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A machine learning algorithm that locates the most similar vectors to a given item in a dataset. Process of establishing the identity of a user or application. Process of establishing permissions to database resources through roles. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z Pausing or blocking the buffering of incoming requests after reaching the threshold until the internal processing of buffered requests catches up. An off-heap structure associated with each SSTable that checks if any data for the requested row exists in the SSTable before doing any disk I/O. The process by which new nodes join the cluster transparently gathering the data needed from existing nodes. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z The number of unique values in a column. For example, a column of ID numbers unique for each employee would have high cardinality while a column of employee ZIP codes would have low cardinality because multiple employees can have the same ZIP code. An index on a column with low cardinality can boost read performance because the index is significantly smaller than the column. An index for a high-cardinality column may reduce performance. If your application requires a search on a high-cardinality column, a materialized view is ideal. The smallest increment of stored data. Contains a value in a row-column intersection. Two or more database instances that exchange messages using the gossip protocol. The storage engine process that creates an index and keeps data in order based on the index. In the table definition, a clustering column is a column that is part of the compound primary key definition. Note that the clustering column cannot be the first column because that position is reserved for the partition key. Columns are clustered in multiple rows within a single partition. The clustering order is determined by the position of columns in the compound primary key definition. Strategy to combine multiple network messages into a single packet for outbound TCP connections to nodes in the same data center (intra-DC) or to nodes in a different data center (inter-DC). A coalescing strategy is provided with a blocking queue of pending messages and an output collection for messages to send. The smallest increment of data. Contains a name, a value, and a timestamp. A container for rows, similar to the table in a relational system. Called a table in CQL 3. A file to which the database appends changed data for recovery in the event of a hardware failure. The process of consolidating SSTables, discarding tombstones, and regenerating the SSTable index. The available compaction strategies are: UnifiedCompactionStrategy (UCS) LeveledCompactionStrategy (LCS) SizeTieredCompactionStrategy (STCS) TimeWindowCompactionStrategy (TWCS) A partition key consisting of multiple columns. A primary key consisting of the partition key, which determines the node on which data is stored, and one or more additional columns that determine clustering. The synchronization of data on replicas in a cluster. Consistency is categorized as weak or strong. A setting that defines a successful write or read by the number of cluster replicas that acknowledge the write or respond to the read request, respectively. The node that determines which nodes in the ring should get the request based on the cluster configured snitch. A metric measuring the similarity between two non-zero vectors in a multi-dimensional space. It quantifies the cosine of the angle between the vectors; the angle representing each vector’s orientation and direction relative to each other. Zero (0) indicates complete dissimilarity. Negative one (-1) indicates exact opposite orientation of the vectors. One (1) indicates complete similarity. The Cassandra Query Language shell (cqlsh) utility. A technique for optimizing replication across datacenters by sending data from one datacenter to a node in another datacenter. The receiving node then forwards the data to other nodes in its data center. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A group of related nodes that are configured together within a cluster for replication and workload segregation purposes. Not necessarily a separate location or physical data center. Datacenter names are case sensitive and cannot be changed. A particular kind of data item, defined by the values it can take or the operations that can be performed on it. Denormalization refers to the process of optimizing the read performance of a database by adding redundant data or by grouping data. This process is accomplished by duplicating data in multiple tables or by grouping data for queries. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z EBNF (Extended Backus-Naur Form) syntax expresses a context-free grammar that formally describes a language. EBNF extends its precursor BNF (Backus-Naur Form) with additional operators allowed in expansions. Syntax (railroad) diagrams graphically depict EBNF grammars. A mathematical technique in machine learning where complex, high-dimensional data is represented as points in a lower-dimensional space. The process of creating an embedding preserves the relevant properties of the original data, such as distance and similarity, enabling easier computational processing. For instance, words with similar meanings in Natural Language Processing (NLP) can be set close to each other in the reduced space, facilitating their use in machine learning models. A coordinate geometry non-negative distance metric between two points, quantifying the similarity or dissimilarity between those data points represented as vectors. Use it to compare generated samples to real data points. The database maximizes availability and partition tolerance. The database ensures eventual data consistency by updating all replicas during read operations and periodically checking and updating any replicas not directly accessed. The updating and checking ensures that any query always returns the most recent version of the result set and that all replicas of any given row eventually become completely consistent with each other. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A Java background process that frees heap memory when it is no longer in use by the program. The main Java algorithms to allocate and clean up memory are Continuous Mark Sweep (CMS) and Garbage-First (G1). A peer-to-peer communication protocol for exchanging location and state information between nodes. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A hard disk drive (HDD) or spinning disk is a data storage device used for storing and retrieving digital information using one or more rigid rapidly rotating disks. Compare to SSD. Hadoop Distributed File System (HDFS) stores data on nodes to improve performance. HDFS is a necessary component in addition to MapReduce in a Hadoop distribution. The amount of disk space required by a process (such as compaction) in addition to the space occupied by the data being processed. One of the three ways, in addition to read-repair and full/incremental anti-entropy repair, that Cassandra implements the eventual consistency guarantee that all updates are eventually received by all replicas. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z An operation that can occur multiple times without changing the result, such as performing the same update multiple times without affecting the outcome. Data on a disk that cannot be overwritten. A native capability for finding a column in the database that does not involve using the primary key. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A measure of similarity between two sets of features or elements in generated data and real data. The mathematical calculation is the size of the intersection of two sets divided by the size of their union, and ranges from zero (0) to one (1). One (1) indicates identical sets. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A namespace container that defines how data is replicated on nodes in each datacenter. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z This compaction strategy creates SSTables of a fixed, relatively small size that are grouped into levels. Within each level, SSTables are guaranteed to be non-overlapping. Each level (L0, L1, L2, and so on) is ten times as large as the previous level. Disk I/O is more uniform and predictable on higher levels than on lower levels as SSTables are continuously being compacted into progressively larger levels. At each level, row keys are merged into non-overlapping SSTables in the next level. This process improves performance for reads because the database can determine which SSTables in each level to check for the existence of row key data. Also called serializable consistency, linearizable consistency is the restriction that one operation cannot be executed unless and until another operation has completed. The database supports Lightweight transactions to ensure linearizable consistency in writes. The first phase of a Lightweight transaction works at SERIAL consistency and follows the Paxos protocol to ensure that the required operation succeeds. If this phase succeeds, the write is performed at the consistency level specified for the operation. Reads performed at the SERIAL consistency level execute without database built-in read repair operations. Address or interface to bind to and tell other Cassandra nodes to connect to a node. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A branch of artificial intelligence (AI) and computer science that uses and develops computer systems capable of learning and adapting without explicit instruction. ML uses algorithms and statistical models to analyze data and identify patterns, make decisions, and improve its system. Hadoop’s parallel processing engine that quickly processes large data sets. A necessary component in addition to MapReduce in a Hadoop distribution. A materialized view is a table with data that is automatically inserted and updated from another base table. Has a primary key that differs from the base table, allowing the implementation of different queries. A database table-specific, in-memory data structure that resembles a write-back cache. A mutation is either an insertion or a deletion. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z Helps computers interpret and share the human language to offer the best use for the user. A Java virtual machine (a platform-independent execution environment that converts Java bytecode into machine language and executes it) that runs an instance of the Licensed Software. A process that makes all data on a replica consistent. Normalization refers to a series of steps used to eliminate redundancy and reduce the chances of data inconsistency in a database’s schema. In DataStax Enterprise, this process is inefficient because joining data in multiple tables for queries requires accessing more nodes. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z Online transaction processing (OLTP) is characterized by a large number of short on-line transactions for data entry and retrieval. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A partition is a collection of data addressable by a key. This data resides on one node in a Cassandra cluster. A partition is replicated on as many nodes as the replication factor specifies. A list of primary keys and the start position of data. A partition keys represents a logical entity which helps a Cassandra cluster know on which node some requested data resides. The partition key is the first column declared in the primary key definition. In a compound key, multiple columns can declare the columns that form the primary key. The limits of the partition that differ depending on the configured partitioner. Murmur3Partitioner (default) range is -263 to +263 and RandomPartitioner range is 0 to 2127-1. A subset of the partition index. By default, 1 partition key out of every 128 is sampled. Distributes data across a cluster. The types of partitioners are Murmur3Partitioner (default), RandomPartitioner, and OrderPreservingPartitioner. Unresolved include directive in modules/ROOT/pages/glossary.adoc - include::ROOT:partial$persistent-volume.adoc[] Unresolved include directive in modules/ROOT/pages/glossary.adoc - include::ROOT:partial$persistent-volume-claim.adoc[] The partition key. One or more columns that uniquely identify a row in a table. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A change in the expanse of tokens assigned to a node. A process that updates database replicas with the most recent version of frequently-read data. A copy of a portion of the whole database. Each node holds some replicas. A specification that determines the replicas for each row of data. The total number of replicas across the cluster, abbreviated as RF. A replication factor of 1 means that there is only one copy of each row in the cluster. If the node containing the row goes down, the row cannot be retrieved. A replication factor of 2 indicates two copies of each row and that each copy is on a different node. All replicas are equally important; there is no primary or master replica. See datacenter. A set of permissions assigned to users that limits their access to database resources. When using internal authentication, roles can also have passwords and represent a single user, DSE client tool, or application. A procedure that is performed during upgrading nodes in a cluster for zero downtime. Nodes are upgraded and restarted one at a time while other nodes continue to operate online. 1) Columns that have the same primary key. 2) A collection of cells per combination of columns in the storage engine. A database component for improving the performance of read-intensive operations. In off-heap memory, the row cache holds the most recently read rows from the local SSTables. Each local read operation stores its result set in the row cache and sends it to the coordinator node. The next read first checks the row cache. If the required data is there, the database returns it immediately. This initial read can save further seeks in the Bloom filter, partition key cache, partition summary, partition index, and SSTables. The database uses LRU (least-recently-used) eviction to ensure that the row cache is refreshed with the most frequently accessed rows. The size of the row cache can be configured in the cassandra.yaml file. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A seed, or seed node, is used to bootstrap the gossip process for new nodes joining a cluster. A seed node provides no other function and is not a single point of failure for a cluster. Unresolved include directive in modules/ROOT/pages/glossary.adoc - include::ROOT:partial$segment.adoc[] See linearizable consistency. The default compaction strategy. This strategy triggers a minor compaction when there are a number of similar sized SSTables on disk as configured by the table subproperty, min_threshold. A minor compaction does not involve all the tables in a keyspace. Also see STCS compaction subproperties in the relevant CQL documentation. A set of clustered columns in a partition that you query as a set using, for example, a conditional WHERE clause. The mapping from the IP addresses of nodes to physical and virtual locations, such as racks and datacenters. The request routing mechanism is affected by which of the several types of snitches is used. A solid-state drive (SSD) is a solid-state storage device that uses integrated circuits to persistently store data. Compare to HDD. A sorted string table (SSTable) is an immutable data file to which the database writes memtables periodically. SSTables are stored on disk sequentially and maintained for each database table. A special column that is shared by all rows of a partition. A component that handles data exchange among nodes in a cluster. It is part of the SSTable file. Examples include: When bootstrapping a new node, the new node gets data from existing nodes using streaming. When running nodetool repair, nodes exchange out-of-sync data using streaming. When bulkloading data from backup, sstableloader uses streaming to complete a task. As a database reads data it performs a read repair before returning results. Superuser is a role attribute that provides root database access. Superusers have all permissions on all objects. Apache Cassandra databases include the superuser role cassandra with password cassandra by default. This account runs queries, including logins, with a consistency level of QUORUM. It is recommended that users create a superuser for deployments and remove the cassandra role. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A collection of columns ordered by name and fetched by row. A row consists of columns and has a primary key; the first part of the key is a column name. Subsequent parts of a compound key are other column names that define the order of columns in the table. This compaction strategy compacts SSTables based on a series of time windows. During the current time window, the SSTables are compacted into one or more SSTables. At the end of the current time window, all SSTables are compacted into a single larger SSTable. The compaction process repeats at the start of the next time window. Each TWCS time window contains data within a specified range and contains varying amounts of data. An element on the ring that depends on the partitioner. Determines the node’s position on the ring and the portion of data for which it is responsible. The range for the Murmur3Partitioner (default) is -263 to +263. The range for the RandomPartitioner is 0 to 2127-1. A marker in a row that indicates a column was deleted. During compaction, marked columns are deleted. Time-to-live (TTL) is an optional expiration date for values that are inserted into a column. The database ensures that all replicas of any given row eventually become completely consistent. For situations requiring immediate and complete consistency, the database can be tuned to provide 100% consistency for specified operations, datacenters, or clusters. The database cannot be tuned to complete consistency for all data and operations. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z This compaction strategy compacts SSTables based on a series of time windows. During the current time window, the SSTables are compacted into one or more SSTables. At the end of the current time window, all SSTables are compacted into a single larger SSTable. The compaction process repeats at the start of the next time window. Each TWCS time window contains data within a specified range and contains varying amounts of data. Covers the applications of levelled, tiered and time-windowed compaction strategies, including combinations of levelled and tiered in different levels of the compaction hierarchy. This compaction can work in modes similar to [STCS] (with w = T4 matching STCS’s default threshold of 4), LCS (with w = L10 to match LCS’s default fan factor of 10), and can also work well enough for time-series workloads when used with a large tiered fan factor (e.g. w = T20). Read-heavy workloads, especially ones that cannot benefit from bloom filters or time order (i.e. wide partition non-time-series) are best served by levelled configurations. Write-heavy, time series or key-value workloads are best served by tiered ones. A change in the database that updates a specified column in a row if the column exists. If the column does not exist, then that column is inserted. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z An array of floating point type that represents a specific object or entity. Reviews data on a database to determine the distance between the vectors. The closer they are, the more similar the data. The more the distance, the less similar the data. Vnode is a virtual node. Normally, nodes are responsible for a single partitioning range in the full token range of a cluster. With vnodes enabled, each node is responsible for several virtual nodes, effectively spreading a partitioning range across more nodes in the cluster. Enabling vnodes can reduce the risk of hotspotting or straining one node in the cluster. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z When reading data, the database performs read repair after returning results. A data partition that CQL 3 transposes into familiar row-based resultsets. A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | X, Y, Z A row or cell that reappears in a database table after deletion. This can happen if a node goes down for a long period of time and is then restored without being repaired. Deleted data is not erased from database tables; it is marked with tombstones until compaction. The tombstones created on one node must be propagated to the nodes containing the deleted data. If one of these nodes goes down before this happens, the node may not receive the most up-to-date tombstones. If the node is not repaired before it comes back online, the database finds the non-tombstoned items and propagates them to other nodes as new data. 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commitlog-archiving.properties configuration file can optionally set commands that are executed when archiving or restoring a commitlog segment. One command can be inserted with %path and %name arguments. %path is the fully qualified path of the commitlog segment to archive. %name is the filename of the commitlog. STDOUT, STDIN, or multiple commands cannot be executed. If multiple commands are required, add a pointer to a script in this option. Example: archive_command=/bin/ln %path /backup/%name Default value: blank One command can be inserted with %from and %to arguments. %from is the fully qualified path to an archived commitlog segment using the specified restore directories. %to defines the directory to the live commitlog location. Example: restore_command=/bin/cp -f %from %to Default value: blank Defines the directory to scan the recovery files into. Example: restore_directories=/path/to/restore_dir_location Default value: blank Restore mutations created up to and including this timestamp in GMT in the format yyyy:MM:dd HH:mm:ss. 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If the JVM settings are static and do not need to be computed from the node characteristics, the cassandra-jvm-options files should be used instead. For example, commonly computed values are the heap sizes, using the system values. For example, add JVM_OPTS=\"$JVM_OPTS -Dcassandra.load_ring_state=false\" to the cassandra_env.sh file and run the command-line cassandra to start. The option is set from the cassandra-env.sh file, and is equivalent to starting Cassandra with the command-line option cassandra -Dcassandra.load_ring_state=false. The -D option specifies the start-up parameters in both the command line and cassandra-env.sh file. The following options are available: Facilitates setting auto_bootstrap to false on initial set-up of the cluster. The next time you start the cluster, you do not need to change the cassandra.yaml file on each node to revert to true, the default value. In a multi-instance deployment, multiple Cassandra instances will independently assume that all CPU processors are available to it. This setting allows you to specify a smaller set of processors. If JNA fails to initialize, Cassandra fails to boot. Use this command to boot Cassandra without JNA. The directory location of the cassandra.yaml file. The default location depends on the type of installation. Setting this property to true causes the dynamic snitch to ignore the severity indicator from gossip when scoring nodes. Explore failure detection and recovery and dynamic snitching for more information. Default: false Use when virtual nodes (vnodes) are not used. Sets the initial partitioner token for a node the first time the node is started. Note: Vnodes are highly recommended as they automatically select tokens. Default: disabled Set to false to start Cassandra on a node but not have the node join the cluster. You can use nodetool join and a JMX call to join the ring afterwards. Default: true Set to false to clear all gossip state for the node on restart. Default: true Enable pluggable metrics reporter. Explore pluggable metrics reporting for more information. Set the partitioner. Default: org.apache.cassandra.dht.Murmur3Partitioner Set the cache size for prepared statements. To replace a node that has died, restart a new node in its place specifying the listen_address or broadcast_address that the new node is assuming. The new node must not have any data in its data directory, the same state as before bootstrapping. Note: The broadcast_address defaults to the listen_address except when using the Ec2MultiRegionSnitch. Allow restoring specific tables from an archived commit log. Defines the amount of time a node waits to hear from other nodes before formally joining the ring. Default: 1000ms Set the port on which the CQL native transport listens for clients. Default: 9042 Set the port for the Thrift RPC service, which is used for client connections. Default: 9160 Set the port for inter-node communication. Default: 7000 Set the SSL port for encrypted communication. Default: 7001 Enable or disable the native transport server. See start_native_transport in cassandra.yaml. Default: true Enable or disable the Thrift RPC server. Default: true Set the default location for the trigger JARs. Default: conf/triggers For testing new compaction and compression strategies. It allows you to experiment with different strategies and benchmark write performance differences without affecting the production workload. Set to true makes Cassandra perform bootstrap safely without violating consistency. False disables this.","title":"cassandra-env.sh file","component":"Cassandra","version":"4.1","name":"cass_env_sh_file","url":"/Cassandra/4.1/cassandra/configuration/cass_env_sh_file.html","titles":[{"text":"cassandra.auto_bootstrap=false","id":"cassandra-auto_bootstrapfalse"},{"text":"cassandra.available_processors=<number_of_processors>","id":"cassandra-available_processorsnumber_of_processors"},{"text":"cassandra.boot_without_jna=true","id":"cassandra-boot_without_jnatrue"},{"text":"cassandra.config=<directory>","id":"cassandra-configdirectory"},{"text":"cassandra.ignore_dynamic_snitch_severity=true|false","id":"cassandra-ignore_dynamic_snitch_severitytruefalse"},{"text":"cassandra.initial_token=<token>","id":"cassandra-initial_tokentoken"},{"text":"cassandra.join_ring=true|false","id":"cassandra-join_ringtruefalse"},{"text":"cassandra.load_ring_state=true|false","id":"cassandra-load_ring_statetruefalse"},{"text":"cassandra.metricsReporterConfigFile=<filename>","id":"cassandra-metricsreporterconfigfilefilename"},{"text":"cassandra.partitioner=<partitioner>","id":"cassandra-partitionerpartitioner"},{"text":"cassandra.prepared_statements_cache_size_in_bytes=<cache_size>","id":"cassandra-prepared_statements_cache_size_in_bytescache_size"},{"text":"cassandra.replace_address=<listen_address of dead node>|<broadcast_address of dead node>","id":"cassandra-replace_addresslisten_address-of-dead-nodebroadcast_address-of-dead-node"},{"text":"cassandra.replayList=<table>","id":"cassandra-replaylisttable"},{"text":"cassandra.ring_delay_ms=<number_of_ms>","id":"cassandra-ring_delay_msnumber_of_ms"},{"text":"cassandra.native_transport_port=<port>","id":"cassandra-native_transport_portport"},{"text":"cassandra.rpc_port=<port>","id":"cassandra-rpc_portport"},{"text":"cassandra.storage_port=<port>","id":"cassandra-storage_portport"},{"text":"cassandra.ssl_storage_port=<port>","id":"cassandra-ssl_storage_portport"},{"text":"cassandra.start_native_transport=true|false","id":"cassandra-start_native_transporttruefalse"},{"text":"cassandra.start_rpc=true|false","id":"cassandra-start_rpctruefalse"},{"text":"cassandra.triggers_dir=<directory>","id":"cassandra-triggers_dirdirectory"},{"text":"cassandra.write_survey=true","id":"cassandra-write_surveytrue"},{"text":"consistent.rangemovement=true|false","id":"consistent-rangemovementtruefalse"}]},"/Cassandra/4.1/cassandra/configuration/cass_jvm_options_file.html":{"text":"Several files for JVM configuration are included in Cassandra. 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The jvm- files only store static JVM settings.","title":"jvm-* files","component":"Cassandra","version":"4.1","name":"cass_jvm_options_file","url":"/Cassandra/4.1/cassandra/configuration/cass_jvm_options_file.html","titles":[]},"/Cassandra/4.1/cassandra/configuration/cass_logback_xml_file.html":{"text":"The logback.xml configuration file can optionally set logging levels for the logs written to system.log and debug.log. The logging levels can also be set using nodetool setlogginglevels. Specify log type and settings. Possible appender names are: SYSTEMLOG, DEBUGLOG, ASYNCDEBUGLOG, and STDOUT. SYSTEMLOG ensures that WARN and ERROR message are written synchronously to the specified file. DEBUGLOG and ASYNCDEBUGLOG ensure that DEBUG messages are written either synchronously or asynchronously, respectively, to the specified file. STDOUT writes all messages to the console in a human-readable format. Example: Specify the filename for a log. Example: $\\{cassandra.logdir}/system.log Specify the level for a log. Part of the filter. Levels are: ALL, TRACE, DEBUG, INFO, WARN, ERROR, OFF. TRACE creates the most verbose log, ERROR the least. Note Note: Increasing logging levels can generate heavy logging output on a moderately trafficked cluster. You can use the nodetool getlogginglevels command to see the current logging configuration. Default: INFO Example: INFO Specify the policy for rolling logs over to an archive. Example: Specify the pattern information for rolling over the log to archive. Part of the rolling policy. Example: $\\{cassandra.logdir}/system.log.%d{yyyy-MM-dd}.%i.zip Specify the maximum file size to trigger rolling a log. Part of the rolling policy. Example: 50MB Specify the maximum history in days to trigger rolling a log. Part of the rolling policy. Example: 7 Specify the format of the message. Part of the rolling policy. Example: 7 Example: %-5level [%thread] %date{ISO8601} %F:%L - %msg%n INFO ${cassandra.logdir}/system.log ${cassandra.logdir}/system.log.%d{yyyy-MM-dd}.%i.zip 50MB 7 5GB %-5level [%thread] %date{ISO8601} %F:%L - %msg%n ${cassandra.logdir}/debug.log ${cassandra.logdir}/debug.log.%d{yyyy-MM-dd}.%i.zip 50MB 7 5GB %-5level [%thread] %date{ISO8601} %F:%L - %msg%n 1024 0 true INFO %-5level [%thread] %date{ISO8601} %F:%L - %msg%n --> -->","title":"logback.xml file","component":"Cassandra","version":"4.1","name":"cass_logback_xml_file","url":"/Cassandra/4.1/cassandra/configuration/cass_logback_xml_file.html","titles":[{"text":"Options","id":"options"},{"text":"appender name=\"<appender_choice>\"…​</appender>","id":"appender-nameappender_choiceappender"},{"text":"<file> <filename> </file>","id":"file-filename-file"},{"text":"<level> <log_level> </level>","id":"level-log_level-level"},{"text":"<rollingPolicy class=\"<rolling_policy_choice>\" <fileNamePattern><pattern_info></fileNamePattern> …​ </rollingPolicy>","id":"rollingpolicy-classrolling_policy_choice-filenamepatternpattern_infofilenamepattern-rollingpolicy"},{"text":"<fileNamePattern> <pattern_info> </fileNamePattern>","id":"filenamepattern-pattern_info-filenamepattern"},{"text":"<maxFileSize> <size> </maxFileSize>","id":"maxfilesize-size-maxfilesize"},{"text":"<maxHistory> <number_of_days> </maxHistory>","id":"maxhistory-number_of_days-maxhistory"},{"text":"<encoder> <pattern>…​</pattern> </encoder>","id":"encoder-patternpattern-encoder"},{"text":"Contents of default logback.xml","id":"contents-of-default-logback-xml"}]},"/Cassandra/4.1/cassandra/configuration/cass_rackdc_file.html":{"text":"Several snitch options use the cassandra-rackdc.properties configuration file to determine which datacenters and racks cluster nodes belong to. Information about the network topology allows requests to be routed efficiently and to distribute replicas evenly. The following snitches can be configured here: GossipingPropertyFileSnitch AWS EC2 single-region snitch AWS EC2 multi-region snitch The GossipingPropertyFileSnitch is recommended for production. This snitch uses the datacenter and rack information configured in a local node’s cassandra-rackdc.properties file and propagates the information to other nodes using gossip. It is the default snitch and the settings in this properties file are enabled. The AWS EC2 snitches are configured for clusters in AWS. This snitch uses the cassandra-rackdc.properties options to designate one of two AWS EC2 datacenter and rack naming conventions: legacy: Datacenter name is the part of the availability zone name preceding the last \"-\" when the zone ends in -1 and includes the number if not -1. Rack name is the portion of the availability zone name following the last \"-\". Examples: us-west-1a ⇒ dc: us-west, rack: 1a; us-west-2b ⇒ dc: us-west-2, rack: 2b; standard: Datacenter name is the standard AWS region name, including the number. Rack name is the region plus the availability zone letter. Examples: us-west-1a ⇒ dc: us-west-1, rack: us-west-1a; us-west-2b ⇒ dc: us-west-2, rack: us-west-2b; Either snitch can set to use the local or internal IP address when multiple datacenters are not communicating. Name of the datacenter. The value is case-sensitive. Default value: DC1 Rack designation. The value is case-sensitive. Default value: RAC1 Datacenter and rack naming convention. Options are legacy or standard (default). This option is commented out by default. Default value: standard Note YOU MUST USE THE legacy VALUE IF YOU ARE UPGRADING A PRE-4.0 CLUSTER. Option to use the local or internal IP address when communication is not across different datacenters. This option is commented out by default. Default value: true","title":"cassandra-rackdc.properties file","component":"Cassandra","version":"4.1","name":"cass_rackdc_file","url":"/Cassandra/4.1/cassandra/configuration/cass_rackdc_file.html","titles":[{"text":"GossipingPropertyFileSnitch","id":"gossipingpropertyfilesnitch"},{"text":"dc","id":"dc"},{"text":"rack","id":"rack"},{"text":"AWS EC2 snitch","id":"aws-ec2-snitch"},{"text":"ec2_naming_scheme","id":"ec2_naming_scheme"},{"text":"Either snitch","id":"either-snitch"},{"text":"prefer_local","id":"prefer_local"}]},"/Cassandra/4.1/cassandra/configuration/cass_topo_file.html":{"text":"The PropertyFileSnitch snitch option uses the cassandra-topologies.properties configuration file to determine which datacenters and racks cluster nodes belong to. If other snitches are used, the cassandra-rackdc.properties must be used. The snitch determines network topology (proximity by rack and datacenter) so that requests are routed efficiently and allows the database to distribute replicas evenly. Include every node in the cluster in the properties file, defining your datacenter names as in the keyspace definition. The datacenter and rack names are case-sensitive. The cassandra-topologies.properties file must be copied identically to every node in the cluster. This example uses three datacenters: # datacenter One 175.56.12.105=DC1:RAC1 175.50.13.200=DC1:RAC1 175.54.35.197=DC1:RAC1 120.53.24.101=DC1:RAC2 120.55.16.200=DC1:RAC2 120.57.102.103=DC1:RAC2 # datacenter Two 110.56.12.120=DC2:RAC1 110.50.13.201=DC2:RAC1 110.54.35.184=DC2:RAC1 50.33.23.120=DC2:RAC2 50.45.14.220=DC2:RAC2 50.17.10.203=DC2:RAC2 # datacenter Three 172.106.12.120=DC3:RAC1 172.106.12.121=DC3:RAC1 172.106.12.122=DC3:RAC1 # default for unknown nodes default =DC3:RAC1","title":"cassandra-topologies.properties file","component":"Cassandra","version":"4.1","name":"cass_topo_file","url":"/Cassandra/4.1/cassandra/configuration/cass_topo_file.html","titles":[{"text":"Example","id":"example"}]},"/Cassandra/4.1/cassandra/configuration/cass_yaml_file.html":{"text":"The name of the cluster. This is mainly used to prevent machines in one logical cluster from joining another. Default Value: 'Test Cluster' This defines the number of tokens randomly assigned to this node on the ring The more tokens, relative to other nodes, the larger the proportion of data that this node will store. You probably want all nodes to have the same number of tokens assuming they have equal hardware capability. If you leave this unspecified, Cassandra will use the default of 1 token for legacy compatibility, and will use the initial_token as described below. Specifying initial_token will override this setting on the node’s initial start, on subsequent starts, this setting will apply even if initial token is set. See cassandra.apache.org/doc/latest/getting_started/production.html#tokens for best practice information about num_tokens. Default Value: 16 This option is commented out by default. Triggers automatic allocation of num_tokens tokens for this node. The allocation algorithm attempts to choose tokens in a way that optimizes replicated load over the nodes in the datacenter for the replica factor. The load assigned to each node will be close to proportional to its number of vnodes. Only supported with the Murmur3Partitioner. Replica factor is determined via the replication strategy used by the specified keyspace. Default Value: KEYSPACE Replica factor is explicitly set, regardless of keyspace or datacenter. This is the replica factor within the datacenter, like NTS. Default Value: 3 This option is commented out by default. initial_token allows you to specify tokens manually. While you can use it with vnodes (num_tokens > 1, above) — in which case you should provide a comma-separated list — it’s primarily used when adding nodes to legacy clusters that do not have vnodes enabled. May either be \"true\" or \"false\" to enable globally Default Value: true This option is commented out by default. When hinted_handoff_enabled is true, a black list of data centers that will not perform hinted handoff Default Value (complex option): # - DC1 # - DC2 this defines the maximum amount of time a dead host will have hints generated. After it has been dead this long, new hints for it will not be created until it has been seen alive and gone down again. Min unit: ms Default Value: 3h Maximum throttle in KiBs per second, per delivery thread. This will be reduced proportionally to the number of nodes in the cluster. (If there are two nodes in the cluster, each delivery thread will use the maximum rate; if there are three, each will throttle to half of the maximum, since we expect two nodes to be delivering hints simultaneously.) Min unit: KiB Default Value: 1024KiB Number of threads with which to deliver hints; Consider increasing this number when you have multi-dc deployments, since cross-dc handoff tends to be slower Default Value: 2 This option is commented out by default. Directory where Cassandra should store hints. If not set, the default directory is $CASSANDRA_HOME/data/hints. Default Value: /var/lib/cassandra/hints How often hints should be flushed from the internal buffers to disk. Will not trigger fsync. Min unit: ms Default Value: 10000ms Maximum size for a single hints file, in mebibytes. Min unit: MiB Default Value: 128MiB This option is commented out by default. The file size limit to store hints for an unreachable host, in mebibytes. Once the local hints files have reached the limit, no more new hints will be created. Set a non-positive value will disable the size limit. Default Value: 0MiB Enable / disable automatic cleanup for the expired and orphaned hints file. Disable the option in order to preserve those hints on the disk. Default Value: false This option is commented out by default. Enable/disable transfering hints to a peer during decommission. Even when enabled, this does not guarantee consistency for logged batches, and it may delay decommission when coupled with a strict hinted_handoff_throttle. Default: true Default Value: true This option is commented out by default. Compression to apply to the hint files. If omitted, hints files will be written uncompressed. LZ4, Snappy, and Deflate compressors are supported. Default Value (complex option): # - class_name: LZ4Compressor # parameters: # - This option is commented out by default. Enable / disable persistent hint windows. If set to false, a hint will be stored only in case a respective node that hint is for is down less than or equal to max_hint_window. If set to true, a hint will be stored in case there is not any hint which was stored earlier than max_hint_window. This is for cases when a node keeps to restart and hints are not delivered yet, we would be saving hints for that node indefinitely. Defaults to true. Default Value: true Maximum throttle in KiBs per second, total. This will be reduced proportionally to the number of nodes in the cluster. Min unit: KiB Default Value: 1024KiB Authentication backend, implementing IAuthenticator; used to identify users Out of the box, Cassandra provides org.apache.cassandra.auth.{AllowAllAuthenticator, PasswordAuthenticator}. AllowAllAuthenticator performs no checks - set it to disable authentication. PasswordAuthenticator relies on username/password pairs to authenticate users. It keeps usernames and hashed passwords in system_auth.roles table. Please increase system_auth keyspace replication factor if you use this authenticator. If using PasswordAuthenticator, CassandraRoleManager must also be used (see below) Default Value: AllowAllAuthenticator Authorization backend, implementing IAuthorizer; used to limit access/provide permissions Out of the box, Cassandra provides org.apache.cassandra.auth.{AllowAllAuthorizer, CassandraAuthorizer}. AllowAllAuthorizer allows any action to any user - set it to disable authorization. CassandraAuthorizer stores permissions in system_auth.role_permissions table. Please increase system_auth keyspace replication factor if you use this authorizer. Default Value: AllowAllAuthorizer Part of the Authentication & Authorization backend, implementing IRoleManager; used to maintain grants and memberships between roles. Out of the box, Cassandra provides org.apache.cassandra.auth.CassandraRoleManager, which stores role information in the system_auth keyspace. Most functions of the IRoleManager require an authenticated login, so unless the configured IAuthenticator actually implements authentication, most of this functionality will be unavailable. CassandraRoleManager stores role data in the system_auth keyspace. Please increase system_auth keyspace replication factor if you use this role manager. Default Value: CassandraRoleManager Network authorization backend, implementing INetworkAuthorizer; used to restrict user access to certain DCs Out of the box, Cassandra provides org.apache.cassandra.auth.{AllowAllNetworkAuthorizer, CassandraNetworkAuthorizer}. AllowAllNetworkAuthorizer allows access to any DC to any user - set it to disable authorization. CassandraNetworkAuthorizer stores permissions in system_auth.network_permissions table. Please increase system_auth keyspace replication factor if you use this authorizer. Default Value: AllowAllNetworkAuthorizer This option is commented out by default. Depending on the auth strategy of the cluster, it can be beneficial to iterate from root to table (root → ks → table) instead of table to root (table → ks → root). As the auth entries are whitelisting, once a permission is found you know it to be valid. We default to false as the legacy behavior is to query at the table level then move back up to the root. See CASSANDRA-17016 for details. Default Value: false Validity period for roles cache (fetching granted roles can be an expensive operation depending on the role manager, CassandraRoleManager is one example) Granted roles are cached for authenticated sessions in AuthenticatedUser and after the period specified here, become eligible for (async) reload. Defaults to 2000, set to 0 to disable caching entirely. Will be disabled automatically for AllowAllAuthenticator. For a long-running cache using roles_cache_active_update, consider setting to something longer such as a daily validation: 86400000 Min unit: ms Default Value: 2000ms This option is commented out by default. Refresh interval for roles cache (if enabled). After this interval, cache entries become eligible for refresh. Upon next access, an async reload is scheduled and the old value returned until it completes. If roles_validity is non-zero, then this must be also. This setting is also used to inform the interval of auto-updating if using roles_cache_active_update. Defaults to the same value as roles_validity. For a long-running cache, consider setting this to 60000 (1 hour) etc. Min unit: ms Default Value: 2000ms This option is commented out by default. If true, cache contents are actively updated by a background task at the interval set by roles_update_interval. If false, cache entries become eligible for refresh after their update interval. Upon next access, an async reload is scheduled and the old value returned until it completes. Default Value: false Validity period for permissions cache (fetching permissions can be an expensive operation depending on the authorizer, CassandraAuthorizer is one example). Defaults to 2000, set to 0 to disable. Will be disabled automatically for AllowAllAuthorizer. For a long-running cache using permissions_cache_active_update, consider setting to something longer such as a daily validation: 86400000ms Min unit: ms Default Value: 2000ms This option is commented out by default. Refresh interval for permissions cache (if enabled). After this interval, cache entries become eligible for refresh. Upon next access, an async reload is scheduled and the old value returned until it completes. If permissions_validity is non-zero, then this must be also. This setting is also used to inform the interval of auto-updating if using permissions_cache_active_update. Defaults to the same value as permissions_validity. For a longer-running permissions cache, consider setting to update hourly (60000) Min unit: ms Default Value: 2000ms This option is commented out by default. If true, cache contents are actively updated by a background task at the interval set by permissions_update_interval. If false, cache entries become eligible for refresh after their update interval. Upon next access, an async reload is scheduled and the old value returned until it completes. Default Value: false Validity period for credentials cache. This cache is tightly coupled to the provided PasswordAuthenticator implementation of IAuthenticator. If another IAuthenticator implementation is configured, this cache will not be automatically used and so the following settings will have no effect. Please note, credentials are cached in their encrypted form, so while activating this cache may reduce the number of queries made to the underlying table, it may not bring a significant reduction in the latency of individual authentication attempts. Defaults to 2000, set to 0 to disable credentials caching. For a long-running cache using credentials_cache_active_update, consider setting to something longer such as a daily validation: 86400000 Min unit: ms Default Value: 2000ms This option is commented out by default. Refresh interval for credentials cache (if enabled). After this interval, cache entries become eligible for refresh. Upon next access, an async reload is scheduled and the old value returned until it completes. If credentials_validity is non-zero, then this must be also. This setting is also used to inform the interval of auto-updating if using credentials_cache_active_update. Defaults to the same value as credentials_validity. For a longer-running permissions cache, consider setting to update hourly (60000) Min unit: ms Default Value: 2000ms This option is commented out by default. If true, cache contents are actively updated by a background task at the interval set by credentials_update_interval. If false (default), cache entries become eligible for refresh after their update interval. Upon next access, an async reload is scheduled and the old value returned until it completes. Default Value: false The partitioner is responsible for distributing groups of rows (by partition key) across nodes in the cluster. The partitioner can NOT be changed without reloading all data. If you are adding nodes or upgrading, you should set this to the same partitioner that you are currently using. The default partitioner is the Murmur3Partitioner. Older partitioners such as the RandomPartitioner, ByteOrderedPartitioner, and OrderPreservingPartitioner have been included for backward compatibility only. For new clusters, you should NOT change this value. Default Value: org.apache.cassandra.dht.Murmur3Partitioner This option is commented out by default. Directories where Cassandra should store data on disk. If multiple directories are specified, Cassandra will spread data evenly across them by partitioning the token ranges. If not set, the default directory is $CASSANDRA_HOME/data/data. Default Value (complex option): # - /var/lib/cassandra/data This option is commented out by default. Directory were Cassandra should store the data of the local system keyspaces. By default Cassandra will store the data of the local system keyspaces in the first of the data directories specified by data_file_directories. This approach ensures that if one of the other disks is lost Cassandra can continue to operate. For extra security this setting allows to store those data on a different directory that provides redundancy. This option is commented out by default. commit log. when running on magnetic HDD, this should be a separate spindle than the data directories. If not set, the default directory is $CASSANDRA_HOME/data/commitlog. Default Value: /var/lib/cassandra/commitlog Enable / disable CDC functionality on a per-node basis. This modifies the logic used for write path allocation rejection (standard: never reject. cdc: reject Mutation containing a CDC-enabled table if at space limit in cdc_raw_directory). Default Value: false This option is commented out by default. CommitLogSegments are moved to this directory on flush if cdc_enabled: true and the segment contains mutations for a CDC-enabled table. This should be placed on a separate spindle than the data directories. If not set, the default directory is $CASSANDRA_HOME/data/cdc_raw. Default Value: /var/lib/cassandra/cdc_raw Policy for data disk failures: die shut down gossip and client transports and kill the JVM for any fs errors or single-sstable errors, so the node can be replaced. stop_paranoid shut down gossip and client transports even for single-sstable errors, kill the JVM for errors during startup. stop shut down gossip and client transports, leaving the node effectively dead, but can still be inspected via JMX, kill the JVM for errors during startup. best_effort stop using the failed disk and respond to requests based on remaining available sstables. This means you WILL see obsolete data at CL.ONE! ignore ignore fatal errors and let requests fail, as in pre-1.2 Cassandra Default Value: stop Policy for commit disk failures: die shut down the node and kill the JVM, so the node can be replaced. stop shut down the node, leaving the node effectively dead, but can still be inspected via JMX. stop_commit shutdown the commit log, letting writes collect but continuing to service reads, as in pre-2.0.5 Cassandra ignore ignore fatal errors and let the batches fail Default Value: stop Maximum size of the native protocol prepared statement cache Valid values are either \"auto\" (omitting the value) or a value greater 0. Note that specifying a too large value will result in long running GCs and possbily out-of-memory errors. Keep the value at a small fraction of the heap. If you constantly see \"prepared statements discarded in the last minute because cache limit reached\" messages, the first step is to investigate the root cause of these messages and check whether prepared statements are used correctly - i.e. use bind markers for variable parts. Do only change the default value, if you really have more prepared statements than fit in the cache. In most cases it is not neccessary to change this value. Constantly re-preparing statements is a performance penalty. Default value (\"auto\") is 1/256th of the heap or 10MiB, whichever is greater Min unit: MiB Maximum size of the key cache in memory. Each key cache hit saves 1 seek and each row cache hit saves 2 seeks at the minimum, sometimes more. The key cache is fairly tiny for the amount of time it saves, so it’s worthwhile to use it at large numbers. The row cache saves even more time, but must contain the entire row, so it is extremely space-intensive. It’s best to only use the row cache if you have hot rows or static rows. if you reduce the size, you may not get you hottest keys loaded on startup. Default value is empty to make it \"auto\" (min(5% of Heap (in MiB), 100MiB)). Set to 0 to disable key cache. Min unit: MiB Duration in seconds after which Cassandra should save the key cache. Caches are saved to saved_caches_directory as specified in this configuration file. Saved caches greatly improve cold-start speeds, and is relatively cheap in terms of I/O for the key cache. Row cache saving is much more expensive and has limited use. Default is 14400 or 4 hours. Min unit: s Default Value: 4h This option is commented out by default. Number of keys from the key cache to save Disabled by default, meaning all keys are going to be saved Default Value: 100 This option is commented out by default. Row cache implementation class name. Available implementations: org.apache.cassandra.cache.OHCProvider Fully off-heap row cache implementation (default). org.apache.cassandra.cache.SerializingCacheProvider This is the row cache implementation availabile in previous releases of Cassandra. Default Value: org.apache.cassandra.cache.OHCProvider Maximum size of the row cache in memory. Please note that OHC cache implementation requires some additional off-heap memory to manage the map structures and some in-flight memory during operations before/after cache entries can be accounted against the cache capacity. This overhead is usually small compared to the whole capacity. Do not specify more memory that the system can afford in the worst usual situation and leave some headroom for OS block level cache. Do never allow your system to swap. Default value is 0, to disable row caching. Min unit: MiB Default Value: 0MiB Duration in seconds after which Cassandra should save the row cache. Caches are saved to saved_caches_directory as specified in this configuration file. Saved caches greatly improve cold-start speeds, and is relatively cheap in terms of I/O for the key cache. Row cache saving is much more expensive and has limited use. Default is 0 to disable saving the row cache. Min unit: s Default Value: 0s This option is commented out by default. Number of keys from the row cache to save. Specify 0 (which is the default), meaning all keys are going to be saved Default Value: 100 Maximum size of the counter cache in memory. Counter cache helps to reduce counter locks' contention for hot counter cells. In case of RF = 1 a counter cache hit will cause Cassandra to skip the read before write entirely. With RF > 1 a counter cache hit will still help to reduce the duration of the lock hold, helping with hot counter cell updates, but will not allow skipping the read entirely. Only the local (clock, count) tuple of a counter cell is kept in memory, not the whole counter, so it’s relatively cheap. if you reduce the size, you may not get you hottest keys loaded on startup. Default value is empty to make it \"auto\" (min(2.5% of Heap (in MiB), 50MiB)). Set to 0 to disable counter cache. NOTE: if you perform counter deletes and rely on low gcgs, you should disable the counter cache. Min unit: MiB Duration in seconds after which Cassandra should save the counter cache (keys only). Caches are saved to saved_caches_directory as specified in this configuration file. Default is 7200 or 2 hours. Min unit: s Default Value: 7200s This option is commented out by default. Number of keys from the counter cache to save Disabled by default, meaning all keys are going to be saved Default Value: 100 This option is commented out by default. saved caches If not set, the default directory is $CASSANDRA_HOME/data/saved_caches. Default Value: /var/lib/cassandra/saved_caches This option is commented out by default. Number of seconds the server will wait for each cache (row, key, etc …​) to load while starting the Cassandra process. Setting this to zero is equivalent to disabling all cache loading on startup while still having the cache during runtime. Min unit: s Default Value: 30s This option is commented out by default. commitlog_sync may be either \"periodic\", \"group\", or \"batch.\" When in batch mode, Cassandra won’t ack writes until the commit log has been flushed to disk. Each incoming write will trigger the flush task. commitlog_sync_batch_window_in_ms is a deprecated value. Previously it had almost no value, and is being removed. Default Value: 2 This option is commented out by default. group mode is similar to batch mode, where Cassandra will not ack writes until the commit log has been flushed to disk. The difference is group mode will wait up to commitlog_sync_group_window between flushes. Min unit: ms Default Value: 1000ms the default option is \"periodic\" where writes may be acked immediately and the CommitLog is simply synced every commitlog_sync_period milliseconds. Default Value: periodic Min unit: ms Default Value: 10000ms This option is commented out by default. When in periodic commitlog mode, the number of milliseconds to block writes while waiting for a slow disk flush to complete. Min unit: ms The size of the individual commitlog file segments. A commitlog segment may be archived, deleted, or recycled once all the data in it (potentially from each columnfamily in the system) has been flushed to sstables. The default size is 32, which is almost always fine, but if you are archiving commitlog segments (see commitlog_archiving.properties), then you probably want a finer granularity of archiving; 8 or 16 MB is reasonable. Max mutation size is also configurable via max_mutation_size setting in cassandra.yaml. The default is half the size commitlog_segment_size in bytes. This should be positive and less than 2048. If max_mutation_size is set explicitly then commitlog_segment_size must be set to at least twice the size of max_mutation_size Min unit: MiB Default Value: 32MiB This option is commented out by default. Compression to apply to the commit log. If omitted, the commit log will be written uncompressed. LZ4, Snappy, and Deflate compressors are supported. Default Value (complex option): # - class_name: LZ4Compressor # parameters: # - This option is commented out by default. Compression to apply to SSTables as they flush for compressed tables. Note that tables without compression enabled do not respect this flag. As high ratio compressors like LZ4HC, Zstd, and Deflate can potentially block flushes for too long, the default is to flush with a known fast compressor in those cases. Options are: none : Flush without compressing blocks but while still doing checksums. fast : Flush with a fast compressor. If the table is already using a fast compressor that compressor is used. Default Value: Always flush with the same compressor that the table uses. This This option is commented out by default. was the pre 4.0 behavior. Default Value: fast any class that implements the SeedProvider interface and has a constructor that takes a Map of parameters will do. Default Value (complex option): # Addresses of hosts that are deemed contact points. # Cassandra nodes use this list of hosts to find each other and learn # the topology of the ring. You must change this if you are running # multiple nodes! - class_name: org.apache.cassandra.locator.SimpleSeedProvider parameters: # seeds is actually a comma-delimited list of addresses. # Ex: \",,\" - seeds: \"127.0.0.1:7000\" For workloads with more data than can fit in memory, Cassandra’s bottleneck will be reads that need to fetch data from disk. \"concurrent_reads\" should be set to (16 * number_of_drives) in order to allow the operations to enqueue low enough in the stack that the OS and drives can reorder them. Same applies to \"concurrent_counter_writes\", since counter writes read the current values before incrementing and writing them back. On the other hand, since writes are almost never IO bound, the ideal number of \"concurrent_writes\" is dependent on the number of cores in your system; (8 * number_of_cores) is a good rule of thumb. Default Value: 32 Default Value: 32 Default Value: 32 For materialized view writes, as there is a read involved, so this should be limited by the less of concurrent reads or concurrent writes. Default Value: 32 This option is commented out by default. Maximum memory to use for inter-node and client-server networking buffers. Defaults to the smaller of 1/16 of heap or 128MB. This pool is allocated off-heap, so is in addition to the memory allocated for heap. The cache also has on-heap overhead which is roughly 128 bytes per chunk (i.e. 0.2% of the reserved size if the default 64k chunk size is used). Memory is only allocated when needed. Min unit: MiB Default Value: 128MiB This option is commented out by default. Enable the sstable chunk cache. The chunk cache will store recently accessed sections of the sstable in-memory as uncompressed buffers. Default Value: false This option is commented out by default. Maximum memory to use for sstable chunk cache and buffer pooling. 32MB of this are reserved for pooling buffers, the rest is used for chunk cache that holds uncompressed sstable chunks. Defaults to the smaller of 1/4 of heap or 512MB. This pool is allocated off-heap, so is in addition to the memory allocated for heap. The cache also has on-heap overhead which is roughly 128 bytes per chunk (i.e. 0.2% of the reserved size if the default 64k chunk size is used). Memory is only allocated when needed. Min unit: MiB Default Value: 512MiB This option is commented out by default. Flag indicating whether to allocate on or off heap when the sstable buffer pool is exhausted, that is when it has exceeded the maximum memory file_cache_size, beyond which it will not cache buffers but allocate on request. Default Value: true This option is commented out by default. The strategy for optimizing disk read Possible values are: ssd (for solid state disks, the default) spinning (for spinning disks) Default Value: ssd This option is commented out by default. Total permitted memory to use for memtables. Cassandra will stop accepting writes when the limit is exceeded until a flush completes, and will trigger a flush based on memtable_cleanup_threshold If omitted, Cassandra will set both to 1/4 the size of the heap. Min unit: MiB Default Value: 2048MiB This option is commented out by default. Min unit: MiB Default Value: 2048MiB This option is commented out by default. memtable_cleanup_threshold is deprecated. The default calculation is the only reasonable choice. See the comments on memtable_flush_writers for more information. Ratio of occupied non-flushing memtable size to total permitted size that will trigger a flush of the largest memtable. Larger mct will mean larger flushes and hence less compaction, but also less concurrent flush activity which can make it difficult to keep your disks fed under heavy write load. memtable_cleanup_threshold defaults to 1 / (memtable_flush_writers + 1) Default Value: 0.11 Specify the way Cassandra allocates and manages memtable memory. Options are: heap_buffers on heap nio buffers offheap_buffers off heap (direct) nio buffers offheap_objects off heap objects Default Value: heap_buffers This option is commented out by default. Limit memory usage for Merkle tree calculations during repairs of a certain table and common token range. Repair commands targetting multiple tables or virtual nodes can exceed this limit depending on concurrent_merkle_tree_requests. The default is 1/16th of the available heap. The main tradeoff is that smaller trees have less resolution, which can lead to over-streaming data. If you see heap pressure during repairs, consider lowering this, but you cannot go below one mebibyte. If you see lots of over-streaming, consider raising this or using subrange repair. For more details see issues.apache.org/jira/browse/CASSANDRA-14096. Min unit: MiB This option is commented out by default. The number of simultaneous Merkle tree requests during repairs that can be performed by a repair command. The size of each validation request is limited by the repair_session_space property, so setting this to 1 will make sure that a repair command doesn’t exceed that limit, even if the repair command is repairing multiple tables or multiple virtual nodes. There isn’t a limit by default for backwards compatibility, but this can produce OOM for commands repairing multiple tables or multiple virtual nodes. A limit of just 1 simultaneous Merkle tree request is generally recommended with no virtual nodes so repair_session_space, and thereof the Merkle tree resolution, can be high. For virtual nodes a value of 1 with the default repair_session_space value will produce higher resolution Merkle trees at the expense of speed. Alternatively, when working with virtual nodes it can make sense to reduce the repair_session_space and increase the value of concurrent_merkle_tree_requests because each range will contain fewer data. For more details see issues.apache.org/jira/browse/CASSANDRA-19336. A zero value means no limit. Default Value: 0 This option is commented out by default. Total space to use for commit logs on disk. If space gets above this value, Cassandra will flush every dirty CF in the oldest segment and remove it. So a small total commitlog space will tend to cause more flush activity on less-active columnfamilies. The default value is the smaller of 8192, and 1/4 of the total space of the commitlog volume. Default Value: 8192MiB This option is commented out by default. This sets the number of memtable flush writer threads per disk as well as the total number of memtables that can be flushed concurrently. These are generally a combination of compute and IO bound. Memtable flushing is more CPU efficient than memtable ingest and a single thread can keep up with the ingest rate of a whole server on a single fast disk until it temporarily becomes IO bound under contention typically with compaction. At that point you need multiple flush threads. At some point in the future it may become CPU bound all the time. You can tell if flushing is falling behind using the MemtablePool.BlockedOnAllocation metric which should be 0, but will be non-zero if threads are blocked waiting on flushing to free memory. memtable_flush_writers defaults to two for a single data directory. This means that two memtables can be flushed concurrently to the single data directory. If you have multiple data directories the default is one memtable flushing at a time but the flush will use a thread per data directory so you will get two or more writers. Two is generally enough to flush on a fast disk [array] mounted as a single data directory. Adding more flush writers will result in smaller more frequent flushes that introduce more compaction overhead. There is a direct tradeoff between number of memtables that can be flushed concurrently and flush size and frequency. More is not better you just need enough flush writers to never stall waiting for flushing to free memory. Default Value: 2 This option is commented out by default. Total space to use for change-data-capture logs on disk. If space gets above this value, Cassandra will throw WriteTimeoutException on Mutations including tables with CDC enabled. A CDCCompactor is responsible for parsing the raw CDC logs and deleting them when parsing is completed. The default value is the min of 4096 MiB and 1/8th of the total space of the drive where cdc_raw_directory resides. Min unit: MiB Default Value: 4096MiB This option is commented out by default. When we hit our cdc_raw limit and the CDCCompactor is either running behind or experiencing backpressure, we check at the following interval to see if any new space for cdc-tracked tables has been made available. Default to 250ms Min unit: ms Default Value: 250ms A fixed memory pool size in MB for for SSTable index summaries. If left empty, this will default to 5% of the heap size. If the memory usage of all index summaries exceeds this limit, SSTables with low read rates will shrink their index summaries in order to meet this limit. However, this is a best-effort process. In extreme conditions Cassandra may need to use more than this amount of memory. Min unit: KiB How frequently index summaries should be resampled. This is done periodically to redistribute memory from the fixed-size pool to sstables proportional their recent read rates. Setting to null value will disable this process, leaving existing index summaries at their current sampling level. Min unit: m Default Value: 60m Whether to, when doing sequential writing, fsync() at intervals in order to force the operating system to flush the dirty buffers. Enable this to avoid sudden dirty buffer flushing from impacting read latencies. Almost always a good idea on SSDs; not necessarily on platters. Default Value: false Min unit: KiB Default Value: 10240KiB TCP port, for commands and data For security reasons, you should not expose this port to the internet. Firewall it if needed. Default Value: 7000 SSL port, for legacy encrypted communication. This property is unused unless enabled in server_encryption_options (see below). As of cassandra 4.0, this property is deprecated as a single port can be used for either/both secure and insecure connections. For security reasons, you should not expose this port to the internet. Firewall it if needed. Default Value: 7001 Address or interface to bind to and tell other Cassandra nodes to connect to. You must change this if you want multiple nodes to be able to communicate! Set listen_address OR listen_interface, not both. Leaving it blank leaves it up to InetAddress.getLocalHost(). This will always do the Right Thing if the node is properly configured (hostname, name resolution, etc), and the Right Thing is to use the address associated with the hostname (it might not be). If unresolvable it will fall back to InetAddress.getLoopbackAddress(), which is wrong for production systems. Setting listen_address to 0.0.0.0 is always wrong. Default Value: localhost This option is commented out by default. Set listen_address OR listen_interface, not both. Interfaces must correspond to a single address, IP aliasing is not supported. Default Value: eth0 This option is commented out by default. If you choose to specify the interface by name and the interface has an ipv4 and an ipv6 address you can specify which should be chosen using listen_interface_prefer_ipv6. If false the first ipv4 address will be used. If true the first ipv6 address will be used. Defaults to false preferring ipv4. If there is only one address it will be selected regardless of ipv4/ipv6. Default Value: false This option is commented out by default. Address to broadcast to other Cassandra nodes Leaving this blank will set it to the same value as listen_address Default Value: 1.2.3.4 This option is commented out by default. When using multiple physical network interfaces, set this to true to listen on broadcast_address in addition to the listen_address, allowing nodes to communicate in both interfaces. Ignore this property if the network configuration automatically routes between the public and private networks such as EC2. Default Value: false This option is commented out by default. Internode authentication backend, implementing IInternodeAuthenticator; used to allow/disallow connections from peer nodes. Default Value: org.apache.cassandra.auth.AllowAllInternodeAuthenticator Whether to start the native transport server. The address on which the native transport is bound is defined by rpc_address. Default Value: true port for the CQL native transport to listen for clients on For security reasons, you should not expose this port to the internet. Firewall it if needed. Default Value: 9042 This option is commented out by default. Enabling native transport encryption in client_encryption_options allows you to either use encryption for the standard port or to use a dedicated, additional port along with the unencrypted standard native_transport_port. Enabling client encryption and keeping native_transport_port_ssl disabled will use encryption for native_transport_port. Setting native_transport_port_ssl to a different value from native_transport_port will use encryption for native_transport_port_ssl while keeping native_transport_port unencrypted. Default Value: 9142 This option is commented out by default. The maximum threads for handling requests (note that idle threads are stopped after 30 seconds so there is not corresponding minimum setting). Default Value: 128 This option is commented out by default. The maximum size of allowed frame. Frame (requests) larger than this will be rejected as invalid. The default is 16MiB. If you’re changing this parameter, you may want to adjust max_value_size accordingly. This should be positive and less than 2048. Min unit: MiB Default Value: 16MiB This option is commented out by default. The maximum number of concurrent client connections. The default is -1, which means unlimited. Default Value: -1 This option is commented out by default. The maximum number of concurrent client connections per source ip. The default is -1, which means unlimited. Default Value: -1 Controls whether Cassandra honors older, yet currently supported, protocol versions. The default is true, which means all supported protocols will be honored. Default Value: true This option is commented out by default. Controls when idle client connections are closed. Idle connections are ones that had neither reads nor writes for a time period. Clients may implement heartbeats by sending OPTIONS native protocol message after a timeout, which will reset idle timeout timer on the server side. To close idle client connections, corresponding values for heartbeat intervals have to be set on the client side. Idle connection timeouts are disabled by default. Min unit: ms Default Value: 60000ms This option is commented out by default. When enabled, limits the number of native transport requests dispatched for processing per second. Behavior once the limit has been breached depends on the value of THROW_ON_OVERLOAD specified in the STARTUP message sent by the client during connection establishment. (See section \"4.1.1. STARTUP\" in \"CQL BINARY PROTOCOL v5\".) With the THROW_ON_OVERLOAD flag enabled, messages that breach the limit are dropped, and an OverloadedException is thrown for the client to handle. When the flag is not enabled, the server will stop consuming messages from the channel/socket, putting backpressure on the client while already dispatched messages are processed. Default Value: false This option is commented out by default. Default Value: 1000000 The address or interface to bind the native transport server to. Set rpc_address OR rpc_interface, not both. Leaving rpc_address blank has the same effect as on listen_address (i.e. it will be based on the configured hostname of the node). Note that unlike listen_address, you can specify 0.0.0.0, but you must also set broadcast_rpc_address to a value other than 0.0.0.0. For security reasons, you should not expose this port to the internet. Firewall it if needed. Default Value: localhost This option is commented out by default. Set rpc_address OR rpc_interface, not both. Interfaces must correspond to a single address, IP aliasing is not supported. Default Value: eth1 This option is commented out by default. If you choose to specify the interface by name and the interface has an ipv4 and an ipv6 address you can specify which should be chosen using rpc_interface_prefer_ipv6. If false the first ipv4 address will be used. If true the first ipv6 address will be used. Defaults to false preferring ipv4. If there is only one address it will be selected regardless of ipv4/ipv6. Default Value: false This option is commented out by default. RPC address to broadcast to drivers and other Cassandra nodes. This cannot be set to 0.0.0.0. If left blank, this will be set to the value of rpc_address. If rpc_address is set to 0.0.0.0, broadcast_rpc_address must be set. Default Value: 1.2.3.4 enable or disable keepalive on rpc/native connections Default Value: true This option is commented out by default. Uncomment to set socket buffer size for internode communication Note that when setting this, the buffer size is limited by net.core.wmem_max and when not setting it it is defined by net.ipv4.tcp_wmem See also: /proc/sys/net/core/wmem_max /proc/sys/net/core/rmem_max /proc/sys/net/ipv4/tcp_wmem /proc/sys/net/ipv4/tcp_wmem and 'man tcp' Min unit: B This option is commented out by default. Uncomment to set socket buffer size for internode communication Note that when setting this, the buffer size is limited by net.core.wmem_max and when not setting it it is defined by net.ipv4.tcp_wmem Min unit: B Set to true to have Cassandra create a hard link to each sstable flushed or streamed locally in a backups/ subdirectory of the keyspace data. Removing these links is the operator’s responsibility. Default Value: false Whether or not to take a snapshot before each compaction. Be careful using this option, since Cassandra won’t clean up the snapshots for you. Mostly useful if you’re paranoid when there is a data format change. Default Value: false Whether or not a snapshot is taken of the data before keyspace truncation or dropping of column families. The STRONGLY advised default of true should be used to provide data safety. If you set this flag to false, you will lose data on truncation or drop. Default Value: true This option is commented out by default. Adds a time-to-live (TTL) to auto snapshots generated by table truncation or drop (when enabled). After the TTL is elapsed, the snapshot is automatically cleared. By default, auto snapshots do not have TTL, uncomment the property below to enable TTL on auto snapshots. Accepted units: d (days), h (hours) or m (minutes) Default Value: 30d The act of creating or clearing a snapshot involves creating or removing potentially tens of thousands of links, which can cause significant performance impact, especially on consumer grade SSDs. A non-zero value here can be used to throttle these links to avoid negative performance impact of taking and clearing snapshots Default Value: 0 Granularity of the collation index of rows within a partition. Increase if your rows are large, or if you have a very large number of rows per partition. The competing goals are these: a smaller granularity means more index entries are generated and looking up rows withing the partition by collation column is faster but, Cassandra will keep the collation index in memory for hot rows (as part of the key cache), so a larger granularity means you can cache more hot rows Min unit: KiB Default Value: 64KiB Per sstable indexed key cache entries (the collation index in memory mentioned above) exceeding this size will not be held on heap. This means that only partition information is held on heap and the index entries are read from disk. Note that this size refers to the size of the serialized index information and not the size of the partition. Min unit: KiB Default Value: 2KiB This option is commented out by default. Number of simultaneous compactions to allow, NOT including validation \"compactions\" for anti-entropy repair. Simultaneous compactions can help preserve read performance in a mixed read/write workload, by mitigating the tendency of small sstables to accumulate during a single long running compactions. The default is usually fine and if you experience problems with compaction running too slowly or too fast, you should look at compaction_throughput first. concurrent_compactors defaults to the smaller of (number of disks, number of cores), with a minimum of 2 and a maximum of 8. If your data directories are backed by SSD, you should increase this to the number of cores. Default Value: 1 This option is commented out by default. Number of simultaneous repair validations to allow. If not set or set to a value less than 1, it defaults to the value of concurrent_compactors. To set a value greeater than concurrent_compactors at startup, the system property cassandra.allow_unlimited_concurrent_validations must be set to true. To dynamically resize to a value > concurrent_compactors on a running node, first call the bypassConcurrentValidatorsLimit method on the org.apache.cassandra.db:type=StorageService mbean Default Value: 0 Number of simultaneous materialized view builder tasks to allow. Default Value: 1 Throttles compaction to the given total throughput across the entire system. The faster you insert data, the faster you need to compact in order to keep the sstable count down, but in general, setting this to 16 to 32 times the rate you are inserting data is more than sufficient. Setting this to 0 disables throttling. Note that this accounts for all types of compaction, including validation compaction (building Merkle trees for repairs). Default Value: 64MiB/s When compacting, the replacement sstable(s) can be opened before they are completely written, and used in place of the prior sstables for any range that has been written. This helps to smoothly transfer reads between the sstables, reducing page cache churn and keeping hot rows hot Set sstable_preemptive_open_interval to null for disabled which is equivalent to sstable_preemptive_open_interval_in_mb being negative Min unit: MiB Default Value: 50MiB Starting from 4.1 sstables support UUID based generation identifiers. They are disabled by default because once enabled, there is no easy way to downgrade. When the node is restarted with this option set to true, each newly created sstable will have a UUID based generation identifier and such files are not readable by previous Cassandra versions. At some point, this option will become true by default and eventually get removed from the configuration. Default Value: false This option is commented out by default. When enabled, permits Cassandra to zero-copy stream entire eligible SSTables between nodes, including every component. This speeds up the network transfer significantly subject to throttling specified by entire_sstable_stream_throughput_outbound, and entire_sstable_inter_dc_stream_throughput_outbound for inter-DC transfers. Enabling this will reduce the GC pressure on sending and receiving node. When unset, the default is enabled. While this feature tries to keep the disks balanced, it cannot guarantee it. This feature will be automatically disabled if internode encryption is enabled. Default Value: true This option is commented out by default. Throttles entire SSTable outbound streaming file transfers on this node to the given total throughput in Mbps. Setting this value to 0 it disables throttling. When unset, the default is 200 Mbps or 24 MiB/s. Default Value: 24MiB/s This option is commented out by default. Throttles entire SSTable file streaming between datacenters. Setting this value to 0 disables throttling for entire SSTable inter-DC file streaming. When unset, the default is 200 Mbps or 24 MiB/s. Default Value: 24MiB/s This option is commented out by default. Throttles all outbound streaming file transfers on this node to the given total throughput in Mbps. This is necessary because Cassandra does mostly sequential IO when streaming data during bootstrap or repair, which can lead to saturating the network connection and degrading rpc performance. When unset, the default is 200 Mbps or 24 MiB/s. Default Value: 24MiB/s This option is commented out by default. Throttles all streaming file transfer between the datacenters, this setting allows users to throttle inter dc stream throughput in addition to throttling all network stream traffic as configured with stream_throughput_outbound_megabits_per_sec When unset, the default is 200 Mbps or 24 MiB/s. Default Value: 24MiB/s Server side timeouts for requests. The server will return a timeout exception to the client if it can’t complete an operation within the corresponding timeout. Those settings are a protection against: 1) having client wait on an operation that might never terminate due to some failures. 2) operations that use too much CPU/read too much data (leading to memory build up) by putting a limit to how long an operation will execute. For this reason, you should avoid putting these settings too high. In other words, if you are timing out requests because of underlying resource constraints then increasing the timeout will just cause more problems. Of course putting them too low is equally ill-advised since clients could get timeouts even for successful operations just because the timeout setting is too tight. How long the coordinator should wait for read operations to complete. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 5000ms How long the coordinator should wait for seq or index scans to complete. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 10000ms How long the coordinator should wait for writes to complete. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 2000ms How long the coordinator should wait for counter writes to complete. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 5000ms How long a coordinator should continue to retry a CAS operation that contends with other proposals for the same row. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 1000ms How long the coordinator should wait for truncates to complete (This can be much longer, because unless auto_snapshot is disabled we need to flush first so we can snapshot before removing the data.) Lowest acceptable value is 10 ms. Min unit: ms Default Value: 60000ms The default timeout for other, miscellaneous operations. Lowest acceptable value is 10 ms. Min unit: ms Default Value: 10000ms This option is commented out by default. Defensive settings for protecting Cassandra from true network partitions. See (CASSANDRA-14358) for details. The amount of time to wait for internode tcp connections to establish. Min unit: ms Default Value: 2000ms This option is commented out by default. The amount of time unacknowledged data is allowed on a connection before we throw out the connection Note this is only supported on Linux + epoll, and it appears to behave oddly above a setting of 30000 (it takes much longer than 30s) as of Linux 4.12. If you want something that high set this to 0 which picks up the OS default and configure the net.ipv4.tcp_retries2 sysctl to be ~8. Min unit: ms Default Value: 30000ms This option is commented out by default. The amount of time unacknowledged data is allowed on a streaming connection. The default is 5 minutes. Increase it or set it to 0 in order to increase the timeout. Min unit: ms Default Value: 300000ms This option is commented out by default. Global, per-endpoint and per-connection limits imposed on messages queued for delivery to other nodes and waiting to be processed on arrival from other nodes in the cluster. These limits are applied to the on-wire size of the message being sent or received. The basic per-link limit is consumed in isolation before any endpoint or global limit is imposed. Each node-pair has three links: urgent, small and large. So any given node may have a maximum of N*3*(internode_application_send_queue_capacity+internode_application_receive_queue_capacity) messages queued without any coordination between them although in practice, with token-aware routing, only RF*tokens nodes should need to communicate with significant bandwidth. The per-endpoint limit is imposed on all messages exceeding the per-link limit, simultaneously with the global limit, on all links to or from a single node in the cluster. The global limit is imposed on all messages exceeding the per-link limit, simultaneously with the per-endpoint limit, on all links to or from any node in the cluster. Min unit: B Default Value: 4MiB This option is commented out by default. Default Value: 128MiB This option is commented out by default. Default Value: 512MiB This option is commented out by default. Default Value: 4MiB This option is commented out by default. Default Value: 128MiB This option is commented out by default. Default Value: 512MiB How long before a node logs slow queries. Select queries that take longer than this timeout to execute, will generate an aggregated log message, so that slow queries can be identified. Set this value to zero to disable slow query logging. Min unit: ms Default Value: 500ms This option is commented out by default. Enable operation timeout information exchange between nodes to accurately measure request timeouts. If disabled, replicas will assume that requests were forwarded to them instantly by the coordinator, which means that under overload conditions we will waste that much extra time processing already-timed-out requests. Warning: It is generally assumed that users have setup NTP on their clusters, and that clocks are modestly in sync, since this is a requirement for general correctness of last write wins. Default Value: true This option is commented out by default. Set period for idle state control messages for earlier detection of failed streams This node will send a keep-alive message periodically on the streaming’s control channel. This ensures that any eventual SocketTimeoutException will occur within 2 keep-alive cycles If the node cannot send, or timeouts sending, the keep-alive message on the netty control channel the stream session is closed. Default value is 300s (5 minutes), which means stalled streams are detected within 10 minutes Specify 0 to disable. Min unit: s Default Value: 300s This option is commented out by default. Limit number of connections per host for streaming Increase this when you notice that joins are CPU-bound rather that network bound (for example a few nodes with big files). Default Value: 1 This option is commented out by default. Settings for stream stats tracking; used by system_views.streaming table How long before a stream is evicted from tracking; this impacts both historic and currently running streams. Default Value: 3d This option is commented out by default. How much memory may be used for tracking before evicting session from tracking; once crossed historic and currently running streams maybe impacted. Default Value: 40MiB This option is commented out by default. Enable/Disable tracking of streaming stats Default Value: true This option is commented out by default. Allows denying configurable access (rw/rr) to operations on configured ks, table, and partitions, intended for use by operators to manage cluster health vs application access. See CASSANDRA-12106 and CEP-13 for more details. Default Value: false This option is commented out by default. Default Value: true This option is commented out by default. Default Value: true This option is commented out by default. Default Value: true This option is commented out by default. The interval at which keys in the cache for denylisting will \"expire\" and async refresh from the backing DB. Note: this serves only as a fail-safe, as the usage pattern is expected to be \"mutate state, refresh cache\" on any changes to the underlying denylist entries. See documentation for details. Min unit: s Default Value: 600s This option is commented out by default. In the event of errors on attempting to load the denylist cache, retry on this interval. Min unit: s Default Value: 5s This option is commented out by default. We cap the number of denylisted keys allowed per table to keep things from growing unbounded. Nodes will warn above this limit while allowing new denylisted keys to be inserted. Denied keys are loaded in natural query / clustering ordering by partition key in case of overflow. Default Value: 1000 This option is commented out by default. We cap the total number of denylisted keys allowed in the cluster to keep things from growing unbounded. Nodes will warn on initial cache load that there are too many keys and be direct the operator to trim down excess entries to within the configured limits. Default Value: 10000 This option is commented out by default. Since the denylist in many ways serves to protect the health of the cluster from partitions operators have identified as being in a bad state, we usually want more robustness than just CL.ONE on operations to/from these tables to ensure that these safeguards are in place. That said, we allow users to configure this if they’re so inclined. Default Value: QUORUM This option is commented out by default. phi value that must be reached for a host to be marked down. most users should never need to adjust this. Default Value: 8 endpoint_snitch — Set this to a class that implements IEndpointSnitch. The snitch has two functions: it teaches Cassandra enough about your network topology to route requests efficiently it allows Cassandra to spread replicas around your cluster to avoid correlated failures. It does this by grouping machines into \"datacenters\" and \"racks.\" Cassandra will do its best not to have more than one replica on the same \"rack\" (which may not actually be a physical location) CASSANDRA WILL NOT ALLOW YOU TO SWITCH TO AN INCOMPATIBLE SNITCH ONCE DATA IS INSERTED INTO THE CLUSTER. This would cause data loss. This means that if you start with the default SimpleSnitch, which locates every node on \"rack1\" in \"datacenter1\", your only options if you need to add another datacenter are GossipingPropertyFileSnitch (and the older PFS). From there, if you want to migrate to an incompatible snitch like Ec2Snitch you can do it by adding new nodes under Ec2Snitch (which will locate them in a new \"datacenter\") and decommissioning the old ones. Out of the box, Cassandra provides: SimpleSnitch: Treats Strategy order as proximity. This can improve cache locality when disabling read repair. Only appropriate for single-datacenter deployments. GossipingPropertyFileSnitch This should be your go-to snitch for production use. The rack and datacenter for the local node are defined in cassandra-rackdc.properties and propagated to other nodes via gossip. If cassandra-topology.properties exists, it is used as a fallback, allowing migration from the PropertyFileSnitch. PropertyFileSnitch: Proximity is determined by rack and data center, which are explicitly configured in cassandra-topology.properties. Ec2Snitch: Appropriate for EC2 deployments in a single Region. Loads Region and Availability Zone information from the EC2 API. The Region is treated as the datacenter, and the Availability Zone as the rack. Only private IPs are used, so this will not work across multiple Regions. Ec2MultiRegionSnitch: Uses public IPs as broadcast_address to allow cross-region connectivity. (Thus, you should set seed addresses to the public IP as well.) You will need to open the storage_port or ssl_storage_port on the public IP firewall. (For intra-Region traffic, Cassandra will switch to the private IP after establishing a connection.) RackInferringSnitch: Proximity is determined by rack and data center, which are assumed to correspond to the 3rd and 2nd octet of each node’s IP address, respectively. Unless this happens to match your deployment conventions, this is best used as an example of writing a custom Snitch class and is provided in that spirit. You can use a custom Snitch by setting this to the full class name of the snitch, which will be assumed to be on your classpath. Default Value: SimpleSnitch controls how often to perform the more expensive part of host score calculation Min unit: ms Default Value: 100ms controls how often to reset all host scores, allowing a bad host to possibly recover Min unit: ms Default Value: 600000ms if set greater than zero, this will allow 'pinning' of replicas to hosts in order to increase cache capacity. The badness threshold will control how much worse the pinned host has to be before the dynamic snitch will prefer other replicas over it. This is expressed as a double which represents a percentage. Thus, a value of 0.2 means Cassandra would continue to prefer the static snitch values until the pinned host was 20% worse than the fastest. Default Value: 1.0 Configure server-to-server internode encryption JVM and netty defaults for supported SSL socket protocols and cipher suites can be replaced using custom encryption options. This is not recommended unless you have policies in place that dictate certain settings, or need to disable vulnerable ciphers or protocols in case the JVM cannot be updated. FIPS compliant settings can be configured at JVM level and should not involve changing encryption settings here: docs.oracle.com/javase/8/docs/technotes/guides/security/jsse/FIPS.html NOTE this default configuration is an insecure configuration. If you need to enable server-to-server encryption generate server keystores (and truststores for mutual authentication) per: download.oracle.com/javase/8/docs/technotes/guides/security/jsse/JSSERefGuide.html#CreateKeystore Then perform the following configuration changes: Step 1: Set internode_encryption= and explicitly set optional=true. Restart all nodes Step 2: Set optional=false (or remove it) and if you generated truststores and want to use mutual auth set require_client_auth=true. Restart all nodes Default Value (complex option): # On outbound connections, determine which type of peers to securely connect to. # The available options are : # none : Do not encrypt outgoing connections # dc : Encrypt connections to peers in other datacenters but not within datacenters # rack : Encrypt connections to peers in other racks but not within racks # all : Always use encrypted connections internode_encryption: none # When set to true, encrypted and unencrypted connections are allowed on the storage_port # This should _only be true_ while in unencrypted or transitional operation # optional defaults to true if internode_encryption is none # optional: true # If enabled, will open up an encrypted listening socket on ssl_storage_port. Should only be used # during upgrade to 4.0; otherwise, set to false. legacy_ssl_storage_port_enabled: false # Set to a valid keystore if internode_encryption is dc, rack or all keystore: conf/.keystore keystore_password: cassandra # Configure the way Cassandra creates SSL contexts. # To use PEM-based key material, see org.apache.cassandra.security.PEMBasedSslContextFactory # ssl_context_factory: # # Must be an instance of org.apache.cassandra.security.ISslContextFactory # class_name: org.apache.cassandra.security.DefaultSslContextFactory # Verify peer server certificates require_client_auth: false # Set to a valid trustore if require_client_auth is true truststore: conf/.truststore truststore_password: cassandra # Verify that the host name in the certificate matches the connected host require_endpoint_verification: false # More advanced defaults: # protocol: TLS # store_type: JKS # cipher_suites: [ # TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384, TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256, # TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, # TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_128_GCM_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA, # TLS_RSA_WITH_AES_256_CBC_SHA # ] Configure client-to-server encryption. NOTE this default configuration is an insecure configuration. If you need to enable client-to-server encryption generate server keystores (and truststores for mutual authentication) per: download.oracle.com/javase/8/docs/technotes/guides/security/jsse/JSSERefGuide.html#CreateKeystore Then perform the following configuration changes: Step 1: Set enabled=true and explicitly set optional=true. Restart all nodes Step 2: Set optional=false (or remove it) and if you generated truststores and want to use mutual auth set require_client_auth=true. Restart all nodes Default Value (complex option): # Enable client-to-server encryption enabled: false # When set to true, encrypted and unencrypted connections are allowed on the native_transport_port # This should _only be true_ while in unencrypted or transitional operation # optional defaults to true when enabled is false, and false when enabled is true. # optional: true # Set keystore and keystore_password to valid keystores if enabled is true keystore: conf/.keystore keystore_password: cassandra # Configure the way Cassandra creates SSL contexts. # To use PEM-based key material, see org.apache.cassandra.security.PEMBasedSslContextFactory # ssl_context_factory: # # Must be an instance of org.apache.cassandra.security.ISslContextFactory # class_name: org.apache.cassandra.security.DefaultSslContextFactory # Verify client certificates require_client_auth: false # Set trustore and truststore_password if require_client_auth is true # truststore: conf/.truststore # truststore_password: cassandra # More advanced defaults: # protocol: TLS # store_type: JKS # cipher_suites: [ # TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384, TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256, # TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, # TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_128_GCM_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA, # TLS_RSA_WITH_AES_256_CBC_SHA # ] internode_compression controls whether traffic between nodes is compressed. Can be: all all traffic is compressed dc traffic between different datacenters is compressed none nothing is compressed. Default Value: dc Enable or disable tcp_nodelay for inter-dc communication. Disabling it will result in larger (but fewer) network packets being sent, reducing overhead from the TCP protocol itself, at the cost of increasing latency if you block for cross-datacenter responses. Default Value: false TTL for different trace types used during logging of the repair process. Min unit: s Default Value: 1d Min unit: s Default Value: 7d If unset, all GC Pauses greater than gc_log_threshold will log at INFO level UDFs (user defined functions) are disabled by default. As of Cassandra 3.0 there is a sandbox in place that should prevent execution of evil code. Default Value: false Enables scripted UDFs (JavaScript UDFs). Java UDFs are always enabled, if user_defined_functions_enabled is true. Enable this option to be able to use UDFs with \"language javascript\" or any custom JSR-223 provider. This option has no effect, if user_defined_functions_enabled is false. Default Value: false Enables encrypting data at-rest (on disk). Different key providers can be plugged in, but the default reads from a JCE-style keystore. A single keystore can hold multiple keys, but the one referenced by the \"key_alias\" is the only key that will be used for encrypt opertaions; previously used keys can still (and should!) be in the keystore and will be used on decrypt operations (to handle the case of key rotation). It is strongly recommended to download and install Java Cryptography Extension (JCE) Unlimited Strength Jurisdiction Policy Files for your version of the JDK. (current link: www.oracle.com/technetwork/java/javase/downloads/jce8-download-2133166.html) Currently, only the following file types are supported for transparent data encryption, although more are coming in future cassandra releases: commitlog, hints Default Value (complex option): enabled: false chunk_length_kb: 64 cipher: AES/CBC/PKCS5Padding key_alias: testing:1 # CBC IV length for AES needs to be 16 bytes (which is also the default size) # iv_length: 16 key_provider: - class_name: org.apache.cassandra.security.JKSKeyProvider parameters: - keystore: conf/.keystore keystore_password: cassandra store_type: JCEKS key_password: cassandra SAFETY THRESHOLDS # When executing a scan, within or across a partition, we need to keep the tombstones seen in memory so we can return them to the coordinator, which will use them to make sure other replicas also know about the deleted rows. With workloads that generate a lot of tombstones, this can cause performance problems and even exaust the server heap. (www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets) Adjust the thresholds here if you understand the dangers and want to scan more tombstones anyway. These thresholds may also be adjusted at runtime using the StorageService mbean. Default Value: 1000 Default Value: 100000 Filtering and secondary index queries at read consistency levels above ONE/LOCAL_ONE use a mechanism called replica filtering protection to ensure that results from stale replicas do not violate consistency. (See CASSANDRA-8272 and CASSANDRA-15907 for more details.) This mechanism materializes replica results by partition on-heap at the coordinator. The more possibly stale results returned by the replicas, the more rows materialized during the query. Log WARN on any multiple-partition batch size exceeding this value. 5KiB per batch by default. Caution should be taken on increasing the size of this threshold as it can lead to node instability. Min unit: KiB Default Value: 5KiB Fail any multiple-partition batch exceeding this value. 50KiB (10x warn threshold) by default. Min unit: KiB Default Value: 50KiB Log WARN on any batches not of type LOGGED than span across more partitions than this limit Default Value: 10 Log a warning when compacting partitions larger than this value Default Value: 100MiB Log a warning when writing more tombstones than this value to a partition Default Value: 100000 This option is commented out by default. GC Pauses greater than 200 ms will be logged at INFO level This threshold can be adjusted to minimize logging if necessary Min unit: ms Default Value: 200ms This option is commented out by default. GC Pauses greater than gc_warn_threshold will be logged at WARN level Adjust the threshold based on your application throughput requirement. Setting to 0 will deactivate the feature. Min unit: ms Default Value: 1000ms This option is commented out by default. Maximum size of any value in SSTables. Safety measure to detect SSTable corruption early. Any value size larger than this threshold will result into marking an SSTable as corrupted. This should be positive and less than 2GiB. Min unit: MiB Default Value: 256MiB This option is commented out by default. Impact on keyspace creation ** If replication factor is not mentioned as part of keyspace creation, default_keyspace_rf would apply. Changing this configuration would only take effect for keyspaces created after the change, but does not impact existing keyspaces created prior to the change. Impact on keyspace alter ** When altering a keyspace from NetworkTopologyStrategy to SimpleStrategy, default_keyspace_rf is applied if rf is not explicitly mentioned. Impact on system keyspaces ** This would also apply for any system keyspaces that need replication factor. A further note about system keyspaces - system_traces and system_distributed keyspaces take RF of 2 or default, whichever is higher, and system_auth keyspace takes RF of 1 or default, whichever is higher. Suggested value for use in production: 3 Default Value: 1 This option is commented out by default. Track a metric per keyspace indicating whether replication achieved the ideal consistency level for writes without timing out. This is different from the consistency level requested by each write which may be lower in order to facilitate availability. Default Value: EACH_QUORUM This option is commented out by default. Automatically upgrade sstables after upgrade - if there is no ordinary compaction to do, the oldest non-upgraded sstable will get upgraded to the latest version Default Value: false This option is commented out by default. Limit the number of concurrent sstable upgrades Default Value: 1 Audit logging - Logs every incoming CQL command request, authentication to a node. See the docs on audit_logging for full details about the various configuration options. This option is commented out by default. default options for full query logging - these can be overridden from command line when executing nodetool enablefullquerylog This option is commented out by default. validate tombstones on reads and compaction can be either \"disabled\", \"warn\" or \"exception\" Default Value: disabled Diagnostic Events # If enabled, diagnostic events can be helpful for troubleshooting operational issues. Emitted events contain details on internal state and temporal relationships across events, accessible by clients via JMX. Default Value: false This option is commented out by default. Use native transport TCP message coalescing. If on upgrade to 4.0 you found your throughput decreasing, and in particular you run an old kernel or have very fewer client connections, this option might be worth evaluating. Default Value: false Enable tracking of repaired state of data during reads and comparison between replicas Mismatches between the repaired sets of replicas can be characterized as either confirmed or unconfirmed. In this context, unconfirmed indicates that the presence of pending repair sessions, unrepaired partition tombstones, or some other condition means that the disparity cannot be considered conclusive. Confirmed mismatches should be a trigger for investigation as they may be indicative of corruption or data loss. There are separate flags for range vs partition reads as single partition reads are only tracked when CL > 1 and a digest mismatch occurs. Currently, range queries don’t use digests so if enabled for range reads, all range reads will include repaired data tracking. As this adds some overhead, operators may wish to disable it whilst still enabling it for partition reads Default Value: false Default Value: false If false, only confirmed mismatches will be reported. If true, a separate metric for unconfirmed mismatches will also be recorded. This is to avoid potential signal:noise issues are unconfirmed mismatches are less actionable than confirmed ones. Default Value: false This option is commented out by default. Having many tables and/or keyspaces negatively affects performance of many operations in the cluster. When the number of tables/keyspaces in the cluster exceeds the following thresholds a client warning will be sent back to the user when creating a table or keyspace. As of cassandra 4.1, these properties are deprecated in favor of keyspaces_warn_threshold and tables_warn_threshold Default Value: 150 This option is commented out by default. Default Value: 40 This option is commented out by default. configure the read and write consistency levels for modifications to auth tables Default Value: LOCAL_QUORUM This option is commented out by default. Default Value: EACH_QUORUM This option is commented out by default. Delays on auth resolution can lead to a thundering herd problem on reconnects; this option will enable warming of auth caches prior to node completing startup. See CASSANDRA-16958 Default Value: false # EXPERIMENTAL FEATURES # # Enables materialized view creation on this node. Materialized views are considered experimental and are not recommended for production use. Default Value: false Enables SASI index creation on this node. SASI indexes are considered experimental and are not recommended for production use. Default Value: false Enables creation of transiently replicated keyspaces on this node. Transient replication is experimental and is not recommended for production use. Default Value: false Enables the used of 'ALTER …​ DROP COMPACT STORAGE' statements on this node. 'ALTER …​ DROP COMPACT STORAGE' is considered experimental and is not recommended for production use. Default Value: false This option is commented out by default. Whether or not USE is allowed. This is enabled by default to avoid failure on upgrade. Default Value: true This option is commented out by default. When the client triggers a protocol exception or unknown issue (Cassandra bug) we increment a client metric showing this; this logic will exclude specific subnets from updating these metrics This option is commented out by default. subnets: - 127.0.0.1 - 127.0.0.0/31 Enables read thresholds (warn/fail) across all replicas for reporting back to the client. See: CASSANDRA-16850 Default Value: false # scheduled to be set true in 4.2 This option is commented out by default. When read_thresholds_enabled: true, this tracks the materialized size of a query on the coordinator. If coordinator_read_size_warn_threshold is defined, this will emit a warning to clients with details on what query triggered this as well as the size of the result set; if coordinator_read_size_fail_threshold is defined, this will fail the query after it has exceeded this threshold, returning a read error to the user. This option is commented out by default. This option is commented out by default. When read_thresholds_enabled: true, this tracks the size of the local read (as defined by heap size), and will warn/fail based off these thresholds; undefined disables these checks. This option is commented out by default. This option is commented out by default. When read_thresholds_enabled: true, this tracks the expected memory size of the RowIndexEntry and will warn/fail based off these thresholds; undefined disables these checks This option is commented out by default. This option is commented out by default. Guardrail to warn or fail when creating more user keyspaces than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn or fail when creating more user tables than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to enable or disable the ability to create uncompressed tables Default Value: true This option is commented out by default. Guardrail to warn or fail when creating/altering a table with more columns per table than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn or fail when creating more secondary indexes per table than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to enable or disable the creation of secondary indexes Default Value: true This option is commented out by default. Guardrail to warn or fail when creating more materialized views per table than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn about, ignore or reject properties when creating tables. By default all properties are allowed. Default Value: [] This option is commented out by default. Default Value: [] This option is commented out by default. Default Value: [] This option is commented out by default. Guardrail to allow/disallow user-provided timestamps. Defaults to true. Default Value: true This option is commented out by default. Guardrail to allow/disallow GROUP BY functionality. Default Value: true This option is commented out by default. Guardrail to allow/disallow TRUNCATE and DROP TABLE statements Default Value: true This option is commented out by default. Guardrail to warn or fail when using a page size greater than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to allow/disallow list operations that require read before write, i.e. setting list element by index and removing list elements by either index or value. Defaults to true. Default Value: true This option is commented out by default. Guardrail to warn or fail when querying with an IN restriction selecting more partition keys than threshold. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn or fail when an IN query creates a cartesian product with a size exceeding threshold, eg. \"a in (1,2,…​10) and b in (1,2…​10)\" results in cartesian product of 100. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn about or reject read consistency levels. By default, all consistency levels are allowed. Default Value: [] This option is commented out by default. Default Value: [] This option is commented out by default. Guardrail to warn about or reject write consistency levels. By default, all consistency levels are allowed. Default Value: [] This option is commented out by default. Default Value: [] This option is commented out by default. Guardrail to warn or fail when encountering larger size of collection data than threshold. At query time this guardrail is applied only to the collection fragment that is being writen, even though in the case of non-frozen collections there could be unaccounted parts of the collection on the sstables. This is done this way to prevent read-before-write. The guardrail is also checked at sstable write time to detect large non-frozen collections, although in that case exceeding the fail threshold will only log an error message, without interrupting the operation. The two thresholds default to null to disable. Min unit: B This option is commented out by default. Min unit: B This option is commented out by default. Guardrail to warn or fail when encountering more elements in collection than threshold. At query time this guardrail is applied only to the collection fragment that is being writen, even though in the case of non-frozen collections there could be unaccounted parts of the collection on the sstables. This is done this way to prevent read-before-write. The guardrail is also checked at sstable write time to detect large non-frozen collections, although in that case exceeding the fail threshold will only log an error message, without interrupting the operation. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to allow/disallow querying with ALLOW FILTERING. Defaults to true. Default Value: true This option is commented out by default. Guardrail to warn or fail when creating a user-defined-type with more fields in than threshold. Default -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Guardrail to warn or fail when local data disk usage percentage exceeds threshold. Valid values are in [1, 100]. This is only used for the disks storing data directories, so it won’t count any separate disks used for storing the commitlog, hints nor saved caches. The disk usage is the ratio between the amount of space used by the data directories and the addition of that same space and the remaining free space on disk. The main purpose of this guardrail is rejecting user writes when the disks are over the defined usage percentage, so the writes done by background processes such as compaction and streaming don’t fail due to a full disk. The limits should be defined accordingly to the expected data growth due to those background processes, so for example a compaction strategy doubling the size of the data would require to keep the disk usage under 50%. The two thresholds default to -1 to disable. Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Allows defining the max disk size of the data directories when calculating thresholds for disk_usage_percentage_warn_threshold and disk_usage_percentage_fail_threshold, so if this is greater than zero they become percentages of a fixed size on disk instead of percentages of the physically available disk size. This should be useful when we have a large disk and we only want to use a part of it for Cassandra’s data directories. Valid values are in [1, max available disk size of all data directories]. Defaults to null to disable and use the physically available disk size of data directories during calculations. Min unit: B This option is commented out by default. Guardrail to warn or fail when the minimum replication factor is lesser than threshold. This would also apply to system keyspaces. Suggested value for use in production: 2 or higher Default Value: -1 This option is commented out by default. Default Value: -1 This option is commented out by default. Startup Checks are executed as part of Cassandra startup process, not all of them are configurable (so you can disable them) but these which are enumerated bellow. Uncomment the startup checks and configure them appropriately to cover your needs.","title":"cassandra.yaml file 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big things happened as part of CASSANDRA-15234: 1) Renaming of parameters in cassandra.yaml to follow the form noun_verb. 2) Liberating cassandra.yaml parameters from their units (DataStorage, DataRate and Duration) and introducing temporary smallest accepted unit per parameter (only for DataStorage and Duration ones) 3) Backward compatibility framework to support the old names and lack of units support until at least the next major release. The community has decided to allow operators to specify units for Cassandra parameters of types duration, data storage, and data rate. All parameters which had a particular unit (most of the time added as a suffix to their name) can be now set by using the format [value][unit]. The unit suffix has been removed from their names. Supported units: Parameter Type Units Supported Duration d, h, m, s, ms, us, µs, ns Data Storage B, KiB, MiB, GiB Data Rate B/s, MiB/s, KiB/s Example: Old name and value format: permissions_update_interval_ms: 0 New name and possible value formats: permissions_update_interval: 0ms permissions_update_interval: 0s permissions_update_interval: 0d permissions_update_interval: 0us permissions_update_interval: 0µs The work in CASSANDRA-15234 was already quite big, so we decided to introduce the notion of the smallest allowed unit per parameter for duration and data storage parameters. What does this mean? Cassandra’s internals still use the old units for parameters. If, for example, seconds are used internally, but you want to add a value in nanoseconds in cassandra.yaml, you will get a configuration exception that contains the following information: Accepted units: seconds, minutes, hours, days. Why was this needed? Because we can run into precision issues. The full solution to the problem is to convert internally all parameters’ values to be manipulated with the smallest supported by Cassandra unit. A series of tickets to assess and maybe migrate to the smallest unit our parameters (incrementally, post CASSANDRA-15234) will be opened in the future. Old Name New Name The Smallest Supported Unit permissions_validity_in_ms permissions_validity ms permissions_update_interval_in_ms permissions_update_interval ms roles_validity_in_ms roles_validity ms roles_update_interval_in_ms roles_update_interval ms credentials_validity_in_ms credentials_validity ms credentials_update_interval_in_ms credentials_update_interval ms max_hint_window_in_ms max_hint_window ms native_transport_idle_timeout_in_ms native_transport_idle_timeout ms request_timeout_in_ms request_timeout ms read_request_timeout_in_ms read_request_timeout ms range_request_timeout_in_ms range_request_timeout ms write_request_timeout_in_ms write_request_timeout ms counter_write_request_timeout_in_ms counter_write_request_timeout ms cas_contention_timeout_in_ms cas_contention_timeout ms truncate_request_timeout_in_ms truncate_request_timeout ms streaming_keep_alive_period_in_secs streaming_keep_alive_period s cross_node_timeout internode_timeout - slow_query_log_timeout_in_ms slow_query_log_timeout ms memtable_heap_space_in_mb memtable_heap_space MiB memtable_offheap_space_in_mb memtable_offheap_space MiB repair_session_space_in_mb repair_session_space MiB internode_max_message_size_in_bytes internode_max_message_size B internode_send_buff_size_in_bytes internode_socket_send_buffer_size B internode_socket_send_buffer_size_in_bytes internode_socket_send_buffer_size B internode_socket_receive_buffer_size_in_bytes internode_socket_receive_buffer_size B internode_recv_buff_size_in_bytes internode_socket_receive_buffer_size B internode_application_send_queue_capacity_in_bytes internode_application_send_queue_capacity B internode_application_send_queue_reserve_endpoint_capacity_in_bytes internode_application_send_queue_reserve_endpoint_capacity B internode_application_send_queue_reserve_global_capacity_in_bytes internode_application_send_queue_reserve_global_capacity B internode_application_receive_queue_capacity_in_bytes internode_application_receive_queue_capacity B internode_application_receive_queue_reserve_endpoint_capacity_in_bytes internode_application_receive_queue_reserve_endpoint_capacity B internode_application_receive_queue_reserve_global_capacity_in_bytes internode_application_receive_queue_reserve_global_capacity B internode_tcp_connect_timeout_in_ms internode_tcp_connect_timeout ms internode_tcp_user_timeout_in_ms internode_tcp_user_timeout ms internode_streaming_tcp_user_timeout_in_ms internode_streaming_tcp_user_timeout ms native_transport_max_frame_size_in_mb native_transport_max_frame_size MiB max_value_size_in_mb max_value_size MiB column_index_size_in_kb column_index_size KiB column_index_cache_size_in_kb column_index_cache_size KiB batch_size_warn_threshold_in_kb batch_size_warn_threshold KiB batch_size_fail_threshold_in_kb batch_size_fail_threshold KiB compaction_throughput_mb_per_sec compaction_throughput MiB/s compaction_large_partition_warning_threshold_mb compaction_large_partition_warning_threshold MiB min_free_space_per_drive_in_mb min_free_space_per_drive MiB stream_throughput_outbound_megabits_per_sec stream_throughput_outbound MiB/s inter_dc_stream_throughput_outbound_megabits_per_sec inter_dc_stream_throughput_outbound MiB/s commitlog_total_space_in_mb commitlog_total_space MiB commitlog_sync_group_window_in_ms commitlog_sync_group_window ms commitlog_sync_period_in_ms commitlog_sync_period ms commitlog_segment_size_in_mb commitlog_segment_size MiB periodic_commitlog_sync_lag_block_in_ms periodic_commitlog_sync_lag_block ms max_mutation_size_in_kb max_mutation_size KiB cdc_total_space_in_mb cdc_total_space MiB cdc_free_space_check_interval_ms cdc_free_space_check_interval ms dynamic_snitch_update_interval_in_ms dynamic_snitch_update_interval ms dynamic_snitch_reset_interval_in_ms dynamic_snitch_reset_interval ms hinted_handoff_throttle_in_kb hinted_handoff_throttle KiB batchlog_replay_throttle_in_kb batchlog_replay_throttle KiB hints_flush_period_in_ms hints_flush_period ms max_hints_file_size_in_mb max_hints_file_size MiB trickle_fsync_interval_in_kb trickle_fsync_interval KiB sstable_preemptive_open_interval_in_mb sstable_preemptive_open_interval MiB key_cache_size_in_mb key_cache_size MiB row_cache_size_in_mb row_cache_size MiB counter_cache_size_in_mb counter_cache_size MiB networking_cache_size_in_mb networking_cache_size MiB file_cache_size_in_mb file_cache_size MiB index_summary_capacity_in_mb index_summary_capacity MiB index_summary_resize_interval_in_minutes index_summary_resize_interval m gc_log_threshold_in_ms gc_log_threshold ms gc_warn_threshold_in_ms gc_warn_threshold ms tracetype_query_ttl trace_type_query_ttl s tracetype_repair_ttl trace_type_repair_ttl s prepared_statements_cache_size_mb prepared_statements_cache_size MiB enable_user_defined_functions user_defined_functions_enabled - enable_scripted_user_defined_functions scripted_user_defined_functions_enabled - enable_materialized_views materialized_views_enabled - enable_transient_replication transient_replication_enabled - enable_sasi_indexes sasi_indexes_enabled - enable_drop_compact_storage drop_compact_storage_enabled - enable_user_defined_functions_threads user_defined_functions_threads_enabled - enable_legacy_ssl_storage_port legacy_ssl_storage_port_enabled - user_defined_function_fail_timeout user_defined_functions_fail_timeout ms user_defined_function_warn_timeout user_defined_functions_warn_timeout ms cache_load_timeout_seconds cache_load_timeout s Another TO DO is to add JMX methods supporting the new format. However, we may abandon this if virtual tables support configuration changes in the near future. Notes for Cassandra Developers: Most of our parameters are already moved to the new framework as part of CASSANDRA-15234. @Replaces is the annotation to be used when you make changes to any configuration parameters in Config class and cassandra.yaml, and you want to add backward compatibility with previous Cassandra versions. Converters class enumerates the different methods used for backward compatibility. IDENTITY is the one used for name change only. For more information about the other Converters, please, check the JavaDoc in the class. For backward compatibility virtual table Settings contains both the old and the new parameters with the old and the new value format. Only exception at the moment are the following three parameters: key_cache_save_period, row_cache_save_period and counter_cache_save_period which appear only once with the new value format. The old names and value format still can be used at least until the next major release. Deprecation warning is emitted on startup. If the parameter is of type duration, data rate or data storage, its value should be accompanied by a unit when new name is used. Please follow the new format noun_verb when adding new configuration parameters. Please consider adding any new parameters with the lowest supported by Cassandra unit when possible. Our new types also support long and integer upper bound, depending on your needs. All options for configuration parameters' types are nested classes in our three main abstract classes - DurationSpec, DataStorageSpec, DataRateSpec. If for some reason you consider the smallest unit for a new parameter shouldn’t be the one that is supported as such in Cassandra, you can use the rest of the nested classes in DurationSpec, DataStorageSpec. The smallest allowed unit is the one we use internally for the property, so we don’t have to do conversions to bigger units which will lead to precision problems. This is a problem only with DurationSpec and DataStorageSpec. DataRateSpec is handled internally in double. New parameters should be added as non-negative numbers. For parameters where you would have set -1 to disable in the past, you might want to consider a separate flag parameter or null value. In case you use the null value, please, ensure that any default value introduced in the DatabaseDescriptor to handle it is also duplicated in any related setters. Parameters of type data storage, duration and data rate cannot be set to Long.MAX_VALUE (former parameters of long type) and Integer.MAX_VALUE (former parameters of int type). That numbers are used during conversion between units to prevent an overflow from happening. Any time you add @Replaces with a name change, we need to add an entry in this Python dictionary in CCM to support the same backward compatibility as SnakeYAML. Please follow the instructions in requirements.txt in the DTest repo how to retag CCM after committing any changes. You might want to test also with tagging in your repo to ensure that there will be no surprise after retagging the official CCM. Please be sure to run a full CI after any changes as CCM affects a few of our testing suites. Some configuration parameters are not announced in cassandra.yaml, but they are presented in the Config class for advanced users. Those also should be using the new framework and naming conventions. As we have backward compatibility, we didn’t have to rework all python DTests to set config in the new format, and we exercise the backward compatibility while testing. Please consider adding any new tests using the new names and value format though. In-JVM upgrade tests do not support per-version configuration at the moment, so we have to keep the old names and value format. Currently, if we try to use the new config for a newer version, that will be silently ignored and default config will be used. SnakeYAML supports overloading of parameters. This means that if you add a configuration parameter more than once in your cassandra.yaml - the latest occasion will be the one to load in Config during Cassandra startup. In order to make upgrades as less disruptive as possible, we continue supporting that behavior also with adding old and new names of a parameter into cassandra.yaml. Please ensure that any JMX setters/getters update the Config class properties and not some local copies. Settings Virtual Table reports the configuration loaded at any time from the Config class. Example: If you add the following to cassandra.yaml: hinted_handoff_enabled: true enabled_hinted_handolff: false you will get loaded in Config: hinted_handoff_enabled: false CASSANDRA-17379 was opened to improve the user experience and deprecate the overloading. By default, we refuse starting Cassandra with a config containing both old and new config keys for the same parameter. Start Cassandra with -Dcassandra.allow_new_old_config_keys=true to override. For historical reasons duplicate config keys in cassandra.yaml are allowed by default, start Cassandra with -Dcassandra.allow_duplicate_config_keys=false to disallow this. Please note that key_cache_save_period, row_cache_save_period, counter_cache_save_period will be affected only by -Dcassandra.allow_duplicate_config_keys.","title":"Liberating cassandra.yaml Parameters' Names from Their Units","component":"Cassandra","version":"4.1","name":"configuration","url":"/Cassandra/4.1/cassandra/configuration/configuration.html","titles":[{"text":"Objective","id":"objective"},{"text":"Renamed Parameters","id":"renamed-parameters"}]},"/Cassandra/4.1/cassandra/configuration/index.html":{"text":"This section describes how to configure Apache Cassandra. cassandra.yaml cassandra-rackdc.properties cassandra-env.sh cassandra-topologies.properties commitlog-archiving.properties logback.xml jvm-* files","title":"Configuring Cassandra","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/configuration/index.html","titles":[]},"/Cassandra/4.1/cassandra/tools/cassandra_stress.html":{"text":"The cassandra-stress tool is used to benchmark and load-test a Cassandra cluster. cassandra-stress supports testing arbitrary CQL tables and queries, allowing users to benchmark their own data model. This documentation focuses on user mode to test personal schema. There are several operation types: write-only, read-only, and mixed workloads of standard data write-only and read-only workloads for counter columns user configured workloads, running custom queries on custom schemas The syntax is cassandra-stress [options]. For more information on a given command or options, run cassandra-stress help . Commands read: Multiple concurrent reads - the cluster must first be populated by a write test write: Multiple concurrent writes against the cluster mixed: Interleaving of any basic commands, with configurable ratio and distribution - the cluster must first be populated by a write test counter_write: Multiple concurrent updates of counters. counter_read: Multiple concurrent reads of counters. The cluster must first be populated by a counterwrite test. user: Interleaving of user provided queries, with configurable ratio and distribution. help: Print help for a command or option print: Inspect the output of a distribution definition legacy: Legacy support mode Primary Options -pop: Population distribution and intra-partition visit order -insert: Insert specific options relating to various methods for batching and splitting partition updates -col: Column details such as size and count distribution, data generator, names, comparator and if super columns should be used -rate: Thread count, rate limit or automatic mode (default is auto) -mode: Thrift or CQL with options -errors: How to handle errors when encountered during stress -sample: Specify the number of samples to collect for measuring latency -schema: Replication settings, compression, compaction, etc. -node: Nodes to connect to -log: Where to log progress to, and the interval at which to do it -transport: Custom transport factories -port: The port to connect to cassandra nodes on -graph: Graph recorded metrics -tokenrange: Token range settings Suboptions Every command and primary option has its own collection of suboptions. These are too numerous to list here. For information on the suboptions for each command or option, please use the help command, cassandra-stress help . User mode allows you to stress your own schemas, to save you time in the long run. Find out if your application can scale using stress test with your schema. User mode defines a profile using YAML. Multiple YAML files may be specified, in which case operations in the ops argument are referenced as specname.opname. An identifier for the profile: specname: staff_activities The keyspace for the test: keyspace: staff CQL for the keyspace. Optional if the keyspace already exists: keyspace_definition: | CREATE KEYSPACE stresscql WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3}; The table to be stressed: table: staff_activities CQL for the table. Optional if the table already exists: table_definition: | CREATE TABLE staff_activities ( name text, when timeuuid, what text, PRIMARY KEY(name, when, what) ) Optional meta-information on the generated columns in the above table. The min and max only apply to text and blob types. The distribution field represents the total unique population distribution of that column across rows: columnspec: - name: name size: uniform(5..10) # The names of the staff members are between 5-10 characters population: uniform(1..10) # 10 possible staff members to pick from - name: when cluster: uniform(20..500) # Staff members do between 20 and 500 events - name: what size: normal(10..100,50) Supported types are: An exponential distribution over the range [min..max]: EXP(min..max) An extreme value (Weibull) distribution over the range [min..max]: EXTREME(min..max,shape) A gaussian/normal distribution, where mean=(min+max)/2, and stdev is (mean-min)/stdvrng: GAUSSIAN(min..max,stdvrng) A gaussian/normal distribution, with explicitly defined mean and stdev: GAUSSIAN(min..max,mean,stdev) A uniform distribution over the range [min, max]: UNIFORM(min..max) A fixed distribution, always returning the same value: FIXED(val) If preceded by ~, the distribution is inverted Defaults for all columns are size: uniform(4..8), population: uniform(1..100B), cluster: fixed(1) Insert distributions: insert: # How many partition to insert per batch partitions: fixed(1) # How many rows to update per partition select: fixed(1)/500 # UNLOGGED or LOGGED batch for insert batchtype: UNLOGGED Currently all inserts are done inside batches. Read statements to use during the test: queries: events: cql: select * from staff_activities where name = ? fields: samerow latest_event: cql: select * from staff_activities where name = ? LIMIT 1 fields: samerow Running a user mode test: cassandra-stress user profile=./example.yaml duration=1m \"ops(insert=1,latest_event=1,events=1)\" truncate=once This will create the schema then run tests for 1 minute with an equal number of inserts, latest_event queries and events queries. Additionally the table will be truncated once before the test. The full example can be found here: spacenam: example # idenitifier for this spec if running with multiple yaml files keyspace: example # Would almost always be network topology unless running something locally keyspace_definition: | CREATE KEYSPACE example WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3}; table: staff_activities # The table under test. Start with a partition per staff member # Is this a good idea? table_definition: | CREATE TABLE staff_activities ( name text, when timeuuid, what text, PRIMARY KEY(name, when) ) columnspec: - name: name size: uniform(5..10) # The names of the staff members are between 5-10 characters population: uniform(1..10) # 10 possible staff members to pick from - name: when cluster: uniform(20..500) # Staff members do between 20 and 500 events - name: what size: normal(10..100,50) insert: # we only update a single partition in any given insert partitions: fixed(1) # we want to insert a single row per partition and we have between 20 and 500 # rows per partition select: fixed(1)/500 batchtype: UNLOGGED # Single partition unlogged batches are essentially noops queries: events: cql: select * from staff_activities where name = ? fields: samerow latest_event: cql: select * from staff_activities where name = ? LIMIT 1 fields: samerow Running a user mode test with multiple yaml files cassandra-stress user profile=./example.yaml,./example2.yaml duration=1m \"ops(ex1.insert=1,ex1.latest_event=1,ex2.insert=2)\" truncate=once This will run operations as specified in both the example.yaml and example2.yaml files. example.yaml and example2.yaml can reference the same table, although care must be taken that the table definition is identical (data generation specs can be different). cassandra-stress supports lightweight transactions. To use this feature, the command will first read current data from Cassandra, and then uses read values to fulfill lightweight transaction conditions. Lightweight transaction update query: queries: regularupdate: cql: update blogposts set author = ? where domain = ? and published_date = ? fields: samerow updatewithlwt: cql: update blogposts set author = ? where domain = ? and published_date = ? IF body = ? AND url = ? fields: samerow The full example can be found here: # Keyspace Name keyspace: stresscql # The CQL for creating a keyspace (optional if it already exists) # Would almost always be network topology unless running something locall keyspace_definition: | CREATE KEYSPACE stresscql WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1}; # Table name table: blogposts # The CQL for creating a table you wish to stress (optional if it already exists) table_definition: | CREATE TABLE blogposts ( domain text, published_date timeuuid, url text, author text, title text, body text, PRIMARY KEY(domain, published_date) ) WITH CLUSTERING ORDER BY (published_date DESC) AND compaction = { 'class':'LeveledCompactionStrategy' } AND comment='A table to hold blog posts' ### Column Distribution Specifications ### columnspec: - name: domain size: gaussian(5..100) #domain names are relatively short population: uniform(1..10M) #10M possible domains to pick from - name: published_date cluster: fixed(1000) #under each domain we will have max 1000 posts - name: url size: uniform(30..300) - name: title #titles shouldn't go beyond 200 chars size: gaussian(10..200) - name: author size: uniform(5..20) #author names should be short - name: body size: gaussian(100..5000) #the body of the blog post can be long ### Batch Ratio Distribution Specifications ### insert: partitions: fixed(1) # Our partition key is the domain so only insert one per batch select: fixed(1)/1000 # We have 1000 posts per domain so 1/1000 will allow 1 post per batch batchtype: UNLOGGED # Unlogged batches # # A list of queries you wish to run against the schema # queries: singlepost: cql: select * from blogposts where domain = ? LIMIT 1 fields: samerow regularupdate: cql: update blogposts set author = ? where domain = ? and published_date = ? fields: samerow updatewithlwt: cql: update blogposts set author = ? where domain = ? and published_date = ? IF body = ? AND url = ? fields: samerow Graphs can be generated for each run of stress. To create a new graph: cassandra-stress user profile=./stress-example.yaml \"ops(insert=1,latest_event=1,events=1)\" -graph file=graph.html title=\"Awesome graph\" To add a new run to an existing graph point to an existing file and add a revision name: cassandra-stress user profile=./stress-example.yaml duration=1m \"ops(insert=1,latest_event=1,events=1)\" -graph file=graph.html title=\"Awesome graph\" revision=\"Second run\" How do you use NetworkTopologyStrategy for the keyspace? Use the schema option making sure to either escape the parenthesis or enclose in quotes: cassandra-stress write -schema \"replication(strategy=NetworkTopologyStrategy,datacenter1=3)\" How do you use SSL? Use the transport option: cassandra-stress \"write n=100k cl=ONE no-warmup\" -transport \"truststore=$HOME/jks/truststore.jks truststore-password=cassandra\" Is Cassandra Stress a secured tool? Cassandra stress is not a secured tool. Serialization and other aspects of the tool offer no security guarantees.","title":"Cassandra Stress","component":"Cassandra","version":"4.1","name":"cassandra_stress","url":"/Cassandra/4.1/cassandra/tools/cassandra_stress.html","titles":[{"text":"Usage","id":"usage"},{"text":"User mode","id":"user-mode"},{"text":"Profile","id":"profile"},{"text":"Lightweight transaction support","id":"lightweight-transaction-support"},{"text":"Graphing","id":"graphing"},{"text":"FAQ","id":"faq"}]},"/Cassandra/4.1/cassandra/tools/cqlsh.html":{"text":"cqlsh is a command-line interface for interacting with Cassandra using CQL (the Cassandra Query Language). It is shipped with every Cassandra package, and can be found in the bin/ directory alongside the cassandra executable. cqlsh is implemented with the Python native protocol driver, and connects to the single specified node. cqlsh is compatible with Python 2.7. In general, a given version of cqlsh is only guaranteed to work with the version of Cassandra that it was released with. In some cases, cqlsh may work with older or newer versions of Cassandra, but this is not officially supported. cqlsh ships with all essential dependencies. However, there are some optional dependencies that can be installed to improve the capabilities of cqlsh. By default, cqlsh displays all timestamps with a UTC timezone. To support display of timestamps with another timezone, install the pytz library. See the timezone option in cqlshrc for specifying a timezone to use. The performance of cqlsh’s COPY operations can be improved by installing cython. This will compile the python modules that are central to the performance of COPY. The cqlshrc file holds configuration options for cqlsh. By default, the file is locagted the user’s home directory at ~/.cassandra/cqlsh, but a custom location can be specified with the --cqlshrc option. Example config values and documentation can be found in the conf/cqlshrc.sample file of a tarball installation. You can also view the latest version of the cqlshrc file online. All CQL commands you execute are written to a history file. By default, CQL history will be written to ~/.cassandra/cql_history. You can change this default by setting the environment variable CQL_HISTORY like ~/some/other/path/to/cqlsh_history where cqlsh_history is a file. All parent directories to history file will be created if they do not exist. If you do not want to persist history, you can do so by setting CQL_HISTORY to /dev/null. This feature is supported from Cassandra 4.1. Usage: cqlsh.py [options] [host [port]] CQL Shell for Apache Cassandra Options: --version show program’s version number and exit -h --help show this help message and exit -C --color Always use color output --no-color Never use color output --browser=BROWSER The browser to use to display CQL help, where BROWSER can be: one of the supported browsers in docs.python.org/3/library/webbrowser.html. browser path followed by %s, example: /usr/bin/google-chrome-stable %s --ssl Use SSL -u USERNAME --username=USERNAME Authenticate as user. -p PASSWORD --password=PASSWORD Authenticate using password. -k KEYSPACE --keyspace=KEYSPACE Authenticate to the given keyspace. -f FILE --file=FILE Execute commands from FILE, then exit --debug Show additional debugging information --coverage Collect coverage data --encoding=ENCODING Specify a non-default encoding for output. (Default: utf-8) --cqlshrc=CQLSHRC Specify an alternative cqlshrc file location. --credentials=CREDENTIALS Specify an alternative credentials file location. --cqlversion=CQLVERSION Specify a particular CQL version, by default the highest version supported by the server will be used. Examples: \"3.0.3\", \"3.1.0\" --protocol-version=PROTOCOL_VERSION Specify a specific protcol version otherwise the client will default and downgrade as necessary -e EXECUTE --execute=EXECUTE Execute the statement and quit. --connect-timeout=CONNECT_TIMEOUT Specify the connection timeout in seconds (default: 5 seconds). --request-timeout=REQUEST_TIMEOUT Specify the default request timeout in seconds (default: 10 seconds). -t, --tty Force tty mode (command prompt). -v --v Print the current version of cqlsh. In addition to supporting regular CQL statements, cqlsh also supports a number of special commands that are not part of CQL. These are detailed below. Usage: CONSISTENCY Sets the consistency level for operations to follow. Valid arguments include: ANY ONE TWO THREE QUORUM ALL LOCAL_QUORUM LOCAL_ONE SERIAL LOCAL_SERIAL Usage: SERIAL CONSISTENCY Sets the serial consistency level for operations to follow. Valid arguments include: SERIAL LOCAL_SERIAL The serial consistency level is only used by conditional updates (INSERT, UPDATE and DELETE with an IF condition). For those, the serial consistency level defines the consistency level of the serial phase (or “paxos” phase) while the normal consistency level defines the consistency for the “learn” phase, i.e. what type of reads will be guaranteed to see the update right away. For example, if a conditional write has a consistency level of QUORUM (and is successful), then a QUORUM read is guaranteed to see that write. But if the regular consistency level of that write is ANY, then only a read with a consistency level of SERIAL is guaranteed to see it (even a read with consistency ALL is not guaranteed to be enough). Prints the cqlsh, Cassandra, CQL, and native protocol versions in use. Example: cqlsh> SHOW VERSION [cqlsh 5.0.1 | Cassandra 3.8 | CQL spec 3.4.2 | Native protocol v4] Prints the IP address and port of the Cassandra node that cqlsh is connected to in addition to the cluster name. Example: cqlsh> SHOW HOST Connected to Prod_Cluster at 192.0.0.1:9042. Pretty prints a specific tracing session. Usage: SHOW SESSION Example usage: cqlsh> SHOW SESSION 95ac6470-327e-11e6-beca-dfb660d92ad8 Tracing session: 95ac6470-327e-11e6-beca-dfb660d92ad8 activity | timestamp | source | source_elapsed | client -----------------------------------------------------------+----------------------------+-----------+----------------+----------- Execute CQL3 query | 2016-06-14 17:23:13.979000 | 127.0.0.1 | 0 | 127.0.0.1 Parsing SELECT * FROM system.local; [SharedPool-Worker-1] | 2016-06-14 17:23:13.982000 | 127.0.0.1 | 3843 | 127.0.0.1 ... Reads the contents of a file and executes each line as a CQL statement or special cqlsh command. Usage: SOURCE Example usage: cqlsh> SOURCE '/home/calvinhobbs/commands.cql' Begins capturing command output and appending it to a specified file. Output will not be shown at the console while it is captured. Usage: CAPTURE ''; CAPTURE OFF; CAPTURE; That is, the path to the file to be appended to must be given inside a string literal. The path is interpreted relative to the current working directory. The tilde shorthand notation ('~/mydir') is supported for referring to $HOME. Only query result output is captured. Errors and output from cqlsh-only commands will still be shown in the cqlsh session. To stop capturing output and show it in the cqlsh session again, use CAPTURE OFF. To inspect the current capture configuration, use CAPTURE with no arguments. Gives information about cqlsh commands. To see available topics, enter HELP without any arguments. To see help on a topic, use HELP . Also see the --browser argument for controlling what browser is used to display help. Enables or disables tracing for queries. When tracing is enabled, once a query completes, a trace of the events during the query will be printed. Usage: TRACING ON TRACING OFF Enables paging, disables paging, or sets the page size for read queries. When paging is enabled, only one page of data will be fetched at a time and a prompt will appear to fetch the next page. Generally, it’s a good idea to leave paging enabled in an interactive session to avoid fetching and printing large amounts of data at once. Usage: PAGING ON PAGING OFF PAGING Enables or disables vertical printing of rows. Enabling EXPAND is useful when many columns are fetched, or the contents of a single column are large. Usage: EXPAND ON EXPAND OFF Authenticate as a specified Cassandra user for the current session. Usage: LOGIN [] Ends the current session and terminates the cqlsh process. Usage: EXIT QUIT Clears the console. Usage: CLEAR CLS Prints a description (typically a series of DDL statements) of a schema element or the cluster. This is useful for dumping all or portions of the schema. Usage: DESCRIBE CLUSTER DESCRIBE SCHEMA DESCRIBE KEYSPACES DESCRIBE KEYSPACE DESCRIBE TABLES DESCRIBE TABLE DESCRIBE INDEX DESCRIBE MATERIALIZED VIEW DESCRIBE TYPES DESCRIBE TYPE DESCRIBE FUNCTIONS DESCRIBE FUNCTION DESCRIBE AGGREGATES DESCRIBE AGGREGATE In any of the commands, DESC may be used in place of DESCRIBE. The DESCRIBE CLUSTER command prints the cluster name and partitioner: cqlsh> DESCRIBE CLUSTER Cluster: Test Cluster Partitioner: Murmur3Partitioner The DESCRIBE SCHEMA command prints the DDL statements needed to recreate the entire schema. This is especially useful for dumping the schema in order to clone a cluster or restore from a backup. Copies data from a table to a CSV file. Usage: COPY [(, ...)] TO WITH [AND ...] If no columns are specified, all columns from the table will be copied to the CSV file. A subset of columns to copy may be specified by adding a comma-separated list of column names surrounded by parenthesis after the table name. The should be a string literal (with single quotes) representing a path to the destination file. This can also the special value STDOUT (without single quotes) to print the CSV to stdout. See shared-copy-options for options that apply to both COPY TO and COPY FROM. MAXREQUESTS The maximum number token ranges to fetch simultaneously. Defaults to 6. PAGESIZE The number of rows to fetch in a single page. Defaults to 1000. PAGETIMEOUT By default the page timeout is 10 seconds per 1000 entries in the page size or 10 seconds if pagesize is smaller. BEGINTOKEN, ENDTOKEN Token range to export. Defaults to exporting the full ring. MAXOUTPUTSIZE The maximum size of the output file measured in number of lines; beyond this maximum the output file will be split into segments. -1 means unlimited, and is the default. ENCODING The encoding used for characters. Defaults to utf8. Copies data from a CSV file to table. Usage: COPY [(, ...)] FROM WITH [AND ...] If no columns are specified, all columns from the CSV file will be copied to the table. A subset of columns to copy may be specified by adding a comma-separated list of column names surrounded by parenthesis after the table name. The should be a string literal (with single quotes) representing a path to the source file. This can also the special value STDIN (without single quotes) to read the CSV data from stdin. See shared-copy-options for options that apply to both COPY TO and COPY FROM. INGESTRATE The maximum number of rows to process per second. Defaults to 100000. MAXROWS The maximum number of rows to import. -1 means unlimited, and is the default. SKIPROWS A number of initial rows to skip. Defaults to 0. SKIPCOLS A comma-separated list of column names to ignore. By default, no columns are skipped. MAXPARSEERRORS The maximum global number of parsing errors to ignore. -1 means unlimited, and is the default. MAXINSERTERRORS The maximum global number of insert errors to ignore. -1 means unlimited. The default is 1000. ERRFILE = A file to store all rows that could not be imported, by default this is import__.err where is your keyspace and is your table name. MAXBATCHSIZE The max number of rows inserted in a single batch. Defaults to 20. MINBATCHSIZE The min number of rows inserted in a single batch. Defaults to 2. CHUNKSIZE The number of rows that are passed to child worker processes from the main process at a time. Defaults to 1000. Options that are common to both COPY TO and COPY FROM. NULLVAL The string placeholder for null values. Defaults to null. HEADER For COPY TO, controls whether the first line in the CSV output file will contain the column names. For COPY FROM, specifies whether the first line in the CSV input file contains column names. Defaults to false. DECIMALSEP The character that is used as the decimal point separator. Defaults to .. THOUSANDSSEP The character that is used to separate thousands. Defaults to the empty string. BOOLSTYlE The string literal format for boolean values. Defaults to True,False. NUMPROCESSES The number of child worker processes to create for COPY tasks. Defaults to a max of 4 for COPY FROM and 16 for COPY TO. However, at most (num_cores - 1) processes will be created. MAXATTEMPTS The maximum number of failed attempts to fetch a range of data (when using COPY TO) or insert a chunk of data (when using COPY FROM) before giving up. Defaults to 5. REPORTFREQUENCY How often status updates are refreshed, in seconds. Defaults to 0.25. RATEFILE An optional file to output rate statistics to. By default, statistics are not output to a file.","title":"cqlsh: the CQL shell","component":"Cassandra","version":"4.1","name":"cqlsh","url":"/Cassandra/4.1/cassandra/tools/cqlsh.html","titles":[{"text":"Compatibility","id":"compatibility"},{"text":"Optional Dependencies","id":"optional-dependencies"},{"text":"pytz","id":"pytz"},{"text":"cython","id":"cython"},{"text":"cqlshrc","id":"cqlshrc"},{"text":"cql history","id":"cql_history"},{"text":"Command Line Options","id":"command-line-options"},{"text":"Special Commands","id":"special-commands"},{"text":"CONSISTENCY","id":"consistency"},{"text":"SERIAL CONSISTENCY","id":"serial-consistency"},{"text":"SHOW VERSION","id":"show-version"},{"text":"SHOW HOST","id":"show-host"},{"text":"SHOW SESSION","id":"show-session"},{"text":"SOURCE","id":"source"},{"text":"CAPTURE","id":"capture"},{"text":"HELP","id":"help"},{"text":"TRACING","id":"tracing"},{"text":"PAGING","id":"paging"},{"text":"EXPAND","id":"expand"},{"text":"LOGIN","id":"login"},{"text":"EXIT","id":"exit"},{"text":"CLEAR","id":"clear"},{"text":"DESCRIBE","id":"describe"},{"text":"COPY TO","id":"copy-to"},{"text":"Options for COPY TO","id":"options-for-copy-to"},{"text":"COPY FROM","id":"copy-from"},{"text":"Options for COPY TO","id":"options-for-copy-to-2"},{"text":"Shared COPY Options","id":"shared-copy-options"}]},"/Cassandra/4.1/cassandra/tools/hash_password.html":{"text":"The hash_password tool is used to get the jBcrypt hash of a password. This hash can be used in CREATE/ALTER ROLE/USER statements for improved security. This feature can be useful if we want to make sure no intermediate system, logging or any other possible plain text password leak can happen. hash_password -h,--help Displays help message -e,--environment-var Use value of the specified environment variable as the password -i,--input Input is a file (or - for stdin) to read the password from. Make sure that the whole input including newlines is considered. For example, the shell command echo -n foobar | hash_password -i - will work as intended and just hash 'foobar'. -p,--plain Argument is the plain text password -r,--logrounds Number of hash rounds (default: 10). One of the options --environment-var, --plain or --input must be used.","title":"Hash password","component":"Cassandra","version":"4.1","name":"hash_password","url":"/Cassandra/4.1/cassandra/tools/hash_password.html","titles":[{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/index.html":{"text":"This section describes the command line tools provided with Apache Cassandra. CQL shell nodetool SSTable tools cassandra-stress tool hash password tool","title":"Cassandra Tools","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/tools/index.html","titles":[]},"/Cassandra/4.1/cassandra/tools/nodetool/assassinate.html":{"text":"NAME nodetool assassinate - Forcefully remove a dead node without re-replicating any data. Use as a last resort if you cannot removenode SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] assassinate [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options IP address of the endpoint to assassinate","title":"","component":"Cassandra","version":"4.1","name":"assassinate","url":"/Cassandra/4.1/cassandra/tools/nodetool/assassinate.html","titles":[{"text":"assassinate","id":"assassinate"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/bootstrap.html":{"text":"NAME nodetool bootstrap - Monitor/manage node's bootstrap process SYNOPSIS nodetool bootstrap nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] bootstrap resume OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username COMMANDS With no arguments, Display help information resume Resume bootstrap streaming","title":"","component":"Cassandra","version":"4.1","name":"bootstrap","url":"/Cassandra/4.1/cassandra/tools/nodetool/bootstrap.html","titles":[{"text":"bootstrap","id":"bootstrap"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/cleanup.html":{"text":"NAME nodetool cleanup - Triggers the immediate cleanup of keys no longer belonging to a node. By default, clean all keyspaces SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] cleanup [(-j | --jobs )] [--] [ ...] OPTIONS -h , --host Node hostname or ip address -j , --jobs Number of sstables to cleanup simultanously, set to 0 to use all available compaction threads -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"cleanup","url":"/Cassandra/4.1/cassandra/tools/nodetool/cleanup.html","titles":[{"text":"cleanup","id":"cleanup"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/clearsnapshot.html":{"text":"NAME nodetool clearsnapshot - Remove the snapshot with the given name from the given keyspaces SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] clearsnapshot [--all] [-t ] [--] [...] OPTIONS --all Removes all snapshots -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -t Remove the snapshot with a given name -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] Remove snapshots from the given keyspaces","title":"","component":"Cassandra","version":"4.1","name":"clearsnapshot","url":"/Cassandra/4.1/cassandra/tools/nodetool/clearsnapshot.html","titles":[{"text":"clearsnapshot","id":"clearsnapshot"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/clientstats.html":{"text":"NAME nodetool clientstats - Print information about connected clients SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] clientstats [--all] [--by-protocol] [--clear-history] [--client-options] OPTIONS --all Lists all connections --by-protocol Lists most recent client connections by protocol version --clear-history Clear the history of connected clients --client-options Lists all connections and the client options -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"clientstats","url":"/Cassandra/4.1/cassandra/tools/nodetool/clientstats.html","titles":[{"text":"clientstats","id":"clientstats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/compact.html":{"text":"NAME nodetool compact - Force a (major) compaction on one or more tables or user-defined compaction on given SSTables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] compact [(-et | --end-token )] [--partition ] [(-s | --split-output)] [(-st | --start-token )] [--user-defined] [--] [ ...] or ... OPTIONS -et , --end-token Use -et to specify a token at which compaction range ends (inclusive) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number --partition String representation of the partition key -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -s, --split-output Use -s to not create a single big file -st , --start-token Use -st to specify a token at which the compaction range starts (inclusive) -u , --username Remote jmx agent username --user-defined Use --user-defined to submit listed files for user-defined compaction -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] or ... The keyspace followed by one or many tables or list of SSTable data files when using --user-defined","title":"","component":"Cassandra","version":"4.1","name":"compact","url":"/Cassandra/4.1/cassandra/tools/nodetool/compact.html","titles":[{"text":"compact","id":"compact"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/compactionhistory.html":{"text":"NAME nodetool compactionhistory - Print history of compaction SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] compactionhistory [(-F | --format )] OPTIONS -F , --format Output format (json, yaml) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"compactionhistory","url":"/Cassandra/4.1/cassandra/tools/nodetool/compactionhistory.html","titles":[{"text":"compactionhistory","id":"compactionhistory"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/compactionstats.html":{"text":"NAME nodetool compactionstats - Print statistics on compactions SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] compactionstats [(-H | --human-readable)] [(-V | --vtable)] OPTIONS -h , --host Node hostname or ip address -H, --human-readable Display bytes in human readable form, i.e. KiB, MiB, GiB, TiB -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -V, --vtable Display fields matching vtable output","title":"","component":"Cassandra","version":"4.1","name":"compactionstats","url":"/Cassandra/4.1/cassandra/tools/nodetool/compactionstats.html","titles":[{"text":"compactionstats","id":"compactionstats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/datapaths.html":{"text":"NAME nodetool datapaths - Print all directories where data of tables are stored SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] datapaths [(-F | --format )] [--] [...] OPTIONS -F , --format Output format (json, yaml) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of table (or keyspace) names","title":"","component":"Cassandra","version":"4.1","name":"datapaths","url":"/Cassandra/4.1/cassandra/tools/nodetool/datapaths.html","titles":[{"text":"datapaths","id":"datapaths"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/decommission.html":{"text":"NAME nodetool decommission - Decommission the *node I am connecting to* SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] decommission [(-f | --force)] OPTIONS -f, --force Force decommission of this node even when it reduces the number of replicas to below configured RF -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"decommission","url":"/Cassandra/4.1/cassandra/tools/nodetool/decommission.html","titles":[{"text":"decommission","id":"decommission"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/describecluster.html":{"text":"NAME nodetool describecluster - Print the name, snitch, partitioner and schema version of a cluster SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] describecluster OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"describecluster","url":"/Cassandra/4.1/cassandra/tools/nodetool/describecluster.html","titles":[{"text":"describecluster","id":"describecluster"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/describering.html":{"text":"NAME nodetool describering - Shows the token ranges info of a given keyspace SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] describering [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace name","title":"","component":"Cassandra","version":"4.1","name":"describering","url":"/Cassandra/4.1/cassandra/tools/nodetool/describering.html","titles":[{"text":"describering","id":"describering"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disableauditlog.html":{"text":"NAME nodetool disableauditlog - Disable the audit log SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disableauditlog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disableauditlog","url":"/Cassandra/4.1/cassandra/tools/nodetool/disableauditlog.html","titles":[{"text":"disableauditlog","id":"disableauditlog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disableautocompaction.html":{"text":"NAME nodetool disableautocompaction - Disable autocompaction for the given keyspace and table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disableautocompaction [--] [ ...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"disableautocompaction","url":"/Cassandra/4.1/cassandra/tools/nodetool/disableautocompaction.html","titles":[{"text":"disableautocompaction","id":"disableautocompaction"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablebackup.html":{"text":"NAME nodetool disablebackup - Disable incremental backup SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablebackup OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disablebackup","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablebackup.html","titles":[{"text":"disablebackup","id":"disablebackup"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablebinary.html":{"text":"NAME nodetool disablebinary - Disable native transport (binary protocol) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablebinary OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disablebinary","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablebinary.html","titles":[{"text":"disablebinary","id":"disablebinary"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablefullquerylog.html":{"text":"NAME nodetool disablefullquerylog - Disable the full query log SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablefullquerylog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disablefullquerylog","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablefullquerylog.html","titles":[{"text":"disablefullquerylog","id":"disablefullquerylog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablegossip.html":{"text":"NAME nodetool disablegossip - Disable gossip (effectively marking the node down) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablegossip OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disablegossip","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablegossip.html","titles":[{"text":"disablegossip","id":"disablegossip"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablehandoff.html":{"text":"NAME nodetool disablehandoff - Disable storing hinted handoffs SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablehandoff OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disablehandoff","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablehandoff.html","titles":[{"text":"disablehandoff","id":"disablehandoff"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disablehintsfordc.html":{"text":"NAME nodetool disablehintsfordc - Disable hints for a data center SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disablehintsfordc [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The data center to disable","title":"","component":"Cassandra","version":"4.1","name":"disablehintsfordc","url":"/Cassandra/4.1/cassandra/tools/nodetool/disablehintsfordc.html","titles":[{"text":"disablehintsfordc","id":"disablehintsfordc"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/disableoldprotocolversions.html":{"text":"NAME nodetool disableoldprotocolversions - Disable old protocol versions SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] disableoldprotocolversions OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"disableoldprotocolversions","url":"/Cassandra/4.1/cassandra/tools/nodetool/disableoldprotocolversions.html","titles":[{"text":"disableoldprotocolversions","id":"disableoldprotocolversions"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/drain.html":{"text":"NAME nodetool drain - Drain the node (stop accepting writes and flush all tables) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] drain OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"drain","url":"/Cassandra/4.1/cassandra/tools/nodetool/drain.html","titles":[{"text":"drain","id":"drain"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enableauditlog.html":{"text":"NAME nodetool enableauditlog - Enable the audit log SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enableauditlog [--archive-command ] [--blocking ] [--excluded-categories ] [--excluded-keyspaces ] [--excluded-users ] [--included-categories ] [--included-keyspaces ] [--included-users ] [--logger ] [--max-archive-retries ] [--max-log-size ] [--max-queue-weight ] [--roll-cycle ] OPTIONS --archive-command Command that will handle archiving rolled audit log files. Format is \"/path/to/script.sh %path\" where %path will be replaced with the file to archive Enable this by setting the audit_logging_options.allow_nodetool_archive_command: true in the config. --blocking If the queue is full whether to block producers or drop samples [true|false]. --excluded-categories Comma separated list of Audit Log Categories to be excluded for audit log. If not set the value from cassandra.yaml will be used --excluded-keyspaces Comma separated list of keyspaces to be excluded for audit log. If not set the value from cassandra.yaml will be used --excluded-users Comma separated list of users to be excluded for audit log. If not set the value from cassandra.yaml will be used -h , --host Node hostname or ip address --included-categories Comma separated list of Audit Log Categories to be included for audit log. If not set the value from cassandra.yaml will be used --included-keyspaces Comma separated list of keyspaces to be included for audit log. If not set the value from cassandra.yaml will be used --included-users Comma separated list of users to be included for audit log. If not set the value from cassandra.yaml will be used --logger Logger name to be used for AuditLogging. Default BinAuditLogger. If not set the value from cassandra.yaml will be used --max-archive-retries Max number of archive retries. --max-log-size How many bytes of log data to store before dropping segments. Might not be respected if a log file hasn't rolled so it can be deleted. --max-queue-weight Maximum number of bytes of query data to queue to disk before blocking or dropping samples. -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file --roll-cycle How often to roll the log file (MINUTELY, HOURLY, DAILY). -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enableauditlog","url":"/Cassandra/4.1/cassandra/tools/nodetool/enableauditlog.html","titles":[{"text":"enableauditlog","id":"enableauditlog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enableautocompaction.html":{"text":"NAME nodetool enableautocompaction - Enable autocompaction for the given keyspace and table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enableautocompaction [--] [ ...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"enableautocompaction","url":"/Cassandra/4.1/cassandra/tools/nodetool/enableautocompaction.html","titles":[{"text":"enableautocompaction","id":"enableautocompaction"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablebackup.html":{"text":"NAME nodetool enablebackup - Enable incremental backup SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablebackup OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enablebackup","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablebackup.html","titles":[{"text":"enablebackup","id":"enablebackup"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablebinary.html":{"text":"NAME nodetool enablebinary - Reenable native transport (binary protocol) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablebinary OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enablebinary","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablebinary.html","titles":[{"text":"enablebinary","id":"enablebinary"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablefullquerylog.html":{"text":"NAME nodetool enablefullquerylog - Enable full query logging, defaults for the options are configured in cassandra.yaml SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablefullquerylog [--archive-command ] [--blocking ] [--max-archive-retries ] [--max-log-size ] [--max-queue-weight ] [--path ] [--roll-cycle ] OPTIONS --archive-command Command that will handle archiving rolled full query log files. Format is \"/path/to/script.sh %path\" where %path will be replaced with the file to archive Enable this by setting the full_query_logging_options.allow_nodetool_archive_command: true in the config. --blocking If the queue is full whether to block producers or drop samples [true|false]. -h , --host Node hostname or ip address --max-archive-retries Max number of archive retries. --max-log-size How many bytes of log data to store before dropping segments. Might not be respected if a log file hasn't rolled so it can be deleted. --max-queue-weight Maximum number of bytes of query data to queue to disk before blocking or dropping samples. -p , --port Remote jmx agent port number --path Path to store the full query log at. Will have it's contents recursively deleted. -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file --roll-cycle How often to roll the log file (MINUTELY, HOURLY, DAILY). -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enablefullquerylog","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablefullquerylog.html","titles":[{"text":"enablefullquerylog","id":"enablefullquerylog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablegossip.html":{"text":"NAME nodetool enablegossip - Reenable gossip SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablegossip OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enablegossip","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablegossip.html","titles":[{"text":"enablegossip","id":"enablegossip"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablehandoff.html":{"text":"NAME nodetool enablehandoff - Reenable future hints storing on the current node SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablehandoff OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enablehandoff","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablehandoff.html","titles":[{"text":"enablehandoff","id":"enablehandoff"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enablehintsfordc.html":{"text":"NAME nodetool enablehintsfordc - Enable hints for a data center that was previsouly disabled SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablehintsfordc [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The data center to enable","title":"","component":"Cassandra","version":"4.1","name":"enablehintsfordc","url":"/Cassandra/4.1/cassandra/tools/nodetool/enablehintsfordc.html","titles":[{"text":"enablehintsfordc","id":"enablehintsfordc"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/enableoldprotocolversions.html":{"text":"NAME nodetool enableoldprotocolversions - Enable old protocol versions SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enableoldprotocolversions OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"enableoldprotocolversions","url":"/Cassandra/4.1/cassandra/tools/nodetool/enableoldprotocolversions.html","titles":[{"text":"enableoldprotocolversions","id":"enableoldprotocolversions"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/failuredetector.html":{"text":"NAME nodetool failuredetector - Shows the failure detector information for the cluster SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] failuredetector OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"failuredetector","url":"/Cassandra/4.1/cassandra/tools/nodetool/failuredetector.html","titles":[{"text":"failuredetector","id":"failuredetector"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/flush.html":{"text":"NAME nodetool flush - Flush one or more tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] flush [--] [ ...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"flush","url":"/Cassandra/4.1/cassandra/tools/nodetool/flush.html","titles":[{"text":"flush","id":"flush"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/garbagecollect.html":{"text":"NAME nodetool garbagecollect - Remove deleted data from one or more tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] garbagecollect [(-g | --granularity )] [(-j | --jobs )] [--] [ ...] OPTIONS -g , --granularity Granularity of garbage removal. ROW (default) removes deleted partitions and rows, CELL also removes overwritten or deleted cells. -h , --host Node hostname or ip address -j , --jobs Number of sstables to cleanup simultanously, set to 0 to use all available compaction threads. Defaults to 1 so that collections of newer tables can see the data is deleted and also remove tombstones. -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"garbagecollect","url":"/Cassandra/4.1/cassandra/tools/nodetool/garbagecollect.html","titles":[{"text":"garbagecollect","id":"garbagecollect"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/gcstats.html":{"text":"NAME nodetool gcstats - Print GC Statistics SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] gcstats OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"gcstats","url":"/Cassandra/4.1/cassandra/tools/nodetool/gcstats.html","titles":[{"text":"gcstats","id":"gcstats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getauditlog.html":{"text":"NAME nodetool getauditlog - Print configuration of audit log if enabled, otherwise the configuration reflected in cassandra.yaml SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getauditlog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getauditlog","url":"/Cassandra/4.1/cassandra/tools/nodetool/getauditlog.html","titles":[{"text":"getauditlog","id":"getauditlog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getauthcacheconfig.html":{"text":"NAME nodetool getauthcacheconfig - Get configuration of Auth cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getauthcacheconfig --cache-name OPTIONS --cache-name Name of Auth cache (required) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getauthcacheconfig","url":"/Cassandra/4.1/cassandra/tools/nodetool/getauthcacheconfig.html","titles":[{"text":"getauthcacheconfig","id":"getauthcacheconfig"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getbatchlogreplaythrottle.html":{"text":"NAME nodetool getbatchlogreplaythrottle - Print batchlog replay throttle in KB/s. This is reduced proportionally to the number of nodes in the cluster. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getbatchlogreplaythrottle OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getbatchlogreplaythrottle","url":"/Cassandra/4.1/cassandra/tools/nodetool/getbatchlogreplaythrottle.html","titles":[{"text":"getbatchlogreplaythrottle","id":"getbatchlogreplaythrottle"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getcolumnindexsize.html":{"text":"NAME nodetool getcolumnindexsize - Print the granularity of the collation index of rows within a partition in KiB SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getcolumnindexsize OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getcolumnindexsize","url":"/Cassandra/4.1/cassandra/tools/nodetool/getcolumnindexsize.html","titles":[{"text":"getcolumnindexsize","id":"getcolumnindexsize"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getcompactionthreshold.html":{"text":"NAME nodetool getcompactionthreshold - Print min and max compaction thresholds for a given table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getcompactionthreshold [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace with a table","title":"","component":"Cassandra","version":"4.1","name":"getcompactionthreshold","url":"/Cassandra/4.1/cassandra/tools/nodetool/getcompactionthreshold.html","titles":[{"text":"getcompactionthreshold","id":"getcompactionthreshold"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getcompactionthroughput.html":{"text":"NAME nodetool getcompactionthroughput - Print the MiB/s throughput cap for compaction in the system as a rounded number SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getcompactionthroughput [(-d | --precise-mib)] OPTIONS -d, --precise-mib Print the MiB/s throughput cap for compaction in the system as a precise number (double) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getcompactionthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/getcompactionthroughput.html","titles":[{"text":"getcompactionthroughput","id":"getcompactionthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrency.html":{"text":"NAME nodetool getconcurrency - Get maximum concurrency for processing stages SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getconcurrency [--] [stage-names] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [stage-names] optional list of stage names, otherwise display all stages","title":"","component":"Cassandra","version":"4.1","name":"getconcurrency","url":"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrency.html","titles":[{"text":"getconcurrency","id":"getconcurrency"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrentcompactors.html":{"text":"NAME nodetool getconcurrentcompactors - Get the number of concurrent compactors in the system. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getconcurrentcompactors OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getconcurrentcompactors","url":"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrentcompactors.html","titles":[{"text":"getconcurrentcompactors","id":"getconcurrentcompactors"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrentviewbuilders.html":{"text":"NAME nodetool getconcurrentviewbuilders - Get the number of concurrent view builders in the system SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getconcurrentviewbuilders OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getconcurrentviewbuilders","url":"/Cassandra/4.1/cassandra/tools/nodetool/getconcurrentviewbuilders.html","titles":[{"text":"getconcurrentviewbuilders","id":"getconcurrentviewbuilders"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getdefaultrf.html":{"text":"NAME nodetool getdefaultrf - Gets default keyspace replication factor. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getdefaultrf OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getdefaultrf","url":"/Cassandra/4.1/cassandra/tools/nodetool/getdefaultrf.html","titles":[{"text":"getdefaultrf","id":"getdefaultrf"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getendpoints.html":{"text":"NAME nodetool getendpoints - Print the end points that owns the key SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getendpoints [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace, the table, and the partition key for which we need to find the endpoint","title":"","component":"Cassandra","version":"4.1","name":"getendpoints","url":"/Cassandra/4.1/cassandra/tools/nodetool/getendpoints.html","titles":[{"text":"getendpoints","id":"getendpoints"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getfullquerylog.html":{"text":"NAME nodetool getfullquerylog - Print configuration of fql if enabled, otherwise the configuration reflected in cassandra.yaml SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getfullquerylog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getfullquerylog","url":"/Cassandra/4.1/cassandra/tools/nodetool/getfullquerylog.html","titles":[{"text":"getfullquerylog","id":"getfullquerylog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getinterdcstreamthroughput.html":{"text":"NAME nodetool getinterdcstreamthroughput - Print the throughput cap for inter-datacenter streaming and entire SSTable inter-datacenter streaming in the systemin rounded megabits. For precise number, please, use option -d SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getinterdcstreamthroughput [(-d | --precise-mbit)] [(-e | --entire-sstable-throughput)] [(-m | --mib)] OPTIONS -d, --precise-mbit Print the throughput cap for inter-datacenter streaming in precise Mbits (double) -e, --entire-sstable-throughput Print entire SSTable streaming throughput in MiB/s -h , --host Node hostname or ip address -m, --mib Print the throughput cap for inter-datacenter streaming in MiB/s -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getinterdcstreamthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/getinterdcstreamthroughput.html","titles":[{"text":"getinterdcstreamthroughput","id":"getinterdcstreamthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getlogginglevels.html":{"text":"NAME nodetool getlogginglevels - Get the runtime logging levels SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getlogginglevels OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getlogginglevels","url":"/Cassandra/4.1/cassandra/tools/nodetool/getlogginglevels.html","titles":[{"text":"getlogginglevels","id":"getlogginglevels"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getmaxhintwindow.html":{"text":"NAME nodetool getmaxhintwindow - Print the max hint window in ms SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getmaxhintwindow OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getmaxhintwindow","url":"/Cassandra/4.1/cassandra/tools/nodetool/getmaxhintwindow.html","titles":[{"text":"getmaxhintwindow","id":"getmaxhintwindow"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getseeds.html":{"text":"NAME nodetool getseeds - Get the currently in use seed node IP list excluding the node IP SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getseeds OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getseeds","url":"/Cassandra/4.1/cassandra/tools/nodetool/getseeds.html","titles":[{"text":"getseeds","id":"getseeds"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getsnapshotthrottle.html":{"text":"NAME nodetool getsnapshotthrottle - Print the snapshot_links_per_second throttle for snapshot/clearsnapshot SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getsnapshotthrottle OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getsnapshotthrottle","url":"/Cassandra/4.1/cassandra/tools/nodetool/getsnapshotthrottle.html","titles":[{"text":"getsnapshotthrottle","id":"getsnapshotthrottle"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getsstables.html":{"text":"NAME nodetool getsstables - Print the sstable filenames that own the key SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getsstables [(-hf | --hex-format)] [--] OPTIONS -h , --host Node hostname or ip address -hf, --hex-format Specify the key in hexadecimal string format -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace, the column family, and the key","title":"","component":"Cassandra","version":"4.1","name":"getsstables","url":"/Cassandra/4.1/cassandra/tools/nodetool/getsstables.html","titles":[{"text":"getsstables","id":"getsstables"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/getstreamthroughput.html":{"text":"NAME nodetool getstreamthroughput - Print the throughput cap for streaming and entire SSTable streaming in the system in rounded megabits. For precise number, please, use option -d SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] getstreamthroughput [(-d | --precise-mbit)] [(-e | --entire-sstable-throughput)] [(-m | --mib)] OPTIONS -d, --precise-mbit Print the throughput cap for streaming in precise Mbits (double) -e, --entire-sstable-throughput Print entire SSTable streaming throughput in MiB/s -h , --host Node hostname or ip address -m, --mib Print the throughput cap for streaming in MiB/s -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"getstreamthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/getstreamthroughput.html","titles":[{"text":"getstreamthroughput","id":"getstreamthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/gettimeout.html":{"text":"NAME nodetool gettimeout - Print the timeout of the given type in ms SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] gettimeout [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The timeout type, one of (read, range, write, counterwrite, cascontention, truncate, internodeconnect, internodeuser, internodestreaminguser, misc (general rpc_timeout_in_ms))","title":"","component":"Cassandra","version":"4.1","name":"gettimeout","url":"/Cassandra/4.1/cassandra/tools/nodetool/gettimeout.html","titles":[{"text":"gettimeout","id":"gettimeout"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/gettraceprobability.html":{"text":"NAME nodetool gettraceprobability - Print the current trace probability value SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] gettraceprobability OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"gettraceprobability","url":"/Cassandra/4.1/cassandra/tools/nodetool/gettraceprobability.html","titles":[{"text":"gettraceprobability","id":"gettraceprobability"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/gossipinfo.html":{"text":"NAME nodetool gossipinfo - Shows the gossip information for the cluster SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] gossipinfo [(-r | --resolve-ip)] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -r, --resolve-ip Show node domain names instead of IPs -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"gossipinfo","url":"/Cassandra/4.1/cassandra/tools/nodetool/gossipinfo.html","titles":[{"text":"gossipinfo","id":"gossipinfo"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/help.html":{"text":"NAME nodetool help - Display help information SYNOPSIS nodetool help [--] [...] OPTIONS -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options","title":"","component":"Cassandra","version":"4.1","name":"help","url":"/Cassandra/4.1/cassandra/tools/nodetool/help.html","titles":[{"text":"help","id":"help"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/import.html":{"text":"NAME nodetool import - Import new SSTables to the system SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] import [(-c | --no-invalidate-caches)] [(-e | --extended-verify)] [(-l | --keep-level)] [(-p | --copy-data)] [(-q | --quick)] [(-r | --keep-repaired)] [(-t | --no-tokens)] [(-v | --no-verify)] [--] ... OPTIONS -c, --no-invalidate-caches Don't invalidate the row cache when importing -e, --extended-verify Run an extended verify, verifying all values in the new sstables -h , --host Node hostname or ip address -l, --keep-level Keep the level on the new sstables -p, --copy-data Copy data from source directories instead of moving them -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -q, --quick Do a quick import without verifying sstables, clearing row cache or checking in which data directory to put the file -r, --keep-repaired Keep any repaired information from the sstables -t, --no-tokens Don't verify that all tokens in the new sstable are owned by the current node -u , --username Remote jmx agent username -v, --no-verify Don't verify new sstables -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options ... The keyspace, table name and directories to import sstables from","title":"","component":"Cassandra","version":"4.1","name":"import","url":"/Cassandra/4.1/cassandra/tools/nodetool/import.html","titles":[{"text":"import","id":"import"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/info.html":{"text":"NAME nodetool info - Print node information (uptime, load, ...) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] info [(-T | --tokens)] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -T, --tokens Display all tokens -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"info","url":"/Cassandra/4.1/cassandra/tools/nodetool/info.html","titles":[{"text":"info","id":"info"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatecountercache.html":{"text":"NAME nodetool invalidatecountercache - Invalidate the counter cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatecountercache OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"invalidatecountercache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatecountercache.html","titles":[{"text":"invalidatecountercache","id":"invalidatecountercache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatecredentialscache.html":{"text":"NAME nodetool invalidatecredentialscache - Invalidate the credentials cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatecredentialscache [--] [...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of roles to invalidate. By default, all roles","title":"","component":"Cassandra","version":"4.1","name":"invalidatecredentialscache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatecredentialscache.html","titles":[{"text":"invalidatecredentialscache","id":"invalidatecredentialscache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatejmxpermissionscache.html":{"text":"NAME nodetool invalidatejmxpermissionscache - Invalidate the JMX permissions cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatejmxpermissionscache [--] [...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of roles to invalidate. By default, all roles","title":"","component":"Cassandra","version":"4.1","name":"invalidatejmxpermissionscache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatejmxpermissionscache.html","titles":[{"text":"invalidatejmxpermissionscache","id":"invalidatejmxpermissionscache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatekeycache.html":{"text":"NAME nodetool invalidatekeycache - Invalidate the key cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatekeycache OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"invalidatekeycache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatekeycache.html","titles":[{"text":"invalidatekeycache","id":"invalidatekeycache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatenetworkpermissionscache.html":{"text":"NAME nodetool invalidatenetworkpermissionscache - Invalidate the network permissions cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatenetworkpermissionscache [--] [...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of roles to invalidate. By default, all roles","title":"","component":"Cassandra","version":"4.1","name":"invalidatenetworkpermissionscache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatenetworkpermissionscache.html","titles":[{"text":"invalidatenetworkpermissionscache","id":"invalidatenetworkpermissionscache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidatepermissionscache.html":{"text":"NAME nodetool invalidatepermissionscache - Invalidate the permissions cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidatepermissionscache [--all-functions] [--all-keyspaces] [--all-mbeans] [--all-roles] [--all-tables] [--function ] [--functions-in-keyspace ] [--keyspace ] [--mbean ] [--role ] [--table ] [--] [] OPTIONS --all-functions Invalidate permissions for 'ALL FUNCTIONS' --all-keyspaces Invalidate permissions for 'ALL KEYSPACES' --all-mbeans Invalidate permissions for 'ALL MBEANS' --all-roles Invalidate permissions for 'ALL ROLES' --all-tables Invalidate permissions for 'ALL TABLES' --function Function to invalidate permissions for (you must specify --functions-in-keyspace for using this option; function format: name[arg1^..^agrN], for example: foo[Int32Type^DoubleType]) --functions-in-keyspace Keyspace to invalidate permissions for -h , --host Node hostname or ip address --keyspace Keyspace to invalidate permissions for --mbean MBean to invalidate permissions for -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file --role Role to invalidate permissions for --table Table to invalidate permissions for (you must specify --keyspace for using this option) -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [] A role for which permissions to specified resources need to be invalidated","title":"","component":"Cassandra","version":"4.1","name":"invalidatepermissionscache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidatepermissionscache.html","titles":[{"text":"invalidatepermissionscache","id":"invalidatepermissionscache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidaterolescache.html":{"text":"NAME nodetool invalidaterolescache - Invalidate the roles cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidaterolescache [--] [...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of roles to invalidate. By default, all roles","title":"","component":"Cassandra","version":"4.1","name":"invalidaterolescache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidaterolescache.html","titles":[{"text":"invalidaterolescache","id":"invalidaterolescache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/invalidaterowcache.html":{"text":"NAME nodetool invalidaterowcache - Invalidate the row cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] invalidaterowcache OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"invalidaterowcache","url":"/Cassandra/4.1/cassandra/tools/nodetool/invalidaterowcache.html","titles":[{"text":"invalidaterowcache","id":"invalidaterowcache"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/join.html":{"text":"NAME nodetool join - Join the ring SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] join OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"join","url":"/Cassandra/4.1/cassandra/tools/nodetool/join.html","titles":[{"text":"join","id":"join"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/listpendinghints.html":{"text":"NAME nodetool listpendinghints - Print all pending hints that this node has SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] listpendinghints OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"listpendinghints","url":"/Cassandra/4.1/cassandra/tools/nodetool/listpendinghints.html","titles":[{"text":"listpendinghints","id":"listpendinghints"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/listsnapshots.html":{"text":"NAME nodetool listsnapshots - Lists all the snapshots along with the size on disk and true size. True size is the total size of all SSTables which are not backed up to disk. Size on disk is total size of the snapshot on disk. Total TrueDiskSpaceUsed does not make any SSTable deduplication. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] listsnapshots [(-nt | --no-ttl)] OPTIONS -h , --host Node hostname or ip address -nt, --no-ttl Skip snapshots with TTL -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"listsnapshots","url":"/Cassandra/4.1/cassandra/tools/nodetool/listsnapshots.html","titles":[{"text":"listsnapshots","id":"listsnapshots"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/move.html":{"text":"NAME nodetool move - Move node on the token ring to a new token SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] move [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The new token.","title":"","component":"Cassandra","version":"4.1","name":"move","url":"/Cassandra/4.1/cassandra/tools/nodetool/move.html","titles":[{"text":"move","id":"move"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/netstats.html":{"text":"NAME nodetool netstats - Print network information on provided host (connecting node by default) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] netstats [(-H | --human-readable)] OPTIONS -h , --host Node hostname or ip address -H, --human-readable Display bytes in human readable form, i.e. KiB, MiB, GiB, TiB -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"netstats","url":"/Cassandra/4.1/cassandra/tools/nodetool/netstats.html","titles":[{"text":"netstats","id":"netstats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/nodetool.html":{"text":"usage: nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] [] The most commonly used nodetool commands are: assassinate - Forcefully remove a dead node without re-replicating any data. Use as a last resort if you cannot removenode bootstrap - Monitor/manage node’s bootstrap process cleanup - Triggers the immediate cleanup of keys no longer belonging to a node. By default, clean all keyspaces clearsnapshot - Remove the snapshot with the given name from the given keyspaces clientstats - Print information about connected clients compact - Force a (major) compaction on one or more tables or user-defined compaction on given SSTables compactionhistory - Print history of compaction compactionstats - Print statistics on compactions datapaths - Print all directories where data of tables are stored decommission - Decommission the node I am connecting to describecluster - Print the name, snitch, partitioner and schema version of a cluster describering - Shows the token ranges info of a given keyspace disableauditlog - Disable the audit log disableautocompaction - Disable autocompaction for the given keyspace and table disablebackup - Disable incremental backup disablebinary - Disable native transport (binary protocol) disablefullquerylog - Disable the full query log disablegossip - Disable gossip (effectively marking the node down) disablehandoff - Disable storing hinted handoffs disablehintsfordc - Disable hints for a data center disableoldprotocolversions - Disable old protocol versions drain - Drain the node (stop accepting writes and flush all tables) enableauditlog - Enable the audit log enableautocompaction - Enable autocompaction for the given keyspace and table enablebackup - Enable incremental backup enablebinary - Reenable native transport (binary protocol) enablefullquerylog - Enable full query logging, defaults for the options are configured in cassandra.yaml enablegossip - Reenable gossip enablehandoff - Reenable future hints storing on the current node enablehintsfordc - Enable hints for a data center that was previsouly disabled enableoldprotocolversions - Enable old protocol versions failuredetector - Shows the failure detector information for the cluster flush - Flush one or more tables garbagecollect - Remove deleted data from one or more tables gcstats - Print GC Statistics getauditlog - Print configuration of audit log if enabled, otherwise the configuration reflected in cassandra.yaml getauthcacheconfig - Get configuration of Auth cache getbatchlogreplaythrottle - Print batchlog replay throttle in KB/s. This is reduced proportionally to the number of nodes in the cluster. getcolumnindexsize - Print the granularity of the collation index of rows within a partition in KiB getcompactionthreshold - Print min and max compaction thresholds for a given table getcompactionthroughput - Print the MiB/s throughput cap for compaction in the system as a rounded number getconcurrency - Get maximum concurrency for processing stages getconcurrentcompactors - Get the number of concurrent compactors in the system. getconcurrentviewbuilders - Get the number of concurrent view builders in the system getdefaultrf - Gets default keyspace replication factor. getendpoints - Print the end points that owns the key getfullquerylog - Print configuration of fql if enabled, otherwise the configuration reflected in cassandra.yaml getinterdcstreamthroughput - Print the throughput cap for inter-datacenter streaming and entire SSTable inter-datacenter streaming in the systemin rounded megabits. For precise number, please, use option -d getlogginglevels - Get the runtime logging levels getmaxhintwindow - Print the max hint window in ms getseeds - Get the currently in use seed node IP list excluding the node IP getsnapshotthrottle - Print the snapshot_links_per_second throttle for snapshot/clearsnapshot getsstables - Print the sstable filenames that own the key getstreamthroughput - Print the throughput cap for streaming and entire SSTable streaming in the system in rounded megabits. For precise number, please, use option -d gettimeout - Print the timeout of the given type in ms gettraceprobability - Print the current trace probability value gossipinfo - Shows the gossip information for the cluster help - Display help information import - Import new SSTables to the system info - Print node information (uptime, load, …​) invalidatecountercache - Invalidate the counter cache invalidatecredentialscache - Invalidate the credentials cache invalidatejmxpermissionscache - Invalidate the JMX permissions cache invalidatekeycache - Invalidate the key cache invalidatenetworkpermissionscache - Invalidate the network permissions cache invalidatepermissionscache - Invalidate the permissions cache invalidaterolescache - Invalidate the roles cache invalidaterowcache - Invalidate the row cache join - Join the ring listpendinghints - Print all pending hints that this node has listsnapshots - Lists all the snapshots along with the size on disk and true size. True size is the total size of all SSTables which are not backed up to disk. Size on disk is total size of the snapshot on disk. Total TrueDiskSpaceUsed does not make any SSTable deduplication. move - Move node on the token ring to a new token netstats - Print network information on provided host (connecting node by default) pausehandoff - Pause hints delivery process profileload - Low footprint profiling of activity for a period of time proxyhistograms - Print statistic histograms for network operations rangekeysample - Shows the sampled keys held across all keyspaces rebuild - Rebuild data by streaming from other nodes (similarly to bootstrap) rebuild_index - A full rebuild of native secondary indexes for a given table recompress_sstables - Rewrite sstables (for the requested tables) that have compression configuration different from the current refresh - Load newly placed SSTables to the system without restart refreshsizeestimates - Refresh system.size_estimates reloadlocalschema - Reload local node schema from system tables reloadseeds - Reload the seed node list from the seed node provider reloadssl - Signals Cassandra to reload SSL certificates reloadtriggers - Reload trigger classes relocatesstables - Relocates sstables to the correct disk removenode - Show status of current node removal, force completion of pending removal or remove provided ID repair - Repair one or more tables repair_admin - list - and fail incremental repair sessions replaybatchlog - Kick off batchlog replay and wait for finish resetfullquerylog - Stop the full query log and clean files in the configured full query log directory from cassandra.yaml as well as JMX resetlocalschema - Reset node’s local schema and resync resumehandoff - Resume hints delivery process ring - Print information about the token ring scrub - Scrub (rebuild sstables for) one or more tables setauthcacheconfig - Set configuration for Auth cache setbatchlogreplaythrottle - Set batchlog replay throttle in KB per second, or 0 to disable throttling. This will be reduced proportionally to the number of nodes in the cluster. setcachecapacity - Set global key, row, and counter cache capacities (in MB units) setcachekeystosave - Set number of keys saved by each cache for faster post-restart warmup. 0 to disable setcolumnindexsize - Set the granularity of the collation index of rows within a partition in KiB setcompactionthreshold - Set min and max compaction thresholds for a given table setcompactionthroughput - Set the MiB/s throughput cap for compaction in the system, or 0 to disable throttling setconcurrency - Set maximum concurrency for processing stage setconcurrentcompactors - Set number of concurrent compactors in the system. setconcurrentviewbuilders - Set the number of concurrent view builders in the system setdefaultrf - Sets default keyspace replication factor. sethintedhandoffthrottlekb - Set hinted handoff throttle in KiB per second, per delivery thread. setinterdcstreamthroughput - Set the throughput cap for inter-datacenter streaming and entire SSTable inter-datacenter streaming in the system, or 0 to disable throttling setlogginglevel - Set the log level threshold for a given component or class. Will reset to the initial configuration if called with no parameters. setmaxhintwindow - Set the specified max hint window in ms setsnapshotthrottle - Set the snapshot_links_per_second cap for snapshot and clearsnapshot throttling setstreamthroughput - Set throughput cap for streaming and entire SSTable streaming in the system, or 0 to disable throttling settimeout - Set the specified timeout in ms, or 0 to disable timeout settraceprobability - Sets the probability for tracing any given request to value. 0 disables, 1 enables for all requests, 0 is the default sjk - Run commands of 'Swiss Java Knife'. Run 'nodetool sjk --help' for more information. snapshot - Take a snapshot of specified keyspaces or a snapshot of the specified table status - Print cluster information (state, load, IDs, …​) statusautocompaction - status - of autocompaction of the given keyspace and table statusbackup - Status of incremental backup statusbinary - Status of native transport (binary protocol) statusgossip - Status of gossip statushandoff - Status of storing future hints on the current node stop - Stop compaction stopdaemon - Stop cassandra daemon tablehistograms - Print statistic histograms for a given table tablestats - Print statistics on tables toppartitions - Sample and print the most active partitions tpstats - Print usage statistics of thread pools truncatehints - Truncate all hints on the local node, or truncate hints for the endpoint(s) specified. upgradesstables - Rewrite sstables (for the requested tables) that are not on the current version (thus upgrading them to said current version) verify - Verify (check data checksum for) one or more tables version - Print cassandra version viewbuildstatus - Show progress of a materialized view build See 'nodetool help ' for more information on a specific command.","title":"","component":"Cassandra","version":"4.1","name":"nodetool","url":"/Cassandra/4.1/cassandra/tools/nodetool/nodetool.html","titles":[{"text":"Nodetool","id":"nodetool"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/pausehandoff.html":{"text":"NAME nodetool pausehandoff - Pause hints delivery process SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] pausehandoff OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"pausehandoff","url":"/Cassandra/4.1/cassandra/tools/nodetool/pausehandoff.html","titles":[{"text":"pausehandoff","id":"pausehandoff"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/profileload.html":{"text":"NAME nodetool profileload - Low footprint profiling of activity for a period of time SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] profileload [-a ] [-k ] [-s ] [--] OPTIONS -a Comma separated list of samplers to use (Default: all) -h , --host Node hostname or ip address -k Number of the top samples to list (Default: 10) -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -s Capacity of the sampler, higher for more accuracy (Default: 256) -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace, column family name, and duration in milliseconds","title":"","component":"Cassandra","version":"4.1","name":"profileload","url":"/Cassandra/4.1/cassandra/tools/nodetool/profileload.html","titles":[{"text":"profileload","id":"profileload"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/proxyhistograms.html":{"text":"NAME nodetool proxyhistograms - Print statistic histograms for network operations SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] proxyhistograms OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"proxyhistograms","url":"/Cassandra/4.1/cassandra/tools/nodetool/proxyhistograms.html","titles":[{"text":"proxyhistograms","id":"proxyhistograms"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/rangekeysample.html":{"text":"NAME nodetool rangekeysample - Shows the sampled keys held across all keyspaces SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] rangekeysample OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"rangekeysample","url":"/Cassandra/4.1/cassandra/tools/nodetool/rangekeysample.html","titles":[{"text":"rangekeysample","id":"rangekeysample"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/rebuild.html":{"text":"NAME nodetool rebuild - Rebuild data by streaming from other nodes (similarly to bootstrap) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] rebuild [(-ks | --keyspace )] [(-s | --sources )] [(-ts | --tokens )] [--] OPTIONS -h , --host Node hostname or ip address -ks , --keyspace Use -ks to rebuild specific keyspace. -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -s , --sources Use -s to specify hosts that this node should stream from when -ts is used. Multiple hosts should be separated using commas (e.g. 127.0.0.1,127.0.0.2,...) -ts , --tokens Use -ts to rebuild specific token ranges, in the format of \"(start_token_1,end_token_1],(start_token_2,end_token_2],...(start_token_n,end_token_n]\". -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Name of DC from which to select sources for streaming. By default, pick any DC","title":"","component":"Cassandra","version":"4.1","name":"rebuild","url":"/Cassandra/4.1/cassandra/tools/nodetool/rebuild.html","titles":[{"text":"rebuild","id":"rebuild"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/rebuild_index.html":{"text":"NAME nodetool rebuild_index - A full rebuild of native secondary indexes for a given table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] rebuild_index [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace and table name followed by a list of index names","title":"","component":"Cassandra","version":"4.1","name":"rebuild_index","url":"/Cassandra/4.1/cassandra/tools/nodetool/rebuild_index.html","titles":[{"text":"rebuild_index","id":"rebuild_index"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/recompress_sstables.html":{"text":"NAME nodetool recompress_sstables - Rewrite sstables (for the requested tables) that have compression configuration different from the current SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] recompress_sstables [(-j | --jobs )] [--] [ ...] OPTIONS -h , --host Node hostname or ip address -j , --jobs Number of sstables to upgrade simultanously, set to 0 to use all available compaction threads -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"recompress_sstables","url":"/Cassandra/4.1/cassandra/tools/nodetool/recompress_sstables.html","titles":[{"text":"recompress_sstables","id":"recompress_sstables"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/refresh.html":{"text":"NAME nodetool refresh - Load newly placed SSTables to the system without restart SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] refresh [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace and table name","title":"","component":"Cassandra","version":"4.1","name":"refresh","url":"/Cassandra/4.1/cassandra/tools/nodetool/refresh.html","titles":[{"text":"refresh","id":"refresh"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/refreshsizeestimates.html":{"text":"NAME nodetool refreshsizeestimates - Refresh system.size_estimates SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] refreshsizeestimates OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"refreshsizeestimates","url":"/Cassandra/4.1/cassandra/tools/nodetool/refreshsizeestimates.html","titles":[{"text":"refreshsizeestimates","id":"refreshsizeestimates"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/reloadlocalschema.html":{"text":"NAME nodetool reloadlocalschema - Reload local node schema from system tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] reloadlocalschema OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"reloadlocalschema","url":"/Cassandra/4.1/cassandra/tools/nodetool/reloadlocalschema.html","titles":[{"text":"reloadlocalschema","id":"reloadlocalschema"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/reloadseeds.html":{"text":"NAME nodetool reloadseeds - Reload the seed node list from the seed node provider SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] reloadseeds OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"reloadseeds","url":"/Cassandra/4.1/cassandra/tools/nodetool/reloadseeds.html","titles":[{"text":"reloadseeds","id":"reloadseeds"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/reloadssl.html":{"text":"NAME nodetool reloadssl - Signals Cassandra to reload SSL certificates SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] reloadssl OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"reloadssl","url":"/Cassandra/4.1/cassandra/tools/nodetool/reloadssl.html","titles":[{"text":"reloadssl","id":"reloadssl"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/reloadtriggers.html":{"text":"NAME nodetool reloadtriggers - Reload trigger classes SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] reloadtriggers OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"reloadtriggers","url":"/Cassandra/4.1/cassandra/tools/nodetool/reloadtriggers.html","titles":[{"text":"reloadtriggers","id":"reloadtriggers"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/relocatesstables.html":{"text":"NAME nodetool relocatesstables - Relocates sstables to the correct disk SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] relocatesstables [(-j | --jobs )] [--] OPTIONS -h , --host Node hostname or ip address -j , --jobs Number of sstables to relocate simultanously, set to 0 to use all available compaction threads -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace and table name","title":"","component":"Cassandra","version":"4.1","name":"relocatesstables","url":"/Cassandra/4.1/cassandra/tools/nodetool/relocatesstables.html","titles":[{"text":"relocatesstables","id":"relocatesstables"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/removenode.html":{"text":"NAME nodetool removenode - Show status of current node removal, force completion of pending removal or remove provided ID SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] removenode [--] || OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options || Show status of current node removal, force completion of pending removal, or remove provided ID","title":"","component":"Cassandra","version":"4.1","name":"removenode","url":"/Cassandra/4.1/cassandra/tools/nodetool/removenode.html","titles":[{"text":"removenode","id":"removenode"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/repair.html":{"text":"NAME nodetool repair - Repair one or more tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] repair [(-dc | --in-dc )...] [(-dcpar | --dc-parallel)] [(-et | --end-token )] [(-force | --force)] [(-full | --full)] [(-hosts | --in-hosts )...] [(-iuk | --ignore-unreplicated-keyspaces)] [(-j | --job-threads )] [(-local | --in-local-dc)] [(-os | --optimise-streams)] [(-paxos-only | --paxos-only)] [(-pl | --pull)] [(-pr | --partitioner-range)] [(-prv | --preview)] [(-seq | --sequential)] [(-skip-paxos | --skip-paxos)] [(-st | --start-token )] [(-tr | --trace)] [(-vd | --validate)] [--] [ ...] OPTIONS -dc , --in-dc Use -dc to repair specific datacenters -dcpar, --dc-parallel Use -dcpar to repair data centers in parallel. -et , --end-token Use -et to specify a token at which repair range ends (inclusive) -force, --force Use -force to filter out down endpoints -full, --full Use -full to issue a full repair. -h , --host Node hostname or ip address -hosts , --in-hosts Use -hosts to repair specific hosts -iuk, --ignore-unreplicated-keyspaces Use --ignore-unreplicated-keyspaces to ignore keyspaces which are not replicated, otherwise the repair will fail -j , --job-threads Number of threads to run repair jobs. Usually this means number of CFs to repair concurrently. WARNING: increasing this puts more load on repairing nodes, so be careful. (default: 1, max: 4) -local, --in-local-dc Use -local to only repair against nodes in the same datacenter -os, --optimise-streams Use --optimise-streams to try to reduce the number of streams we do (EXPERIMENTAL, see CASSANDRA-3200). -p , --port Remote jmx agent port number -paxos-only, --paxos-only If the --paxos-only flag is included, no table data is repaired, only paxos operations.. -pl, --pull Use --pull to perform a one way repair where data is only streamed from a remote node to this node. -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pr, --partitioner-range Use -pr to repair only the first range returned by the partitioner -prv, --preview Determine ranges and amount of data to be streamed, but don't actually perform repair -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -seq, --sequential Use -seq to carry out a sequential repair -skip-paxos, --skip-paxos If the --skip-paxos flag is included, the paxos repair step is skipped. Paxos repair is also skipped for preview repairs. -st , --start-token Use -st to specify a token at which the repair range starts (exclusive) -tr, --trace Use -tr to trace the repair. Traces are logged to system_traces.events. -u , --username Remote jmx agent username -vd, --validate Checks that repaired data is in sync between nodes. Out of sync repaired data indicates a full repair should be run. -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"repair","url":"/Cassandra/4.1/cassandra/tools/nodetool/repair.html","titles":[{"text":"repair","id":"repair"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/repair_admin.html":{"text":"NAME nodetool repair_admin - list and fail incremental repair sessions SYNOPSIS nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin cancel [(-f | --force)] (-s | --session ) nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin cleanup [(-f | --force)] [(-st | --start-token )] [(-et | --end-token )] nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin list [(-a | --all)] [(-st | --start-token )] [(-et | --end-token )] nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin summarize-pending [(-st | --start-token )] [(-v | --verbose)] [(-et | --end-token )] nodetool [(-pw | --password )] [(-h | --host )] [(-pwf | --password-file )] [(-pp | --print-port)] [(-p | --port )] [(-u | --username )] repair_admin summarize-repaired [(-st | --start-token )] [(-v | --verbose)] [(-et | --end-token )] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username COMMANDS With no arguments, list repair sessions list list repair sessions With --all option, include completed and failed sessions With --start-token option, Use -st to specify a token at which the repair range starts With --end-token option, Use -et to specify a token at which repair range ends cancel cancel an incremental repair session. Use --force to cancel from a node other than the repair coordinator Attempting to cancel FINALIZED or FAILED sessions is an error. With --force option, Force a cancellation. With --session option, The session to cancel cleanup cleans up pending data from completed sessions. This happens automatically, but the command is provided for situations where it needs to be expedited. Use --force to cancel compactions that are preventing promotion With --force option, Force a cleanup. With --start-token option, Use -st to specify a token at which the repair range starts With --end-token option, Use -et to specify a token at which repair range ends summarize-pending report the amount of data marked pending repair for the given token range (or all replicated range if no tokens are provided With --start-token option, Use -st to specify a token at which the repair range starts With --verbose option, print additional info With --end-token option, Use -et to specify a token at which repair range ends summarize-repaired return the most recent repairedAt timestamp for the given token range (or all replicated ranges if no tokens are provided) With --start-token option, Use -st to specify a token at which the repair range starts With --verbose option, print additional info With --end-token option, Use -et to specify a token at which repair range ends","title":"","component":"Cassandra","version":"4.1","name":"repair_admin","url":"/Cassandra/4.1/cassandra/tools/nodetool/repair_admin.html","titles":[{"text":"repair_admin","id":"repair_admin"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/replaybatchlog.html":{"text":"NAME nodetool replaybatchlog - Kick off batchlog replay and wait for finish SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] replaybatchlog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"replaybatchlog","url":"/Cassandra/4.1/cassandra/tools/nodetool/replaybatchlog.html","titles":[{"text":"replaybatchlog","id":"replaybatchlog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/resetfullquerylog.html":{"text":"NAME nodetool resetfullquerylog - Stop the full query log and clean files in the configured full query log directory from cassandra.yaml as well as JMX SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] resetfullquerylog OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"resetfullquerylog","url":"/Cassandra/4.1/cassandra/tools/nodetool/resetfullquerylog.html","titles":[{"text":"resetfullquerylog","id":"resetfullquerylog"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/resetlocalschema.html":{"text":"NAME nodetool resetlocalschema - Reset node's local schema and resync SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] resetlocalschema OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"resetlocalschema","url":"/Cassandra/4.1/cassandra/tools/nodetool/resetlocalschema.html","titles":[{"text":"resetlocalschema","id":"resetlocalschema"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/resumehandoff.html":{"text":"NAME nodetool resumehandoff - Resume hints delivery process SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] resumehandoff OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"resumehandoff","url":"/Cassandra/4.1/cassandra/tools/nodetool/resumehandoff.html","titles":[{"text":"resumehandoff","id":"resumehandoff"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/ring.html":{"text":"NAME nodetool ring - Print information about the token ring SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] ring [(-r | --resolve-ip)] [--] [] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -r, --resolve-ip Show node domain names instead of IPs -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Specify a keyspace for accurate ownership information (topology awareness)","title":"","component":"Cassandra","version":"4.1","name":"ring","url":"/Cassandra/4.1/cassandra/tools/nodetool/ring.html","titles":[{"text":"ring","id":"ring"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/scrub.html":{"text":"NAME nodetool scrub - Scrub (rebuild sstables for) one or more tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] scrub [(-j | --jobs )] [(-n | --no-validate)] [(-ns | --no-snapshot)] [(-r | --reinsert-overflowed-ttl)] [(-s | --skip-corrupted)] [--] [ ...] OPTIONS -h , --host Node hostname or ip address -j , --jobs Number of sstables to scrub simultanously, set to 0 to use all available compaction threads -n, --no-validate Do not validate columns using column validator -ns, --no-snapshot Scrubbed CFs will be snapshotted first, if disableSnapshot is false. (default false) -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -r, --reinsert-overflowed-ttl Rewrites rows with overflowed expiration date affected by CASSANDRA-14092 with the maximum supported expiration date of 2038-01-19T03:14:06+00:00. The rows are rewritten with the original timestamp incremented by one millisecond to override/supersede any potential tombstone that may have been generated during compaction of the affected rows. -s, --skip-corrupted Skip corrupted partitions even when scrubbing counter tables. (default false) -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"scrub","url":"/Cassandra/4.1/cassandra/tools/nodetool/scrub.html","titles":[{"text":"scrub","id":"scrub"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setauthcacheconfig.html":{"text":"NAME nodetool setauthcacheconfig - Set configuration for Auth cache SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setauthcacheconfig --cache-name [--disable-active-update] [--enable-active-update] [--max-entries ] [--update-interval ] [--validity-period ] OPTIONS --cache-name Name of Auth cache (required) --disable-active-update Disable active update --enable-active-update Enable active update -h , --host Node hostname or ip address --max-entries Max entries -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username --update-interval Update interval in milliseconds --validity-period Validity period in milliseconds","title":"","component":"Cassandra","version":"4.1","name":"setauthcacheconfig","url":"/Cassandra/4.1/cassandra/tools/nodetool/setauthcacheconfig.html","titles":[{"text":"setauthcacheconfig","id":"setauthcacheconfig"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setbatchlogreplaythrottle.html":{"text":"NAME nodetool setbatchlogreplaythrottle - Set batchlog replay throttle in KB per second, or 0 to disable throttling. This will be reduced proportionally to the number of nodes in the cluster. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setbatchlogreplaythrottle [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in KiB per second, 0 to disable throttling","title":"","component":"Cassandra","version":"4.1","name":"setbatchlogreplaythrottle","url":"/Cassandra/4.1/cassandra/tools/nodetool/setbatchlogreplaythrottle.html","titles":[{"text":"setbatchlogreplaythrottle","id":"setbatchlogreplaythrottle"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setcachecapacity.html":{"text":"NAME nodetool setcachecapacity - Set global key, row, and counter cache capacities (in MB units) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setcachecapacity [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Key cache, row cache, and counter cache (in MB)","title":"","component":"Cassandra","version":"4.1","name":"setcachecapacity","url":"/Cassandra/4.1/cassandra/tools/nodetool/setcachecapacity.html","titles":[{"text":"setcachecapacity","id":"setcachecapacity"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setcachekeystosave.html":{"text":"NAME nodetool setcachekeystosave - Set number of keys saved by each cache for faster post-restart warmup. 0 to disable SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setcachekeystosave [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The number of keys saved by each cache. 0 to disable","title":"","component":"Cassandra","version":"4.1","name":"setcachekeystosave","url":"/Cassandra/4.1/cassandra/tools/nodetool/setcachekeystosave.html","titles":[{"text":"setcachekeystosave","id":"setcachekeystosave"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setcolumnindexsize.html":{"text":"NAME nodetool setcolumnindexsize - Set the granularity of the collation index of rows within a partition in KiB SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setcolumnindexsize [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in KiB","title":"","component":"Cassandra","version":"4.1","name":"setcolumnindexsize","url":"/Cassandra/4.1/cassandra/tools/nodetool/setcolumnindexsize.html","titles":[{"text":"setcolumnindexsize","id":"setcolumnindexsize"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setcompactionthreshold.html":{"text":"NAME nodetool setcompactionthreshold - Set min and max compaction thresholds for a given table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setcompactionthreshold [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace, the table, min and max threshold","title":"","component":"Cassandra","version":"4.1","name":"setcompactionthreshold","url":"/Cassandra/4.1/cassandra/tools/nodetool/setcompactionthreshold.html","titles":[{"text":"setcompactionthreshold","id":"setcompactionthreshold"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setcompactionthroughput.html":{"text":"NAME nodetool setcompactionthroughput - Set the MiB/s throughput cap for compaction in the system, or 0 to disable throttling SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setcompactionthroughput [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in MiB, 0 to disable throttling","title":"","component":"Cassandra","version":"4.1","name":"setcompactionthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/setcompactionthroughput.html","titles":[{"text":"setcompactionthroughput","id":"setcompactionthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrency.html":{"text":"NAME nodetool setconcurrency - Set maximum concurrency for processing stage SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setconcurrency [--] | OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options | Set concurrency for processing stage","title":"","component":"Cassandra","version":"4.1","name":"setconcurrency","url":"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrency.html","titles":[{"text":"setconcurrency","id":"setconcurrency"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrentcompactors.html":{"text":"NAME nodetool setconcurrentcompactors - Set number of concurrent compactors in the system. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setconcurrentcompactors [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Number of concurrent compactors, greater than 0.","title":"","component":"Cassandra","version":"4.1","name":"setconcurrentcompactors","url":"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrentcompactors.html","titles":[{"text":"setconcurrentcompactors","id":"setconcurrentcompactors"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrentviewbuilders.html":{"text":"NAME nodetool setconcurrentviewbuilders - Set the number of concurrent view builders in the system SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setconcurrentviewbuilders [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Number of concurrent view builders, greater than 0.","title":"","component":"Cassandra","version":"4.1","name":"setconcurrentviewbuilders","url":"/Cassandra/4.1/cassandra/tools/nodetool/setconcurrentviewbuilders.html","titles":[{"text":"setconcurrentviewbuilders","id":"setconcurrentviewbuilders"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setdefaultrf.html":{"text":"NAME nodetool setdefaultrf - Sets default keyspace replication factor. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setdefaultrf [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Default replication factor","title":"","component":"Cassandra","version":"4.1","name":"setdefaultrf","url":"/Cassandra/4.1/cassandra/tools/nodetool/setdefaultrf.html","titles":[{"text":"setdefaultrf","id":"setdefaultrf"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/sethintedhandoffthrottlekb.html":{"text":"NAME nodetool sethintedhandoffthrottlekb - Set hinted handoff throttle in KiB per second, per delivery thread. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] sethintedhandoffthrottlekb [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in KiB per second","title":"","component":"Cassandra","version":"4.1","name":"sethintedhandoffthrottlekb","url":"/Cassandra/4.1/cassandra/tools/nodetool/sethintedhandoffthrottlekb.html","titles":[{"text":"sethintedhandoffthrottlekb","id":"sethintedhandoffthrottlekb"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setinterdcstreamthroughput.html":{"text":"NAME nodetool setinterdcstreamthroughput - Set the throughput cap for inter-datacenter streaming and entire SSTable inter-datacenter streaming in the system, or 0 to disable throttling SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setinterdcstreamthroughput [(-e | --entire-sstable-throughput)] [(-m | --mib)] [--] OPTIONS -e, --entire-sstable-throughput Set entire SSTable streaming throughput in MiB/s -h , --host Node hostname or ip address -m, --mib Set streaming throughput in MiB/s -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in megabits, 0 to disable throttling","title":"","component":"Cassandra","version":"4.1","name":"setinterdcstreamthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/setinterdcstreamthroughput.html","titles":[{"text":"setinterdcstreamthroughput","id":"setinterdcstreamthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setlogginglevel.html":{"text":"NAME nodetool setlogginglevel - Set the log level threshold for a given component or class. Will reset to the initial configuration if called with no parameters. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setlogginglevel [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The component or class to change the level for and the log level threshold to set. Will reset to initial level if omitted. Available components: bootstrap, compaction, repair, streaming, cql, ring","title":"","component":"Cassandra","version":"4.1","name":"setlogginglevel","url":"/Cassandra/4.1/cassandra/tools/nodetool/setlogginglevel.html","titles":[{"text":"setlogginglevel","id":"setlogginglevel"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setmaxhintwindow.html":{"text":"NAME nodetool setmaxhintwindow - Set the specified max hint window in ms SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setmaxhintwindow [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value of maxhintwindow in ms","title":"","component":"Cassandra","version":"4.1","name":"setmaxhintwindow","url":"/Cassandra/4.1/cassandra/tools/nodetool/setmaxhintwindow.html","titles":[{"text":"setmaxhintwindow","id":"setmaxhintwindow"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setsnapshotthrottle.html":{"text":"NAME nodetool setsnapshotthrottle - Set the snapshot_links_per_second cap for snapshot and clearsnapshot throttling SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setsnapshotthrottle [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value represents hardlinks per second ( snapshot_links_per_second ) , 0 to disable throttling","title":"","component":"Cassandra","version":"4.1","name":"setsnapshotthrottle","url":"/Cassandra/4.1/cassandra/tools/nodetool/setsnapshotthrottle.html","titles":[{"text":"setsnapshotthrottle","id":"setsnapshotthrottle"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/setstreamthroughput.html":{"text":"NAME nodetool setstreamthroughput - Set throughput cap for streaming and entire SSTable streaming in the system, or 0 to disable throttling SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] setstreamthroughput [(-e | --entire-sstable-throughput)] [(-m | --mib)] [--] OPTIONS -e, --entire-sstable-throughput Set entire SSTable streaming throughput in MiB/s -h , --host Node hostname or ip address -m, --mib Set streaming throughput in MiB/s -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Value in megabits, 0 to disable throttling","title":"","component":"Cassandra","version":"4.1","name":"setstreamthroughput","url":"/Cassandra/4.1/cassandra/tools/nodetool/setstreamthroughput.html","titles":[{"text":"setstreamthroughput","id":"setstreamthroughput"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/settimeout.html":{"text":"NAME nodetool settimeout - Set the specified timeout in ms, or 0 to disable timeout SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] settimeout [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Timeout type followed by value in ms (0 disables socket streaming timeout). Type should be one of (read, range, write, counterwrite, cascontention, truncate, internodeconnect, internodeuser, internodestreaminguser, misc (general rpc_timeout_in_ms))","title":"","component":"Cassandra","version":"4.1","name":"settimeout","url":"/Cassandra/4.1/cassandra/tools/nodetool/settimeout.html","titles":[{"text":"settimeout","id":"settimeout"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/settraceprobability.html":{"text":"NAME nodetool settraceprobability - Sets the probability for tracing any given request to value. 0 disables, 1 enables for all requests, 0 is the default SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] settraceprobability [--] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Trace probability between 0 and 1 (ex: 0.2)","title":"","component":"Cassandra","version":"4.1","name":"settraceprobability","url":"/Cassandra/4.1/cassandra/tools/nodetool/settraceprobability.html","titles":[{"text":"settraceprobability","id":"settraceprobability"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/sjk.html":{"text":"NAME nodetool sjk - Run commands of 'Swiss Java Knife'. Run 'nodetool sjk --help' for more information. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] sjk [--] [...] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Arguments passed as is to 'Swiss Java Knife'.","title":"","component":"Cassandra","version":"4.1","name":"sjk","url":"/Cassandra/4.1/cassandra/tools/nodetool/sjk.html","titles":[{"text":"sjk","id":"sjk"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/snapshot.html":{"text":"NAME nodetool snapshot - Take a snapshot of specified keyspaces or a snapshot of the specified table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] snapshot [(-cf | --column-family | --table )] [(-kt | --kt-list | -kc | --kc.list )] [(-sf | --skip-flush)] [(-t | --tag )] [--ttl ] [--] [] OPTIONS -cf , --column-family , --table The table name (you must specify one and only one keyspace for using this option) -h , --host Node hostname or ip address -kt , --kt-list , -kc , --kc.list The list of Keyspace.table to take snapshot.(you must not specify only keyspace) -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -sf, --skip-flush Do not flush memtables before snapshotting (snapshot will not contain unflushed data) -t , --tag The name of the snapshot --ttl Specify a TTL of created snapshot -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [] List of keyspaces. By default, all keyspaces","title":"","component":"Cassandra","version":"4.1","name":"snapshot","url":"/Cassandra/4.1/cassandra/tools/nodetool/snapshot.html","titles":[{"text":"snapshot","id":"snapshot"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/status.html":{"text":"NAME nodetool status - Print cluster information (state, load, IDs, ...) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] status [(-r | --resolve-ip)] [--] [] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -r, --resolve-ip Show node domain names instead of IPs -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [] The keyspace name","title":"","component":"Cassandra","version":"4.1","name":"status","url":"/Cassandra/4.1/cassandra/tools/nodetool/status.html","titles":[{"text":"status","id":"status"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/statusautocompaction.html":{"text":"NAME nodetool statusautocompaction - status of autocompaction of the given keyspace and table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] statusautocompaction [(-a | --all)] [--] [ ...] OPTIONS -a, --all Show auto compaction status for each keyspace/table -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"statusautocompaction","url":"/Cassandra/4.1/cassandra/tools/nodetool/statusautocompaction.html","titles":[{"text":"statusautocompaction","id":"statusautocompaction"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/statusbackup.html":{"text":"NAME nodetool statusbackup - Status of incremental backup SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] statusbackup OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"statusbackup","url":"/Cassandra/4.1/cassandra/tools/nodetool/statusbackup.html","titles":[{"text":"statusbackup","id":"statusbackup"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/statusbinary.html":{"text":"NAME nodetool statusbinary - Status of native transport (binary protocol) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] statusbinary OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"statusbinary","url":"/Cassandra/4.1/cassandra/tools/nodetool/statusbinary.html","titles":[{"text":"statusbinary","id":"statusbinary"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/statusgossip.html":{"text":"NAME nodetool statusgossip - Status of gossip SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] statusgossip OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"statusgossip","url":"/Cassandra/4.1/cassandra/tools/nodetool/statusgossip.html","titles":[{"text":"statusgossip","id":"statusgossip"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/statushandoff.html":{"text":"NAME nodetool statushandoff - Status of storing future hints on the current node SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] statushandoff OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"statushandoff","url":"/Cassandra/4.1/cassandra/tools/nodetool/statushandoff.html","titles":[{"text":"statushandoff","id":"statushandoff"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/stop.html":{"text":"NAME nodetool stop - Stop compaction SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] stop [(-id | --compaction-id )] [--] OPTIONS -h , --host Node hostname or ip address -id , --compaction-id Use -id to stop a compaction by the specified id. Ids can be found in the transaction log files whose name starts with compaction_, located in the table transactions folder. -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Supported types are COMPACTION, VALIDATION, CLEANUP, SCRUB, UPGRADE_SSTABLES, INDEX_BUILD, TOMBSTONE_COMPACTION, ANTICOMPACTION, VERIFY, VIEW_BUILD, INDEX_SUMMARY, RELOCATE, GARBAGE_COLLECT","title":"","component":"Cassandra","version":"4.1","name":"stop","url":"/Cassandra/4.1/cassandra/tools/nodetool/stop.html","titles":[{"text":"stop","id":"stop"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/stopdaemon.html":{"text":"NAME nodetool stopdaemon - Stop cassandra daemon SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] stopdaemon OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"stopdaemon","url":"/Cassandra/4.1/cassandra/tools/nodetool/stopdaemon.html","titles":[{"text":"stopdaemon","id":"stopdaemon"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/tablehistograms.html":{"text":"NAME nodetool tablehistograms - Print statistic histograms for a given table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] tablehistograms [--] [ | ] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ | ] The keyspace and table name","title":"","component":"Cassandra","version":"4.1","name":"tablehistograms","url":"/Cassandra/4.1/cassandra/tools/nodetool/tablehistograms.html","titles":[{"text":"tablehistograms","id":"tablehistograms"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/tablestats.html":{"text":"NAME nodetool tablestats - Print statistics on tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] tablestats [(-F | --format )] [(-H | --human-readable)] [-i] [(-l | --sstable-location-check)] [(-s | --sort )] [(-t | --top )] [--] [...] OPTIONS -F , --format Output format (json, yaml) -h , --host Node hostname or ip address -H, --human-readable Display bytes in human readable form, i.e. KiB, MiB, GiB, TiB -i Ignore the list of tables and display the remaining tables -l, --sstable-location-check Check whether or not the SSTables are in the correct location. -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -s , --sort Sort tables by specified sort key (average_live_cells_per_slice_last_five_minutes, average_tombstones_per_slice_last_five_minutes, bloom_filter_false_positives, bloom_filter_false_ratio, bloom_filter_off_heap_memory_used, bloom_filter_space_used, compacted_partition_maximum_bytes, compacted_partition_mean_bytes, compacted_partition_minimum_bytes, compression_metadata_off_heap_memory_used, dropped_mutations, full_name, index_summary_off_heap_memory_used, local_read_count, local_read_latency_ms, local_write_latency_ms, maximum_live_cells_per_slice_last_five_minutes, maximum_tombstones_per_slice_last_five_minutes, memtable_cell_count, memtable_data_size, memtable_off_heap_memory_used, memtable_switch_count, number_of_partitions_estimate, off_heap_memory_used_total, pending_flushes, percent_repaired, read_latency, reads, space_used_by_snapshots_total, space_used_live, space_used_total, sstable_compression_ratio, sstable_count, table_name, write_latency, writes) -t , --top Show only the top K tables for the sort key (specify the number K of tables to be shown -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] List of tables (or keyspace) names","title":"","component":"Cassandra","version":"4.1","name":"tablestats","url":"/Cassandra/4.1/cassandra/tools/nodetool/tablestats.html","titles":[{"text":"tablestats","id":"tablestats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/toppartitions.html":{"text":"NAME nodetool toppartitions - Sample and print the most active partitions SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] toppartitions [-a ] [-k ] [-s ] [--] OPTIONS -a Comma separated list of samplers to use (Default: all) -h , --host Node hostname or ip address -k Number of the top samples to list (Default: 10) -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -s Capacity of the sampler, higher for more accuracy (Default: 256) -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options The keyspace, column family name, and duration in milliseconds","title":"","component":"Cassandra","version":"4.1","name":"toppartitions","url":"/Cassandra/4.1/cassandra/tools/nodetool/toppartitions.html","titles":[{"text":"toppartitions","id":"toppartitions"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/tpstats.html":{"text":"NAME nodetool tpstats - Print usage statistics of thread pools SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] tpstats [(-F | --format )] OPTIONS -F , --format Output format (json, yaml) -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"tpstats","url":"/Cassandra/4.1/cassandra/tools/nodetool/tpstats.html","titles":[{"text":"tpstats","id":"tpstats"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/truncatehints.html":{"text":"NAME nodetool truncatehints - Truncate all hints on the local node, or truncate hints for the endpoint(s) specified. SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] truncatehints [--] [endpoint ... ] OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [endpoint ... ] Endpoint address(es) to delete hints for, either ip address (\"127.0.0.1\") or hostname","title":"","component":"Cassandra","version":"4.1","name":"truncatehints","url":"/Cassandra/4.1/cassandra/tools/nodetool/truncatehints.html","titles":[{"text":"truncatehints","id":"truncatehints"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/upgradesstables.html":{"text":"NAME nodetool upgradesstables - Rewrite sstables (for the requested tables) that are not on the current version (thus upgrading them to said current version) SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] upgradesstables [(-a | --include-all-sstables)] [(-j | --jobs )] [(-t | --max-timestamp )] [--] [ ...] OPTIONS -a, --include-all-sstables Use -a to include all sstables, even those already on the current version -h , --host Node hostname or ip address -j , --jobs Number of sstables to upgrade simultanously, set to 0 to use all available compaction threads -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -t , --max-timestamp Use -t to compact only SSTables that have local creation time _older_ than the given timestamp -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"upgradesstables","url":"/Cassandra/4.1/cassandra/tools/nodetool/upgradesstables.html","titles":[{"text":"upgradesstables","id":"upgradesstables"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/verify.html":{"text":"NAME nodetool verify - Verify (check data checksum for) one or more tables SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] verify [(-c | --check-version)] [(-d | --dfp)] [(-e | --extended-verify)] [(-f | --force)] [(-q | --quick)] [(-r | --rsc)] [(-t | --check-tokens)] [--] [ ...] OPTIONS -c, --check-version Also check that all sstables are the latest version -d, --dfp Invoke the disk failure policy if a corrupt sstable is found -e, --extended-verify Verify each cell data, beyond simply checking sstable checksums -f, --force Override disabling of verify tool - see CASSANDRA-9947 for caveats -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -q, --quick Do a quick check - avoid reading all data to verify checksums -r, --rsc Mutate the repair status on corrupt sstables -t, --check-tokens Verify that all tokens in sstables are owned by this node -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [ ...] The keyspace followed by one or many tables","title":"","component":"Cassandra","version":"4.1","name":"verify","url":"/Cassandra/4.1/cassandra/tools/nodetool/verify.html","titles":[{"text":"verify","id":"verify"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/version.html":{"text":"NAME nodetool version - Print cassandra version SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] version OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username","title":"","component":"Cassandra","version":"4.1","name":"version","url":"/Cassandra/4.1/cassandra/tools/nodetool/version.html","titles":[{"text":"version","id":"version"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/tools/nodetool/viewbuildstatus.html":{"text":"NAME nodetool viewbuildstatus - Show progress of a materialized view build SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] viewbuildstatus [--] | OPTIONS -h , --host Node hostname or ip address -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options | The keyspace and view name","title":"","component":"Cassandra","version":"4.1","name":"viewbuildstatus","url":"/Cassandra/4.1/cassandra/tools/nodetool/viewbuildstatus.html","titles":[{"text":"viewbuildstatus","id":"viewbuildstatus"},{"text":"Usage","id":"usage"}]},"/Cassandra/4.1/cassandra/faq/index.html":{"text":"Cassandra is a gossip-based distributed system and listen_address is the address a node tells other nodes to reach it at. Telling other nodes \"contact me on any of my addresses\" is a bad idea; if different nodes in the cluster pick different addresses for you, Bad Things happen. If you don’t want to manually specify an IP to listen_address for each node in your cluster (understandable!), leave it blank and Cassandra will use InetAddress.getLocalHost() to pick an address. Then it’s up to you or your ops team to make things resolve correctly (/etc/hosts/, dns, etc). One exception to this process is JMX, which by default binds to 0.0.0.0 (Java bug 6425769). See 256 and 43 for more gory details. By default, Cassandra uses 7000 for cluster communication (7001 if SSL is enabled), 9042 for native protocol clients, and 7199 for JMX. The internode communication and native protocol ports are configurable in the cassandra-yaml. The JMX port is configurable in cassandra-env.sh (through JVM options). All ports are TCP. When a new nodes joins a cluster, it will automatically contact the other nodes in the cluster and copy the right data to itself. See topology-changes. Data you write to Cassandra gets persisted to SSTables. Since SSTables are immutable, the data can’t actually be removed when you perform a delete, instead, a marker (also called a \"tombstone\") is written to indicate the value’s new status. Never fear though, on the first compaction that occurs between the data and the tombstone, the data will be expunged completely and the corresponding disk space recovered. See compaction for more detail. This happens when you have the same token assigned to each node. Don’t do that. Most often this bites people who deploy by installing Cassandra on a VM (especially when using the Debian package, which auto-starts Cassandra after installation, thus generating and saving a token), then cloning that VM to other nodes. The easiest fix is to wipe the data and commitlog directories, thus making sure that each node will generate a random token on the next restart. Yes, but it will require running a full repair (or cleanup) to change the replica count of existing data: Alter the replication factor for desired keyspace (using cqlsh for instance). If you’re reducing the replication factor, run nodetool cleanup on the cluster to remove surplus replicated data. Cleanup runs on a per-node basis. If you’re increasing the replication factor, run nodetool repair -full to ensure data is replicated according to the new configuration. Repair runs on a per-replica set basis. This is an intensive process that may result in adverse cluster performance. It’s highly recommended to do rolling repairs, as an attempt to repair the entire cluster at once will most likely swamp it. Note that you will need to run a full repair (-full) to make sure that already repaired sstables are not skipped. Cassandra isn’t optimized for large file or BLOB storage and a single blob value is always read and send to the client entirely. As such, storing small blobs (less than single digit MB) should not be a problem, but it is advised to manually split large blobs into smaller chunks. Please note in particular that by default, any value greater than 16MiB will be rejected by Cassandra due the max_mutation_size configuration of the cassandra-yaml file (which default to half of commitlog_segment_size, which itself default to 32MiB). Nodetool relies on JMX, which in turn relies on RMI, which in turn sets up its own listeners and connectors as needed on each end of the exchange. Normally all of this happens behind the scenes transparently, but incorrect name resolution for either the host connecting, or the one being connected to, can result in crossed wires and confusing exceptions. If you are not using DNS, then make sure that your /etc/hosts files are accurate on both ends. If that fails, try setting the -Djava.rmi.server.hostname= JVM option near the bottom of cassandra-env.sh to an interface that you can reach from the remote machine. No. Using batches to load data will generally just add \"spikes\" of latency. Use asynchronous INSERTs instead, or use true bulk-loading. An exception is batching updates to a single partition, which can be a Good Thing (as long as the size of a single batch stay reasonable). But never ever blindly batch everything! Check if SELinux is on; if it is, turn it off. Send an email to user-unsubscribe@cassandra.apache.org. Cassandra uses Memory Mapped Files (mmap) internally. That is, we use the operating system’s virtual memory system to map a number of on-disk files into the Cassandra process' address space. This will \"use\" virtual memory; i.e. address space, and will be reported by tools like top accordingly, but on 64 bit systems virtual address space is effectively unlimited so you should not worry about that. What matters from the perspective of \"memory use\" in the sense as it is normally meant, is the amount of data allocated on brk() or mmap’d /dev/zero, which represent real memory used. The key issue is that for a mmap’d file, there is never a need to retain the data resident in physical memory. Thus, whatever you do keep resident in physical memory is essentially just there as a cache, in the same way as normal I/O will cause the kernel page cache to retain data that you read/write. The difference between normal I/O and mmap() is that in the mmap() case the memory is actually mapped to the process, thus affecting the virtual size as reported by top. The main argument for using mmap() instead of standard I/O is the fact that reading entails just touching memory - in the case of the memory being resident, you just read it - you don’t even take a page fault (so no overhead in entering the kernel and doing a semi-context switch). This is covered in more detail here. Seeds are used during startup to discover the cluster. If you configure your nodes to refer some node as seed, nodes in your ring tend to send Gossip message to seeds more often (also see the section on gossip ) than to non-seeds. In other words, seeds are worked as hubs of Gossip network. With seeds, each node can detect status changes of other nodes quickly. Seeds are also referred by new nodes on bootstrap to learn other nodes in ring. When you add a new node to ring, you need to specify at least one live seed to contact. Once a node join the ring, it learns about the other nodes, so it doesn’t need seed on subsequent boot. You can make a seed a node at any time. There is nothing special about seed nodes. If you list the node in seed list it is a seed Seeds do not auto bootstrap (i.e. if a node has itself in its seed list it will not automatically transfer data to itself) If you want a node to do that, bootstrap it first and then add it to seeds later. If you have no data (new install) you do not have to worry about bootstrap at all. Recommended usage of seeds: pick two (or more) nodes per data center as seed nodes. sync the seed list to all your nodes The ring can operate or boot without a seed; however, you will not be able to add new nodes to the cluster. It is recommended to configure multiple seeds in production system. Some of JMX operations use array argument and as jconsole doesn’t support array argument, those operations can’t be called with jconsole (the buttons are inactive for them). You need to write a JMX client to call such operations or need array-capable JMX monitoring tool. This is a symptom of load shedding — Cassandra defending itself against more requests than it can handle. Internode messages which are received by a node, but do not get not to be processed within their proper timeout (see read_request_timeout, write_request_timeout, …​ in the cassandra-yaml), are dropped rather than processed (since the as the coordinator node will no longer be waiting for a response). For writes, this means that the mutation was not applied to all replicas it was sent to. The inconsistency will be repaired by read repair, hints or a manual repair. The write operation may also have timeouted as a result. For reads, this means a read request may not have completed. Load shedding is part of the Cassandra architecture, if this is a persistent issue it is generally a sign of an overloaded node or cluster. If Cassandra is dying specifically with the \"Map failed\" message, it means the OS is denying java the ability to lock more memory. In linux, this typically means memlock is limited. Check /proc//limits to verify this and raise it (eg, via ulimit in bash). You may also need to increase vm.max_map_count. Note that the debian package handles this for you automatically. Updates must be commutative, since they may arrive in different orders on different replicas. As long as Cassandra has a deterministic way to pick the winner (in a timestamp tie), the one selected is as valid as any other, and the specifics should be treated as an implementation detail. That said, in the case of a timestamp tie, Cassandra follows two rules: first, deletes take precedence over inserts/updates. Second, if there are two updates, the one with the lexically larger value is selected. Two main possibilities: the GC may be creating long pauses disrupting the streaming process compactions happening in the background hold streaming long enough that the TCP connection fails In the first case, regular GC tuning advices apply. In the second case, you need to set TCP keepalive to a lower value (default is very high on Linux). Try to just run the following: $ sudo /sbin/sysctl -w net.ipv4.tcp_keepalive_time=60 net.ipv4.tcp_keepalive_intvl=60 net.ipv4.tcp_keepalive_probes=5 To make those settings permanent, add them to your /etc/sysctl.conf file. Note: GCE's firewall will always interrupt TCP connections that are inactive for more than 10 min. Running the above command is highly recommended in that environment.","title":"Frequently Asked Questions","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/faq/index.html","titles":[{"text":"Why can’t I set listen_address to listen on 0.0.0.0 (all my addresses)?","id":"why-cant-list-all"},{"text":"What ports does Cassandra use?","id":"what-ports"},{"text":"What happens to existing data in my cluster when I add new nodes?","id":"what-happens-on-joins"},{"text":"I delete data from Cassandra, but disk usage stays the same. What gives?","id":"asynch-deletes"},{"text":"Why does nodetool ring only show one entry, even though my nodes logged that they see each other joining the ring?","id":"one-entry-ring"},{"text":"Can I change the replication factor (a a keyspace) on a live cluster?","id":"change-replication-factor"},{"text":"Can I Store (large) BLOBs in Cassandra?","id":"can-large-blob"},{"text":"Nodetool says \"Connection refused to host: 127.0.1.1\" for any remote host. What gives?","id":"nodetool-connection-refused"},{"text":"Will batching my operations speed up my bulk load?","id":"to-batch-or-not-to-batch"},{"text":"On RHEL nodes are unable to join the ring","id":"selinux"},{"text":"How do I unsubscribe from the email list?","id":"how-to-unsubscribe"},{"text":"Why does top report that Cassandra is using a lot more memory than the Java heap max?","id":"cassandra-eats-all-my-memory"},{"text":"What are seeds?","id":"what-are-seeds"},{"text":"Does single seed mean single point of failure?","id":"are-seeds-SPOF"},{"text":"Why can’t I call jmx method X on jconsole?","id":"cant-call-jmx-method"},{"text":"Why do I see \"…​ messages dropped …​\" in the logs?","id":"why-message-dropped"},{"text":"Cassandra dies with java.lang.OutOfMemoryError: Map failed","id":"oom-map-failed"},{"text":"What happens if two updates are made with the same timestamp?","id":"what-on-same-timestamp-update"},{"text":"Why bootstrapping a new node fails with a \"Stream failed\" error?","id":"why-bootstrapping-stream-error"}]},"/Cassandra/4.1/cassandra/new/index.html":{"text":"This section covers the new features in Apache Cassandra 4.1. Paxos v2 Guardrails New and Improved Configuration Format Client-side Password Hashing Partition Denylist Lots of CQL improvements New SSTable Identifiers Native Transport rate limiting Top partition tracking per table Hint Window consistency Pluggability Memtable Encryption CQLSH Authentication and much more","title":"New Features in Apache Cassandra 4.1","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/new/index.html","titles":[]},"/Cassandra/4.1/cassandra/new/virtualtables.html":{"text":"Apache Cassandra 4.0 implements virtual tables (CASSANDRA-7622). Virtual tables are tables backed by an API instead of data explicitly managed and stored as SSTables. Apache Cassandra 4.0 implements a virtual keyspace interface for virtual tables. Virtual tables are specific to each node. Some of the features of virtual tables are the ability to: expose metrics through CQL expose YAML configuration information Virtual keyspaces and tables are quite different from regular tables and keyspaces: Virtual tables are created in special keyspaces and not just any keyspace. Virtual tables are managed by Cassandra. Users cannot run DDL to create new virtual tables or DML to modify existing virtual tables. Virtual tables are currently read-only, although that may change in a later version. Virtual tables are local only, non-distributed, and thus not replicated. Virtual tables have no associated SSTables. Consistency level of the queries sent to virtual tables are ignored. All existing virtual tables use LocalPartitioner. Since a virtual table is not replicated the partitioner sorts in order of partition keys instead of by their hash. Making advanced queries using ALLOW FILTERING and aggregation functions can be executed in virtual tables, even though in normal tables we dont recommend it. Apache Cassandra 4.0 has added two new keyspaces for virtual tables: system_virtual_schema system_views. The system_virtual_schema keyspace has three tables: keyspaces, columns and tables for the virtual keyspace, table, and column definitions, respectively. These tables contain schema information for the virtual tables. It is used by Cassandra internally and a user should not access it directly. The system_views keyspace contains the actual virtual tables. Before disccusing virtual keyspaces and tables, note that virtual keyspaces and tables have some limitations. These limitations are subject to change. Virtual keyspaces cannot be altered or dropped. In fact, no operations can be performed against virtual keyspaces. Virtual tables cannot be created in virtual keyspaces. Virtual tables cannot be altered, dropped, or truncated. Secondary indexes, types, functions, aggregates, materialized views, and triggers cannot be created for virtual tables. Expiring time-to-live (TTL) columns cannot be created. Virtual tables do not support conditional updates or deletes. Aggregates may be run in SELECT statements. Conditional batch statements cannot include mutations for virtual tables, nor can a virtual table statement be included in a logged batch. In fact, mutations for virtual and regular tables cannot occur in the same batch table. Each of the virtual tables in the system_views virtual keyspace contain different information. The following table describes the virtual tables: Virtual Table Description caches Displays the general cache information including cache name, capacity_bytes, entry_count, hit_count, hit_ratio double, recent_hit_rate_per_second, recent_request_rate_per_second, request_count, and size_bytes. clients Lists information about all connected clients. coordinator_read_latency Records counts, keyspace_name, table_name, max, median, and per_second for coordinator reads. coordinator_scan Records counts, keyspace_name, table_name, max, median, and per_second for coordinator scans. coordinator_write_latency Records counts, keyspace_name, table_name, max, median, and per_second for coordinator writes. cql_metrics Metrics specific to CQL prepared statement caching. disk_usage Records disk usage including disk_space, keyspace_name, and table_name, sorted by system keyspaces. gossip_info Lists the gossip information for the cluster. internode_inbound Lists information about the inbound internode messaging. internode_outbound Information about the outbound internode messaging. local_read_latency Records counts, keyspace_name, table_name, max, median, and per_second for local reads. local_scan Records counts, keyspace_name, table_name, max, median, and per_second for local scans. local_write_latency Records counts, keyspace_name, table_name, max, median, and per_second for local writes. max_partition_size A table metric for maximum partition size. rows_per_read Records counts, keyspace_name, tablek_name, max, and median for rows read. settings Displays configuration settings in cassandra.yaml. sstable_tasks Lists currently running tasks and progress on SSTables, for operations like compaction and upgrade. system_properties Displays environmental system properties set on the node. thread_pools Lists metrics for each thread pool. tombstones_per_read Records counts, keyspace_name, tablek_name, max, and median for tombstones. We shall discuss some of the virtual tables in more detail next. The clients virtual table lists all active connections (connected clients) including their ip address, port, client_options, connection stage, driver name, driver version, hostname, protocol version, request count, ssl enabled, ssl protocol and user name: cqlsh> EXPAND ON ; Now Expanded output is enabled cqlsh> SELECT * FROM system_views.clients; @ Row 1 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50687 client_options | {'CQL_VERSION': '3.4.6', 'DRIVER_NAME': 'DataStax Python Driver', 'DRIVER_VERSION': '3.25.0'} connection_stage | ready driver_name | DataStax Python Driver driver_version | 3.25.0 hostname | localhost protocol_version | 5 request_count | 16 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 2 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50688 client_options | {'CQL_VERSION': '3.4.6', 'DRIVER_NAME': 'DataStax Python Driver', 'DRIVER_VERSION': '3.25.0'} connection_stage | ready driver_name | DataStax Python Driver driver_version | 3.25.0 hostname | localhost protocol_version | 5 request_count | 4 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 3 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50753 client_options | {'APPLICATION_NAME': 'TestApp', 'APPLICATION_VERSION': '1.0.0', 'CLIENT_ID': '55b3efbd-c56b-469d-8cca-016b860b2f03', 'CQL_VERSION': '3.0.0', 'DRIVER_NAME': 'DataStax Java driver for Apache Cassandra(R)', 'DRIVER_VERSION': '4.13.0'} connection_stage | ready driver_name | DataStax Java driver for Apache Cassandra(R) driver_version | 4.13.0 hostname | localhost protocol_version | 5 request_count | 18 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 4 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50755 client_options | {'APPLICATION_NAME': 'TestApp', 'APPLICATION_VERSION': '1.0.0', 'CLIENT_ID': '55b3efbd-c56b-469d-8cca-016b860b2f03', 'CQL_VERSION': '3.0.0', 'DRIVER_NAME': 'DataStax Java driver for Apache Cassandra(R)', 'DRIVER_VERSION': '4.13.0'} connection_stage | ready driver_name | DataStax Java driver for Apache Cassandra(R) driver_version | 4.13.0 hostname | localhost protocol_version | 5 request_count | 7 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous (4 rows) Some examples of how clients can be used are: To find applications using old incompatible versions of drivers before upgrading and with nodetool enableoldprotocolversions and nodetool disableoldprotocolversions during upgrades. To identify clients sending too many requests. To find if SSL is enabled during the migration to and from ssl. To identify all options the client is sending, e.g. APPLICATION_NAME and APPLICATION_VERSION The virtual tables may be described with DESCRIBE statement. The DDL listed however cannot be run to create a virtual table. As an example describe the system_views.clients virtual table: cqlsh> DESCRIBE TABLE system_views.clients; /* Warning: Table system_views.clients is a virtual table and cannot be recreated with CQL. Structure, for reference: VIRTUAL TABLE system_views.clients ( address inet, port int, client_options frozen>, connection_stage text, driver_name text, driver_version text, hostname text, protocol_version int, request_count bigint, ssl_cipher_suite text, ssl_enabled boolean, ssl_protocol text, username text, PRIMARY KEY (address, port) ) WITH CLUSTERING ORDER BY (port ASC) AND comment = 'currently connected clients'; */ The caches virtual table lists information about the caches. The four caches presently created are chunks, counters, keys and rows. A query on the caches virtual table returns the following details: cqlsh:system_views> SELECT * FROM system_views.caches; name | capacity_bytes | entry_count | hit_count | hit_ratio | recent_hit_rate_per_second | recent_request_rate_per_second | request_count | size_bytes ---------+----------------+-------------+-----------+-----------+----------------------------+--------------------------------+---------------+------------ chunks | 229638144 | 29 | 166 | 0.83 | 5 | 6 | 200 | 475136 counters | 26214400 | 0 | 0 | NaN | 0 | 0 | 0 | 0 keys | 52428800 | 14 | 124 | 0.873239 | 4 | 4 | 142 | 1248 rows | 0 | 0 | 0 | NaN | 0 | 0 | 0 | 0 (4 rows) The cql_metrics virtual table lists metrics specific to CQL prepared statement caching. A query on cql_metrics virtual table lists below metrics. cqlsh> select * from system_views.cql_metrics ; name | value ------------------------------+------- prepared_statements_count | 0 prepared_statements_evicted | 0 prepared_statements_executed | 0 prepared_statements_ratio | 0 regular_statements_executed | 17 The settings table is rather useful and lists all the current configuration settings from the cassandra.yaml. The encryption options are overridden to hide the sensitive truststore information or passwords. The configuration settings however cannot be set using DML on the virtual table presently: : cqlsh:system_views> SELECT * FROM system_views.settings; name | value -------------------------------------+-------------------- allocate_tokens_for_keyspace | null audit_logging_options_enabled | false auto_snapshot | true automatic_sstable_upgrade | false cluster_name | Test Cluster transient_replication_enabled | false hinted_handoff_enabled | true hints_directory | /home/ec2-user/cassandra/data/hints incremental_backups | false initial_token | null ... ... ... rpc_address | localhost ssl_storage_port | 7001 start_native_transport | true storage_port | 7000 stream_entire_sstables | true (224 rows) The settings table can be really useful if yaml file has been changed since startup and dont know running configuration, or to find if they have been modified via jmx/nodetool or virtual tables. The thread_pools table lists information about all thread pools. Thread pool information includes active tasks, active tasks limit, blocked tasks, blocked tasks all time, completed tasks, and pending tasks. A query on the thread_pools returns following details: cqlsh:system_views> select * from system_views.thread_pools; name | active_tasks | active_tasks_limit | blocked_tasks | blocked_tasks_all_time | completed_tasks | pending_tasks ------------------------------+--------------+--------------------+---------------+------------------------+-----------------+--------------- AntiEntropyStage | 0 | 1 | 0 | 0 | 0 | 0 CacheCleanupExecutor | 0 | 1 | 0 | 0 | 0 | 0 CompactionExecutor | 0 | 2 | 0 | 0 | 881 | 0 CounterMutationStage | 0 | 32 | 0 | 0 | 0 | 0 GossipStage | 0 | 1 | 0 | 0 | 0 | 0 HintsDispatcher | 0 | 2 | 0 | 0 | 0 | 0 InternalResponseStage | 0 | 2 | 0 | 0 | 0 | 0 MemtableFlushWriter | 0 | 2 | 0 | 0 | 1 | 0 MemtablePostFlush | 0 | 1 | 0 | 0 | 2 | 0 MemtableReclaimMemory | 0 | 1 | 0 | 0 | 1 | 0 MigrationStage | 0 | 1 | 0 | 0 | 0 | 0 MiscStage | 0 | 1 | 0 | 0 | 0 | 0 MutationStage | 0 | 32 | 0 | 0 | 0 | 0 Native-Transport-Requests | 1 | 128 | 0 | 0 | 130 | 0 PendingRangeCalculator | 0 | 1 | 0 | 0 | 1 | 0 PerDiskMemtableFlushWriter_0 | 0 | 2 | 0 | 0 | 1 | 0 ReadStage | 0 | 32 | 0 | 0 | 13 | 0 Repair-Task | 0 | 2147483647 | 0 | 0 | 0 | 0 RequestResponseStage | 0 | 2 | 0 | 0 | 0 | 0 Sampler | 0 | 1 | 0 | 0 | 0 | 0 SecondaryIndexManagement | 0 | 1 | 0 | 0 | 0 | 0 ValidationExecutor | 0 | 2147483647 | 0 | 0 | 0 | 0 ViewBuildExecutor | 0 | 1 | 0 | 0 | 0 | 0 ViewMutationStage | 0 | 32 | 0 | 0 | 0 | 0 (24 rows) The internode_inbound virtual table is for the internode inbound messaging. Initially no internode inbound messaging may get listed. In addition to the address, port, datacenter and rack information includes corrupt frames recovered, corrupt frames unrecovered, error bytes, error count, expired bytes, expired count, processed bytes, processed count, received bytes, received count, scheduled bytes, scheduled count, throttled count, throttled nanos, using bytes, using reserve bytes. A query on the internode_inbound returns following details: cqlsh:system_views> SELECT * FROM system_views.internode_inbound; address | port | dc | rack | corrupt_frames_recovered | corrupt_frames_unrecovered | error_bytes | error_count | expired_bytes | expired_count | processed_bytes | processed_count | received_bytes | received_count | scheduled_bytes | scheduled_count | throttled_count | throttled_nanos | using_bytes | using_reserve_bytes ---------+------+----+------+--------------------------+----------------------------+- ---------- (0 rows) The sstable_tasks could be used to get information about running tasks. It lists following columns: cqlsh:system_views> SELECT * FROM sstable_tasks; keyspace_name | table_name | task_id | kind | progress | total | unit ---------------+------------+--------------------------------------+------------+----------+----------+------- basic | wide2 | c3909740-cdf7-11e9-a8ed-0f03de2d9ae1 | compaction | 60418761 | 70882110 | bytes basic | wide2 | c7556770-cdf7-11e9-a8ed-0f03de2d9ae1 | compaction | 2995623 | 40314679 | bytes As another example, to find how much time is remaining for SSTable tasks, use the following query: SELECT total - progress AS remaining FROM system_views.sstable_tasks; The gossip_info virtual table lists the Gossip information for the cluster. An example query is as follows: cqlsh> select address, port, generation, heartbeat, load, dc, rack from system_views.gossip_info; address | port | generation | heartbeat | load | dc | rack -----------+------+------------+-----------+---------+-------------+------- 127.0.0.1 | 7000 | 1645575140 | 312 | 70542.0 | datacenter1 | rack1 127.0.0.2 | 7000 | 1645575135 | 318 | 70499.0 | datacenter1 | rack1 127.0.0.3 | 7000 | 1645575140 | 312 | 70504.0 | datacenter1 | rack1 127.0.0.4 | 7000 | 1645575141 | 311 | 70502.0 | datacenter1 | rack1 127.0.0.5 | 7000 | 1645575136 | 315 | 70500.0 | datacenter1 | rack1 (5 rows) Some examples of using other virtual tables are as follows. Find tables with most disk usage: cqlsh> SELECT * FROM disk_usage WHERE mebibytes > 1 ALLOW FILTERING; keyspace_name | table_name | mebibytes ---------------+------------+----------- keyspace1 | standard1 | 288 tlp_stress | keyvalue | 3211 Find queries on table/s with greatest read latency: cqlsh> SELECT * FROM local_read_latency WHERE per_second > 1 ALLOW FILTERING; keyspace_name | table_name | p50th_ms | p99th_ms | count | max_ms | per_second ---------------+------------+----------+----------+----------+---------+------------ tlp_stress | keyvalue | 0.043 | 0.152 | 49785158 | 186.563 | 11418.356 To list the keyspaces, enter cqlsh and run the CQL command DESCRIBE KEYSPACES: cqlsh> DESC KEYSPACES; system_schema system system_distributed system_virtual_schema system_auth system_traces system_views To view the virtual table schema, run the CQL commands USE system_virtual_schema and SELECT * FROM tables: cqlsh> USE system_virtual_schema; cqlsh> SELECT * FROM tables; results in: keyspace_name | table_name | comment -----------------------+---------------------------+-------------------------------------- system_views | caches | system caches system_views | clients | currently connected clients system_views | coordinator_read_latency | system_views | coordinator_scan_latency | system_views | coordinator_write_latency | system_views | disk_usage | system_views | internode_inbound | system_views | internode_outbound | system_views | local_read_latency | system_views | local_scan_latency | system_views | local_write_latency | system_views | max_partition_size | system_views | rows_per_read | system_views | settings | current settings system_views | sstable_tasks | current sstable tasks system_views | system_properties | Cassandra relevant system properties system_views | thread_pools | system_views | tombstones_per_read | system_virtual_schema | columns | virtual column definitions system_virtual_schema | keyspaces | virtual keyspace definitions system_virtual_schema | tables | virtual table definitions (21 rows) To view the virtual tables, run the CQL commands USE system_view and DESCRIBE tables: cqlsh> USE system_view;; cqlsh> DESCRIBE tables; results in: sstable_tasks clients coordinator_write_latency disk_usage local_write_latency tombstones_per_read thread_pools internode_outbound settings local_scan_latency coordinator_scan_latency system_properties internode_inbound coordinator_read_latency max_partition_size local_read_latency rows_per_read caches To look at any table data, run the CQL command SELECT: cqlsh> USE system_view;; cqlsh> SELECT * FROM clients LIMIT 2; results in: address | port | connection_stage | driver_name | driver_version | hostname | protocol_version | request_count | ssl_cipher_suite | ssl_enabled | ssl_protocol | username -----------+-------+------------------+------------------------+----------------+-----------+------------------+---------------+------------------+-------------+--------------+----------- 127.0.0.1 | 37308 | ready | DataStax Python Driver | 3.21.0.post0 | localhost | 4 | 17 | null | False | null | anonymous 127.0.0.1 | 37310 | ready | DataStax Python Driver | 3.21.0.post0 | localhost | 4 | 8 | null | False | null | anonymous (2 rows)","title":"Virtual Tables","component":"Cassandra","version":"4.1","name":"virtualtables","url":"/Cassandra/4.1/cassandra/new/virtualtables.html","titles":[{"text":"Virtual Keyspaces","id":"virtual-keyspaces"},{"text":"Virtual Table Limitations","id":"virtual-table-limitations"},{"text":"Virtual Tables","id":"virtual-tables"},{"text":"Clients Virtual Table","id":"clients-virtual-table"},{"text":"Caches Virtual Table","id":"caches-virtual-table"},{"text":"CQL metrics Virtual Table","id":"cql-metrics-virtual-table"},{"text":"Settings Virtual Table","id":"settings-virtual-table"},{"text":"Thread Pools Virtual Table","id":"thread-pools-virtual-table"},{"text":"Internode Inbound Messaging Virtual Table","id":"internode-inbound-messaging-virtual-table"},{"text":"SSTables Tasks Virtual Table","id":"sstables-tasks-virtual-table"},{"text":"Gossip Information Virtual Table","id":"gossip-information-virtual-table"},{"text":"Other Virtual Tables","id":"other-virtual-tables"},{"text":"Example","id":"example"}]},"/Cassandra/4.1/cassandra/plugins/index.html":{"text":"Available third-party plugins for Apache Cassandra The Coherent Accelerator Process Interface (CAPI) is a general term for the infrastructure of attaching a Coherent accelerator to an IBM POWER system. A key innovation in IBM POWER8’s open architecture is the CAPI. It provides a high bandwidth, low latency path between external devices, the POWER8 core, and the system’s open memory architecture. IBM Data Engine for NoSQL is an integrated platform for large and fast growing NoSQL data stores. It builds on the CAPI capability of POWER8 systems and provides super-fast access to large flash storage capacity and addresses the challenges associated with typical x86 server based scale-out deployments. The official page for the CAPI-Rowcache plugin contains further details how to build/run/download the plugin. Stratio’s Lucene index is a Cassandra secondary index implementation based on Apache Lucene. It extends Cassandra’s functionality to provide near real-time distributed search engine capabilities such as with ElasticSearch or Apache Solr, including full text search capabilities, free multivariable, geospatial and bitemporal search, relevance queries and sorting based on column value, relevance or distance. Each node indexes its own data, so high availability and scalability is guaranteed. The official Github repository Cassandra Lucene Index contains everything you need to build/run/configure the plugin.","title":"Third-Party Plugins","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/plugins/index.html","titles":[{"text":"CAPI-Rowcache","id":"capi-rowcache"},{"text":"Stratio’s Cassandra Lucene Index","id":"stratios-cassandra-lucene-index"}]},"/Cassandra/4.1/cassandra/architecture/dynamo.html":{"text":"Apache Cassandra relies on a number of techniques from Amazon’s Dynamo distributed storage key-value system. Each node in the Dynamo system has three main components: Request coordination over a partitioned dataset Ring membership and failure detection A local persistence (storage) engine Cassandra primarily draws from the first two clustering components, while using a storage engine based on a Log Structured Merge Tree (LSM). In particular, Cassandra relies on Dynamo style: Dataset partitioning using consistent hashing Multi-master replication using versioned data and tunable consistency Distributed cluster membership and failure detection via a gossip protocol Incremental scale-out on commodity hardware Cassandra was designed this way to meet large-scale (PiB+) business-critical storage requirements. In particular, as applications demanded full global replication of petabyte scale datasets along with always available low-latency reads and writes, it became imperative to design a new kind of database model as the relational database systems of the time struggled to meet the new requirements of global scale applications. Cassandra achieves horizontal scalability by partitioning all data stored in the system using a hash function. Each partition is replicated to multiple physical nodes, often across failure domains such as racks and even datacenters. As every replica can independently accept mutations to every key that it owns, every key must be versioned. Unlike in the original Dynamo paper where deterministic versions and vector clocks were used to reconcile concurrent updates to a key, Cassandra uses a simpler last write wins model where every mutation is timestamped (including deletes) and then the latest version of data is the \"winning\" value. Formally speaking, Cassandra uses a Last-Write-Wins Element-Set conflict-free replicated data type for each CQL row, or en.wikipedia.org/wiki/Conflict-free_replicated_data_type LWW-Element-Set_(Last-Write-Wins-Element-Set)[LWW-Element-Set CRDT], to resolve conflicting mutations on replica sets. Cassandra partitions data over storage nodes using a special form of hashing called consistent hashing. In naive data hashing, you typically allocate keys to buckets by taking a hash of the key modulo the number of buckets. For example, if you want to distribute data to 100 nodes using naive hashing you might assign every node to a bucket between 0 and 100, hash the input key modulo 100, and store the data on the associated bucket. In this naive scheme, however, adding a single node might invalidate almost all of the mappings. Cassandra instead maps every node to one or more tokens on a continuous hash ring, and defines ownership by hashing a key onto the ring and then \"walking\" the ring in one direction, similar to the Chord algorithm. The main difference of consistent hashing to naive data hashing is that when the number of nodes (buckets) to hash into changes, consistent hashing only has to move a small fraction of the keys. For example, if we have an eight node cluster with evenly spaced tokens, and a replication factor (RF) of 3, then to find the owning nodes for a key we first hash that key to generate a token (which is just the hash of the key), and then we \"walk\" the ring in a clockwise fashion until we encounter three distinct nodes, at which point we have found all the replicas of that key. This example of an eight node cluster with gRF=3 can be visualized as follows: You can see that in a Dynamo like system, ranges of keys, also known as token ranges, map to the same physical set of nodes. In this example, all keys that fall in the token range excluding token 1 and including token 2 (grange(t1, t2]) are stored on nodes 2, 3 and 4. Simple single token consistent hashing works well if you have many physical nodes to spread data over, but with evenly spaced tokens and a small number of physical nodes, incremental scaling (adding just a few nodes of capacity) is difficult because there are no token selections for new nodes that can leave the ring balanced. Cassandra seeks to avoid token imbalance because uneven token ranges lead to uneven request load. For example, in the previous example there is no way to add a ninth token without causing imbalance; instead we would have to insert 8 tokens in the midpoints of the existing ranges. The Dynamo paper advocates for the use of \"virtual nodes\" to solve this imbalance problem. Virtual nodes solve the problem by assigning multiple tokens in the token ring to each physical node. By allowing a single physical node to take multiple positions in the ring, we can make small clusters look larger and therefore even with a single physical node addition we can make it look like we added many more nodes, effectively taking many smaller pieces of data from more ring neighbors when we add even a single node. Cassandra introduces some nomenclature to handle these concepts: Token: A single position on the dynamo style hash ring. Endpoint: A single physical IP and port on the network. Host ID: A unique identifier for a single \"physical\" node, usually present at one gEndpoint and containing one or more gTokens. Virtual Node (or vnode): A gToken on the hash ring owned by the same physical node, one with the same gHost ID. The mapping of Tokens to Endpoints gives rise to the Token Map where Cassandra keeps track of what ring positions map to which physical endpoints. For example, in the following figure we can represent an eight node cluster using only four physical nodes by assigning two tokens to every node: Multiple tokens per physical node provide the following benefits: When a new node is added it accepts approximately equal amounts of data from other nodes in the ring, resulting in equal distribution of data across the cluster. When a node is decommissioned, it loses data roughly equally to other members of the ring, again keeping equal distribution of data across the cluster. If a node becomes unavailable, query load (especially token aware query load), is evenly distributed across many other nodes. Multiple tokens, however, can also have disadvantages: Every token introduces up to 2 * (RF - 1) additional neighbors on the token ring, which means that there are more combinations of node failures where we lose availability for a portion of the token ring. The more tokens you have, the higher the probability of an outage. Cluster-wide maintenance operations are often slowed. For example, as the number of tokens per node is increased, the number of discrete repair operations the cluster must do also increases. Performance of operations that span token ranges could be affected. Note that in Cassandra 2.x, the only token allocation algorithm available was picking random tokens, which meant that to keep balance the default number of tokens per node had to be quite high, at 256. This had the effect of coupling many physical endpoints together, increasing the risk of unavailability. That is why in 3.x + the new deterministic token allocator was added which intelligently picks tokens such that the ring is optimally balanced while requiring a much lower number of tokens per physical node. Cassandra replicates every partition of data to many nodes across the cluster to maintain high availability and durability. When a mutation occurs, the coordinator hashes the partition key to determine the token range the data belongs to and then replicates the mutation to the replicas of that data according to the Replication Strategy. All replication strategies have the notion of a replication factor (RF), which indicates to Cassandra how many copies of the partition should exist. For example with a RF=3 keyspace, the data will be written to three distinct replicas. Replicas are always chosen such that they are distinct physical nodes which is achieved by skipping virtual nodes if needed. Replication strategies may also choose to skip nodes present in the same failure domain such as racks or datacenters so that Cassandra clusters can tolerate failures of whole racks and even datacenters of nodes. Cassandra supports pluggable replication strategies, which determine which physical nodes act as replicas for a given token range. Every keyspace of data has its own replication strategy. All production deployments should use the NetworkTopologyStrategy while the SimpleStrategy replication strategy is useful only for testing clusters where you do not yet know the datacenter layout of the cluster. NetworkTopologyStrategy requires a specified replication factor for each datacenter in the cluster. Even if your cluster only uses a single datacenter, NetworkTopologyStrategy is recommended over SimpleStrategy to make it easier to add new physical or virtual datacenters to the cluster later, if required. In addition to allowing the replication factor to be specified individually by datacenter, NetworkTopologyStrategy also attempts to choose replicas within a datacenter from different racks as specified by the Snitch. If the number of racks is greater than or equal to the replication factor for the datacenter, each replica is guaranteed to be chosen from a different rack. Otherwise, each rack will hold at least one replica, but some racks may hold more than one. Note that this rack-aware behavior has some potentially surprising implications. For example, if there are not an even number of nodes in each rack, the data load on the smallest rack may be much higher. Similarly, if a single node is bootstrapped into a brand new rack, it will be considered a replica for the entire ring. For this reason, many operators choose to configure all nodes in a single availability zone or similar failure domain as a single \"rack\". SimpleStrategy allows a single integer replication_factor to be defined. This determines the number of nodes that should contain a copy of each row. For example, if replication_factor is 3, then three different nodes should store a copy of each row. SimpleStrategy treats all nodes identically, ignoring any configured datacenters or racks. To determine the replicas for a token range, Cassandra iterates through the tokens in the ring, starting with the token range of interest. For each token, it checks whether the owning node has been added to the set of replicas, and if it has not, it is added to the set. This process continues until replication_factor distinct nodes have been added to the set of replicas. Transient replication is an experimental feature in Cassandra 4.0 not present in the original Dynamo paper. This feature allows configuration of a subset of replicas to replicate only data that hasn’t been incrementally repaired. This configuration decouples data redundancy from availability. For instance, if you have a keyspace replicated at RF=3, and alter it to RF=5 with two transient replicas, you go from tolerating one failed replica to tolerating two, without corresponding increase in storage usage. Now, three nodes will replicate all the data for a given token range, and the other two will only replicate data that hasn’t been incrementally repaired. To use transient replication, first enable the option in cassandra.yaml. Once enabled, both SimpleStrategy and NetworkTopologyStrategy can be configured to transiently replicate data. Configure it by specifying replication factor as / in the read path and Hinted handoff in the write path. These techniques are only best-effort, however, and to guarantee eventual consistency Cassandra implements anti-entropy repair where replicas calculate hierarchical hash-trees over their datasets called Merkle trees that can then be compared across replicas to identify mismatched data. Like the original Dynamo paper Cassandra supports full repairs where replicas hash their entire dataset, create Merkle trees, send them to each other and sync any ranges that don’t match. Unlike the original Dynamo paper, Cassandra also implements sub-range repair and incremental repair. Sub-range repair allows Cassandra to increase the resolution of the hash trees (potentially down to the single partition level) by creating a larger number of trees that span only a portion of the data range. Incremental repair allows Cassandra to only repair the partitions that have changed since the last repair. Cassandra supports a per-operation tradeoff between consistency and availability through Consistency Levels. Cassandra’s consistency levels are a version of Dynamo’s R + W > N consistency mechanism where operators could configure the number of nodes that must participate in reads (R) and writes (W) to be larger than the replication factor (N). In Cassandra, you instead choose from a menu of common consistency levels which allow the operator to pick R and W behavior without knowing the replication factor. Generally writes will be visible to subsequent reads when the read consistency level contains enough nodes to guarantee a quorum intersection with the write consistency level. The following consistency levels are available: ONE Only a single replica must respond. TWO Two replicas must respond. THREE Three replicas must respond. QUORUM A majority (n/2 + 1) of the replicas must respond. ALL All of the replicas must respond. LOCAL_QUORUM A majority of the replicas in the local datacenter (whichever datacenter the coordinator is in) must respond. EACH_QUORUM A majority of the replicas in each datacenter must respond. LOCAL_ONE Only a single replica must respond. In a multi-datacenter cluster, this also gaurantees that read requests are not sent to replicas in a remote datacenter. ANY A single replica may respond, or the coordinator may store a hint. If a hint is stored, the coordinator will later attempt to replay the hint and deliver the mutation to the replicas. This consistency level is only accepted for write operations. Write operations are always sent to all replicas, regardless of consistency level. The consistency level simply controls how many responses the coordinator waits for before responding to the client. For read operations, the coordinator generally only issues read commands to enough replicas to satisfy the consistency level. The one exception to this is when speculative retry may issue a redundant read request to an extra replica if the original replicas have not responded within a specified time window. It is common to pick read and write consistency levels such that the replica sets overlap, resulting in all acknowledged writes being visible to subsequent reads. This is typically expressed in the same terms Dynamo does, in that W + R > RF, where W is the write consistency level, R is the read consistency level, and RF is the replication factor. For example, if RF = 3, a QUORUM request will require responses from at least 2/3 replicas. If QUORUM is used for both writes and reads, at least one of the replicas is guaranteed to participate in both the write and the read request, which in turn guarantees that the quorums will overlap and the write will be visible to the read. In a multi-datacenter environment, LOCAL_QUORUM can be used to provide a weaker but still useful guarantee: reads are guaranteed to see the latest write from within the same datacenter. This is often sufficient as clients homed to a single datacenter will read their own writes. If this type of strong consistency isn’t required, lower consistency levels like LOCAL_ONE or ONE may be used to improve throughput, latency, and availability. With replication spanning multiple datacenters, LOCAL_ONE is typically less available than ONE but is faster as a rule. Indeed ONE will succeed if a single replica is available in any datacenter. The replication protocols and dataset partitioning rely on knowing which nodes are alive and dead in the cluster so that write and read operations can be optimally routed. In Cassandra liveness information is shared in a distributed fashion through a failure detection mechanism based on a gossip protocol. Gossip is how Cassandra propagates basic cluster bootstrapping information such as endpoint membership and internode network protocol versions. In Cassandra’s gossip system, nodes exchange state information not only about themselves but also about other nodes they know about. This information is versioned with a vector clock of (generation, version) tuples, where the generation is a monotonic timestamp and version is a logical clock the increments roughly every second. These logical clocks allow Cassandra gossip to ignore old versions of cluster state just by inspecting the logical clocks presented with gossip messages. Every node in the Cassandra cluster runs the gossip task independently and periodically. Every second, every node in the cluster: Updates the local node’s heartbeat state (the version) and constructs the node’s local view of the cluster gossip endpoint state. Picks a random other node in the cluster to exchange gossip endpoint state with. Probabilistically attempts to gossip with any unreachable nodes (if one exists) Gossips with a seed node if that didn’t happen in step 2. When an operator first bootstraps a Cassandra cluster they designate certain nodes as seed nodes. Any node can be a seed node and the only difference between seed and non-seed nodes is seed nodes are allowed to bootstrap into the ring without seeing any other seed nodes. Furthermore, once a cluster is bootstrapped, seed nodes become hotspots for gossip due to step 4 above. As non-seed nodes must be able to contact at least one seed node in order to bootstrap into the cluster, it is common to include multiple seed nodes, often one for each rack or datacenter. Seed nodes are often chosen using existing off-the-shelf service discovery mechanisms. Note Nodes do not have to agree on the seed nodes, and indeed once a cluster is bootstrapped, newly launched nodes can be configured to use any existing nodes as seeds. The only advantage to picking the same nodes as seeds is it increases their usefullness as gossip hotspots. Currently, gossip also propagates token metadata and schema version information. This information forms the control plane for scheduling data movements and schema pulls. For example, if a node sees a mismatch in schema version in gossip state, it will schedule a schema sync task with the other nodes. As token information propagates via gossip it is also the control plane for teaching nodes which endpoints own what data. Gossip forms the basis of ring membership, but the failure detector ultimately makes decisions about if nodes are UP or DOWN. Every node in Cassandra runs a variant of the Phi Accrual Failure Detector, in which every node is constantly making an independent decision of if their peer nodes are available or not. This decision is primarily based on received heartbeat state. For example, if a node does not see an increasing heartbeat from a node for a certain amount of time, the failure detector \"convicts\" that node, at which point Cassandra will stop routing reads to it (writes will typically be written to hints). If/when the node starts heartbeating again, Cassandra will try to reach out and connect, and if it can open communication channels it will mark that node as available. Note UP and DOWN state are local node decisions and are not propagated with gossip. Heartbeat state is propagated with gossip, but nodes will not consider each other as UP until they can successfully message each other over an actual network channel. Cassandra will never remove a node from gossip state without explicit instruction from an operator via a decommission operation or a new node bootstrapping with a replace_address_first_boot option. This choice is intentional to allow Cassandra nodes to temporarily fail without causing data to needlessly re-balance. This also helps to prevent simultaneous range movements, where multiple replicas of a token range are moving at the same time, which can violate monotonic consistency and can even cause data loss. Cassandra scales-out to meet the requirements of growth in data size and request rates. Scaling-out means adding additional nodes to the ring, and every additional node brings linear improvements in compute and storage. In contrast, scaling-up implies adding more capacity to the existing database nodes. Cassandra is also capable of scale-up, and in certain environments it may be preferable depending on the deployment. Cassandra gives operators the flexibility to chose either scale-out or scale-up. One key aspect of Dynamo that Cassandra follows is to attempt to run on commodity hardware, and many engineering choices are made under this assumption. For example, Cassandra assumes nodes can fail at any time, auto-tunes to make the best use of CPU and memory resources available and makes heavy use of advanced compression and caching techniques to get the most storage out of limited memory and storage capabilities. Cassandra, like Dynamo, chooses not to provide cross-partition transactions that are common in SQL Relational Database Management Systems (RDBMS). This both gives the programmer a simpler read and write API, and allows Cassandra to more easily scale horizontally since multi-partition transactions spanning multiple nodes are notoriously difficult to implement and typically very latent. Instead, Cassanda chooses to offer fast, consistent, latency at any scale for single partition operations, allowing retrieval of entire partitions or only subsets of partitions based on primary key filters. Furthermore, Cassandra does support single partition compare and swap functionality via the lightweight transaction CQL API. Cassandra, in a slight departure from Dynamo, chooses a storage interface that is more sophisticated then \"simple key value\" stores but significantly less complex than SQL relational data models. Cassandra presents a wide-column store interface, where partitions of data contain multiple rows, each of which contains a flexible set of individually typed columns. Every row is uniquely identified by the partition key and one or more clustering keys, and every row can have as many columns as needed. This allows users to flexibly add new columns to existing datasets as new requirements surface. Schema changes involve only metadata changes and run fully concurrently with live workloads. Therefore, users can safely add columns to existing Cassandra databases while remaining confident that query performance will not degrade.","title":"Dynamo","component":"Cassandra","version":"4.1","name":"dynamo","url":"/Cassandra/4.1/cassandra/architecture/dynamo.html","titles":[{"text":"Dataset Partitioning: Consistent Hashing","id":"dataset-partitioning-consistent-hashing"},{"text":"Consistent Hashing using a Token Ring","id":"consistent-hashing-using-a-token-ring"},{"text":"Multiple Tokens per Physical Node (vnodes)","id":"multiple-tokens-per-physical-node-vnodes"},{"text":"Multi-master Replication: Versioned Data and Tunable Consistency","id":"multi-master-replication-versioned-data-and-tunable-consistency"},{"text":"Replication Strategy","id":"replication-strategy"},{"text":"NetworkTopologyStrategy","id":"network-topology-strategy"},{"text":"SimpleStrategy","id":"simple-strategy"},{"text":"Transient Replication","id":"transient-replication"},{"text":"Data Versioning","id":"data-versioning"},{"text":"Replica Synchronization","id":"replica-synchronization"},{"text":"Tunable Consistency","id":"tunable-consistency"},{"text":"Picking Consistency Levels","id":"picking-consistency-levels"},{"text":"Distributed Cluster Membership and Failure Detection","id":"distributed-cluster-membership-and-failure-detection"},{"text":"Gossip","id":"gossip"},{"text":"Ring Membership and Failure Detection","id":"ring-membership-and-failure-detection"},{"text":"Incremental Scale-out on Commodity Hardware","id":"incremental-scale-out-on-commodity-hardware"},{"text":"Simple Query Model","id":"simple-query-model"},{"text":"Simple Interface for Storing Records","id":"simple-interface-for-storing-records"}]},"/Cassandra/4.1/cassandra/architecture/guarantees.html":{"text":"Apache Cassandra is a highly scalable and reliable database. Cassandra is used in web based applications that serve large number of clients and the quantity of data processed is web-scale (Petabyte) large. Cassandra makes some guarantees about its scalability, availability and reliability. To fully understand the inherent limitations of a storage system in an environment in which a certain level of network partition failure is to be expected and taken into account when designing the system it is important to first briefly introduce the CAP theorem. According to the CAP theorem it is not possible for a distributed data store to provide more than two of the following guarantees simultaneously. Consistency: Consistency implies that every read receives the most recent write or errors out Availability: Availability implies that every request receives a response. It is not guaranteed that the response contains the most recent write or data. Partition tolerance: Partition tolerance refers to the tolerance of a storage system to failure of a network partition. Even if some of the messages are dropped or delayed the system continues to operate. CAP theorem implies that when using a network partition, with the inherent risk of partition failure, one has to choose between consistency and availability and both cannot be guaranteed at the same time. CAP theorem is illustrated in Figure 1. Figure 1. CAP Theorem High availability is a priority in web based applications and to this objective Cassandra chooses Availability and Partition Tolerance from the CAP guarantees, compromising on data Consistency to some extent. Cassandra makes the following guarantees. High Scalability High Availability Durability Eventual Consistency of writes to a single table Lightweight transactions with linearizable consistency Batched writes across multiple tables are guaranteed to succeed completely or not at all Secondary indexes are guaranteed to be consistent with their local replicas data Cassandra is a highly scalable storage system in which nodes may be added/removed as needed. Using gossip-based protocol a unified and consistent membership list is kept at each node. Cassandra guarantees high availability of data by implementing a fault-tolerant storage system. Failure detection in a node is detected using a gossip-based protocol. Cassandra guarantees data durability by using replicas. Replicas are multiple copies of a data stored on different nodes in a cluster. In a multi-datacenter environment the replicas may be stored on different datacenters. If one replica is lost due to unrecoverable node/datacenter failure the data is not completely lost as replicas are still available. Meeting the requirements of performance, reliability, scalability and high availability in production Cassandra is an eventually consistent storage system. Eventually consistent implies that all updates reach all replicas eventually. Divergent versions of the same data may exist temporarily but they are eventually reconciled to a consistent state. Eventual consistency is a tradeoff to achieve high availability and it involves some read and write latencies. Data must be read and written in a sequential order. Paxos consensus protocol is used to implement lightweight transactions. Paxos protocol implements lightweight transactions that are able to handle concurrent operations using linearizable consistency. Linearizable consistency is sequential consistency with real-time constraints and it ensures transaction isolation with compare and set (CAS) transaction. With CAS replica data is compared and data that is found to be out of date is set to the most consistent value. Reads with linearizable consistency allow reading the current state of the data, which may possibly be uncommitted, without making a new addition or update. The guarantee for batched writes across multiple tables is that they will eventually succeed, or none will. Batch data is first written to batchlog system data, and when the batch data has been successfully stored in the cluster the batchlog data is removed. The batch is replicated to another node to ensure the full batch completes in the event the coordinator node fails. A secondary index is an index on a column and is used to query a table that is normally not queryable. Secondary indexes when built are guaranteed to be consistent with their local replicas.","title":"Guarantees","component":"Cassandra","version":"4.1","name":"guarantees","url":"/Cassandra/4.1/cassandra/architecture/guarantees.html","titles":[{"text":"What is CAP?","id":"what-is-cap"},{"text":"High Scalability","id":"high-scalability"},{"text":"High Availability","id":"high-availability"},{"text":"Durability","id":"durability"},{"text":"Eventual Consistency","id":"eventual-consistency"},{"text":"Lightweight transactions with linearizable consistency","id":"lightweight-transactions-with-linearizable-consistency"},{"text":"Batched Writes","id":"batched-writes"},{"text":"Secondary Indexes","id":"secondary-indexes"}]},"/Cassandra/4.1/cassandra/architecture/index.html":{"text":"This section describes the general architecture of Apache Cassandra. Overview Dynamo Storage Engine Guarantees Snitches","title":"Architecture","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/architecture/index.html","titles":[]},"/Cassandra/4.1/cassandra/architecture/messaging.html":{"text":"Apache Cassandra 4.0 has added several new improvements to internode messaging. The internode messaging protocol has been optimized (CASSANDRA-14485). Previously the IPAddressAndPort of the sender was included with each message that was sent even though the IPAddressAndPort had already been sent once when the initial connection/session was established. In Cassandra 4.0 IPAddressAndPort has been removed from every separate message sent and only sent when connection/session is initiated. Another improvement is that at several instances (listed) a fixed 4-byte integer value has been replaced with vint as a vint is almost always less than 1 byte: The paramSize (the number of parameters in the header) Each individual parameter value The payloadSize In Cassandra 4.0 peer-to-peer (internode) messaging has been switched to non-blocking I/O (NIO) via Netty (CASSANDRA-8457). As serialization format, each message contains a header with several fixed fields, an optional key-value parameters section, and then the message payload itself. Note: the IP address in the header may be either IPv4 (4 bytes) or IPv6 (16 bytes). The diagram below shows the IPv4 address for brevity. 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 6 6 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | PROTOCOL MAGIC | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Message ID | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Timestamp | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Addr len | IP Address (IPv4) / +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ / | Verb / +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ / | Parameters size / +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ / | Parameter data / +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ / | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Payload size | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | / / Payload / / | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ An individual parameter has a String key and a byte array value. The key is serialized with its length, encoded as two bytes, followed by the UTF-8 byte encoding of the string. The body is serialized with its length, encoded as four bytes, followed by the bytes of the value. System stability is improved by enforcing strict resource limits (CASSANDRA-15066) on the number of outbound messages that are queued, measured by the serializedSize of the message. There are three separate limits imposed simultaneously to ensure that progress is always made without any reasonable combination of failures impacting a node’s stability. Global, per-endpoint and per-connection limits are imposed on messages queued for delivery to other nodes and waiting to be processed on arrival from other nodes in the cluster. These limits are applied to the on-wire size of the message being sent or received. The basic per-link limit is consumed in isolation before any endpoint or global limit is imposed. Each node-pair has three links: urgent, small and large. Any given node may have a maximum of N*3 * (internode_application_send_queue_capacity in bytes + internode_application_receive_queue_capacity in bytes) of messages data queued without any coordination between them although in practice, with token-aware routing, only RF*tokens nodes should need to communicate with significant bandwidth. The per-endpoint limit is imposed on all messages exceeding the per-link limit, simultaneously with the global limit, on all links to or from a single node in the cluster. The global limit is imposed on all messages exceeding the per-link limit, simultaneously with the per-endpoint limit, on all links to or from any node in the cluster. The following configuration settings have been added to cassandra.yaml for resource limits on queued messages. internode_application_send_queue_capacity: 4MiB internode_application_send_queue_reserve_endpoint_capacity: 128MiB internode_application_send_queue_reserve_global_capacity: 512MiB internode_application_receive_queue_capacity: 4MiB internode_application_receive_queue_reserve_endpoint_capacity: 128MiB internode_application_receive_queue_reserve_global_capacity: 512MiB Metrics is improved by keeping metrics using virtual tables for inter-node inbound and outbound messaging (CASSANDRA-15066). For inbound messaging a virtual table (internode_inbound) has been added to keep metrics for: Bytes and count of messages that could not be serialized or flushed due to an error Bytes and count of messages scheduled Bytes and count of messages successfully processed Bytes and count of messages successfully received Nanos and count of messages throttled Bytes and count of messages expired Corrupt frames recovered and unrecovered A separate virtual table (internode_outbound) has been added for outbound inter-node messaging. The outbound virtual table keeps metrics for: Bytes and count of messages pending Bytes and count of messages sent Bytes and count of messages expired Bytes and count of messages that could not be sent due to an error Bytes and count of messages overloaded Active Connection Count Connection Attempts Successful Connection Attempts A specialized version of hint message that takes an already encoded in a ByteBuffer hint and sends it verbatim has been added. It is an optimization for when dispatching a hint file of the current messaging version to a node of the same messaging version, which is the most common case. It saves on extra ByteBuffer allocations one redundant hint deserialization-serialization cycle. A configuration setting has been added to cassandra.yaml for the maximum continuous period a connection may be unwritable in application space. # internode_application_timeout_in_ms = 30000 Some other new features include logging of message size to trace message for tracing a query. In pre-4.0 Paxos prepare and propose messages always go through entire MessagingService stack in Cassandra even if request is to be served locally, we can enhance and make local requests severed w/o involving MessagingService. Similar things are done elsewhere in Cassandra which skips MessagingService stage for local requests. This is what it looks like in pre 4.0 if we have tracing on and run a light-weight transaction: Sending PAXOS_PREPARE message to /A.B.C.D [MessagingService-Outgoing-/A.B.C.D] | 2017-09-11 21:55:18.971000 | A.B.C.D | 15045 … REQUEST_RESPONSE message received from /A.B.C.D [MessagingService-Incoming-/A.B.C.D] | 2017-09-11 21:55:18.976000 | A.B.C.D | 20270 … Processing response from /A.B.C.D [SharedPool-Worker-4] | 2017-09-11 21:55:18.976000 | A.B.C.D | 20372 Same thing applies for Propose stage as well. In version 4.0 Paxos prepare and propose stage for local requests are optimized (CASSANDRA-13862). Several other quality assurance improvements have been made in version 4.0 (CASSANDRA-15066). Version 4.0 introduces framing to all internode messages, i.e. the grouping of messages into a single logical payload with headers and trailers; these frames are guaranteed to either contain at most one message, that is split into its own unique sequence of frames (for large messages), or that a frame contains only complete messages. Previously, intra-datacenter internode messages would be unprotected from corruption by default, as only LZ4 provided any integrity checks. All messages to post 4.0 nodes are written to explicit frames, which may be: LZ4 encoded CRC protected The Unprotected option is still available. For resilience, all frames are written with a separate CRC protected header, of 8 and 6 bytes respectively. If corruption occurs in this header, the connection must be reset, as before. If corruption occurs anywhere outside of the header, the corrupt frame will be skipped, leaving the connection intact and avoiding the loss of any messages unnecessarily. Previously, any issue at any point in the stream would result in the connection being reset, with the loss of any in-flight messages. The overall memory usage, and number of byte shuffles, on both inbound and outbound messages is reduced. Outbound the Netty LZ4 encoder maintains a chunk size buffer (64KiB), that is filled before any compressed frame can be produced. Our frame encoders avoid this redundant copy, as well as freeing 192KiB per endpoint. Inbound, frame decoders guarantee only to copy the number of bytes necessary to parse a frame, and to never store more bytes than necessary. This improvement applies twice to LZ4 connections, improving both the message decode and the LZ4 frame decode. Version 4.0 introduces several improvements to the inbound path. An appropriate message handler is used based on whether large or small messages are expected on a particular connection as set in a flag. NonblockingBufferHandler, running on event loop, is used for small messages, and BlockingBufferHandler, running off event loop, for large messages. The single implementation of InboundMessageHandler handles messages of any size effectively by deriving size of the incoming message from the byte stream. In addition to deriving size of the message from the stream, incoming message expiration time is proactively read, before attempting to deserialize the entire message. If it’s expired at the time when a message is encountered the message is just skipped in the byte stream altogether. And if a message fails to be deserialized while still on the receiving side - say, because of table id or column being unknown - bytes are skipped, without dropping the entire connection and losing all the buffered messages. An immediately reply back is sent to the coordinator node with the failure reason, rather than waiting for the coordinator callback to expire. This logic is extended to a corrupted frame; a corrupted frame is safely skipped over without dropping the connection. Inbound path imposes strict limits on memory utilization. Specifically, the memory occupied by all parsed, but unprocessed messages is bound - on per-connection, per-endpoint, and global basis. Once a connection exceeds its local unprocessed capacity and cannot borrow any permits from per-endpoint and global reserve, it simply stops processing further messages, providing natural backpressure - until sufficient capacity is regained. A consistent approach is adopted for all kinds of failure to connect, including: refused by endpoint, incompatible versions, or unexpected exceptions; Retry forever, until either success or no messages waiting to deliver. Wait incrementally longer periods before reconnecting, up to a maximum of 1s. While failing to connect, no reserve queue limits are acquired. Correctly drains outbound messages that are waiting to be delivered (unless disconnected and fail to reconnect). Messages written to a closing connection are either delivered or rejected, with a new connection being opened if the old is irrevocably closed. Unused connections are pruned eventually. We sometimes need to reconnect a perfectly valid connection, e.g. if the preferred IP address changes. We ensure that the underlying connection has no in-progress operations before closing it and reconnecting. Propagates to callbacks instantly, better preventing overload by reclaiming committed memory. No longer experiences head-of-line blocking (e.g. undroppable message preventing all droppable messages from being expired). While overloaded, expiry is attempted eagerly on enqueuing threads. While disconnected we schedule regular pruning, to handle the case where messages are no longer being sent, but we have a large backlog to expire. Tracked by bytes queued, as opposed to number of messages. Do not result in the connection being invalidated; the message is simply completed with failure, and then erased from the frame. Includes detected mismatch between calculated serialization size to actual. Failures to flush to network, perhaps because the connection has been reset are not currently notified to callback handlers, as the necessary information has been discarded, though it would be possible to do so in future if we decide it is worth our while. \"Gossip\" connection has been replaced with a general purpose \"Urgent\" connection, for any small messages impacting system stability. We track, and expose via Virtual Table and JMX, the number of messages and bytes that: we could not serialize or flush due to an error, we dropped due to overload or timeout, are pending, and have successfully sent. Cassandra pre-4.0 doesn’t protect the server from allocating huge buffers for the inter-node Message objects. Adding a message size limit would be good to deal with issues such as a malfunctioning cluster participant. Version 4.0 introduced max message size config param, akin to max mutation size - set to endpoint reserve capacity by default. As discussed in (CASSANDRA-9289) it would be nice to gracefully recover from seeing an unknown table in a message from another node. Pre-4.0, we close the connection and reconnect, which can cause other concurrent queries to fail. Version 4.0 fixes the issue by wrapping message in-stream with TrackedDataInputPlus, catching UnknownCFException, and skipping the remaining bytes in this message. TCP won’t be closed and it will remain connected for other messages.","title":"Improved Internode Messaging","component":"Cassandra","version":"4.1","name":"messaging","url":"/Cassandra/4.1/cassandra/architecture/messaging.html","titles":[{"text":"Optimized Internode Messaging Protocol","id":"optimized-internode-messaging-protocol"},{"text":"NIO Messaging","id":"nio-messaging"},{"text":"Resource limits on Queued Messages","id":"resource-limits-on-queued-messages"},{"text":"Virtual Tables for Messaging Metrics","id":"virtual-tables-for-messaging-metrics"},{"text":"Hint Messaging","id":"hint-messaging"},{"text":"Internode Application Timeout","id":"internode-application-timeout"},{"text":"Paxos prepare and propose stage for local requests optimized","id":"paxos-prepare-and-propose-stage-for-local-requests-optimized"},{"text":"Quality Assurance","id":"quality-assurance"},{"text":"Framing","id":"framing"},{"text":"Corruption prevention","id":"corruption-prevention"},{"text":"Resilience","id":"resilience"},{"text":"Efficiency","id":"efficiency"},{"text":"Inbound Path","id":"inbound-path"},{"text":"Outbound Connections","id":"outbound-connections"},{"text":"Opening a connection","id":"opening-a-connection"},{"text":"Closing a connection","id":"closing-a-connection"},{"text":"Reconnecting","id":"reconnecting"},{"text":"Message Failure","id":"message-failure"},{"text":"Expiry","id":"expiry"},{"text":"Overload","id":"overload"},{"text":"Serialization Errors","id":"serialization-errors"},{"text":"QoS","id":"qos"},{"text":"Metrics","id":"metrics"},{"text":"Added a Message size limit","id":"added-a-message-size-limit"},{"text":"Recover from unknown table when deserializing internode messages","id":"recover-from-unknown-table-when-deserializing-internode-messages"}]},"/Cassandra/4.1/cassandra/architecture/overview.html":{"text":"Apache Cassandra is an open source, distributed, NoSQL database. It presents a partitioned wide column storage model with eventually consistent semantics. Apache Cassandra was initially designed at Facebook using a staged event-driven architecture (SEDA) to implement a combination of Amazon’s Dynamo distributed storage and replication techniques and Google’s Bigtable data and storage engine model. Dynamo and Bigtable were both developed to meet emerging requirements for scalable, reliable and highly available storage systems, but each had areas that could be improved. Cassandra was designed as a best-in-class combination of both systems to meet emerging largescale, both in data footprint and query volume, storage requirements. As applications began to require full global replication and always available low-latency reads and writes, it became imperative to design a new kind of database model as the relational database systems of the time struggled to meet the new requirements of global scale applications. Systems like Cassandra are designed for these challenges and seek the following design objectives: Full multi-master database replication Global availability at low latency Scaling out on commodity hardware Linear throughput increase with each additional processor Online load balancing and cluster growth Partitioned key-oriented queries Flexible schema Cassandra provides the Cassandra Query Language (CQL), an SQL-like language, to create and update database schema and access data. CQL allows users to organize data within a cluster of Cassandra nodes using: Keyspace: Defines how a dataset is replicated, per datacenter. Replication is the number of copies saved per cluster. Keyspaces contain tables. Table: Defines the typed schema for a collection of partitions. Tables contain partitions, which contain rows, which contain columns. Cassandra tables can flexibly add new columns to tables with zero downtime. Partition: Defines the mandatory part of the primary key all rows in Cassandra must have to identify the node in a cluster where the row is stored. All performant queries supply the partition key in the query. Row: Contains a collection of columns identified by a unique primary key made up of the partition key and optionally additional clustering keys. Column: A single datum with a type which belongs to a row. CQL supports numerous advanced features over a partitioned dataset such as: Single partition lightweight transactions with atomic compare and set semantics. User-defined types, functions and aggregates Collection types including sets, maps, and lists. Local secondary indices (Experimental) materialized views Cassandra explicitly chooses not to implement operations that require cross partition coordination as they are typically slow and hard to provide highly available global semantics. For example Cassandra does not support: Cross partition transactions Distributed joins Foreign keys or referential integrity. Apache Cassandra configuration settings are configured in the cassandra.yaml file that can be edited by hand or with the aid of configuration management tools. Some settings can be manipulated live using an online interface, but others require a restart of the database to take effect. Cassandra provides tools for managing a cluster. The nodetool command interacts with Cassandra’s live control interface, allowing runtime manipulation of many settings from cassandra.yaml. The auditlogviewer is used to view the audit logs. The fqltool is used to view, replay and compare full query logs. The auditlogviewer and fqltool are new tools in Apache Cassandra 4.0. In addition, Cassandra supports out of the box atomic snapshot functionality, which presents a point in time snapshot of Cassandra’s data for easy integration with many backup tools. Cassandra also supports incremental backups where data can be backed up as it is written. Apache Cassandra 4.0 has added several new features including virtual tables, transient replication (experimental), audit logging, full query logging, and support for Java 11 (full support since 4.0.2 version).","title":"Overview","component":"Cassandra","version":"4.1","name":"overview","url":"/Cassandra/4.1/cassandra/architecture/overview.html","titles":[{"text":"Features","id":"features"},{"text":"Operating","id":"operating"}]},"/Cassandra/4.1/cassandra/architecture/snitch.html":{"text":"In cassandra, the snitch has two functions: it teaches Cassandra enough about your network topology to route requests efficiently. it allows Cassandra to spread replicas around your cluster to avoid correlated failures. It does this by grouping machines into \"datacenters\" and \"racks.\" Cassandra will do its best not to have more than one replica on the same \"rack\" (which may not actually be a physical location). The dynamic snitch monitor read latencies to avoid reading from hosts that have slowed down. The dynamic snitch is configured with the following properties on cassandra.yaml: dynamic_snitch: whether the dynamic snitch should be enabled or disabled. dynamic_snitch_update_interval: 100ms, controls how often to perform the more expensive part of host score calculation. dynamic_snitch_reset_interval: 10m, if set greater than zero, this will allow 'pinning' of replicas to hosts in order to increase cache capacity. dynamic_snitch_badness_threshold:: The badness threshold will control how much worse the pinned host has to be before the dynamic snitch will prefer other replicas over it. This is expressed as a double which represents a percentage. Thus, a value of 0.2 means Cassandra would continue to prefer the static snitch values until the pinned host was 20% worse than the fastest. The endpoint_snitch parameter in cassandra.yaml should be set to the class that implements IEndPointSnitch which will be wrapped by the dynamic snitch and decide if two endpoints are in the same data center or on the same rack. Out of the box, Cassandra provides the snitch implementations: GossipingPropertyFileSnitch This should be your go-to snitch for production use. The rack and datacenter for the local node are defined in cassandra-rackdc.properties and propagated to other nodes via gossip. If cassandra-topology.properties exists, it is used as a fallback, allowing migration from the PropertyFileSnitch. SimpleSnitch Treats Strategy order as proximity. This can improve cache locality when disabling read repair. Only appropriate for single-datacenter deployments. PropertyFileSnitch Proximity is determined by rack and data center, which are explicitly configured in cassandra-topology.properties. Ec2Snitch Appropriate for EC2 deployments in a single Region, or in multiple regions with inter-region VPC enabled (available since the end of 2017, see AWS announcement). Loads Region and Availability Zone information from the EC2 API. The Region is treated as the datacenter, and the Availability Zone as the rack. Only private IPs are used, so this will work across multiple regions only if inter-region VPC is enabled. Ec2MultiRegionSnitch Uses public IPs as broadcast_address to allow cross-region connectivity (thus, you should set seed addresses to the public IP as well). You will need to open the storage_port or ssl_storage_port on the public IP firewall (For intra-Region traffic, Cassandra will switch to the private IP after establishing a connection). RackInferringSnitch Proximity is determined by rack and data center, which are assumed to correspond to the 3rd and 2nd octet of each node’s IP address, respectively. Unless this happens to match your deployment conventions, this is best used as an example of writing a custom Snitch class and is provided in that spirit.","title":"Snitch","component":"Cassandra","version":"4.1","name":"snitch","url":"/Cassandra/4.1/cassandra/architecture/snitch.html","titles":[{"text":"Dynamic snitching","id":"dynamic-snitching"},{"text":"Snitch classes","id":"snitch-classes"}]},"/Cassandra/4.1/cassandra/architecture/storage_engine.html":{"text":"Commitlogs are an append only log of all mutations local to a Cassandra node. Any data written to Cassandra will first be written to a commit log before being written to a memtable. This provides durability in the case of unexpected shutdown. On startup, any mutations in the commit log will be applied to memtables. All mutations write optimized by storing in commitlog segments, reducing the number of seeks needed to write to disk. Commitlog Segments are limited by the commitlog_segment_size option, once the size is reached, a new commitlog segment is created. Commitlog segments can be archived, deleted, or recycled once all its data has been flushed to SSTables. Commitlog segments are truncated when Cassandra has written data older than a certain point to the SSTables. Running \"nodetool drain\" before stopping Cassandra will write everything in the memtables to SSTables and remove the need to sync with the commitlogs on startup. commitlog_segment_size: The default size is 32MiB, which is almost always fine, but if you are archiving commitlog segments (see commitlog_archiving.properties), then you probably want a finer granularity of archiving; 8 or 16 MiB is reasonable. commitlog_segment_size also determines the default value of max_mutation_size in cassandra.yaml. By default, max_mutation_size is half the size of commitlog_segment_size. NOTE: If max_mutation_size is set explicitly then commitlog_segment_size must be set to at least twice the size of max_mutation_size. Commitlogs are an append only log of all mutations local to a Cassandra node. Any data written to Cassandra will first be written to a commit log before being written to a memtable. This provides durability in the case of unexpected shutdown. On startup, any mutations in the commit log will be applied. commitlog_sync: may be either periodic or batch. batch: In batch mode, Cassandra won’t ack writes until the commit log has been fsynced to disk. It will wait \"commitlog_sync_batch_window_in_ms\" milliseconds between fsyncs. This window should be kept short because the writer threads will be unable to do extra work while waiting. You may need to increase concurrent_writes for the same reason. commitlog_sync_batch_window_in_ms: Time to wait between \"batch\" fsyncs Default Value: 2 periodic: In periodic mode, writes are immediately ack’ed, and the CommitLog is simply synced every \"commitlog_sync_period\" milliseconds. commitlog_sync_period: Time to wait between \"periodic\" fsyncs Default Value: 10000ms Default Value: batch NOTE: In the event of an unexpected shutdown, Cassandra can lose up to the sync period or more if the sync is delayed. If using \"batch\" mode, it is recommended to store commitlogs in a separate, dedicated device.* commitlog_directory: This option is commented out by default When running on magnetic HDD, this should be a separate spindle than the data directories. If not set, the default directory is $CASSANDRA_HOME/data/commitlog. Default Value: /var/lib/cassandra/commitlog commitlog_compression: Compression to apply to the commitlog. If omitted, the commit log will be written uncompressed. LZ4, Snappy, Deflate and Zstd compressors are supported. (Default Value: (complex option): # - class_name: LZ4Compressor # parameters: commitlog_total_space: Total space to use for commit logs on disk. If space gets above this value, Cassandra will flush every dirty CF in the oldest segment and remove it. So a small total commitlog space will tend to cause more flush activity on less-active columnfamilies. The default value is the smaller of 8192, and 1/4 of the total space of the commitlog volume. Default Value: 8192MiB Memtables are in-memory structures where Cassandra buffers writes. In general, there is one active memtable per table. Eventually, memtables are flushed onto disk and become immutable SSTables. This can be triggered in several ways: The memory usage of the memtables exceeds the configured threshold (see memtable_cleanup_threshold) The commit-log approaches its maximum size, and forces memtable flushes in order to allow commitlog segments to be freed Memtables may be stored entirely on-heap or partially off-heap, depending on memtable_allocation_type. SSTables are the immutable data files that Cassandra uses for persisting data on disk. As SSTables are flushed to disk from memtables or are streamed from other nodes, Cassandra triggers compactions which combine multiple SSTables into one. Once the new SSTable has been written, the old SSTables can be removed. Each SSTable is comprised of multiple components stored in separate files: Data.db The actual data, i.e. the contents of rows. Index.db An index from partition keys to positions in the Data.db file. For wide partitions, this may also include an index to rows within a partition. Summary.db A sampling of (by default) every 128th entry in the Index.db file. Filter.db A Bloom Filter of the partition keys in the SSTable. CompressionInfo.db Metadata about the offsets and lengths of compression chunks in the Data.db file. Statistics.db Stores metadata about the SSTable, including information about timestamps, tombstones, clustering keys, compaction, repair, compression, TTLs, and more. Digest.crc32 A CRC-32 digest of the Data.db file. TOC.txt A plain text list of the component files for the SSTable. Within the Data.db file, rows are organized by partition. These partitions are sorted in token order (i.e. by a hash of the partition key when the default partitioner, Murmur3Partition, is used). Within a partition, rows are stored in the order of their clustering keys. SSTables can be optionally compressed using block-based compression. This section was created using the following gist which utilized this original source. The version numbers, to date are: b (0.7.0): added version to sstable filenames c (0.7.0): bloom filter component computes hashes over raw key bytes instead of strings d (0.7.0): row size in data component becomes a long instead of int e (0.7.0): stores undecorated keys in data and index components f (0.7.0): switched bloom filter implementations in data component g (0.8): tracks flushed-at context in metadata component h (1.0): tracks max client timestamp in metadata component hb (1.0.3): records compression ration in metadata component hc (1.0.4): records partitioner in metadata component hd (1.0.10): includes row tombstones in maxtimestamp he (1.1.3): includes ancestors generation in metadata component hf (1.1.6): marker that replay position corresponds to 1.1.5+ millis-based id (see CASSANDRA-4782) ia (1.2.0): column indexes are promoted to the index file records estimated histogram of deletion times in tombstones bloom filter (keys and columns) upgraded to Murmur3 ib (1.2.1): tracks min client timestamp in metadata component ic (1.2.5): omits per-row bloom filter of column names ja (2.0.0): super columns are serialized as composites (note that there is no real format change, this is mostly a marker to know if we should expect super columns or not. We do need a major version bump however, because we should not allow streaming of super columns into this new format) tracks max local deletiontime in sstable metadata records bloom_filter_fp_chance in metadata component remove data size and column count from data file (CASSANDRA-4180) tracks max/min column values (according to comparator) jb (2.0.1): switch from crc32 to adler32 for compression checksums checksum the compressed data ka (2.1.0): new Statistics.db file format index summaries can be downsampled and the sampling level is persisted switch uncompressed checksums to adler32 tracks presense of legacy (local and remote) counter shards la (2.2.0): new file name format lb (2.2.7): commit log lower bound included ma (3.0.0): swap bf hash order store rows natively mb (3.0.7, 3.7): commit log lower bound included mc (3.0.8, 3.9): commit log intervals included The following example is useful for finding all sstables that do not match the \"ib\" SSTable version include:example$find_sstables.sh[]","title":"Storage Engine","component":"Cassandra","version":"4.1","name":"storage_engine","url":"/Cassandra/4.1/cassandra/architecture/storage_engine.html","titles":[{"text":"CommitLog","id":"commit-log"},{"text":"Memtables","id":"memtables"},{"text":"SSTables","id":"sstables"},{"text":"SSTable Versions","id":"sstable-versions"},{"text":"Version 0","id":"version-0"},{"text":"Version 1","id":"version-1"},{"text":"Version 2","id":"version-2"},{"text":"Version 3","id":"version-3"},{"text":"Example Code","id":"example-code"}]},"/Cassandra/4.1/cassandra/architecture/streaming.html":{"text":"Apache Cassandra 4.0 has made several improvements to streaming. Streaming is the process used by nodes of a cluster to exchange data in the form of SSTables. Streaming of SSTables is performed for several operations, such as: SSTable Repair Host Replacement Range movements Bootstrapping Rebuild Cluster expansion Streaming in Cassandra 4.0 is based on Non-blocking Input/Output (NIO) with Netty (CASSANDRA-12229). It replaces the single-threaded (or sequential), synchronous, blocking model of streaming messages and transfer of files. Netty supports non-blocking, asynchronous, multi-threaded streaming with which multiple connections are opened simultaneously. Non-blocking implies that threads are not blocked as they don’t wait for a response for a sent request. A response could be returned in a different thread. With asynchronous, connections and threads are decoupled and do not have a 1:1 relation. Several more connections than threads may be opened. Pre-4.0, during streaming Cassandra reifies the SSTables into objects. This creates unnecessary garbage and slows down the whole streaming process as some SSTables can be transferred as a whole file rather than individual partitions. Cassandra 4.0 has added support for streaming entire SSTables when possible (CASSANDRA-14556) for faster Streaming using ZeroCopy APIs. If enabled, Cassandra will use ZeroCopy for eligible SSTables significantly speeding up transfers and increasing throughput. A zero-copy path avoids bringing data into user-space on both sending and receiving side. Any streaming related operations will notice corresponding improvement. Zero copy streaming is hardware bound; only limited by the hardware limitations (Network and Disk IO ). In benchmark tests Zero Copy Streaming is 5x faster than partitions based streaming. Faster streaming provides the benefit of improved availability. A cluster’s recovery mainly depends on the streaming speed, Cassandra clusters with failed nodes will be able to recover much more quickly (5x faster). If a node fails, SSTables need to be streamed to a replacement node. During the replacement operation, the new Cassandra node streams SSTables from the neighboring nodes that hold copies of the data belonging to this new node’s token range. Depending on the amount of data stored, this process can require substantial network bandwidth, taking some time to complete. The longer these range movement operations take, the more the cluster availability is lost. Failure of multiple nodes would reduce high availability greatly. The faster the new node completes streaming its data, the faster it can serve traffic, increasing the availability of the cluster. Zero copy streaming is enabled by setting the following setting in cassandra.yaml. stream_entire_sstables: true By default zero copy streaming is enabled. Zero copy streaming is used if all partitions within the SSTable need to be transmitted. This is common when using LeveledCompactionStrategy or when partitioning SSTables by token range has been enabled. All partition keys in the SSTables are iterated over to determine the eligibility for Zero Copy streaming. When enabled, it permits Cassandra to zero-copy stream entire eligible SSTables between nodes, including every component. This speeds up the network transfer significantly subject to throttling specified by stream_throughput_outbound. Enabling this will reduce the GC pressure on sending and receiving node. While this feature tries to keep the disks balanced, it cannot guarantee it. This feature will be automatically disabled if internode encryption is enabled. Currently this can be used with Leveled Compaction. Throttling would reduce the streaming speed. The stream_throughput_outbound throttles all outbound streaming file transfers on a node to the given total throughput in Mbps. When unset, the default is 200 Mbps or 24 MiB/s. stream_throughput_outbound: 24MiB/s To run any Zero Copy streaming benchmark the stream_throughput_outbound must be set to a really high value otherwise, throttling will be significant and the benchmark results will not be meaningful. The inter_dc_stream_throughput_outbound throttles all streaming file transfer between the datacenters, this setting allows users to throttle inter dc stream throughput in addition to throttling all network stream traffic as configured with stream_throughput_outbound. When unset, the default is 200 Mbps or 25 MB/s. inter_dc_stream_throughput_outbound: 24MiB/s Zero Copy Streaming streams entire SSTables. SSTables are made up of multiple components in separate files. SSTable components streamed are listed in Table 1. Table 1. SSTable Components SSTable Component Description Data.db The base data for an SSTable: the remaining components can be regenerated based on the data component. Index.db Index of the row keys with pointers to their positions in the data file. Filter.db Serialized bloom filter for the row keys in the SSTable. CompressionInfo.db File to hold information about uncompressed data length, chunk offsets etc. Statistics.db Statistical metadata about the content of the SSTable. Digest.crc32 Holds CRC32 checksum of the data file size_bytes. CRC.db Holds the CRC32 for chunks in an uncompressed file. Summary.db Holds SSTable Index Summary (sampling of Index component) TOC.txt Table of contents, stores the list of all components for the SSTable. Custom component, used by e.g. custom compaction strategy may also be included. Repair with nodetool repair involves streaming of repaired SSTables and a repair preview has been added to provide an estimate of the amount of repair streaming that would need to be performed. Repair preview (CASSANDRA-13257) is invoke with nodetool repair --preview using option: -prv, --preview It determines ranges and amount of data to be streamed, but doesn’t actually perform repair. The streaming of the different keyspaces for bootstrap and rebuild has been parallelized in Cassandra 4.0 (CASSANDRA-4663). Range Streamer picks unique nodes to stream data from when number of replicas in each DC is three or more (CASSANDRA-4650). What the optimization does is to even out the streaming load across the cluster. Without the optimization, some node can be picked up to stream more data than others. This patch allows to select dedicated node to stream only one range. This will increase the performance of bootstrapping a node and will also put less pressure on nodes serving the data. This does not affect if N < 3 in each DC as then it streams data from only 2 nodes. Stream Operation Types ^^^^^ It is important to know the type or purpose of a certain stream. Version 4.0 (CASSANDRA-13064) adds an enum to distinguish between the different types of streams. Stream types are available both in a stream request and a stream task. The different stream types are: Restore replica count Unbootstrap Relocation Bootstrap Rebuild Bulk Load Repair CASSANDRA-12510 guards against decommission that will drop # of replicas below configured replication factor (RF), and adds the --force option that allows decommission to continue if intentional; force decommission of this node even when it reduces the number of replicas to below configured RF.","title":"Improved Streaming","component":"Cassandra","version":"4.1","name":"streaming","url":"/Cassandra/4.1/cassandra/architecture/streaming.html","titles":[{"text":"Streaming based on Netty","id":"streaming-based-on-netty"},{"text":"Zero Copy Streaming","id":"zero-copy-streaming"},{"text":"High Availability","id":"high-availability"},{"text":"Enabling Zero Copy Streaming","id":"enabling-zero-copy-streaming"},{"text":"SSTables Eligible for Zero Copy Streaming","id":"sstables-eligible-for-zero-copy-streaming"},{"text":"Benefits of Zero Copy Streaming","id":"benefits-of-zero-copy-streaming"},{"text":"Configuring for Zero Copy Streaming","id":"configuring-for-zero-copy-streaming"},{"text":"SSTable Components Streamed with Zero Copy Streaming","id":"sstable-components-streamed-with-zero-copy-streaming"},{"text":"Repair Streaming Preview","id":"repair-streaming-preview"},{"text":"Parallelizing of Streaming of Keyspaces","id":"parallelizing-of-streaming-of-keyspaces"},{"text":"Unique nodes for Streaming in Multi-DC deployment","id":"unique-nodes-for-streaming-in-multi-dc-deployment"},{"text":"Disallow Decommission when number of Replicas will drop below configured RF","id":"disallow-decommission-when-number-of-replicas-will-drop-below-configured-rf"}]},"/Cassandra/4.1/cassandra/cql/SASI.html":{"text":"SASIIndex, or SASI for short, is an implementation of Cassandra’s Index interface that can be used as an alternative to the existing implementations. SASI’s indexing and querying improves on existing implementations by tailoring it specifically to Cassandra’s needs. SASI has superior performance in cases where queries would previously require filtering. In achieving this performance, SASI aims to be significantly less resource intensive than existing implementations, in memory, disk, and CPU usage. In addition, SASI supports prefix and contains queries on strings (similar to SQL’s LIKE = \"foo*\" or LIKE = \"foo\"'). The following goes on describe how to get up and running with SASI, demonstrates usage with examples, and provides some details on its implementation. The examples below walk through creating a table and indexes on its columns, and performing queries on some inserted data. The examples below assume the demo keyspace has been created and is in use. cqlsh> CREATE KEYSPACE demo WITH replication = { ... 'class': 'SimpleStrategy', ... 'replication_factor': '1' ... }; cqlsh> USE demo; All examples are performed on the sasi table: cqlsh:demo> CREATE TABLE sasi (id uuid, first_name text, last_name text, ... age int, height int, created_at bigint, primary key (id)); To create SASI indexes use CQLs CREATE CUSTOM INDEX statement: cqlsh:demo> CREATE CUSTOM INDEX ON sasi (first_name) USING 'org.apache.cassandra.index.sasi.SASIIndex' ... WITH OPTIONS = { ... 'analyzer_class': ... 'org.apache.cassandra.index.sasi.analyzer.NonTokenizingAnalyzer', ... 'case_sensitive': 'false' ... }; cqlsh:demo> CREATE CUSTOM INDEX ON sasi (last_name) USING 'org.apache.cassandra.index.sasi.SASIIndex' ... WITH OPTIONS = {'mode': 'CONTAINS'}; cqlsh:demo> CREATE CUSTOM INDEX ON sasi (age) USING 'org.apache.cassandra.index.sasi.SASIIndex'; cqlsh:demo> CREATE CUSTOM INDEX ON sasi (created_at) USING 'org.apache.cassandra.index.sasi.SASIIndex' ... WITH OPTIONS = {'mode': 'SPARSE'}; The indexes created have some options specified that customize their behaviour and potentially performance. The index on first_name is case-insensitive. The analyzers are discussed more in a subsequent example. The NonTokenizingAnalyzer performs no analysis on the text. Each index has a mode: PREFIX, CONTAINS, or SPARSE, the first being the default. The last_name index is created with the mode CONTAINS which matches terms on suffixes instead of prefix only. Examples of this are available below and more detail can be found in the section on OnDiskIndex.The created_at column is created with its mode set to SPARSE, which is meant to improve performance of querying large, dense number ranges like timestamps for data inserted every millisecond. Details of the SPARSE implementation can also be found in the section on the OnDiskIndex. The age index is created with the default PREFIX mode and no case-sensitivity or text analysis options are specified since the field is numeric. After inserting the following data and performing a nodetool flush, SASI performing index flushes to disk can be seen in Cassandra’s logs – although the direct call to flush is not required (see IndexMemtable for more details). cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (556ebd54-cbe5-4b75-9aae-bf2a31a24500, 'Pavel', 'Yaskevich', 27, 181, 1442959315018); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (5770382a-c56f-4f3f-b755-450e24d55217, 'Jordan', 'West', 26, 173, 1442959315019); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (96053844-45c3-4f15-b1b7-b02c441d3ee1, 'Mikhail', 'Stepura', 36, 173, 1442959315020); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (f5dfcabe-de96-4148-9b80-a1c41ed276b4, 'Michael', 'Kjellman', 26, 180, 1442959315021); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (2970da43-e070-41a8-8bcb-35df7a0e608a, 'Johnny', 'Zhang', 32, 175, 1442959315022); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (6b757016-631d-4fdb-ac62-40b127ccfbc7, 'Jason', 'Brown', 40, 182, 1442959315023); cqlsh:demo> INSERT INTO sasi (id, first_name, last_name, age, height, created_at) ... VALUES (8f909e8a-008e-49dd-8d43-1b0df348ed44, 'Vijay', 'Parthasarathy', 34, 183, 1442959315024); cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi; first_name | last_name | age | height | created_at ------------+---------------+-----+--------+--------------- Michael | Kjellman | 26 | 180 | 1442959315021 Mikhail | Stepura | 36 | 173 | 1442959315020 Jason | Brown | 40 | 182 | 1442959315023 Pavel | Yaskevich | 27 | 181 | 1442959315018 Vijay | Parthasarathy | 34 | 183 | 1442959315024 Jordan | West | 26 | 173 | 1442959315019 Johnny | Zhang | 32 | 175 | 1442959315022 (7 rows) SASI supports all queries already supported by CQL, including LIKE statement for PREFIX, CONTAINS and SUFFIX searches. cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi ... WHERE first_name = 'Pavel'; first_name | last_name | age | height | created_at -------------+-----------+-----+--------+--------------- Pavel | Yaskevich | 27 | 181 | 1442959315018 (1 rows) cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi ... WHERE first_name = 'pavel'; first_name | last_name | age | height | created_at -------------+-----------+-----+--------+--------------- Pavel | Yaskevich | 27 | 181 | 1442959315018 (1 rows) cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi ... WHERE first_name LIKE 'M%'; first_name | last_name | age | height | created_at ------------+-----------+-----+--------+--------------- Michael | Kjellman | 26 | 180 | 1442959315021 Mikhail | Stepura | 36 | 173 | 1442959315020 (2 rows) Of course, the case of the query does not matter for the first_name column because of the options provided at index creation time. cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi ... WHERE first_name LIKE 'm%'; first_name | last_name | age | height | created_at ------------+-----------+-----+--------+--------------- Michael | Kjellman | 26 | 180 | 1442959315021 Mikhail | Stepura | 36 | 173 | 1442959315020 (2 rows) SASI supports queries with multiple predicates, however, due to the nature of the default indexing implementation, CQL requires the user to specify ALLOW FILTERING to opt-in to the potential performance pitfalls of such a query. With SASI, while the requirement to include ALLOW FILTERING remains, to reduce modifications to the grammar, the performance pitfalls do not exist because filtering is not performed. Details on how SASI joins data from multiple predicates is available below in the Implementation Details section. cqlsh:demo> SELECT first_name, last_name, age, height, created_at FROM sasi ... WHERE first_name LIKE 'M%' and age SELECT * FROM sasi WHERE last_name LIKE '%a%'; id | age | created_at | first_name | height | last_name --------------------------------------+-----+---------------+------------+--------+--------------- f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman 96053844-45c3-4f15-b1b7-b02c441d3ee1 | 36 | 1442959315020 | Mikhail | 173 | Stepura 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | 1442959315018 | Pavel | 181 | Yaskevich 8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | 1442959315024 | Vijay | 183 | Parthasarathy 2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang (5 rows) cqlsh:demo> SELECT * FROM sasi WHERE last_name LIKE '%an%'; id | age | created_at | first_name | height | last_name --------------------------------------+-----+---------------+------------+--------+----------- f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman 2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang (2 rows) SASI also supports filtering on non-indexed columns like height. The expression can only narrow down an existing query using AND. cqlsh:demo> SELECT * FROM sasi WHERE last_name LIKE '%a%' AND height >= 175 ALLOW FILTERING; id | age | created_at | first_name | height | last_name --------------------------------------+-----+---------------+------------+--------+--------------- f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | 1442959315021 | Michael | 180 | Kjellman 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | 1442959315018 | Pavel | 181 | Yaskevich 8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | 1442959315024 | Vijay | 183 | Parthasarathy 2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | 1442959315022 | Johnny | 175 | Zhang (4 rows) A simple text analysis provided is delimiter based tokenization. This provides an alternative to indexing collections, as delimiter separated text can be indexed without the overhead of CONTAINS mode nor using PREFIX or SUFFIX queries. cqlsh:demo> ALTER TABLE sasi ADD aliases text; cqlsh:demo> CREATE CUSTOM INDEX on sasi (aliases) USING 'org.apache.cassandra.index.sasi.SASIIndex' ... WITH OPTIONS = { ... 'analyzer_class': 'org.apache.cassandra.index.sasi.analyzer.DelimiterAnalyzer', ... 'delimiter': ',', ... 'mode': 'prefix', ... 'analyzed': 'true'}; cqlsh:demo> UPDATE sasi SET aliases = 'Mike,Mick,Mikey,Mickey' WHERE id = f5dfcabe-de96-4148-9b80-a1c41ed276b4; cqlsh:demo> SELECT * FROM sasi WHERE aliases LIKE 'Mikey' ALLOW FILTERING; id | age | aliases | created_at | first_name | height | last_name --------------------------------------+-----+------------------------+---------------+------------+--------+----------- f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | Mike,Mick,Mikey,Mickey | 1442959315021 | Michael | 180 | Kjellman Lastly, to demonstrate text analysis an additional column is needed on the table. Its definition, index, and statements to update rows are shown below. cqlsh:demo> ALTER TABLE sasi ADD bio text; cqlsh:demo> CREATE CUSTOM INDEX ON sasi (bio) USING 'org.apache.cassandra.index.sasi.SASIIndex' ... WITH OPTIONS = { ... 'analyzer_class': 'org.apache.cassandra.index.sasi.analyzer.StandardAnalyzer', ... 'tokenization_enable_stemming': 'true', ... 'analyzed': 'true', ... 'tokenization_normalize_lowercase': 'true', ... 'tokenization_locale': 'en' ... }; cqlsh:demo> UPDATE sasi SET bio = 'Software Engineer, who likes distributed systems, doesnt like to argue.' WHERE id = 5770382a-c56f-4f3f-b755-450e24d55217; cqlsh:demo> UPDATE sasi SET bio = 'Software Engineer, works on the freight distribution at nights and likes arguing' WHERE id = 556ebd54-cbe5-4b75-9aae-bf2a31a24500; cqlsh:demo> SELECT * FROM sasi; id | age | bio | created_at | first_name | height | last_name --------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+--------------- f5dfcabe-de96-4148-9b80-a1c41ed276b4 | 26 | null | 1442959315021 | Michael | 180 | Kjellman 96053844-45c3-4f15-b1b7-b02c441d3ee1 | 36 | null | 1442959315020 | Mikhail | 173 | Stepura 6b757016-631d-4fdb-ac62-40b127ccfbc7 | 40 | null | 1442959315023 | Jason | 182 | Brown 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich 8f909e8a-008e-49dd-8d43-1b0df348ed44 | 34 | null | 1442959315024 | Vijay | 183 | Parthasarathy 5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West 2970da43-e070-41a8-8bcb-35df7a0e608a | 32 | null | 1442959315022 | Johnny | 175 | Zhang (7 rows) Index terms and query search strings are stemmed for the bio column because it was configured to use the StandardAnalyzer and analyzed is set to true. The tokenization_normalize_lowercase is similar to the case_sensitive property but for the StandardAnalyzer. These query demonstrates the stemming applied by StandardAnalyzer. cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'distributing'; id | age | bio | created_at | first_name | height | last_name --------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+----------- 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich 5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West (2 rows) cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'they argued'; id | age | bio | created_at | first_name | height | last_name --------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+----------- 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich 5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West (2 rows) cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'working at the company'; id | age | bio | created_at | first_name | height | last_name --------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+----------- 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich (1 rows) cqlsh:demo> SELECT * FROM sasi WHERE bio LIKE 'soft eng'; id | age | bio | created_at | first_name | height | last_name --------------------------------------+-----+----------------------------------------------------------------------------------+---------------+------------+--------+----------- 556ebd54-cbe5-4b75-9aae-bf2a31a24500 | 27 | Software Engineer, works on the freight distribution at nights and likes arguing | 1442959315018 | Pavel | 181 | Yaskevich 5770382a-c56f-4f3f-b755-450e24d55217 | 26 | Software Engineer, who likes distributed systems, doesnt like to argue. | 1442959315019 | Jordan | 173 | West (2 rows) While SASI, at the surface, is simply an implementation of the Index interface, at its core there are several data structures and algorithms used to satisfy it. These are described here. Additionally, the changes internal to Cassandra to support SASI’s integration are described. The Index interface divides responsibility of the implementer into two parts: Indexing and Querying. Further, Cassandra makes it possible to divide those responsibilities into the memory and disk components. SASI takes advantage of Cassandra’s write-once, immutable, ordered data model to build indexes along with the flushing of the memtable to disk – this is the origin of the name ``SSTable Attached Secondary Index''. The SASI index data structures are built in memory as the SSTable is being written and they are flushed to disk before the writing of the SSTable completes. The writing of each index file only requires sequential writes to disk. In some cases, partial flushes are performed, and later stitched back together, to reduce memory usage. These data structures are optimized for this use case. Taking advantage of Cassandra’s ordered data model, at query time, candidate indexes are narrowed down for searching, minimizing the amount of work done. Searching is then performed using an efficient method that streams data off disk as needed. Per SSTable, SASI writes an index file for each indexed column. The data for these files is built in memory using the OnDiskIndexBuilder. Once flushed to disk, the data is read using the OnDiskIndex class. These are composed of bytes representing indexed terms, organized for efficient writing or searching respectively. The keys and values they hold represent tokens and positions in an SSTable and these are stored per-indexed term in TokenTreeBuilders for writing, and TokenTrees for querying. These index files are memory mapped after being written to disk, for quicker access. For indexing data in the memtable, SASI uses its IndexMemtable class. Each OnDiskIndex is an instance of a modified Suffix Array data structure. The OnDiskIndex is comprised of page-size blocks of sorted terms and pointers to the terms’ associated data, as well as the data itself, stored also in one or more page-sized blocks. The OnDiskIndex is structured as a tree of arrays, where each level describes the terms in the level below, the final level being the terms themselves. The PointerLevel`s and their `PointerBlock`s contain terms and pointers to other blocks that end with those terms. The `DataLevel, the final level, and its DataBlock`s contain terms and point to the data itself, contained in `TokenTrees. The terms written to the OnDiskIndex vary depending on its mode'': either PREFIX, CONTAINS, or SPARSE. In the PREFIX and SPARSE cases, terms’ exact values are written exactly once per OnDiskIndex. For example, when using a PREFIX index with terms Jason, Jordan, Pavel, all three will be included in the index. A CONTAINS index writes additional terms for each suffix of each term recursively. Continuing with the example, a CONTAINS index storing the previous terms would also store ason, ordan, avel, son, rdan, vel, etc. This allows for queries on the suffix of strings. The SPARSE mode differs from PREFIX in that for every 64 blocks of terms a TokenTree is built merging all the TokenTrees for each term into a single one. This copy of the data is used for efficient iteration of large ranges of e.g. timestamps. The index mode'' is configurable per column at index creation time. The TokenTree is an implementation of the well-known B+-tree that has been modified to optimize for its use-case. In particular, it has been optimized to associate tokens, longs, with a set of positions in an SSTable, also longs. Allowing the set of long values accommodates the possibility of a hash collision in the token, but the data structure is optimized for the unlikely possibility of such a collision. To optimize for its write-once environment the TokenTreeBuilder completely loads its interior nodes as the tree is built and it uses the well-known algorithm optimized for bulk-loading the data structure. TokenTrees provide the means to iterate over tokens, and file positions, that match a given term, and to skip forward in that iteration, an operation used heavily at query time. The IndexMemtable handles indexing the in-memory data held in the memtable. The IndexMemtable in turn manages either a TrieMemIndex or a SkipListMemIndex per-column. The choice of which index type is used is data dependent. The TrieMemIndex is used for literal types. AsciiType and UTF8Type are literal types by default but any column can be configured as a literal type using the is_literal option at index creation time. For non-literal types the SkipListMemIndex is used. The TrieMemIndex is an implementation that can efficiently support prefix queries on character-like data. The SkipListMemIndex, conversely, is better suited for other Cassandra data types like numbers. The TrieMemIndex is built using either the ConcurrentRadixTree or ConcurrentSuffixTree from the com.goooglecode.concurrenttrees package. The choice between the two is made based on the indexing mode, PREFIX or other modes, and CONTAINS mode, respectively. The SkipListMemIndex is built on top of java.util.concurrent.ConcurrentSkipListSet. Responsible for converting the internal IndexExpression representation into SASI’s Operation and Expression trees, optimizing the trees to reduce the amount of work done, and driving the query itself, the QueryPlan is the work horse of SASI’s querying implementation. To efficiently perform union and intersection operations, SASI provides several iterators similar to Cassandra’s MergeIterator, but tailored specifically for SASI’s use while including more features. The RangeUnionIterator, like its name suggests, performs set unions over sets of tokens/keys matching the query, only reading as much data as it needs from each set to satisfy the query. The RangeIntersectionIterator, similar to its counterpart, performs set intersections over its data. The QueryPlan instantiated per search query is at the core of SASI’s querying implementation. Its work can be divided in two stages: analysis and execution. During the analysis phase, QueryPlan converts from Cassandra’s internal representation of IndexExpression`s, which has also been modified to support encoding queries that contain ORs and groupings of expressions using parentheses (see the Cassandra Internal Changes section below for more details). This process produces a tree of `Operations, which in turn may contain Expressions, all of which provide an alternative, more efficient, representation of the query. During execution, the QueryPlan uses the DecoratedKey-generating iterator created from the Operation tree. These keys are read from disk and a final check to ensure they satisfy the query is made, once again using the Operation tree. At the point the desired amount of matching data has been found, or there is no more matching data, the result set is returned to the coordinator through the existing internal components. The number of queries (total/failed/timed-out), and their latencies, are maintined per-table/column family. SASI also supports concurrently iterating terms for the same index across SSTables. The concurrency factor is controlled by the cassandra.search_concurrency_factor system property. The default is 1. Each QueryPlan references a QueryController used throughout the execution phase. The QueryController has two responsibilities: to manage and ensure the proper cleanup of resources (indexes), and to strictly enforce the time bound per query, specified by the user via the range slice timeout. All indexes are accessed via the QueryController so that they can be safely released by it later. The QueryController’s checkpoint function is called in specific places in the execution path to ensure the time-bound is enforced. While in the analysis phase, the QueryPlan performs several potential optimizations to the query. The goal of these optimizations is to reduce the amount of work performed during the execution phase. The simplest optimization performed is compacting multiple expressions joined by logical intersections (AND) into a single Operation with three or more Expressions. For example, the query WHERE age 21 would, without modification, have the following tree: ┌───────┐ ┌────────│ AND │──────┐ │ └───────┘ │ ▼ ▼ ┌───────┐ ┌──────────┐ ┌─────│ AND │─────┐ │age 21 │ └──────────┘ └──────────┘ QueryPlan will remove the redundant right branch whose root is the final AND and has leaves fname != pa* and age > 21. These Expressions will be compacted into the parent AND, a safe operation due to AND being associative and commutative. The resulting tree looks like the following: ┌───────┐ ┌────────│ AND │──────┐ │ └───────┘ │ ▼ ▼ ┌───────┐ ┌──────────┐ ┌───────────│ AND │────────┐ │age 21 │ └──────────┘ ┌──────────┐ └──────────┘ │fname!=pa*│ └──────────┘ When excluding results from the result set, using !=, the QueryPlan determines the best method for handling it. For range queries, for example, it may be optimal to divide the range into multiple parts with a hole for the exclusion. For string queries, such as this one, it is more optimal, however, to simply note which data to skip, or exclude, while scanning the index. Following this optimization the tree looks like this: ┌───────┐ ┌────────│ AND │──────┐ │ └───────┘ │ ▼ ▼ ┌───────┐ ┌──────────┐ ┌───────│ AND │────────┐ │age 21 │ │ exclusions=[pa*] │ └──────────┘ └──────────────────┘ The last type of optimization applied, for this query, is to merge range expressions across branches of the tree – without modifying the meaning of the query, of course. In this case, because the query contains all AND`s the `age expressions can be collapsed. Along with this optimization, the initial collapsing of unneeded `AND`s can also be applied once more to result in this final tree using to execute the query: ┌───────┐ ┌──────│ AND │───────┐ │ └───────┘ │ ▼ ▼ ┌──────────────────┐ ┌────────────────┐ │ fname=p* │ │ 21 < age < 100 │ │ exclusions=[pa*] │ └────────────────┘ └──────────────────┘ As discussed, the QueryPlan optimizes a tree represented by Operations as interior nodes, and Expressions as leaves. The Operation class, more specifically, can have zero, one, or two Operations as children and an unlimited number of expressions. The iterators used to perform the queries, discussed below in the `Range(Union|Intersection)Iterator'' section, implement the necessary logic to merge results transparently regardless of the `Operations children. Besides participating in the optimizations performed by the QueryPlan, Operation is also responsible for taking a row that has been returned by the query and performing a final validation that it in fact does match. This satisfiesBy operation is performed recursively from the root of the Operation tree for a given query. These checks are performed directly on the data in a given row. For more details on how satisfiesBy works, see the documentation in the code. The abstract RangeIterator class provides a unified interface over the two main operations performed by SASI at various layers in the execution path: set intersection and union. These operations are performed in a iterated, or ``streaming'', fashion to prevent unneeded reads of elements from either set. In both the intersection and union cases the algorithms take advantage of the data being pre-sorted using the same sort order, e.g. term or token order. The RangeUnionIterator performs the ``Merge-Join'' portion of the Sort-Merge-Join algorithm, with the properties of an outer-join, or union. It is implemented with several optimizations to improve its performance over a large number of iterators – sets to union. Specifically, the iterator exploits the likely case of the data having many sub-groups of overlapping ranges and the unlikely case that all ranges will overlap each other. For more details see the javadoc. The RangeIntersectionIterator itself is not a subclass of RangeIterator. It is a container for several classes, one of which, AbstractIntersectionIterator, sub-classes RangeIterator. SASI supports two methods of performing the intersection operation, and the ability to be adaptive in choosing between them based on some properties of the data. BounceIntersectionIterator, and the BOUNCE strategy, works like the RangeUnionIterator in that it performs a ``Merge-Join'', however, its nature is similar to a inner-join, where like values are merged by a data-specific merge function (e.g. merging two tokens in a list to lookup in a SSTable later). See the javadoc for more details on its implementation. LookupIntersectionIterator, and the LOOKUP strategy, performs a different operation, more similar to a lookup in an associative data structure, or ``hash lookup'' in database terminology. Once again, details on the implementation can be found in the javadoc. The choice between the two iterators, or the ADAPTIVE strategy, is based upon the ratio of data set sizes of the minimum and maximum range of the sets being intersected. If the number of the elements in minimum range divided by the number of elements is the maximum range is less than or equal to 0.01, then the ADAPTIVE strategy chooses the LookupIntersectionIterator, otherwise the BounceIntersectionIterator is chosen. The above components are glued together by the SASIIndex class which implements Index, and is instantiated per-table containing SASI indexes. It manages all indexes for a table via the sasi.conf.DataTracker and sasi.conf.view.View components, controls writing of all indexes for an SSTable via its PerSSTableIndexWriter, and initiates searches with Searcher. These classes glue the previously mentioned indexing components together with Cassandra’s SSTable life-cycle ensuring indexes are not only written when Memtable’s flush, but also as SSTable’s are compacted. For querying, the Searcher does little but defer to QueryPlan and update e.g. latency metrics exposed by SASI. To support the above changes and integrate them into Cassandra a few minor internal changes were made to Cassandra itself. These are described here. The SSTableFlushObserver is an observer pattern-like interface, whose sub-classes can register to be notified about events in the life-cycle of writing out a SSTable. Sub-classes can be notified when a flush begins and ends, as well as when each next row is about to be written, and each next column. SASI’s PerSSTableIndexWriter, discussed above, is the only current subclass. The following are items that can be addressed in future updates but are not available in this repository or are not currently implemented. The cluster must be configured to use a partitioner that produces LongToken`s, e.g. `Murmur3Partitioner. Other existing partitioners which don’t produce LongToken e.g. ByteOrderedPartitioner and RandomPartitioner will not work with SASI. Not Equals and OR support have been removed in this release while changes are made to Cassandra itself to support them. Pavel Yaskevich Jordan West Michael Kjellman Jason Brown Mikhail Stepura","title":"","component":"Cassandra","version":"4.1","name":"SASI","url":"/Cassandra/4.1/cassandra/cql/SASI.html","titles":[{"text":"SASIIndex","id":"sasiindex"},{"text":"Using SASI","id":"using-sasi"},{"text":"Creating Indexes","id":"creating-indexes"},{"text":"Equality & Prefix Queries","id":"equality-prefix-queries"},{"text":"Compound Queries","id":"compound-queries"},{"text":"Suffix Queries","id":"suffix-queries"},{"text":"Expressions on Non-Indexed Columns","id":"expressions-on-non-indexed-columns"},{"text":"Delimiter based Tokenization Analysis","id":"delimiter-based-tokenization-analysis"},{"text":"Text Analysis (Tokenization and Stemming)","id":"text-analysis-tokenization-and-stemming"},{"text":"Implementation Details","id":"implementation-details"},{"text":"Indexing","id":"indexing"},{"text":"OnDiskIndex(Builder)","id":"ondiskindexbuilder"},{"text":"TokenTree(Builder)","id":"tokentreebuilder"},{"text":"IndexMemtable","id":"indexmemtable"},{"text":"Querying","id":"querying"},{"text":"QueryPlan","id":"queryplan"},{"text":"QueryController","id":"querycontroller"},{"text":"QueryPlan Optimizations","id":"queryplan-optimizations"},{"text":"Operations and Expressions","id":"operations-and-expressions"},{"text":"Range(Union|Intersection)Iterator","id":"rangeunionintersectioniterator"},{"text":"The SASIIndex Class","id":"the-sasiindex-class"},{"text":"Cassandra Internal Changes","id":"cassandra-internal-changes"},{"text":"SSTable Write Life-cycle Notifications","id":"sstable-write-life-cycle-notifications"},{"text":"Limitations and Caveats","id":"limitations-and-caveats"},{"text":"Contributors","id":"contributors"}]},"/Cassandra/4.1/cassandra/cql/appendices.html":{"text":"CQL distinguishes between reserved and non-reserved keywords. Reserved keywords cannot be used as identifier, they are truly reserved for the language (but one can enclose a reserved keyword by double-quotes to use it as an identifier). Non-reserved keywords however only have a specific meaning in certain context but can used as identifier otherwise. The only raison d’être of these non-reserved keywords is convenience: some keyword are non-reserved when it was always easy for the parser to decide whether they were used as keywords or not. Keyword Reserved? ADD yes AGGREGATE no ALL no ALLOW yes ALTER yes AND yes APPLY yes AS no ASC yes ASCII no AUTHORIZE yes BATCH yes BEGIN yes BIGINT no BLOB no BOOLEAN no BY yes CALLED no CLUSTERING no COLUMNFAMILY yes COMPACT no CONTAINS no COUNT no COUNTER no CREATE yes CUSTOM no DATE no DECIMAL no DELETE yes DESC yes DESCRIBE yes DISTINCT no DOUBLE no DROP yes ENTRIES yes EXECUTE yes EXISTS no FILTERING no FINALFUNC no FLOAT no FROM yes FROZEN no FULL yes FUNCTION no FUNCTIONS no GRANT yes IF yes IN yes INDEX yes INET no INFINITY yes INITCOND no INPUT no INSERT yes INT no INTO yes JSON no KEY no KEYS no KEYSPACE yes KEYSPACES no LANGUAGE no LIMIT yes LIST no LOGIN no MAP no MODIFY yes NAN yes NOLOGIN no NORECURSIVE yes NOSUPERUSER no NOT yes NULL yes OF yes ON yes OPTIONS no OR yes ORDER yes PASSWORD no PERMISSION no PERMISSIONS no PRIMARY yes RENAME yes REPLACE yes RETURNS no REVOKE yes ROLE no ROLES no SCHEMA yes SELECT yes SET yes SFUNC no SMALLINT no STATIC no STORAGE no STYPE no SUPERUSER no TABLE yes TEXT no TIME no TIMESTAMP no TIMEUUID no TINYINT no TO yes TOKEN yes TRIGGER no TRUNCATE yes TTL no TUPLE no TYPE no UNLOGGED yes UPDATE yes USE yes USER no USERS no USING yes UUID no VALUES no VARCHAR no VARINT no WHERE yes WITH yes WRITETIME no The following type names are not currently used by CQL, but are reserved for potential future use. User-defined types may not use reserved type names as their name. type bitstring byte complex enum interval macaddr Starting version 4.0, Thrift and COMPACT STORAGE is no longer supported. ALTER …​ DROP COMPACT STORAGE statement makes Compact Tables CQL-compatible, exposing internal structure of Thrift/Compact Tables: CQL-created Compact Tables that have no clustering columns, will expose an additional clustering column column1 with UTF8Type. CQL-created Compact Tables that had no regular columns, will expose a regular column value with BytesType. For CQL-Created Compact Tables, all columns originally defined as regular will be come static CQL-created Compact Tables that have clustering but have no regular columns will have an empty value column (of EmptyType) SuperColumn Tables (can only be created through Thrift) will expose a compact value map with an empty name. Thrift-created Compact Tables will have types corresponding to their Thrift definition.","title":"Appendices","component":"Cassandra","version":"4.1","name":"appendices","url":"/Cassandra/4.1/cassandra/cql/appendices.html","titles":[{"text":"Appendix A: CQL Keywords","id":"appendix-A"},{"text":"Appendix B: CQL Reserved Types","id":"appendix-b-cql-reserved-types"},{"text":"Appendix C: Dropping Compact Storage","id":"appendix-c-dropping-compact-storage"}]},"/Cassandra/4.1/cassandra/cql/changes.html":{"text":"The following describes the changes in each version of CQL. Add support for IF EXISTS and IF NOT EXISTS in ALTER statements (16916) Allow GRANT/REVOKE multiple permissions in a single statement (17030) Pre hashed passwords in CQL (17334) Add support for type casting in WHERE clause components and in the values of INSERT/UPDATE statements (14337) Add support for CONTAINS and CONTAINS KEY in conditional UPDATE and DELETE statement (10537) Allow to grant permission for all tables in a keyspace (17027) Allow to aggregate by time intervals (11871) Adds support for arithmetic operators (11935) Adds support for + and - operations on dates (11936) Adds currentTimestamp, currentDate, currentTime and currentTimeUUID functions (13132) ALTER TABLE ALTER has been removed; a column’s type may not be changed after creation (12443). ALTER TYPE ALTER has been removed; a field’s type may not be changed after creation (12443). Adds a new duration data types (11873). Support for GROUP BY (10707). Adds a DEFAULT UNSET option for INSERT JSON to ignore omitted columns (11424). Allows null as a legal value for TTL on insert and update. It will be treated as equivalent to inserting a 0 (12216). If a table has a non zero default_time_to_live, then explicitly specifying a TTL of 0 in an INSERT or UPDATE statement will result in the new writes not having any expiration (that is, an explicit TTL of 0 cancels the default_time_to_live). This wasn’t the case before and the default_time_to_live was applied even though a TTL had been explicitly set. ALTER TABLE ADD and DROP now allow multiple columns to be added/removed. New PER PARTITION LIMIT option for SELECT statements (see CASSANDRA-7017. User-defined functions can now instantiate UDTValue and TupleValue instances via the new UDFContext interface (see CASSANDRA-10818. User-defined types may now be stored in a non-frozen form, allowing individual fields to be updated and deleted in UPDATE statements and DELETE statements, respectively. (CASSANDRA-7423). Adds CAST functions. Support for materialized views . DELETE support for inequality expressions and IN restrictions on any primary key columns. UPDATE support for IN restrictions on any primary key columns. The syntax TRUNCATE TABLE X is now accepted as an alias for TRUNCATE X. User-defined functions and aggregates are now supported. Allows double-dollar enclosed strings literals as an alternative to single-quote enclosed strings. Introduces Roles to supersede user based authentication and access control New date, time, tinyint and smallint data types have been added. JSON support has been added Adds new time conversion functions and deprecate dateOf and unixTimestampOf. User-defined types supported. CREATE INDEX now supports indexing collection columns, including indexing the keys of map collections through the keys() function Indexes on collections may be queried using the new CONTAINS and CONTAINS KEY operators Tuple types were added to hold fixed-length sets of typed positional fields. DROP INDEX now supports optionally specifying a keyspace. SELECT statements now support selecting multiple rows in a single partition using an IN clause on combinations of clustering columns. IF NOT EXISTS and IF EXISTS syntax is now supported by CREATE USER and DROP USER statements, respectively. A new uuid() method has been added. Support for DELETE …​ IF EXISTS syntax. It is now possible to group clustering columns in a relation, see WHERE clauses. Added support for static columns . CREATE INDEX now allows specifying options when creating CUSTOM indexes. Millisecond precision formats have been added to the timestamp parser. NaN and Infinity has been added as valid float constants. They are now reserved keywords. In the unlikely case you we using them as a column identifier (or keyspace/table one), you will now need to double quote them. SELECT statement now allows listing the partition keys (using the DISTINCT modifier). See CASSANDRA-4536. The syntax c IN ? is now supported in WHERE clauses. In that case, the value expected for the bind variable will be a list of whatever type c is. It is now possible to use named bind variables (using :name instead of ?). ALTER TABLE DROP option added. SELECT statement now supports aliases in select clause. Aliases in WHERE and ORDER BY clauses are not supported. CREATE statements for KEYSPACE, TABLE and INDEX now supports an IF NOT EXISTS condition. Similarly, DROP statements support a IF EXISTS condition. INSERT statements optionally supports a IF NOT EXISTS condition and UPDATE supports IF conditions. SELECT, UPDATE, and DELETE statements now allow empty IN relations (see CASSANDRA-5626. Updated the syntax for custom secondary indexes . Non-equal condition on the partition key are now never supported, even for ordering partitioner as this was not correct (the order was not the one of the type of the partition key). Instead, the token method should always be used for range queries on the partition key (see WHERE clauses ). Support for custom secondary indexes has been added. Type validation for the constants has been fixed. For instance, the implementation used to allow '2' as a valid value for an int column (interpreting it has the equivalent of 2), or 42 as a valid blob value (in which case 42 was interpreted as an hexadecimal representation of the blob). This is no longer the case, type validation of constants is now more strict. See the data types section for details on which constant is allowed for which type. The type validation fixed of the previous point has lead to the introduction of blobs constants to allow the input of blobs. Do note that while the input of blobs as strings constant is still supported by this version (to allow smoother transition to blob constant), it is now deprecated and will be removed by a future version. If you were using strings as blobs, you should thus update your client code ASAP to switch blob constants. A number of functions to convert native types to blobs have also been introduced. Furthermore the token function is now also allowed in select clauses. See the section on functions for details. Date strings (and timestamps) are no longer accepted as valid timeuuid values. Doing so was a bug in the sense that date string are not valid timeuuid, and it was thus resulting in confusing behaviors. However, the following new methods have been added to help working with timeuuid: now, minTimeuuid, maxTimeuuid , dateOf and unixTimestampOf. Float constants now support the exponent notation. In other words, 4.2E10 is now a valid floating point value. Versioning of the CQL language adheres to the Semantic Versioning guidelines. Versions take the form X.Y.Z where X, Y, and Z are integer values representing major, minor, and patch level respectively. There is no correlation between Cassandra release versions and the CQL language version. version description Major The major version must be bumped when backward incompatible changes are introduced. This should rarely occur. Minor Minor version increments occur when new, but backward compatible, functionality is introduced. Patch The patch version is incremented when bugs are fixed.","title":"Changes","component":"Cassandra","version":"4.1","name":"changes","url":"/Cassandra/4.1/cassandra/cql/changes.html","titles":[{"text":"3.4.6","id":"3-4-6"},{"text":"3.4.5","id":"3-4-5"},{"text":"3.4.4","id":"3-4-4"},{"text":"3.4.3","id":"3-4-3"},{"text":"3.4.2","id":"3-4-2"},{"text":"3.4.1","id":"3-4-1"},{"text":"3.4.0","id":"3-4-0"},{"text":"3.3.1","id":"3-3-1"},{"text":"3.3.0","id":"3-3-0"},{"text":"3.2.0","id":"3-2-0"},{"text":"3.1.7","id":"3-1-7"},{"text":"3.1.6","id":"3-1-6"},{"text":"3.1.5","id":"3-1-5"},{"text":"3.1.4","id":"3-1-4"},{"text":"3.1.3","id":"3-1-3"},{"text":"3.1.2","id":"3-1-2"},{"text":"3.1.1","id":"3-1-1"},{"text":"3.1.0","id":"3-1-0"},{"text":"3.0.5","id":"3-0-5"},{"text":"3.0.4","id":"3-0-4"},{"text":"3.0.3","id":"3-0-3"},{"text":"3.0.2","id":"3-0-2"},{"text":"3.0.1","id":"3-0-1"},{"text":"Versioning","id":"versioning"}]},"/Cassandra/4.1/cassandra/cql/cql_singlefile.html":{"text":"\\{toc:maxLevel=3} This document describes the Cassandra Query Language (CQL) version 3. CQL v3 is not backward compatible with CQL v2 and differs from it in numerous ways. Note that this document describes the last version of the languages. However, the changes section provides the diff between the different versions of CQL v3. CQL v3 offers a model very close to SQL in the sense that data is put in tables containing rows of columns. For that reason, when used in this document, these terms (tables, rows and columns) have the same definition than they have in SQL. But please note that as such, they do not refer to the concept of rows and columns found in the internal implementation of Cassandra and in the thrift and CQL v2 API. To aid in specifying the CQL syntax, we will use the following conventions in this document: Language rules will be given in a BNF -like notation: bc(syntax). ::= TERMINAL Nonterminal symbols will have . As additional shortcut notations to BNF, we’ll use traditional regular expression’s symbols (?, + and *) to signify that a given symbol is optional and/or can be repeated. We’ll also allow parentheses to group symbols and the [] notation to represent any one of . The grammar is provided for documentation purposes and leave some minor details out. For instance, the last column definition in a CREATE TABLE statement is optional but supported if present even though the provided grammar in this document suggest it is not supported. Sample code will be provided in a code block: bc(sample). SELECT sample_usage FROM cql; References to keywords or pieces of CQL code in running text will be shown in a fixed-width font. The CQL language uses identifiers (or names) to identify tables, columns and other objects. An identifier is a token matching the regular expression [a-zA-Z0-9_]*. A number of such identifiers, like SELECT or WITH, are keywords. They have a fixed meaning for the language and most are reserved. The list of those keywords can be found in Appendix A. Identifiers and (unquoted) keywords are case insensitive. Thus SELECT is the same than select or sElEcT, and myId is the same than myid or MYID for instance. A convention often used (in particular by the samples of this documentation) is to use upper case for keywords and lower case for other identifiers. There is a second kind of identifiers called quoted identifiers defined by enclosing an arbitrary sequence of characters in double-quotes(\"). Quoted identifiers are never keywords. Thus \"select\" is not a reserved keyword and can be used to refer to a column, while select would raise a parse error. Also, contrarily to unquoted identifiers and keywords, quoted identifiers are case sensitive (\"My Quoted Id\" is different from \"my quoted id\"). A fully lowercase quoted identifier that matches [a-zA-Z0-9_]* is equivalent to the unquoted identifier obtained by removing the double-quote (so \"myid\" is equivalent to myid and to myId but different from \"myId\"). Inside a quoted identifier, the double-quote character can be repeated to escape it, so \"foo \"\" bar\" is a valid identifier. Warning: quoted identifiers allows to declare columns with arbitrary names, and those can sometime clash with specific names used by the server. For instance, when using conditional update, the server will respond with a result-set containing a special result named \"[applied]\". If you’ve declared a column with such a name, this could potentially confuse some tools and should be avoided. In general, unquoted identifiers should be preferred but if you use quoted identifiers, it is strongly advised to avoid any name enclosed by squared brackets (like \"[applied]\") and any name that looks like a function call (like \"f(x)\"). CQL defines the following kind of constants: strings, integers, floats, booleans, uuids and blobs: A string constant is an arbitrary sequence of characters characters enclosed by single-quote('). One can include a single-quote in a string by repeating it, e.g. 'It''s raining today'. Those are not to be confused with quoted identifiers that use double-quotes. An integer constant is defined by '-'?[0-9]+. A float constant is defined by '-'?[0-9]+('.'[0-9]*)?([eE][+-]?[0-9+])?. On top of that, NaN and Infinity are also float constants. A boolean constant is either true or false up to case-insensitivity (i.e. True is a valid boolean constant). A UUID constant is defined by hex{8}-hex{4}-hex{4}-hex{4}-hex{12} where hex is an hexadecimal character, e.g. [0-9a-fA-F] and {4} is the number of such characters. A blob constant is an hexadecimal number defined by 0[xX](hex)+ where hex is an hexadecimal character, e.g. [0-9a-fA-F]. For how these constants are typed, see the data types section. A comment in CQL is a line beginning by either double dashes (--) or double slash (//). Multi-line comments are also supported through enclosure within / and / (but nesting is not supported). bc(sample). — This is a comment /* This is a multi-line comment */ CQL consists of statements. As in SQL, these statements can be divided in 3 categories: Data definition statements, that allow to set and change the way data is stored. Data manipulation statements, that allow to change data Queries, to look up data All statements end with a semicolon (;) but that semicolon can be omitted when dealing with a single statement. The supported statements are described in the following sections. When describing the grammar of said statements, we will reuse the non-terminal symbols defined below: bc(syntax).. ::= any quoted or unquoted identifier, excluding reserved keywords ::= ( `.')? ::= a string constant ::= an integer constant ::= a float constant ::= | ::= a uuid constant ::= a boolean constant ::= a blob constant ::= | | | | ::= ?' | `:' ::= | | | `(' ( (,' )*)? `)' ::= | | ::= `\\{' ( `:' ( `,' `:' )* )? `}' ::= `\\{' ( ( `,' )* )? `}' ::= `[' ( ( `,' )* )? `]' ::= ::= (AND )* ::= =' ( | | ) p. Please note that not every possible productions of the grammar above will be valid in practice. Most notably, ` and nested are currently not allowed inside . A can be either anonymous (a question mark (?)) or named (an identifier preceded by :). Both declare a bind variables for prepared statements. The only difference between an anymous and a named variable is that a named one will be easier to refer to (how exactly depends on the client driver used). The production is use by statement that create and alter keyspaces and tables. Each is either a simple one, in which case it just has a value, or a map one, in which case it’s value is a map grouping sub-options. The following will refer to one or the other as the kind (simple or map) of the property. A will be used to identify a table. This is an identifier representing the table name that can be preceded by a keyspace name. The keyspace name, if provided, allow to identify a table in another keyspace than the currently active one (the currently active keyspace is set through the USE statement). For supported , see the section on functions. Strings can be either enclosed with single quotes or two dollar characters. The second syntax has been introduced to allow strings that contain single quotes. Typical candidates for such strings are source code fragments for user-defined functions. Sample: bc(sample).. `some string value' double-dollar string can contain single ’ quotes p. CQL supports prepared statements. Prepared statement is an optimization that allows to parse a query only once but execute it multiple times with different concrete values. In a statement, each time a column value is expected (in the data manipulation and query statements), a (see above) can be used instead. A statement with bind variables must then be prepared. Once it has been prepared, it can executed by providing concrete values for the bind variables. The exact procedure to prepare a statement and execute a prepared statement depends on the CQL driver used and is beyond the scope of this document. In addition to providing column values, bind markers may be used to provide values for LIMIT, TIMESTAMP, and TTL clauses. If anonymous bind markers are used, the names for the query parameters will be [limit], [timestamp], and [ttl], respectively. Syntax: bc(syntax).. ::= CREATE KEYSPACE (IF NOT EXISTS)? WITH p. Sample: bc(sample).. CREATE KEYSPACE Excelsior WITH replication = \\{’class’: `SimpleStrategy', `replication_factor' : 3}; CREATE KEYSPACE Excalibur WITH replication = \\{’class’: NetworkTopologyStrategy', `DC1' : 1, `DC2' : 3} AND durable_writes = false; p. The `CREATE KEYSPACE statement creates a new top-level keyspace. A keyspace is a namespace that defines a replication strategy and some options for a set of tables. Valid keyspaces names are identifiers composed exclusively of alphanumerical characters and whose length is lesser or equal to 32. Note that as identifiers, keyspace names are case insensitive: use a quoted identifier for case sensitive keyspace names. The supported for CREATE KEYSPACE are: name kind mandatory default description replication map yes The replication strategy and options to use for the keyspace. durable_writes simple no true Whether to use the commit log for updates on this keyspace (disable this option at your own risk!). The replication is mandatory. It must at least contains the 'class' sub-option which defines the replication strategy class to use. The rest of the sub-options depends on that replication strategy class. By default, Cassandra support the following 'class': 'SimpleStrategy': A simple strategy that defines a simple replication factor for the whole cluster. The only sub-options supported is 'replication_factor' to define that replication factor and is mandatory. 'NetworkTopologyStrategy': A replication strategy that allows to set the replication factor independently for each data-center. The rest of the sub-options are key-value pairs where each time the key is the name of a datacenter and the value the replication factor for that data-center. Attempting to create an already existing keyspace will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the keyspace already exists. Syntax: bc(syntax). ::= USE Sample: bc(sample). USE myApp; The USE statement takes an existing keyspace name as argument and set it as the per-connection current working keyspace. All subsequent keyspace-specific actions will be performed in the context of the selected keyspace, unless otherwise specified, until another USE statement is issued or the connection terminates. Syntax: bc(syntax).. ::= ALTER KEYSPACE (IF EXISTS)? WITH p. Sample: bc(sample).. ALTER KEYSPACE Excelsior WITH replication = \\{’class’: `SimpleStrategy', `replication_factor' : 4}; The ALTER KEYSPACE statement alters the properties of an existing keyspace. The supported are the same as for the CREATE KEYSPACE statement. Syntax: bc(syntax). ::= DROP KEYSPACE ( IF EXISTS )? Sample: bc(sample). DROP KEYSPACE myApp; A DROP KEYSPACE statement results in the immediate, irreversible removal of an existing keyspace, including all column families in it, and all data contained in those column families. If the keyspace does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax).. ::= CREATE ( TABLE | COLUMNFAMILY ) ( IF NOT EXISTS )? `(' ( `,' )* `)' ( WITH ( AND )* )? ::= ( STATIC )? ( PRIMARY KEY )? | PRIMARY KEY `(' ( `,' )* `)' ::= | (' (,' )* `)' ::= | COMPACT STORAGE | CLUSTERING ORDER p. Sample: bc(sample).. CREATE TABLE monkeySpecies ( species text PRIMARY KEY, common_name text, population varint, average_size int ) WITH comment=`Important biological records'; CREATE TABLE timeline ( userid uuid, posted_month int, posted_time uuid, body text, posted_by text, PRIMARY KEY (userid, posted_month, posted_time) ) WITH compaction = \\{ class' : `LeveledCompactionStrategy' }; p. The `CREATE TABLE statement creates a new table. Each such table is a set of rows (usually representing related entities) for which it defines a number of properties. A table is defined by a name, it defines the columns composing rows of the table and have a number of options. Note that the CREATE COLUMNFAMILY syntax is supported as an alias for CREATE TABLE (for historical reasons). Attempting to create an already existing table will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the table already exists. Valid table names are the same as valid keyspace names (up to 32 characters long alphanumerical identifiers). If the table name is provided alone, the table is created within the current keyspace (see USE), but if it is prefixed by an existing keyspace name (see grammar), it is created in the specified keyspace (but does not change the current keyspace). A CREATE TABLE statement defines the columns that rows of the table can have. A column is defined by its name (an identifier) and its type (see the data types section for more details on allowed types and their properties). Within a table, a row is uniquely identified by its PRIMARY KEY (or more simply the key), and hence all table definitions must define a PRIMARY KEY (and only one). A PRIMARY KEY is composed of one or more of the columns defined in the table. If the PRIMARY KEY is only one column, this can be specified directly after the column definition. Otherwise, it must be specified by following PRIMARY KEY by the comma-separated list of column names composing the key within parenthesis. Note that: bc(sample). CREATE TABLE t ( k int PRIMARY KEY, other text ) is equivalent to bc(sample). CREATE TABLE t ( k int, other text, PRIMARY KEY (k) ) In CQL, the order in which columns are defined for the PRIMARY KEY matters. The first column of the key is called the partition key. It has the property that all the rows sharing the same partition key (even across table in fact) are stored on the same physical node. Also, insertion/update/deletion on rows sharing the same partition key for a given table are performed atomically and in isolation. Note that it is possible to have a composite partition key, i.e. a partition key formed of multiple columns, using an extra set of parentheses to define which columns forms the partition key. The remaining columns of the PRIMARY KEY definition, if any, are called __clustering columns. On a given physical node, rows for a given partition key are stored in the order induced by the clustering columns, making the retrieval of rows in that clustering order particularly efficient (see SELECT). Some columns can be declared as STATIC in a table definition. A column that is static will be ``shared'' by all the rows belonging to the same partition (having the same partition key). For instance, in: bc(sample). CREATE TABLE test ( pk int, t int, v text, s text static, PRIMARY KEY (pk, t) ); INSERT INTO test(pk, t, v, s) VALUES (0, 0, `val0', `static0'); INSERT INTO test(pk, t, v, s) VALUES (0, 1, `val1', `static1'); SELECT * FROM test WHERE pk=0 AND t=0; the last query will return 'static1' as value for s, since s is static and thus the 2nd insertion modified this `shared'' value. Note however that static columns are only static within a given partition, and if in the example above both rows where from different partitions (i.e. if they had different value for `pk), then the 2nd insertion would not have modified the value of s for the first row. A few restrictions applies to when static columns are allowed: tables with the COMPACT STORAGE option (see below) cannot have them a table without clustering columns cannot have static columns (in a table without clustering columns, every partition has only one row, and so every column is inherently static). only non PRIMARY KEY columns can be static The CREATE TABLE statement supports a number of options that controls the configuration of a new table. These options can be specified after the WITH keyword. The first of these option is COMPACT STORAGE. This option is mainly targeted towards backward compatibility for definitions created before CQL3 (see www.datastax.com/dev/blog/thrift-to-cql3 for more details). The option also provides a slightly more compact layout of data on disk but at the price of diminished flexibility and extensibility for the table. Most notably, COMPACT STORAGE tables cannot have collections nor static columns and a COMPACT STORAGE table with at least one clustering column supports exactly one (as in not 0 nor more than 1) column not part of the PRIMARY KEY definition (which imply in particular that you cannot add nor remove columns after creation). For those reasons, COMPACT STORAGE is not recommended outside of the backward compatibility reason evoked above. Another option is CLUSTERING ORDER. It allows to define the ordering of rows on disk. It takes the list of the clustering column names with, for each of them, the on-disk order (Ascending or descending). Note that this option affects what ORDER BY are allowed during SELECT. Table creation supports the following other : option kind default description comment simple none A free-form, human-readable comment. gc_grace_seconds simple 864000 Time to wait before garbage collecting tombstones (deletion markers). bloom_filter_fp_chance simple 0.00075 The target probability of false positive of the sstable bloom filters. Said bloom filters will be sized to provide the provided probability (thus lowering this value impact the size of bloom filters in-memory and on-disk) default_time_to_live simple 0 The default expiration time (``TTL'') in seconds for a table. compaction map see below Compaction options, see below. compression map see below Compression options, see below. caching map see below Caching options, see below. The compaction property must at least define the 'class' sub-option, that defines the compaction strategy class to use. The default supported class are 'SizeTieredCompactionStrategy', 'LeveledCompactionStrategy', 'DateTieredCompactionStrategy' and 'TimeWindowCompactionStrategy'. Custom strategy can be provided by specifying the full class name as a string constant. The rest of the sub-options depends on the chosen class. The sub-options supported by the default classes are: option supported compaction strategy default description enabled all true A boolean denoting whether compaction should be enabled or not. tombstone_threshold all 0.2 A ratio such that if a sstable has more than this ratio of gcable tombstones over all contained columns, the sstable will be compacted (with no other sstables) for the purpose of purging those tombstones. tombstone_compaction_interval all 1 day The minimum time to wait after an sstable creation time before considering it for tombstone compaction'', where tombstone compaction'' is the compaction triggered if the sstable has more gcable tombstones than tombstone_threshold. unchecked_tombstone_compaction all false Setting this to true enables more aggressive tombstone compactions - single sstable tombstone compactions will run without checking how likely it is that they will be successful. min_sstable_size SizeTieredCompactionStrategy 50MB The size tiered strategy groups SSTables to compact in buckets. A bucket groups SSTables that differs from less than 50% in size. However, for small sizes, this would result in a bucketing that is too fine grained. min_sstable_size defines a size threshold (in bytes) below which all SSTables belong to one unique bucket min_threshold SizeTieredCompactionStrategy 4 Minimum number of SSTables needed to start a minor compaction. max_threshold SizeTieredCompactionStrategy 32 Maximum number of SSTables processed by one minor compaction. bucket_low SizeTieredCompactionStrategy 0.5 Size tiered consider sstables to be within the same bucket if their size is within [average_size * bucket_low, average_size * bucket_high ] (i.e the default groups sstable whose sizes diverges by at most 50%) bucket_high SizeTieredCompactionStrategy 1.5 Size tiered consider sstables to be within the same bucket if their size is within [average_size * bucket_low, average_size * bucket_high ] (i.e the default groups sstable whose sizes diverges by at most 50%). sstable_size_in_mb LeveledCompactionStrategy 5MB The target size (in MB) for sstables in the leveled strategy. Note that while sstable sizes should stay less or equal to sstable_size_in_mb, it is possible to exceptionally have a larger sstable as during compaction, data for a given partition key are never split into 2 sstables timestamp_resolution DateTieredCompactionStrategy MICROSECONDS The timestamp resolution used when inserting data, could be MILLISECONDS, MICROSECONDS etc (should be understandable by Java TimeUnit) - don’t change this unless you do mutations with USING TIMESTAMP (or equivalent directly in the client) base_time_seconds DateTieredCompactionStrategy 60 The base size of the time windows. max_sstable_age_days DateTieredCompactionStrategy 365 SSTables only containing data that is older than this will never be compacted. timestamp_resolution TimeWindowCompactionStrategy MICROSECONDS The timestamp resolution used when inserting data, could be MILLISECONDS, MICROSECONDS etc (should be understandable by Java TimeUnit) - don’t change this unless you do mutations with USING TIMESTAMP (or equivalent directly in the client) compaction_window_unit TimeWindowCompactionStrategy DAYS The Java TimeUnit used for the window size, set in conjunction with compaction_window_size. Must be one of DAYS, HOURS, MINUTES compaction_window_size TimeWindowCompactionStrategy 1 The number of compaction_window_unit units that make up a time window. unsafe_aggressive_sstable_expiration TimeWindowCompactionStrategy false Expired sstables will be dropped without checking its data is shadowing other sstables. This is a potentially risky option that can lead to data loss or deleted data re-appearing, going beyond what unchecked_tombstone_compaction does for single sstable compaction. Due to the risk the jvm must also be started with -Dcassandra.unsafe_aggressive_sstable_expiration=true. For the compression property, the following sub-options are available: option default description class LZ4Compressor The compression algorithm to use. Default compressor are: LZ4Compressor, SnappyCompressor and DeflateCompressor. Use 'enabled' : false to disable compression. Custom compressor can be provided by specifying the full class name as a string constant. enabled true By default compression is enabled. To disable it, set enabled to false chunk_length_in_kb 64KB On disk SSTables are compressed by block (to allow random reads). This defines the size (in KB) of said block. Bigger values may improve the compression rate, but increases the minimum size of data to be read from disk for a read crc_check_chance 1.0 When compression is enabled, each compressed block includes a checksum of that block for the purpose of detecting disk bitrot and avoiding the propagation of corruption to other replica. This option defines the probability with which those checksums are checked during read. By default they are always checked. Set to 0 to disable checksum checking and to 0.5 for instance to check them every other read For the caching property, the following sub-options are available: option default description keys ALL Whether to cache keys (`key cache'') for this table. Valid values are: `ALL and NONE. rows_per_partition NONE The amount of rows to cache per partition (`row cache''). If an integer `n is specified, the first n queried rows of a partition will be cached. Other possible options are ALL, to cache all rows of a queried partition, or NONE to disable row caching. When inserting / updating a given row, not all columns needs to be defined (except for those part of the key), and missing columns occupy no space on disk. Furthermore, adding new columns (see ALTER TABLE) is a constant time operation. There is thus no need to try to anticipate future usage (or to cry when you haven’t) when creating a table. Syntax: bc(syntax).. ::= ALTER (TABLE | COLUMNFAMILY) (IF EXISTS)? ::= ADD (IF NOT EXISTS)? | ADD (IF NOT EXISTS)? ( ( , )* ) | DROP (IF EXISTS)? | DROP (IF EXISTS)? ( ( , )* ) | RENAME (IF EXISTS)? TO (AND TO)* | WITH ( AND )* p. Sample: bc(sample).. ALTER TABLE addamsFamily ALTER TABLE addamsFamily ADD gravesite varchar; ALTER TABLE addamsFamily WITH comment = A most excellent and useful column family'; p. The `ALTER statement is used to manipulate table definitions. It allows for adding new columns, dropping existing ones, or updating the table options. As with table creation, ALTER COLUMNFAMILY is allowed as an alias for ALTER TABLE. If the table does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. The is the table name optionally preceded by the keyspace name. The defines the alteration to perform: ADD: Adds a new column to the table. The for the new column must not conflict with an existing column. Moreover, columns cannot be added to tables defined with the COMPACT STORAGE option. If the new column already exists, the statement will return an error, unless IF NOT EXISTS is used in which case the operation is a no-op. DROP: Removes a column from the table. Dropped columns will immediately become unavailable in the queries and will not be included in compacted sstables in the future. If a column is readded, queries won’t return values written before the column was last dropped. It is assumed that timestamps represent actual time, so if this is not your case, you should NOT read previously dropped columns. Columns can’t be dropped from tables defined with the COMPACT STORAGE option. If the dropped column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. RENAME a primary key column of a table. Non primary key columns cannot be renamed. Furthermore, renaming a column to another name which already exists isn’t allowed. It’s important to keep in mind that renamed columns shouldn’t have dependent seconday indexes. If the renamed column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. WITH: Allows to update the options of the table. The supported (and syntax) are the same as for the CREATE TABLE statement except that COMPACT STORAGE is not supported. Note that setting any compaction sub-options has the effect of erasing all previous compaction options, so you need to re-specify all the sub-options if you want to keep them. The same note applies to the set of compression sub-options. CQL data types may be converted only as the following table. Data type may be altered to: Data type timestamp bigint ascii, bigint, boolean, date, decimal, double, float, inet, int, smallint, text, time, timestamp, timeuuid, tinyint, uuid, varchar, varint blob int date ascii, varchar text bigint time bigint timestamp timeuuid uuid ascii, text varchar bigint, int, timestamp varint Clustering columns have stricter requirements, only the below conversions are allowed. Data type may be altered to: Data type ascii, text, varchar blob ascii, varchar text ascii, text varchar Syntax: bc(syntax). ::= DROP TABLE ( IF EXISTS )? Sample: bc(sample). DROP TABLE worldSeriesAttendees; The DROP TABLE statement results in the immediate, irreversible removal of a table, including all data contained in it. As for table creation, DROP COLUMNFAMILY is allowed as an alias for DROP TABLE. If the table does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax). ::= TRUNCATE ( TABLE | COLUMNFAMILY )? Sample: bc(sample). TRUNCATE superImportantData; The TRUNCATE statement permanently removes all data from a table. Syntax: bc(syntax).. ::= CREATE ( CUSTOM )? INDEX ( IF NOT EXISTS )? ( )? ON `(' `)' ( USING ( WITH OPTIONS = )? )? ::= | keys( ) p. Sample: bc(sample). CREATE INDEX userIndex ON NerdMovies (user); CREATE INDEX ON Mutants (abilityId); CREATE INDEX ON users (keys(favs)); CREATE CUSTOM INDEX ON users (email) USING `path.to.the.IndexClass'; CREATE CUSTOM INDEX ON users (email) USING `path.to.the.IndexClass' WITH OPTIONS = \\{’storage’: `/mnt/ssd/indexes/'}; The CREATE INDEX statement is used to create a new (automatic) secondary index for a given (existing) column in a given table. A name for the index itself can be specified before the ON keyword, if desired. If data already exists for the column, it will be indexed asynchronously. After the index is created, new data for the column is indexed automatically at insertion time. Attempting to create an already existing index will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the index already exists. When creating an index on a map column, you may index either the keys or the values. If the column identifier is placed within the keys() function, the index will be on the map keys, allowing you to use CONTAINS KEY in WHERE clauses. Otherwise, the index will be on the map values. Syntax: bc(syntax). ::= DROP INDEX ( IF EXISTS )? ( `.' )? Sample: bc(sample).. DROP INDEX userIndex; DROP INDEX userkeyspace.address_index; p. The DROP INDEX statement is used to drop an existing secondary index. The argument of the statement is the index name, which may optionally specify the keyspace of the index. If the index does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax).. ::= CREATE MATERIALIZED VIEW ( IF NOT EXISTS )? AS SELECT ( `(' ( `,' ) * `)' | `' ) FROM ( WHERE )? PRIMARY KEY `(' ( `,' ) `)' ( WITH ( AND )* )? p. Sample: bc(sample).. CREATE MATERIALIZED VIEW monkeySpecies_by_population AS SELECT * FROM monkeySpecies WHERE population IS NOT NULL AND species IS NOT NULL PRIMARY KEY (population, species) WITH comment=Allow query by population instead of species'; p. The `CREATE MATERIALIZED VIEW statement creates a new materialized view. Each such view is a set of rows which corresponds to rows which are present in the underlying, or base, table specified in the SELECT statement. A materialized view cannot be directly updated, but updates to the base table will cause corresponding updates in the view. Attempting to create an already existing materialized view will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the materialized view already exists. The is similar to the where clause of a SELECT statement, with a few differences. First, the where clause must contain an expression that disallows NULL values in columns in the view’s primary key. If no other restriction is desired, this can be accomplished with an IS NOT NULL expression. Second, only columns which are in the base table’s primary key may be restricted with expressions other than IS NOT NULL. (Note that this second restriction may be lifted in the future.) Syntax: bc(syntax). ::= ALTER MATERIALIZED VIEW WITH ( AND )* The ALTER MATERIALIZED VIEW statement allows options to be update; these options are the same as CREATE TABLE’s options. Syntax: bc(syntax). ::= DROP MATERIALIZED VIEW ( IF EXISTS )? Sample: bc(sample). DROP MATERIALIZED VIEW monkeySpecies_by_population; The DROP MATERIALIZED VIEW statement is used to drop an existing materialized view. If the materialized view does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax).. ::= CREATE TYPE ( IF NOT EXISTS )? `(' ( `,' )* `)' ::= ( `.' )? ::= Sample: bc(sample).. CREATE TYPE address ( street_name text, street_number int, city text, state text, zip int ) CREATE TYPE work_and_home_addresses ( home_address address, work_address address ) p. The CREATE TYPE statement creates a new user-defined type. Each type is a set of named, typed fields. Field types may be any valid type, including collections and other existing user-defined types. Attempting to create an already existing type will result in an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the type already exists. Valid type names are identifiers. The names of existing CQL types and reserved type names may not be used. If the type name is provided alone, the type is created with the current keyspace (see USE). If it is prefixed by an existing keyspace name, the type is created within the specified keyspace instead of the current keyspace. Syntax: bc(syntax).. ::= ALTER TYPE (IF EXISTS)? ::= ADD (IF NOT EXISTS)? | RENAME (IF EXISTS)? TO ( AND TO )* p. Sample: bc(sample).. ALTER TYPE address ADD country text ALTER TYPE address RENAME zip TO zipcode AND street_name TO street p. The ALTER TYPE statement is used to manipulate type definitions. It allows for adding new fields, renaming existing fields, or changing the type of existing fields. If the type does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax).. ::= DROP TYPE ( IF EXISTS )? p. The DROP TYPE statement results in the immediate, irreversible removal of a type. Attempting to drop a type that is still in use by another type or a table will result in an error. If the type does not exist, an error will be returned unless IF EXISTS is used, in which case the operation is a no-op. Syntax: bc(syntax).. ::= CREATE TRIGGER ( IF NOT EXISTS )? ( )? ON USING Sample: bc(sample). CREATE TRIGGER myTrigger ON myTable USING `org.apache.cassandra.triggers.InvertedIndex'; The actual logic that makes up the trigger can be written in any Java (JVM) language and exists outside the database. You place the trigger code in a lib/triggers subdirectory of the Cassandra installation directory, it loads during cluster startup, and exists on every node that participates in a cluster. The trigger defined on a table fires before a requested DML statement occurs, which ensures the atomicity of the transaction. Syntax: bc(syntax).. ::= DROP TRIGGER ( IF EXISTS )? ( )? ON p. Sample: bc(sample). DROP TRIGGER myTrigger ON myTable; DROP TRIGGER statement removes the registration of a trigger created using CREATE TRIGGER. Syntax: bc(syntax).. ::= CREATE ( OR REPLACE )? FUNCTION ( IF NOT EXISTS )? ( `.' )? `(' ( `,' )* `)' ( CALLED | RETURNS NULL ) ON NULL INPUT RETURNS LANGUAGE AS Sample: bc(sample). CREATE OR REPLACE FUNCTION somefunction ( somearg int, anotherarg text, complexarg frozen, listarg list ) RETURNS NULL ON NULL INPUT RETURNS text LANGUAGE java AS + ; CREATE FUNCTION akeyspace.fname IF NOT EXISTS ( someArg int ) CALLED ON NULL INPUT RETURNS text LANGUAGE java AS + ; CREATE FUNCTION creates or replaces a user-defined function. Signatures are used to distinguish individual functions. The signature consists of: The fully qualified function name - i.e keyspace plus function-name The concatenated list of all argument types Note that keyspace names, function names and argument types are subject to the default naming conventions and case-sensitivity rules. CREATE FUNCTION with the optional OR REPLACE keywords either creates a function or replaces an existing one with the same signature. A CREATE FUNCTION without OR REPLACE fails if a function with the same signature already exists. Behavior on invocation with null values must be defined for each function. There are two options: RETURNS NULL ON NULL INPUT declares that the function will always return null if any of the input arguments is null. CALLED ON NULL INPUT declares that the function will always be executed. If the optional IF NOT EXISTS keywords are used, the function will only be created if another function with the same signature does not exist. OR REPLACE and IF NOT EXIST cannot be used together. Functions belong to a keyspace. If no keyspace is specified in , the current keyspace is used (i.e. the keyspace specified using the USE statement). It is not possible to create a user-defined function in one of the system keyspaces. See the section on user-defined functions for more information. Syntax: bc(syntax).. ::= DROP FUNCTION ( IF EXISTS )? ( `.' )? ( `(' ( `,' )* `)' )? Sample: bc(sample). DROP FUNCTION myfunction; DROP FUNCTION mykeyspace.afunction; DROP FUNCTION afunction ( int ); DROP FUNCTION afunction ( text ); DROP FUNCTION statement removes a function created using CREATE FUNCTION. You must specify the argument types (signature ) of the function to drop if there are multiple functions with the same name but a different signature (overloaded functions). DROP FUNCTION with the optional IF EXISTS keywords drops a function if it exists. Syntax: bc(syntax).. ::= CREATE ( OR REPLACE )? AGGREGATE ( IF NOT EXISTS )? ( `.' )? `(' ( `,' )* `)' SFUNC STYPE ( FINALFUNC )? ( INITCOND )? p. Sample: bc(sample). CREATE AGGREGATE myaggregate ( val text ) SFUNC myaggregate_state STYPE text FINALFUNC myaggregate_final INITCOND `foo'; See the section on user-defined aggregates for a complete example. CREATE AGGREGATE creates or replaces a user-defined aggregate. CREATE AGGREGATE with the optional OR REPLACE keywords either creates an aggregate or replaces an existing one with the same signature. A CREATE AGGREGATE without OR REPLACE fails if an aggregate with the same signature already exists. CREATE AGGREGATE with the optional IF NOT EXISTS keywords either creates an aggregate if it does not already exist. OR REPLACE and IF NOT EXIST cannot be used together. Aggregates belong to a keyspace. If no keyspace is specified in , the current keyspace is used (i.e. the keyspace specified using the USE statement). It is not possible to create a user-defined aggregate in one of the system keyspaces. Signatures for user-defined aggregates follow the same rules as for user-defined functions. STYPE defines the type of the state value and must be specified. The optional INITCOND defines the initial state value for the aggregate. It defaults to null. A non-null INITCOND must be specified for state functions that are declared with RETURNS NULL ON NULL INPUT. SFUNC references an existing function to be used as the state modifying function. The type of first argument of the state function must match STYPE. The remaining argument types of the state function must match the argument types of the aggregate function. State is not updated for state functions declared with RETURNS NULL ON NULL INPUT and called with null. The optional FINALFUNC is called just before the aggregate result is returned. It must take only one argument with type STYPE. The return type of the FINALFUNC may be a different type. A final function declared with RETURNS NULL ON NULL INPUT means that the aggregate’s return value will be null, if the last state is null. If no FINALFUNC is defined, the overall return type of the aggregate function is STYPE. If a FINALFUNC is defined, it is the return type of that function. See the section on user-defined aggregates for more information. Syntax: bc(syntax).. ::= DROP AGGREGATE ( IF EXISTS )? ( `.' )? ( `(' ( `,' )* `)' )? p. Sample: bc(sample). DROP AGGREGATE myAggregate; DROP AGGREGATE myKeyspace.anAggregate; DROP AGGREGATE someAggregate ( int ); DROP AGGREGATE someAggregate ( text ); The DROP AGGREGATE statement removes an aggregate created using CREATE AGGREGATE. You must specify the argument types of the aggregate to drop if there are multiple aggregates with the same name but a different signature (overloaded aggregates). DROP AGGREGATE with the optional IF EXISTS keywords drops an aggregate if it exists, and does nothing if a function with the signature does not exist. Signatures for user-defined aggregates follow the same rules as for user-defined functions. Syntax: bc(syntax).. ::= INSERT INTO ( ( VALUES ) | ( JSON )) ( IF NOT EXISTS )? ( USING ( AND )* )? ::= `(' ( `,' )* `)' ::= `(' ( `,' )* `)' ::= TIMESTAMP | TTL p. Sample: bc(sample).. INSERT INTO NerdMovies (movie, director, main_actor, year) VALUES (`Serenity', `Joss Whedon', `Nathan Fillion', 2005) USING TTL 86400; INSERT INTO NerdMovies JSON \\{movie'': Serenity'', director'': Joss Whedon'', ``year'': 2005}' p. The `INSERT statement writes one or more columns for a given row in a table. Note that since a row is identified by its PRIMARY KEY, at least the columns composing it must be specified. The list of columns to insert to must be supplied when using the VALUES syntax. When using the JSON syntax, they are optional. See the section on INSERT JSON for more details. Note that unlike in SQL, INSERT does not check the prior existence of the row by default: the row is created if none existed before, and updated otherwise. Furthermore, there is no mean to know which of creation or update happened. It is however possible to use the IF NOT EXISTS condition to only insert if the row does not exist prior to the insertion. But please note that using IF NOT EXISTS will incur a non negligible performance cost (internally, Paxos will be used) so this should be used sparingly. All updates for an INSERT are applied atomically and in isolation. Please refer to the UPDATE section for information on the available and to the collections section for use of . Also note that INSERT does not support counters, while UPDATE does. Syntax: bc(syntax).. ::= UPDATE ( USING ( AND )* )? SET ( `,' )* WHERE ( IF ( AND condition )* )? ::= =' | `=' (+' | `-') ( | | ) | `=' `+' | `[' `]' `=' | `.' `=' ::= | CONTAINS (KEY)? | IN | `[' `]' | `[' `]' IN | `.' | `.' IN ::= `=' | `>' ::= ( | `(' ( ( `,' )* )? `)') ::= ( AND )* ::= =' | `(' (,' )* )' `=' | IN `(' ( ( `,' )* )? `)' | IN | `(' (,' )* )' IN `(' ( ( `,' )* )? `)' | `(' (,' )* `)' IN ::= TIMESTAMP | TTL p. Sample: bc(sample).. UPDATE NerdMovies USING TTL 400 SET director = `Joss Whedon', main_actor = `Nathan Fillion', year = 2005 WHERE movie = `Serenity'; UPDATE UserActions SET total = total + 2 WHERE user = B70DE1D0-9908-4AE3-BE34-5573E5B09F14 AND action = click'; p. The `UPDATE statement writes one or more columns for a given row in a table. The is used to select the row to update and must include all columns composing the PRIMARY KEY. Other columns values are specified through after the SET keyword. Note that unlike in SQL, UPDATE does not check the prior existence of the row by default (except through the use of , see below): the row is created if none existed before, and updated otherwise. Furthermore, there are no means to know whether a creation or update occurred. It is however possible to use the conditions on some columns through IF, in which case the row will not be updated unless the conditions are met. But, please note that using IF conditions will incur a non-negligible performance cost (internally, Paxos will be used) so this should be used sparingly. In an UPDATE statement, all updates within the same partition key are applied atomically and in isolation. The c = c + 3 form of is used to increment/decrement counters. The identifier after the `=' sign must be the same than the one before the `=' sign (Only increment/decrement is supported on counters, not the assignment of a specific value). The id = id + and id[value1] = value2 forms of are for collections. Please refer to the relevant section for more details. The id.field = form of is for setting the value of a single field on a non-frozen user-defined types. The UPDATE and INSERT statements support the following options: TIMESTAMP: sets the timestamp for the operation. If not specified, the coordinator will use the current time (in microseconds) at the start of statement execution as the timestamp. This is usually a suitable default. TTL: specifies an optional Time To Live (in seconds) for the inserted values. If set, the inserted values are automatically removed from the database after the specified time. Note that the TTL concerns the inserted values, not the columns themselves. This means that any subsequent update of the column will also reset the TTL (to whatever TTL is specified in that update). By default, values never expire. A TTL of 0 is equivalent to no TTL. If the table has a default_time_to_live, a TTL of 0 will remove the TTL for the inserted or updated values. Syntax: bc(syntax).. ::= DELETE ( ( `,' )* )? FROM ( USING TIMESTAMP )? WHERE ( IF ( EXISTS | ( ( AND )*) ) )? ::= | `[' `]' | `.' ::= ( AND )* ::= | (' (,' )* )' | IN `(' ( ( `,' )* )? `)' | IN | `(' (,' )* )' IN `(' ( ( `,' )* )? `)' | `(' (,' )* `)' IN ::= `=' | `' | `⇐' | `>=' ::= ( | `(' ( ( `,' )* )? `)') ::= ( | `!=') | CONTAINS (KEY)? | IN | `[' `]' ( | `!=') | `[' `]' IN | `.' ( | `!=') | `.' IN Sample: bc(sample).. DELETE FROM NerdMovies USING TIMESTAMP 1240003134 WHERE movie = `Serenity'; DELETE phone FROM Users WHERE userid IN (C73DE1D3-AF08-40F3-B124-3FF3E5109F22, B70DE1D0-9908-4AE3-BE34-5573E5B09F14); p. The DELETE statement deletes columns and rows. If column names are provided directly after the DELETE keyword, only those columns are deleted from the row indicated by the . The id[value] syntax in is for non-frozen collections (please refer to the collection section for more details). The id.field syntax is for the deletion of non-frozen user-defined types. Otherwise, whole rows are removed. The specifies which rows are to be deleted. Multiple rows may be deleted with one statement by using an IN clause. A range of rows may be deleted using an inequality operator (such as >=). DELETE supports the TIMESTAMP option with the same semantics as the UPDATE statement. In a DELETE statement, all deletions within the same partition key are applied atomically and in isolation. A DELETE operation can be conditional through the use of an IF clause, similar to UPDATE and INSERT statements. However, as with INSERT and UPDATE statements, this will incur a non-negligible performance cost (internally, Paxos will be used) and so should be used sparingly. Syntax: bc(syntax).. ::= BEGIN ( UNLOGGED | COUNTER ) BATCH ( USING ( AND )* )? ( `;' )* APPLY BATCH ::= | | ::= TIMESTAMP p. Sample: bc(sample). BEGIN BATCH INSERT INTO users (userid, password, name) VALUES (`user2', `ch@ngem3b', `second user'); UPDATE users SET password = `ps22dhds' WHERE userid = `user3'; INSERT INTO users (userid, password) VALUES (`user4', `ch@ngem3c'); DELETE name FROM users WHERE userid = `user1'; APPLY BATCH; The BATCH statement group multiple modification statements (insertions/updates and deletions) into a single statement. It serves several purposes: It saves network round-trips between the client and the server (and sometimes between the server coordinator and the replicas) when batching multiple updates. All updates in a BATCH belonging to a given partition key are performed in isolation. By default, all operations in the batch are performed as LOGGED, to ensure all mutations eventually complete (or none will). See the notes on UNLOGGED for more details. Note that: BATCH statements may only contain UPDATE, INSERT and DELETE statements. Batches are not a full analogue for SQL transactions. If a timestamp is not specified for each operation, then all operations will be applied with the same timestamp. Due to Cassandra’s conflict resolution procedure in the case of timestamp ties, operations may be applied in an order that is different from the order they are listed in the BATCH statement. To force a particular operation ordering, you must specify per-operation timestamps. By default, Cassandra uses a batch log to ensure all operations in a batch eventually complete or none will (note however that operations are only isolated within a single partition). There is a performance penalty for batch atomicity when a batch spans multiple partitions. If you do not want to incur this penalty, you can tell Cassandra to skip the batchlog with the UNLOGGED option. If the UNLOGGED option is used, a failed batch might leave the patch only partly applied. Use the COUNTER option for batched counter updates. Unlike other updates in Cassandra, counter updates are not idempotent. BATCH supports both the TIMESTAMP option, with similar semantic to the one described in the UPDATE statement (the timestamp applies to all the statement inside the batch). However, if used, TIMESTAMP must not be used in the statements within the batch. Syntax: bc(syntax).. ::= SELECT ( JSON )? FROM ( WHERE )? ( GROUP BY )? ( ORDER BY )? ( PER PARTITION LIMIT )? ( LIMIT )? ( ALLOW FILTERING )? ::= DISTINCT? ::= (AS )? ( `,' (AS )? )* | `*' ::= | | WRITETIME (' `)' | COUNT `(' `' `)' | TTL `(' `)' | CAST `(' AS `)' | `(' ( (,' ))? `)' | `.' | `[' `]' | `[' ? .. ? `]' ::= ( AND )* ::= | (' (,' )* )' | IN `(' ( ( `,' )* )? `)' | `(' (,' )* `)' IN `(' ( ( `,' )* )? `)' | TOKEN `(' ( `,' )* `)' ::= =' | `' | `⇐' | `>=' | CONTAINS | CONTAINS KEY ::= (,' )* ::= ( ,' )* ::= ( ASC | DESC )? ::= `(' (,' )* `)' p. Sample: bc(sample).. SELECT name, occupation FROM users WHERE userid IN (199, 200, 207); SELECT JSON name, occupation FROM users WHERE userid = 199; SELECT name AS user_name, occupation AS user_occupation FROM users; SELECT time, value FROM events WHERE event_type = `myEvent' AND time > `2011-02-03' AND time ⇐ `2012-01-01' SELECT COUNT (*) FROM users; SELECT COUNT (*) AS user_count FROM users; The SELECT statements reads one or more columns for one or more rows in a table. It returns a result-set of rows, where each row contains the collection of columns corresponding to the query. If the JSON keyword is used, the results for each row will contain only a single column named `json''. See the section on `SELECT JSON for more details. The determines which columns needs to be queried and returned in the result-set. It consists of either the comma-separated list of or the wildcard character (*) to select all the columns defined for the table. Please note that for wildcard SELECT queries the order of columns returned is not specified and is not guaranteed to be stable between Cassandra versions. A is either a column name to retrieve or a of one or more `s. The function allowed are the same as for ` and are described in the function section. In addition to these generic functions, the WRITETIME (resp. TTL) function allows to select the timestamp of when the column was inserted (resp. the time to live (in seconds) for the column (or null if the column has no expiration set)) and the CAST function can be used to convert one data type to another. The WRITETIME and TTL functions can’t be used on multi-cell columns such as non-frozen collections or non-frozen user-defined types. Additionally, individual values of maps and sets can be selected using [ ]. For maps, this will return the value corresponding to the key, if such entry exists. For sets, this will return the key that is selected if it exists and is thus mainly a way to check element existence. It is also possible to select a slice of a set or map with `[ …​ `], where both bound can be omitted. Any can be aliased using AS keyword (see examples). Please note that and clause should refer to the columns by their original names and not by their aliases. The COUNT keyword can be used with parenthesis enclosing *. If so, the query will return a single result: the number of rows matching the query. Note that COUNT(1) is supported as an alias. The specifies which rows must be queried. It is composed of relations on the columns that are part of the PRIMARY KEY and/or have a secondary index defined on them. Not all relations are allowed in a query. For instance, non-equal relations (where IN is considered as an equal relation) on a partition key are not supported (but see the use of the TOKEN method below to do non-equal queries on the partition key). Moreover, for a given partition key, the clustering columns induce an ordering of rows and relations on them is restricted to the relations that allow to select a contiguous (for the ordering) set of rows. For instance, given bc(sample). CREATE TABLE posts ( userid text, blog_title text, posted_at timestamp, entry_title text, content text, category int, PRIMARY KEY (userid, blog_title, posted_at) ) The following query is allowed: bc(sample). SELECT entry_title, content FROM posts WHERE userid=`john doe' AND blog_title=`John'`s Blog' AND posted_at >= `2012-01-01' AND posted_at = `2012-01-01' AND posted_at token(0) in particular). Example: bc(sample). SELECT * FROM posts WHERE token(userid) > token(`tom') AND token(userid) (`John'`s Blog', `2012-01-01') will request all rows that sorts after the one having `John’s Blog'' as `blog_tile and 2012-01-01' for `posted_at in the clustering order. In particular, rows having a post_at ⇐ '2012-01-01' will be returned as long as their blog_title > 'John''s Blog', which wouldn’t be the case for: bc(sample). SELECT * FROM posts WHERE userid=`john doe' AND blog_title > `John'`s Blog' AND posted_at > `2012-01-01' The tuple notation may also be used for IN clauses on CLUSTERING COLUMNS: bc(sample). SELECT * FROM posts WHERE userid=`john doe' AND (blog_title, posted_at) IN `John'`s Blog', `2012-01-01), (’Extreme Chess', `2014-06-01' The CONTAINS operator may only be used on collection columns (lists, sets, and maps). In the case of maps, CONTAINS applies to the map values. The CONTAINS KEY operator may only be used on map columns and applies to the map keys. The ORDER BY option allows to select the order of the returned results. It takes as argument a list of column names along with the order for the column (ASC for ascendant and DESC for descendant, omitting the order being equivalent to ASC). Currently the possible orderings are limited (which depends on the table CLUSTERING ORDER ): if the table has been defined without any specific CLUSTERING ORDER, then then allowed orderings are the order induced by the clustering columns and the reverse of that one. otherwise, the orderings allowed are the order of the CLUSTERING ORDER option and the reversed one. The GROUP BY option allows to condense into a single row all selected rows that share the same values for a set of columns. Using the GROUP BY option, it is only possible to group rows at the partition key level or at a clustering column level. By consequence, the GROUP BY option only accept as arguments primary key column names in the primary key order. If a primary key column is restricted by an equality restriction it is not required to be present in the GROUP BY clause. Aggregate functions will produce a separate value for each group. If no GROUP BY clause is specified, aggregates functions will produce a single value for all the rows. If a column is selected without an aggregate function, in a statement with a GROUP BY, the first value encounter in each group will be returned. The LIMIT option to a SELECT statement limits the number of rows returned by a query, while the PER PARTITION LIMIT option limits the number of rows returned for a given partition by the query. Note that both type of limit can used in the same statement. By default, CQL only allows select queries that don’t involve filtering'' server side, i.e. queries where we know that all (live) record read will be returned (maybe partly) in the result set. The reasoning is that those non filtering'' queries have predictable performance in the sense that they will execute in a time that is proportional to the amount of data returned by the query (which can be controlled through LIMIT). The ALLOW FILTERING option allows to explicitly allow (some) queries that require filtering. Please note that a query using ALLOW FILTERING may thus have unpredictable performance (for the definition above), i.e. even a query that selects a handful of records may exhibit performance that depends on the total amount of data stored in the cluster. For instance, considering the following table holding user profiles with their year of birth (with a secondary index on it) and country of residence: bc(sample).. CREATE TABLE users ( username text PRIMARY KEY, firstname text, lastname text, birth_year int, country text ) CREATE INDEX ON users(birth_year); p. Then the following queries are valid: bc(sample). SELECT * FROM users; SELECT firstname, lastname FROM users WHERE birth_year = 1981; because in both case, Cassandra guarantees that these queries performance will be proportional to the amount of data returned. In particular, if no users are born in 1981, then the second query performance will not depend of the number of user profile stored in the database (not directly at least: due to secondary index implementation consideration, this query may still depend on the number of node in the cluster, which indirectly depends on the amount of data stored. Nevertheless, the number of nodes will always be multiple number of magnitude lower than the number of user profile stored). Of course, both query may return very large result set in practice, but the amount of data returned can always be controlled by adding a LIMIT. However, the following query will be rejected: bc(sample). SELECT firstname, lastname FROM users WHERE birth_year = 1981 AND country = `FR'; because Cassandra cannot guarantee that it won’t have to scan large amount of data even if the result to those query is small. Typically, it will scan all the index entries for users born in 1981 even if only a handful are actually from France. However, if you `know what you are doing'', you can force the execution of this query by using `ALLOW FILTERING and so the following query is valid: bc(sample). SELECT firstname, lastname FROM users WHERE birth_year = 1981 AND country = `FR' ALLOW FILTERING; Syntax: bc(syntax).. ::= CREATE ROLE ( IF NOT EXISTS )? ( WITH ( AND )* )? ::= PASSWORD = | LOGIN = | SUPERUSER = | OPTIONS = p. Sample: bc(sample). CREATE ROLE new_role; CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true; CREATE ROLE bob WITH PASSWORD = `password_b' AND LOGIN = true AND SUPERUSER = true; CREATE ROLE carlos WITH OPTIONS = \\{ `custom_option1' : `option1_value', `custom_option2' : 99 }; By default roles do not possess LOGIN privileges or SUPERUSER status. Permissions on database resources are granted to roles; types of resources include keyspaces, tables, functions and roles themselves. Roles may be granted to other roles to create hierarchical permissions structures; in these hierarchies, permissions and SUPERUSER status are inherited, but the LOGIN privilege is not. If a role has the LOGIN privilege, clients may identify as that role when connecting. For the duration of that connection, the client will acquire any roles and privileges granted to that role. Only a client with with the CREATE permission on the database roles resource may issue CREATE ROLE requests (see the relevant section below), unless the client is a SUPERUSER. Role management in Cassandra is pluggable and custom implementations may support only a subset of the listed options. Role names should be quoted if they contain non-alphanumeric characters. Use the WITH PASSWORD clause to set a password for internal authentication, enclosing the password in single quotation marks. If internal authentication has not been set up or the role does not have LOGIN privileges, the WITH PASSWORD clause is not necessary. Attempting to create an existing role results in an invalid query condition unless the IF NOT EXISTS option is used. If the option is used and the role exists, the statement is a no-op. bc(sample). CREATE ROLE other_role; CREATE ROLE IF NOT EXISTS other_role; Syntax: bc(syntax).. ::= ALTER ROLE (IF EXISTS)? ( WITH ( AND )* )? ::= PASSWORD = | LOGIN = | SUPERUSER = | OPTIONS = p. Sample: bc(sample). ALTER ROLE bob WITH PASSWORD = `PASSWORD_B' AND SUPERUSER = false; If the role does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Conditions on executing ALTER ROLE statements: A client must have SUPERUSER status to alter the SUPERUSER status of another role A client cannot alter the SUPERUSER status of any role it currently holds A client can only modify certain properties of the role with which it identified at login (e.g. PASSWORD) To modify properties of a role, the client must be granted ALTER permission on that role Syntax: bc(syntax).. ::= DROP ROLE ( IF EXISTS )? p. Sample: bc(sample). DROP ROLE alice; DROP ROLE IF EXISTS bob; DROP ROLE requires the client to have DROP permission on the role in question. In addition, client may not DROP the role with which it identified at login. Finaly, only a client with SUPERUSER status may DROP another SUPERUSER role. Attempting to drop a role which does not exist results in an invalid query condition unless the IF EXISTS option is used. If the option is used and the role does not exist the statement is a no-op. Syntax: bc(syntax). ::= GRANT TO Sample: bc(sample). GRANT report_writer TO alice; This statement grants the report_writer role to alice. Any permissions granted to report_writer are also acquired by alice. Roles are modelled as a directed acyclic graph, so circular grants are not permitted. The following examples result in error conditions: bc(sample). GRANT role_a TO role_b; GRANT role_b TO role_a; bc(sample). GRANT role_a TO role_b; GRANT role_b TO role_c; GRANT role_c TO role_a; Syntax: bc(syntax). ::= REVOKE FROM Sample: bc(sample). REVOKE report_writer FROM alice; This statement revokes the report_writer role from alice. Any permissions that alice has acquired via the report_writer role are also revoked. Syntax: bc(syntax). ::= LIST ROLES ( OF )? ( NORECURSIVE )? Sample: bc(sample). LIST ROLES; Return all known roles in the system, this requires DESCRIBE permission on the database roles resource. bc(sample). LIST ROLES OF alice; Enumerate all roles granted to alice, including those transitively aquired. bc(sample). LIST ROLES OF bob NORECURSIVE List all roles directly granted to bob. Prior to the introduction of roles in Cassandra 2.2, authentication and authorization were based around the concept of a USER. For backward compatibility, the legacy syntax has been preserved with USER centric statments becoming synonyms for the ROLE based equivalents. Syntax: bc(syntax).. ::= CREATE USER ( IF NOT EXISTS )? ( WITH PASSWORD )? ()? ::= SUPERUSER | NOSUPERUSER p. Sample: bc(sample). CREATE USER alice WITH PASSWORD `password_a' SUPERUSER; CREATE USER bob WITH PASSWORD `password_b' NOSUPERUSER; CREATE USER is equivalent to CREATE ROLE where the LOGIN option is true. So, the following pairs of statements are equivalent: bc(sample).. CREATE USER alice WITH PASSWORD `password_a' SUPERUSER; CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = true; CREATE USER IF NOT EXISTS alice WITH PASSWORD `password_a' SUPERUSER; CREATE ROLE IF NOT EXISTS alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = true; CREATE USER alice WITH PASSWORD `password_a' NOSUPERUSER; CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true AND SUPERUSER = false; CREATE USER alice WITH PASSWORD `password_a' NOSUPERUSER; CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true; CREATE USER alice WITH PASSWORD `password_a'; CREATE ROLE alice WITH PASSWORD = `password_a' AND LOGIN = true; p. Syntax: bc(syntax).. ::= ALTER USER (IF EXISTS)? ( WITH PASSWORD )? ( )? ::= SUPERUSER | NOSUPERUSER p. bc(sample). ALTER USER alice WITH PASSWORD `PASSWORD_A'; ALTER USER bob SUPERUSER; If the user does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Syntax: bc(syntax).. ::= DROP USER ( IF EXISTS )? p. Sample: bc(sample). DROP USER alice; DROP USER IF EXISTS bob; Syntax: bc(syntax). ::= LIST USERS; Sample: bc(sample). LIST USERS; This statement is equivalent to bc(sample). LIST ROLES; but only roles with the LOGIN privilege are included in the output. Permissions on resources are granted to roles; there are several different types of resources in Cassandra and each type is modelled hierarchically: The hierarchy of Data resources, Keyspaces and Tables has the structure ALL KEYSPACES → KEYSPACE → TABLE Function resources have the structure ALL FUNCTIONS → KEYSPACE → FUNCTION Resources representing roles have the structure ALL ROLES → ROLE Resources representing JMX ObjectNames, which map to sets of MBeans/MXBeans, have the structure ALL MBEANS → MBEAN Permissions can be granted at any level of these hierarchies and they flow downwards. So granting a permission on a resource higher up the chain automatically grants that same permission on all resources lower down. For example, granting SELECT on a KEYSPACE automatically grants it on all TABLES in that KEYSPACE. Likewise, granting a permission on ALL FUNCTIONS grants it on every defined function, regardless of which keyspace it is scoped in. It is also possible to grant permissions on all functions scoped to a particular keyspace. Modifications to permissions are visible to existing client sessions; that is, connections need not be re-established following permissions changes. The full set of available permissions is: CREATE ALTER DROP SELECT MODIFY AUTHORIZE DESCRIBE EXECUTE Not all permissions are applicable to every type of resource. For instance, EXECUTE is only relevant in the context of functions or mbeans; granting EXECUTE on a resource representing a table is nonsensical. Attempting to GRANT a permission on resource to which it cannot be applied results in an error response. The following illustrates which permissions can be granted on which types of resource, and which statements are enabled by that permission. permission resource operations CREATE ALL KEYSPACES CREATE KEYSPACE CREATE TABLE in any keyspace CREATE KEYSPACE CREATE TABLE in specified keyspace CREATE ALL FUNCTIONS CREATE FUNCTION in any keyspace CREATE AGGREGATE in any keyspace CREATE ALL FUNCTIONS IN KEYSPACE CREATE FUNCTION in keyspace CREATE AGGREGATE in keyspace CREATE ALL ROLES CREATE ROLE ALTER ALL KEYSPACES ALTER KEYSPACE ALTER TABLE in any keyspace ALTER KEYSPACE ALTER KEYSPACE ALTER TABLE in keyspace ALTER TABLE ALTER TABLE ALTER ALL FUNCTIONS CREATE FUNCTION replacing any existing CREATE AGGREGATE replacing any existing ALTER ALL FUNCTIONS IN KEYSPACE CREATE FUNCTION replacing existing in keyspace CREATE AGGREGATE replacing any existing in keyspace ALTER FUNCTION CREATE FUNCTION replacing existing CREATE AGGREGATE replacing existing ALTER ALL ROLES ALTER ROLE on any role ALTER ROLE ALTER ROLE DROP ALL KEYSPACES DROP KEYSPACE DROP TABLE in any keyspace DROP KEYSPACE DROP TABLE in specified keyspace DROP TABLE DROP TABLE DROP ALL FUNCTIONS DROP FUNCTION in any keyspace DROP AGGREGATE in any existing DROP ALL FUNCTIONS IN KEYSPACE DROP FUNCTION in keyspace DROP AGGREGATE in existing DROP FUNCTION DROP FUNCTION DROP ALL ROLES DROP ROLE on any role DROP ROLE DROP ROLE SELECT ALL KEYSPACES SELECT on any table SELECT KEYSPACE SELECT on any table in keyspace SELECT TABLE SELECT on specified table SELECT ALL MBEANS Call getter methods on any mbean SELECT MBEANS Call getter methods on any mbean matching a wildcard pattern SELECT MBEAN Call getter methods on named mbean MODIFY ALL KEYSPACES INSERT on any table UPDATE on any table DELETE on any table TRUNCATE on any table MODIFY KEYSPACE INSERT on any table in keyspace UPDATE on any table in keyspace DELETE on any table in keyspace TRUNCATE on any table in keyspace MODIFY TABLE INSERT UPDATE DELETE TRUNCATE MODIFY ALL MBEANS Call setter methods on any mbean MODIFY MBEANS Call setter methods on any mbean matching a wildcard pattern MODIFY MBEAN Call setter methods on named mbean AUTHORIZE ALL KEYSPACES GRANT PERMISSION on any table REVOKE PERMISSION on any table AUTHORIZE KEYSPACE GRANT PERMISSION on table in keyspace REVOKE PERMISSION on table in keyspace AUTHORIZE TABLE GRANT PERMISSION REVOKE PERMISSION AUTHORIZE ALL FUNCTIONS GRANT PERMISSION on any function REVOKE PERMISSION on any function AUTHORIZE ALL FUNCTIONS IN KEYSPACE GRANT PERMISSION in keyspace REVOKE PERMISSION in keyspace AUTHORIZE ALL FUNCTIONS IN KEYSPACE GRANT PERMISSION in keyspace REVOKE PERMISSION in keyspace AUTHORIZE FUNCTION GRANT PERMISSION REVOKE PERMISSION AUTHORIZE ALL MBEANS GRANT PERMISSION on any mbean REVOKE PERMISSION on any mbean AUTHORIZE MBEANS GRANT PERMISSION on any mbean matching a wildcard pattern REVOKE PERMISSION on any mbean matching a wildcard pattern AUTHORIZE MBEAN GRANT PERMISSION on named mbean REVOKE PERMISSION on named mbean AUTHORIZE ALL ROLES GRANT ROLE grant any role REVOKE ROLE revoke any role AUTHORIZE ROLES GRANT ROLE grant role REVOKE ROLE revoke role DESCRIBE ALL ROLES LIST ROLES all roles or only roles granted to another, specified role DESCRIBE @ALL MBEANS Retrieve metadata about any mbean from the platform’s MBeanServer DESCRIBE @MBEANS Retrieve metadata about any mbean matching a wildcard patter from the platform’s MBeanServer DESCRIBE @MBEAN Retrieve metadata about a named mbean from the platform’s MBeanServer EXECUTE ALL FUNCTIONS SELECT, INSERT, UPDATE using any function use of any function in CREATE AGGREGATE EXECUTE ALL FUNCTIONS IN KEYSPACE SELECT, INSERT, UPDATE using any function in keyspace use of any function in keyspace in CREATE AGGREGATE EXECUTE FUNCTION SELECT, INSERT, UPDATE using function use of function in CREATE AGGREGATE EXECUTE ALL MBEANS Execute operations on any mbean EXECUTE MBEANS Execute operations on any mbean matching a wildcard pattern EXECUTE MBEAN Execute operations on named mbean Syntax: bc(syntax).. ::= GRANT ( ALL ( PERMISSIONS )? | ( PERMISSION )? ) ON TO ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESRIBE | EXECUTE ::= ALL KEYSPACES | KEYSPACE | ( TABLE )? | ALL ROLES | ROLE | ALL FUNCTIONS ( IN KEYSPACE )? | FUNCTION | ALL MBEANS | ( MBEAN | MBEANS ) p. Sample: bc(sample). GRANT SELECT ON ALL KEYSPACES TO data_reader; This gives any user with the role data_reader permission to execute SELECT statements on any table across all keyspaces bc(sample). GRANT MODIFY ON KEYSPACE keyspace1 TO data_writer; This give any user with the role data_writer permission to perform UPDATE, INSERT, UPDATE, DELETE and TRUNCATE queries on all tables in the keyspace1 keyspace bc(sample). GRANT DROP ON keyspace1.table1 TO schema_owner; This gives any user with the schema_owner role permissions to DROP keyspace1.table1. bc(sample). GRANT EXECUTE ON FUNCTION keyspace1.user_function( int ) TO report_writer; This grants any user with the report_writer role permission to execute SELECT, INSERT and UPDATE queries which use the function keyspace1.user_function( int ) bc(sample). GRANT DESCRIBE ON ALL ROLES TO role_admin; This grants any user with the role_admin role permission to view any and all roles in the system with a LIST ROLES statement When the GRANT ALL form is used, the appropriate set of permissions is determined automatically based on the target resource. When a resource is created, via a CREATE KEYSPACE, CREATE TABLE, CREATE FUNCTION, CREATE AGGREGATE or CREATE ROLE statement, the creator (the role the database user who issues the statement is identified as), is automatically granted all applicable permissions on the new resource. Syntax: bc(syntax).. ::= REVOKE ( ALL ( PERMISSIONS )? | ( PERMISSION )? ) ON FROM ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESRIBE | EXECUTE ::= ALL KEYSPACES | KEYSPACE | ( TABLE )? | ALL ROLES | ROLE | ALL FUNCTIONS ( IN KEYSPACE )? | FUNCTION | ALL MBEANS | ( MBEAN | MBEANS ) p. Sample: bc(sample).. REVOKE SELECT ON ALL KEYSPACES FROM data_reader; REVOKE MODIFY ON KEYSPACE keyspace1 FROM data_writer; REVOKE DROP ON keyspace1.table1 FROM schema_owner; REVOKE EXECUTE ON FUNCTION keyspace1.user_function( int ) FROM report_writer; REVOKE DESCRIBE ON ALL ROLES FROM role_admin; p. Syntax: bc(syntax).. ::= LIST ( ALL ( PERMISSIONS )? | ) ( ON )? ( OF ( NORECURSIVE )? )? ::= ALL KEYSPACES | KEYSPACE | ( TABLE )? | ALL ROLES | ROLE | ALL FUNCTIONS ( IN KEYSPACE )? | FUNCTION | ALL MBEANS | ( MBEAN | MBEANS ) p. Sample: bc(sample). LIST ALL PERMISSIONS OF alice; Show all permissions granted to alice, including those acquired transitively from any other roles. bc(sample). LIST ALL PERMISSIONS ON keyspace1.table1 OF bob; Show all permissions on keyspace1.table1 granted to bob, including those acquired transitively from any other roles. This also includes any permissions higher up the resource hierarchy which can be applied to keyspace1.table1. For example, should bob have ALTER permission on keyspace1, that would be included in the results of this query. Adding the NORECURSIVE switch restricts the results to only those permissions which were directly granted to bob or one of bob’s roles. bc(sample). LIST SELECT PERMISSIONS OF carlos; Show any permissions granted to carlos or any of carlos’s roles, limited to SELECT permissions on any resource. CQL supports a rich set of data types for columns defined in a table, including collection types. On top of those native and collection types, users can also provide custom types (through a JAVA class extending AbstractType loadable by Cassandra). The syntax of types is thus: bc(syntax).. ::= | | | // Used for custom types. The fully-qualified name of a JAVA class ::= ascii | bigint | blob | boolean | counter | date | decimal | double | float | inet | int | smallint | text | time | timestamp | timeuuid | tinyint | uuid | varchar | varint ::= list ' | set `' | map `' ::= tuple `' p. Note that the native types are keywords and as such are case-insensitive. They are however not reserved ones. The following table gives additional informations on the native data types, and on which kind of constants each type supports: type constants supported description ascii strings ASCII character string bigint integers 64-bit signed long blob blobs Arbitrary bytes (no validation) boolean booleans true or false counter integers Counter column (64-bit signed value). See Counters for details date integers, strings A date (with no corresponding time value). See Working with dates below for more information. decimal integers, floats Variable-precision decimal double integers 64-bit IEEE-754 floating point float integers, floats 32-bit IEEE-754 floating point inet strings An IP address. It can be either 4 bytes long (IPv4) or 16 bytes long (IPv6). There is no inet constant, IP address should be inputed as strings int integers 32-bit signed int smallint integers 16-bit signed int text strings UTF8 encoded string time integers, strings A time with nanosecond precision. See Working with time below for more information. timestamp integers, strings A timestamp. Strings constant are allow to input timestamps as dates, see Working with timestamps below for more information. timeuuid uuids Type 1 UUID. This is generally used as a ``conflict-free'' timestamp. Also see the functions on Timeuuid tinyint integers 8-bit signed int uuid uuids Type 1 or type 4 UUID varchar strings UTF8 encoded string varint integers Arbitrary-precision integer For more information on how to use the collection types, see the Working with collections section below. Values of the timestamp type are encoded as 64-bit signed integers representing a number of milliseconds since the standard base time known as ``the epoch'': January 1 1970 at 00:00:00 GMT. Timestamp can be input in CQL as simple long integers, giving the number of milliseconds since the epoch, as defined above. They can also be input as string literals in any of the following ISO 8601 formats, each representing the time and date Mar 2, 2011, at 04:05:00 AM, GMT.: 2011-02-03 04:05+0000 2011-02-03 04:05:00+0000 2011-02-03 04:05:00.000+0000 2011-02-03T04:05+0000 2011-02-03T04:05:00+0000 2011-02-03T04:05:00.000+0000 The +0000 above is an RFC 822 4-digit time zone specification; +0000 refers to GMT. US Pacific Standard Time is -0800. The time zone may be omitted if desired— the date will be interpreted as being in the time zone under which the coordinating Cassandra node is configured. 2011-02-03 04:05 2011-02-03 04:05:00 2011-02-03 04:05:00.000 2011-02-03T04:05 2011-02-03T04:05:00 2011-02-03T04:05:00.000 There are clear difficulties inherent in relying on the time zone configuration being as expected, though, so it is recommended that the time zone always be specified for timestamps when feasible. The time of day may also be omitted, if the date is the only piece that matters: 2011-02-03 2011-02-03+0000 In that case, the time of day will default to 00:00:00, in the specified or default time zone. Values of the date type are encoded as 32-bit unsigned integers representing a number of days with ``the epoch'' at the center of the range (2^31). Epoch is January 1st, 1970 A date can be input in CQL as an unsigned integer as defined above. They can also be input as string literals in the following format: 2014-01-01 Values of the time type are encoded as 64-bit signed integers representing the number of nanoseconds since midnight. A time can be input in CQL as simple long integers, giving the number of nanoseconds since midnight. They can also be input as string literals in any of the following formats: 08:12:54 08:12:54.123 08:12:54.123456 08:12:54.123456789 The counter type is used to define counter columns. A counter column is a column whose value is a 64-bit signed integer and on which 2 operations are supported: incrementation and decrementation (see UPDATE for syntax). Note the value of a counter cannot be set. A counter doesn’t exist until first incremented/decremented, and the first incrementation/decrementation is made as if the previous value was 0. Deletion of counter columns is supported but have some limitations (see the Cassandra Wiki for more information). The use of the counter type is limited in the following way: It cannot be used for column that is part of the PRIMARY KEY of a table. A table that contains a counter can only contain counters. In other words, either all the columns of a table outside the PRIMARY KEY have the counter type, or none of them have it. Collections are meant for storing/denormalizing relatively small amount of data. They work well for things like the phone numbers of a given user'', labels applied to an email'', etc. But when items are expected to grow unbounded (all the messages sent by a given user'', events registered by a sensor'', …), then collections are not appropriate anymore and a specific table (with clustering columns) should be used. Concretely, collections have the following limitations: Collections are always read in their entirety (and reading one is not paged internally). Collections cannot have more than 65535 elements. More precisely, while it may be possible to insert more than 65535 elements, it is not possible to read more than the 65535 first elements (see CASSANDRA-5428 for details). While insertion operations on sets and maps never incur a read-before-write internally, some operations on lists do (see the section on lists below for details). It is thus advised to prefer sets over lists when possible. Please note that while some of those limitations may or may not be loosen in the future, the general rule that collections are for denormalizing small amount of data is meant to stay. A map is a typed set of key-value pairs, where keys are unique. Furthermore, note that the map are internally sorted by their keys and will thus always be returned in that order. To create a column of type map, use the map keyword suffixed with comma-separated key and value types, enclosed in angle brackets. For example: bc(sample). CREATE TABLE users ( id text PRIMARY KEY, given text, surname text, favs map // A map of text keys, and text values ) Writing map data is accomplished with a JSON-inspired syntax. To write a record using INSERT, specify the entire map as a JSON-style associative array. Note: This form will always replace the entire map. bc(sample). INSERT INTO users (id, given, surname, favs) VALUES (`jsmith', `John', `Smith', \\{ `fruit' : `apple', `band' : `Beatles' }) Adding or updating key-values of a (potentially) existing map can be accomplished either by subscripting the map column in an UPDATE statement or by adding a new map literal: bc(sample). UPDATE users SET favs[`author'] = `Ed Poe' WHERE id = `jsmith' UPDATE users SET favs = favs + \\{ `movie' : `Cassablanca' } WHERE id = `jsmith' Note that TTLs are allowed for both INSERT and UPDATE, but in both case the TTL set only apply to the newly inserted/updated values. In other words, bc(sample). UPDATE users USING TTL 10 SET favs[`color'] = `green' WHERE id = `jsmith' will only apply the TTL to the { 'color' : 'green' } record, the rest of the map remaining unaffected. Deleting a map record is done with: bc(sample). DELETE favs[`author'] FROM users WHERE id = `jsmith' A set is a typed collection of unique values. Sets are ordered by their values. To create a column of type set, use the set keyword suffixed with the value type enclosed in angle brackets. For example: bc(sample). CREATE TABLE images ( name text PRIMARY KEY, owner text, date timestamp, tags set ); Writing a set is accomplished by comma separating the set values, and enclosing them in curly braces. Note: An INSERT will always replace the entire set. bc(sample). INSERT INTO images (name, owner, date, tags) VALUES (`cat.jpg', `jsmith', `now', \\{ `kitten', `cat', `pet' }); Adding and removing values of a set can be accomplished with an UPDATE by adding/removing new set values to an existing set column. bc(sample). UPDATE images SET tags = tags + \\{ `cute', `cuddly' } WHERE name = `cat.jpg'; UPDATE images SET tags = tags - \\{ `lame' } WHERE name = `cat.jpg'; As with maps, TTLs if used only apply to the newly inserted/updated values. A list is a typed collection of non-unique values where elements are ordered by there position in the list. To create a column of type list, use the list keyword suffixed with the value type enclosed in angle brackets. For example: bc(sample). CREATE TABLE plays ( id text PRIMARY KEY, game text, players int, scores list ) Do note that as explained below, lists have some limitations and performance considerations to take into account, and it is advised to prefer sets over lists when this is possible. Writing list data is accomplished with a JSON-style syntax. To write a record using INSERT, specify the entire list as a JSON array. Note: An INSERT will always replace the entire list. bc(sample). INSERT INTO plays (id, game, players, scores) VALUES (`123-afde', `quake', 3, [17, 4, 2]); Adding (appending or prepending) values to a list can be accomplished by adding a new JSON-style array to an existing list column. bc(sample). UPDATE plays SET players = 5, scores = scores + [ 14, 21 ] WHERE id = `123-afde'; UPDATE plays SET players = 5, scores = [ 12 ] + scores WHERE id = `123-afde'; It should be noted that append and prepend are not idempotent operations. This means that if during an append or a prepend the operation timeout, it is not always safe to retry the operation (as this could result in the record appended or prepended twice). Lists also provides the following operation: setting an element by its position in the list, removing an element by its position in the list and remove all the occurrence of a given value in the list. However, and contrarily to all the other collection operations, these three operations induce an internal read before the update, and will thus typically have slower performance characteristics. Those operations have the following syntax: bc(sample). UPDATE plays SET scores[1] = 7 WHERE id = `123-afde'; // sets the 2nd element of scores to 7 (raises an error is scores has less than 2 elements) DELETE scores[1] FROM plays WHERE id = `123-afde'; // deletes the 2nd element of scores (raises an error is scores has less than 2 elements) UPDATE plays SET scores = scores - [ 12, 21 ] WHERE id = `123-afde'; // removes all occurrences of 12 and 21 from scores As with maps, TTLs if used only apply to the newly inserted/updated values. CQL3 distinguishes between built-in functions (so called `native functions') and user-defined functions. CQL3 includes several native functions, described below: The cast function can be used to converts one native datatype to another. The following table describes the conversions supported by the cast function. Cassandra will silently ignore any cast converting a datatype into its own datatype. from to ascii text, varchar bigint tinyint, smallint, int, float, double, decimal, varint, text, varchar boolean text, varchar counter tinyint, smallint, int, bigint, float, double, decimal, varint, text, varchar date timestamp decimal tinyint, smallint, int, bigint, float, double, varint, text, varchar double tinyint, smallint, int, bigint, float, decimal, varint, text, varchar float tinyint, smallint, int, bigint, double, decimal, varint, text, varchar inet text, varchar int tinyint, smallint, bigint, float, double, decimal, varint, text, varchar smallint tinyint, int, bigint, float, double, decimal, varint, text, varchar time text, varchar timestamp date, text, varchar timeuuid timestamp, date, text, varchar tinyint tinyint, smallint, int, bigint, float, double, decimal, varint, text, varchar uuid text, varchar varint tinyint, smallint, int, bigint, float, double, decimal, text, varchar The conversions rely strictly on Java’s semantics. For example, the double value 1 will be converted to the text value `1.0'. bc(sample). SELECT avg(cast(count as double)) FROM myTable The token function allows to compute the token for a given partition key. The exact signature of the token function depends on the table concerned and of the partitioner used by the cluster. The type of the arguments of the token depend on the type of the partition key columns. The return type depend on the partitioner in use: For Murmur3Partitioner, the return type is bigint. For RandomPartitioner, the return type is varint. For ByteOrderedPartitioner, the return type is blob. For instance, in a cluster using the default Murmur3Partitioner, if a table is defined by bc(sample). CREATE TABLE users ( userid text PRIMARY KEY, username text, … ) then the token function will take a single argument of type text (in that case, the partition key is userid (there is no clustering columns so the partition key is the same than the primary key)), and the return type will be bigint. The uuid function takes no parameters and generates a random type 4 uuid suitable for use in INSERT or SET statements. The now function takes no arguments and generates, on the coordinator node, a new unique timeuuid (at the time where the statement using it is executed). Note that this method is useful for insertion but is largely non-sensical in WHERE clauses. For instance, a query of the form bc(sample). SELECT * FROM myTable WHERE t = now() will never return any result by design, since the value returned by now() is guaranteed to be unique. The minTimeuuid (resp. maxTimeuuid) function takes a timestamp value t (which can be either a timestamp or a date string ) and return a fake timeuuid corresponding to the smallest (resp. biggest) possible timeuuid having for timestamp t. So for instance: bc(sample). SELECT * FROM myTable WHERE t > maxTimeuuid(`2013-01-01 00:05+0000') AND t = maxTimeuuid('2013-01-01 00:05+0000') would still not select a timeuuid generated exactly at 2013-01-01 00:05+0000' and is essentially equivalent to `t > maxTimeuuid('2013-01-01 00:05+0000'). Warning: We called the values generated by minTimeuuid and maxTimeuuid fake UUID because they do no respect the Time-Based UUID generation process specified by the RFC 4122. In particular, the value returned by these 2 methods will not be unique. This means you should only use those methods for querying (as in the example above). Inserting the result of those methods is almost certainly a bad idea. A number of functions are provided to `convert'' a `timeuuid, a timestamp or a date into another native type. function name input type description toDate timeuuid Converts the timeuuid argument into a date type toDate timestamp Converts the timestamp argument into a date type toTimestamp timeuuid Converts the timeuuid argument into a timestamp type toTimestamp date Converts the date argument into a timestamp type toUnixTimestamp timeuuid Converts the timeuuid argument into a bigInt raw value toUnixTimestamp timestamp Converts the timestamp argument into a bigInt raw value toUnixTimestamp date Converts the date argument into a bigInt raw value dateOf timeuuid Similar to toTimestamp(timeuuid) (DEPRECATED) unixTimestampOf timeuuid Similar to toUnixTimestamp(timeuuid) (DEPRECATED) A number of functions are provided to `convert'' the native types into binary data (`blob). For every type supported by CQL3 (a notable exceptions is blob, for obvious reasons), the function typeAsBlob takes a argument of type type and return it as a blob. Conversely, the function blobAsType takes a 64-bit blob argument and convert it to a bigint value. And so for instance, bigintAsBlob(3) is 0x0000000000000003 and blobAsBigint(0x0000000000000003) is 3. Aggregate functions work on a set of rows. They receive values for each row and returns one value for the whole set. If normal columns, scalar functions, UDT fields, writetime or ttl are selected together with aggregate functions, the values returned for them will be the ones of the first row matching the query. CQL3 distinguishes between built-in aggregates (so called `native aggregates') and user-defined aggregates. CQL3 includes several native aggregates, described below: The count function can be used to count the rows returned by a query. Example: bc(sample). SELECT COUNT (*) FROM plays; SELECT COUNT (1) FROM plays; It also can be used to count the non null value of a given column. Example: bc(sample). SELECT COUNT (scores) FROM plays; The max and min functions can be used to compute the maximum and the minimum value returned by a query for a given column. bc(sample). SELECT MIN (players), MAX (players) FROM plays WHERE game = `quake'; The sum function can be used to sum up all the values returned by a query for a given column. bc(sample). SELECT SUM (players) FROM plays; The avg function can be used to compute the average of all the values returned by a query for a given column. bc(sample). SELECT AVG (players) FROM plays; User-defined functions allow execution of user-provided code in Cassandra. By default, Cassandra supports defining functions in Java and JavaScript. Support for other JSR 223 compliant scripting languages (such as Python, Ruby, and Scala) has been removed in 3.0.11. UDFs are part of the Cassandra schema. As such, they are automatically propagated to all nodes in the cluster. UDFs can be overloaded - i.e. multiple UDFs with different argument types but the same function name. Example: bc(sample). CREATE FUNCTION sample ( arg int ) …; CREATE FUNCTION sample ( arg text ) …; User-defined functions are susceptible to all of the normal problems with the chosen programming language. Accordingly, implementations should be safe against null pointer exceptions, illegal arguments, or any other potential source of exceptions. An exception during function execution will result in the entire statement failing. It is valid to use complex types like collections, tuple types and user-defined types as argument and return types. Tuple types and user-defined types are handled by the conversion functions of the DataStax Java Driver. Please see the documentation of the Java Driver for details on handling tuple types and user-defined types. Arguments for functions can be literals or terms. Prepared statement placeholders can be used, too. Note that you can use the double-quoted string syntax to enclose the UDF source code. For example: bc(sample).. CREATE FUNCTION some_function ( arg int ) RETURNS NULL ON NULL INPUT RETURNS int LANGUAGE java AS return arg; ; SELECT some_function(column) FROM atable …; UPDATE atable SET col = some_function(?) …; p. bc(sample). CREATE TYPE custom_type (txt text, i int); CREATE FUNCTION fct_using_udt ( udtarg frozen ) RETURNS NULL ON NULL INPUT RETURNS text LANGUAGE java AS return udtarg.getString(``txt''); ; User-defined functions can be used in SELECT, INSERT and UPDATE statements. The implicitly available udfContext field (or binding for script UDFs) provides the neccessary functionality to create new UDT and tuple values. bc(sample). CREATE TYPE custom_type (txt text, i int); CREATE FUNCTION fct_using_udt ( somearg int ) RETURNS NULL ON NULL INPUT RETURNS custom_type LANGUAGE java AS + UDTValue udt = udfContext.newReturnUDTValue(); + udt.setString(``txt'', ``some string''); + udt.setInt(``i'', 42); + return udt; + ; The definition of the UDFContext interface can be found in the Apache Cassandra source code for org.apache.cassandra.cql3.functions.UDFContext. bc(sample). public interface UDFContext \\{ UDTValue newArgUDTValue(String argName); UDTValue newArgUDTValue(int argNum); UDTValue newReturnUDTValue(); UDTValue newUDTValue(String udtName); TupleValue newArgTupleValue(String argName); TupleValue newArgTupleValue(int argNum); TupleValue newReturnTupleValue(); TupleValue newTupleValue(String cqlDefinition); } Java UDFs already have some imports for common interfaces and classes defined. These imports are: Please note, that these convenience imports are not available for script UDFs. bc(sample). import java.nio.ByteBuffer; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.cassandra.cql3.functions.UDFContext; import com.datastax.driver.core.TypeCodec; import com.datastax.driver.core.TupleValue; import com.datastax.driver.core.UDTValue; See CREATE FUNCTION and DROP FUNCTION. User-defined aggregates allow creation of custom aggregate functions using UDFs. Common examples of aggregate functions are count, min, and max. Each aggregate requires an initial state (INITCOND, which defaults to null) of type STYPE. The first argument of the state function must have type STYPE. The remaining arguments of the state function must match the types of the user-defined aggregate arguments. The state function is called once for each row, and the value returned by the state function becomes the new state. After all rows are processed, the optional FINALFUNC is executed with last state value as its argument. STYPE is mandatory in order to be able to distinguish possibly overloaded versions of the state and/or final function (since the overload can appear after creation of the aggregate). User-defined aggregates can be used in SELECT statement. A complete working example for user-defined aggregates (assuming that a keyspace has been selected using the USE statement): bc(sample).. CREATE OR REPLACE FUNCTION averageState ( state tuple, val int ) CALLED ON NULL INPUT RETURNS tuple LANGUAGE java AS ’ if (val != null) \\{ state.setInt(0, state.getInt(0)+1); state.setLong(1, state.getLong(1)+val.intValue()); } return state; ’; CREATE OR REPLACE FUNCTION averageFinal ( state tuple ) CALLED ON NULL INPUT RETURNS double LANGUAGE java AS ’ double r = 0; if (state.getInt(0) == 0) return null; r = state.getLong(1); r /= state.getInt(0); return Double.valueOf®; ’; CREATE OR REPLACE AGGREGATE average ( int ) SFUNC averageState STYPE tuple FINALFUNC averageFinal INITCOND (0, 0); CREATE TABLE atable ( pk int PRIMARY KEY, val int); INSERT INTO atable (pk, val) VALUES (1,1); INSERT INTO atable (pk, val) VALUES (2,2); INSERT INTO atable (pk, val) VALUES (3,3); INSERT INTO atable (pk, val) VALUES (4,4); SELECT average(val) FROM atable; p. See CREATE AGGREGATE and DROP AGGREGATE. Cassandra 2.2 introduces JSON support to SELECT and INSERT statements. This support does not fundamentally alter the CQL API (for example, the schema is still enforced), it simply provides a convenient way to work with JSON documents. With SELECT statements, the new JSON keyword can be used to return each row as a single JSON encoded map. The remainder of the SELECT statment behavior is the same. The result map keys are the same as the column names in a normal result set. For example, a statement like SELECT JSON a, ttl(b) FROM …​’' would result in a map with keys `\"a\" and \"ttl(b)\". However, this is one notable exception: for symmetry with INSERT JSON behavior, case-sensitive column names with upper-case letters will be surrounded with double quotes. For example, SELECT JSON myColumn FROM …​’' would result in a map key `\"\\\"myColumn\\\"\" (note the escaped quotes). The map values will JSON-encoded representations (as described below) of the result set values. With INSERT statements, the new JSON keyword can be used to enable inserting a JSON encoded map as a single row. The format of the JSON map should generally match that returned by a SELECT JSON statement on the same table. In particular, case-sensitive column names should be surrounded with double quotes. For example, to insert into a table with two columns named myKey'' and value'', you would do the following: bc(sample). INSERT INTO mytable JSON `\\{\\''myKey\\'': 0, ``value'': 0}' Any columns which are ommitted from the JSON map will be defaulted to a NULL value (which will result in a tombstone being created). Where possible, Cassandra will represent and accept data types in their native JSON representation. Cassandra will also accept string representations matching the CQL literal format for all single-field types. For example, floats, ints, UUIDs, and dates can be represented by CQL literal strings. However, compound types, such as collections, tuples, and user-defined types must be represented by native JSON collections (maps and lists) or a JSON-encoded string representation of the collection. The following table describes the encodings that Cassandra will accept in INSERT JSON values (and fromJson() arguments) as well as the format Cassandra will use when returning data for SELECT JSON statements (and fromJson()): type formats accepted return format notes ascii string string Uses JSON’s \\u character escape bigint integer, string integer String must be valid 64 bit integer blob string string String should be 0x followed by an even number of hex digits boolean boolean, string boolean String must be true'' or false'' date string string Date in format YYYY-MM-DD, timezone UTC decimal integer, float, string float May exceed 32 or 64-bit IEEE-754 floating point precision in client-side decoder double integer, float, string float String must be valid integer or float float integer, float, string float String must be valid integer or float inet string string IPv4 or IPv6 address int integer, string integer String must be valid 32 bit integer list list, string list Uses JSON’s native list representation map map, string map Uses JSON’s native map representation smallint integer, string integer String must be valid 16 bit integer set list, string list Uses JSON’s native list representation text string string Uses JSON’s \\u character escape time string string Time of day in format HH-MM-SS[.fffffffff] timestamp integer, string string A timestamp. Strings constant are allow to input timestamps as dates, see Working with dates below for more information. Datestamps with format YYYY-MM-DD HH:MM:SS.SSS are returned. timeuuid string string Type 1 UUID. See Constants for the UUID format tinyint integer, string integer String must be valid 8 bit integer tuple list, string list Uses JSON’s native list representation UDT map, string map Uses JSON’s native map representation with field names as keys uuid string string See Constants for the UUID format varchar string string Uses JSON’s \\u character escape varint integer, string integer Variable length; may overflow 32 or 64 bit integers in client-side decoder The fromJson() function may be used similarly to INSERT JSON, but for a single column value. It may only be used in the VALUES clause of an INSERT statement or as one of the column values in an UPDATE, DELETE, or SELECT statement. For example, it cannot be used in the selection clause of a SELECT statement. The toJson() function may be used similarly to SELECT JSON, but for a single column value. It may only be used in the selection clause of a SELECT statement. CQL distinguishes between reserved and non-reserved keywords. Reserved keywords cannot be used as identifier, they are truly reserved for the language (but one can enclose a reserved keyword by double-quotes to use it as an identifier). Non-reserved keywords however only have a specific meaning in certain context but can used as identifer otherwise. The only raison d’être of these non-reserved keywords is convenience: some keyword are non-reserved when it was always easy for the parser to decide whether they were used as keywords or not. Keyword Reserved? ADD yes AGGREGATE no ALL no ALLOW yes ALTER yes AND yes APPLY yes AS no ASC yes ASCII no AUTHORIZE yes BATCH yes BEGIN yes BIGINT no BLOB no BOOLEAN no BY yes CALLED no CAST no CLUSTERING no COLUMNFAMILY yes COMPACT no CONTAINS no COUNT no COUNTER no CREATE yes CUSTOM no DATE no DECIMAL no DEFAULT yes DELETE yes DESC yes DESCRIBE yes DISTINCT no DOUBLE no DROP yes DURATION no ENTRIES yes EXECUTE yes EXISTS no FILTERING no FINALFUNC no FLOAT no FROM yes FROZEN no FULL yes FUNCTION no FUNCTIONS no GRANT yes GROUP no IF yes IN yes INDEX yes INET no INFINITY yes INITCOND no INPUT no INSERT yes INT no INTO yes IS yes JSON no KEY no KEYS no KEYSPACE yes KEYSPACES no LANGUAGE no LIKE no LIMIT yes LIST no LOGIN no MAP no MATERIALIZED yes MBEAN yes MBEANS yes MODIFY yes NAN yes NOLOGIN no NORECURSIVE yes NOSUPERUSER no NOT yes NULL yes OF yes ON yes OPTIONS no OR yes ORDER yes PARTITION no PASSWORD no PER no PERMISSION no PERMISSIONS no PRIMARY yes RENAME yes REPLACE yes RETURNS no REVOKE yes ROLE no ROLES no SCHEMA yes SELECT yes SET yes SFUNC no SMALLINT no STATIC no STORAGE no STYPE no SUPERUSER no TABLE yes TEXT no TIME no TIMESTAMP no TIMEUUID no TINYINT no TO yes TOKEN yes TRIGGER no TRUNCATE yes TTL no TUPLE no TYPE no UNLOGGED yes UNSET yes UPDATE yes USE yes USER no USERS no USING yes UUID no VALUES no VARCHAR no VARINT no VIEW yes WHERE yes WITH yes WRITETIME no The following type names are not currently used by CQL, but are reserved for potential future use. User-defined types may not use reserved type names as their name. type bitstring byte complex date enum interval macaddr The following describes the changes in each version of CQL. Support for GROUP BY. See (see CASSANDRA-10707. Support for selecting elements and slices of a collection (CASSANDRA-7396. INSERT/UPDATE options for tables having a default_time_to_live specifying a TTL of 0 will remove the TTL from the inserted or updated values ALTER TABLE ADD and DROP now allow mutiple columns to be added/removed New PER PARTITION LIMIT option (see CASSANDRA-7017. User-defined functions can now instantiate UDTValue and TupleValue instances via the new UDFContext interface (see CASSANDRA-10818. `User-defined types''#createTypeStmt may now be stored in a non-frozen form, allowing individual fields to be updated and deleted in `UPDATE statements and DELETE statements, respectively. (CASSANDRA-7423 Adds CAST functions. See Cast. Support for materialized views DELETE support for inequality expressions and IN restrictions on any primary key columns UPDATE support for IN restrictions on any primary key columns The syntax TRUNCATE TABLE X is now accepted as an alias for TRUNCATE X Adds new aggregates User-defined functions are now supported through CREATE FUNCTION and DROP FUNCTION. User-defined aggregates are now supported through CREATE AGGREGATE and DROP AGGREGATE. Allows double-dollar enclosed strings literals as an alternative to single-quote enclosed strings. Introduces Roles to supercede user based authentication and access control Date and Time data types have been added JSON support has been added Tinyint and Smallint data types have been added Adds new time conversion functions and deprecate dateOf and unixTimestampOf. See Time conversion functions User-defined types are now supported through CREATE TYPE, ALTER TYPE, and DROP TYPE CREATE INDEX now supports indexing collection columns, including indexing the keys of map collections through the keys() function Indexes on collections may be queried using the new CONTAINS and CONTAINS KEY operators Tuple types were added to hold fixed-length sets of typed positional fields (see the section on types ) DROP INDEX now supports optionally specifying a keyspace SELECT statements now support selecting multiple rows in a single partition using an IN clause on combinations of clustering columns. See SELECT WHERE clauses. IF NOT EXISTS and IF EXISTS syntax is now supported by CREATE USER and DROP USER statmenets, respectively. A new uuid method has been added. Support for DELETE …​ IF EXISTS syntax. It is now possible to group clustering columns in a relatiion, see SELECT WHERE clauses. Added support for STATIC columns, see static in CREATE TABLE. CREATE INDEX now allows specifying options when creating CUSTOM indexes (see CREATE INDEX reference ). Millisecond precision formats have been added to the timestamp parser (see working with dates ). NaN and Infinity has been added as valid float contants. They are now reserved keywords. In the unlikely case you we using them as a column identifier (or keyspace/table one), you will noew need to double quote them (see quote identifiers ). SELECT statement now allows listing the partition keys (using the DISTINCT modifier). See CASSANDRA-4536. The syntax c IN ? is now supported in WHERE clauses. In that case, the value expected for the bind variable will be a list of whatever type c is. It is now possible to use named bind variables (using :name instead of ?). ALTER TABLE DROP option has been reenabled for CQL3 tables and has new semantics now: the space formerly used by dropped columns will now be eventually reclaimed (post-compaction). You should not readd previously dropped columns unless you use timestamps with microsecond precision (see CASSANDRA-3919 for more details). SELECT statement now supports aliases in select clause. Aliases in WHERE and ORDER BY clauses are not supported. See the section on select for details. CREATE statements for KEYSPACE, TABLE and INDEX now supports an IF NOT EXISTS condition. Similarly, DROP statements support a IF EXISTS condition. INSERT statements optionally supports a IF NOT EXISTS condition and UPDATE supports IF conditions. SELECT, UPDATE, and DELETE statements now allow empty IN relations (see CASSANDRA-5626. Updated the syntax for custom secondary indexes. Non-equal condition on the partition key are now never supported, even for ordering partitioner as this was not correct (the order was not the one of the type of the partition key). Instead, the token method should always be used for range queries on the partition key (see WHERE clauses ). Support for custom secondary indexes has been added. Type validation for the constants has been fixed. For instance, the implementation used to allow '2' as a valid value for an int column (interpreting it has the equivalent of 2), or 42 as a valid blob value (in which case 42 was interpreted as an hexadecimal representation of the blob). This is no longer the case, type validation of constants is now more strict. See the data types section for details on which constant is allowed for which type. The type validation fixed of the previous point has lead to the introduction of blobs constants to allow inputing blobs. Do note that while inputing blobs as strings constant is still supported by this version (to allow smoother transition to blob constant), it is now deprecated (in particular the data types section does not list strings constants as valid blobs) and will be removed by a future version. If you were using strings as blobs, you should thus update your client code ASAP to switch blob constants. A number of functions to convert native types to blobs have also been introduced. Furthermore the token function is now also allowed in select clauses. See the section on functions for details. Date strings (and timestamps) are no longer accepted as valid timeuuid values. Doing so was a bug in the sense that date string are not valid timeuuid, and it was thus resulting in confusing behaviors. However, the following new methods have been added to help working with timeuuid: now, minTimeuuid, maxTimeuuid , dateOf and unixTimestampOf. See the section dedicated to these methods for more detail. `Float constants''#constants now support the exponent notation. In other words, `4.2E10 is now a valid floating point value. Versioning of the CQL language adheres to the Semantic Versioning guidelines. Versions take the form X.Y.Z where X, Y, and Z are integer values representing major, minor, and patch level respectively. There is no correlation between Cassandra release versions and the CQL language version. version description Major The major version must be bumped when backward incompatible changes are introduced. This should rarely occur. Minor Minor version increments occur when new, but backward compatible, functionality is introduced. Patch The patch version is incremented when bugs are fixed.","title":"","component":"Cassandra","version":"4.1","name":"cql_singlefile","url":"/Cassandra/4.1/cassandra/cql/cql_singlefile.html","titles":[{"text":"Cassandra Query Language (CQL) v3.4.3","id":"cassandra-query-language-cql-v3-4-3"},{"text":"CQL Syntax","id":"cql-syntax"},{"text":"Preamble","id":"preamble"},{"text":"Conventions","id":"conventions"},{"text":"Identifiers and keywords","id":"identifiers"},{"text":"Constants","id":"constants"},{"text":"Comments","id":"comments"},{"text":"Statements","id":"statements"},{"text":"Prepared Statement","id":"preparedStatement"},{"text":"Data Definition","id":"dataDefinition"},{"text":"CREATE KEYSPACE","id":"createKeyspaceStmt"},{"text":"USE","id":"useStmt"},{"text":"ALTER KEYSPACE","id":"alterKeyspaceStmt"},{"text":"DROP KEYSPACE","id":"dropKeyspaceStmt"},{"text":"CREATE TABLE","id":"createTableStmt"},{"text":"<tablename>","id":"createTableName"},{"text":"<column-definition>","id":"createTableColumn"},{"text":"Partition key and clustering columns","id":"createTablepartitionClustering"},{"text":"STATIC columns","id":"createTableStatic"},{"text":"<option>","id":"createTableOptions"},{"text":"Compaction options","id":"compactionOptions"},{"text":"Compression options","id":"compressionOptions"},{"text":"Caching options","id":"cachingOptions"},{"text":"Other considerations:","id":"other-considerations"},{"text":"ALTER TABLE","id":"alterTableStmt"},{"text":"CQL type compatibility:","id":"cql-type-compatibility"},{"text":"DROP TABLE","id":"dropTableStmt"},{"text":"TRUNCATE","id":"truncateStmt"},{"text":"CREATE INDEX","id":"createIndexStmt"},{"text":"Indexes on Map Keys","id":"keysIndex"},{"text":"DROP INDEX","id":"dropIndexStmt"},{"text":"CREATE MATERIALIZED VIEW","id":"createMVStmt"},{"text":"WHERE Clause","id":"createMVWhere"},{"text":"ALTER MATERIALIZED VIEW","id":"alterMVStmt"},{"text":"DROP MATERIALIZED VIEW","id":"dropMVStmt"},{"text":"CREATE TYPE","id":"createTypeStmt"},{"text":"<typename>","id":"createTypeName"},{"text":"ALTER TYPE","id":"alterTypeStmt"},{"text":"DROP TYPE","id":"dropTypeStmt"},{"text":"CREATE TRIGGER","id":"createTriggerStmt"},{"text":"DROP TRIGGER","id":"dropTriggerStmt"},{"text":"CREATE FUNCTION","id":"createFunctionStmt"},{"text":"Function Signature","id":"functionSignature"},{"text":"DROP FUNCTION","id":"dropFunctionStmt"},{"text":"CREATE AGGREGATE","id":"createAggregateStmt"},{"text":"DROP AGGREGATE","id":"dropAggregateStmt"},{"text":"Data Manipulation","id":"dataManipulation"},{"text":"INSERT","id":"insertStmt"},{"text":"UPDATE","id":"updateStmt"},{"text":"<options>","id":"updateOptions"},{"text":"DELETE","id":"deleteStmt"},{"text":"BATCH","id":"batchStmt"},{"text":"UNLOGGED","id":"unloggedBatch"},{"text":"COUNTER","id":"counterBatch"},{"text":"<option>","id":"batchOptions"},{"text":"Queries","id":"queries"},{"text":"SELECT","id":"selectStmt"},{"text":"<select-clause>","id":"selectSelection"},{"text":"<where-clause>","id":"selectWhere"},{"text":"<order-by>","id":"selectOrderBy"},{"text":"<group-by>","id":"selectGroupBy"},{"text":"LIMIT and PER PARTITION LIMIT","id":"selectLimit"},{"text":"ALLOW FILTERING","id":"selectAllowFiltering"},{"text":"Database Roles","id":"databaseRoles"},{"text":"CREATE ROLE","id":"createRoleStmt"},{"text":"Setting credentials for internal authentication","id":"createRolePwd"},{"text":"Creating a role conditionally","id":"createRoleConditional"},{"text":"ALTER ROLE","id":"alterRoleStmt"},{"text":"DROP ROLE","id":"dropRoleStmt"},{"text":"GRANT ROLE","id":"grantRoleStmt"},{"text":"REVOKE ROLE","id":"revokeRoleStmt"},{"text":"LIST ROLES","id":"listRolesStmt"},{"text":"CREATE USER","id":"createUserStmt"},{"text":"ALTER USER","id":"alterUserStmt"},{"text":"DROP USER","id":"dropUserStmt"},{"text":"LIST USERS","id":"listUsersStmt"},{"text":"Data Control","id":"dataControl"},{"text":"Permissions","id":"permissions"},{"text":"GRANT PERMISSION","id":"grantPermissionsStmt"},{"text":"GRANT ALL","id":"grantAll"},{"text":"Automatic Granting","id":"autoGrantPermissions"},{"text":"REVOKE PERMISSION","id":"revokePermissionsStmt"},{"text":"LIST PERMISSIONS","id":"listPermissionsStmt"},{"text":"Data Types","id":"types"},{"text":"Working with timestamps","id":"usingtimestamps"},{"text":"Working with dates","id":"usingdates"},{"text":"Working with time","id":"usingtime"},{"text":"Counters","id":"counters"},{"text":"Working with collections","id":"collections"},{"text":"Noteworthy characteristics","id":"noteworthy-characteristics"},{"text":"Maps","id":"map"},{"text":"Sets","id":"set"},{"text":"Lists","id":"list"},{"text":"Functions","id":"functions"},{"text":"Cast","id":"castFun"},{"text":"Token","id":"tokenFun"},{"text":"Uuid","id":"uuidFun"},{"text":"Timeuuid functions","id":"timeuuidFun"},{"text":"now","id":"now"},{"text":"minTimeuuid and maxTimeuuid","id":"mintimeuuid-and-maxtimeuuid"},{"text":"Time conversion functions","id":"timeFun"},{"text":"Blob conversion functions","id":"blobFun"},{"text":"Aggregates","id":"aggregates"},{"text":"Count","id":"countFct"},{"text":"Max and Min","id":"maxMinFcts"},{"text":"Sum","id":"sumFct"},{"text":"Avg","id":"avgFct"},{"text":"User-Defined Functions","id":"udfs"},{"text":"User-Defined Aggregates","id":"udas"},{"text":"JSON Support","id":"json"},{"text":"SELECT JSON","id":"selectJson"},{"text":"INSERT JSON","id":"insertJson"},{"text":"JSON Encoding of Cassandra Data Types","id":"jsonEncoding"},{"text":"The fromJson() Function","id":"fromJson"},{"text":"The toJson() Function","id":"toJson"},{"text":"Appendix A: CQL Keywords","id":"appendixA"},{"text":"Appendix B: CQL Reserved Types","id":"appendixB"},{"text":"Changes","id":"changes"},{"text":"3.4.3","id":"3-4-3"},{"text":"3.4.2","id":"3-4-2"},{"text":"3.4.2","id":"3-4-2-2"},{"text":"3.4.1","id":"3-4-1"},{"text":"3.4.0","id":"3-4-0"},{"text":"3.3.1","id":"3-3-1"},{"text":"3.3.0","id":"3-3-0"},{"text":"3.2.0","id":"3-2-0"},{"text":"3.1.7","id":"3-1-7"},{"text":"3.1.6","id":"3-1-6"},{"text":"3.1.5","id":"3-1-5"},{"text":"3.1.4","id":"3-1-4"},{"text":"3.1.3","id":"3-1-3"},{"text":"3.1.2","id":"3-1-2"},{"text":"3.1.1","id":"3-1-1"},{"text":"3.1.0","id":"3-1-0"},{"text":"3.0.5","id":"3-0-5"},{"text":"3.0.4","id":"3-0-4"},{"text":"3.0.3","id":"3-0-3"},{"text":"3.0.2","id":"3-0-2"},{"text":"3.0.1","id":"3-0-1"},{"text":"Versioning","id":"versioning"}]},"/Cassandra/4.1/cassandra/cql/ddl.html":{"text":"CQL stores data in tables, whose schema defines the layout of the data in the table. Tables are located in keyspaces. A keyspace defines options that apply to all the keyspace’s tables. The replication strategy is an important keyspace option, as is the replication factor. A good general rule is one keyspace per application. It is common for a cluster to define only one keyspace for an actie application. This section describes the statements used to create, modify, and remove those keyspace and tables. The names of the keyspaces and tables are defined by the following grammar: keyspace_name::= name table_name::= [keyspace_name '.' ] name name::= unquoted_name | quoted_name unquoted_name::= re('[a-zA-Z_0-9]\\{1, 48}') quoted_name::= '\"' unquoted_name '\"' Both keyspace and table name should be comprised of only alphanumeric characters, cannot be empty and are limited in size to 48 characters (that limit exists mostly to avoid filenames (which may include the keyspace and table name) to go over the limits of certain file systems). By default, keyspace and table names are case-insensitive (myTable is equivalent to mytable) but case sensitivity can be forced by using double-quotes (\"myTable\" is different from mytable). Further, a table is always part of a keyspace and a table name can be provided fully-qualified by the keyspace it is part of. If is is not fully-qualified, the table is assumed to be in the current keyspace (see USE statement. Further, the valid names for columns are defined as: column_name::= identifier We also define the notion of statement options for use in the following section: options::= option ( AND option )* option::= identifier '=' ( identifier | constant | map_literal ) A keyspace is created with a CREATE KEYSPACE statement: create_keyspace_statement::= CREATE KEYSPACE [ IF NOT EXISTS ] keyspace_name WITH options For example: CREATE KEYSPACE excelsior WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 3}; CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1' : 1, 'DC2' : 3} AND durable_writes = false; Attempting to create a keyspace that already exists will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the keyspace already exists. The supported options are: name kind mandatory default description replication map yes n/a The replication strategy and options to use for the keyspace (see details below). durable_writes simple no true Whether to use the commit log for updates on this keyspace (disable this option at your own risk!). The replication property is mandatory and must contain the 'class' sub-option that defines the desired replication strategy class. The rest of the sub-options depend on which replication strategy is used. By default, Cassandra supports the following 'class' values: A simple strategy that defines a replication factor for data to be spread across the entire cluster. This is generally not a wise choice for production, as it does not respect datacenter layouts and can lead to wildly varying query latency. For production, use NetworkTopologyStrategy. SimpleStrategy supports a single mandatory argument: sub-option type since description 'replication_factor' int all The number of replicas to store per range A production-ready replication strategy that sets the replication factor independently for each data-center. The rest of the sub-options are key-value pairs, with a key set to a data-center name and its value set to the associated replication factor. Options: sub-option type description '' int The number of replicas to store per range in the provided datacenter. 'replication_factor' int When later altering keyspaces and changing the replication_factor, auto-expansion will only add new datacenters for safety, it will not alter existing datacenters or remove any, even if they are no longer in the cluster. If you want to remove datacenters while setting the replication_factor, explicitly zero out the datacenter you want to have zero replicas. An example of auto-expanding datacenters with two datacenters: DC1 and DC2: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3}; DESCRIBE KEYSPACE excalibur; will result in: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3', 'DC2': '3'} AND durable_writes = true; An example of auto-expanding and overriding a datacenter: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3, 'DC2': 2}; DESCRIBE KEYSPACE excalibur; will result in: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3', 'DC2': '2'} AND durable_writes = true; An example that excludes a datacenter while using replication_factor: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor' : 3, 'DC2': 0}; DESCRIBE KEYSPACE excalibur; will result in: CREATE KEYSPACE excalibur WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': '3'} AND durable_writes = true; If transient replication has been enabled, transient replicas can be configured for both SimpleStrategy and NetworkTopologyStrategy by defining replication factors in the format '/' For instance, this keyspace will have 3 replicas in DC1, 1 of which is transient, and 5 replicas in DC2, 2 of which are transient: CREATE KEYSPACE some_keyspace WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1' : '3/1'', 'DC2' : '5/2'}; The USE statement changes the current keyspace to the specified keyspace. A number of objects in CQL are bound to a keyspace (tables, user-defined types, functions, etc.) and the current keyspace is the default keyspace used when those objects are referred to in a query without a fully-qualified name (without a prefixed keyspace name). A USE statement specifies the keyspace to use as an argument: use_statement::= USE keyspace_name Using CQL: USE excelsior; An ALTER KEYSPACE statement modifies the options of a keyspace: alter_keyspace_statement::= ALTER KEYSPACE [ IF EXISTS ] keyspace_name WITH options For example: ALTER KEYSPACE excelsior WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 4}; If the keyspace does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. The supported options are the same as for creating a keyspace. Dropping a keyspace is done with the DROP KEYSPACE statement: drop_keyspace_statement::= DROP KEYSPACE [ IF EXISTS ] keyspace_name For example: DROP KEYSPACE excelsior; Dropping a keyspace results in the immediate, irreversible removal of that keyspace, including all the tables, user-defined types, user-defined functions, and all the data contained in those tables. If the keyspace does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Creating a new table uses the CREATE TABLE statement: create_table_statement::= CREATE TABLE [ IF NOT EXISTS ] table_name '(' column_definition ( ',' column_definition )* [ ',' PRIMARY KEY '(' primary_key ')' ] ')' [ WITH table_options ] column_definition::= column_name cql_type [ STATIC ] [ PRIMARY KEY] primary_key::= partition_key [ ',' clustering_columns ] partition_key::= column_name | '(' column_name ( ',' column_name )* ')' clustering_columns::= column_name ( ',' column_name )* table_options:=: COMPACT STORAGE [ AND table_options ] | CLUSTERING ORDER BY '(' clustering_order ')' [ AND table_options ] | options clustering_order::= column_name (ASC | DESC) ( ',' column_name (ASC | DESC) )* For example, here are some CQL statements to create tables: CREATE TABLE monkey_species ( species text PRIMARY KEY, common_name text, population varint, average_size int ) WITH comment='Important biological records'; CREATE TABLE timeline ( userid uuid, posted_month int, posted_time uuid, body text, posted_by text, PRIMARY KEY (userid, posted_month, posted_time) ) WITH compaction = { 'class' : 'LeveledCompactionStrategy' }; CREATE TABLE loads ( machine inet, cpu int, mtime timeuuid, load float, PRIMARY KEY ((machine, cpu), mtime) ) WITH CLUSTERING ORDER BY (mtime DESC); A CQL table has a name and is composed of a set of rows. Creating a table amounts to defining which columns each rows will have, which of those columns comprise the primary key, as well as defined options for the table. Attempting to create an already existing table will return an error unless the IF NOT EXISTS directive is used. If it is used, the statement will be a no-op if the table already exists. Every row in a CQL table will have the predefined columns defined at table creation. Columns can be added later using an alter statement. A column_definition is comprised of the name of the column and its type, restricting the values that are accepted for that column. Additionally, a column definition can have the following modifiers: STATIC: declares the column as a static column PRIMARY KEY: declares the column as the sole component of the primary key of the table Some columns can be declared as STATIC in a table definition. A column that is static will be “shared” by all the rows belonging to the same partition (having the same partition key. For example: Code Results CREATE TABLE t ( pk int, t int, v text, s text static, PRIMARY KEY (pk, t) ); INSERT INTO t (pk, t, v, s) VALUES (0, 0, 'val0', 'static0'); INSERT INTO t (pk, t, v, s) VALUES (0, 1, 'val1', 'static1'); SELECT * FROM t; pk | t | v | s ----+---+--------+----------- 0 | 0 | 'val0' | 'static1' 0 | 1 | 'val1' | 'static1' As can be seen, the s value is the same (static1) for both of the rows in the partition (the partition key being pk, and both rows are in the same partition): the second insertion overrides the value for s. The use of static columns has the following restrictions: A table without clustering columns cannot have static columns. In a table without clustering columns, every partition has only one row, and so every column is inherently static) Only non-primary key columns can be static. Within a table, a row is uniquely identified by its PRIMARY KEY, and hence all tables must define a single PRIMARY KEY. A PRIMARY KEY is composed of one or more of the defined columns in the table. Syntactically, the primary key is defined with the phrase PRIMARY KEY followed by a comma-separated list of the column names within parenthesis. If the primary key has only one column, you can alternatively add the PRIMARY KEY phrase to that column in the table definition. The order of the columns in the primary key definition defines the partition key and clustering columns. A CQL primary key is composed of two parts: partition key It is the first component of the primary key definition. It can be a single column or, using an additional set of parenthesis, can be multiple columns. A table must have at least one partition key, the smallest possible table definition is: CREATE TABLE t (k text PRIMARY KEY); clustering columns The columns are the columns that follow the partition key in the primary key definition. The order of those columns define the clustering order. Some examples of primary key definition are: PRIMARY KEY (a): a is the single partition key and there are no clustering columns PRIMARY KEY (a, b, c) : a is the single partition key and b and c are the clustering columns PRIMARY KEY ((a, b), c) : a and b compose the composite partition key and c is the clustering column The primary key uniquely identifies a row in the table, as described above. A consequence of this uniqueness is that if another row is inserted using the same primary key, then an UPSERT occurs and an existing row with the same primary key is replaced. Columns that are not part of the primary key cannot define uniqueness. Within a table, CQL defines the notion of a partition that defines the location of data within a Cassandra cluster. A partition is the set of rows that share the same value for their partition key. Note that if the partition key is composed of multiple columns, then rows belong to the same partition when they have the same values for all those partition key columns. A hash is computed from the partition key columns and that hash value defines the partition location. So, for instance, given the following table definition and content: CREATE TABLE t ( a int, b int, c int, d int, PRIMARY KEY ((a, b), c, d) ); INSERT INTO t (a, b, c, d) VALUES (0,0,0,0); INSERT INTO t (a, b, c, d) VALUES (0,0,1,1); INSERT INTO t (a, b, c, d) VALUES (0,1,2,2); INSERT INTO t (a, b, c, d) VALUES (0,1,3,3); INSERT INTO t (a, b, c, d) VALUES (1,1,4,4); SELECT * FROM t; will result in a | b | c | d ---+---+---+--- 0 | 0 | 0 | 0 (1) 0 | 0 | 1 | 1 0 | 1 | 2 | 2 (2) 0 | 1 | 3 | 3 1 | 1 | 4 | 4 (3) (5 rows) 1 Rows 1 and 2 are in the same partition, because both columns a and b are zero. 2 Rows 3 and 4 are in the same partition, but a different one, because column a is zero and column b is 1 in both rows. 3 Row 5 is in a third partition by itself, because both columns a and b are 1. Note that a table always has a partition key, and that if the table has no clustering columns, then every partition of that table has a single row. because the partition key, compound or otherwise, identifies a single location. The most important property of partition is that all the rows belonging to the same partition are guaranteed to be stored on the same set of replica nodes. In other words, the partition key of a table defines which rows will be localized on the same node in the cluster. The localization of data is important to the efficient retrieval of data, requiring the Cassandra coordinator to contact as few nodes as possible. However, there is a flip-side to this guarantee, and all rows sharing a partition key will be stored on the same node, creating a hotspot for both reading and writing. While selecting a primary key that groups table rows assists batch updates and can ensure that the updates are atomic and done in isolation, the partitions must be sized \"just right, not too big nor too small\". Data modeling that considers the querying patterns and assigns primary keys based on the queries will have the lowest latency in fetching data. The clustering columns of a table define the clustering order for the partition of that table. For a given partition, all rows are ordered by that clustering order. Clustering columns also add uniqueness to a row in a table. For instance, given: CREATE TABLE t2 ( a int, b int, c int, d int, PRIMARY KEY (a, b, c) ); INSERT INTO t2 (a, b, c, d) VALUES (0,0,0,0); INSERT INTO t2 (a, b, c, d) VALUES (0,0,1,1); INSERT INTO t2 (a, b, c, d) VALUES (0,1,2,2); INSERT INTO t2 (a, b, c, d) VALUES (0,1,3,3); INSERT INTO t2 (a, b, c, d) VALUES (1,1,4,4); SELECT * FROM t2; will result in a | b | c | d ---+---+---+--- 1 | 1 | 4 | 4 (1) 0 | 0 | 0 | 0 0 | 0 | 1 | 1 0 | 1 | 2 | 2 0 | 1 | 3 | 3 (5 rows) 1 Row 1 is in one partition, and Rows 2-5 are in a different one. The display order is also different. Looking more closely at the four rows in the same partition, the b clustering column defines the order in which those rows are displayed. Whereas the partition key of the table groups rows on the same node, the clustering columns control how those rows are stored on the node. That sorting allows the very efficient retrieval of a range of rows within a partition: SELECT * FROM t2 WHERE a = 0 AND b > 0 and b <= 3; will result in a | b | c | d ---+---+---+--- 0 | 1 | 2 | 2 0 | 1 | 3 | 3 (2 rows) A CQL table has a number of options that can be set at creation (and, for most of them, altered later). These options are specified after the WITH keyword. One important option that cannot be changed after creation, CLUSTERING ORDER BY, influences how queries can be done against the table. It is worth discussing in more detail here. The clustering order of a table is defined by the clustering columns. By default, the clustering order is ascending for the clustering column’s data types. For example, integers order from 1, 2, …​ n, while text orders from A to Z. The CLUSTERING ORDER BY table option uses a comma-separated list of the clustering columns, each set for either ASC (for ascending order) or DESC (for _descending order). The default is ascending for all clustering columns if the CLUSTERING ORDER BY option is not set. This option is basically a hint for the storage engine that changes the order in which it stores the row. Beware of the consequences of setting this option: It changes the default ascending order of results when queried with a SELECT statement with no ORDER BY clause. It limits how the ORDER BY clause is used in SELECT statements on that table. Results can only be ordered with either the original clustering order or the reverse clustering order. Suppose you create a table with two clustering columns a and b, defined WITH CLUSTERING ORDER BY (a DESC, b ASC). Queries on the table can use ORDER BY (a DESC, b ASC) or ORDER BY (a ASC, b DESC). Mixed order, such as ORDER BY (a ASC, b ASC) or ORDER BY (a DESC, b DESC) will not return expected order. It has a performance impact on queries. Queries in reverse clustering order are slower than the default ascending order. If you plan to query mostly in descending order, declare the clustering order in the table schema using WITH CLUSTERING ORDER BY (). This optimization is common for time series, to retrieve the data from newest to oldest. A table supports the following options: option kind default description comment simple none A free-form, human-readable comment speculative_retry simple 99PERCENTILE Speculative retry options cdc boolean false Create a Change Data Capture (CDC) log on the table additional_write_policy simple 99PERCENTILE Same as speculative_retry gc_grace_seconds simple 864000 Time to wait before garbage collecting tombstones (deletion markers) bloom_filter_fp_chance simple 0.00075 The target probability of false positive of the sstable bloom filters. Said bloom filters will be sized to provide the provided probability, thus lowering this value impact the size of bloom filters in-memory and on-disk. default_time_to_live simple 0 Default expiration time (“TTL”) in seconds for a table compaction map see below Compaction options compression map see below Compression options caching map see below Caching options memtable_flush_period_in_ms simple 0 Time (in ms) before Cassandra flushes memtables to disk read_repair simple BLOCKING Sets read repair behavior (see below) By default, Cassandra read coordinators only query as many replicas as necessary to satisfy consistency levels: one for consistency level ONE, a quorum for QUORUM, and so on. speculative_retry determines when coordinators may query additional replicas, a useful action when replicas are slow or unresponsive. Speculative retries reduce the latency. The speculative_retry option configures rapid read protection, where a coordinator sends more requests than needed to satisfy the consistency level. Frequently reading from additional replicas can hurt cluster performance. When in doubt, keep the default 99PERCENTILE. Pre-Cassandra 4.0 speculative retry policy takes a single string as a parameter: NONE ALWAYS 99PERCENTILE (PERCENTILE) 50MS (CUSTOM) An example of setting speculative retry sets a custom value: ALTER TABLE users WITH speculative_retry = '10ms'; This example uses a percentile for the setting: ALTER TABLE users WITH speculative_retry = '99PERCENTILE'; A percentile setting can backfire. If a single host becomes unavailable, it can force up the percentiles. A value of p99 will not speculate as intended because the value at the specified percentile has increased too much. If the consistency level is set to ALL, all replicas are queried regardless of the speculative retry setting. Cassandra 4.0 supports case-insensitivity for speculative retry values (CASSANDRA-14293). For example, assigning the value as none, None, or NONE has the same effect. Additionally, the following values are added: Format Example Description XPERCENTILE 90.5PERCENTILE Coordinators record average per-table response times for all replicas. If a replica takes longer than X percent of this table’s average response time, the coordinator queries an additional replica. X must be between 0 and 100. XP 90.5P Same as XPERCENTILE Yms 25ms If a replica takes more than Y milliseconds to respond, the coordinator queries an additional replica. MIN(XPERCENTILE,YMS) MIN(99PERCENTILE,35MS) A hybrid policy that uses either the specified percentile or fixed milliseconds depending on which value is lower at the time of calculation. Parameters are XPERCENTILE, XP, or Yms. This setting helps protect against a single slow instance. MAX(XPERCENTILE,YMS) ALWAYS NEVER MAX(90.5P,25ms) A hybrid policy that uses either the specified percentile or fixed milliseconds depending on which value is higher at the time of calculation. Cassandra 4.0 adds support for hybrid MIN() and MAX() speculative retry policies, with a mix and match of either MIN(), MAX(), MIN(), MIN(), or MAX(), MAX() (CASSANDRA-14293). The hybrid mode will still speculate if the normal p99 for the table is < 50ms, the minimum value. But if the p99 level goes higher than the maximum value, then that value can be used. In a hybrid value, one value must be a fixed time (ms) value and the other a percentile value. To illustrate variations, the following examples are all valid: min(99percentile,50ms) max(99p,50MS) MAX(99P,50ms) MIN(99.9PERCENTILE,50ms) max(90percentile,100MS) MAX(100.0PERCENTILE,60ms) The additional_write_policy setting specifies the threshold at which a cheap quorum write will be upgraded to include transient replicas. The compaction options must minimally define the 'class' sub-option, to specify the compaction strategy class to use. The supported classes are: 'SizeTieredCompactionStrategy', STCS (Default) 'LeveledCompactionStrategy', LCS 'TimeWindowCompactionStrategy', TWCS The 'DateTieredCompactionStrategy' is also supported but deprecated; 'TimeWindowCompactionStrategy' should be used. If a custom strategies is required, specify the full class name as a string constant. All default strategies support a number of common options, as well as options specific to the strategy chosen. See the section corresponding to your strategy for details: STCS, LCS, TWCS. The compression options define if and how the SSTables of the table are compressed. Compression is configured on a per-table basis as an optional argument to CREATE TABLE or ALTER TABLE. The following sub-options are available: Option Default Description class LZ4Compressor The compression algorithm to use. Default compressor are: LZ4Compressor, SnappyCompressor, DeflateCompressor and ZstdCompressor. Use 'enabled' : false to disable compression. Custom compressor can be provided by specifying the full class name as a string constant. enabled true Enable/disable sstable compression. If the enabled option is set to false, no other options must be specified. chunk_length_in_kb 64 On disk SSTables are compressed by block (to allow random reads). This option defines the size (in KB) of said block. See note for further information. crc_check_chance 1.0 Determines how likely Cassandra is to verify the checksum on each compression chunk during reads. compression_level 3 Compression level. Only applicable for ZstdCompressor. Accepts values between -131072 and 22. Bigger values may improve the compression rate, but will increase the minimum size of data to be read from disk for a read. The default value is an optimal value for compressing tables. Chunk length must be a power of 2 when computing the chunk number from an uncompressed file offset. Block size may be adjusted based on read/write access patterns such as: How much data is typically requested at once Average size of rows in the table For instance, to create a table with LZ4Compressor and a chunk_length_in_kb of 4 KB: CREATE TABLE simple ( id int, key text, value text, PRIMARY KEY (key, value) ) WITH compression = {'class': 'LZ4Compressor', 'chunk_length_in_kb': 4}; Caching optimizes the use of cache memory of a table. The cached data is weighed by size and access frequency. The caching options can configure both the key cache and the row cache for the table. The following sub-options are available: Option Default Description keys ALL Whether to cache keys (key cache) for this table. Valid values are: ALL and NONE. rows_per_partition NONE The amount of rows to cache per partition (row cache). If an integer n is specified, the first n queried rows of a partition will be cached. Valid values are: ALL, to cache all rows of a queried partition, or NONE to disable row caching. For instance, to create a table with both a key cache and 10 rows cached per partition: CREATE TABLE simple ( id int, key text, value text, PRIMARY KEY (key, value) ) WITH caching = {'keys': 'ALL', 'rows_per_partition': 10}; The read_repair options configure the read repair behavior, tuning for various performance and consistency behaviors. The values are: Option Default Description BLOCKING yes If a read repair is triggered, the read blocks writes sent to other replicas until the consistency level is reached by the writes. NONE no If set, the coordinator reconciles any differences between replicas, but doesn’t attempt to repair them. Two consistency properties are affected by read repair behavior. Monotonic quorum reads: Monotonic quorum reads prevents reads from appearing to go back in time in some circumstances. When monotonic quorum reads are not provided and a write fails to reach a quorum of replicas, the read values may be visible in one read, and then disappear in a subsequent read. BLOCKING provides this behavior. Write atomicity: Write atomicity prevents reads from returning partially-applied writes. Cassandra attempts to provide partition-level write atomicity, but since only the data covered by a SELECT statement is repaired by a read repair, read repair can break write atomicity when data is read at a more granular level than it is written. For example, read repair can break write atomicity if you write multiple rows to a clustered partition in a batch, but then select a single row by specifying the clustering column in a SELECT statement. NONE provides this behavior. Adding new columns (see ALTER TABLE below) is a constant time operation. Thus, there is no need to anticipate future usage while initially creating a table. Altering an existing table uses the ALTER TABLE statement: alter_table_statement::= ALTER TABLE [ IF EXISTS ] table_name alter_table_instruction alter_table_instruction::= ADD [ IF NOT EXISTS ] column_name cql_type ( ',' column_name cql_type )* | DROP [ IF EXISTS ] column_name ( column_name )* | RENAME [ IF EXISTS ] column_name to column_name (AND column_name to column_name)* | WITH options If the table does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. For example: ALTER TABLE addamsFamily ADD gravesite varchar; ALTER TABLE addamsFamily WITH comment = 'A most excellent and useful table'; The ALTER TABLE statement can: ADD a new column to a table. The primary key of a table cannot ever be altered. A new column, thus, cannot be part of the primary key. Adding a column is a constant-time operation based on the amount of data in the table. If the new column already exists, the statement will return an error, unless IF NOT EXISTS is used in which case the operation is a no-op. DROP a column from a table. This command drops both the column and all its content. Be aware that, while the column becomes immediately unavailable, its content are removed lazily during compaction. Because of this lazy removal, the command is a constant-time operation based on the amount of data in the table. Also, it is important to know that once a column is dropped, a column with the same name can be re-added, unless the dropped column was a non-frozen column like a collection. If the dropped column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Warning Dropping a column assumes that the timestamps used for the value of this column are \"real\" timestamp in microseconds. Using \"real\" timestamps in microseconds is the default is and is strongly recommended but as Cassandra allows the client to provide any timestamp on any table, it is theoretically possible to use another convention. Please be aware that if you do so, dropping a column will not correctly execute. RENAME a primary key column of a table. Non primary key columns cannot be renamed. Furthermore, renaming a column to another name which already exists isn’t allowed. It’s important to keep in mind that renamed columns shouldn’t have dependent seconday indexes. If the renamed column does not already exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Use WITH to change a table option. The supported options are the same as those used when creating a table, with the exception of CLUSTERING ORDER. However, setting any compaction sub-options will erase ALL previous compaction options, so you need to re-specify all the sub-options you wish to keep. The same is true for compression sub-options. Dropping a table uses the DROP TABLE statement: drop_table_statement::= DROP TABLE [ IF EXISTS ] table_name Dropping a table results in the immediate, irreversible removal of the table, including all data it contains. If the table does not exist, the statement will return an error, unless IF EXISTS is used, when the operation is a no-op. A table can be truncated using the TRUNCATE statement: truncate_statement::= TRUNCATE [ TABLE ] table_name TRUNCATE TABLE foo is the preferred syntax for consistency with other DDL statements. However, tables are the only object that can be truncated currently, and the TABLE keyword can be omitted. Truncating a table permanently removes all existing data from the table, but without removing the table itself.","title":"Data Definition","component":"Cassandra","version":"4.1","name":"ddl","url":"/Cassandra/4.1/cassandra/cql/ddl.html","titles":[{"text":"Common definitions","id":"common-definitions"},{"text":"CREATE KEYSPACE","id":"create-keyspace-statement"},{"text":"SimpleStrategy","id":"replication-strategy"},{"text":"NetworkTopologyStrategy","id":"networktopologystrategy"},{"text":"USE","id":"use-statement"},{"text":"ALTER KEYSPACE","id":"alter-keyspace-statement"},{"text":"DROP KEYSPACE","id":"drop-keyspace-statement"},{"text":"CREATE TABLE","id":"create-table-statement"},{"text":"Column definitions","id":"column-definition"},{"text":"Static columns","id":"static-column"},{"text":"The Primary key","id":"primary-key"},{"text":"Partition key","id":"partition-key"},{"text":"Clustering columns","id":"clustering-columns"},{"text":"Table options","id":"create-table-options"},{"text":"Clustering order","id":"clustering-order"},{"text":"Other table options","id":"create-table-general-options"},{"text":"Speculative retry options","id":"spec_retry"},{"text":"Compaction options","id":"cql-compaction-options"},{"text":"Compression options","id":"cql-compression-options"},{"text":"Caching options","id":"cql-caching-options"},{"text":"Read Repair options","id":"read-repair-options"},{"text":"Other considerations:","id":"other-considerations"},{"text":"ALTER TABLE","id":"alter-table-statement"},{"text":"DROP TABLE","id":"drop-table-statement"},{"text":"TRUNCATE","id":"truncate-statement"}]},"/Cassandra/4.1/cassandra/cql/definitions.html":{"text":"To aid in specifying the CQL syntax, we will use the following conventions in this document: Language rules will be given in an informal BNF variant notation. In particular, we’ll use square brakets ([ item ]) for optional items, * and + for repeated items (where + imply at least one). The grammar will also use the following convention for convenience: non-terminal term will be lowercase (and link to their definition) while terminal keywords will be provided \"all caps\". Note however that keywords are identifiers and are thus case insensitive in practice. We will also define some early construction using regexp, which we’ll indicate with re(). The grammar is provided for documentation purposes and leave some minor details out. For instance, the comma on the last column definition in a CREATE TABLE statement is optional but supported if present even though the grammar in this document suggests otherwise. Also, not everything accepted by the grammar is necessarily valid CQL. References to keywords or pieces of CQL code in running text will be shown in a fixed-width font. The CQL language uses identifiers (or names) to identify tables, columns and other objects. An identifier is a token matching the regular expression [a-zA-Z][a-zA-Z0-9_]*. A number of such identifiers, like SELECT or WITH, are keywords. They have a fixed meaning for the language and most are reserved. The list of those keywords can be found in Appendix A. Identifiers and (unquoted) keywords are case insensitive. Thus SELECT is the same than select or sElEcT, and myId is the same than myid or MYID. A convention often used (in particular by the samples of this documentation) is to use uppercase for keywords and lowercase for other identifiers. There is a second kind of identifier called a quoted identifier defined by enclosing an arbitrary sequence of characters (non-empty) in double-quotes(\"). Quoted identifiers are never keywords. Thus \"select\" is not a reserved keyword and can be used to refer to a column (note that using this is particularly ill-advised), while select would raise a parsing error. Also, unlike unquoted identifiers and keywords, quoted identifiers are case sensitive (\"My Quoted Id\" is different from \"my quoted id\"). A fully lowercase quoted identifier that matches [a-zA-Z][a-zA-Z0-9_]* is however equivalent to the unquoted identifier obtained by removing the double-quote (so \"myid\" is equivalent to myid and to myId but different from \"myId\"). Inside a quoted identifier, the double-quote character can be repeated to escape it, so \"foo \"\" bar\" is a valid identifier. Note The quoted identifier can declare columns with arbitrary names, and these can sometime clash with specific names used by the server. For instance, when using conditional update, the server will respond with a result set containing a special result named \"[applied]\". If you’ve declared a column with such a name, this could potentially confuse some tools and should be avoided. In general, unquoted identifiers should be preferred but if you use quoted identifiers, it is strongly advised that you avoid any name enclosed by squared brackets (like \"[applied]\") and any name that looks like a function call (like \"f(x)\"). More formally, we have: identifier::= unquoted_identifier | quoted_identifier unquoted_identifier::= re('[a-zA-Z][link:[a-zA-Z0-9]]*') quoted_identifier::= '\"' (any character where \" can appear if doubled)+ '\"' CQL defines the following constants: constant::= string | integer | float | boolean | uuid | blob | NULL string::= ''' (any character where ' can appear if doubled)+ ''' : '$$' (any character other than '$$') '$$' integer::= re('-?[0-9]+') float::= re('-?[0-9]+(.[0-9]*)?([eE][+-]?[0-9+])?') | NAN | INFINITY boolean::= TRUE | FALSE uuid::= hex\\{8}-hex\\{4}-hex\\{4}-hex\\{4}-hex\\{12} hex::= re(\"[0-9a-fA-F]\") blob::= '0' ('x' | 'X') hex+ In other words: A string constant is an arbitrary sequence of characters enclosed by single-quote('). A single-quote can be included by repeating it, e.g. 'It''s raining today'. Those are not to be confused with quoted identifiers that use double-quotes. Alternatively, a string can be defined by enclosing the arbitrary sequence of characters by two dollar characters, in which case single-quote can be used without escaping (It's raining today). That latter form is often used when defining user-defined functions to avoid having to escape single-quote characters in function body (as they are more likely to occur than $$). Integer, float and boolean constant are defined as expected. Note however than float allows the special NaN and Infinity constants. CQL supports UUID constants. Blobs content are provided in hexadecimal and prefixed by 0x. The special NULL constant denotes the absence of value. For how these constants are typed, see the Data types section. CQL has the notion of a term, which denotes the kind of values that CQL support. Terms are defined by: term::= constant | literal | function_call | arithmetic_operation | type_hint | bind_marker literal::= collection_literal | udt_literal | tuple_literal function_call::= identifier '(' [ term (',' term)* ] ')' arithmetic_operation::= '-' term | term ('+' | '-' | '*' | '/' | '%') term type_hint::= '(' cql_type ')' term bind_marker::= '?' | ':' identifier A term is thus one of: A constant A literal for either a collection, a user-defined type or a tuple A function call, either a native function or a user-defined function An arithmetic operation between terms A type hint A bind marker, which denotes a variable to be bound at execution time. See the section on prepared-statements for details. A bind marker can be either anonymous (?) or named (:some_name). The latter form provides a more convenient way to refer to the variable for binding it and should generally be preferred. A comment in CQL is a line beginning by either double dashes (--) or double slash (//). Multi-line comments are also supported through enclosure within / and / (but nesting is not supported). -- This is a comment // This is a comment too /* This is a multi-line comment */ CQL consists of statements that can be divided in the following categories: data-definition statements, to define and change how the data is stored (keyspaces and tables). data-manipulation statements, for selecting, inserting and deleting data. secondary-indexes statements. materialized-views statements. cql-roles statements. cql-permissions statements. User-Defined Functions (UDFs) statements. udts statements. cql-triggers statements. All the statements are listed below and are described in the rest of this documentation (see links above): cql_statement::= statement [ ';' ] statement:=: ddl_statement : | dml_statement | secondary_index_statement | materialized_view_statement | role_or_permission_statement | udf_statement | udt_statement | trigger_statement ddl_statement::= use_statement | create_keyspace_statement | alter_keyspace_statement | drop_keyspace_statement | create_table_statement | alter_table_statement | drop_table_statement | truncate_statement dml_statement::= select_statement | insert_statement | update_statement | delete_statement | batch_statement secondary_index_statement::= create_index_statement | drop_index_statement materialized_view_statement::= create_materialized_view_statement | drop_materialized_view_statement role_or_permission_statement::= create_role_statement | alter_role_statement | drop_role_statement | grant_role_statement | revoke_role_statement | list_roles_statement | grant_permission_statement | revoke_permission_statement | list_permissions_statement | create_user_statement | alter_user_statement | drop_user_statement | list_users_statement udf_statement::= create_function_statement | drop_function_statement | create_aggregate_statement | drop_aggregate_statement udt_statement::= create_type_statement | alter_type_statement | drop_type_statement trigger_statement::= create_trigger_statement | drop_trigger_statement CQL supports prepared statements. Prepared statements are an optimization that allows to parse a query only once but execute it multiple times with different concrete values. Any statement that uses at least one bind marker (see bind_marker) will need to be prepared. After which the statement can be executed by provided concrete values for each of its marker. The exact details of how a statement is prepared and then executed depends on the CQL driver used and you should refer to your driver documentation.","title":"Definitions","component":"Cassandra","version":"4.1","name":"definitions","url":"/Cassandra/4.1/cassandra/cql/definitions.html","titles":[{"text":"Conventions","id":"conventions"},{"text":"Identifiers and keywords","id":"identifiers"},{"text":"Constants","id":"constants"},{"text":"Terms","id":"terms"},{"text":"Comments","id":"comments"},{"text":"Statements","id":"statements"},{"text":"Prepared Statements","id":"prepared-statements"}]},"/Cassandra/4.1/cassandra/cql/dml.html":{"text":"This section describes the statements supported by CQL to insert, update, delete and query data. Querying data from data is done using a SELECT statement: select_statement::= SELECT [ JSON | DISTINCT ] ( select_clause | '*' ) FROM `table_name` [ WHERE `where_clause` ] [ GROUP BY `group_by_clause` ] [ ORDER BY `ordering_clause` ] [ PER PARTITION LIMIT (`integer` | `bind_marker`) ] [ LIMIT (`integer` | `bind_marker`) ] [ ALLOW FILTERING ] select_clause::= `selector` [ AS `identifier` ] ( ',' `selector` [ AS `identifier` ] ) selector::== `column_name` | `term` | CAST '(' `selector` AS `cql_type` ')' | `function_name` '(' [ `selector` ( ',' `selector` )_ ] ')' | COUNT '(' '_' ')' where_clause::= `relation` ( AND `relation` )* relation::= column_name operator term '(' column_name ( ',' column_name )* ')' operator tuple_literal TOKEN '(' column_name# ( ',' column_name )* ')' operator term operator::= '=' | '' | '=' | '!=' | IN | CONTAINS | CONTAINS KEY group_by_clause::= column_name ( ',' column_name )* ordering_clause::= column_name [ ASC | DESC ] ( ',' column_name [ ASC | DESC ] )* For example: SELECT name, occupation FROM users WHERE userid IN (199, 200, 207); SELECT JSON name, occupation FROM users WHERE userid = 199; SELECT name AS user_name, occupation AS user_occupation FROM users; SELECT time, value FROM events WHERE event_type = 'myEvent' AND time > '2011-02-03' AND time = '2012-01-01' AND posted_at = '2012-01-01' AND posted_at token(0) in particular). For example: SELECT * FROM posts WHERE token(userid) > token('tom') AND token(userid) ('John''s Blog', '2012-01-01'); This query will return all rows that sort after the one having “John’s Blog” as blog_tile and '2012-01-01' for posted_at in the clustering order. In particular, rows having a post_at ⇐ '2012-01-01' will be returned, as long as their blog_title > 'John''s Blog'. That would not be the case for this example: SELECT * FROM posts WHERE userid = 'john doe' AND blog_title > 'John''s Blog' AND posted_at > '2012-01-01'; The tuple notation may also be used for IN clauses on clustering columns: SELECT * FROM posts WHERE userid = 'john doe' AND (blog_title, posted_at) IN (('John''s Blog', '2012-01-01'), ('Extreme Chess', '2014-06-01')); The CONTAINS operator may only be used for collection columns (lists, sets, and maps). In the case of maps, CONTAINS applies to the map values. The CONTAINS KEY operator may only be used on map columns and applies to the map keys. The GROUP BY option can condense all selected rows that share the same values for a set of columns into a single row. Using the GROUP BY option, rows can be grouped at the partition key or clustering column level. Consequently, the GROUP BY option only accepts primary key columns in defined order as arguments. If a primary key column is restricted by an equality restriction, it is not included in the GROUP BY clause. Aggregate functions will produce a separate value for each group. If no GROUP BY clause is specified, aggregates functions will produce a single value for all the rows. If a column is selected without an aggregate function, in a statement with a GROUP BY, the first value encounter in each group will be returned. The ORDER BY clause selects the order of the returned results. The argument is a list of column names and each column’s order (ASC for ascendant and DESC for descendant, The possible orderings are limited by the clustering order defined on the table: if the table has been defined without any specific CLUSTERING ORDER, then the order is as defined by the clustering columns or the reverse otherwise, the order is defined by the CLUSTERING ORDER option and the reversed one. The LIMIT option to a SELECT statement limits the number of rows returned by a query. The PER PARTITION LIMIT option limits the number of rows returned for a given partition by the query. Both types of limits can used in the same statement. By default, CQL only allows select queries that don’t involve a full scan of all partitions. If all partitions are scanned, then returning the results may experience a significant latency proportional to the amount of data in the table. The ALLOW FILTERING option explicitly executes a full scan. Thus, the performance of the query can be unpredictable. For example, consider the following table of user profiles with birth year and country of residence. The birth year has a secondary index defined. CREATE TABLE users ( username text PRIMARY KEY, firstname text, lastname text, birth_year int, country text ); CREATE INDEX ON users(birth_year); The following queries are valid: // All users are returned SELECT * FROM users; // All users with a particular birth year are returned SELECT * FROM users WHERE birth_year = 1981; In both cases, the query performance is proportional to the amount of data returned. The first query returns all rows, because all users are selected. The second query returns only the rows defined by the secondary index, a per-node implementation; the results will depend on the number of nodes in the cluster, and is indirectly proportional to the amount of data stored. The number of nodes will always be multiple number of magnitude lower than the number of user profiles stored. Both queries may return very large result sets, but the addition of a LIMIT clause can reduced the latency. The following query will be rejected: SELECT * FROM users WHERE birth_year = 1981 AND country = 'FR'; Cassandra cannot guarantee that large amounts of data won’t have to scanned amount of data, even if the result is small. If you know that the dataset is small, and the performance will be reasonable, add ALLOW FILTERING to allow the query to execute: SELECT * FROM users WHERE birth_year = 1981 AND country = 'FR' ALLOW FILTERING; Inserting data for a row is done using an INSERT statement: insert_statement::= INSERT INTO table_name ( names_values | json_clause ) [ IF NOT EXISTS ] [ USING update_parameter ( AND update_parameter )* ] names_values::= names VALUES tuple_literal json_clause::= JSON string [ DEFAULT ( NULL | UNSET ) ] names::= '(' column_name ( ',' column_name )* ')' For example: INSERT INTO NerdMovies (movie, director, main_actor, year) VALUES ('Serenity', 'Joss Whedon', 'Nathan Fillion', 2005) USING TTL 86400; INSERT INTO NerdMovies JSON '{\"movie\": \"Serenity\", \"director\": \"Joss Whedon\", \"year\": 2005}'; The INSERT statement writes one or more columns for a given row in a table. Since a row is identified by its PRIMARY KEY, at least one columns must be specified. The list of columns to insert must be supplied with the VALUES syntax. When using the JSON syntax, VALUES are optional. See the section on JSON support for more detail. All updates for an INSERT are applied atomically and in isolation. Unlike in SQL, INSERT does not check the prior existence of the row by default. The row is created if none existed before, and updated otherwise. Furthermore, there is no means of knowing which action occurred. The IF NOT EXISTS condition can restrict the insertion if the row does not exist. However, note that using IF NOT EXISTS will incur a non-negligible performance cost, because Paxos is used, so this should be used sparingly. Please refer to the UPDATE section for informations on the update_parameter. Also note that INSERT does not support counters, while UPDATE does. Updating a row is done using an UPDATE statement: update_statement ::= UPDATE table_name [ USING update_parameter ( AND update_parameter )* ] SET assignment( ',' assignment )* WHERE where_clause [ IF ( EXISTS | condition ( AND condition)*) ] update_parameter ::= ( TIMESTAMP | TTL ) ( integer | bind_marker ) assignment: simple_selection'=' term `| column_name'=' column_name ( '+' | '-' ) term | column_name'=' list_literal'+' column_name simple_selection ::= column_name | column_name '[' term']' | column_name'.' field_name condition ::= `simple_selection operator term For instance: UPDATE NerdMovies USING TTL 400 SET director = 'Joss Whedon', main_actor = 'Nathan Fillion', year = 2005 WHERE movie = 'Serenity'; UPDATE UserActions SET total = total + 2 WHERE user = B70DE1D0-9908-4AE3-BE34-5573E5B09F14 AND action = 'click'; The UPDATE statement writes one or more columns for a given row in a table. The WHERE`clause is used to select the row to update and must include all columns of the `PRIMARY KEY. Non-primary key columns are set using the SET keyword. In an UPDATE statement, all updates within the same partition key are applied atomically and in isolation. Unlike in SQL, UPDATE does not check the prior existence of the row by default. The row is created if none existed before, and updated otherwise. Furthermore, there is no means of knowing which action occurred. The IF condition can be used to choose whether the row is updated or not if a particular condition is met. However, like the IF NOT EXISTS condition, a non-negligible performance cost can be incurred. Regarding the SET assignment: c = c + 3 will increment/decrement counters, the only operation allowed. The column name after the '=' sign must be the same than the one before the '=' sign. Increment/decrement is only allowed on counters. See the section on counters for details. id = id + and id[value1] = value2 are for collections. See the collections for details. id.field = 3 is for setting the value of a field on a non-frozen user-defined types. See the UDTs for details. UPDATE and INSERT statements support the following parameters: TTL: specifies an optional Time To Live (in seconds) for the inserted values. If set, the inserted values are automatically removed from the database after the specified time. Note that the TTL concerns the inserted values, not the columns themselves. This means that any subsequent update of the column will also reset the TTL (to whatever TTL is specified in that update). By default, values never expire. A TTL of 0 is equivalent to no TTL. If the table has a default_time_to_live, a TTL of 0 will remove the TTL for the inserted or updated values. A TTL of null is equivalent to inserting with a TTL of 0. UPDATE, INSERT, DELETE and BATCH statements support the following parameters: TIMESTAMP: sets the timestamp for the operation. If not specified, the coordinator will use the current time (in microseconds) at the start of statement execution as the timestamp. This is usually a suitable default. Deleting rows or parts of rows uses the DELETE statement: delete_statement::= DELETE [ simple_selection ( ',' simple_selection ) ] FROM table_name [ USING update_parameter ( AND update_parameter# )* ] WHERE where_clause [ IF ( EXISTS | condition ( AND condition)*) ] For example: DELETE FROM NerdMovies USING TIMESTAMP 1240003134 WHERE movie = 'Serenity'; DELETE phone FROM Users WHERE userid IN (C73DE1D3-AF08-40F3-B124-3FF3E5109F22, B70DE1D0-9908-4AE3-BE34-5573E5B09F14); The DELETE statement deletes columns and rows. If column names are provided directly after the DELETE keyword, only those columns are deleted from the row indicated by the WHERE clause. Otherwise, whole rows are removed. The WHERE clause specifies which rows are to be deleted. Multiple rows may be deleted with one statement by using an IN operator. A range of rows may be deleted using an inequality operator (such as >=). DELETE supports the TIMESTAMP option with the same semantics as in updates. In a DELETE statement, all deletions within the same partition key are applied atomically and in isolation. A DELETE operation can be conditional through the use of an IF clause, similar to UPDATE and INSERT statements. However, as with INSERT and UPDATE statements, this will incur a non-negligible performance cost because Paxos is used, and should be used sparingly. Multiple INSERT, UPDATE and DELETE can be executed in a single statement by grouping them through a BATCH statement: batch_statement ::= BEGIN [ UNLOGGED | COUNTER ] BATCH [ USING update_parameter( AND update_parameter)* ] modification_statement ( ';' modification_statement )* APPLY BATCH modification_statement ::= insert_statement | update_statement | delete_statement For instance: BEGIN BATCH INSERT INTO users (userid, password, name) VALUES ('user2', 'ch@ngem3b', 'second user'); UPDATE users SET password = 'ps22dhds' WHERE userid = 'user3'; INSERT INTO users (userid, password) VALUES ('user4', 'ch@ngem3c'); DELETE name FROM users WHERE userid = 'user1'; APPLY BATCH; The BATCH statement group multiple modification statements (insertions/updates and deletions) into a single statement. It serves several purposes: It saves network round-trips between the client and the server (and sometimes between the server coordinator and the replicas) when batching multiple updates. All updates in a BATCH belonging to a given partition key are performed in isolation. By default, all operations in the batch are performed as logged, to ensure all mutations eventually complete (or none will). See the notes on UNLOGGED batches for more details. Note that: BATCH statements may only contain UPDATE, INSERT and DELETE statements (not other batches for instance). Batches are not a full analogue for SQL transactions. If a timestamp is not specified for each operation, then all operations will be applied with the same timestamp (either one generated automatically, or the timestamp provided at the batch level). Due to Cassandra’s conflict resolution procedure in the case of timestamp ties, operations may be applied in an order that is different from the order they are listed in the BATCH statement. To force a particular operation ordering, you must specify per-operation timestamps. A LOGGED batch to a single partition will be converted to an UNLOGGED batch as an optimization. By default, Cassandra uses a batch log to ensure all operations in a batch eventually complete or none will (note however that operations are only isolated within a single partition). There is a performance penalty for batch atomicity when a batch spans multiple partitions. If you do not want to incur this penalty, you can tell Cassandra to skip the batchlog with the UNLOGGED option. If the UNLOGGED option is used, a failed batch might leave the patch only partly applied. Use the COUNTER option for batched counter updates. Unlike other updates in Cassandra, counter updates are not idempotent.","title":"Data Manipulation","component":"Cassandra","version":"4.1","name":"dml","url":"/Cassandra/4.1/cassandra/cql/dml.html","titles":[{"text":"SELECT","id":"select-statement"},{"text":"Selection clause","id":"selection-clause"},{"text":"Selectors","id":"selectors"},{"text":"Aliases","id":"aliases"},{"text":"WRITETIME and TTL function","id":"writetime-and-ttl-function"},{"text":"The WHERE clause","id":"where-clause"},{"text":"Grouping results","id":"group-by-clause"},{"text":"Ordering results","id":"ordering-clause"},{"text":"Limiting results","id":"limit-clause"},{"text":"Allowing filtering","id":"allow-filtering"},{"text":"INSERT","id":"insert-statement"},{"text":"UPDATE","id":"update-statement"},{"text":"Update parameters","id":"update-parameters"},{"text":"DELETE","id":"delete_statement"},{"text":"BATCH","id":"batch_statement"},{"text":"UNLOGGED batches","id":"unlogged-batches"},{"text":"COUNTER batches","id":"counter-batches"}]},"/Cassandra/4.1/cassandra/cql/functions.html":{"text":"CQL supports 2 main categories of functions: scalar functions that take a number of values and produce an output aggregate functions that aggregate multiple rows resulting from a SELECT statement In both cases, CQL provides a number of native \"hard-coded\" functions as well as the ability to create new user-defined functions. Note By default, the use of user-defined functions is disabled by default for security concerns (even when enabled, the execution of user-defined functions is sandboxed and a \"rogue\" function should not be allowed to do evil, but no sandbox is perfect so using user-defined functions is opt-in). See the user_defined_functions_enabled in cassandra.yaml to enable them. A function is identifier by its name: function_name ::= [ keyspace_name'.' ] name The cast function can be used to converts one native datatype to another. The following table describes the conversions supported by the cast function. Cassandra will silently ignore any cast converting a datatype into its own datatype. From To ascii text, varchar bigint tinyint, smallint, int, float, double, decimal, varint, text, varchar boolean text, varchar counter tinyint, smallint, int, bigint, float, double, decimal, varint, text, varchar date timestamp decimal tinyint, smallint, int, bigint, float, double, varint, text, varchar double tinyint, smallint, int, bigint, float, decimal, varint, text, varchar float tinyint, smallint, int, bigint, double, decimal, varint, text, varchar inet text, varchar int tinyint, smallint, bigint, float, double, decimal, varint, text, varchar smallint tinyint, int, bigint, float, double, decimal, varint, text, varchar time text, varchar timestamp date, text, varchar timeuuid timestamp, date, text, varchar tinyint tinyint, smallint, int, bigint, float, double, decimal, varint, text, varchar uuid text, varchar varint tinyint, smallint, int, bigint, float, double, decimal, text, varchar The conversions rely strictly on Java’s semantics. For example, the double value 1 will be converted to the text value '1.0'. For instance: SELECT avg(cast(count as double)) FROM myTable The token function computes the token for a given partition key. The exact signature of the token function depends on the table concerned and the partitioner used by the cluster. The type of the arguments of the token depend on the partition key column type. The returned type depends on the defined partitioner: Partitioner Returned type Murmur3Partitioner bigint RandomPartitioner varint ByteOrderedPartitioner blob For example, consider the following table: CREATE TABLE users ( userid text PRIMARY KEY, username text, ); The table uses the default Murmur3Partitioner. The token function uses the single argument text, because the partition key is userid of text type. The returned type will be bigint. The uuid function takes no parameters and generates a random type 4 uuid suitable for use in INSERT or UPDATE statements. The now function takes no arguments and generates, on the coordinator node, a new unique timeuuid at the time the function is invoked. Note that this method is useful for insertion but is largely non-sensical in WHERE clauses. For example, a query of the form: SELECT * FROM myTable WHERE t = now(); will not return a result, by design, since the value returned by now() is guaranteed to be unique. currentTimeUUID is an alias of now. The minTimeuuid function takes a timestamp value t, either a timestamp or a date string. It returns a fake timeuuid corresponding to the smallest possible timeuuid for timestamp t. The maxTimeuuid works similarly, but returns the largest possible timeuuid. For example: SELECT * FROM myTable WHERE t > maxTimeuuid('2013-01-01 00:05+0000') AND t = maxTimeuuid('2013-01-01 00:05+0000') would still not select a timeuuid generated exactly at '2013-01-01 00:05+0000', and is essentially equivalent to t > maxTimeuuid('2013-01-01 00:05+0000'). Note The values generated by minTimeuuid and maxTimeuuid are called fake UUID because they do no respect the time-based UUID generation process specified by the IETF RFC 4122. In particular, the value returned by these two methods will not be unique. Thus, only use these methods for querying, not for insertion, to prevent possible data overwriting. The following functions can be used to retrieve the date/time at the time where the function is invoked: Function name Output type currentTimestamp timestamp currentDate date currentTime time currentTimeUUID timeUUID For example the last two days of data can be retrieved using: SELECT * FROM myTable WHERE date >= currentDate() - 2d; A number of functions are provided to convert a timeuuid, a timestamp or a date into another native type. Function name Input type Description toDate timeuuid Converts the timeuuid argument into a date type toDate timestamp Converts the timestamp argument into a date type toTimestamp timeuuid Converts the timeuuid argument into a timestamp type toTimestamp date Converts the date argument into a timestamp type toUnixTimestamp timeuuid Converts the timeuuid argument into a bigInt raw value toUnixTimestamp timestamp Converts the timestamp argument into a bigInt raw value toUnixTimestamp date Converts the date argument into a bigInt raw value dateOf timeuuid Similar to toTimestamp(timeuuid) (DEPRECATED) unixTimestampOf timeuuid Similar to toUnixTimestamp(timeuuid) (DEPRECATED) A number of functions are provided to convert the native types into binary data, or a blob. For every type supported by CQL, the function typeAsBlob takes a argument of type type and returns it as a blob. Conversely, the function blobAsType takes a 64-bit blob argument and converts it to a bigint value. For example, bigintAsBlob(3) returns 0x0000000000000003 and blobAsBigint(0x0000000000000003) returns 3. User-defined functions (UDFs) execute user-provided code in Cassandra. By default, Cassandra supports defining functions in Java and JavaScript. Support for other JSR 223 compliant scripting languages, such as Python, Ruby, and Scala, is possible by adding a JAR to the classpath. UDFs are part of the Cassandra schema, and are automatically propagated to all nodes in the cluster. UDFs can be overloaded, so that multiple UDFs with different argument types can have the same function name. Note JavaScript user-defined functions have been deprecated. They are planned for removal in the next major release. For example: CREATE FUNCTION sample ( arg int ) ...; CREATE FUNCTION sample ( arg text ) ...; UDFs are susceptible to all of the normal problems with the chosen programming language. Accordingly, implementations should be safe against null pointer exceptions, illegal arguments, or any other potential source of exceptions. An exception during function execution will result in the entire statement failing. Valid queries for UDF use are SELECT, INSERT and UPDATE statements. Complex types like collections, tuple types and user-defined types are valid argument and return types in UDFs. Tuple types and user-defined types use the DataStax Java Driver conversion functions. Please see the Java Driver documentation for details on handling tuple types and user-defined types. Arguments for functions can be literals or terms. Prepared statement placeholders can be used, too. Note the use the double dollar-sign syntax to enclose the UDF source code. For example: CREATE FUNCTION some_function ( arg int ) RETURNS NULL ON NULL INPUT RETURNS int LANGUAGE java AS $$ return arg; $$; SELECT some_function(column) FROM atable ...; UPDATE atable SET col = some_function(?) ...; CREATE TYPE custom_type (txt text, i int); CREATE FUNCTION fct_using_udt ( udtarg frozen ) RETURNS NULL ON NULL INPUT RETURNS text LANGUAGE java AS $$ return udtarg.getString(\"txt\"); $$; The implicitly available udfContext field (or binding for script UDFs) provides the necessary functionality to create new UDT and tuple values: CREATE TYPE custom_type (txt text, i int); CREATE FUNCTION fct\\_using\\_udt ( somearg int ) RETURNS NULL ON NULL INPUT RETURNS custom_type LANGUAGE java AS $$ UDTValue udt = udfContext.newReturnUDTValue(); udt.setString(\"txt\", \"some string\"); udt.setInt(\"i\", 42); return udt; $$; The definition of the UDFContext interface can be found in the Apache Cassandra source code for org.apache.cassandra.cql3.functions.UDFContext. public interface UDFContext { UDTValue newArgUDTValue(String argName); UDTValue newArgUDTValue(int argNum); UDTValue newReturnUDTValue(); UDTValue newUDTValue(String udtName); TupleValue newArgTupleValue(String argName); TupleValue newArgTupleValue(int argNum); TupleValue newReturnTupleValue(); TupleValue newTupleValue(String cqlDefinition); } Java UDFs already have some imports for common interfaces and classes defined. These imports are: import java.nio.ByteBuffer; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.cassandra.cql3.functions.UDFContext; import com.datastax.driver.core.TypeCodec; import com.datastax.driver.core.TupleValue; import com.datastax.driver.core.UDTValue; Please note, that these convenience imports are not available for script UDFs. Creating a new user-defined function uses the CREATE FUNCTION statement: create_function_statement::= CREATE [ OR REPLACE ] FUNCTION [ IF NOT EXISTS] function_name '(' arguments_declaration ')' [ CALLED | RETURNS NULL ] ON NULL INPUT RETURNS cql_type LANGUAGE identifier AS string arguments_declaration: identifier cql_type ( ',' identifier cql_type )* For example: CREATE OR REPLACE FUNCTION somefunction(somearg int, anotherarg text, complexarg frozen, listarg list) RETURNS NULL ON NULL INPUT RETURNS text LANGUAGE java AS $$ // some Java code $$; CREATE FUNCTION IF NOT EXISTS akeyspace.fname(someArg int) CALLED ON NULL INPUT RETURNS text LANGUAGE java AS $$ // some Java code $$; CREATE FUNCTION with the optional OR REPLACE keywords creates either a function or replaces an existing one with the same signature. A CREATE FUNCTION without OR REPLACE fails if a function with the same signature already exists. If the optional IF NOT EXISTS keywords are used, the function will only be created only if another function with the same signature does not exist. OR REPLACE and IF NOT EXISTS cannot be used together. Behavior for null input values must be defined for each function: RETURNS NULL ON NULL INPUT declares that the function will always return null if any of the input arguments is null. CALLED ON NULL INPUT declares that the function will always be executed. Signatures are used to distinguish individual functions. The signature consists of a fully-qualified function name of the . and a concatenated list of all the argument types. Note that keyspace names, function names and argument types are subject to the default naming conventions and case-sensitivity rules. Functions belong to a keyspace; if no keyspace is specified, the current keyspace is used. User-defined functions are not allowed in the system keyspaces. Dropping a function uses the DROP FUNCTION statement: drop_function_statement::= DROP FUNCTION [ IF EXISTS ] function_name [ '(' arguments_signature ')' ] arguments_signature::= cql_type ( ',' cql_type )* For example: DROP FUNCTION myfunction; DROP FUNCTION mykeyspace.afunction; DROP FUNCTION afunction ( int ); DROP FUNCTION afunction ( text ); You must specify the argument types of the function, the arguments_signature, in the drop command if there are multiple overloaded functions with the same name but different signatures. DROP FUNCTION with the optional IF EXISTS keywords drops a function if it exists, but does not throw an error if it doesn’t. Aggregate functions work on a set of rows. Values for each row are input, to return a single value for the set of rows aggregated. If normal columns, scalar functions, UDT fields, writetime, or ttl are selected together with aggregate functions, the values returned for them will be the ones of the first row matching the query. The count function can be used to count the rows returned by a query. For example: SELECT COUNT (*) FROM plays; SELECT COUNT (1) FROM plays; It also can count the non-null values of a given column: SELECT COUNT (scores) FROM plays; The max and min functions compute the maximum and the minimum value returned by a query for a given column. For example: SELECT MIN (players), MAX (players) FROM plays WHERE game = 'quake'; The sum function sums up all the values returned by a query for a given column. For example: SELECT SUM (players) FROM plays; The avg function computes the average of all the values returned by a query for a given column. For example: SELECT AVG (players) FROM plays; User-defined aggregates allow the creation of custom aggregate functions. User-defined aggregates can be used in SELECT statement. Each aggregate requires an initial state of type STYPE defined with the INITCOND`value (default value: `null). The first argument of the state function must have type STYPE. The remaining arguments of the state function must match the types of the user-defined aggregate arguments. The state function is called once for each row, and the value returned by the state function becomes the new state. After all rows are processed, the optional FINALFUNC is executed with last state value as its argument. The STYPE value is mandatory in order to distinguish possibly overloaded versions of the state and/or final function, since the overload can appear after creation of the aggregate. A complete working example for user-defined aggregates (assuming that a keyspace has been selected using the USE statement): CREATE OR REPLACE FUNCTION test.averageState(state tuple, val int) CALLED ON NULL INPUT RETURNS tuple LANGUAGE java AS $$ if (val != null) { state.setInt(0, state.getInt(0)+1); state.setLong(1, state.getLong(1)+val.intValue()); } return state; $$; CREATE OR REPLACE FUNCTION test.averageFinal (state tuple) CALLED ON NULL INPUT RETURNS double LANGUAGE java AS $$ double r = 0; if (state.getInt(0) == 0) return null; r = state.getLong(1); r /= state.getInt(0); return Double.valueOf(r); $$; CREATE OR REPLACE AGGREGATE test.average(int) SFUNC averageState STYPE tuple FINALFUNC averageFinal INITCOND (0, 0); CREATE TABLE test.atable ( pk int PRIMARY KEY, val int ); INSERT INTO test.atable (pk, val) VALUES (1,1); INSERT INTO test.atable (pk, val) VALUES (2,2); INSERT INTO test.atable (pk, val) VALUES (3,3); INSERT INTO test.atable (pk, val) VALUES (4,4); SELECT test.average(val) FROM atable; Creating (or replacing) a user-defined aggregate function uses the CREATE AGGREGATE statement: create_aggregate_statement ::= CREATE [ OR REPLACE ] AGGREGATE [ IF NOT EXISTS ] function_name '(' arguments_signature')' SFUNC function_name STYPE cql_type: [ FINALFUNC function_name] [ INITCOND term ] See above for a complete example. The CREATE AGGREGATE command with the optional OR REPLACE keywords creates either an aggregate or replaces an existing one with the same signature. A CREATE AGGREGATE without OR REPLACE fails if an aggregate with the same signature already exists. The CREATE AGGREGATE command with the optional IF NOT EXISTS keywords creates an aggregate if it does not already exist. The OR REPLACE and IF NOT EXISTS phrases cannot be used together. The STYPE value defines the type of the state value and must be specified. The optional INITCOND defines the initial state value for the aggregate; the default value is null. A non-null INITCOND must be specified for state functions that are declared with RETURNS NULL ON NULL INPUT. The SFUNC value references an existing function to use as the state-modifying function. The first argument of the state function must have type STYPE. The remaining arguments of the state function must match the types of the user-defined aggregate arguments. The state function is called once for each row, and the value returned by the state function becomes the new state. State is not updated for state functions declared with RETURNS NULL ON NULL INPUT and called with null. After all rows are processed, the optional FINALFUNC is executed with last state value as its argument. It must take only one argument with type STYPE, but the return type of the FINALFUNC may be a different type. A final function declared with RETURNS NULL ON NULL INPUT means that the aggregate’s return value will be null, if the last state is null. If no FINALFUNC is defined, the overall return type of the aggregate function is STYPE. If a FINALFUNC is defined, it is the return type of that function. Dropping an user-defined aggregate function uses the DROP AGGREGATE statement: drop_aggregate_statement::= DROP AGGREGATE [ IF EXISTS ] function_name[ '(' arguments_signature ')' ] For instance: DROP AGGREGATE myAggregate; DROP AGGREGATE myKeyspace.anAggregate; DROP AGGREGATE someAggregate ( int ); DROP AGGREGATE someAggregate ( text ); The DROP AGGREGATE statement removes an aggregate created using CREATE AGGREGATE. You must specify the argument types of the aggregate to drop if there are multiple overloaded aggregates with the same name but a different signature. The DROP AGGREGATE command with the optional IF EXISTS keywords drops an aggregate if it exists, and does nothing if a function with the signature does not exist.","title":"","component":"Cassandra","version":"4.1","name":"functions","url":"/Cassandra/4.1/cassandra/cql/functions.html","titles":[{"text":"Functions","id":"functions"},{"text":"Scalar functions","id":"scalar-functions"},{"text":"Native functions","id":"scalar-native-functions"},{"text":"Cast","id":"cast"},{"text":"Token","id":"token"},{"text":"Uuid","id":"uuid"},{"text":"Timeuuid functions","id":"timeuuid-functions"},{"text":"now","id":"now"},{"text":"minTimeuuid and maxTimeuuid","id":"mintimeuuid-and-maxtimeuuid"},{"text":"Datetime functions","id":"datetime-functions"},{"text":"Retrieving the current date/time","id":"retrieving-the-current-datetime"},{"text":"Time conversion functions","id":"time-conversion-functions"},{"text":"Blob conversion functions","id":"blob-conversion-functions"},{"text":"User-defined functions","id":"user-defined-scalar-functions"},{"text":"CREATE FUNCTION statement","id":"create-function-statement"},{"text":"Function Signature","id":"function-signature"},{"text":"DROP FUNCTION statement","id":"drop-function-statement"},{"text":"Aggregate functions","id":"aggregate-functions"},{"text":"Native aggregates","id":"native-aggregates"},{"text":"Count","id":"count-function"},{"text":"Max and Min","id":"max-and-min"},{"text":"Sum","id":"sum"},{"text":"Avg","id":"avg"},{"text":"User-Defined Aggregates (UDAs)","id":"user-defined-aggregates-functions"},{"text":"CREATE AGGREGATE statement","id":"create-aggregate-statement"},{"text":"DROP AGGREGATE statement","id":"drop-aggregate-statement"}]},"/Cassandra/4.1/cassandra/cql/index.html":{"text":"This document describes the Cassandra Query Language (CQL) version 3. Note that this document describes the last version of the language. However, the changes section provides the differences between the versions of CQL since version 3.0. CQL offers a model similar to SQL. The data is stored in tables containing rows of columns. For that reason, when used in this document, these terms (tables, rows and columns) have the same definition that they have in SQL. Definitions Data types Data definition language Data manipulation language Operators Secondary indexes Materialized views Functions JSON CQL security Triggers Appendices Changes","title":"The Cassandra Query Language (CQL)","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/cql/index.html","titles":[]},"/Cassandra/4.1/cassandra/cql/indexes.html":{"text":"CQL supports creating secondary indexes on tables, allowing queries on the table to use those indexes. A secondary index is identified by a name defined by: index_name::= re('[a-zA-Z_0-9]+') Creating a secondary index on a table uses the CREATE INDEX statement: create_index_statement::= CREATE [ CUSTOM ] INDEX [ IF NOT EXISTS ] [ index_name ] ON table_name '(' index_identifier ')' [ USING string [ WITH OPTIONS = map_literal ] ] index_identifier::= column_name | ( KEYS | VALUES | ENTRIES | FULL ) '(' column_name ')' For instance: CREATE INDEX userIndex ON NerdMovies (user); CREATE INDEX ON Mutants (abilityId); CREATE INDEX ON users (keys(favs)); CREATE CUSTOM INDEX ON users (email) USING 'path.to.the.IndexClass'; CREATE CUSTOM INDEX ON users (email) USING 'path.to.the.IndexClass' WITH OPTIONS = {'storage': '/mnt/ssd/indexes/'}; The CREATE INDEX statement is used to create a new (automatic) secondary index for a given (existing) column in a given table. A name for the index itself can be specified before the ON keyword, if desired. If data already exists for the column, it will be indexed asynchronously. After the index is created, new data for the column is indexed automatically at insertion time. Attempting to create an already existing index will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the index already exists. When creating an index on a maps , you may index either the keys or the values. If the column identifier is placed within the keys() function, the index will be on the map keys, allowing you to use CONTAINS KEY in WHERE clauses. Otherwise, the index will be on the map values. Dropping a secondary index uses the DROP INDEX statement: drop_index_statement::= DROP INDEX [ IF EXISTS ] index_name The DROP INDEX statement is used to drop an existing secondary index. The argument of the statement is the index name, which may optionally specify the keyspace of the index. If the index does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op.","title":"Secondary Indexes","component":"Cassandra","version":"4.1","name":"indexes","url":"/Cassandra/4.1/cassandra/cql/indexes.html","titles":[{"text":"CREATE INDEX","id":"create-index-statement"},{"text":"Indexes on Map Keys","id":"indexes-on-map-keys"},{"text":"DROP INDEX","id":"drop-index-statement"}]},"/Cassandra/4.1/cassandra/cql/json.html":{"text":"Cassandra 2.2 introduces JSON support to SELECT and INSERT statements. This support does not fundamentally alter the CQL API (for example, the schema is still enforced). It simply provides a convenient way to work with JSON documents. With SELECT statements, the JSON keyword is used to return each row as a single JSON encoded map. The remainder of the SELECT statement behavior is the same. The result map keys match the column names in a normal result set. For example, a statement like SELECT JSON a, ttl(b) FROM …​ would result in a map with keys \"a\" and \"ttl(b)\". However, there is one notable exception: for symmetry with INSERT JSON behavior, case-sensitive column names with upper-case letters will be surrounded with double quotes. For example, SELECT JSON myColumn FROM …​ would result in a map key \"\\\"myColumn\\\"\" with escaped quotes). The map values will JSON-encoded representations (as described below) of the result set values. With INSERT statements, the new JSON keyword can be used to enable inserting a JSON encoded map as a single row. The format of the JSON map should generally match that returned by a SELECT JSON statement on the same table. In particular, case-sensitive column names should be surrounded with double quotes. For example, to insert into a table with two columns named \"myKey\" and \"value\", you would do the following: INSERT INTO mytable JSON '{ \"\\\"myKey\\\"\": 0, \"value\": 0}'; By default (or if DEFAULT NULL is explicitly used), a column omitted from the JSON map will be set to NULL, meaning that any pre-existing value for that column will be removed (resulting in a tombstone being created). Alternatively, if the DEFAULT UNSET directive is used after the value, omitted column values will be left unset, meaning that pre-existing values for those column will be preserved. Where possible, Cassandra will represent and accept data types in their native JSON representation. Cassandra will also accept string representations matching the CQL literal format for all single-field types. For example, floats, ints, UUIDs, and dates can be represented by CQL literal strings. However, compound types, such as collections, tuples, and user-defined types must be represented by native JSON collections (maps and lists) or a JSON-encoded string representation of the collection. The following table describes the encodings that Cassandra will accept in INSERT JSON values (and fromJson() arguments) as well as the format Cassandra will use when returning data for SELECT JSON statements (and fromJson()): Type Formats accepted Return format Notes ascii string string Uses JSON’s \\u character escape bigint integer, string integer String must be valid 64 bit integer blob string string String should be 0x followed by an even number of hex digits boolean boolean, string boolean String must be \"true\" or \"false\" date string string Date in format YYYY-MM-DD, timezone UTC decimal integer, float, string float May exceed 32 or 64-bit IEEE-754 floating point precision in client-side decoder double integer, float, string float String must be valid integer or float float integer, float, string float String must be valid integer or float inet string string IPv4 or IPv6 address int integer, string integer String must be valid 32 bit integer list list, string list Uses JSON’s native list representation map map, string map Uses JSON’s native map representation smallint integer, string integer String must be valid 16 bit integer set list, string list Uses JSON’s native list representation text string string Uses JSON’s \\u character escape time string string Time of day in format HH-MM-SS[.fffffffff] timestamp integer, string string A timestamp. Strings constant allows to input timestamps as dates . Datestamps with format YYYY-MM-DD HH:MM:SS.SSS are returned. timeuuid string string Type 1 UUID. See constant for the UUID format tinyint integer, string integer String must be valid 8 bit integer tuple list, string list Uses JSON’s native list representation UDT map, string map Uses JSON’s native map representation with field names as keys uuid string string See constant for the UUID format varchar string string Uses JSON’s \\u character escape varint integer, string integer Variable length; may overflow 32 or 64 bit integers in client-side decoder The fromJson() function may be used similarly to INSERT JSON, but for a single column value. It may only be used in the VALUES clause of an INSERT statement or as one of the column values in an UPDATE, DELETE, or SELECT statement. For example, it cannot be used in the selection clause of a SELECT statement. The toJson() function may be used similarly to SELECT JSON, but for a single column value. It may only be used in the selection clause of a SELECT statement.","title":"JSON Support","component":"Cassandra","version":"4.1","name":"json","url":"/Cassandra/4.1/cassandra/cql/json.html","titles":[{"text":"SELECT JSON","id":"select-json"},{"text":"INSERT JSON","id":"insert-json"},{"text":"JSON Encoding of Cassandra Data Types","id":"json-encoding-of-cassandra-data-types"},{"text":"The fromJson() Function","id":"the-fromjson-function"},{"text":"The toJson() Function","id":"the-tojson-function"}]},"/Cassandra/4.1/cassandra/cql/mvs.html":{"text":"Materialized views names are defined by: view_name::= re('[a-zA-Z_0-9]+') You can create a materialized view on a table using a CREATE MATERIALIZED VIEW statement: create_materialized_view_statement::= CREATE MATERIALIZED VIEW [ IF NOT EXISTS ] view_name AS select_statement PRIMARY KEY '(' primary_key')' WITH table_options For instance: CREATE MATERIALIZED VIEW monkeySpecies_by_population AS SELECT * FROM monkeySpecies WHERE population IS NOT NULL AND species IS NOT NULL PRIMARY KEY (population, species) WITH comment='Allow query by population instead of species'; The CREATE MATERIALIZED VIEW statement creates a new materialized view. Each such view is a set of rows which corresponds to rows which are present in the underlying, or base, table specified in the SELECT statement. A materialized view cannot be directly updated, but updates to the base table will cause corresponding updates in the view. Creating a materialized view has 3 main parts: The select statement that restrict the data included in the view. The primary key definition for the view. The options for the view. Attempting to create an already existing materialized view will return an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the materialized view already exists. Note By default, materialized views are built in a single thread. The initial build can be parallelized by increasing the number of threads specified by the property concurrent_materialized_view_builders in cassandra.yaml. This property can also be manipulated at runtime through both JMX and the setconcurrentviewbuilders and getconcurrentviewbuilders nodetool commands. The select statement of a materialized view creation defines which of the base table is included in the view. That statement is limited in a number of ways: the selection is limited to those that only select columns of the base table. In other words, you can’t use any function (aggregate or not), casting, term, etc. Aliases are also not supported. You can however use * as a shortcut of selecting all columns. Further, static columns cannot be included in a materialized view. Thus, a SELECT * command isn’t allowed if the base table has static columns. The WHERE clause has the following restrictions: cannot include any bind_marker cannot have columns that are not part of the base table primary key that are not restricted by an IS NOT NULL restriction no other restriction is allowed cannot have columns that are part of the view primary key be null, they must always be at least restricted by a IS NOT NULL restriction (or any other restriction, but they must have one). cannot have an ordering clause, a limit, or xref:cql/dml.adoc#allow-filtering[ALLOW FILTERING A view must have a primary key and that primary key must conform to the following restrictions: it must contain all the primary key columns of the base table. This ensures that every row of the view correspond to exactly one row of the base table. it can only contain a single column that is not a primary key column in the base table. So for instance, give the following base table definition: CREATE TABLE t ( k int, c1 int, c2 int, v1 int, v2 int, PRIMARY KEY (k, c1, c2) ); then the following view definitions are allowed: CREATE MATERIALIZED VIEW mv1 AS SELECT * FROM t WHERE k IS NOT NULL AND c1 IS NOT NULL AND c2 IS NOT NULL PRIMARY KEY (c1, k, c2); CREATE MATERIALIZED VIEW mv1 AS SELECT * FROM t WHERE k IS NOT NULL AND c1 IS NOT NULL AND c2 IS NOT NULL PRIMARY KEY (v1, k, c1, c2); but the following ones are not allowed: // Error: cannot include both v1 and v2 in the primary key as both are not in the base table primary key CREATE MATERIALIZED VIEW mv1 AS SELECT * FROM t WHERE k IS NOT NULL AND c1 IS NOT NULL AND c2 IS NOT NULL AND v1 IS NOT NULL PRIMARY KEY (v1, v2, k, c1, c2); // Error: must include k in the primary as it's a base table primary key column CREATE MATERIALIZED VIEW mv1 AS SELECT * FROM t WHERE c1 IS NOT NULL AND c2 IS NOT NULL PRIMARY KEY (c1, c2); A materialized view is internally implemented by a table and as such, creating a MV allows the same options than creating a table . After creation, you can alter the options of a materialized view using the ALTER MATERIALIZED VIEW statement: alter_materialized_view_statement::= ALTER MATERIALIZED VIEW [ IF EXISTS ] view_name WITH table_options The options that can be updated are the same than at creation time and thus the same than for tables . If the view does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Dropping a materialized view using the DROP MATERIALIZED VIEW statement: drop_materialized_view_statement::= DROP MATERIALIZED VIEW [ IF EXISTS ] view_name; If the materialized view does not exists, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. Note Removal of columns not selected in the Materialized View (via UPDATE base SET unselected_column = null or DELETE unselected_column FROM base) may shadow missed updates to other columns received by hints or repair. For this reason, we advise against doing deletions on base columns not selected in views until this is fixed on CASSANDRA-13826.","title":"Materialized Views","component":"Cassandra","version":"4.1","name":"mvs","url":"/Cassandra/4.1/cassandra/cql/mvs.html","titles":[{"text":"CREATE MATERIALIZED VIEW","id":"create-materialized-view-statement"},{"text":"MV select statement","id":"mv-select"},{"text":"MV primary key","id":"mv-primary-key"},{"text":"MV options","id":"mv-options"},{"text":"ALTER MATERIALIZED VIEW","id":"alter-materialized-view-statement"},{"text":"DROP MATERIALIZED VIEW","id":"drop-materialized-view-statement"},{"text":"MV Limitations","id":"mv-limitations"}]},"/Cassandra/4.1/cassandra/cql/operators.html":{"text":"CQL supports the following operators: Operator Description - (unary) Negates operand + Addition - Substraction * Multiplication / Division % Returns the remainder of a division All arithmetic operations are supported on numeric types or counters. The return type of the operation will be based on the operand types: left/right tinyint smallint int bigint counter float double varint decimal tinyint tinyint smallint int bigint bigint float double varint decimal smallint smallint smallint int bigint bigint float double varint decimal int int int int bigint bigint float double varint decimal bigint bigint bigint bigint bigint bigint double double varint decimal counter bigint bigint bigint bigint bigint double double varint decimal float float float float double double float double decimal decimal double double double double double double double double decimal decimal varint varint varint varint decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal decimal *, / and % operators have a higher precedence level than + and - operator. By consequence, they will be evaluated before. If two operator in an expression have the same precedence level, they will be evaluated left to right based on their position in the expression. A duration can be added (+) or substracted (-) from a timestamp or a date to create a new timestamp or date. So for instance: SELECT * FROM myTable WHERE t = '2017-01-01' - 2d; will select all the records with a value of t which is in the last 2 days of 2016.","title":"Arithmetic Operators","component":"Cassandra","version":"4.1","name":"operators","url":"/Cassandra/4.1/cassandra/cql/operators.html","titles":[{"text":"Number Arithmetic","id":"number-arithmetic"},{"text":"Datetime Arithmetic","id":"datetime--arithmetic"}]},"/Cassandra/4.1/cassandra/cql/security.html":{"text":"role_name ::= identifier | string= Security CQL uses database roles to represent users and group of users. Syntactically, a role is defined by: role_name ::= identifier | string Creating a role uses the CREATE ROLE statement: create_role_statement ::= CREATE ROLE [ IF NOT EXISTS ] role_name [ WITH role_options# ] role_options ::= role_option ( AND role_option)* role_option ::= PASSWORD '=' string | HASHED PASSWORD '=' string | LOGIN '=' boolean | SUPERUSER '=' boolean | OPTIONS '=' map_literal | ACCESS TO DATACENTERS set_literal | ACCESS TO ALL DATACENTERS For instance: CREATE ROLE new_role; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true; CREATE ROLE alice WITH HASHED PASSWORD = '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' AND LOGIN = true; CREATE ROLE bob WITH PASSWORD = 'password_b' AND LOGIN = true AND SUPERUSER = true; CREATE ROLE carlos WITH OPTIONS = { 'custom_option1' : 'option1_value', 'custom_option2' : 99 }; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND ACCESS TO DATACENTERS {'DC1', 'DC3'}; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND ACCESS TO ALL DATACENTERS; By default roles do not possess LOGIN privileges or SUPERUSER status. Permissions on database resources are granted to roles; types of resources include keyspaces, tables, functions and roles themselves. Roles may be granted to other roles to create hierarchical permissions structures; in these hierarchies, permissions and SUPERUSER status are inherited, but the LOGIN privilege is not. If a role has the LOGIN privilege, clients may identify as that role when connecting. For the duration of that connection, the client will acquire any roles and privileges granted to that role. Only a client with with the CREATE permission on the database roles resource may issue CREATE ROLE requests (see the relevant section), unless the client is a SUPERUSER. Role management in Cassandra is pluggable and custom implementations may support only a subset of the listed options. Role names should be quoted if they contain non-alphanumeric characters. Use the WITH PASSWORD clause to set a password for internal authentication, enclosing the password in single quotation marks. If internal authentication has not been set up or the role does not have LOGIN privileges, the WITH PASSWORD clause is not necessary. USE WITH HASHED PASSWORD to provide the jBcrypt hashed password directly. See the hash_password tool. If a network_authorizer has been configured, you can restrict login roles to specific datacenters with the ACCESS TO DATACENTERS clause followed by a set literal of datacenters the user can access. Not specifiying datacenters implicitly grants access to all datacenters. The clause ACCESS TO ALL DATACENTERS can be used for explicitness, but there’s no functional difference. Attempting to create an existing role results in an invalid query condition unless the IF NOT EXISTS option is used. If the option is used and the role exists, the statement is a no-op: CREATE ROLE other_role; CREATE ROLE IF NOT EXISTS other_role; Altering a role options uses the ALTER ROLE statement: alter_role_statement ::= ALTER ROLE [ IF EXISTS ] role_name WITH role_options For example: ALTER ROLE bob WITH PASSWORD = 'PASSWORD_B' AND SUPERUSER = false; ALTER ROLE bob WITH HASHED PASSWORD = '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' AND SUPERUSER = false; If the role does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. USE WITH HASHED PASSWORD to provide the jBcrypt hashed password directly. See the hash_password tool. If a network_authorizer has been configured, you can restrict login roles to specific datacenters with the ACCESS TO DATACENTERS clause followed by a set literal of datacenters the user can access. To remove any data center restrictions, use the ACCESS TO ALL DATACENTERS clause. Conditions on executing ALTER ROLE statements: a client must have SUPERUSER status to alter the SUPERUSER status of another role a client cannot alter the SUPERUSER status of any role it currently holds a client can only modify certain properties of the role with which it identified at login (e.g. PASSWORD) to modify properties of a role, the client must be granted ALTER permission on that role Dropping a role uses the DROP ROLE statement: drop_role_statement ::= DROP ROLE [ IF EXISTS ] role_name DROP ROLE requires the client to have DROP permission on the role in question. In addition, client may not DROP the role with which it identified at login. Finally, only a client with SUPERUSER status may DROP another SUPERUSER role. Attempting to drop a role which does not exist results in an invalid query condition unless the IF EXISTS option is used. If the option is used and the role does not exist the statement is a no-op. Note DROP ROLE intentionally does not terminate any open user sessions. Currently connected sessions will remain connected and will retain the ability to perform any database actions which do not require authorization. However, if authorization is enabled, permissions of the dropped role are also revoked, subject to the caching options configured in cassandra-yaml file. Should a dropped role be subsequently recreated and have new permissions or roles` granted to it, any client sessions still connected will acquire the newly granted permissions and roles. Granting a role to another uses the GRANT ROLE statement: grant_role_statement ::= GRANT role_name TO role_name For example: GRANT report_writer TO alice; This statement grants the report_writer role to alice. Any permissions granted to report_writer are also acquired by alice. Roles are modelled as a directed acyclic graph, so circular grants are not permitted. The following examples result in error conditions: GRANT role_a TO role_b; GRANT role_b TO role_a; GRANT role_a TO role_b; GRANT role_b TO role_c; GRANT role_c TO role_a; Revoking a role uses the REVOKE ROLE statement: revoke_role_statement ::= REVOKE role_name FROM role_name For example: REVOKE report_writer FROM alice; This statement revokes the report_writer role from alice. Any permissions that alice has acquired via the report_writer role are also revoked. All the known roles (in the system or granted to specific role) can be listed using the LIST ROLES statement: list_roles_statement ::= LIST ROLES [ OF role_name] [ NORECURSIVE ] For instance: LIST ROLES; returns all known roles in the system, this requires DESCRIBE permission on the database roles resource. This example enumerates all roles granted to alice, including those transitively acquired: LIST ROLES OF alice; This example lists all roles directly granted to bob without including any of the transitively acquired ones: LIST ROLES OF bob NORECURSIVE; Prior to the introduction of roles in Cassandra 2.2, authentication and authorization were based around the concept of a USER. For backward compatibility, the legacy syntax has been preserved with USER centric statements becoming synonyms for the ROLE based equivalents. In other words, creating/updating a user is just a different syntax for creating/updating a role. Creating a user uses the CREATE USER statement: create_user_statement ::= CREATE USER [ IF NOT EXISTS ] role_name [ WITH [ HASHED ] PASSWORD string ] [ user_option ] user_option: SUPERUSER | NOSUPERUSER For example: CREATE USER alice WITH PASSWORD 'password_a' SUPERUSER; CREATE USER bob WITH PASSWORD 'password_b' NOSUPERUSER; CREATE USER bob WITH HASHED PASSWORD '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG' NOSUPERUSER; The CREATE USER command is equivalent to CREATE ROLE where the LOGIN option is true. So, the following pairs of statements are equivalent: CREATE USER alice WITH PASSWORD 'password_a' SUPERUSER; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND SUPERUSER = true; CREATE USER IF NOT EXISTS alice WITH PASSWORD 'password_a' SUPERUSER; CREATE ROLE IF NOT EXISTS alice WITH PASSWORD = 'password_a' AND LOGIN = true AND SUPERUSER = true; CREATE USER alice WITH PASSWORD 'password_a' NOSUPERUSER; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true AND SUPERUSER = false; CREATE USER alice WITH PASSWORD 'password_a' NOSUPERUSER; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true; CREATE USER alice WITH PASSWORD 'password_a'; CREATE ROLE alice WITH PASSWORD = 'password_a' AND LOGIN = true; Altering the options of a user uses the ALTER USER statement: alter_user_statement ::= ALTER USER [ IF EXISTS ] role_name [ WITH [ HASHED ] PASSWORD string] [ user_option] If the role does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. For example: ALTER USER alice WITH PASSWORD 'PASSWORD_A'; ALTER USER alice WITH HASHED PASSWORD '$2a$10$JSJEMFm6GeaW9XxT5JIheuEtPvat6i7uKbnTcxX3c1wshIIsGyUtG'; ALTER USER bob SUPERUSER; Dropping a user uses the DROP USER statement: drop_user_statement ::= DROP USER [ IF EXISTS ] role_name Existing users can be listed using the LIST USERS statement: list_users_statement::= LIST USERS Note that this statement is equivalent to LIST ROLES, but only roles with the `LOGIN privilege are included in the output. Permissions on resources are granted to roles; there are several different types of resources in Cassandra and each type is modelled hierarchically: The hierarchy of Data resources, Keyspaces and Tables has the structure ALL KEYSPACES → KEYSPACE → TABLE. Function resources have the structure ALL FUNCTIONS → KEYSPACE → FUNCTION Resources representing roles have the structure ALL ROLES → ROLE Resources representing JMX ObjectNames, which map to sets of MBeans/MXBeans, have the structure ALL MBEANS → MBEAN Permissions can be granted at any level of these hierarchies and they flow downwards. So granting a permission on a resource higher up the chain automatically grants that same permission on all resources lower down. For example, granting SELECT on a KEYSPACE automatically grants it on all TABLES in that KEYSPACE. Likewise, granting a permission on ALL FUNCTIONS grants it on every defined function, regardless of which keyspace it is scoped in. It is also possible to grant permissions on all functions scoped to a particular keyspace. Modifications to permissions are visible to existing client sessions; that is, connections need not be re-established following permissions changes. The full set of available permissions is: CREATE ALTER DROP SELECT MODIFY AUTHORIZE DESCRIBE EXECUTE Not all permissions are applicable to every type of resource. For instance, EXECUTE is only relevant in the context of functions or mbeans; granting EXECUTE on a resource representing a table is nonsensical. Attempting to GRANT a permission on resource to which it cannot be applied results in an error response. The following illustrates which permissions can be granted on which types of resource, and which statements are enabled by that permission. Permission Resource Operations CREATE ALL KEYSPACES CREATE KEYSPACE and CREATE TABLE in any keyspace CREATE KEYSPACE CREATE TABLE in specified keyspace CREATE ALL FUNCTIONS CREATE FUNCTION in any keyspace and CREATE AGGREGATE in any keyspace CREATE ALL FUNCTIONS IN KEYSPACE CREATE FUNCTION and CREATE AGGREGATE in specified keyspace CREATE ALL ROLES CREATE ROLE ALTER ALL KEYSPACES ALTER KEYSPACE and ALTER TABLE in any keyspace ALTER KEYSPACE ALTER KEYSPACE and ALTER TABLE in specified keyspace ALTER TABLE ALTER TABLE ALTER ALL FUNCTIONS CREATE FUNCTION and CREATE AGGREGATE: replacing any existing ALTER ALL FUNCTIONS IN KEYSPACE CREATE FUNCTION and CREATE AGGREGATE: replacing existing in specified keyspace ALTER FUNCTION CREATE FUNCTION and CREATE AGGREGATE: replacing existing ALTER ALL ROLES ALTER ROLE on any role ALTER ROLE ALTER ROLE DROP ALL KEYSPACES DROP KEYSPACE and DROP TABLE in any keyspace DROP KEYSPACE DROP TABLE in specified keyspace DROP TABLE DROP TABLE DROP ALL FUNCTIONS DROP FUNCTION and DROP AGGREGATE in any keyspace DROP ALL FUNCTIONS IN KEYSPACE DROP FUNCTION and DROP AGGREGATE in specified keyspace DROP FUNCTION DROP FUNCTION DROP ALL ROLES DROP ROLE on any role DROP ROLE DROP ROLE SELECT ALL KEYSPACES SELECT on any table SELECT KEYSPACE SELECT on any table in specified keyspace SELECT TABLE SELECT on specified table SELECT ALL MBEANS Call getter methods on any mbean SELECT MBEANS Call getter methods on any mbean matching a wildcard pattern SELECT MBEAN Call getter methods on named mbean MODIFY ALL KEYSPACES INSERT, UPDATE, DELETE and TRUNCATE on any table MODIFY KEYSPACE INSERT, UPDATE, DELETE and TRUNCATE on any table in specified keyspace MODIFY TABLE INSERT, UPDATE, DELETE and TRUNCATE on specified table MODIFY ALL MBEANS Call setter methods on any mbean MODIFY MBEANS Call setter methods on any mbean matching a wildcard pattern MODIFY MBEAN Call setter methods on named mbean AUTHORIZE ALL KEYSPACES GRANT PERMISSION and REVOKE PERMISSION on any table AUTHORIZE KEYSPACE GRANT PERMISSION and REVOKE PERMISSION on any table in specified keyspace AUTHORIZE TABLE GRANT PERMISSION and REVOKE PERMISSION on specified table AUTHORIZE ALL FUNCTIONS GRANT PERMISSION and REVOKE PERMISSION on any function AUTHORIZE ALL FUNCTIONS IN KEYSPACE GRANT PERMISSION and REVOKE PERMISSION in specified keyspace AUTHORIZE FUNCTION GRANT PERMISSION and REVOKE PERMISSION on specified function AUTHORIZE ALL MBEANS GRANT PERMISSION and REVOKE PERMISSION on any mbean AUTHORIZE MBEANS GRANT PERMISSION and REVOKE PERMISSION on any mbean matching a wildcard pattern AUTHORIZE MBEAN GRANT PERMISSION and REVOKE PERMISSION on named mbean AUTHORIZE ALL ROLES GRANT ROLE and REVOKE ROLE on any role AUTHORIZE ROLES GRANT ROLE and REVOKE ROLE on specified roles DESCRIBE ALL ROLES LIST ROLES on all roles or only roles granted to another, specified role DESCRIBE ALL MBEANS Retrieve metadata about any mbean from the platform’s MBeanServer DESCRIBE MBEANS Retrieve metadata about any mbean matching a wildcard patter from the platform’s MBeanServer DESCRIBE MBEAN Retrieve metadata about a named mbean from the platform’s MBeanServer EXECUTE ALL FUNCTIONS SELECT, INSERT and UPDATE using any function, and use of any function in CREATE AGGREGATE EXECUTE ALL FUNCTIONS IN KEYSPACE SELECT, INSERT and UPDATE using any function in specified keyspace and use of any function in keyspace in CREATE AGGREGATE EXECUTE FUNCTION SELECT, INSERT and UPDATE using specified function and use of the function in CREATE AGGREGATE EXECUTE ALL MBEANS Execute operations on any mbean EXECUTE MBEANS Execute operations on any mbean matching a wildcard pattern EXECUTE MBEAN Execute operations on named mbean Granting a permission uses the GRANT PERMISSION statement: grant_permission_statement ::= GRANT permissions ON resource TO role_name permissions ::= ALL [ PERMISSIONS ] | permission [ PERMISSION ] permission ::= CREATE | ALTER | DROP | SELECT | MODIFY | AUTHORIZE | DESCRIBE | EXECUTE resource ::= ALL KEYSPACES | KEYSPACE keyspace_name | [ TABLE ] table_name | ALL ROLES | ROLE role_name | ALL FUNCTIONS [ IN KEYSPACE keyspace_name ] | FUNCTION function_name '(' [ cql_type( ',' cql_type )* ] ')' | ALL MBEANS | ( MBEAN | MBEANS ) string For example: GRANT SELECT ON ALL KEYSPACES TO data_reader; This example gives any user with the role data_reader permission to execute SELECT statements on any table across all keyspaces: GRANT MODIFY ON KEYSPACE keyspace1 TO data_writer; To give any user with the role data_writer permission to perform UPDATE, INSERT, UPDATE, DELETE and TRUNCATE queries on all tables in the keyspace1 keyspace: GRANT DROP ON keyspace1.table1 TO schema_owner; To give any user with the schema_owner role permissions to DROP a specific keyspace1.table1: GRANT EXECUTE ON FUNCTION keyspace1.user_function( int ) TO report_writer; This command grants any user with the report_writer role permission to execute SELECT, INSERT and UPDATE queries which use the function keyspace1.user_function( int ): GRANT DESCRIBE ON ALL ROLES TO role_admin; This grants any user with the role_admin role permission to view any and all roles in the system with a LIST ROLES statement. When the GRANT ALL form is used, the appropriate set of permissions is determined automatically based on the target resource. When a resource is created, via a CREATE KEYSPACE, CREATE TABLE, CREATE FUNCTION, CREATE AGGREGATE or CREATE ROLE statement, the creator (the role the database user who issues the statement is identified as), is automatically granted all applicable permissions on the new resource. Revoking a permission from a role uses the REVOKE PERMISSION statement: revoke_permission_statement ::= REVOKE permissions ON resource FROM role_name For example: REVOKE SELECT ON ALL KEYSPACES FROM data_reader; REVOKE MODIFY ON KEYSPACE keyspace1 FROM data_writer; REVOKE DROP ON keyspace1.table1 FROM schema_owner; REVOKE EXECUTE ON FUNCTION keyspace1.user_function( int ) FROM report_writer; REVOKE DESCRIBE ON ALL ROLES FROM role_admin; Because of their function in normal driver operations, certain tables cannot have their SELECT permissions revoked. The following tables will be available to all authorized users regardless of their assigned role: * `system_schema.keyspaces` * `system_schema.columns` * `system_schema.tables` * `system.local` * `system.peers` Listing granted permissions uses the LIST PERMISSIONS statement: list_permissions_statement ::= LIST permissions [ ON resource] [ OF role_name[ NORECURSIVE ] ] For example: LIST ALL PERMISSIONS OF alice; Show all permissions granted to alice, including those acquired transitively from any other roles: LIST ALL PERMISSIONS ON keyspace1.table1 OF bob; Show all permissions on keyspace1.table1 granted to bob, including those acquired transitively from any other roles. This also includes any permissions higher up the resource hierarchy which can be applied to keyspace1.table1. For example, should bob have ALTER permission on keyspace1, that would be included in the results of this query. Adding the NORECURSIVE switch restricts the results to only those permissions which were directly granted to bob or one of `bob’s roles: LIST SELECT PERMISSIONS OF carlos; Show any permissions granted to carlos or any of carlos’s roles, limited to `SELECT permissions on any resource.","title":"","component":"Cassandra","version":"4.1","name":"security","url":"/Cassandra/4.1/cassandra/cql/security.html","titles":[{"text":"Database Roles","id":"cql-roles"},{"text":"CREATE ROLE","id":"create-role-statement"},{"text":"Setting credentials for internal authentication","id":"setting-credentials-for-internal-authentication"},{"text":"Restricting connections to specific datacenters","id":"restricting-connections-to-specific-datacenters"},{"text":"Creating a role conditionally","id":"creating-a-role-conditionally"},{"text":"ALTER ROLE","id":"alter-role-statement"},{"text":"Restricting connections to specific datacenters","id":"restricting-connections-to-specific-datacenters-2"},{"text":"DROP ROLE","id":"drop-role-statement"},{"text":"GRANT ROLE","id":"grant-role-statement"},{"text":"REVOKE ROLE","id":"revoke-role-statement"},{"text":"LIST ROLES","id":"list-roles-statement"},{"text":"Users","id":"users"},{"text":"CREATE USER","id":"create-user-statement"},{"text":"ALTER USER","id":"alter-user-statement"},{"text":"DROP USER","id":"drop-user-statement"},{"text":"LIST USERS","id":"list-users-statement"},{"text":"Data Control","id":"data-control"},{"text":"Permissions","id":"cql-permissions"},{"text":"GRANT PERMISSION","id":"grant-permission-statement"},{"text":"GRANT ALL","id":"grant-all"},{"text":"Automatic Granting","id":"automatic-granting"},{"text":"REVOKE PERMISSION","id":"revoke-permission-statement"},{"text":"LIST PERMISSIONS","id":"list-permissions-statement"}]},"/Cassandra/4.1/cassandra/cql/triggers.html":{"text":"Triggers are identified with a name defined by: trigger_name ::= identifier Creating a new trigger uses the CREATE TRIGGER statement: create_trigger_statement ::= CREATE TRIGGER [ IF NOT EXISTS ] trigger_name ON table_name USING string For instance: CREATE TRIGGER myTrigger ON myTable USING 'org.apache.cassandra.triggers.InvertedIndex'; The actual logic that makes up the trigger can be written in any Java (JVM) language and exists outside the database. You place the trigger code in a lib/triggers subdirectory of the Cassandra installation directory, it loads during cluster startup, and exists on every node that participates in a cluster. The trigger defined on a table fires before a requested DML statement occurs, which ensures the atomicity of the transaction. Dropping a trigger uses the DROP TRIGGER statement: drop_trigger_statement ::= DROP TRIGGER [ IF EXISTS ] trigger_nameON table_name For instance: DROP TRIGGER myTrigger ON myTable;","title":"Triggers","component":"Cassandra","version":"4.1","name":"triggers","url":"/Cassandra/4.1/cassandra/cql/triggers.html","titles":[{"text":"CREATE TRIGGER","id":"create-trigger-statement"},{"text":"DROP TRIGGER","id":"drop-trigger-statement"}]},"/Cassandra/4.1/cassandra/cql/types.html":{"text":"CQL is a typed language and supports a rich set of data types, including native types, collection types, user-defined types, tuple types, and custom types: cql_type::= native_type| collection_type| user_defined_type | tuple_type | custom_type The native types supported by CQL are: native_type::= ASCII | BIGINT | BLOB | BOOLEAN | COUNTER | DATE | DECIMAL | DOUBLE | DURATION | FLOAT | INET | INT | SMALLINT | TEXT | TIME | TIMESTAMP | TIMEUUID | TINYINT | UUID | VARCHAR | VARINT The following table gives additional informations on the native data types, and on which kind of constants each type supports: Type Constants supported Description ascii string ASCII character string bigint integer 64-bit signed long blob blob Arbitrary bytes (no validation) boolean boolean Either true or false counter integer Counter column (64-bit signed value). See counters for details. date integer, string A date (with no corresponding time value). See dates below for details. decimal integer, float Variable-precision decimal double integer float 64-bit IEEE-754 floating point duration duration, A duration with nanosecond precision. See durations below for details. float integer, float 32-bit IEEE-754 floating point inet string An IP address, either IPv4 (4 bytes long) or IPv6 (16 bytes long). Note that there is no inet constant, IP address should be input as strings. int integer 32-bit signed int smallint integer 16-bit signed int text string UTF8 encoded string time integer, string A time (with no corresponding date value) with nanosecond precision. See times below for details. timestamp integer, string A timestamp (date and time) with millisecond precision. See timestamps below for details. timeuuid uuid Version 1 UUID, generally used as a “conflict-free” timestamp. Also see timeuuid-functions. tinyint integer 8-bit signed int uuid uuid A UUID (of any version) varchar string UTF8 encoded string varint integer Arbitrary-precision integer The counter type is used to define counter columns. A counter column is a column whose value is a 64-bit signed integer and on which 2 operations are supported: incrementing and decrementing (see the UPDATE statement for syntax). Note that the value of a counter cannot be set: a counter does not exist until first incremented/decremented, and that first increment/decrement is made as if the prior value was 0. Counters have a number of important limitations: They cannot be used for columns part of the PRIMARY KEY of a table. A table that contains a counter can only contain counters. In other words, either all the columns of a table outside the PRIMARY KEY have the counter type, or none of them have it. Counters do not support expiration. The deletion of counters is supported, but is only guaranteed to work the first time you delete a counter. In other words, you should not re-update a counter that you have deleted (if you do, proper behavior is not guaranteed). Counter updates are, by nature, not idemptotent. An important consequence is that if a counter update fails unexpectedly (timeout or loss of connection to the coordinator node), the client has no way to know if the update has been applied or not. In particular, replaying the update may or may not lead to an over count. Values of the timestamp type are encoded as 64-bit signed integers representing a number of milliseconds since the standard base time known as the epoch: January 1 1970 at 00:00:00 GMT. Timestamps can be input in CQL either using their value as an integer, or using a string that represents an ISO 8601 date. For instance, all of the values below are valid timestamp values for Mar 2, 2011, at 04:05:00 AM, GMT: 1299038700000 '2011-02-03 04:05+0000' '2011-02-03 04:05:00+0000' '2011-02-03 04:05:00.000+0000' '2011-02-03T04:05+0000' '2011-02-03T04:05:00+0000' '2011-02-03T04:05:00.000+0000' The +0000 above is an RFC 822 4-digit time zone specification; +0000 refers to GMT. US Pacific Standard Time is -0800. The time zone may be omitted if desired ('2011-02-03 04:05:00'), and if so, the date will be interpreted as being in the time zone under which the coordinating Cassandra node is configured. There are however difficulties inherent in relying on the time zone configuration being as expected, so it is recommended that the time zone always be specified for timestamps when feasible. The time of day may also be omitted ('2011-02-03' or '2011-02-03+0000'), in which case the time of day will default to 00:00:00 in the specified or default time zone. However, if only the date part is relevant, consider using the date type. Values of the date type are encoded as 32-bit unsigned integers representing a number of days with “the epoch” at the center of the range (2^31). Epoch is January 1st, 1970 For timestamps, a date can be input either as an integer or using a date string. In the later case, the format should be yyyy-mm-dd (so '2011-02-03' for instance). Values of the time type are encoded as 64-bit signed integers representing the number of nanoseconds since midnight. For timestamps, a time can be input either as an integer or using a string representing the time. In the later case, the format should be hh:mm:ss[.fffffffff] (where the sub-second precision is optional and if provided, can be less than the nanosecond). So for instance, the following are valid inputs for a time: '08:12:54' '08:12:54.123' '08:12:54.123456' '08:12:54.123456789' Values of the duration type are encoded as 3 signed integer of variable lengths. The first integer represents the number of months, the second the number of days and the third the number of nanoseconds. This is due to the fact that the number of days in a month can change, and a day can have 23 or 25 hours depending on the daylight saving. Internally, the number of months and days are decoded as 32 bits integers whereas the number of nanoseconds is decoded as a 64 bits integer. A duration can be input as: (quantity unit)+ like 12h30m where the unit can be: y: years (12 months) mo: months (1 month) w: weeks (7 days) d: days (1 day) h: hours (3,600,000,000,000 nanoseconds) m: minutes (60,000,000,000 nanoseconds) s: seconds (1,000,000,000 nanoseconds) ms: milliseconds (1,000,000 nanoseconds) us or µs : microseconds (1000 nanoseconds) ns: nanoseconds (1 nanosecond) ISO 8601 format: P[n]Y[n]M[n]DT[n]H[n]M[n]S or P[n]W ISO 8601 alternative format: P[YYYY]-[MM]-[DD]T[hh]:[mm]:[ss] For example: INSERT INTO RiderResults (rider, race, result) VALUES ('Christopher Froome', 'Tour de France', 89h4m48s); INSERT INTO RiderResults (rider, race, result) VALUES ('BARDET Romain', 'Tour de France', PT89H8M53S); INSERT INTO RiderResults (rider, race, result) VALUES ('QUINTANA Nairo', 'Tour de France', P0000-00-00T89:09:09); Duration columns cannot be used in a table’s PRIMARY KEY. This limitation is due to the fact that durations cannot be ordered. It is effectively not possible to know if 1mo is greater than 29d without a date context. A 1d duration is not equal to a 24h one as the duration type has been created to be able to support daylight saving. CQL supports three kinds of collections: maps, sets and lists. The types of those collections is defined by: collection_type::= MAP '' | SET '' | LIST '' and their values can be inputd using collection literals: collection_literal::= map_literal | set_literal | list_literal map_literal::= '\\{' [ term ':' term (',' term : term)* ] '}' set_literal::= '\\{' [ term (',' term)* ] '}' list_literal::= '[' [ term (',' term)* ] ']' Note however that neither bind_marker nor NULL are supported inside collection literals. Collections are meant for storing/denormalizing relatively small amount of data. They work well for things like “the phone numbers of a given user”, “labels applied to an email”, etc. But when items are expected to grow unbounded (“all messages sent by a user”, “events registered by a sensor”…​), then collections are not appropriate and a specific table (with clustering columns) should be used. Concretely, (non-frozen) collections have the following noteworthy characteristics and limitations: Individual collections are not indexed internally. Which means that even to access a single element of a collection, the while collection has to be read (and reading one is not paged internally). While insertion operations on sets and maps never incur a read-before-write internally, some operations on lists do. Further, some lists operations are not idempotent by nature (see the section on lists below for details), making their retry in case of timeout problematic. It is thus advised to prefer sets over lists when possible. Please note that while some of those limitations may or may not be removed/improved upon in the future, it is a anti-pattern to use a (single) collection to store large amounts of data. A map is a (sorted) set of key-value pairs, where keys are unique and the map is sorted by its keys. You can define and insert a map with: CREATE TABLE users ( id text PRIMARY KEY, name text, favs map // A map of text keys, and text values ); INSERT INTO users (id, name, favs) VALUES ('jsmith', 'John Smith', { 'fruit' : 'Apple', 'band' : 'Beatles' }); // Replace the existing map entirely. UPDATE users SET favs = { 'fruit' : 'Banana' } WHERE id = 'jsmith'; Further, maps support: Updating or inserting one or more elements: UPDATE users SET favs['author'] = 'Ed Poe' WHERE id = 'jsmith'; UPDATE users SET favs = favs + { 'movie' : 'Cassablanca', 'band' : 'ZZ Top' } WHERE id = 'jsmith'; Removing one or more element (if an element doesn’t exist, removing it is a no-op but no error is thrown): DELETE favs['author'] FROM users WHERE id = 'jsmith'; UPDATE users SET favs = favs - { 'movie', 'band'} WHERE id = 'jsmith'; Note that for removing multiple elements in a map, you remove from it a set of keys. Lastly, TTLs are allowed for both INSERT and UPDATE, but in both case the TTL set only apply to the newly inserted/updated elements. In other words: UPDATE users USING TTL 10 SET favs['color'] = 'green' WHERE id = 'jsmith'; will only apply the TTL to the { 'color' : 'green' } record, the rest of the map remaining unaffected. A set is a (sorted) collection of unique values. You can define and insert a map with: CREATE TABLE images ( name text PRIMARY KEY, owner text, tags set // A set of text values ); INSERT INTO images (name, owner, tags) VALUES ('cat.jpg', 'jsmith', { 'pet', 'cute' }); // Replace the existing set entirely UPDATE images SET tags = { 'kitten', 'cat', 'lol' } WHERE name = 'cat.jpg'; Further, sets support: Adding one or multiple elements (as this is a set, inserting an already existing element is a no-op): UPDATE images SET tags = tags + { 'gray', 'cuddly' } WHERE name = 'cat.jpg'; Removing one or multiple elements (if an element doesn’t exist, removing it is a no-op but no error is thrown): UPDATE images SET tags = tags - { 'cat' } WHERE name = 'cat.jpg'; Lastly, for sets, TTLs are only applied to newly inserted values. Note As mentioned above and further discussed at the end of this section, lists have limitations and specific performance considerations that you should take into account before using them. In general, if you can use a set instead of list, always prefer a set. A list is a (sorted) collection of non-unique values where elements are ordered by there position in the list. You can define and insert a list with: CREATE TABLE plays ( id text PRIMARY KEY, game text, players int, scores list // A list of integers ) INSERT INTO plays (id, game, players, scores) VALUES ('123-afde', 'quake', 3, [17, 4, 2]); // Replace the existing list entirely UPDATE plays SET scores = [ 3, 9, 4] WHERE id = '123-afde'; Further, lists support: Appending and prepending values to a list: UPDATE plays SET players = 5, scores = scores + [ 14, 21 ] WHERE id = '123-afde'; UPDATE plays SET players = 6, scores = [ 3 ] + scores WHERE id = '123-afde'; Warning The append and prepend operations are not idempotent by nature. So in particular, if one of these operation timeout, then retrying the operation is not safe and it may (or may not) lead to appending/prepending the value twice. Setting the value at a particular position in a list that has a pre-existing element for that position. An error will be thrown if the list does not have the position.: UPDATE plays SET scores[1] = 7 WHERE id = '123-afde'; Removing an element by its position in the list that has a pre-existing element for that position. An error will be thrown if the list does not have the position. Further, as the operation removes an element from the list, the list size will decrease by one element, shifting the position of all the following elements one forward: DELETE scores[1] FROM plays WHERE id = '123-afde'; Deleting all the occurrences of particular values in the list (if a particular element doesn’t occur at all in the list, it is simply ignored and no error is thrown): UPDATE plays SET scores = scores - [ 12, 21 ] WHERE id = '123-afde'; Warning Setting and removing an element by position and removing occurences of particular values incur an internal read-before-write. These operations will run slowly and use more resources than usual updates (with the exclusion of conditional write that have their own cost). Lastly, for lists, TTLs only apply to newly inserted values. CQL support the definition of user-defined types (UDTs). Such a type can be created, modified and removed using the create_type_statement, alter_type_statement and drop_type_statement described below. But once created, a UDT is simply referred to by its name: user_defined_type::= udt_name udt_name::= [ keyspace_name '.' ] identifier Creating a new user-defined type is done using a CREATE TYPE statement defined by: create_type_statement::= CREATE TYPE [ IF NOT EXISTS ] udt_name '(' field_definition ( ',' field_definition)* ')' field_definition::= identifier cql_type A UDT has a name (used to declared columns of that type) and is a set of named and typed fields. Fields name can be any type, including collections or other UDT. For instance: CREATE TYPE phone ( country_code int, number text, ); CREATE TYPE address ( street text, city text, zip text, phones map ); CREATE TABLE user ( name text PRIMARY KEY, addresses map> ); Things to keep in mind about UDTs: Attempting to create an already existing type will result in an error unless the IF NOT EXISTS option is used. If it is used, the statement will be a no-op if the type already exists. A type is intrinsically bound to the keyspace in which it is created, and can only be used in that keyspace. At creation, if the type name is prefixed by a keyspace name, it is created in that keyspace. Otherwise, it is created in the current keyspace. As of Cassandra , UDT have to be frozen in most cases, hence the frozen in the table definition above. Please see the section on frozen for more details. Once a used-defined type has been created, value can be input using a UDT literal: udt_literal::= '{' identifier ':' term ( ',' identifier ':' term)* '}' In other words, a UDT literal is like a map` literal but its keys are the names of the fields of the type. For instance, one could insert into the table define in the previous section using: INSERT INTO user (name, addresses) VALUES ('z3 Pr3z1den7', { 'home' : { street: '1600 Pennsylvania Ave NW', city: 'Washington', zip: '20500', phones: { 'cell' : { country_code: 1, number: '202 456-1111' }, 'landline' : { country_code: 1, number: '...' } } }, 'work' : { street: '1600 Pennsylvania Ave NW', city: 'Washington', zip: '20500', phones: { 'fax' : { country_code: 1, number: '...' } } } } ); To be valid, a UDT literal can only include fields defined by the type it is a literal of, but it can omit some fields (these will be set to NULL). An existing user-defined type can be modified using an ALTER TYPE statement: alter_type_statement::= ALTER TYPE [ IF EXISTS ] udt_name alter_type_modification alter_type_modification::= ADD [ IF NOT EXISTS ] field_definition | RENAME [ IF EXISTS ] identifier TO identifier (AND identifier TO identifier )* If the type does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. You can: Add a new field to the type (ALTER TYPE address ADD country text). That new field will be NULL for any values of the type created before the addition. If the new field exists, the statement will return an error, unless IF NOT EXISTS is used in which case the operation is a no-op. Rename the fields of the type. If the field(s) does not exist, the statement will return an error, unless IF EXISTS is used in which case the operation is a no-op. ALTER TYPE address RENAME zip TO zipcode; You can drop an existing user-defined type using a DROP TYPE statement: drop_type_statement::= DROP TYPE [ IF EXISTS ] udt_name Dropping a type results in the immediate, irreversible removal of that type. However, attempting to drop a type that is still in use by another type, table or function will result in an error. If the type dropped does not exist, an error will be returned unless IF EXISTS is used, in which case the operation is a no-op. CQL also support tuples and tuple types (where the elements can be of different types). Functionally, tuples can be though as anonymous UDT with anonymous fields. Tuple types and tuple literals are defined by: tuple_type::= TUPLE '' tuple_literal::= '(' term( ',' term )* ')' and can be created: CREATE TABLE durations ( event text, duration tuple, ); INSERT INTO durations (event, duration) VALUES ('ev1', (3, 'hours')); Unlike other composed types, like collections and UDTs, a tuple is always frozen (without the need of the frozen keyword) and it is not possible to update only some elements of a tuple (without updating the whole tuple). Also, a tuple literal should always have the same number of value than declared in the type it is a tuple of (some of those values can be null but they need to be explicitly declared as so). Note Custom types exists mostly for backward compatibility purposes and their usage is discouraged. Their usage is complex, not user friendly and the other provided types, particularly user-defined types, should almost always be enough. A custom type is defined by: custom_type::= string A custom type is a string that contains the name of Java class that extends the server side AbstractType class and that can be loaded by Cassandra (it should thus be in the CLASSPATH of every node running Cassandra). That class will define what values are valid for the type and how the time sorts when used for a clustering column. For any other purpose, a value of a custom type is the same than that of a blob, and can in particular be input using the blob literal syntax.","title":"Data Types","component":"Cassandra","version":"4.1","name":"types","url":"/Cassandra/4.1/cassandra/cql/types.html","titles":[{"text":"Native types","id":"native-types"},{"text":"Counters","id":"counters"},{"text":"Working with timestamps","id":"timestamps"},{"text":"Date type","id":"dates"},{"text":"Time type","id":"times"},{"text":"Duration type","id":"durations"},{"text":"Collections","id":"collections"},{"text":"Noteworthy characteristics","id":"noteworthy-characteristics"},{"text":"Maps","id":"maps"},{"text":"Sets","id":"sets"},{"text":"Lists","id":"lists"},{"text":"User-Defined Types (UDTs)","id":"udts"},{"text":"Creating a UDT","id":"creating-a-udt"},{"text":"UDT literals","id":"udt-literals"},{"text":"Altering a UDT","id":"altering-a-udt"},{"text":"Dropping a UDT","id":"dropping-a-udt"},{"text":"Tuples","id":"tuples"},{"text":"Custom Types","id":"custom-types"}]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_conceptual.html":{"text":"First, let’s create a simple domain model that is easy to understand in the relational world, and then see how you might map it from a relational to a distributed hashtable model in Cassandra. Let’s use an example that is complex enough to show the various data structures and design patterns, but not something that will bog you down with details. Also, a domain that’s familiar to everyone will allow you to concentrate on how to work with Cassandra, not on what the application domain is all about. For example, let’s use a domain that is easily understood and that everyone can relate to: making hotel reservations. The conceptual domain includes hotels, guests that stay in the hotels, a collection of rooms for each hotel, the rates and availability of those rooms, and a record of reservations booked for guests. Hotels typically also maintain a collection of “points of interest,” which are parks, museums, shopping galleries, monuments, or other places near the hotel that guests might want to visit during their stay. Both hotels and points of interest need to maintain geolocation data so that they can be found on maps for mashups, and to calculate distances. The conceptual domain is depicted below using the entity–relationship model popularized by Peter Chen. This simple diagram represents the entities in the domain with rectangles, and attributes of those entities with ovals. Attributes that represent unique identifiers for items are underlined. Relationships between entities are represented as diamonds, and the connectors between the relationship and each entity show the multiplicity of the connection. Obviously, in the real world, there would be many more considerations and much more complexity. For example, hotel rates are notoriously dynamic, and calculating them involves a wide array of factors. Here you’re defining something complex enough to be interesting and touch on the important points, but simple enough to maintain the focus on learning Cassandra. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Conceptual Data Modeling","component":"Cassandra","version":"4.1","name":"data_modeling_conceptual","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_conceptual.html","titles":[]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_logical.html":{"text":"Now that you have defined your queries, you’re ready to begin designing Cassandra tables. First, create a logical model containing a table for each query, capturing entities and relationships from the conceptual model. To name each table, you’ll identify the primary entity type for which you are querying and use that to start the entity name. If you are querying by attributes of other related entities, append those to the table name, separated with by. For example, hotels_by_poi. Next, you identify the primary key for the table, adding partition key columns based on the required query attributes, and clustering columns in order to guarantee uniqueness and support desired sort ordering. The design of the primary key is extremely important, as it will determine how much data will be stored in each partition and how that data is organized on disk, which in turn will affect how quickly Cassandra processes reads. Complete each table by adding any additional attributes identified by the query. If any of these additional attributes are the same for every instance of the partition key, mark the column as static. Now that was a pretty quick description of a fairly involved process, so it will be worthwhile to work through a detailed example. First, let’s introduce a notation that you can use to represent logical models. Several individuals within the Cassandra community have proposed notations for capturing data models in diagrammatic form. This document uses a notation popularized by Artem Chebotko which provides a simple, informative way to visualize the relationships between queries and tables in your designs. This figure shows the Chebotko notation for a logical data model. Each table is shown with its title and a list of columns. Primary key columns are identified via symbols such as K for partition key columns and C↑ or C↓ to represent clustering columns. Lines are shown entering tables or between tables to indicate the queries that each table is designed to support. The figure below shows a Chebotko logical data model for the queries involving hotels, points of interest, rooms, and amenities. One thing you’ll notice immediately is that the Cassandra design doesn’t include dedicated tables for rooms or amenities, as you had in the relational design. This is because the workflow didn’t identify any queries requiring this direct access. Let’s explore the details of each of these tables. The first query Q1 is to find hotels near a point of interest, so you’ll call this table hotels_by_poi. Searching by a named point of interest is a clue that the point of interest should be a part of the primary key. Let’s reference the point of interest by name, because according to the workflow that is how users will start their search. You’ll note that you certainly could have more than one hotel near a given point of interest, so you’ll need another component in the primary key in order to make sure you have a unique partition for each hotel. So you add the hotel key as a clustering column. An important consideration in designing your table’s primary key is making sure that it defines a unique data element. Otherwise you run the risk of accidentally overwriting data. Now for the second query (Q2), you’ll need a table to get information about a specific hotel. One approach would have been to put all of the attributes of a hotel in the hotels_by_poi table, but you added only those attributes that were required by the application workflow. From the workflow diagram, you know that the hotels_by_poi table is used to display a list of hotels with basic information on each hotel, and the application knows the unique identifiers of the hotels returned. When the user selects a hotel to view details, you can then use Q2, which is used to obtain details about the hotel. Because you already have the hotel_id from Q1, you use that as a reference to the hotel you’re looking for. Therefore the second table is just called hotels. Another option would have been to store a set of poi_names in the hotels table. This is an equally valid approach. You’ll learn through experience which approach is best for your application. Q3 is just a reverse of Q1—looking for points of interest near a hotel, rather than hotels near a point of interest. This time, however, you need to access the details of each point of interest, as represented by the pois_by_hotel table. As previously, you add the point of interest name as a clustering key to guarantee uniqueness. At this point, let’s now consider how to support query Q4 to help the user find available rooms at a selected hotel for the nights they are interested in staying. Note that this query involves both a start date and an end date. Because you’re querying over a range instead of a single date, you know that you’ll need to use the date as a clustering key. Use the hotel_id as a primary key to group room data for each hotel on a single partition, which should help searches be super fast. Let’s call this the available_rooms_by_hotel_date table. To support searching over a range, use clustering columns to store attributes that you need to access in a range query. Remember that the order of the clustering columns is important. The design of the available_rooms_by_hotel_date table is an instance of the wide partition pattern. This pattern is sometimes called the wide row pattern when discussing databases that support similar models, but wide partition is a more accurate description from a Cassandra perspective. The essence of the pattern is to group multiple related rows in a partition in order to support fast access to multiple rows within the partition in a single query. In order to round out the shopping portion of the data model, add the amenities_by_room table to support Q5. This will allow users to view the amenities of one of the rooms that is available for the desired stay dates. Now let’s switch gears to look at the reservation queries. The figure shows a logical data model for reservations. You’ll notice that these tables represent a denormalized design; the same data appears in multiple tables, with differing keys. In order to satisfy Q6, the reservations_by_guest table can be used to look up the reservation by guest name. You could envision query Q7 being used on behalf of a guest on a self-serve website or a call center agent trying to assist the guest. Because the guest name might not be unique, you include the guest ID here as a clustering column as well. Q8 and Q9 in particular help to remind you to create queries that support various stakeholders of the application, not just customers but staff as well, and perhaps even the analytics team, suppliers, and so on. The hotel staff might wish to see a record of upcoming reservations by date in order to get insight into how the hotel is performing, such as what dates the hotel is sold out or undersold. Q8 supports the retrieval of reservations for a given hotel by date. Finally, you create a guests table. This provides a single location that used to store guest information. In this case, you specify a separate unique identifier for guest records, as it is not uncommon for guests to have the same name. In many organizations, a customer database such as the guests table would be part of a separate customer management application, which is why other guest access patterns were omitted from the example. As with other types of software design, there are some well-known patterns and anti-patterns for data modeling in Cassandra. You’ve already used one of the most common patterns in this hotel model—the wide partition pattern. The time series pattern is an extension of the wide partition pattern. In this pattern, a series of measurements at specific time intervals are stored in a wide partition, where the measurement time is used as part of the partition key. This pattern is frequently used in domains including business analysis, sensor data management, and scientific experiments. The time series pattern is also useful for data other than measurements. Consider the example of a banking application. You could store each customer’s balance in a row, but that might lead to a lot of read and write contention as various customers check their balance or make transactions. You’d probably be tempted to wrap a transaction around writes just to protect the balance from being updated in error. In contrast, a time series–style design would store each transaction as a timestamped row and leave the work of calculating the current balance to the application. One design trap that many new users fall into is attempting to use Cassandra as a queue. Each item in the queue is stored with a timestamp in a wide partition. Items are appended to the end of the queue and read from the front, being deleted after they are read. This is a design that seems attractive, especially given its apparent similarity to the time series pattern. The problem with this approach is that the deleted items are now tombstones that Cassandra must scan past in order to read from the front of the queue. Over time, a growing number of tombstones begins to degrade read performance. The queue anti-pattern serves as a reminder that any design that relies on the deletion of data is potentially a poorly performing design. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Logical Data Modeling","component":"Cassandra","version":"4.1","name":"data_modeling_logical","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_logical.html","titles":[{"text":"Hotel Logical Data Model","id":"hotel-logical-data-model"},{"text":"Reservation Logical Data Model","id":"reservation-logical-data-model"},{"text":"Patterns and Anti-Patterns","id":"patterns-and-anti-patterns"}]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_physical.html":{"text":"Once you have a logical data model defined, creating the physical model is a relatively simple process. You walk through each of the logical model tables, assigning types to each item. You can use any valid CQL data type , including the basic types, collections, and user-defined types. You may identify additional user-defined types that can be created to simplify your design. After you’ve assigned data types, you analyze the model by performing size calculations and testing out how the model works. You may make some adjustments based on your findings. Once again let’s cover the data modeling process in more detail by working through an example. Before getting started, let’s look at a few additions to the Chebotko notation for physical data models. To draw physical models, you need to be able to add the typing information for each column. This figure shows the addition of a type for each column in a sample table. The figure includes a designation of the keyspace containing each table and visual cues for columns represented using collections and user-defined types. Note the designation of static columns and secondary index columns. There is no restriction on assigning these as part of a logical model, but they are typically more of a physical data modeling concern. Now let’s get to work on the physical model. First, you need keyspaces to contain the tables. To keep the design relatively simple, create a hotel keyspace to contain tables for hotel and availability data, and a reservation keyspace to contain tables for reservation and guest data. In a real system, you might divide the tables across even more keyspaces in order to separate concerns. For the hotels table, use Cassandra’s text type to represent the hotel’s id. For the address, create an address user defined type. Use the text type to represent the phone number, as there is considerable variance in the formatting of numbers between countries. While it would make sense to use the uuid type for attributes such as the hotel_id, this document uses mostly text attributes as identifiers, to keep the samples simple and readable. For example, a common convention in the hospitality industry is to reference properties by short codes like \"AZ123\" or \"NY229\". This example uses these values for hotel_ids, while acknowledging they are not necessarily globally unique. You’ll find that it’s often helpful to use unique IDs to uniquely reference elements, and to use these uuids as references in tables representing other entities. This helps to minimize coupling between different entity types. This may prove especially effective if you are using a microservice architectural style for your application, in which there are separate services responsible for each entity type. As you work to create physical representations of various tables in the logical hotel data model, you use the same approach. The resulting design is shown in this figure: Note that the address type is also included in the design. It is designated with an asterisk to denote that it is a user-defined type, and has no primary key columns identified. This type is used in the hotels and hotels_by_poi tables. User-defined types are frequently used to help reduce duplication of non-primary key columns, as was done with the address user-defined type. This can reduce complexity in the design. Remember that the scope of a UDT is the keyspace in which it is defined. To use address in the reservation keyspace defined below design, you’ll have to declare it again. This is just one of the many trade-offs you have to make in data model design. Now, let’s examine reservation tables in the design. Remember that the logical model contained three denormalized tables to support queries for reservations by confirmation number, guest, and hotel and date. For the first iteration of your physical data model design, assume you’re going to manage this denormalization manually. Note that this design could be revised to use Cassandra’s (experimental) materialized view feature. Note that the address type is reproduced in this keyspace and guest_id is modeled as a uuid type in all of the tables. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Physical Data Modeling","component":"Cassandra","version":"4.1","name":"data_modeling_physical","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_physical.html","titles":[{"text":"Hotel Physical Data Model","id":"hotel-physical-data-model"},{"text":"Reservation Physical Data Model","id":"reservation-physical-data-model"}]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_queries.html":{"text":"Let’s try the query-first approach to start designing the data model for a hotel application. The user interface design for the application is often a great artifact to use to begin identifying queries. Let’s assume that you’ve talked with the project stakeholders and your UX designers have produced user interface designs or wireframes for the key use cases. You’ll likely have a list of shopping queries like the following: Q1. Find hotels near a given point of interest. Q2. Find information about a given hotel, such as its name and location. Q3. Find points of interest near a given hotel. Q4. Find an available room in a given date range. Q5. Find the rate and amenities for a room. It is often helpful to be able to refer to queries by a shorthand number rather that explaining them in full. The queries listed here are numbered Q1, Q2, and so on, which is how they are referenced in diagrams throughout the example. Now if the application is to be a success, you’ll certainly want customers to be able to book reservations at hotels. This includes steps such as selecting an available room and entering their guest information. So clearly you will also need some queries that address the reservation and guest entities from the conceptual data model. Even here, however, you’ll want to think not only from the customer perspective in terms of how the data is written, but also in terms of how the data will be queried by downstream use cases. Your natural tendency might be to focus first on designing the tables to store reservation and guest records, and only then start thinking about the queries that would access them. You may have felt a similar tension already when discussing the shopping queries before, thinking “but where did the hotel and point of interest data come from?” Don’t worry, you will see soon enough. Here are some queries that describe how users will access reservations: Q6. Lookup a reservation by confirmation number. Q7. Lookup a reservation by hotel, date, and guest name. Q8. Lookup all reservations by guest name. Q9. View guest details. All of the queries are shown in the context of the workflow of the application in the figure below. Each box on the diagram represents a step in the application workflow, with arrows indicating the flows between steps and the associated query. If you’ve modeled the application well, each step of the workflow accomplishes a task that “unlocks” subsequent steps. For example, the “View hotels near POI” task helps the application learn about several hotels, including their unique keys. The key for a selected hotel may be used as part of Q2, in order to obtain detailed description of the hotel. The act of booking a room creates a reservation record that may be accessed by the guest and hotel staff at a later time through various additional queries. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Defining Application Queries","component":"Cassandra","version":"4.1","name":"data_modeling_queries","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_queries.html","titles":[]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_rdbms.html":{"text":"When you set out to build a new data-driven application that will use a relational database, you might start by modeling the domain as a set of properly normalized tables and use foreign keys to reference related data in other tables. The figure below shows how you might represent the data storage for your application using a relational database model. The relational model includes a couple of “join” tables in order to realize the many-to-many relationships from the conceptual model of hotels-to-points of interest, rooms-to-amenities, rooms-to-availability, and guests-to-rooms (via a reservation). Let’s take a minute to highlight some of the key differences in doing data modeling for Cassandra versus a relational database. You cannot perform joins in Cassandra. If you have designed a data model and find that you need something like a join, you’ll have to either do the work on the client side, or create a denormalized second table that represents the join results for you. This latter option is preferred in Cassandra data modeling. Performing joins on the client should be a very rare case; you really want to duplicate (denormalize) the data instead. Although Cassandra supports features such as lightweight transactions and batches, Cassandra itself has no concept of referential integrity across tables. In a relational database, you could specify foreign keys in a table to reference the primary key of a record in another table. But Cassandra does not enforce this. It is still a common design requirement to store IDs related to other entities in your tables, but operations such as cascading deletes are not available. In relational database design, you are often taught the importance of normalization. This is not an advantage when working with Cassandra because it performs best when the data model is denormalized. It is often the case that companies end up denormalizing data in relational databases as well. There are two common reasons for this. One is performance. Companies simply can’t get the performance they need when they have to do so many joins on years’ worth of data, so they denormalize along the lines of known queries. This ends up working, but goes against the grain of how relational databases are intended to be designed, and ultimately makes one question whether using a relational database is the best approach in these circumstances. A second reason that relational databases get denormalized on purpose is a business document structure that requires retention. That is, you have an enclosing table that refers to a lot of external tables whose data could change over time, but you need to preserve the enclosing document as a snapshot in history. The common example here is with invoices. You already have customer and product tables, and you’d think that you could just make an invoice that refers to those tables. But this should never be done in practice. Customer or price information could change, and then you would lose the integrity of the invoice document as it was on the invoice date, which could violate audits, reports, or laws, and cause other problems. In the relational world, denormalization violates Codd’s normal forms, and you try to avoid it. But in Cassandra, denormalization is, well, perfectly normal. It’s not required if your data model is simple. But don’t be afraid of it. Historically, denormalization in Cassandra has required designing and managing multiple tables using techniques described in this documentation. Beginning with the 3.0 release, Cassandra provides a feature known as materialized views which allows you to create multiple denormalized views of data based on a base table design. Cassandra manages materialized views on the server, including the work of keeping the views in sync with the table. Relational modeling, in simple terms, means that you start from the conceptual domain and then represent the nouns in the domain in tables. You then assign primary keys and foreign keys to model relationships. When you have a many-to-many relationship, you create the join tables that represent just those keys. The join tables don’t exist in the real world, and are a necessary side effect of the way relational models work. After you have all your tables laid out, you can start writing queries that pull together disparate data using the relationships defined by the keys. The queries in the relational world are very much secondary. It is assumed that you can always get the data you want as long as you have your tables modeled properly. Even if you have to use several complex subqueries or join statements, this is usually true. By contrast, in Cassandra you don’t start with the data model; you start with the query model. Instead of modeling the data first and then writing queries, with Cassandra you model the queries and let the data be organized around them. Think of the most common query paths your application will use, and then create the tables that you need to support them. Detractors have suggested that designing the queries first is overly constraining on application design, not to mention database modeling. But it is perfectly reasonable to expect that you should think hard about the queries in your application, just as you would, presumably, think hard about your relational domain. You may get it wrong, and then you’ll have problems in either world. Or your query needs might change over time, and then you’ll have to work to update your data set. But this is no different from defining the wrong tables, or needing additional tables, in an RDBMS. In a relational database, it is frequently transparent to the user how tables are stored on disk, and it is rare to hear of recommendations about data modeling based on how the RDBMS might store tables on disk. However, that is an important consideration in Cassandra. Because Cassandra tables are each stored in separate files on disk, it’s important to keep related columns defined together in the same table. A key goal that you will see as you begin creating data models in Cassandra is to minimize the number of partitions that must be searched in order to satisfy a given query. Because the partition is a unit of storage that does not get divided across nodes, a query that searches a single partition will typically yield the best performance. In an RDBMS, you can easily change the order in which records are returned to you by using ORDER BY in your query. The default sort order is not configurable; by default, records are returned in the order in which they are written. If you want to change the order, you just modify your query, and you can sort by any list of columns. In Cassandra, however, sorting is treated differently; it is a design decision. The sort order available on queries is fixed, and is determined entirely by the selection of clustering columns you supply in the CREATE TABLE command. The CQL SELECT statement does support ORDER BY semantics, but only in the order specified by the clustering columns. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"RDBMS Design","component":"Cassandra","version":"4.1","name":"data_modeling_rdbms","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_rdbms.html","titles":[{"text":"Design Differences Between RDBMS and Cassandra","id":"design-differences-between-rdbms-and-cassandra"},{"text":"No joins","id":"no-joins"},{"text":"No referential integrity","id":"no-referential-integrity"},{"text":"Denormalization","id":"denormalization"},{"text":"Query-first design","id":"query-first-design"},{"text":"Designing for optimal storage","id":"designing-for-optimal-storage"},{"text":"Sorting is a design decision","id":"sorting-is-a-design-decision"}]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_refining.html":{"text":"Once you’ve created a physical model, there are some steps you’ll want to take to evaluate and refine table designs to help ensure optimal performance. The first thing that you want to look for is whether your tables will have partitions that will be overly large, or to put it another way, too wide. Partition size is measured by the number of cells (values) that are stored in the partition. Cassandra’s hard limit is 2 billion cells per partition, but you’ll likely run into performance issues before reaching that limit. In order to calculate the size of partitions, use the following formula: \\[N_v = N_r (N_c - N_{pk} - N_s) + N_s\\] The number of values (or cells) in the partition (Nv) is equal to the number of static columns (Ns) plus the product of the number of rows (Nr) and the number of of values per row. The number of values per row is defined as the number of columns (Nc) minus the number of primary key columns (Npk) and static columns (Ns). The number of columns tends to be relatively static, although it is possible to alter tables at runtime. For this reason, a primary driver of partition size is the number of rows in the partition. This is a key factor that you must consider in determining whether a partition has the potential to get too large. Two billion values sounds like a lot, but in a sensor system where tens or hundreds of values are measured every millisecond, the number of values starts to add up pretty fast. Let’s take a look at one of the tables to analyze the partition size. Because it has a wide partition design with one partition per hotel, look at the available_rooms_by_hotel_date table. The table has four columns total (Nc = 4), including three primary key columns (Npk = 3) and no static columns (Ns = 0). Plugging these values into the formula, the result is: \\[N_v = N_r (4 - 3 - 0) + 0 = 1N_r\\] Therefore the number of values for this table is equal to the number of rows. You still need to determine a number of rows. To do this, make estimates based on the application design. The table is storing a record for each room, in each of hotel, for every night. Let’s assume the system will be used to store two years of inventory at a time, and there are 5,000 hotels in the system, with an average of 100 rooms in each hotel. Since there is a partition for each hotel, the estimated number of rows per partition is as follows: \\[N_r = 100 rooms/hotel \\times 730 days = 73,000 rows\\] This relatively small number of rows per partition is not going to get you in too much trouble, but if you start storing more dates of inventory, or don’t manage the size of the inventory well using TTL, you could start having issues. You still might want to look at breaking up this large partition, which you’ll see how to do shortly. When performing sizing calculations, it is tempting to assume the nominal or average case for variables such as the number of rows. Consider calculating the worst case as well, as these sorts of predictions have a way of coming true in successful systems. In addition to calculating the size of a partition, it is also an excellent idea to estimate the amount of disk space that will be required for each table you plan to store in the cluster. In order to determine the size, use the following formula to determine the size St of a partition: \\[S_t = \\displaystyle\\sum_i sizeOf\\big (c_{k_i}\\big) + \\displaystyle\\sum_j sizeOf\\big(c_{s_j}\\big) + N_r\\times \\bigg(\\displaystyle\\sum_k sizeOf\\big(c_{r_k}\\big) + \\displaystyle\\sum_l sizeOf\\big(c_{c_l}\\big)\\bigg) +\\] \\[N_v\\times sizeOf\\big(t_{avg}\\big)\\] This is a bit more complex than the previous formula, but let’s break it down a bit at a time. Let’s take a look at the notation first: In this formula, ck refers to partition key columns, cs to static columns, cr to regular columns, and cc to clustering columns. The term tavg refers to the average number of bytes of metadata stored per cell, such as timestamps. It is typical to use an estimate of 8 bytes for this value. You’ll recognize the number of rows Nr and number of values Nv from previous calculations. The sizeOf() function refers to the size in bytes of the CQL data type of each referenced column. The first term asks you to sum the size of the partition key columns. For this example, the available_rooms_by_hotel_date table has a single partition key column, the hotel_id, which is of type text. Assuming that hotel identifiers are simple 5-character codes, you have a 5-byte value, so the sum of the partition key column sizes is 5 bytes. The second term asks you to sum the size of the static columns. This table has no static columns, so the size is 0 bytes. The third term is the most involved, and for good reason—it is calculating the size of the cells in the partition. Sum the size of the clustering columns and regular columns. The two clustering columns are the date, which is 4 bytes, and the room_number, which is a 2-byte short integer, giving a sum of 6 bytes. There is only a single regular column, the boolean is_available, which is 1 byte in size. Summing the regular column size (1 byte) plus the clustering column size (6 bytes) gives a total of 7 bytes. To finish up the term, multiply this value by the number of rows (73,000), giving a result of 511,000 bytes (0.51 MB). The fourth term is simply counting the metadata that that Cassandra stores for each cell. In the storage format used by Cassandra 3.0 and later, the amount of metadata for a given cell varies based on the type of data being stored, and whether or not custom timestamp or TTL values are specified for individual cells. For this table, reuse the number of values from the previous calculation (73,000) and multiply by 8, which gives 0.58 MB. Adding these terms together, you get a final estimate: \\[Partition size = 16 bytes + 0 bytes + 0.51 MB + 0.58 MB = 1.1 MB\\] This formula is an approximation of the actual size of a partition on disk, but is accurate enough to be quite useful. Remembering that the partition must be able to fit on a single node, it looks like the table design will not put a lot of strain on disk storage. Cassandra’s storage engine was re-implemented for the 3.0 release, including a new format for SSTable files. The previous format stored a separate copy of the clustering columns as part of the record for each cell. The newer format eliminates this duplication, which reduces the size of stored data and simplifies the formula for computing that size. Keep in mind also that this estimate only counts a single replica of data. You will need to multiply the value obtained here by the number of partitions and the number of replicas specified by the keyspace’s replication strategy in order to determine the total required total capacity for each table. This will come in handy when you plan your cluster. As discussed previously, the goal is to design tables that can provide the data you need with queries that touch a single partition, or failing that, the minimum possible number of partitions. However, as shown in the examples, it is quite possible to design wide partition-style tables that approach Cassandra’s built-in limits. Performing sizing analysis on tables may reveal partitions that are potentially too large, either in number of values, size on disk, or both. The technique for splitting a large partition is straightforward: add an additional column to the partition key. In most cases, moving one of the existing columns into the partition key will be sufficient. Another option is to introduce an additional column to the table to act as a sharding key, but this requires additional application logic. Continuing to examine the available rooms example, if you add the date column to the partition key for the available_rooms_by_hotel_date table, each partition would then represent the availability of rooms at a specific hotel on a specific date. This will certainly yield partitions that are significantly smaller, perhaps too small, as the data for consecutive days will likely be on separate nodes. Another technique known as bucketing is often used to break the data into moderate-size partitions. For example, you could bucketize the available_rooms_by_hotel_date table by adding a month column to the partition key, perhaps represented as an integer. The comparision with the original design is shown in the figure below. While the month column is partially duplicative of the date, it provides a nice way of grouping related data in a partition that will not get too large. If you really felt strongly about preserving a wide partition design, you could instead add the room_id to the partition key, so that each partition would represent the availability of the room across all dates. Because there was no query identified that involves searching availability of a specific room, the first or second design approach is most suitable to the application needs. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Evaluating and Refining Data Models","component":"Cassandra","version":"4.1","name":"data_modeling_refining","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_refining.html","titles":[{"text":"Calculating Partition Size","id":"calculating-partition-size"},{"text":"Calculating Size on Disk","id":"calculating-size-on-disk"},{"text":"Breaking Up Large Partitions","id":"breaking-up-large-partitions"}]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_schema.html":{"text":"Once you have finished evaluating and refining the physical model, you’re ready to implement the schema in CQL. Here is the schema for the hotel keyspace, using CQL’s comment feature to document the query pattern supported by each table: CREATE KEYSPACE hotel WITH replication = {‘class’: ‘SimpleStrategy’, ‘replication_factor’ : 3}; CREATE TYPE hotel.address ( street text, city text, state_or_province text, postal_code text, country text ); CREATE TABLE hotel.hotels_by_poi ( poi_name text, hotel_id text, name text, phone text, address frozen, PRIMARY KEY ((poi_name), hotel_id) ) WITH comment = ‘Q1. Find hotels near given poi’ AND CLUSTERING ORDER BY (hotel_id ASC) ; CREATE TABLE hotel.hotels ( id text PRIMARY KEY, name text, phone text, address frozen, pois set ) WITH comment = ‘Q2. Find information about a hotel’; CREATE TABLE hotel.pois_by_hotel ( poi_name text, hotel_id text, description text, PRIMARY KEY ((hotel_id), poi_name) ) WITH comment = Q3. Find pois near a hotel’; CREATE TABLE hotel.available_rooms_by_hotel_date ( hotel_id text, date date, room_number smallint, is_available boolean, PRIMARY KEY ((hotel_id), date, room_number) ) WITH comment = ‘Q4. Find available rooms by hotel date’; CREATE TABLE hotel.amenities_by_room ( hotel_id text, room_number smallint, amenity_name text, description text, PRIMARY KEY ((hotel_id, room_number), amenity_name) ) WITH comment = ‘Q5. Find amenities for a room’; Notice that the elements of the partition key are surrounded with parentheses, even though the partition key consists of the single column poi_name. This is a best practice that makes the selection of partition key more explicit to others reading your CQL. Similarly, here is the schema for the reservation keyspace: CREATE KEYSPACE reservation WITH replication = {‘class’: ‘SimpleStrategy’, ‘replication_factor’ : 3}; CREATE TYPE reservation.address ( street text, city text, state_or_province text, postal_code text, country text ); CREATE TABLE reservation.reservations_by_confirmation ( confirm_number text, hotel_id text, start_date date, end_date date, room_number smallint, guest_id uuid, PRIMARY KEY (confirm_number) ) WITH comment = ‘Q6. Find reservations by confirmation number’; CREATE TABLE reservation.reservations_by_hotel_date ( hotel_id text, start_date date, end_date date, room_number smallint, confirm_number text, guest_id uuid, PRIMARY KEY ((hotel_id, start_date), room_number) ) WITH comment = ‘Q7. Find reservations by hotel and date’; CREATE TABLE reservation.reservations_by_guest ( guest_last_name text, hotel_id text, start_date date, end_date date, room_number smallint, confirm_number text, guest_id uuid, PRIMARY KEY ((guest_last_name), hotel_id) ) WITH comment = ‘Q8. Find reservations by guest name’; CREATE TABLE reservation.guests ( guest_id uuid PRIMARY KEY, first_name text, last_name text, title text, emails set, phone_numbers list, addresses map, confirm_number text ) WITH comment = ‘Q9. Find guest by ID’; You now have a complete Cassandra schema for storing data for a hotel application. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Defining Database Schema","component":"Cassandra","version":"4.1","name":"data_modeling_schema","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_schema.html","titles":[]},"/Cassandra/4.1/cassandra/data_modeling/data_modeling_tools.html":{"text":"There are several tools available to help you design and manage your Cassandra schema and build queries. Hackolade is a data modeling tool that supports schema design for Cassandra and many other NoSQL databases. Hackolade supports the unique concepts of CQL such as partition keys and clustering columns, as well as data types including collections and UDTs. It also provides the ability to create Chebotko diagrams. Kashlev Data Modeler is a Cassandra data modeling tool that automates the data modeling methodology described in this documentation, including identifying access patterns, conceptual, logical, and physical data modeling, and schema generation. It also includes model patterns that you can optionally leverage as a starting point for your designs. DataStax DevCenter is a tool for managing schema, executing queries and viewing results. While the tool is no longer actively supported, it is still popular with many developers and is available as a free download. DevCenter features syntax highlighting for CQL commands, types, and name literals. DevCenter provides command completion as you type out CQL commands and interprets the commands you type, highlighting any errors you make. The tool provides panes for managing multiple CQL scripts and connections to multiple clusters. The connections are used to run CQL commands against live clusters and view the results. The tool also has a query trace feature that is useful for gaining insight into the performance of your queries. IDE Plugins - There are CQL plugins available for several Integrated Development Environments (IDEs), such as IntelliJ IDEA and Apache NetBeans. These plugins typically provide features such as schema management and query execution. Some IDEs and tools that claim to support Cassandra do not actually support CQL natively, but instead access Cassandra using a JDBC/ODBC driver and interact with Cassandra as if it were a relational database with SQL support. Wnen selecting tools for working with Cassandra you’ll want to make sure they support CQL and reinforce Cassandra best practices for data modeling as presented in this documentation. Material adapted from Cassandra, The Definitive Guide. Published by O’Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. All rights reserved. Used with permission.","title":"Cassandra Data Modeling Tools","component":"Cassandra","version":"4.1","name":"data_modeling_tools","url":"/Cassandra/4.1/cassandra/data_modeling/data_modeling_tools.html","titles":[]},"/Cassandra/4.1/cassandra/data_modeling/index.html":{"text":"Introduction RDBMS Conceptual Logical Physical Schema Queries Refining Tools","title":"Data Modeling","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/data_modeling/index.html","titles":[]},"/Cassandra/4.1/cassandra/data_modeling/intro.html":{"text":"Apache Cassandra stores data in tables, with each table consisting of rows and columns. CQL (Cassandra Query Language) is used to query the data stored in tables. Apache Cassandra data model is based around and optimized for querying. Cassandra does not support relational data modeling intended for relational databases. Data modeling is the process of identifying entities and their relationships. In relational databases, data is placed in normalized tables with foreign keys used to reference related data in other tables. Queries that the application will make are driven by the structure of the tables and related data are queried as table joins. In Cassandra, data modeling is query-driven. The data access patterns and application queries determine the structure and organization of data which then used to design the database tables. Data is modeled around specific queries. Queries are best designed to access a single table, which implies that all entities involved in a query must be in the same table to make data access (reads) very fast. Data is modeled to best suit a query or a set of queries. A table could have one or more entities as best suits a query. As entities do typically have relationships among them and queries could involve entities with relationships among them, a single entity may be included in multiple tables. Unlike a relational database model in which queries make use of table joins to get data from multiple tables, joins are not supported in Cassandra so all required fields (columns) must be grouped together in a single table. Since each query is backed by a table, data is duplicated across multiple tables in a process known as denormalization. Data duplication and a high write throughput are used to achieve a high read performance. The choice of the primary key and partition key is important to distribute data evenly across the cluster. Keeping the number of partitions read for a query to a minimum is also important because different partitions could be located on different nodes and the coordinator would need to send a request to each node adding to the request overhead and latency. Even if the different partitions involved in a query are on the same node, fewer partitions make for a more efficient query. Apache Cassandra is a distributed database that stores data across a cluster of nodes. A partition key is used to partition data among the nodes. Cassandra partitions data over the storage nodes using a variant of consistent hashing for data distribution. Hashing is a technique used to map data with which given a key, a hash function generates a hash value (or simply a hash) that is stored in a hash table. A partition key is generated from the first field of a primary key. Data partitioned into hash tables using partition keys provides for rapid lookup. Fewer the partitions used for a query faster is the response time for the query. As an example of partitioning, consider table t in which id is the only field in the primary key. CREATE TABLE t ( id int, k int, v text, PRIMARY KEY (id) ); The partition key is generated from the primary key id for data distribution across the nodes in a cluster. Consider a variation of table t that has two fields constituting the primary key to make a composite or compound primary key. CREATE TABLE t ( id int, c text, k int, v text, PRIMARY KEY (id,c) ); For the table t with a composite primary key the first field id is used to generate the partition key and the second field c is the clustering key used for sorting within a partition. Using clustering keys to sort data makes retrieval of adjacent data more efficient. In general, the first field or component of a primary key is hashed to generate the partition key and the remaining fields or components are the clustering keys that are used to sort data within a partition. Partitioning data improves the efficiency of reads and writes. The other fields that are not primary key fields may be indexed separately to further improve query performance. The partition key could be generated from multiple fields if they are grouped as the first component of a primary key. As another variation of the table t, consider a table with the first component of the primary key made of two fields grouped using parentheses. CREATE TABLE t ( id1 int, id2 int, c1 text, c2 text k int, v text, PRIMARY KEY ((id1,id2),c1,c2) ); For the preceding table t the first component of the primary key constituting fields id1 and id2 is used to generate the partition key and the rest of the fields c1 and c2 are the clustering keys used for sorting within a partition. Relational databases store data in tables that have relations with other tables using foreign keys. A relational database’s approach to data modeling is table-centric. Queries must use table joins to get data from multiple tables that have a relation between them. Apache Cassandra does not have the concept of foreign keys or relational integrity. Apache Cassandra’s data model is based around designing efficient queries; queries that don’t involve multiple tables. Relational databases normalize data to avoid duplication. Apache Cassandra in contrast de-normalizes data by duplicating data in multiple tables for a query-centric data model. If a Cassandra data model cannot fully integrate the complexity of relationships between the different entities for a particular query, client-side joins in application code may be used. As an example, a magazine data set consists of data for magazines with attributes such as magazine id, magazine name, publication frequency, publication date, and publisher. A basic query (Q1) for magazine data is to list all the magazine names including their publication frequency. As not all data attributes are needed for Q1 the data model would only consist of id ( for partition key), magazine name and publication frequency as shown in Figure 1. Figure 1. Data Model for Q1 Another query (Q2) is to list all the magazine names by publisher. For Q2 the data model would consist of an additional attribute publisher for the partition key. The id would become the clustering key for sorting within a partition. Data model for Q2 is illustrated in Figure 2. Figure 2. Data Model for Q2 After the conceptual data model has been created a schema may be designed for a query. For Q1 the following schema may be used. CREATE TABLE magazine_name (id int PRIMARY KEY, name text, publicationFrequency text) For Q2 the schema definition would include a clustering key for sorting. CREATE TABLE magazine_publisher (publisher text,id int,name text, publicationFrequency text, PRIMARY KEY (publisher, id)) WITH CLUSTERING ORDER BY (id DESC) The data model is a conceptual model that must be analyzed and optimized based on storage, capacity, redundancy and consistency. A data model may need to be modified as a result of the analysis. Considerations or limitations that are used in data model analysis include: Partition Size Data Redundancy Disk space Lightweight Transactions (LWT) The two measures of partition size are the number of values in a partition and partition size on disk. Though requirements for these measures may vary based on the application a general guideline is to keep number of values per partition to below 100,000 and disk space per partition to below 100MB. Data redundancies as duplicate data in tables and multiple partition replicates are to be expected in the design of a data model , but nevertheless should be kept in consideration as a parameter to keep to the minimum. LWT transactions (compare-and-set, conditional update) could affect performance and queries using LWT should be kept to the minimum. Warning Materialized views (MVs) are experimental in the latest (4.0) release. Materialized views (MVs) could be used to implement multiple queries for a single table. A materialized view is a table built from data from another table, the base table, with new primary key and new properties. Changes to the base table data automatically add and update data in a MV. Different queries may be implemented using a materialized view as an MV’s primary key differs from the base table. Queries are optimized by the primary key definition.","title":"Introduction","component":"Cassandra","version":"4.1","name":"intro","url":"/Cassandra/4.1/cassandra/data_modeling/intro.html","titles":[{"text":"What is Data Modeling?","id":"what-is-data-modeling"},{"text":"Query-driven modeling","id":"query-driven-modeling"},{"text":"Goals","id":"goals"},{"text":"Partitions","id":"partitions"},{"text":"Comparing with Relational Data Model","id":"comparing-with-relational-data-model"},{"text":"Examples of Data Modeling","id":"examples-of-data-modeling"},{"text":"Designing Schema","id":"designing-schema"},{"text":"Data Model Analysis","id":"data-model-analysis"},{"text":"Using Materialized Views","id":"using-materialized-views"}]},"/Cassandra/4.1/cassandra/getting_started/configuring.html":{"text":"The Cassandra configuration files location varies, depending on the type of installation: docker: /etc/cassandra directory tarball: conf directory within the tarball install location package: /etc/cassandra directory Cassandra’s default configuration file, cassandra.yaml, is sufficient to explore a simple single-node cluster. However, anything beyond running a single-node cluster locally requires additional configuration to various Cassandra configuration files. Some examples that require non-default configuration are deploying a multi-node cluster or using clients that are not running on a cluster node. cassandra.yaml: the main configuration file for Cassandra cassandra-env.sh: environment variables can be set cassandra-rackdc.properties OR cassandra-topology.properties: set rack and datacenter information for a cluster logback.xml: logging configuration including logging levels jvm-*: a number of JVM configuration files for both the server and clients commitlog_archiving.properties: set archiving parameters for the commitlog Two sample configuration files can also be found in ./conf: metrics-reporter-config-sample.yaml: configuring what the metrics-report will collect cqlshrc.sample: how the CQL shell, cqlsh, can be configured Configuring Cassandra is done by setting yaml properties in the cassandra.yaml file. At a minimum you should consider setting the following properties: cluster_name: Set the name of your cluster. seeds: A comma separated list of the IP addresses of your cluster seed nodes. storage_port: Check that you don’t have the default port of 7000 blocked by a firewall. listen_address: The listen address is the IP address of a node that allows it to communicate with other nodes in the cluster. Set to localhost by default. Alternatively, you can set listen_interface to tell Cassandra which interface to use, and consecutively which address to use. Set one property, not both. native_transport_port: Check that you don’t have the default port of 9042 blocked by a firewall, so that clients like cqlsh can communicate with Cassandra on this port. The following yaml properties control the location of directories: data_file_directories: One or more directories where data files, like SSTables are located. commitlog_directory: The directory where commitlog files are located. saved_caches_directory: The directory where saved caches are located. hints_directory: The directory where hints are located. For performance reasons, if you have multiple disks, consider putting commitlog and data files on different disks. JVM-level settings such as heap size can be set in cassandra-env.sh. You can add any additional JVM command line argument to the JVM_OPTS environment variable; when Cassandra starts, these arguments will be passed to the JVM. The default logger is logback. By default it will log: INFO level in system.log DEBUG level in debug.log When running in the foreground, it will also log at INFO level to the console. You can change logging properties by editing logback.xml or by running the nodetool setlogginglevel command.","title":"Configuring Cassandra","component":"Cassandra","version":"4.1","name":"configuring","url":"/Cassandra/4.1/cassandra/getting_started/configuring.html","titles":[{"text":"Main runtime properties","id":"main-runtime-properties"},{"text":"Changing the location of directories","id":"changing-the-location-of-directories"},{"text":"Environment variables","id":"environment-variables"},{"text":"Logging","id":"logging"}]},"/Cassandra/4.1/cassandra/getting_started/drivers.html":{"text":"Here are known Cassandra client drivers organized by language. Before choosing a driver, you should verify the Cassandra version and functionality supported by a specific driver. Achilles Astyanax Casser Datastax Java driver Kundera PlayORM Datastax Python driver Datastax Ruby driver Cassandra Sharp Datastax C# driver Fluent Cassandra Datastax Nodejs driver CQL | PHP Datastax PHP driver PHP-Cassandra PHP Library for Cassandra Datastax C++ driver libQTCassandra Datastax Spark connector Phantom Quill Alia Cassaforte Hayt CQerl Erlcass CQLc Gocassa GoCQL Cassy Rust CQL Cassandra::Client and DBD::Cassandra Xandra CQEx dart_cassandra_cql","title":"Client drivers","component":"Cassandra","version":"4.1","name":"drivers","url":"/Cassandra/4.1/cassandra/getting_started/drivers.html","titles":[{"text":"Java","id":"java"},{"text":"Python","id":"python"},{"text":"Ruby","id":"ruby"},{"text":"C# / .NET","id":"c-net"},{"text":"Nodejs","id":"nodejs"},{"text":"PHP","id":"php"},{"text":"C++","id":"c"},{"text":"Scala","id":"scala"},{"text":"Clojure","id":"clojure"},{"text":"Erlang","id":"erlang"},{"text":"Go","id":"go"},{"text":"Haskell","id":"haskell"},{"text":"Rust","id":"rust"},{"text":"Perl","id":"perl"},{"text":"Elixir","id":"elixir"},{"text":"Dart","id":"dart"}]},"/Cassandra/4.1/cassandra/getting_started/index.html":{"text":"This section covers how to get started using Apache Cassandra and should be the first thing to read if you are new to Cassandra. Installing Cassandra: Installation instructions plus information on choosing a method. [ Docker ] [ tarball ] [ Debian ] [ RPM ] Configuring Cassandra Inserting and querying data Client drivers: Drivers for various languages. [ Java ] [ Python ] [ Ruby ] [ C# / .NET ] [ Node.js ] [ PHP ] [ C++ ] [ Scala ] [ Clojure ] [ Erlang ] [ Go ] [ Haskell ] [ Rust ] [ Perl ] [ Elixir ] [ Dart ] Production recommendations","title":"Getting Started","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/getting_started/index.html","titles":[]},"/Cassandra/4.1/cassandra/getting_started/installing.html":{"text":"These are the instructions for deploying the supported releases of Apache Cassandra on Linux servers. Cassandra runs on a wide array of Linux distributions including (but not limited to): Ubuntu, most notably LTS releases 16.04 to 18.04 CentOS & RedHat Enterprise Linux (RHEL) including 6.6 to 7.7 Amazon Linux AMIs including 2016.09 through to Linux 2 Debian versions 8 & 9 SUSE Enterprise Linux 12 This is not an exhaustive list of operating system platforms, nor is it prescriptive. However, users will be well-advised to conduct exhaustive tests of their own particularly for less-popular distributions of Linux. Deploying on older versions is not recommended unless you have previous experience with the older distribution in a production environment. Install the latest version of Java 8 or Java 11, either the Oracle Java Standard Edition 8 / Oracle Java Standard Edition 11 (Long Term Support) or OpenJDK 8 / OpenJDK 11. To verify that you have the correct version of java installed, type java -version. NOTE: Experimental support for Java 11 was added in Cassandra 4.0 (CASSANDRA-9608). Full support is effective Cassandra 4.0.2 version (CASSANDRA-16894) For more information, see NEWS.txt. For using cqlsh, the latest version of Python 3.6+ or Python 2.7 (support deprecated). To verify that you have the correct version of Python installed, type python --version. There are three methods of installing Cassandra that are common: Docker image Tarball binary file Package installation (RPM, YUM) If you are a current Docker user, installing a Docker image is simple. You’ll need to install Docker Desktop for Mac, Docker Desktop for Windows, or have docker installed on Linux. Pull the appropriate image and then start Cassandra with a run command. For most users, installing the binary tarball is also a simple choice. The tarball unpacks all its contents into a single location with binaries and configuration files located in their own subdirectories. The most obvious attribute of the tarball installation is it does not require root permissions and can be installed on any Linux distribution. Packaged installations require root permissions, and are most appropriate for production installs. Install the RPM build on CentOS and RHEL-based distributions if you want to install Cassandra using YUM. Install the Debian build on Ubuntu and other Debian-based distributions if you want to install Cassandra using APT. Note that both the YUM and APT methods required root permissions and will install the binaries and configuration files as the cassandra OS user. Pull the docker image. For the latest image, use: docker pull cassandra:latest This docker pull command will get the latest version of the 'Docker Official' Apache Cassandra image available from the Dockerhub. Start Cassandra with a docker run command: docker run --name cass_cluster cassandra:latest The --name option will be the name of the Cassandra cluster created. Start the CQL shell, cqlsh to interact with the Cassandra node created: docker exec -it cass_cluster cqlsh Verify the version of Java installed. For example: Command Result $ java -version openjdk version \"1.8.0_222\" OpenJDK Runtime Environment (build 1.8.0_222-8u222-b10-1ubuntu1~16.04.1-b10) OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode) Download the binary tarball from one of the mirrors on the Apache Cassandra Download site. For example, to download Cassandra 4.0: $ curl -OL http://apache.mirror.digitalpacific.com.au/cassandra/4.0.0/apache-cassandra-4.0.0-bin.tar.gz The mirrors only host the latest versions of each major supported release. To download an earlier version of Cassandra, visit the Apache Archives. OPTIONAL: Verify the integrity of the downloaded tarball using one of the methods here. For example, to verify the hash of the downloaded file using GPG: Command Result $ gpg --print-md SHA256 apache-cassandra-4.0.0-bin.tar.gz apache-cassandra-4.0.0-bin.tar.gz: 28757DDE 589F7041 0F9A6A95 C39EE7E6 CDE63440 E2B06B91 AE6B2006 14FA364D Compare the signature with the SHA256 file from the Downloads site: Command Result $ curl -L https://downloads.apache.org/cassandra/4.0.0/apache-cassandra-4.0.0-bin.tar.gz.sha256 28757dde589f70410f9a6a95c39ee7e6cde63440e2b06b91ae6b200614fa364d Unpack the tarball: $ tar xzvf apache-cassandra-4.0.0-bin.tar.gz The files will be extracted to the apache-cassandra-4.0.0/ directory. This is the tarball installation location. Located in the tarball installation location are the directories for the scripts, binaries, utilities, configuration, data and log files: / bin/ (1) conf/ (2) data/ (3) doc/ interface/ javadoc/ lib/ logs/ (4) pylib/ tools/ (5) 1 location of the commands to run cassandra, cqlsh, nodetool, and SSTable tools 2 location of cassandra.yaml and other configuration files 3 location of the commit logs, hints, and SSTables 4 location of system and debug logs location of cassandra-stress tool For information on how to configure your installation, see Configuring Cassandra. Start Cassandra: $ cd apache-cassandra-4.0.0/ && bin/cassandra This will run Cassandra as the authenticated Linux user. Monitor the progress of the startup with: Command Result $ tail -f logs/system.log Cassandra is ready when you see an entry like this in the system.log: INFO [main] 2019-12-17 03:03:37,526 Server.java:156 - Starting listening for CQL clients on localhost/127.0.0.1:9042 (unencrypted)... You can monitor the progress of the startup with: Command Result $ tail -f logs/system.log Cassandra is ready when you see an entry like this in the system.log: INFO [main] 2019-12-17 03:03:37,526 Server.java:156 - Starting listening for CQL clients on localhost/127.0.0.1:9042 (unencrypted)... For information on how to configure your installation, see Configuring Cassandra. Check the status of Cassandra: $ bin/nodetool status The status column in the output should report UN which stands for \"Up/Normal\". Alternatively, connect to the database with: $ bin/cqlsh Verify the version of Java installed. For example: Command Result $ java -version openjdk version \"1.8.0_222\" OpenJDK Runtime Environment (build 1.8.0_222-8u222-b10-1ubuntu1~16.04.1-b10) OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode) Add the Apache repository of Cassandra to the file cassandra.sources.list. The latest major version is {41_version} and the corresponding distribution name is 41x (with an \"x\" as the suffix). For older releases use: 41x for C* {41_version} series 40x for C* 4.0 series 311x for C* {311_version} series 30x for C* {30_version} series For example, to add the repository for version {41_version} (41x): $ echo \"deb [signed-by=/etc/apt/keyrings/apache-cassandra.asc] https://debian.cassandra.apache.org 41x main\" | sudo tee -a /etc/apt/sources.list.d/cassandra.sources.list deb https://debian.cassandra.apache.org 41x main Add the Apache Cassandra repository keys to the list of trusted keys on the server: Command Result $ curl -o /etc/apt/keyrings/apache-cassandra.asc https://downloads.apache.org/cassandra/KEYS % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 266k 100 266k 0 0 320k 0 --:--:-- --:--:-- --:--:-- 320k OK Update the package index from sources: $ sudo apt-get update Install Cassandra with APT: $ sudo apt-get install cassandra For information on how to configure your installation, see Configuring Cassandra. Monitor the progress of the startup with: Command Result $ tail -f logs/system.log Cassandra is ready when you see an entry like this in the system.log: INFO [main] 2019-12-17 03:03:37,526 Server.java:156 - Starting listening for CQL clients on localhost/127.0.0.1:9042 (unencrypted)... For information on how to configure your installation, see Configuring Cassandra. Check the status of Cassandra: $ nodetool status The status column in the output should report UN which stands for \"Up/Normal\". Alternatively, connect to the database with: $ cqlsh Verify the version of Java installed. For example: Command Result $ java -version openjdk version \"1.8.0_222\" OpenJDK Runtime Environment (build 1.8.0_222-8u222-b10-1ubuntu1~16.04.1-b10) OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode) Add the Apache repository of Cassandra to the file /etc/yum.repos.d/cassandra.repo (as the root user). The latest major version is {41_version} and the corresponding distribution name is 41x (with an \"x\" as the suffix). For older releases use: 41x for C* {41_version} series 40x for C* 4.0 series 311x for C* {311_version} series 30x for C* {30_version} series For example, to add the repository for version {41_version} (41x): [cassandra] name=Apache Cassandra baseurl=https://redhat.cassandra.apache.org/41x/ gpgcheck=1 repo_gpgcheck=1 gpgkey=https://downloads.apache.org/cassandra/KEYS Update the package index from sources: $ sudo yum update Install Cassandra with YUM: $ sudo yum install cassandra A new Linux user cassandra will get created as part of the installation. The Cassandra service will also be run as this user. Start the Cassandra service: $ sudo service cassandra start Monitor the progress of the startup with: Command Result $ tail -f logs/system.log Cassandra is ready when you see an entry like this in the system.log: INFO [main] 2019-12-17 03:03:37,526 Server.java:156 - Starting listening for CQL clients on localhost/127.0.0.1:9042 (unencrypted)... For information on how to configure your installation, see Configuring Cassandra. Check the status of Cassandra: $ nodetool status The status column in the output should report UN which stands for \"Up/Normal\". Alternatively, connect to the database with: $ cqlsh For help with installation issues, see the Troubleshooting section.","title":"Installing Cassandra","component":"Cassandra","version":"4.1","name":"installing","url":"/Cassandra/4.1/cassandra/getting_started/installing.html","titles":[{"text":"Prerequisites","id":"prerequisites"},{"text":"Choosing an installation method","id":"choosing-an-installation-method"},{"text":"Installing the docker image","id":"installing-the-docker-image"},{"text":"Installing the binary tarball","id":"installing-the-binary-tarball"},{"text":"Installing the Debian packages","id":"installing-the-debian-packages"},{"text":"Installing the RPM packages","id":"installing-the-rpm-packages"},{"text":"Further installation info","id":"further-installation-info"}]},"/Cassandra/4.1/cassandra/getting_started/java11.html":{"text":"In the new Java release cadence a new Java version is made available every six months. The more frequent release cycle is favored as it brings new Java features to the developers as and when they are developed without the wait that the earlier 3 year release model incurred. Not every Java version is a Long Term Support (LTS) version. After Java 8 the next LTS version is Java 11. Java 9, 10, 12 and 13 are all non-LTS versions. One of the objectives of the Apache Cassandra 4.0 version is to support the recent LTS Java versions 8 and 11 (CASSANDRA-9608). Java 8 and Java 11 may be used to build and run Apache Cassandra 4.0. Effective Cassandra 4.0.2 there is full Java 11 support, it is not experimental anymore. The support matrix for the Java versions for compiling and running Apache Cassandra 4.0 is detailed in Table 1. The build version is along the vertical axis and the run version is along the horizontal axis. Table 1 : Support Matrix for Java Java 8 (Run) Java 11 (Run) Java 8 (Build) Supported Supported Java 11(Build) Not Supported Supported Essentially Apache 4.0 source code built with Java 11 cannot be run with Java 8. Next, we shall discuss using each of Java 8 and 11 to build and run Apache Cassandra 4.0. To start with, install Java 8. As an example, for installing Java 8 on RedHat Linux the command is as follows: $ sudo yum install java-1.8.0-openjdk-devel Set JAVA_HOME and JRE_HOME environment variables in the shell bash script. First, open the bash script: $ sudo vi ~/.bashrc Set the environment variables including the PATH. $ export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk $ export JRE_HOME=/usr/lib/jvm/java-1.8.0-openjdk/jre $ export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin Download and install Apache Cassandra 4.0 source code from the Git along with the dependencies. $ git clone https://github.com/apache/cassandra.git If Cassandra is already running stop Cassandra with the following command. [ec2-user@ip-172-30-3-146 bin]$ ./nodetool stopdaemon Build the source code from the cassandra directory, which has the build.xml build script. The Apache Ant uses the Java version set in the JAVA_HOME environment variable. $ cd ~/cassandra $ ant Apache Cassandra 4.0 gets built with Java 8. Set the environment variable for CASSANDRA_HOME in the bash script. Also add the CASSANDRA_HOME/bin to the PATH variable. $ export CASSANDRA_HOME=~/cassandra $ export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin:$CASSANDRA_HOME/bin To run Apache Cassandra 4.0 with either of Java 8 or Java 11 run the Cassandra application in the CASSANDRA_HOME/bin directory, which is in the PATH env variable. $ cassandra The Java version used to run Cassandra gets output as Cassandra is getting started. As an example if Java 11 is used, the run output should include similar to the following output snippet: INFO [main] 2019-07-31 21:18:16,862 CassandraDaemon.java:480 - Hostname: ip-172-30-3- 146.ec2.internal:7000:7001 INFO [main] 2019-07-31 21:18:16,862 CassandraDaemon.java:487 - JVM vendor/version: OpenJDK 64-Bit Server VM/11.0.3 INFO [main] 2019-07-31 21:18:16,863 CassandraDaemon.java:488 - Heap size: 1004.000MiB/1004.000MiB The following output indicates a single node Cassandra 4.0 cluster has started. INFO [main] 2019-07-31 21:18:19,687 InboundConnectionInitiator.java:130 - Listening on address: (127.0.0.1:7000), nic: lo, encryption: enabled (openssl) ... ... INFO [main] 2019-07-31 21:18:19,850 StorageService.java:512 - Unable to gossip with any peers but continuing anyway since node is in its own seed list INFO [main] 2019-07-31 21:18:19,864 StorageService.java:695 - Loading persisted ring state INFO [main] 2019-07-31 21:18:19,865 StorageService.java:814 - Starting up server gossip INFO [main] 2019-07-31 21:18:20,088 BufferPool.java:216 - Global buffer pool is enabled, when pool is exhausted (max is 251.000MiB) it will allocate on heap INFO [main] 2019-07-31 21:18:20,110 StorageService.java:875 - This node will not auto bootstrap because it is configured to be a seed node. ... ... INFO [main] 2019-07-31 21:18:20,809 StorageService.java:1507 - JOINING: Finish joining ring INFO [main] 2019-07-31 21:18:20,921 StorageService.java:2508 - Node 127.0.0.1:7000 state jump to NORMAL If Java 11 is used to build Apache Cassandra 4.0, first Java 11 must be installed and the environment variables set. As an example, to download and install Java 11 on RedHat Linux run the following command. $ yum install java-11-openjdk-devel Set the environment variables in the bash script for Java 11. The first command is to open the bash script. $ sudo vi ~/.bashrc $ export JAVA_HOME=/usr/lib/jvm/java-11-openjdk $ export JRE_HOME=/usr/lib/jvm/java-11-openjdk/jre $ export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin To build source code with Java 11 one of the following two options must be used. Include Apache Ant command-line option -Duse.jdk=11 as follows $ ant -Duse.jdk=11 Set environment variable CASSANDRA_USE_JDK11 to true $ export CASSANDRA_USE_JDK11=true As an example, set the environment variable CASSANDRA_USE_JDK11 to true. [ec2-user@ip-172-30-3-146 cassandra]$ export CASSANDRA_USE_JDK11=true [ec2-user@ip-172-30-3-146 cassandra]$ ant Buildfile: /home/ec2-user/cassandra/build.xml Or, set the command-line option. [ec2-user@ip-172-30-3-146 cassandra]$ ant -Duse.jdk11=true The build output should include the following. _build_java: [echo] Compiling for Java 11 ... ... build: _main-jar: [copy] Copying 1 file to /home/ec2-user/cassandra/build/classes/main/META-INF [jar] Building jar: /home/ec2-user/cassandra/build/apache-cassandra-4.0-SNAPSHOT.jar ... ... _build-test: [javac] Compiling 739 source files to /home/ec2-user/cassandra/build/test/classes [copy] Copying 25 files to /home/ec2-user/cassandra/build/test/classes ... ... jar: [mkdir] Created dir: /home/ec2-user/cassandra/build/classes/stress/META-INF [mkdir] Created dir: /home/ec2-user/cassandra/build/tools/lib [jar] Building jar: /home/ec2-user/cassandra/build/tools/lib/stress.jar [mkdir] Created dir: /home/ec2-user/cassandra/build/classes/fqltool/META-INF [jar] Building jar: /home/ec2-user/cassandra/build/tools/lib/fqltool.jar BUILD SUCCESSFUL Total time: 1 minute 3 seconds [ec2-user@ip-172-30-3-146 cassandra]$ One of the two options mentioned must be used to compile with JDK 11 or the build fails and the following error message is output. [ec2-user@ip-172-30-3-146 cassandra]$ ant Buildfile: /home/ec2-user/cassandra/build.xml validate-build-conf: BUILD FAILED /home/ec2-user/cassandra/build.xml:293: -Duse.jdk11=true or $CASSANDRA_USE_JDK11=true must be set when building from java 11 Total time: 1 second [ec2-user@ip-172-30-3-146 cassandra]$ The Java 11 built Apache Cassandra 4.0 source code may be run with Java 11 only. If a Java 11 built code is run with Java 8 the following error message gets output. [root@localhost ~]# ssh -i cassandra.pem ec2-user@ec2-3-85-85-75.compute-1.amazonaws.com Last login: Wed Jul 31 20:47:26 2019 from 75.155.255.51 [ec2-user@ip-172-30-3-146 ~]$ echo $JAVA_HOME /usr/lib/jvm/java-1.8.0-openjdk [ec2-user@ip-172-30-3-146 ~]$ cassandra ... ... Error: A JNI error has occurred, please check your installation and try again Exception in thread \"main\" java.lang.UnsupportedClassVersionError: org/apache/cassandra/service/CassandraDaemon has been compiled by a more recent version of the Java Runtime (class file version 55.0), this version of the Java Runtime only recognizes class file versions up to 52.0 at java.lang.ClassLoader.defineClass1(Native Method) at java.lang.ClassLoader.defineClass(ClassLoader.java:763) at ... ... The CASSANDRA_USE_JDK11 variable or the command-line option -Duse.jdk11 cannot be used to build with Java 8. To demonstrate set JAVA_HOME to version 8. [root@localhost ~]# ssh -i cassandra.pem ec2-user@ec2-3-85-85-75.compute-1.amazonaws.com Last login: Wed Jul 31 21:41:50 2019 from 75.155.255.51 [ec2-user@ip-172-30-3-146 ~]$ echo $JAVA_HOME /usr/lib/jvm/java-1.8.0-openjdk Set the CASSANDRA_USE_JDK11=true or command-line option -Duse.jdk11=true. Subsequently, run Apache Ant to start the build. The build fails with error message listed. [ec2-user@ip-172-30-3-146 ~]$ cd cassandra [ec2-user@ip-172-30-3-146 cassandra]$ export CASSANDRA_USE_JDK11=true [ec2-user@ip-172-30-3-146 cassandra]$ ant Buildfile: /home/ec2-user/cassandra/build.xml validate-build-conf: BUILD FAILED /home/ec2-user/cassandra/build.xml:285: -Duse.jdk11=true or $CASSANDRA_USE_JDK11=true cannot be set when building from java 8 Total time: 0 seconds","title":"Support for Java 11","component":"Cassandra","version":"4.1","name":"java11","url":"/Cassandra/4.1/cassandra/getting_started/java11.html","titles":[{"text":"Support Matrix","id":"support-matrix"},{"text":"Using Java 8 to Build","id":"using-java-8-to-build"},{"text":"Using Java 11 to Build","id":"using-java-11-to-build"},{"text":"Common Issues","id":"common-issues"}]},"/Cassandra/4.1/cassandra/getting_started/production.html":{"text":"The cassandra.yaml and jvm.options files have a number of notes and recommendations for production usage. This page expands on some of the information in the files. Using more than one token-range per node is referred to as virtual nodes, or vnodes. vnodes facilitate flexible expansion with more streaming peers when a new node bootstraps into a cluster. Limiting the negative impact of streaming (I/O and CPU overhead) enables incremental cluster expansion. However, more tokens leads to sharing data with more peers, and results in decreased availability. These two factors must be balanced based on a cluster’s characteristic reads and writes. To learn more, Cassandra Availability in Virtual Nodes, Joseph Lynch and Josh Snyder is recommended reading. Change the number of tokens using the setting in the cassandra.yaml file: num_tokens: 16 Here are the most common token counts with a brief explanation of when and why you would use each one. Token Count Description 1 Maximum availablility, maximum cluster size, fewest peers, but inflexible expansion. Must always double size of cluster to expand and remain balanced. 4 A healthy mix of elasticity and availability. Recommended for clusters which will eventually reach over 30 nodes. Requires adding approximately 20% more nodes to remain balanced. Shrinking a cluster may result in cluster imbalance. 8 Using 8 vnodes distributes the workload between systems with a ~10% variance and has minimal impact on performance. 16 Best for heavily elastic clusters which expand and shrink regularly, but may have issues availability with larger clusters. Not recommended for clusters over 50 nodes. In addition to setting the token count, it’s extremely important that allocate_tokens_for_local_replication_factor in cassandra.yaml is set to an appropriate number of replicates, to ensure even token allocation. Read ahead is an operating system feature that attempts to keep as much data as possible loaded in the page cache. Spinning disks can have long seek times causing high latency, so additional throughput on reads using page cache can improve performance. By leveraging read ahead, the OS can pull additional data into memory without the cost of additional seeks. This method works well when the available RAM is greater than the size of the hot dataset, but can be problematic when the reverse is true (dataset > RAM). The larger the hot dataset, the less read ahead is useful. Read ahead is definitely not useful in the following cases: Small partitions, such as tables with a single partition key Solid state drives (SSDs) Read ahead can actually increase disk usage, and in some cases result in as much as a 5x latency and throughput performance penalty. Read-heavy, key/value tables with small (under 1KB) rows are especially prone to this problem. The recommended read ahead settings are: Hardware Initial Recommendation Spinning Disks 64KB SSD 4KB Read ahead can be adjusted on Linux systems using the blockdev tool. For example, set the read ahead of the disk /dev/sda1\\ to 4KB: $ blockdev --setra 8 /dev/sda1 The blockdev setting sets the number of 512 byte sectors to read ahead. The argument of 8 above is equivalent to 4KB, or 8 * 512 bytes. All systems are different, so use these recommendations as a starting point and tune, based on your SLA and throughput requirements. To understand how read ahead impacts disk resource usage, we recommend carefully reading through the Diving Deep, using external tools section. Compressed data is stored by compressing fixed size byte buffers and writing the data to disk. The buffer size is determined by the chunk_length_in_kb element in the compression map of a table’s schema settings for WITH COMPRESSION. The default setting is 16KB starting with Cassandra 4.0. Since the entire compressed buffer must be read off-disk, using a compression chunk length that is too large can lead to significant overhead when reading small records. Combined with the default read ahead setting, the result can be massive read amplification for certain workloads. Therefore, picking an appropriate value for this setting is important. LZ4Compressor is the default and recommended compression algorithm. If you need additional information on compression, read The Last Pickle blogpost on compression performance. There are different compaction strategies available for different workloads. We recommend reading about the different strategies to understand which is the best for your environment. Different tables may, and frequently do use different compaction strategies in the same cluster. It is significantly better to set up peer-to-peer encryption and client server encryption when setting up your production cluster. Setting it up after the cluster is serving production traffic is challenging to do correctly. If you ever plan to use network encryption of any type, we recommend setting it up when initially configuring your cluster. Changing these configurations later is not impossible, but mistakes can result in downtime or data loss. Production clusters should never use SimpleStrategy. Production keyspaces should use the NetworkTopologyStrategy (NTS). For example: CREATE KEYSPACE mykeyspace WITH replication = { 'class': 'NetworkTopologyStrategy', 'datacenter1': 3 }; Cassandra clusters initialized with NetworkTopologyStrategy can take advantage of the ability to configure multiple racks and data centers. Correctly configuring or changing racks after a cluster has been provisioned is an unsupported process. Migrating from a single rack to multiple racks is also unsupported and can result in data loss. Using GossipingPropertyFileSnitch is the most flexible solution for on premise or mixed cloud environments. Ec2Snitch is reliable for AWS EC2 only environments.","title":"Production recommendations","component":"Cassandra","version":"4.1","name":"production","url":"/Cassandra/4.1/cassandra/getting_started/production.html","titles":[{"text":"Tokens","id":"tokens"},{"text":"Read ahead","id":"read-ahead"},{"text":"Compression","id":"compression"},{"text":"Compaction","id":"compaction"},{"text":"Encryption","id":"encryption"},{"text":"Ensure keyspaces are created with NetworkTopologyStrategy","id":"ensure-keyspaces-are-created-with-networktopologystrategy"},{"text":"Configure racks and snitch","id":"configure-racks-and-snitch"}]},"/Cassandra/4.1/cassandra/getting_started/querying.html":{"text":"The API for Cassandra is CQL, the Cassandra Query Language. To use CQL, you will need to connect to the cluster, using either: cqlsh, a shell for CQL a client driver for Cassandra for the adventurous, check out Apache Zeppelin, a notebook-style tool cqlsh is a command-line shell for interacting with Cassandra using CQL. It is shipped with every Cassandra package, and can be found in the bin directory alongside the cassandra executable. It connects to the single node specified on the command line. For example: $ bin/cqlsh localhost Connected to Test Cluster at localhost:9042. [cqlsh 5.0.1 | Cassandra 3.8 | CQL spec 3.4.2 | Native protocol v4] Use HELP for help. cqlsh> SELECT cluster_name, listen_address FROM system.local; cluster_name | listen_address --------------+---------------- Test Cluster | 127.0.0.1 (1 rows) cqlsh> If the command is used without specifying a node, localhost is the default. See the cqlsh section for full documentation. A lot of client drivers are provided by the Community and a list of known drivers is provided. You should refer to the documentation of each driver for more information.","title":"Inserting and querying","component":"Cassandra","version":"4.1","name":"querying","url":"/Cassandra/4.1/cassandra/getting_started/querying.html","titles":[{"text":"CQLSH","id":"cqlsh"},{"text":"Client drivers","id":"client-drivers"}]},"/Cassandra/4.1/cassandra/getting_started/quickstart.html":{"text":"Interested in getting started with Cassandra? Follow these instructions. STEP 1: GET CASSANDRA USING DOCKER You’ll need to have Docker Desktop for Mac, Docker Desktop for Windows, or similar software installed on your computer. docker pull cassandra:latest Apache Cassandra is also available as a tarball or package download. STEP 2: START CASSANDRA docker run --name cassandra cassandra STEP 3: CREATE FILES In the directory where you plan to run the next step, create these two files so that some data can be automatically inserted in the next step. A cqlshrc file will log into the Cassandra database with the default superuser: [authentication] username = cassandra password = cassandra Create a scripts directory and change to that directory. The following data.cql file will create a keyspace, the layer at which Cassandra replicates its data, a table to hold the data, and insert some data: # Create a keyspace CREATE KEYSPACE IF NOT EXISTS store WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : '1' }; # Create a table CREATE TABLE IF NOT EXISTS store.shopping_cart ( userid text PRIMARY KEY, item_count int, last_update_timestamp timestamp ); # Insert some data INSERT INTO store.shopping_cart (userid, item_count, last_update_timestamp) VALUES ('9876', 2, toTimeStamp(toDate(now)))); INSERT INTO store.shopping_cart (userid, item_count, last_update_timestamp) VALUES (1234, 5, toTimeStamp(toDate(now)))); You should now have a cqlshrc file and /scripts/data.cql file. STEP 4: RUN CQLSH TO INTERACT Cassandra is a distributed database that can read and write data across multiple nodes with peer-to-peer replication. The Cassandra Query Language (CQL) is similar to SQL but suited for the JOINless structure of Cassandra. The CQL shell, or cqlsh, is one tool to use in interacting with the database. docker run --rm -it -v //scripts:/scripts \\ -v /<currentdir/cqlshrc:/.cassandra/cqlshrc \\ --env CQLSH_HOST=host.docker.internal --env CQLSH_PORT=9042 nuvo/docker-cqlsh For this quickstart, this cqlsh docker image also loads some data automatically, so you can start running queries. STEP 5: READ SOME DATA SELECT * FROM store.shopping_cart; STEP 6: WRITE SOME MORE DATA INSERT (userid, item_count) VALUES (4567, 20) INTO store.shopping_cart; STEP 7: TERMINATE CASSANDRA docker rm cassandra CONGRATULATIONS! Hey, that wasn’t so hard, was it? To learn more, we suggest the following next steps: Read through the need link[Overview] to learn main concepts and how Cassandra works at a high level. To understand Cassandra in more detail, head over to the Docs. Browse through the Case Studies to learn how other users in our worldwide community are getting value out of Cassandra.","title":"Apache Cassandra Quickstart","component":"Cassandra","version":"4.1","name":"quickstart","url":"/Cassandra/4.1/cassandra/getting_started/quickstart.html","titles":[]},"/Cassandra/4.1/cassandra/operating/audit_logging.html":{"text":"Audit logging in Cassandra logs every incoming CQL command request, as well as authentication (successful/unsuccessful login) to a Cassandra node. Currently, there are two implementations provided. The custom logger can be implemented and injected with the class name as a parameter in the cassandra.yaml file. BinAuditLogger: an efficient way to log events to file in a binary format (community-recommended logger for performance) FileAuditLogger: logs events to audit/audit.log file using slf4j logger Audit logging captures following events: Successful as well as unsuccessful login attempts All database commands executed via native CQL protocol attempted or successfully executed Executing prepared statements will log the query as provided by the client in the prepare call, along with the execution timestamp and all other attributes (see below). Actual values bound for prepared statement execution will not show up in the audit log. Each audit log implementation has access to the following attributes, and for the default text based logger these fields are concatenated with pipes to yield the final message. user: User name(if available) host: Host IP, where the command is being executed source ip address: Source IP address from where the request initiated source port: Source port number from where the request initiated timestamp: unix time stamp type: Type of the request (SELECT, INSERT, etc.,) category - Category of the request (DDL, DML, etc.,) keyspace - Keyspace(If applicable) on which request is targeted to be executed scope - Table/Aggregate name/ function name/ trigger name etc., as applicable operation - CQL command being executed Auditlog can be configured using the cassandra.yaml file. To use audit logging on one node, either edit that file or enable and configure using nodetool. The following options are supported: enabled: This option enables/ disables audit log logger: Class name of the logger/ custom logger. audit_logs_dir: Auditlogs directory location, if not set, default to cassandra.logdir.audit or cassandra.logdir /audit/ included_keyspaces: Comma separated list of keyspaces to be included in audit log, default - includes all keyspaces excluded_keyspaces: Comma separated list of keyspaces to be excluded from audit log, default - excludes no keyspace except system, system_schema and system_virtual_schema included_categories: Comma separated list of Audit Log Categories to be included in audit log, default - includes all categories excluded_categories: Comma separated list of Audit Log Categories to be excluded from audit log, default - excludes no category included_users: Comma separated list of users to be included in audit log, default - includes all users excluded_users: Comma separated list of users to be excluded from audit log, default - excludes no user List of available categories are: QUERY, DML, DDL, DCL, OTHER, AUTH, ERROR, PREPARE The nodetool enableauditlog command enables AuditLog with the cassandra.yaml file defaults. Those defaults can be overridden using options with this nodetool command. nodetool enableauditlog --excluded-categories Comma separated list of Audit Log Categories to be excluded for audit log. If not set the value from cassandra.yaml will be used --excluded-keyspaces Comma separated list of keyspaces to be excluded for audit log. If not set the value from cassandra.yaml will be used. Please remeber that system, system_schema and system_virtual_schema are excluded by default, if you are overwriting this option via nodetool, remember to add these keyspaces back if you dont want them in audit logs --excluded-users Comma separated list of users to be excluded for audit log. If not set the value from cassandra.yaml will be used --included-categories Comma separated list of Audit Log Categories to be included for audit log. If not set the value from cassandra.yaml will be used --included-keyspaces Comma separated list of keyspaces to be included for audit log. If not set the value from cassandra.yaml will be used --included-users Comma separated list of users to be included for audit log. If not set the value from cassandra.yaml will be used --logger Logger name to be used for AuditLogging. Default BinAuditLogger. If not set the value from cassandra.yaml will be used The nodetool disableauditlog command disables AuditLog. nodetool disableuditlog The nodetool enableauditlog command can be used to reload auditlog filters with either defaults or previous loggername and updated filters: nodetool enableauditlog --loggername --included-keyspaces The auditlogviewer is used to view the contents of the audit binlog file in human readable text format. auditlogviewer [...] [options] -f,--follow Upon reacahing the end of the log continue indefinitely waiting for more records -r,--roll_cycle How often to roll the log file was rolled. May be necessary for Chronicle to correctly parse file names. (MINUTELY, HOURLY, DAILY). Default HOURLY. -h,--help display this help message For example, to dump the contents of audit log files to the console: auditlogviewer /logs/cassandra/audit results in LogMessage: user:anonymous|host:localhost/X.X.X.X|source:/X.X.X.X|port:60878|timestamp:1521158923615|type:USE_KS|category:DDL|ks:dev1|operation:USE \"dev1\" To use BinAuditLogger as a logger in AuditLogging, set the logger to BinAuditLogger in the cassandra.yaml file under the audit_logging_options section. BinAuditLogger can be futher configued using its advanced options in cassandra.yaml. block Indicates if the AuditLog should block if the it falls behind or should drop audit log records. Default is set to true so that AuditLog records wont be lost max_queue_weight Maximum weight of in memory queue for records waiting to be written to the audit log file before blocking or dropping the log records. Default is set to 256 * 1024 * 1024 max_log_size Maximum size of the rolled files to retain on disk before deleting the oldest file. Default is set to 16L * 1024L * 1024L * 1024L roll_cycle How often to roll Audit log segments so they can potentially be reclaimed. Available options are: MINUTELY, HOURLY, DAILY, LARGE_DAILY, XLARGE_DAILY, HUGE_DAILY.For more options, refer: net.openhft.chronicle.queue.RollCycles. Default is set to \"HOURLY\" To use FileAuditLogger as a logger in AuditLogging, set the class name in the cassandra.yaml file and configure the audit log events to flow through separate log file instead of system.log. ${cassandra.logdir}/audit/audit.log ${cassandra.logdir}/audit/audit.log.%d{yyyy-MM-dd}.%i.zip 50MB 30 5GB %-5level [%thread] %date{ISO8601} %F:%L - %msg%n","title":"Audit Logging","component":"Cassandra","version":"4.1","name":"audit_logging","url":"/Cassandra/4.1/cassandra/operating/audit_logging.html","titles":[{"text":"What does audit logging captures","id":"what-does-audit-logging-captures"},{"text":"Limitations","id":"limitations"},{"text":"What does audit logging logs","id":"what-does-audit-logging-logs"},{"text":"How to configure","id":"how-to-configure"},{"text":"cassandra.yaml configurations for AuditLog","id":"cassandra-yaml-configurations-for-auditlog"},{"text":"NodeTool command to enable AuditLog","id":"nodetool-command-to-enable-auditlog"},{"text":"Options","id":"options"},{"text":"NodeTool command to disable AuditLog","id":"nodetool-command-to-disable-auditlog"},{"text":"NodeTool command to reload AuditLog filters","id":"nodetool-command-to-reload-auditlog-filters"},{"text":"View the contents of AuditLog Files","id":"view-the-contents-of-auditlog-files"},{"text":"Options","id":"options-2"},{"text":"Configuring BinAuditLogger","id":"configuring-binauditlogger"},{"text":"Advanced Options for BinAuditLogger","id":"advanced-options-for-binauditlogger"},{"text":"Configuring FileAuditLogger","id":"configuring-fileauditlogger"}]},"/Cassandra/4.1/cassandra/operating/auditlogging.html":{"text":"Audit Logging is a new feature in Apache Cassandra 4.0 (CASSANDRA-12151). This new feature is safe for production use, with configurable limits to heap memory and disk space to prevent out-of-memory errors. All database activity is logged per-node as file-based records to a specified local filesystem directory. The audit log files are rolled periodically based on a configurable value. Some of the features of audit logging are: No additional database capacity is needed to store audit logs. No query tool is required to store the audit logs. Latency of database operations is not affected, so there is no performance impact. Heap memory usage is bounded by a weighted queue, with configurable maximum weight sitting in front of logging thread. Disk utilization is bounded by a configurable size, deleting old log segments once the limit is reached. Can be enabled, disabled, or reset (to delete on-disk data) using the JMX tool, nodetool. Can configure the settings in either the cassandra.yaml file or by using nodetool. Audit logging includes all CQL requests, both successful and failed. It also captures all successful and failed authentication and authorization events, such as login attempts. The difference between Full Query Logging (FQL) and audit logging is that FQL captures only successful CQL requests, which allow replay or comparison of logs. Audit logs are useful for compliance and debugging, while FQL is useful for debugging, performance benchmarking, testing and auditing CQL queries. The audit log contains: all events in the configured keyspaces to include all events in the configured categories to include all events executed by the configured users to include The audit log does not contain: configuration changes made in cassandra.yaml file nodetool commands Passwords mentioned as part of DCL statements: Passwords will be obfuscated as *\\**\\**\\*\\*. Statements that fail to parse will have everything after the appearance of the word password obfuscated as *\\**\\**\\*\\*. Statements with a mistyped word 'password' will be logged without obfuscation. Please make sure to use a different password on retries. The audit log is a series of log entries. An audit log entry contains: keyspace (String) - Keyspace on which request is made operation (String) - Database operation such as CQL command user (String) - User name scope (String) - Scope of request such as Table/Function/Aggregate name type (AuditLogEntryType) - Type of request CQL Audit Log Entry Type Common Audit Log Entry Type source (InetAddressAndPort) - Source IP Address from which request originated timestamp (long ) - Timestamp of the request batch (UUID) - Batch of request options (QueryOptions) - CQL Query options state (QueryState) - State related to a given query Each entry contains all applicable attributes for the given event, concatenated with a pipe (|). CQL audit log entry types are the following CQL commands. Each command is assigned to a particular specified category to log: Category CQL commands DDL ALTER_KEYSPACE, CREATE_KEYSPACE, DROP_KEYSPACE, ALTER_TABLE, CREATE_TABLE, DROP_TABLE, CREATE_FUNCTION, DROP_FUNCTION, CREATE_AGGREGATE, DROP_AGGREGATE, CREATE_INDEX, DROP_INDEX, ALTER_TYPE, CREATE_TYPE, DROP_TYPE, CREATE_TRIGGER, DROP_TRIGGER, ALTER_VIEW, CREATE_VIEW, DROP_VIEW, TRUNCATE DML BATCH, DELETE, UPDATE DCL GRANT, REVOKE, ALTER_ROLE, CREATE_ROLE, DROP_ROLE, LIST_ROLES, LIST_PERMISSIONS, LIST_USERS OTHER USE_KEYSPACE QUERY SELECT PREPARE PREPARE_STATEMENT Common audit log entry types are one of the following: Category CQL commands AUTH LOGIN_SUCCESS, LOGIN_ERROR, UNAUTHORIZED_ATTEMPT ERROR REQUEST_FAILURE The cassandra.yaml file can be used to configure and enable audit logging. Configuration and enablement may be the same or different on each node, depending on the cassandra.yaml file settings. Audit logs are generated on each enabled node, so logs on each node will have that node’s queries. All options for audit logging can be set in the cassandra.yaml file under the audit_logging_options:. The file includes the following options that can be uncommented for use: # Audit logging - Logs every incoming CQL command request, authentication to a node. See the docs # on audit_logging for full details about the various configuration options. audit_logging_options: enabled: false logger: - class_name: BinAuditLogger # audit_logs_dir: # included_keyspaces: # excluded_keyspaces: system, system_schema, system_virtual_schema # included_categories: # excluded_categories: # included_users: # excluded_users: # roll_cycle: HOURLY # block: true # max_queue_weight: 268435456 # 256 MiB # max_log_size: 17179869184 # 16 GiB ## archive command is \"/path/to/script.sh %path\" where %path is replaced with the file being rolled: # archive_command: # max_archive_retries: 10 Audit logging is enabled by setting the enabled option to true in the audit_logging_options setting. If this option is enabled, audit logging will start when Cassandra is started. For example, enabled: true. The type of audit logger is set with the logger option. Supported values are: BinAuditLogger (default), FileAuditLogger and NoOpAuditLogger. BinAuditLogger logs events to a file in binary format. FileAuditLogger uses the standard logging mechanism, slf4j to log events to the audit/audit.log file. It is a synchronous, file-based audit logger. The roll_cycle will be set in the logback.xml file. NoOpAuditLogger is a no-op implementation of the audit logger that shoudl be specified when audit logging is disabled. For example: logger: - class_name: FileAuditLogger To write audit logs, an existing directory must be set in audit_logs_dir. The directory must have appropriate permissions set to allow reading, writing, and executing. Logging will recursively delete the directory contents as needed. Do not place links in this directory to other sections of the filesystem. For example, audit_logs_dir: /cassandra/audit/logs/hourly. The audit log directory can also be configured using the system property cassandra.logdir.audit, which by default is set to cassandra.logdir + /audit/. Set the keyspaces to include with the included_keyspaces option and the keyspaces to exclude with the excluded_keyspaces option. By default, system, system_schema and system_virtual_schema are excluded, and all other keyspaces are included. For example: included_keyspaces: test, demo excluded_keyspaces: system, system_schema, system_virtual_schema The categories of database operations to include are specified with the included_categories option as a comma-separated list. The categories of database operations to exclude are specified with excluded_categories option as a comma-separated list. The supported categories for audit log are: AUTH, DCL, DDL, DML, ERROR, OTHER, PREPARE, and QUERY. By default all supported categories are included, and no category is excluded. included_categories: AUTH, ERROR, DCL excluded_categories: DDL, DML, QUERY, PREPARE Users to audit log are set with the included_users and excluded_users options. The included_users option specifies a comma-separated list of users to include explicitly. The excluded_users option specifies a comma-separated list of users to exclude explicitly. By default all users are included, and no users are excluded. included_users: excluded_users: john, mary The roll_cycle defines the frequency with which the audit log segments are rolled. Supported values are HOURLY (default), MINUTELY, and DAILY. For example: roll_cycle: DAILY The block option specifies whether audit logging should block writing or drop log records if the audit logging falls behind. Supported boolean values are true (default) or false. For example: block: false to drop records The max_queue_weight option sets the maximum weight of in-memory queue for records waiting to be written to the file before blocking or dropping. The option must be set to a positive value. The default value is 268435456, or 256 MiB. For example, to change the default: max_queue_weight: 134217728 # 128 MiB The max_log_size option sets the maximum size of the rolled files to retain on disk before deleting the oldest file. The option must be set to a positive value. The default is 17179869184, or 16 GiB. For example, to change the default: max_log_size: 34359738368 # 32 GiB The archive_command option sets the user-defined archive script to execute on rolled log files. For example: archive_command: /usr/local/bin/archiveit.sh %path # %path is the file being rolled The max_archive_retries option sets the max number of retries of failed archive commands. The default is 10. For example: max_archive_retries: 10 An audit log file could get rolled for other reasons as well such as a log file reaches the configured size threshold. Audit logging can also be configured using `nodetool when enabling the feature, and will override any values set in the cassandra.yaml file, as discussed in the next section. Audit logging is enabled on a per-node basis using the nodetool enableauditlog command. The logging directory must be defined with audit_logs_dir in the cassandra.yaml file or uses the default value cassandra.logdir.audit. The syntax of the nodetool enableauditlog command has all the same options that can be set in the cassandra.yaml file except audit_logs_dir. In addition, nodetool has options to set which host and port to run the command on, and username and password if the command requires authentication. nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enableauditlog [--excluded-categories ] [--excluded-keyspaces ] [--excluded-users ] [--included-categories ] [--included-keyspaces ] [--included-users ] [--logger ] OPTIONS --excluded-categories Comma separated list of Audit Log Categories to be excluded for audit log. If not set the value from cassandra.yaml will be used --excluded-keyspaces Comma separated list of keyspaces to be excluded for audit log. If not set the value from cassandra.yaml will be used --excluded-users Comma separated list of users to be excluded for audit log. If not set the value from cassandra.yaml will be used -h , --host Node hostname or ip address --included-categories Comma separated list of Audit Log Categories to be included for audit log. If not set the value from cassandra.yaml will be used --included-keyspaces Comma separated list of keyspaces to be included for audit log. If not set the value from cassandra.yaml will be used --included-users Comma separated list of users to be included for audit log. If not set the value from cassandra.yaml will be used --logger Logger name to be used for AuditLogging. Default BinAuditLogger. If not set the value from cassandra.yaml will be used -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -u , --username Remote jmx agent username To enable audit logging, run following command on each node in the cluster on which you want to enable logging: $ nodetool enableauditlog Use the nodetool disableauditlog command to disable audit logging. The auditlogviewer tool is used to view (dump) audit logs if the logger was BinAuditLogger.. auditlogviewer converts the binary log files into human-readable format; only the audit log directory must be supplied as a command-line option. If the logger FileAuditLogger was set, the log file are already in human-readable format and auditlogviewer is not needed to read files. The syntax of auditlogviewer is: auditlogviewer Audit log files directory path is a required argument. usage: auditlogviewer [...] [options] -- View the audit log contents in human readable format -- Options are: -f,--follow Upon reaching the end of the log continue indefinitely waiting for more records -h,--help display this help message -r,--roll_cycle How often to roll the log file was rolled. May be necessary for Chronicle to correctly parse file names. (MINUTELY, HOURLY, DAILY). Default HOURLY. To demonstrate audit logging, first configure the cassandra.yaml file with the following settings: audit_logging_options: enabled: true logger: BinAuditLogger audit_logs_dir: \"/cassandra/audit/logs/hourly\" # included_keyspaces: # excluded_keyspaces: system, system_schema, system_virtual_schema # included_categories: # excluded_categories: # included_users: # excluded_users: roll_cycle: HOURLY # block: true # max_queue_weight: 268435456 # 256 MiB # max_log_size: 17179869184 # 16 GiB ## archive command is \"/path/to/script.sh %path\" where %path is replaced with the file being rolled: # archive_command: # max_archive_retries: 10 Create the audit log directory /cassandra/audit/logs/hourly and set the directory permissions to read, write, and execute for all. Now create a demo keyspace and table and insert some data using cqlsh: cqlsh> CREATE KEYSPACE auditlogkeyspace ... WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 1}; cqlsh> USE auditlogkeyspace; cqlsh:auditlogkeyspace> CREATE TABLE t ( ...id int, ...k int, ...v text, ...PRIMARY KEY (id) ... ); cqlsh:auditlogkeyspace> INSERT INTO t (id, k, v) VALUES (0, 0, 'val0'); cqlsh:auditlogkeyspace> INSERT INTO t (id, k, v) VALUES (0, 1, 'val1'); All the supported CQL commands will be logged to the audit log directory. Change directory to the audit logs directory. $ cd /cassandra/audit/logs/hourly List the audit log files and directories. $ ls -l You should see results similar to: total 28 -rw-rw-r--. 1 ec2-user ec2-user 65536 Aug 2 03:01 directory-listing.cq4t -rw-rw-r--. 1 ec2-user ec2-user 83886080 Aug 2 03:01 20190802-02.cq4 -rw-rw-r--. 1 ec2-user ec2-user 83886080 Aug 2 03:01 20190802-03.cq4 The audit log files will all be listed with a .cq4 file type. The audit directory is of .cq4t type. Run auditlogviewer tool to view the audit logs. $ auditlogviewer /cassandra/audit/logs/hourly This command will return a readable version of the log. Here is a partial sample of the log for the commands in this demo: WARN 03:12:11,124 Using Pauser.sleepy() as not enough processors, have 2, needs 8+ Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564711427328|type :USE_KEYSPACE|category:OTHER|ks:auditlogkeyspace|operation:USE AuditLogKeyspace; Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564711427329|type :USE_KEYSPACE|category:OTHER|ks:auditlogkeyspace|operation:USE \"auditlogkeyspace\" Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564711446279|type :SELECT|category:QUERY|ks:auditlogkeyspace|scope:t|operation:SELECT * FROM t; Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564713878834|type :DROP_TABLE|category:DDL|ks:auditlogkeyspace|scope:t|operation:DROP TABLE IF EXISTS AuditLogKeyspace.t; Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/3.91.56.164|port:42382|timestamp:1564714618360|ty pe:REQUEST_FAILURE|category:ERROR|operation:CREATE KEYSPACE AuditLogKeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 1};; Cannot add existing keyspace \"auditlogkeyspace\" Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564714690968|type :DROP_KEYSPACE|category:DDL|ks:auditlogkeyspace|operation:DROP KEYSPACE AuditLogKeyspace; Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/3.91.56.164|port:42406|timestamp:1564714708329|ty pe:CREATE_KEYSPACE|category:DDL|ks:auditlogkeyspace|operation:CREATE KEYSPACE AuditLogKeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 1}; Type: AuditLog LogMessage: user:anonymous|host:10.0.2.238:7000|source:/127.0.0.1|port:46264|timestamp:1564714870678|type :USE_KEYSPACE|category:OTHER|ks:auditlogkeyspace|operation:USE auditlogkeyspace; Password obfuscation examples: LogMessage: user:cassandra|host:localhost/127.0.0.1:7000|source:/127.0.0.1|port:65282|timestamp:1622630496708|type:CREATE_ROLE|category:DCL|operation:CREATE ROLE role1 WITH PASSWORD = '*******'; Type: audit LogMessage: user:cassandra|host:localhost/127.0.0.1:7000|source:/127.0.0.1|port:65282|timestamp:1622630634552|type:ALTER_ROLE|category:DCL|operation:ATLER ROLE role1 WITH PASSWORD = '*******'; Type: audit LogMessage: user:cassandra|host:localhost/127.0.0.1:7000|source:/127.0.0.1|port:65282|timestamp:1622630698686|type:CREATE_ROLE|category:DCL|operation:CREATE USER user1 WITH PASSWORD '*******'; Type: audit LogMessage: user:cassandra|host:localhost/127.0.0.1:7000|source:/127.0.0.1|port:65282|timestamp:1622630747344|type:ALTER_ROLE|category:DCL|operation:ALTER USER user1 WITH PASSWORD '*******'; Any native transport-enabled client can subscribe to audit log events for diagnosing cluster issues. These events can be consumed by external tools to implement a Cassandra user auditing solution.","title":"Audit Logging","component":"Cassandra","version":"4.1","name":"auditlogging","url":"/Cassandra/4.1/cassandra/operating/auditlogging.html","titles":[{"text":"Audit information logged","id":"audit-information-logged"},{"text":"Configuring audit logging in cassandra.yaml","id":"configuring-audit-logging-in-cassandra-yaml"},{"text":"enabled","id":"enabled"},{"text":"logger","id":"logger"},{"text":"audit_logs_dir","id":"audit_logs_dir"},{"text":"included_keyspaces and excluded_keyspaces","id":"included_keyspaces-and-excluded_keyspaces"},{"text":"included_categories and excluded_categories","id":"included_categories-and-excluded_categories"},{"text":"included_users and excluded_users","id":"included_users-and-excluded_users"},{"text":"roll_cycle","id":"roll_cycle"},{"text":"block","id":"block"},{"text":"max_queue_weight","id":"max_queue_weight"},{"text":"max_log_size","id":"max_log_size"},{"text":"archive_command","id":"archive_command"},{"text":"max_archive_retries","id":"max_archive_retries"},{"text":"Enabling Audit Logging with nodetool","id":"enabling-audit-logging-with-nodetool"},{"text":"Disabling audit logging","id":"disabling-audit-logging"},{"text":"Viewing audit logs","id":"viewing-audit-logs"},{"text":"Example","id":"example"},{"text":"Diagnostic events for user audit logging","id":"diagnostic-events-for-user-audit-logging"}]},"/Cassandra/4.1/cassandra/operating/backups.html":{"text":"Apache Cassandra stores data in immutable SSTable files. Backups in Apache Cassandra database are backup copies of the database data that is stored as SSTable files. Backups are used for several purposes including the following: To store a data copy for durability To be able to restore a table if table data is lost due to node/partition/network failure To be able to transfer the SSTable files to a different machine; for portability Apache Cassandra supports two kinds of backup strategies. Snapshots Incremental Backups A snapshot is a copy of a table’s SSTable files at a given time, created via hard links. The DDL to create the table is stored as well. Snapshots may be created by a user or created automatically. The setting snapshot_before_compaction in the cassandra.yaml file determines if snapshots are created before each compaction. By default, snapshot_before_compaction is set to false. Snapshots may be created automatically before keyspace truncation or dropping of a table by setting auto_snapshot to true (default) in cassandra.yaml. Truncates could be delayed due to the auto snapshots and another setting in cassandra.yaml determines how long the coordinator should wait for truncates to complete. By default Cassandra waits 60 seconds for auto snapshots to complete. An incremental backup is a copy of a table’s SSTable files created by a hard link when memtables are flushed to disk as SSTables. Typically incremental backups are paired with snapshots to reduce the backup time as well as reduce disk space. Incremental backups are not enabled by default and must be enabled explicitly in cassandra.yaml (with incremental_backups setting) or with nodetool. Once enabled, Cassandra creates a hard link to each SSTable flushed or streamed locally in a backups/ subdirectory of the keyspace data. Incremental backups of system tables are also created. The directory structure of Cassandra data consists of different directories for keyspaces, and tables with the data files within the table directories. Directories backups and snapshots to store backups and snapshots respectively for a particular table are also stored within the table directory. The directory structure for Cassandra is illustrated in Figure 1. Figure 1. Directory Structure for Cassandra Data In this section we shall create some example data that could be used to demonstrate incremental backups and snapshots. We have used a three node Cassandra cluster. First, the keyspaces are created. Then tables are created within a keyspace and table data is added. We have used two keyspaces cqlkeyspace and catalogkeyspace with two tables within each. Create the keyspace cqlkeyspace: CREATE KEYSPACE cqlkeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 3}; Create two tables t and t2 in the cqlkeyspace keyspace. USE cqlkeyspace; CREATE TABLE t ( id int, k int, v text, PRIMARY KEY (id) ); CREATE TABLE t2 ( id int, k int, v text, PRIMARY KEY (id) ); Add data to the tables: INSERT INTO t (id, k, v) VALUES (0, 0, 'val0'); INSERT INTO t (id, k, v) VALUES (1, 1, 'val1'); INSERT INTO t2 (id, k, v) VALUES (0, 0, 'val0'); INSERT INTO t2 (id, k, v) VALUES (1, 1, 'val1'); INSERT INTO t2 (id, k, v) VALUES (2, 2, 'val2'); Query the table to list the data: SELECT * FROM t; SELECT * FROM t2; results in id | k | v ----+---+------ 1 | 1 | val1 0 | 0 | val0 (2 rows) id | k | v ----+---+------ 1 | 1 | val1 0 | 0 | val0 2 | 2 | val2 (3 rows) Create a second keyspace catalogkeyspace: CREATE KEYSPACE catalogkeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 3}; Create two tables journal and magazine in catalogkeyspace: USE catalogkeyspace; CREATE TABLE journal ( id int, name text, publisher text, PRIMARY KEY (id) ); CREATE TABLE magazine ( id int, name text, publisher text, PRIMARY KEY (id) ); Add data to the tables: INSERT INTO journal (id, name, publisher) VALUES (0, 'Apache Cassandra Magazine', 'Apache Cassandra'); INSERT INTO journal (id, name, publisher) VALUES (1, 'Couchbase Magazine', 'Couchbase'); INSERT INTO magazine (id, name, publisher) VALUES (0, 'Apache Cassandra Magazine', 'Apache Cassandra'); INSERT INTO magazine (id, name, publisher) VALUES (1, 'Couchbase Magazine', 'Couchbase'); Query the tables to list the data: SELECT * FROM catalogkeyspace.journal; SELECT * FROM catalogkeyspace.magazine; results in id | name | publisher ----+---------------------------+------------------ 1 | Couchbase Magazine | Couchbase 0 | Apache Cassandra Magazine | Apache Cassandra (2 rows) id | name | publisher ----+---------------------------+------------------ 1 | Couchbase Magazine | Couchbase 0 | Apache Cassandra Magazine | Apache Cassandra (2 rows) In this section, we demonstrate creating snapshots. The command used to create a snapshot is nodetool snapshot with the usage: $ nodetool help snapshot results in NAME nodetool snapshot - Take a snapshot of specified keyspaces or a snapshot of the specified table SYNOPSIS nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] snapshot [(-cf | --column-family | --table )] [(-kt | --kt-list | -kc | --kc.list )] [(-sf | --skip-flush)] [(-t | --tag )] [--] [] OPTIONS -cf , --column-family , --table The table name (you must specify one and only one keyspace for using this option) -h , --host Node hostname or ip address -kt , --kt-list , -kc , --kc.list The list of Keyspace.table to take snapshot.(you must not specify only keyspace) -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -sf, --skip-flush Do not flush memtables before snapshotting (snapshot will not contain unflushed data) -t , --tag The name of the snapshot -u , --username Remote jmx agent username -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [] List of keyspaces. By default, all keyspaces To demonstrate creating snapshots with Nodetool on the commandline we have set auto_snapshots setting to false in the cassandra.yaml file: auto_snapshot: false Also set snapshot_before_compaction to false to disable creating snapshots automatically before compaction: snapshot_before_compaction: false Before creating any snapshots, search for snapshots and none will be listed: $ find -name snapshots We shall be using the example keyspaces and tables to create snapshots. Using the syntax above, create a snapshot called catalog-ks for all the tables in the catalogkeyspace keyspace: $ nodetool snapshot --tag catalog-ks catalogkeyspace results in Requested creating snapshot(s) for [catalogkeyspace] with snapshot name [catalog-ks] and options {skipFlush=false} Snapshot directory: catalog-ks Using the find command above, the snapshots and snapshots directories are now found with listed files similar to: ./cassandra/data/data/catalogkeyspace/journal-296a2d30c22a11e9b1350d927649052c/snapshots ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/snapshots Snapshots of all tables in multiple keyspaces may be created similarly: $ nodetool snapshot --tag catalog-cql-ks catalogkeyspace, cqlkeyspace To take a snapshot of a single table the nodetool snapshot command syntax becomes as follows: $ nodetool snapshot --tag --table -- Using the syntax above, create a snapshot for table magazine in keyspace catalogkeyspace: $ nodetool snapshot --tag magazine --table magazine catalogkeyspace results in Requested creating snapshot(s) for [catalogkeyspace] with snapshot name [magazine] and options {skipFlush=false} Snapshot directory: magazine To take snapshots of multiple tables in a keyspace the list of Keyspace.table must be specified with option --kt-list. For example, create snapshots for tables t and t2 in the cqlkeyspace keyspace: $ nodetool snapshot --kt-list cqlkeyspace.t,cqlkeyspace.t2 --tag multi-table results in Requested creating snapshot(s) for [\"CQLKeyspace\".t,\"CQLKeyspace\".t2] with snapshot name [multi- table] and options {skipFlush=false} Snapshot directory: multi-table Multiple snapshots of the same set of tables may be created and tagged with a different name. As an example, create another snapshot for the same set of tables t and t2 in the cqlkeyspace keyspace and tag the snapshots differently: $ nodetool snapshot --kt-list cqlkeyspace.t, cqlkeyspace.t2 --tag multi-table-2 results in Requested creating snapshot(s) for [\"CQLKeyspace\".t,\"CQLKeyspace\".t2] with snapshot name [multi- table-2] and options {skipFlush=false} Snapshot directory: multi-table-2 To take snapshots of multiple tables that are in different keyspaces the command syntax is the same as when multiple tables are in the same keyspace. Each . must be specified separately in the --kt-list option. For example, create a snapshot for table t in the cqlkeyspace and table journal in the catalogkeyspace and tag the snapshot multi-ks. $ nodetool snapshot --kt-list catalogkeyspace.journal,cqlkeyspace.t --tag multi-ks results in Requested creating snapshot(s) for [catalogkeyspace.journal,cqlkeyspace.t] with snapshot name [multi-ks] and options {skipFlush=false} Snapshot directory: multi-ks To list snapshots use the nodetool listsnapshots command. All the snapshots that we created in the preceding examples get listed: $ nodetool listsnapshots results in Snapshot Details: Snapshot name Keyspace name Column family name True size Size on disk multi-table cqlkeyspace t2 4.86 KiB 5.67 KiB multi-table cqlkeyspace t 4.89 KiB 5.7 KiB multi-ks cqlkeyspace t 4.89 KiB 5.7 KiB multi-ks catalogkeyspace journal 4.9 KiB 5.73 KiB magazine catalogkeyspace magazine 4.9 KiB 5.73 KiB multi-table-2 cqlkeyspace t2 4.86 KiB 5.67 KiB multi-table-2 cqlkeyspace t 4.89 KiB 5.7 KiB catalog-ks catalogkeyspace journal 4.9 KiB 5.73 KiB catalog-ks catalogkeyspace magazine 4.9 KiB 5.73 KiB Total TrueDiskSpaceUsed: 44.02 KiB The snapshots directories may be listed with find –name snapshots command: $ find -name snapshots results in ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/snapshots ./cassandra/data/data/cqlkeyspace/t2-d993a390c22911e9b1350d927649052c/snapshots ./cassandra/data/data/catalogkeyspace/journal-296a2d30c22a11e9b1350d927649052c/snapshots ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/snapshots To list the snapshots for a particular table first change to the snapshots directory for that table. For example, list the snapshots for the catalogkeyspace/journal table: $ cd ./cassandra/data/data/catalogkeyspace/journal-296a2d30c22a11e9b1350d927649052c/snapshots && ls -l results in total 0 drwxrwxr-x. 2 ec2-user ec2-user 265 Aug 19 02:44 catalog-ks drwxrwxr-x. 2 ec2-user ec2-user 265 Aug 19 02:52 multi-ks A snapshots directory lists the SSTable files in the snapshot. A schema.cql file is also created in each snapshot that defines schema that can recreate the table with CQL when restoring from a snapshot: $ cd catalog-ks && ls -l results in total 44 -rw-rw-r--. 1 ec2-user ec2-user 31 Aug 19 02:44 manifest.jsonZ -rw-rw-r--. 4 ec2-user ec2-user 47 Aug 19 02:38 na-1-big-CompressionInfo.db -rw-rw-r--. 4 ec2-user ec2-user 97 Aug 19 02:38 na-1-big-Data.db -rw-rw-r--. 4 ec2-user ec2-user 10 Aug 19 02:38 na-1-big-Digest.crc32 -rw-rw-r--. 4 ec2-user ec2-user 16 Aug 19 02:38 na-1-big-Filter.db -rw-rw-r--. 4 ec2-user ec2-user 16 Aug 19 02:38 na-1-big-Index.db -rw-rw-r--. 4 ec2-user ec2-user 4687 Aug 19 02:38 na-1-big-Statistics.db -rw-rw-r--. 4 ec2-user ec2-user 56 Aug 19 02:38 na-1-big-Summary.db -rw-rw-r--. 4 ec2-user ec2-user 92 Aug 19 02:38 na-1-big-TOC.txt -rw-rw-r--. 1 ec2-user ec2-user 814 Aug 19 02:44 schema.cql Snapshots may be cleared or deleted with the nodetool clearsnapshot command. Either a specific snapshot name must be specified or the –all option must be specified. For example, delete a snapshot called magazine from keyspace cqlkeyspace: $ nodetool clearsnapshot -t magazine cqlkeyspace or delete all snapshots from cqlkeyspace with the –all option: $ nodetool clearsnapshot -all cqlkeyspace In the following sections, we shall discuss configuring and creating incremental backups. To create incremental backups set incremental_backups to true in cassandra.yaml. incremental_backups: true This is the only setting needed to create incremental backups. By default incremental_backups setting is set to false because a new set of SSTable files is created for each data flush and if several CQL statements are to be run the backups directory could fill up quickly and use up storage that is needed to store table data. Incremental backups may also be enabled on the command line with the nodetool command nodetool enablebackup. Incremental backups may be disabled with nodetool disablebackup command. Status of incremental backups, whether they are enabled may be checked with nodetool statusbackup. After each table is created flush the table data with nodetool flush command. Incremental backups get created. $ nodetool flush cqlkeyspace t $ nodetool flush cqlkeyspace t2 $ nodetool flush catalogkeyspace journal magazine Incremental backups are created within the Cassandra’s data directory within a table directory. Backups may be found with following command. $ find -name backups results in ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/backups ./cassandra/data/data/cqlkeyspace/t2-d993a390c22911e9b1350d927649052c/backups ./cassandra/data/data/catalogkeyspace/journal-296a2d30c22a11e9b1350d927649052c/backups ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/backups This section discusses how incremental backups are created in more detail using the keyspace and table previously created. Flush the keyspace and table: $ nodetool flush cqlkeyspace t A search for backups and a backups directory will list a backup directory, even if we have added no table data yet. $ find -name backups results in ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/backups Checking the backups directory will show that there are also no backup files: $ cd ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/backups && ls -l results in total 0 If a row of data is added to the data, running the nodetool flush command will flush the table data and an incremental backup will be created: $ nodetool flush cqlkeyspace t $ cd ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/backups && ls -l results in total 36 -rw-rw-r--. 2 ec2-user ec2-user 47 Aug 19 00:32 na-1-big-CompressionInfo.db -rw-rw-r--. 2 ec2-user ec2-user 43 Aug 19 00:32 na-1-big-Data.db -rw-rw-r--. 2 ec2-user ec2-user 10 Aug 19 00:32 na-1-big-Digest.crc32 -rw-rw-r--. 2 ec2-user ec2-user 16 Aug 19 00:32 na-1-big-Filter.db -rw-rw-r--. 2 ec2-user ec2-user 8 Aug 19 00:32 na-1-big-Index.db -rw-rw-r--. 2 ec2-user ec2-user 4673 Aug 19 00:32 na-1-big-Statistics.db -rw-rw-r--. 2 ec2-user ec2-user 56 Aug 19 00:32 na-1-big-Summary.db -rw-rw-r--. 2 ec2-user ec2-user 92 Aug 19 00:32 na-1-big-TOC.txt note The backups directory for any table, such as cqlkeyspace/t is created in the data directory for that table. Adding another row of data and flushing will result in another set of incremental backup files. The SSTable files are timestamped, which distinguishes the first incremental backup from the second: total 72 -rw-rw-r--. 2 ec2-user ec2-user 47 Aug 19 00:32 na-1-big-CompressionInfo.db -rw-rw-r--. 2 ec2-user ec2-user 43 Aug 19 00:32 na-1-big-Data.db -rw-rw-r--. 2 ec2-user ec2-user 10 Aug 19 00:32 na-1-big-Digest.crc32 -rw-rw-r--. 2 ec2-user ec2-user 16 Aug 19 00:32 na-1-big-Filter.db -rw-rw-r--. 2 ec2-user ec2-user 8 Aug 19 00:32 na-1-big-Index.db -rw-rw-r--. 2 ec2-user ec2-user 4673 Aug 19 00:32 na-1-big-Statistics.db -rw-rw-r--. 2 ec2-user ec2-user 56 Aug 19 00:32 na-1-big-Summary.db -rw-rw-r--. 2 ec2-user ec2-user 92 Aug 19 00:32 na-1-big-TOC.txt -rw-rw-r--. 2 ec2-user ec2-user 47 Aug 19 00:35 na-2-big-CompressionInfo.db -rw-rw-r--. 2 ec2-user ec2-user 41 Aug 19 00:35 na-2-big-Data.db -rw-rw-r--. 2 ec2-user ec2-user 10 Aug 19 00:35 na-2-big-Digest.crc32 -rw-rw-r--. 2 ec2-user ec2-user 16 Aug 19 00:35 na-2-big-Filter.db -rw-rw-r--. 2 ec2-user ec2-user 8 Aug 19 00:35 na-2-big-Index.db -rw-rw-r--. 2 ec2-user ec2-user 4673 Aug 19 00:35 na-2-big-Statistics.db -rw-rw-r--. 2 ec2-user ec2-user 56 Aug 19 00:35 na-2-big-Summary.db -rw-rw-r--. 2 ec2-user ec2-user 92 Aug 19 00:35 na-2-big-TOC.txt The two main tools/commands for restoring a table after it has been dropped are: sstableloader nodetool import A snapshot contains essentially the same set of SSTable files as an incremental backup does with a few additional files. A snapshot includes a schema.cql file for the schema DDL to create a table in CQL. A table backup does not include DDL which must be obtained from a snapshot when restoring from an incremental backup.","title":"Backups","component":"Cassandra","version":"4.1","name":"backups","url":"/Cassandra/4.1/cassandra/operating/backups.html","titles":[{"text":"Types of Backups","id":"types-of-backups"},{"text":"Data Directory Structure","id":"data-directory-structure"},{"text":"Setting Up Example Tables for Backups and Snapshots","id":"setting-up-example-tables-for-backups-and-snapshots"},{"text":"Snapshots","id":"snapshots"},{"text":"Configuring for Snapshots","id":"configuring-for-snapshots"},{"text":"Creating Snapshots","id":"creating-snapshots"},{"text":"Taking Snapshots of all Tables in a Keyspace","id":"taking-snapshots-of-all-tables-in-a-keyspace"},{"text":"Taking Snapshots of Single Table in a Keyspace","id":"taking-snapshots-of-single-table-in-a-keyspace"},{"text":"Taking Snapshot of Multiple Tables from same Keyspace","id":"taking-snapshot-of-multiple-tables-from-same-keyspace"},{"text":"Taking Snapshot of Multiple Tables from Different Keyspaces","id":"taking-snapshot-of-multiple-tables-from-different-keyspaces"},{"text":"Listing Snapshots","id":"listing-snapshots"},{"text":"Finding Snapshots Directories","id":"finding-snapshots-directories"},{"text":"Clearing Snapshots","id":"clearing-snapshots"},{"text":"Incremental Backups","id":"incremental-backups"},{"text":"Configuring for Incremental Backups","id":"configuring-for-incremental-backups"},{"text":"Creating Incremental Backups","id":"creating-incremental-backups"},{"text":"Finding Incremental Backups","id":"finding-incremental-backups"},{"text":"Creating an Incremental Backup","id":"creating-an-incremental-backup"},{"text":"Restoring from Incremental Backups and Snapshots","id":"restoring-from-incremental-backups-and-snapshots"}]},"/Cassandra/4.1/cassandra/operating/bloom_filters.html":{"text":"In the read path, Cassandra merges data on disk (in SSTables) with data in RAM (in memtables). To avoid checking every SSTable data file for the partition being requested, Cassandra employs a data structure known as a bloom filter. Bloom filters are a probabilistic data structure that allows Cassandra to determine one of two possible states: - The data definitely does not exist in the given file, or - The data probably exists in the given file. While bloom filters can not guarantee that the data exists in a given SSTable, bloom filters can be made more accurate by allowing them to consume more RAM. Operators have the opportunity to tune this behavior per table by adjusting the the bloom_filter_fp_chance to a float between 0 and 1. The default value for bloom_filter_fp_chance is 0.1 for tables using LeveledCompactionStrategy and 0.01 for all other cases. Bloom filters are stored in RAM, but are stored offheap, so operators should not consider bloom filters when selecting the maximum heap size. As accuracy improves (as the bloom_filter_fp_chance gets closer to 0), memory usage increases non-linearly - the bloom filter for bloom_filter_fp_chance = 0.01 will require about three times as much memory as the same table with bloom_filter_fp_chance = 0.1. Typical values for bloom_filter_fp_chance are usually between 0.01 (1%) to 0.1 (10%) false-positive chance, where Cassandra may scan an SSTable for a row, only to find that it does not exist on the disk. The parameter should be tuned by use case: Users with more RAM and slower disks may benefit from setting the bloom_filter_fp_chance to a numerically lower number (such as 0.01) to avoid excess IO operations Users with less RAM, more dense nodes, or very fast disks may tolerate a higher bloom_filter_fp_chance in order to save RAM at the expense of excess IO operations In workloads that rarely read, or that only perform reads by scanning the entire data set (such as analytics workloads), setting the bloom_filter_fp_chance to a much higher number is acceptable. The bloom filter false positive chance is visible in the DESCRIBE TABLE output as the field bloom_filter_fp_chance. Operators can change the value with an ALTER TABLE statement: : ALTER TABLE keyspace.table WITH bloom_filter_fp_chance=0.01 Operators should be aware, however, that this change is not immediate: the bloom filter is calculated when the file is written, and persisted on disk as the Filter component of the SSTable. Upon issuing an ALTER TABLE statement, new files on disk will be written with the new bloom_filter_fp_chance, but existing sstables will not be modified until they are compacted - if an operator needs a change to bloom_filter_fp_chance to take effect, they can trigger an SSTable rewrite using nodetool scrub or nodetool upgradesstables -a, both of which will rebuild the sstables on disk, regenerating the bloom filters in the progress.","title":"Bloom Filters","component":"Cassandra","version":"4.1","name":"bloom_filters","url":"/Cassandra/4.1/cassandra/operating/bloom_filters.html","titles":[{"text":"Changing","id":"changing"}]},"/Cassandra/4.1/cassandra/operating/bulk_loading.html":{"text":"Bulk loading Apache Cassandra data is supported by different tools. The data to bulk load must be in the form of SSTables. Cassandra does not support loading data in any other format such as CSV, JSON, and XML directly. Although the cqlsh COPY command can load CSV data, it is not a good option for amounts of data. Bulk loading is used to: Restore incremental backups and snapshots. Backups and snapshots are already in the form of SSTables. Load existing SSTables into another cluster. The data can have a different number of nodes or replication strategy. Load external data to a cluster. Cassandra provides two commands or tools for bulk loading data: Cassandra Bulk loader, also called sstableloader The nodetool import command The sstableloader and nodetool import are accessible if the Cassandra installation bin directory is in the PATH environment variable. Or these may be accessed directly from the bin directory. The examples use the keyspaces and tables created in Backups. The sstableloader is the main tool for bulk uploading data. sstableloader streams SSTable data files to a running cluster, conforming to the replication strategy and replication factor. The table to upload data to does need not to be empty. The only requirements to run sstableloader are: One or more comma separated initial hosts to connect to and get ring information A directory path for the SSTables to load sstableloader [options] Sstableloader bulk loads the SSTables found in the directory to the configured cluster. The is used as the target keyspace/table name. For example, to load an SSTable named Standard1-g-1-Data.db into Keyspace1/Standard1, you will need to have the files Standard1-g-1-Data.db and Standard1-g-1-Index.db in a directory /path/to/Keyspace1/Standard1/. Often as part of a backup strategy, some Cassandra DBAs store an entire data directory. When corruption in the data is found, restoring data in the same cluster (for large clusters 200 nodes) is common, but with a different keyspace name. Currently sstableloader derives keyspace name from the folder structure. As an option, to specify target keyspace name as part of sstableloader, version 4.0 adds support for the --target-keyspace option (CASSANDRA-13884). The following options are supported, with -d,--nodes required: -alg,--ssl-alg Client SSL: algorithm -ap,--auth-provider Custom AuthProvider class name for cassandra authentication -ciphers,--ssl-ciphers Client SSL: comma-separated list of encryption suites to use -cph,--connections-per-host Number of concurrent connections-per-host. -d,--nodes Required. Try to connect to these hosts (comma separated) initially for ring information --entire-sstable-throttle-mib Entire SSTable throttle speed in MiB/s (default 0 for unlimited). --entire-sstable-inter-dc-throttle-mib Entire SSTable inter-datacenter throttle speed in MiB/s (default 0 for unlimited). -f,--conf-path cassandra.yaml file path for streaming throughput and client/server SSL. -h,--help Display this help message -i,--ignore Don't stream to this (comma separated) list of nodes -idct,--inter-dc-throttle (deprecated) Inter-datacenter throttle speed in Mbits (default 0 for unlimited). Use --inter-dc-throttle-mib instead. --inter-dc-throttle-mib Inter-datacenter throttle speed in MiB/s (default 0 for unlimited) -k,--target-keyspace Target keyspace name -ks,--keystore Client SSL: full path to keystore -kspw,--keystore-password Client SSL: password of the keystore --no-progress Don't display progress -p,--port Port used for native connection (default 9042) -prtcl,--ssl-protocol Client SSL: connections protocol to use (default: TLS) -pw,--password Password for cassandra authentication -sp,--storage-port Port used for internode communication (default 7000) -spd,--server-port-discovery Use ports published by server to decide how to connect. With SSL requires StartTLS to be used. -ssp,--ssl-storage-port Port used for TLS internode communication (default 7001) -st,--store-type Client SSL: type of store -t,--throttle (deprecated) Throttle speed in Mbits (default 0 for unlimited). Use --throttle-mib instead. --throttle-mib Throttle speed in MiB/s (default 0 for unlimited) -ts,--truststore Client SSL: full path to truststore -tspw,--truststore-password Client SSL: Password of the truststore -u,--username Username for cassandra authentication -v,--verbose verbose output The cassandra.yaml file can be provided on the command-line with -f option to set up streaming throughput, client and server encryption options. Only stream_throughput_outbound_megabits_per_sec, server_encryption_options and client_encryption_options are read from the cassandra.yaml file. You can override options read from cassandra.yaml with corresponding command line options. An example shows how to use sstableloader to upload incremental backup data for the table catalogkeyspace.magazine. In addition, a snapshot of the same table is created to bulk upload, also with sstableloader. The backups and snapshots for the catalogkeyspace.magazine table are listed as follows: $ cd ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c && ls -l results in total 0 drwxrwxr-x. 2 ec2-user ec2-user 226 Aug 19 02:38 backups drwxrwxr-x. 4 ec2-user ec2-user 40 Aug 19 02:45 snapshots The directory path structure of SSTables to be uploaded using sstableloader is used as the target keyspace/table. You can directly upload from the backups and snapshots directories respectively, if the directory structure is in the format used by sstableloader. But the directory path of backups and snapshots for SSTables is /catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/backups and /catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/snapshots respectively, and cannot be used to upload SSTables to catalogkeyspace.magazine table. The directory path structure must be /catalogkeyspace/magazine/ to use sstableloader. Create a new directory structure to upload SSTables with sstableloader located at /catalogkeyspace/magazine and set appropriate permissions. $ sudo mkdir -p /catalogkeyspace/magazine $ sudo chmod -R 777 /catalogkeyspace/magazine An incremental backup does not include the DDL for a table; the table must already exist. If the table was dropped, it can be created using the schema.cql file generated with every snapshot of a table. Prior to using sstableloader to load SSTables to the magazine table, the table must exist. The table does not need to be empty but we have used an empty table as indicated by a CQL query: SELECT * FROM magazine; results in id | name | publisher ----+------+----------- (0 rows) After creating the table to upload to, copy the SSTable files from the backups directory to the /catalogkeyspace/magazine/ directory. $ sudo cp ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/backups/* \\ /catalogkeyspace/magazine/ Run the sstableloader to upload SSTables from the /catalogkeyspace/magazine/ directory. $ sstableloader --nodes 10.0.2.238 /catalogkeyspace/magazine/ The output from the sstableloader command should be similar to this listing: $ sstableloader --nodes 10.0.2.238 /catalogkeyspace/magazine/ results in Opening SSTables and calculating sections to stream Streaming relevant part of /catalogkeyspace/magazine/na-1-big-Data.db /catalogkeyspace/magazine/na-2-big-Data.db to [35.173.233.153:7000, 10.0.2.238:7000, 54.158.45.75:7000] progress: [35.173.233.153:7000]0:1/2 88 % total: 88% 0.018KiB/s (avg: 0.018KiB/s) progress: [35.173.233.153:7000]0:2/2 176% total: 176% 33.807KiB/s (avg: 0.036KiB/s) progress: [35.173.233.153:7000]0:2/2 176% total: 176% 0.000KiB/s (avg: 0.029KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:1/2 39 % total: 81% 0.115KiB/s (avg: 0.024KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:2/2 78 % total: 108% 97.683KiB/s (avg: 0.033KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:2/2 78 % [54.158.45.75:7000]0:1/2 39 % total: 80% 0.233KiB/s (avg: 0.040KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:2/2 78 % [54.158.45.75:7000]0:2/2 78 % total: 96% 88.522KiB/s (avg: 0.049KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:2/2 78 % [54.158.45.75:7000]0:2/2 78 % total: 96% 0.000KiB/s (avg: 0.045KiB/s) progress: [35.173.233.153:7000]0:2/2 176% [10.0.2.238:7000]0:2/2 78 % [54.158.45.75:7000]0:2/2 78 % total: 96% 0.000KiB/s (avg: 0.044KiB/s) After the sstableloader has finished loading the data, run a query the magazine table to check: SELECT * FROM magazine; results in id | name | publisher ----+---------------------------+------------------ 1 | Couchbase Magazine | Couchbase 0 | Apache Cassandra Magazine | Apache Cassandra (2 rows) Restoring a snapshot of a table to the same table can be easily accomplished: If the directory structure needed to load SSTables to catalogkeyspace.magazine does not exist create the directories and set appropriate permissions: $ sudo mkdir -p /catalogkeyspace/magazine $ sudo chmod -R 777 /catalogkeyspace/magazine Remove any files from the directory, so that the snapshot files can be copied without interference: $ sudo rm /catalogkeyspace/magazine/* $ cd /catalogkeyspace/magazine/ $ ls -l results in total 0 Copy the snapshot files to the /catalogkeyspace/magazine directory. $ sudo cp ./cassandra/data/data/catalogkeyspace/magazine-446eae30c22a11e9b1350d927649052c/snapshots/magazine/* \\ /catalogkeyspace/magazine List the files in the /catalogkeyspace/magazine directory. The schema.cql will also be listed. $ cd /catalogkeyspace/magazine && ls -l results in total 44 -rw-r--r--. 1 root root 31 Aug 19 04:13 manifest.json -rw-r--r--. 1 root root 47 Aug 19 04:13 na-1-big-CompressionInfo.db -rw-r--r--. 1 root root 97 Aug 19 04:13 na-1-big-Data.db -rw-r--r--. 1 root root 10 Aug 19 04:13 na-1-big-Digest.crc32 -rw-r--r--. 1 root root 16 Aug 19 04:13 na-1-big-Filter.db -rw-r--r--. 1 root root 16 Aug 19 04:13 na-1-big-Index.db -rw-r--r--. 1 root root 4687 Aug 19 04:13 na-1-big-Statistics.db -rw-r--r--. 1 root root 56 Aug 19 04:13 na-1-big-Summary.db -rw-r--r--. 1 root root 92 Aug 19 04:13 na-1-big-TOC.txt -rw-r--r--. 1 root root 815 Aug 19 04:13 schema.cql Alternatively create symlinks to the snapshot folder instead of copying the data: $ mkdir $ ln -s / If the magazine table was dropped, run the DDL in the schema.cql to create the table. Run the sstableloader with the following command: $ sstableloader --nodes 10.0.2.238 /catalogkeyspace/magazine/ As the output from the command indicates, SSTables get streamed to the cluster: Established connection to initial hosts Opening SSTables and calculating sections to stream Streaming relevant part of /catalogkeyspace/magazine/na-1-big-Data.db to [35.173.233.153:7000, 10.0.2.238:7000, 54.158.45.75:7000] progress: [35.173.233.153:7000]0:1/1 176% total: 176% 0.017KiB/s (avg: 0.017KiB/s) progress: [35.173.233.153:7000]0:1/1 176% total: 176% 0.000KiB/s (avg: 0.014KiB/s) progress: [35.173.233.153:7000]0:1/1 176% [10.0.2.238:7000]0:1/1 78 % total: 108% 0.115KiB/s (avg: 0.017KiB/s) progress: [35.173.233.153:7000]0:1/1 176% [10.0.2.238:7000]0:1/1 78 % [54.158.45.75:7000]0:1/1 78 % total: 96% 0.232KiB/s (avg: 0.024KiB/s) progress: [35.173.233.153:7000]0:1/1 176% [10.0.2.238:7000]0:1/1 78 % [54.158.45.75:7000]0:1/1 78 % total: 96% 0.000KiB/s (avg: 0.022KiB/s) progress: [35.173.233.153:7000]0:1/1 176% [10.0.2.238:7000]0:1/1 78 % [54.158.45.75:7000]0:1/1 78 % total: 96% 0.000KiB/s (avg: 0.021KiB/s) Some other requirements of sstableloader that should be kept into consideration are: The SSTables loaded must be compatible with the Cassandra version being loaded into. Repairing tables that have been loaded into a different cluster does not repair the source tables. Sstableloader makes use of port 7000 for internode communication. Before restoring incremental backups, run nodetool flush to backup any data in memtables. Importing SSTables into a table using the nodetool import command is recommended instead of the deprecated nodetool refresh command. The nodetool import command has an option to load new SSTables from a separate directory. The command usage is as follows: nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] import [(-c | --no-invalidate-caches)] [(-e | --extended-verify)] [(-l | --keep-level)] [(-q | --quick)] [(-r | --keep-repaired)] [(-t | --no-tokens)] [(-v | --no-verify)] [--] ... The arguments keyspace, table name and directory are required. The following options are supported: -c, --no-invalidate-caches Don't invalidate the row cache when importing -e, --extended-verify Run an extended verify, verifying all values in the new SSTables -h , --host Node hostname or ip address -l, --keep-level Keep the level on the new SSTables -p , --port Remote jmx agent port number -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file -q, --quick Do a quick import without verifying SSTables, clearing row cache or checking in which data directory to put the file -r, --keep-repaired Keep any repaired information from the SSTables -t, --no-tokens Don't verify that all tokens in the new SSTable are owned by the current node -u , --username Remote jmx agent username -v, --no-verify Don't verify new SSTables -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options Because the keyspace and table are specified on the command line for nodetool import, there is not the same requirement as with sstableloader, to have the SSTables in a specific directory path. When importing snapshots or incremental backups with nodetool import, the SSTables don’t need to be copied to another directory. Using nodetool import to import SSTables from an incremental backup, and restoring the table is shown below. DROP table t; An incremental backup for a table does not include the schema definition for the table. If the schema definition is not kept as a separate backup, the schema.cql from a backup of the table may be used to create the table as follows: CREATE TABLE IF NOT EXISTS cqlkeyspace.t ( id int PRIMARY KEY, k int, v text) WITH ID = d132e240-c217-11e9-bbee-19821dcea330 AND bloom_filter_fp_chance = 0.01 AND crc_check_chance = 1.0 AND default_time_to_live = 0 AND gc_grace_seconds = 864000 AND min_index_interval = 128 AND max_index_interval = 2048 AND memtable_flush_period_in_ms = 0 AND speculative_retry = '99p' AND additional_write_policy = '99p' AND comment = '' AND caching = { 'keys': 'ALL', 'rows_per_partition': 'NONE' } AND compaction = { 'max_threshold': '32', 'min_threshold': '4', 'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy' } AND compression = { 'chunk_length_in_kb': '16', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor' } AND cdc = false AND extensions = { } ; Initially the table could be empty, but does not have to be. SELECT * FROM t; id | k | v ----+---+--- (0 rows) Run the nodetool import command, providing the keyspace, table and the backups directory. Don’t copy the table backups to another directory, as with sstableloader. $ nodetool import -- cqlkeyspace t \\ ./cassandra/data/data/cqlkeyspace/t-d132e240c21711e9bbee19821dcea330/backups The SSTables are imported into the table. Run a query in cqlsh to check: SELECT * FROM t; id | k | v ----+---+------ 1 | 1 | val1 0 | 0 | val0 (2 rows) Importing SSTables from a snapshot with the nodetool import command is similar to importing SSTables from an incremental backup. Shown here is an import of a snapshot for table catalogkeyspace.journal, after dropping the table to demonstrate the restore. USE CATALOGKEYSPACE; DROP TABLE journal; Use the catalog-ks snapshot for the journal table. Check the files in the snapshot, and note the existence of the schema.cql file. $ ls -l total 44 -rw-rw-r--. 1 ec2-user ec2-user 31 Aug 19 02:44 manifest.json -rw-rw-r--. 3 ec2-user ec2-user 47 Aug 19 02:38 na-1-big-CompressionInfo.db -rw-rw-r--. 3 ec2-user ec2-user 97 Aug 19 02:38 na-1-big-Data.db -rw-rw-r--. 3 ec2-user ec2-user 10 Aug 19 02:38 na-1-big-Digest.crc32 -rw-rw-r--. 3 ec2-user ec2-user 16 Aug 19 02:38 na-1-big-Filter.db -rw-rw-r--. 3 ec2-user ec2-user 16 Aug 19 02:38 na-1-big-Index.db -rw-rw-r--. 3 ec2-user ec2-user 4687 Aug 19 02:38 na-1-big-Statistics.db -rw-rw-r--. 3 ec2-user ec2-user 56 Aug 19 02:38 na-1-big-Summary.db -rw-rw-r--. 3 ec2-user ec2-user 92 Aug 19 02:38 na-1-big-TOC.txt -rw-rw-r--. 1 ec2-user ec2-user 814 Aug 19 02:44 schema.cql Copy the DDL from the schema.cql and run in cqlsh to create the catalogkeyspace.journal table: CREATE TABLE IF NOT EXISTS catalogkeyspace.journal ( id int PRIMARY KEY, name text, publisher text) WITH ID = 296a2d30-c22a-11e9-b135-0d927649052c AND bloom_filter_fp_chance = 0.01 AND crc_check_chance = 1.0 AND default_time_to_live = 0 AND gc_grace_seconds = 864000 AND min_index_interval = 128 AND max_index_interval = 2048 AND memtable_flush_period_in_ms = 0 AND speculative_retry = '99p' AND additional_write_policy = '99p' AND comment = '' AND caching = { 'keys': 'ALL', 'rows_per_partition': 'NONE' } AND compaction = { 'min_threshold': '4', 'max_threshold': '32', 'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy' } AND compression = { 'chunk_length_in_kb': '16', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor' } AND cdc = false AND extensions = { } ; Run the nodetool import command to import the SSTables for the snapshot: $ nodetool import -- catalogkeyspace journal \\ ./cassandra/data/data/catalogkeyspace/journal- 296a2d30c22a11e9b1350d927649052c/snapshots/catalog-ks/ Subsequently run a CQL query on the journal table to check the imported data: SELECT * FROM journal; id | name | publisher ----+---------------------------+------------------ 1 | Couchbase Magazine | Couchbase 0 | Apache Cassandra Magazine | Apache Cassandra (2 rows) Bulk loading external data directly is not supported by any of the tools we have discussed which include sstableloader and nodetool import. The sstableloader and nodetool import require data to be in the form of SSTables. Apache Cassandra supports a Java API for generating SSTables from input data, using the org.apache.cassandra.io.sstable.CQLSSTableWriter Java class. Subsequently, either sstableloader or nodetool import is used to bulk load the SSTables. To generate SSTables using the CQLSSTableWriter class the following are required: An output directory to generate the SSTable in The schema for the SSTable A prepared statement for the INSERT A partitioner The output directory must exist before starting. Create a directory (/sstables as an example) and set appropriate permissions. $ sudo mkdir /sstables $ sudo chmod 777 -R /sstables To use CQLSSTableWriter in a Java application, create a Java constant for the output directory. public static final String OUTPUT_DIR = \"./sstables\"; CQLSSTableWriter Java API can create a user-defined type. Create a new type to store int data: String type = \"CREATE TYPE CQLKeyspace.intType (a int, b int)\"; // Define a String variable for the SSTable schema. String schema = \"CREATE TABLE CQLKeyspace.t (\" + \" id int PRIMARY KEY,\" + \" k int,\" + \" v1 text,\" + \" v2 intType,\" + \")\"; Define a String variable for the prepared statement to use: String insertStmt = \"INSERT INTO CQLKeyspace.t (id, k, v1, v2) VALUES (?, ?, ?, ?)\"; The partitioner to use only needs setting if the default partitioner Murmur3Partitioner is not used. All these variables or settings are used by the builder class CQLSSTableWriter.Builder to create a CQLSSTableWriter object. Create a File object for the output directory. File outputDir = new File(OUTPUT_DIR + File.separator + \"CQLKeyspace\" + File.separator + \"t\"); Obtain a CQLSSTableWriter.Builder object using static method CQLSSTableWriter.builder(). Set the following items: output directory File object user-defined type SSTable schema buffer size prepared statement optionally any of the other builder options and invoke the build() method to create a CQLSSTableWriter object: CQLSSTableWriter writer = CQLSSTableWriter.builder() .inDirectory(outputDir) .withType(type) .forTable(schema) .withBufferSizeInMB(256) .using(insertStmt).build(); Set the SSTable data. If any user-defined types are used, obtain a UserType object for each type: UserType userType = writer.getUDType(\"intType\"); Add data rows for the resulting SSTable: writer.addRow(0, 0, \"val0\", userType.newValue().setInt(\"a\", 0).setInt(\"b\", 0)); writer.addRow(1, 1, \"val1\", userType.newValue().setInt(\"a\", 1).setInt(\"b\", 1)); writer.addRow(2, 2, \"val2\", userType.newValue().setInt(\"a\", 2).setInt(\"b\", 2)); Close the writer, finalizing the SSTable: writer.close(); Other public methods the CQLSSTableWriter class provides are: Method Description addRow(java.util.List values) Adds a new row to the writer. Returns a CQLSSTableWriter object. Each provided value type should correspond to the types of the CQL column the value is for. The correspondence between java type and CQL type is the same one than the one documented at www.datastax.com/drivers/java/2.0/apidocs/com/datastax/driver/core/DataType.Name.html#asJavaC lass(). addRow(java.util.Map values) Adds a new row to the writer. Returns a CQLSSTableWriter object. This is equivalent to the other addRow methods, but takes a map whose keys are the names of the columns to add instead of taking a list of the values in the order of the insert statement used during construction of this SSTable writer. The column names in the map keys must be in lowercase unless the declared column name is a case-sensitive quoted identifier in which case the map key must use the exact case of the column. The values parameter is a map of column name to column values representing the new row to add. If a column is not included in the map, it’s value will be null. If the map contains keys that do not correspond to one of the columns of the insert statement used when creating this SSTable writer, the corresponding value is ignored. addRow(java.lang.Object…​ values) Adds a new row to the writer. Returns a CQLSSTableWriter object. CQLSSTableWriter.builder() Returns a new builder for a CQLSSTableWriter. close() Closes the writer. rawAddRow(java.nio.ByteBuffer…​ values) Adds a new row to the writer given already serialized binary values. Returns a CQLSSTableWriter object. The row values must correspond to the bind variables of the insertion statement used when creating by this SSTable writer. rawAddRow(java.util.List values) Adds a new row to the writer given already serialized binary values. Returns a CQLSSTableWriter object. The row values must correspond to the bind variables of the insertion statement used when creating by this SSTable writer. rawAddRow(java.util.Map values) Adds a new row to the writer given already serialized binary values. Returns a CQLSSTableWriter object. The row values must correspond to the bind variables of the insertion statement used when creating by this SSTable writer. getUDType(String dataType) Returns the User Defined type used in this SSTable Writer that can be used to create UDTValue instances. Other public methods the CQLSSTableWriter.Builder class provides are: Method Description inDirectory(String directory) The directory where to write the SSTables. This is a mandatory option. The directory to use should already exist and be writable. inDirectory(File directory) The directory where to write the SSTables. This is a mandatory option. The directory to use should already exist and be writable. forTable(String schema) The schema (CREATE TABLE statement) for the table for which SSTable is to be created. The provided CREATE TABLE statement must use a fully-qualified table name, one that includes the keyspace name. This is a mandatory option. withPartitioner(IPartitioner partitioner) The partitioner to use. By default, Murmur3Partitioner will be used. If this is not the partitioner used by the cluster for which the SSTables are created, the correct partitioner needs to be provided. using(String insert) The INSERT or UPDATE statement defining the order of the values to add for a given CQL row. The provided INSERT statement must use a fully-qualified table name, one that includes the keyspace name. Moreover, said statement must use bind variables since these variables will be bound to values by the resulting SSTable writer. This is a mandatory option. withBufferSizeInMiB(int size) The size of the buffer to use. This defines how much data will be buffered before being written as a new SSTable. This corresponds roughly to the data size that will have the created SSTable. The default is 128MB, which should be reasonable for a 1GB heap. If OutOfMemory exception gets generated while using the SSTable writer, should lower this value. withBufferSizeInMB(int size) Deprecated, and it will be available at least until next major release. Please use withBufferSizeInMiB(int size) which is the same method with a new name. sorted() Creates a CQLSSTableWriter that expects sorted inputs. If this option is used, the resulting SSTable writer will expect rows to be added in SSTable sorted order (and an exception will be thrown if that is not the case during row insertion). The SSTable sorted order means that rows are added such that their partition keys respect the partitioner order. This option should only be used if the rows can be provided in order, which is rarely the case. If the rows can be provided in order however, using this sorted might be more efficient. If this option is used, some option like withBufferSizeInMB will be ignored. build() Builds a CQLSSTableWriter object.","title":"Bulk Loading","component":"Cassandra","version":"4.1","name":"bulk_loading","url":"/Cassandra/4.1/cassandra/operating/bulk_loading.html","titles":[{"text":"Tools for Bulk Loading","id":"tools-for-bulk-loading"},{"text":"Using sstableloader","id":"using-sstableloader"},{"text":"Sstableloader Option to accept Target keyspace name","id":"sstableloader-option-to-accept-target-keyspace-name"},{"text":"A sstableloader Demo","id":"a-sstableloader-demo"},{"text":"Bulk Loading from an Incremental Backup","id":"bulk-loading-from-an-incremental-backup"},{"text":"Bulk Loading from a Snapshot","id":"bulk-loading-from-a-snapshot"},{"text":"Using nodetool import","id":"using-nodetool-import"},{"text":"Importing Data from an Incremental Backup","id":"importing-data-from-an-incremental-backup"},{"text":"Importing Data from a Snapshot","id":"importing-data-from-a-snapshot"},{"text":"Bulk Loading External Data","id":"bulk-loading-external-data"},{"text":"Generating SSTables with CQLSSTableWriter Java API","id":"generating-sstables-with-cqlsstablewriter-java-api"}]},"/Cassandra/4.1/cassandra/operating/cdc.html":{"text":"Change data capture (CDC) provides a mechanism to flag specific tables for archival as well as rejecting writes to those tables once a configurable size-on-disk for the CDC log is reached. An operator can enable CDC on a table by setting the table property cdc=true (either when creating the table or altering it). Upon CommitLogSegment creation, a hard-link to the segment is created in the directory specified in cassandra.yaml. On segment fsync to disk, if CDC data is present anywhere in the segment a _cdc.idx file is also created with the integer offset of how much data in the original segment is persisted to disk. Upon final segment flush, a second line with the human-readable word \"COMPLETED\" will be added to the _cdc.idx file indicating that Cassandra has completed all processing on the file. We use an index file rather than just encouraging clients to parse the log realtime off a memory mapped handle as data can be reflected in a kernel buffer that is not yet persisted to disk. Parsing only up to the listed offset in the _cdc.idx file will ensure that you only parse CDC data for data that is durable. Please note that in rare chances, e.g. slow disk, it is possible for the consumer to read an empty value from the _cdc.idx file because update is achieved with first truncating the file then write to the file. In such case, the consumer should retry read the index file. A threshold of total disk space allowed is specified in the yaml at which time newly allocated CommitLogSegments will not allow CDC data until a consumer parses and removes files from the specified cdc_raw directory. CDC is enable or disable through the cdc table property, for instance: CREATE TABLE foo (a int, b text, PRIMARY KEY(a)) WITH cdc=true; ALTER TABLE foo WITH cdc=true; ALTER TABLE foo WITH cdc=false; The following cassandra.yaml options are available for CDC: cdc_enabled (default: false) Enable or disable CDC operations node-wide. cdc_raw_directory (default: $CASSANDRA_HOME/data/cdc_raw) Destination for CommitLogSegments to be moved after all corresponding memtables are flushed. cdc_total_space: (default: min of 4096MiB and 1/8th volume space) Calculated as sum of all active CommitLogSegments that permit CDC all flushed CDC segments in cdc_raw_directory. cdc_free_space_check_interval (default: 250ms) When at capacity, we limit the frequency with which we re-calculate the space taken up by cdc_raw_directory to prevent burning CPU cycles unnecessarily. Default is to check 4 times per second. Use a CommitLogReader.java. Usage is fairly straightforward with a variety of signatures available for use. In order to handle mutations read from disk, implement CommitLogReadHandler. Do not enable CDC without some kind of consumption process in-place. If CDC is enabled on a node and then on a table, the cdc_free_space_in_mb will fill up and then writes to CDC-enabled tables will be rejected unless some consumption process is in place. JIRA ticket JIRA ticket","title":"Change Data Capture","component":"Cassandra","version":"4.1","name":"cdc","url":"/Cassandra/4.1/cassandra/operating/cdc.html","titles":[{"text":"Overview","id":"overview"},{"text":"Configuration","id":"configuration"},{"text":"Enabling or disabling CDC on a table","id":"enabling-or-disabling-cdc-on-a-table"},{"text":"cassandra.yaml parameters","id":"cassandra-yaml-parameters"},{"text":"Reading CommitLogSegments","id":"reading-commitlogsegments"},{"text":"Warnings","id":"warnings"},{"text":"Further Reading","id":"further-reading"}]},"/Cassandra/4.1/cassandra/operating/compression.html":{"text":"Cassandra offers operators the ability to configure compression on a per-table basis. Compression reduces the size of data on disk by compressing the SSTable in user-configurable compression chunk_length_in_kb. As Cassandra SSTables are immutable, the CPU cost of compressing is only necessary when the SSTable is written - subsequent updates to data will land in different SSTables, so Cassandra will not need to decompress, overwrite, and recompress data when UPDATE commands are issued. On reads, Cassandra will locate the relevant compressed chunks on disk, decompress the full chunk, and then proceed with the remainder of the read path (merging data from disks and memtables, read repair, and so on). Compression algorithms typically trade off between the following three areas: Compression speed: How fast does the compression algorithm compress data. This is critical in the flush and compaction paths because data must be compressed before it is written to disk. Decompression speed: How fast does the compression algorithm de-compress data. This is critical in the read and compaction paths as data must be read off disk in a full chunk and decompressed before it can be returned. Ratio: By what ratio is the uncompressed data reduced by. Cassandra typically measures this as the size of data on disk relative to the uncompressed size. For example a ratio of 0.5 means that the data on disk is 50% the size of the uncompressed data. Cassandra exposes this ratio per table as the SSTable Compression Ratio field of nodetool tablestats. Cassandra offers five compression algorithms by default that make different tradeoffs in these areas. While benchmarking compression algorithms depends on many factors (algorithm parameters such as compression level, the compressibility of the input data, underlying processor class, etc …​), the following table should help you pick a starting point based on your application’s requirements with an extremely rough grading of the different choices by their performance in these areas (A is relatively good, F is relatively bad): Compression Algorithm Cassandra Class Compression Decompression Ratio C* Version LZ4 LZ4Compressor A+ A+ C+ >=1.2.2 LZ4HC LZ4Compressor C+ A+ B+ >= 3.6 Zstd ZstdCompressor A- A- A+ >= 4.0 Snappy SnappyCompressor A- A C >= 1.0 Deflate (zlib) DeflateCompressor C C A >= 1.0 Generally speaking for a performance critical (latency or throughput) application LZ4 is the right choice as it gets excellent ratio per CPU cycle spent. This is why it is the default choice in Cassandra. For storage critical applications (disk footprint), however, Zstd may be a better choice as it can get significant additional ratio to LZ4. Snappy is kept for backwards compatibility and LZ4 will typically be preferable. Deflate is kept for backwards compatibility and Zstd will typically be preferable. Compression is configured on a per-table basis as an optional argument to CREATE TABLE or ALTER TABLE. Three options are available for all compressors: class (default: LZ4Compressor): specifies the compression class to use. The two \"fast\" compressors are LZ4Compressor and SnappyCompressor and the two \"good\" ratio compressors are ZstdCompressor and DeflateCompressor. chunk_length_in_kb (default: 16KiB): specifies the number of kilobytes of data per compression chunk. The main tradeoff here is that larger chunk sizes give compression algorithms more context and improve their ratio, but require reads to deserialize and read more off disk. crc_check_chance (default: 1.0): determines how likely Cassandra is to verify the checksum on each compression chunk during reads to protect against data corruption. Unless you have profiles indicating this is a performance problem it is highly encouraged not to turn this off as it is Cassandra’s only protection against bitrot. The LZ4Compressor supports the following additional options: lz4_compressor_type (default fast): specifies if we should use the high (a.k.a LZ4HC) ratio version or the fast (a.k.a LZ4) version of LZ4. The high mode supports a configurable level, which can allow operators to tune the performance <→ ratio tradeoff via the lz4_high_compressor_level option. Note that in 4.0 and above it may be preferable to use the Zstd compressor. lz4_high_compressor_level (default 9): A number between 1 and 17 inclusive that represents how much CPU time to spend trying to get more compression ratio. Generally lower levels are \"faster\" but they get less ratio and higher levels are slower but get more compression ratio. The ZstdCompressor supports the following options in addition: compression_level (default 3): A number between -131072 and 22 inclusive that represents how much CPU time to spend trying to get more compression ratio. The lower the level, the faster the speed (at the cost of ratio). Values from 20 to 22 are called \"ultra levels\" and should be used with caution, as they require more memory. The default of 3 is a good choice for competing with Deflate ratios and 1 is a good choice for competing with LZ4. Users can set compression using the following syntax: CREATE TABLE keyspace.table (id int PRIMARY KEY) WITH compression = {'class': 'LZ4Compressor'}; Or ALTER TABLE keyspace.table WITH compression = {'class': 'LZ4Compressor', 'chunk_length_in_kb': 64, 'crc_check_chance': 0.5}; Once enabled, compression can be disabled with ALTER TABLE setting enabled to false: ALTER TABLE keyspace.table WITH compression = {'enabled':'false'}; Operators should be aware, however, that changing compression is not immediate. The data is compressed when the SSTable is written, and as SSTables are immutable, the compression will not be modified until the table is compacted. Upon issuing a change to the compression options via ALTER TABLE, the existing SSTables will not be modified until they are compacted - if an operator needs compression changes to take effect immediately, the operator can trigger an SSTable rewrite using nodetool scrub or nodetool upgradesstables -a, both of which will rebuild the SSTables on disk, re-compressing the data in the process. Compression’s primary benefit is that it reduces the amount of data written to disk. Not only does the reduced size save in storage requirements, it often increases read and write throughput, as the CPU overhead of compressing data is faster than the time it would take to read or write the larger volume of uncompressed data from disk. Compression is most useful in tables comprised of many rows, where the rows are similar in nature. Tables containing similar text columns (such as repeated JSON blobs) often compress very well. Tables containing data that has already been compressed or random data (e.g. benchmark datasets) do not typically compress well. Compression metadata is stored off-heap and scales with data on disk. This often requires 1-3GB of off-heap RAM per terabyte of data on disk, though the exact usage varies with chunk_length_in_kb and compression ratios. Streaming operations involve compressing and decompressing data on compressed tables - in some code paths (such as non-vnode bootstrap), the CPU overhead of compression can be a limiting factor. To prevent slow compressors (Zstd, Deflate, LZ4HC) from blocking flushes for too long, all three flush with the default fast LZ4 compressor and then rely on normal compaction to re-compress the data into the desired compression strategy. See CASSANDRA-15379 issues.apache.org/jira/browse/CASSANDRA-15379 for more details. The compression path checksums data to ensure correctness - while the traditional Cassandra read path does not have a way to ensure correctness of data on disk, compressed tables allow the user to set crc_check_chance (a float from 0.0 to 1.0) to allow Cassandra to probabilistically validate chunks on read to verify bits on disk are not corrupt. Advanced users can provide their own compression class by implementing the interface at org.apache.cassandra.io.compress.ICompressor.","title":"Compression","component":"Cassandra","version":"4.1","name":"compression","url":"/Cassandra/4.1/cassandra/operating/compression.html","titles":[{"text":"Configuring Compression","id":"configuring-compression"},{"text":"Benefits and Uses","id":"benefits-and-uses"},{"text":"Operational Impact","id":"operational-impact"},{"text":"Advanced Use","id":"advanced-use"}]},"/Cassandra/4.1/cassandra/operating/fqllogging.html":{"text":"Apache Cassandra 4.0 adds a new highly performant feature that supports live query logging (CASSANDRA-13983). FQL is safe for production use, with configurable limits to heap memory and disk space to prevent out-of-memory errors. This feature is useful for live traffic capture, as well as traffic replay. The tool provided can be used for both debugging query traffic and migration. New nodetool options are also added to enable, disable or reset FQL, as well as a new tool to read and replay the binary logs. The full query logging (FQL) capability uses Chronicle-Queue to rotate a log of queries. Full query logs will be referred to as logs for the remainder of the page. Some of the features of FQL are: The impact on query latency is reduced by asynchronous single-thread log entry writes to disk. Heap memory usage is bounded by a weighted queue, with configurable maximum weight sitting in front of logging thread. If the weighted queue is full, producers can be blocked or samples can be dropped. Disk utilization is bounded by a configurable size, deleting old log segments once the limit is reached. A flexible schema binary format, Chronicle-Wire, for on-disk serialization that can skip unrecognized fields, add new ones, and omit old ones. Can be enabled, disabled, or reset (to delete on-disk data) using the JMX tool, nodetool. Can configure the settings in either the cassandra.yaml file or by using nodetool. Introduces new fqltool that currently can Dump the binary logs to a readable format. Other options are Replay and Compare. FQL logs all successful Cassandra Query Language (CQL) requests, both events that modify the data and those that query. While audit logs also include CQL requests, FQL logs only the CQL request. This difference means that FQL can be used to replay or compare logs, which audit logging cannot. FQL is useful for debugging, performance benchmarking, testing and auditing CQL queries, while audit logs are useful for compliance. In performance testing, FQL appears to have little or no overhead in WRITE only workloads, and a minor overhead in MIXED workload. The query log contains: all queries invoked approximate time they were invoked any parameters necessary to bind wildcard values all query options The logger writes single or batched CQL queries after they finish, so only successfully completed queries are logged. Failed or timed-out queries are not logged. Different data is logged, depending on the type of query. A single CQL query log entry contains: query - CQL query text queryOptions - Options associated with the query invocation queryState - Timestamp state associated with the query invocation queryTimeMillis - Approximate time in milliseconds since the epoch since the query was invoked A batch CQL query log entry contains: queries - CQL text of the queries queryOptions - Options associated with the query invocation queryState - Timestamp state associated with the query invocation batchTimeMillis - Approximate time in milliseconds since the epoch since the batch was invoked type - The type of the batch values - Values to bind to as parameters for the queries Because FQL is backed by Binlog, the performance and footprint are predictable, with minimal impact on log record producers. Performance safety prevents the producers from overloading the log, using a weighted queue to drop records if the logging falls behind. Single-thread asynchronous writing produces the logs. Chronicle-Queue provides an easy method of rolling the logs. FQL also tracks information about the stored log files: Stored log files that are added and their storage impact. Deletes them if over storage limit. The log files in Chronicle-Queue that have already rolled The number of bytes in the log files that have already rolled The logger follows a well-defined sequence of events: The consumer thread that writes log records is started. This action can occur only once. The consumer thread offers a record to the log. If the in-memory queue is full, the record will be dropped and offer returns a false value. If accepted, the record is entered into the log. If the in-memory queue is full, the putting thread will be blocked until there is space or it is interrupted. The buffers are cleaned up at thread exit. Finalization will check again, to ensure there are no stragglers in the queue. The consumer thread is stopped. It can be called multiple times. To use FQL, two actions must be completed. FQL must be configured using either the cassandra.yaml file or nodetool, and logging must be enabled using nodetool enablefullquerylog. With either method, at a minimum, the path to the log directory must be specified. Both actions are completed on a per-node basis. Full query logs are generated on each enabled node, so logs on each node will have that node’s queries. The cassandra.yaml file can be used to configure FQL before enabling the feature with nodetool. The file includes the following options that can be uncommented for use: # default options for full query logging - these can be overridden from command line # when executing nodetool enablefullquerylog #full_query_logging_options: # log_dir: # roll_cycle: HOURLY # block: true # max_queue_weight: 268435456 # 256 MiB # max_log_size: 17179869184 # 16 GiB # archive command is \"/path/to/script.sh %path\" where %path is replaced with the file being rolled: # archive_command: # max_archive_retries: 10 To write logs, an existing directory must be set in log_dir. The directory must have appropriate permissions set to allow reading, writing, and executing. Logging will recursively delete the directory contents as needed. Do not place links in this directory to other sections of the filesystem. For example, log_dir: /tmp/cassandrafullquerylog. The roll_cycle defines the frequency with which the log segments are rolled. Supported values are HOURLY (default), MINUTELY, and DAILY. For example: roll_cycle: DAILY The block option specifies whether FQL should block writing or drop log records if FQL falls behind. Supported boolean values are true (default) or false. For example: block: false to drop records The max_queue_weight option sets the maximum weight of in-memory queue for records waiting to be written to the file before blocking or dropping. The option must be set to a positive value. The default value is 268435456, or 256 MiB. For example, to change the default: max_queue_weight: 134217728 # 128 MiB The max_log_size option sets the maximum size of the rolled files to retain on disk before deleting the oldest file. The option must be set to a positive value. The default is 17179869184, or 16 GiB. For example, to change the default: max_log_size: 34359738368 # 32 GiB The archive_command option sets the user-defined archive script to execute on rolled log files. When not defined, files are deleted, with the default \"\" which then maps to org.apache.cassandra.utils.binlog.DeletingArchiver. For example: archive_command: /usr/local/bin/archiveit.sh %path # %path is the file being rolled The max_archive_retries option sets the max number of retries of failed archive commands. The default is 10. For example: max_archive_retries: 10 FQL can also be configured using nodetool when enabling the feature, and will override any values set in the cassandra.yaml file, as discussed in the next section. FQL is enabled on a per-node basis using the nodetool enablefullquerylog command. At a minimum, the path to the logging directory must be defined, if log_dir is not set in the cassandra.yaml file. The syntax of the nodetool enablefullquerylog command has all the same options that can be set in the cassandra.yaml file. In addition, nodetool has options to set which host and port to run the command on, and username and password if the command requires authentication. nodetool [(-h | --host )] [(-p | --port )] [(-pp | --print-port)] [(-pw | --password )] [(-pwf | --password-file )] [(-u | --username )] enablefullquerylog [--archive-command ] [--blocking] [--max-archive-retries ] [--max-log-size ] [--max-queue-weight ] [--path ] [--roll-cycle ] OPTIONS --archive-command Command that will handle archiving rolled full query log files. Format is \"/path/to/script.sh %path\" where %path will be replaced with the file to archive --blocking If the queue is full whether to block producers or drop samples. -h , --host Node hostname or ip address --max-archive-retries Max number of archive retries. --max-log-size How many bytes of log data to store before dropping segments. Might not be respected if a log file hasn't rolled so it can be deleted. --max-queue-weight Maximum number of bytes of query data to queue to disk before blocking or dropping samples. -p , --port Remote jmx agent port number --path Path to store the full query log at. Will have it's contents recursively deleted. -pp, --print-port Operate in 4.0 mode with hosts disambiguated by port number -pw , --password Remote jmx agent password -pwf , --password-file Path to the JMX password file --roll-cycle How often to roll the log file (MINUTELY, HOURLY, DAILY). -u , --username Remote jmx agent username To enable FQL, run the following command on each node in the cluster on which you want to enable logging: $ nodetool enablefullquerylog --path /tmp/cassandrafullquerylog Use the nodetool disablefullquerylog to disable logging. Use nodetool resetfullquerylog to stop FQL and clear the log files in the configured directory. IMPORTANT: Using nodetool resetfullquerylog will delete the log files! Do not use this command unless you need to delete all log files. The fqltool command is used to view (dump), replay, or compare logs. fqltool dump converts the binary log files into human-readable format; only the log directory must be supplied as a command-line option. fqltool replay (CASSANDRA-14618) enables replay of logs. The command can run from a different machine or cluster for testing, debugging, or performance benchmarking. The command can also be used to recreate a dropped database object. Use fqltool replay to record and compare different runs of production traffic against different versions/configurations of Cassandra or different clusters. Another use is to gather logs from several machines and replay them in “order” by the timestamps recorded. The syntax of fqltool replay is: fqltool replay [--keyspace ] [--results ] [--store-queries ] --target ... [--] [...] OPTIONS --keyspace Only replay queries against this keyspace and queries without keyspace set. --results Where to store the results of the queries, this should be a directory. Leave this option out to avoid storing results. --store-queries Path to store the queries executed. Stores queries in the same order as the result sets are in the result files. Requires --results --target Hosts to replay the logs to, can be repeated to replay to more hosts. -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] Paths containing the FQ logs to replay. fqltool compare (CASSANDRA-14619) compares result files generated by fqltool replay. The command uses recorded runs from fqltool replay and compareslog, outputting any differences (potentially all queries). It also stores each row as a separate chronicle document to avoid reading the entire result from in-memory when comparing. The syntax of fqltool compare is: fqltool compare --queries [--] [...] OPTIONS --queries Directory to read the queries from. It is produced by the fqltool replay --store-queries option. -- This option can be used to separate command-line options from the list of argument, (useful when arguments might be mistaken for command-line options [...] Directories containing result files to compare. The comparison sets the following marks: Mark the beginning of a query set: version: int16 type: column_definitions column_count: int32; column_definition: text, text column_definition: text, text .... Mark a failed query set: version: int16 type: query_failed message: text Mark a row set: version: int16 type: row row_column_count: int32 column: bytes Mark the end of a result set: version: int16 type: end_resultset To demonstrate FQL, first configure and enable FQL on a node in your cluster: $ nodetool enablefullquerylog --path /tmp/cassandrafullquerylog Now create a demo keyspace and table and insert some data using cqlsh: cqlsh> CREATE KEYSPACE querylogkeyspace ... WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 1}; cqlsh> USE querylogkeyspace; cqlsh:querylogkeyspace> CREATE TABLE t ( ...id int, ...k int, ...v text, ...PRIMARY KEY (id) ... ); cqlsh:querylogkeyspace> INSERT INTO t (id, k, v) VALUES (0, 0, 'val0'); cqlsh:querylogkeyspace> INSERT INTO t (id, k, v) VALUES (0, 1, 'val1'); Then check that the data is inserted: cqlsh:querylogkeyspace> SELECT * FROM t; id | k | v ----+---+------ 0 | 1 | val1 (1 rows) Use the fqltool dump command to view the logs. $ fqltool dump /tmp/cassandrafullquerylog This command will return a readable version of the log. Here is a partial sample of the log for the commands in this demo: WARN [main] 2019-08-02 03:07:53,635 Slf4jExceptionHandler.java:42 - Using Pauser.sleepy() as not enough processors, have 2, needs 8+ Type: single-query Query start time: 1564708322030 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system.peers Values: Type: single-query Query start time: 1564708322054 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system.local WHERE key='local' Values: Type: single-query Query start time: 1564708322109 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.keyspaces Values: Type: single-query Query start time: 1564708322116 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.tables Values: Type: single-query Query start time: 1564708322139 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.columns Values: Type: single-query Query start time: 1564708322142 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.functions Values: Type: single-query Query start time: 1564708322141 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.aggregates Values: Type: single-query Query start time: 1564708322143 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.types Values: Type: single-query Query start time: 1564708322144 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.indexes Values: Type: single-query Query start time: 1564708322145 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708322 Query: SELECT * FROM system_schema.views Values: Type: single-query Query start time: 1564708345408 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:-2147483648 Query: CREATE KEYSPACE querylogkeyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 1}; Values: Type: single-query Query start time: 1564708360873 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:-2147483648 Query: USE querylogkeyspace; Values: Type: single-query Query start time: 1564708360874 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:-2147483648 Query: USE \"querylogkeyspace\" Values: Type: single-query Query start time: 1564708378837 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:-2147483648 Query: CREATE TABLE t ( id int, k int, v text, PRIMARY KEY (id) ); Values: Type: single-query Query start time: 1564708379247 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708379 Query: SELECT * FROM system_schema.tables WHERE keyspace_name = 'querylogkeyspace' AND table_name = 't' Values: Type: single-query Query start time: 1564708397144 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708397 Query: INSERT INTO t (id, k, v) VALUES (0, 0, 'val0'); Values: Type: single-query Query start time: 1564708434782 Protocol version: 4 Generated timestamp:-9223372036854775808 Generated nowInSeconds:1564708434 Query: SELECT * FROM t; Values: To demonstrate fqltool replay, first drop the keyspace. cqlsh:querylogkeyspace> DROP KEYSPACE querylogkeyspace; Now run fqltool replay specifying the directories in which to store the results of the queries and the list of queries run, respectively, in --results and --store-queries: $ fqltool replay \\ --keyspace querylogkeyspace --results /cassandra/fql/logs/results/replay \\ --store-queries /cassandra/fql/logs/queries/replay \\ -- target 3.91.56.164 \\ /tmp/cassandrafullquerylog The --results and --store-queries directories are optional, but if --store-queries is set, then --results must also be set. The --target specifies the node on which to replay to logs. Check that the keyspace was replayed and exists again using the DESCRIBE KEYSPACES command: cqlsh:querylogkeyspace> DESC KEYSPACES; system_schema system system_distributed system_virtual_schema system_auth querylogkeyspace system_traces system_views","title":"Full Query Logging","component":"Cassandra","version":"4.1","name":"fqllogging","url":"/Cassandra/4.1/cassandra/operating/fqllogging.html","titles":[{"text":"Query information logged","id":"query-information-logged"},{"text":"Logging information logged","id":"logging-information-logged"},{"text":"Logging sequence","id":"logging-sequence"},{"text":"Using FQL","id":"using-fql"},{"text":"Configuring FQL in cassandra.yaml","id":"configuring-fql-in-cassandra-yaml"},{"text":"log_dir","id":"log_dir"},{"text":"roll_cycle","id":"roll_cycle"},{"text":"block","id":"block"},{"text":"max_queue_weight","id":"max_queue_weight"},{"text":"max_log_size","id":"max_log_size"},{"text":"archive_command","id":"archive_command"},{"text":"max_archive_retries","id":"max_archive_retries"},{"text":"Enabling FQL","id":"enabling-fql"},{"text":"Disabling or resetting FQL","id":"disabling-or-resetting-fql"},{"text":"fqltool","id":"fqltool"},{"text":"Example","id":"example"}]},"/Cassandra/4.1/cassandra/operating/hardware.html":{"text":"Like most databases, Cassandra throughput improves with more CPU cores, more RAM, and faster disks. While Cassandra can be made to run on small servers for testing or development environments (including Raspberry Pis), a minimal production server requires at least 2 cores, and at least 8GB of RAM. Typical production servers have 8 or more cores and at least 32GB of RAM. Cassandra is highly concurrent, handling many simultaneous requests (both read and write) using multiple threads running on as many CPU cores as possible. The Cassandra write path tends to be heavily optimized (writing to the commitlog and then inserting the data into the memtable), so writes, in particular, tend to be CPU bound. Consequently, adding additional CPU cores often increases throughput of both reads and writes. Cassandra runs within a Java VM, which will pre-allocate a fixed size heap (java’s Xmx system parameter). In addition to the heap, Cassandra will use significant amounts of RAM offheap for compression metadata, bloom filters, row, key, and counter caches, and an in process page cache. Finally, Cassandra will take advantage of the operating system’s page cache, storing recently accessed portions files in RAM for rapid re-use. For optimal performance, operators should benchmark and tune their clusters based on their individual workload. However, basic guidelines suggest: ECC RAM should always be used, as Cassandra has few internal safeguards to protect against bit level corruption The Cassandra heap should be no less than 2GB, and no more than 50% of your system RAM Heaps smaller than 12GB should consider ParNew/ConcurrentMarkSweep garbage collection Heaps larger than 12GB should consider either: 16GB heap with 8-10GB of new gen, a survivor ratio of 4-6, and a maximum tenuring threshold of 6 G1GC Cassandra persists data to disk for two very different purposes. The first is to the commitlog when a new write is made so that it can be replayed after a crash or system shutdown. The second is to the data directory when thresholds are exceeded and memtables are flushed to disk as SSTables. Commitlogs receive every write made to a Cassandra node and have the potential to block client operations, but they are only ever read on node start-up. SSTable (data file) writes on the other hand occur asynchronously, but are read to satisfy client look-ups. SSTables are also periodically merged and rewritten in a process called compaction. The data held in the commitlog directory is data that has not been permanently saved to the SSTable data directories - it will be periodically purged once it is flushed to the SSTable data files. Cassandra performs very well on both spinning hard drives and solid state disks. In both cases, Cassandra’s sorted immutable SSTables allow for linear reads, few seeks, and few overwrites, maximizing throughput for HDDs and lifespan of SSDs by avoiding write amplification. However, when using spinning disks, it’s important that the commitlog (commitlog_directory) be on one physical disk (not simply a partition, but a physical disk), and the data files (data_file_directories) be set to a separate physical disk. By separating the commitlog from the data directory, writes can benefit from sequential appends to the commitlog without having to seek around the platter as reads request data from various SSTables on disk. In most cases, Cassandra is designed to provide redundancy via multiple independent, inexpensive servers. For this reason, using NFS or a SAN for data directories is an antipattern and should typically be avoided. Similarly, servers with multiple disks are often better served by using RAID0 or JBOD than RAID1 or RAID5 - replication provided by Cassandra obsoletes the need for replication at the disk layer, so it’s typically recommended that operators take advantage of the additional throughput of RAID0 rather than protecting against failures with RAID1 or RAID5. Many large users of Cassandra run in various clouds, including AWS, Azure, and GCE - Cassandra will happily run in any of these environments. Users should choose similar hardware to what would be needed in physical space. In EC2, popular options include: i2 instances, which provide both a high RAM:CPU ratio and local ephemeral SSDs i3 instances with NVMe disks EBS works okay if you want easy backups and replacements m4.2xlarge / c4.4xlarge instances, which provide modern CPUs, enhanced networking and work well with EBS GP2 (SSD) storage Generally, disk and network performance increases with instance size and generation, so newer generations of instances and larger instance types within each family often perform better than their smaller or older alternatives.","title":"Hardware Choices","component":"Cassandra","version":"4.1","name":"hardware","url":"/Cassandra/4.1/cassandra/operating/hardware.html","titles":[{"text":"CPU","id":"cpu"},{"text":"Memory","id":"memory"},{"text":"Disks","id":"disks"},{"text":"Common Cloud Choices","id":"common-cloud-choices"}]},"/Cassandra/4.1/cassandra/operating/hints.html":{"text":"Hinting is a data repair technique applied during write operations. When replica nodes are unavailable to accept a mutation, either due to failure or more commonly routine maintenance, coordinators attempting to write to those replicas store temporary hints on their local filesystem for later application to the unavailable replica. Hints are an important way to help reduce the duration of data inconsistency. Coordinators replay hints quickly after unavailable replica nodes return to the ring. Hints are best effort, however, and do not guarantee eventual consistency like anti-entropy repair does. Hints are useful because of how Apache Cassandra replicates data to provide fault tolerance, high availability and durability. Cassandra partitions data across the cluster using consistent hashing, and then replicates keys to multiple nodes along the hash ring. To guarantee availability, all replicas of a key can accept mutations without consensus, but this means it is possible for some replicas to accept a mutation while others do not. When this happens an inconsistency is introduced. Hints are one of the three ways, in addition to read-repair and full/incremental anti-entropy repair, that Cassandra implements the eventual consistency guarantee that all updates are eventually received by all replicas. Hints, like read-repair, are best effort and not an alternative to performing full repair, but they do help reduce the duration of inconsistency between replicas in practice. Hinted handoff is the process by which Cassandra applies hints to unavailable nodes. For example, consider a mutation is to be made at Consistency Level LOCAL_QUORUM against a keyspace with Replication Factor of 3. Normally the client sends the mutation to a single coordinator, who then sends the mutation to all three replicas, and when two of the three replicas acknowledge the mutation the coordinator responds successfully to the client. If a replica node is unavailable, however, the coordinator stores a hint locally to the filesystem for later application. New hints will be retained for up to max_hint_windowin_ms of downtime (defaults to 3 h). If the unavailable replica does return to the cluster before the window expires, the coordinator applies any pending hinted mutations against the replica to ensure that eventual consistency is maintained. (t0): The write is sent by the client, and the coordinator sends it to the three replicas. Unfortunately replica_2 is restarting and cannot receive the mutation. (t1): The client receives a quorum acknowledgement from the coordinator. At this point the client believe the write to be durable and visible to reads (which it is). (t2): After the write timeout (default 2s), the coordinator decides that replica_2 is unavailable and stores a hint to its local disk. (t3): Later, when replica_2 starts back up it sends a gossip message to all nodes, including the coordinator. (t4): The coordinator replays hints including the missed mutation against replica_2. If the node does not return in time, the destination replica will be permanently out of sync until either read-repair or full/incremental anti-entropy repair propagates the mutation. Hints are streamed in bulk, a segment at a time, to the target replica node and the target node replays them locally. After the target node has replayed a segment it deletes the segment and receives the next segment. This continues until all hints are drained. Hints are stored in flat files in the coordinator node’s $CASSANDRA_HOME/data/hints directory. A hint includes a hint id, the target replica node on which the mutation is meant to be stored, the serialized mutation (stored as a blob) that couldn’t be delivered to the replica node, the mutation timestamp, and the Cassandra version used to serialize the mutation. By default hints are compressed using LZ4Compressor. Multiple hints are appended to the same hints file. Since hints contain the original unmodified mutation timestamp, hint application is idempotent and cannot overwrite a future mutation. Hints are also stored for write requests that time out. The write_request_timeout setting in cassandra.yaml configures the timeout for write requests. write_request_timeout: 2000ms The coordinator waits for the configured amount of time for write requests to complete, at which point it will time out and generate a hint for the timed out request. The lowest acceptable value for write_request_timeout is 10 ms. Hints are enabled by default as they are critical for data consistency. The cassandra.yaml configuration file provides several settings for configuring hints: Table 1. Settings for Hints Setting Description Default Value hinted_handoff_enabled Enables/Disables hinted handoffs true hinted_handoff_disabled_datacenters A list of data centers that do not perform hinted handoffs even when handoff is otherwise enabled. Example: hinted_handoff_disabled_datacenters: - DC1 - DC2 unset max_hint_window Defines the maximum amount of time a node shall have hints generated after it has failed. 3h hinted_handoff_throttle Maximum throttle in KiBs per second, per delivery thread. This will be reduced proportionally to the number of nodes in the cluster. (If there are two nodes in the cluster, each delivery thread will use the maximum rate; if there are 3, each will throttle to half of the maximum,since it is expected for two nodes to be delivering hints simultaneously.) 1024KiB max_hints_delivery_threads Number of threads with which to deliver hints; Consider increasing this number when you have multi-dc deployments, since cross-dc handoff tends to be slower 2 hints_directory Directory where Cassandra stores hints. $CASSANDRA_HOME/data/hints hints_flush_period How often hints should be flushed from the internal buffers to disk. Will not trigger fsync. 10000ms `max_hints_file_size Maximum size for a single hints file, in megabytes. 128MiB hints_compression Compression to apply to the hint files. If omitted, hints files will be written uncompressed. LZ4, Snappy, and Deflate compressors are supported. LZ4Compressor nodetool provides several commands for configuring hints or getting hints related information. The nodetool commands override the corresponding settings if any in cassandra.yaml for the node running the command. Table 2. Nodetool Commands for Hints Command Description nodetool disablehandoff Disables storing and delivering hints nodetool disablehintsfordc Disables storing and delivering hints to a data center nodetool enablehandoff Re-enables future hints storing and delivery on the current node nodetool enablehintsfordc Enables hints for a data center that was previously disabled nodetool getmaxhintwindow Prints the max hint window in ms. New in Cassandra 4.0. nodetool handoffwindow Prints current hinted handoff window nodetool pausehandoff Pauses hints delivery process nodetool resumehandoff Resumes hints delivery process nodetool sethintedhandoffthrottlekb Sets hinted handoff throttle in kb per second, per delivery thread nodetool setmaxhintwindow Sets the specified max hint window in ms nodetool statushandoff Status of storing future hints on the current node nodetool truncatehints Truncates all hints on the local node, or truncates hints for the endpoint(s) specified. The default of 1024 kbps handoff throttle is conservative for most modern networks, and it is entirely possible that in a simple node restart you may accumulate many gigabytes hints that may take hours to play back. For example if you are ingesting 100 Mbps of data per node, a single 10 minute long restart will create 10 minutes * (100 megabit / second) ~= 7 GiB of data which at (1024 KiB / second) would take 7.5 GiB / (1024 KiB / second) = 2.03 hours to play back. The exact math depends on the load balancing strategy (round robin is better than token aware), number of tokens per node (more tokens is better than fewer), and naturally the cluster’s write rate, but regardless you may find yourself wanting to increase this throttle at runtime. If you find yourself in such a situation, you may consider raising the hinted_handoff_throttle dynamically via the nodetool sethintedhandoffthrottlekb command. Sometimes a node may be down for more than the normal max_hint_window, (default of three hours), but the hardware and data itself will still be accessible. In such a case you may consider raising the max_hint_window dynamically via the nodetool setmaxhintwindow command added in Cassandra 4.0 (CASSANDRA-11720). This will instruct Cassandra to continue holding hints for the down endpoint for a longer amount of time. This command should be applied on all nodes in the cluster that may be holding hints. If needed, the setting can be applied permanently by setting the max_hint_window setting in cassandra.yaml followed by a rolling restart. Cassandra 4.0 adds histograms available to understand how long it takes to deliver hints which is useful for operators to better identify problems (CASSANDRA-13234). There are also metrics available for tracking Hinted Handoff and Hints Service metrics.","title":"Hints","component":"Cassandra","version":"4.1","name":"hints","url":"/Cassandra/4.1/cassandra/operating/hints.html","titles":[{"text":"Hinted Handoff","id":"hinted-handoff"},{"text":"Application of Hints","id":"application-of-hints"},{"text":"Storage of Hints on Disk","id":"storage-of-hints-on-disk"},{"text":"Hints for Timed Out Write Requests","id":"hints-for-timed-out-write-requests"},{"text":"Configuring Hints","id":"configuring-hints"},{"text":"Configuring Hints at Runtime with nodetool","id":"configuring-hints-at-runtime-with-nodetool"},{"text":"Make Hints Play Faster at Runtime","id":"make-hints-play-faster-at-runtime"},{"text":"Allow a Node to be Down Longer at Runtime","id":"allow-a-node-to-be-down-longer-at-runtime"},{"text":"Monitoring Hint Delivery","id":"monitoring-hint-delivery"}]},"/Cassandra/4.1/cassandra/operating/index.html":{"text":"Hardware Security Topology changes Hints Repair Read repair Backups Compression Compaction Monitoring Bulk loading CDC Bloom filters","title":"","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/operating/index.html","titles":[{"text":"Operating Cassandra","id":"operating-cassandra"}]},"/Cassandra/4.1/cassandra/operating/metrics.html":{"text":"Metrics in Cassandra are managed using the Dropwizard Metrics library. These metrics can be queried via JMX or pushed to external monitoring systems using a number of built in and third party reporter plugins. Metrics are collected for a single node. It’s up to the operator to use an external monitoring system to aggregate them. All metrics reported by cassandra fit into one of the following types. Gauge An instantaneous measurement of a value. Counter A gauge for an AtomicLong instance. Typically this is consumed by monitoring the change since the last call to see if there is a large increase compared to the norm. Histogram Measures the statistical distribution of values in a stream of data. + In addition to minimum, maximum, mean, etc., it also measures median, 75th, 90th, 95th, 98th, 99th, and 99.9th percentiles. Timer Measures both the rate that a particular piece of code is called and the histogram of its duration. Latency Special type that tracks latency (in microseconds) with a Timer plus a Counter that tracks the total latency accrued since starting. The former is useful if you track the change in total latency since the last check. Each metric name of this type will have 'Latency' and 'TotalLatency' appended to it. Meter A meter metric which measures mean throughput and one-, five-, and fifteen-minute exponentially-weighted moving average throughputs. Each table in Cassandra has metrics responsible for tracking its state and performance. The metric names are all appended with the specific Keyspace and Table name. Reported name format: Metric Name org.apache.cassandra.metrics.Table... JMX MBean org.apache.cassandra.metrics:type=Table keyspace= scope= name= Note There is a special table called ‘all’ without a keyspace. This represents the aggregation of metrics across all tables and keyspaces on the node. ====[cols=\",,\",options=\"header\",] Name Type Description MemtableOnHeapSize Gauge Total amount of data stored in the memtable that resides on-heap, including column related overhead and partitions overwritten. MemtableOffHeapSize Gauge Total amount of data stored in the memtable that resides off-heap, including column related overhead and partitions overwritten. MemtableLiveDataSize Gauge Total amount of live data stored in the memtable, excluding any data structure overhead. AllMemtablesOnHeapSize Gauge Total amount of data stored in the memtables (2i and pending flush memtables included) that resides on-heap. AllMemtablesOffHeapSize Gauge Total amount of data stored in the memtables (2i and pending flush memtables included) that resides off-heap. AllMemtablesLiveDataSize Gauge Total amount of live data stored in the memtables (2i and pending flush memtables included) that resides off-heap, excluding any data structure overhead. MemtableColumnsCount Gauge Total number of columns present in the memtable. MemtableSwitchCount Counter Number of times flush has resulted in the memtable being switched out. CompressionRatio Gauge Current compression ratio for all SSTables. EstimatedPartitionSizeHistogram Gauge Histogram of estimated partition size (in bytes). EstimatedPartitionCount Gauge Approximate number of keys in table. EstimatedColumnCountHistogram Gauge Histogram of estimated number of columns. SSTablesPerReadHistogram Histogram Histogram of the number of sstable data files accessed per single partition read. SSTables skipped due to Bloom Filters, min-max key or partition index lookup are not taken into acoount. ReadLatency Latency Local read latency for this table. RangeLatency Latency Local range scan latency for this table. WriteLatency Latency Local write latency for this table. CoordinatorReadLatency Timer Coordinator read latency for this table. CoordinatorWriteLatency Timer Coordinator write latency for this table. CoordinatorScanLatency Timer Coordinator range scan latency for this table. PendingFlushes Counter Estimated number of flush tasks pending for this table. BytesFlushed Counter Total number of bytes flushed since server [re]start. CompactionBytesWritten Counter Total number of bytes written by compaction since server [re]start. PendingCompactions Gauge Estimate of number of pending compactions for this table. LiveSSTableCount Gauge Number of SSTables on disk for this table. LiveDiskSpaceUsed Counter Disk space used by SSTables belonging to this table (in bytes). TotalDiskSpaceUsed Counter Total disk space used by SSTables belonging to this table, including obsolete ones waiting to be GC’d. MinPartitionSize Gauge Size of the smallest compacted partition (in bytes). MaxPartitionSize Gauge Size of the largest compacted partition (in bytes). MeanPartitionSize Gauge Size of the average compacted partition (in bytes). BloomFilterFalsePositives Gauge Number of false positives on table’s bloom filter. BloomFilterFalseRatio Gauge False positive ratio of table’s bloom filter. BloomFilterDiskSpaceUsed Gauge Disk space used by bloom filter (in bytes). BloomFilterOffHeapMemoryUsed Gauge Off-heap memory used by bloom filter. IndexSummaryOffHeapMemoryUsed Gauge Off-heap memory used by index summary. CompressionMetadataOffHeapMemoryUsed Gauge Off-heap memory used by compression meta data. KeyCacheHitRate Gauge Key cache hit rate for this table. TombstoneScannedHistogram Histogram Histogram of tombstones scanned in queries on this table. LiveScannedHistogram Histogram Histogram of live cells scanned in queries on this table. ColUpdateTimeDeltaHistogram Histogram Histogram of column update time delta on this table. ViewLockAcquireTime Timer Time taken acquiring a partition lock for materialized view updates on this table. ViewReadTime Timer Time taken during the local read of a materialized view update. TrueSnapshotsSize Gauge Disk space used by snapshots of this table including all SSTable components. RowCacheHitOutOfRange Counter Number of table row cache hits that do not satisfy the query filter, thus went to disk. RowCacheHit Counter Number of table row cache hits. RowCacheMiss Counter Number of table row cache misses. CasPrepare Latency Latency of paxos prepare round. CasPropose Latency Latency of paxos propose round. CasCommit Latency Latency of paxos commit round. PercentRepaired Gauge Percent of table data that is repaired on disk. BytesRepaired Gauge Size of table data repaired on disk BytesUnrepaired Gauge Size of table data unrepaired on disk BytesPendingRepair Gauge Size of table data isolated for an ongoing incremental repair SpeculativeRetries Counter Number of times speculative retries were sent for this table. SpeculativeFailedRetries Counter Number of speculative retries that failed to prevent a timeout SpeculativeInsufficientReplicas Counter Number of speculative retries that couldn’t be attempted due to lack of replicas SpeculativeSampleLatencyNanos Gauge Number of nanoseconds to wait before speculation is attempted. Value may be statically configured or updated periodically based on coordinator latency. WaitingOnFreeMemtableSpace Histogram Histogram of time spent waiting for free memtable space, either on- or off-heap. DroppedMutations Counter Number of dropped mutations on this table. AnticompactionTime Timer Time spent anticompacting before a consistent repair. ValidationTime Timer Time spent doing validation compaction during repair. SyncTime Timer Time spent doing streaming during repair. BytesValidated Histogram Histogram over the amount of bytes read during validation. PartitionsValidated Histogram Histogram over the number of partitions read during validation. BytesAnticompacted Counter How many bytes we anticompacted. BytesMutatedAnticompaction Counter How many bytes we avoided anticompacting because the sstable was fully contained in the repaired range. MutatedAnticompactionGauge Gauge Ratio of bytes mutated vs total bytes repaired. == Keyspace Metrics Each keyspace in Cassandra has metrics responsible for tracking its state and performance. Most of these metrics are the same as the Table Metrics above, only they are aggregated at the Keyspace level. The keyspace specific metrics are specified in the table below. Reported name format: Metric Name org.apache.cassandra.metrics.keyspace.. JMX MBean org.apache.cassandra.metrics:type=Keyspace scope= name= Name Type Description WriteFailedIdeaCL Counter Number of writes that failed to achieve the configured ideal consistency level or 0 if none is configured IdealCLWriteLatency Latency Coordinator latency of writes at the configured ideal consistency level. No values are recorded if ideal consistency level is not configured RepairTime Timer Total time spent as repair coordinator. RepairPrepareTime Timer Total time spent preparing for repair. == ThreadPool Metrics Cassandra splits work of a particular type into its own thread pool. This provides back-pressure and asynchrony for requests on a node. It’s important to monitor the state of these thread pools since they can tell you how saturated a node is. The metric names are all appended with the specific ThreadPool name. The thread pools are also categorized under a specific type. Reported name format: Metric Name org.apache.cassandra.metrics.ThreadPools... JMX MBean org.apache.cassandra.metrics:type=ThreadPools path= scope= name= Name Type Description ActiveTasks Gauge Number of tasks being actively worked on by this pool. PendingTasks Gauge Number of queued tasks queued up on this pool. CompletedTasks Counter Number of tasks completed. TotalBlockedTasks Counter Number of tasks that were blocked due to queue saturation. CurrentlyBlockedTask Counter Number of tasks that are currently blocked due to queue saturation but on retry will become unblocked. MaxPoolSize Gauge The maximum number of threads in this pool. MaxTasksQueued Gauge The maximum number of tasks queued before a task get blocked. The following thread pools can be monitored. Name Type Description Native-Transport-Requests transport Handles client CQL requests CounterMutationStage request Responsible for counter writes ViewMutationStage request Responsible for materialized view writes MutationStage request Responsible for all other writes ReadRepairStage request ReadRepair happens on this thread pool ReadStage request Local reads run on this thread pool RequestResponseStage request Coordinator requests to the cluster run on this thread pool AntiEntropyStage internal Builds merkle tree for repairs CacheCleanupExecutor internal Cache maintenance performed on this thread pool CompactionExecutor internal Compactions are run on these threads GossipStage internal Handles gossip requests HintsDispatcher internal Performs hinted handoff InternalResponseStage internal Responsible for intra-cluster callbacks MemtableFlushWriter internal Writes memtables to disk MemtablePostFlush internal Cleans up commit log after memtable is written to disk MemtableReclaimMemory internal Memtable recycling MigrationStage internal Runs schema migrations MiscStage internal Misceleneous tasks run here PendingRangeCalculator internal Calculates token range PerDiskMemtableFlushWriter_0 internal Responsible for writing a spec (there is one of these per disk 0-N) Sampler internal Responsible for re-sampling the index summaries of SStables SecondaryIndexManagement internal Performs updates to secondary indexes ValidationExecutor internal Performs validation compaction or scrubbing ViewBuildExecutor internal Performs materialized views initial build == Client Request Metrics Client requests have their own set of metrics that encapsulate the work happening at coordinator level. Different types of client requests are broken down by RequestType. Reported name format: Metric Name org.apache.cassandra.metrics.ClientRequest.. JMX MBean org.apache.cassandra.metrics:type=ClientRequest scope= name= RequestType CASRead Description Metrics related to transactional read requests. Metrics [cols=\",,\",options=\"header\",] |=== |Name |Type |Description |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of transaction failures encountered. | |Latency |Transaction read latency. |Unavailables |Counter |Number of unavailable exceptions encountered. |UnfinishedCommit |Counter |Number of transactions that were committed on read. |ConditionNotMet |Counter |Number of transaction preconditions did not match current values. |ContentionHistogram |Histogram |How many contended reads were encountered |=== RequestType CASWrite Description Metrics related to transactional write requests. Metrics [cols=\",,\",options=\"header\",] |=== |Name |Type |Description |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of transaction failures encountered. | |Latency |Transaction write latency. |UnfinishedCommit |Counter |Number of transactions that were committed on write. |ConditionNotMet |Counter |Number of transaction preconditions did not match current values. |ContentionHistogram |Histogram |How many contended writes were encountered |MutationSizeHistogram |Histogram |Total size in bytes of the requests mutations. |=== RequestType Read Description Metrics related to standard read requests. Metrics [cols=\",,\",options=\"header\",] |=== |Name |Type |Description |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of read failures encountered. | |Latency |Read latency. |Unavailables |Counter |Number of unavailable exceptions encountered. |=== RequestType RangeSlice Description Metrics related to token range read requests. Metrics [cols=\",,\",options=\"header\",] |=== |Name |Type |Description |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of range query failures encountered. | |Latency |Range query latency. |Unavailables |Counter |Number of unavailable exceptions encountered. |=== RequestType Write Description Metrics related to regular write requests. Metrics [cols=\",,\",options=\"header\",] |=== |Name |Type |Description |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of write failures encountered. | |Latency |Write latency. |Unavailables |Counter |Number of unavailable exceptions encountered. |MutationSizeHistogram |Histogram |Total size in bytes of the requests mutations. |=== RequestType ViewWrite Description Metrics related to materialized view write wrtes. Metrics [cols=\",,\",] |=== |Timeouts |Counter |Number of timeouts encountered. |Failures |Counter |Number of transaction failures encountered. |Unavailables |Counter |Number of unavailable exceptions encountered. |ViewReplicasAttempted |Counter |Total number of attempted view replica writes. |ViewReplicasSuccess |Counter |Total number of succeded view replica writes. |ViewPendingMutations |Gauge |ViewReplicasAttempted - ViewReplicasSuccess. |ViewWriteLatency |Timer |Time between when mutation is applied to base table and when CL.ONE is achieved on view. |=== == Cache Metrics Cassandra caches have metrics to track the effectivness of the caches. Though the Table Metrics might be more useful. Reported name format: Metric Name org.apache.cassandra.metrics.Cache.. JMX MBean org.apache.cassandra.metrics:type=Cache scope= name= Name Type Description Capacity Gauge Cache capacity in bytes. Entries Gauge Total number of cache entries. FifteenMinuteCacheHitRate Gauge 15m cache hit rate. FiveMinuteCacheHitRate Gauge 5m cache hit rate. OneMinuteCacheHitRate Gauge 1m cache hit rate. HitRate Gauge All time cache hit rate. Hits Meter Total number of cache hits. Misses Meter Total number of cache misses. MissLatency Timer Latency of misses. Requests Gauge Total number of cache requests. Size Gauge Total size of occupied cache, in bytes. The following caches are covered: Name Description CounterCache Keeps hot counters in memory for performance. ChunkCache In process uncompressed page cache. KeyCache Cache for partition to sstable offsets. RowCache Cache for rows kept in memory. Misses and MissLatency are only defined for the ChunkCache ====== CQL Metrics Metrics specific to CQL prepared statement caching. Reported name format: Metric Name org.apache.cassandra.metrics.CQL. JMX MBean org.apache.cassandra.metrics:type=CQL name= Name Type Description PreparedStatementsCount Gauge Number of cached prepared statements. PreparedStatementsEvicted Counter Number of prepared statements evicted from the prepared statement cache PreparedStatementsExecuted Counter Number of prepared statements executed. RegularStatementsExecuted Counter Number of non prepared statements executed. PreparedStatementsRatio Gauge Percentage of statements that are prepared vs unprepared. Metrics specific to tracking dropped messages for different types of requests. Dropped writes are stored and retried by Hinted Handoff Reported name format: Metric Name org.apache.cassandra.metrics.DroppedMessage.. JMX MBean org.apache.cassandra.metrics:type=DroppedMessage scope= name= Name Type Description CrossNodeDroppedLatency Timer The dropped latency across nodes. InternalDroppedLatency Timer The dropped latency within node. Dropped Meter Number of dropped messages. The different types of messages tracked are: Name Description BATCH_STORE Batchlog write BATCH_REMOVE Batchlog cleanup (after succesfully applied) COUNTER_MUTATION Counter writes HINT Hint replay MUTATION Regular writes READ Regular reads READ_REPAIR Read repair PAGED_SLICE Paged read RANGE_SLICE Token range read REQUEST_RESPONSE RPC Callbacks _TRACE Tracing writes Metrics reported during Streaming operations, such as repair, bootstrap, rebuild. These metrics are specific to a peer endpoint, with the source node being the node you are pulling the metrics from. Reported name format: Metric Name org.apache.cassandra.metrics.Streaming.. JMX MBean org.apache.cassandra.metrics:type=Streaming scope= name= Name Type Description IncomingBytes Counter Number of bytes streamed to this node from the peer. OutgoingBytes Counter Number of bytes streamed to the peer endpoint from this node. Metrics specific to Compaction work. Reported name format: Metric Name org.apache.cassandra.metrics.Compaction. JMX MBean org.apache.cassandra.metrics:type=Compaction name= Name Type Description BytesCompacted Counter Total number of bytes compacted since server [re]start. PendingTasks Gauge Estimated number of compactions remaining to perform. CompletedTasks Gauge Number of completed compactions since server [re]start. TotalCompactionsCompleted Meter Throughput of completed compactions since server [re]start. PendingTasksByTableName Gauge>> Estimated number of compactions remaining to perform, grouped by keyspace and then table name. This info is also kept in Table Metrics. Metrics specific to the CommitLog Reported name format: Metric Name org.apache.cassandra.metrics.CommitLog. JMX MBean org.apache.cassandra.metrics:type=CommitLog name= Name Type Description CompletedTasks Gauge Total number of commit log messages written since [re]start. PendingTasks Gauge Number of commit log messages written but yet to be fsync’d. TotalCommitLogSize Gauge Current size, in bytes, used by all the commit log segments. WaitingOnSegmentAllocation Timer Time spent waiting for a CommitLogSegment to be allocated - under normal conditions this should be zero. WaitingOnCommit Timer The time spent waiting on CL fsync; for Periodic this is only occurs when the sync is lagging its sync interval. Metrics specific to the storage engine. Reported name format: Metric Name org.apache.cassandra.metrics.Storage. JMX MBean org.apache.cassandra.metrics:type=Storage name= Name Type Description Exceptions Counter Number of internal exceptions caught. Under normal exceptions this should be zero. Load Counter Size, in bytes, of the on disk data size this node manages. TotalHints Counter Number of hint messages written to this node since [re]start. Includes one entry for each host to be hinted per hint. TotalHintsInProgress Counter Number of hints attemping to be sent currently. Metrics specific to Hinted Handoff. There are also some metrics related to hints tracked in Storage Metrics These metrics include the peer endpoint in the metric name Reported name format: Metric Name org.apache.cassandra.metrics.HintedHandOffManager. JMX MBean org.apache.cassandra.metrics:type=HintedHandOffManager name= Name Type Description Hints_created- Counter Number of hints on disk for this peer. Hints_not_stored- Counter Number of hints not stored for this peer, due to being down past the configured hint window. Metrics specific to the Hints delivery service. There are also some metrics related to hints tracked in Storage Metrics These metrics include the peer endpoint in the metric name Reported name format: Metric Name org.apache.cassandra.metrics.HintsService. JMX MBean org.apache.cassandra.metrics:type=HintsService name= Name Type Description HintsSucceeded Meter A meter of the hints successfully delivered HintsFailed Meter A meter of the hints that failed deliver HintsTimedOut Meter A meter of the hints that timed out Hint_delays Histogram Histogram of hint delivery delays (in milliseconds) Hint_delays- Histogram Histogram of hint delivery delays (in milliseconds) per peer Metrics specific to the SSTable index metadata. Reported name format: Metric Name org.apache.cassandra.metrics.Index..RowIndexEntry JMX MBean org.apache.cassandra.metrics:type=Index scope=RowIndexEntry name= Name Type Description IndexedEntrySize Histogram Histogram of the on-heap size, in bytes, of the index across all SSTables. IndexInfoCount Histogram Histogram of the number of on-heap index entries managed across all SSTables. IndexInfoGets Histogram Histogram of the number index seeks performed per SSTable. Metrics specific to the internal recycled buffer pool Cassandra manages. This pool is meant to keep allocations and GC lower by recycling on and off heap buffers. Reported name format: Metric Name org.apache.cassandra.metrics.BufferPool. JMX MBean org.apache.cassandra.metrics:type=BufferPool name= Name Type Description Size Gauge Size, in bytes, of the managed buffer pool Misses Meter The rate of misses in the pool. The higher this is the more allocations incurred. Metrics specifc to client managment. Reported name format: Metric Name org.apache.cassandra.metrics.Client. JMX MBean org.apache.cassandra.metrics:type=Client name= Name Type Description connectedNativeClients Gauge Number of clients connected to this nodes native protocol server connections Gauge> List of all connections and their state information connectedNativeClientsByUser Gauge Number of connnective native clients by username Metrics specifc to batch statements. Reported name format: Metric Name org.apache.cassandra.metrics.Batch. JMX MBean org.apache.cassandra.metrics:type=Batch name= Name Type Description PartitionsPerCounterBatch Histogram Distribution of the number of partitions processed per counter batch PartitionsPerLoggedBatch Histogram Distribution of the number of partitions processed per logged batch PartitionsPerUnloggedBatch Histogram Distribution of the number of partitions processed per unlogged batch JVM metrics such as memory and garbage collection statistics can either be accessed by connecting to the JVM using JMX or can be exported using Metric Reporters. Metric Name jvm.buffers.. JMX MBean java.nio:type=BufferPool name= Name Type Description Capacity Gauge Estimated total capacity of the buffers in this pool Count Gauge Estimated number of buffers in the pool Used Gauge Estimated memory that the Java virtual machine is using for this buffer pool Metric Name jvm.fd. JMX MBean java.lang:type=OperatingSystem name= Name Type Description Usage Ratio Ratio of used to total file descriptors Metric Name jvm.gc.. JMX MBean java.lang:type=GarbageCollector name= Name Type Description Count Gauge Total number of collections that have occurred Time Gauge Approximate accumulated collection elapsed time in milliseconds Metric Name jvm.memory.. JMX MBean java.lang:type=Memory Committed Gauge Amount of memory in bytes that is committed for the JVM to use Init Gauge Amount of memory in bytes that the JVM initially requests from the OS Max Gauge Maximum amount of memory in bytes that can be used for memory management Usage Ratio Ratio of used to maximum memory Used Gauge Amount of used memory in bytes Metric Name jvm.memory.pools.. JMX MBean java.lang:type=MemoryPool name= Committed Gauge Amount of memory in bytes that is committed for the JVM to use Init Gauge Amount of memory in bytes that the JVM initially requests from the OS Max Gauge Maximum amount of memory in bytes that can be used for memory management Usage Ratio Ratio of used to maximum memory Used Gauge Amount of used memory in bytes Any JMX based client can access metrics from cassandra. If you wish to access JMX metrics over http it’s possible to download Mx4jTool and place mx4j-tools.jar into the classpath. On startup you will see in the log: HttpAdaptor version 3.0.2 started on port 8081 To choose a different port (8081 is the default) or a different listen address (0.0.0.0 is not the default) edit conf/cassandra-env.sh and uncomment: #MX4J_ADDRESS=\"-Dmx4jaddress=0.0.0.0\" #MX4J_PORT=\"-Dmx4jport=8081\" As mentioned at the top of this section on monitoring the Cassandra metrics can be exported to a number of monitoring system a number of built in and third party reporter plugins. The configuration of these plugins is managed by the metrics reporter config project. There is a sample configuration file located at conf/metrics-reporter-config-sample.yaml. Once configured, you simply start cassandra with the flag -Dcassandra.metricsReporterConfigFile=metrics-reporter-config.yaml. The specified .yaml file plus any 3rd party reporter jars must all be in Cassandra’s classpath.","title":"Monitoring","component":"Cassandra","version":"4.1","name":"metrics","url":"/Cassandra/4.1/cassandra/operating/metrics.html","titles":[{"text":"Metric Types","id":"metric-types"},{"text":"Table Metrics","id":"table-metrics"},{"text":"DroppedMessage Metrics","id":"dropped-metrics"},{"text":"Streaming Metrics","id":"streaming-metrics"},{"text":"Compaction Metrics","id":"compaction-metrics"},{"text":"CommitLog Metrics","id":"commitlog-metrics"},{"text":"Storage Metrics","id":"storage-metrics"},{"text":"HintedHandoff Metrics","id":"handoff-metrics"},{"text":"HintsService Metrics","id":"hintsservice-metrics"},{"text":"SSTable Index Metrics","id":"sstable-index-metrics"},{"text":"BufferPool Metrics","id":"bufferpool-metrics"},{"text":"Client Metrics","id":"client-metrics"},{"text":"Batch Metrics","id":"batch-metrics"},{"text":"JVM Metrics","id":"jvm-metrics"},{"text":"BufferPool","id":"bufferpool"},{"text":"FileDescriptorRatio","id":"filedescriptorratio"},{"text":"GarbageCollector","id":"garbagecollector"},{"text":"Memory","id":"memory"},{"text":"MemoryPool","id":"memorypool"},{"text":"JMX","id":"jmx"},{"text":"Metric Reporters","id":"metric-reporters"}]},"/Cassandra/4.1/cassandra/operating/read_repair.html":{"text":"Read Repair is the process of repairing data replicas during a read request. If all replicas involved in a read request at the given read consistency level are consistent the data is returned to the client and no read repair is needed. But if the replicas involved in a read request at the given consistency level are not consistent a read repair is performed to make replicas involved in the read request consistent. The most up-to-date data is returned to the client. The read repair runs in the foreground and is blocking in that a response is not returned to the client until the read repair has completed and up-to-date data is constructed. Cassandra uses a blocking read repair to ensure the expectation of \"monotonic quorum reads\" i.e. that in 2 successive quorum reads, it’s guaranteed the 2nd one won’t get something older than the 1st one, and this even if a failed quorum write made a write of the most up to date value only to a minority of replicas. \"Quorum\" means majority of nodes among replicas. Cassandra 4.0 adds support for table level configuration of monotonic reads (CASSANDRA-14635). The read_repair table option has been added to table schema, with the options blocking (default), and none. The read_repair option configures the read repair behavior to allow tuning for various performance and consistency behaviors. Two consistency properties are affected by read repair behavior. Monotonic Quorum Reads: Provided by BLOCKING. Monotonic quorum reads prevents reads from appearing to go back in time in some circumstances. When monotonic quorum reads are not provided and a write fails to reach a quorum of replicas, it may be visible in one read, and then disappear in a subsequent read. Write Atomicity: Provided by NONE. Write atomicity prevents reads from returning partially applied writes. Cassandra attempts to provide partition level write atomicity, but since only the data covered by a SELECT statement is repaired by a read repair, read repair can break write atomicity when data is read at a more granular level than it is written. For example read repair can break write atomicity if you write multiple rows to a clustered partition in a batch, but then select a single row by specifying the clustering column in a SELECT statement. The available read repair settings are: The default setting. When read_repair is set to BLOCKING, and a read repair is started, the read will block on writes sent to other replicas until the CL is reached by the writes. Provides monotonic quorum reads, but not partition level write atomicity. When read_repair is set to NONE, the coordinator will reconcile any differences between replicas, but will not attempt to repair them. Provides partition level write atomicity, but not monotonic quorum reads. An example of using the NONE setting for the read_repair option is as follows: CREATE TABLE ks.tbl (k INT, c INT, v INT, PRIMARY KEY (k,c)) with read_repair='NONE'\"); To illustrate read repair with an example, consider that a client sends a read request with read consistency level TWO to a 5-node cluster as illustrated in Figure 1. Read consistency level determines how many replica nodes must return a response before the read request is considered successful. Figure 1. Client sends read request to a 5-node Cluster Three nodes host replicas for the requested data as illustrated in Figure 2. With a read consistency level of TWO two replica nodes must return a response for the read request to be considered successful. If the node the client sends request to hosts a replica of the data requested only one other replica node needs to be sent a read request to. But if the receiving node does not host a replica for the requested data the node becomes a coordinator node and forwards the read request to a node that hosts a replica. A direct read request is forwarded to the fastest node (as determined by dynamic snitch) as shown in Figure 2. A direct read request is a full read and returns the requested data. Figure 2. Direct Read Request sent to Fastest Replica Node Next, the coordinator node sends the requisite number of additional requests to satisfy the consistency level, which is TWO. The coordinator node needs to send one more read request for a total of two. All read requests additional to the first direct read request are digest read requests. A digest read request is not a full read and only returns the hash value of the data. Only a hash value is returned to reduce the network data traffic. In the example being discussed the coordinator node sends one digest read request to a node hosting a replica as illustrated in Figure 3. Figure 3. Coordinator Sends a Digest Read Request The coordinator node has received a full copy of data from one node and a hash value for the data from another node. To compare the data returned a hash value is calculated for the full copy of data. The two hash values are compared. If the hash values are the same no read repair is needed and the full copy of requested data is returned to the client. The coordinator node only performed a total of two replica read request because the read consistency level is TWO in the example. If the consistency level were higher such as THREE, three replica nodes would need to respond to a read request and only if all digest or hash values were to match with the hash value of the full copy of data would the read request be considered successful and the data returned to the client. But, if the hash value/s from the digest read request/s are not the same as the hash value of the data from the full read request of the first replica node it implies that an inconsistency in the replicas exists. To fix the inconsistency a read repair is performed. For example, consider that that digest request returns a hash value that is not the same as the hash value of the data from the direct full read request. We would need to make the replicas consistent for which the coordinator node sends a direct (full) read request to the replica node that it sent a digest read request to earlier as illustrated in Figure 4. Figure 4. Coordinator sends Direct Read Request to Replica Node it had sent Digest Read Request to After receiving the data from the second replica node the coordinator has data from two of the replica nodes. It only needs two replicas as the read consistency level is TWO in the example. Data from the two replicas is compared and based on the timestamps the most recent replica is selected. Data may need to be merged to construct an up-to-date copy of data if one replica has data for only some of the columns. In the example, if the data from the first direct read request is found to be outdated and the data from the second full read request to be the latest read, repair needs to be performed on Replica 2. If a new up-to-date data is constructed by merging the two replicas a read repair would be needed on both the replicas involved. For example, a read repair is performed on Replica 2 as illustrated in Figure 5. Figure 5. Coordinator performs Read Repair The most up-to-date data is returned to the client as illustrated in Figure 6. From the three replicas Replica 1 is not even read and thus not repaired. Replica 2 is repaired. Replica 3 is the most up-to-date and returned to client. Figure 6. Most up-to-date Data returned to Client The read consistency is most significant in determining if a read repair needs to be performed. As discussed in Table 1 a read repair is not needed for all of the consistency levels. Table 1. Read Repair based on Read Consistency Level Read Consistency Level Description ONE Read repair is not performed as the data from the first direct read request satisfies the consistency level ONE. No digest read requests are involved for finding mismatches in data. TWO Read repair is performed if inconsistencies in data are found as determined by the direct and digest read requests. THREE Read repair is performed if inconsistencies in data are found as determined by the direct and digest read requests. LOCAL_ONE Read repair is not performed as the data from the direct read request from the closest replica satisfies the consistency level LOCAL_ONE.No digest read requests are involved for finding mismatches in data. LOCAL_QUORUM Read repair is performed if inconsistencies in data are found as determined by the direct and digest read requests. QUORUM Read repair is performed if inconsistencies in data are found as determined by the direct and digest read requests. If read repair is performed it is made only on the replicas that are not up-to-date and that are involved in the read request. The number of replicas involved in a read request would be based on the read consistency level; in the example it is two. Cassandra 4.0 makes two improvements to read repair blocking behavior (CASSANDRA-10726). Speculative Retry of Full Data Read Requests. Cassandra 4.0 makes use of speculative retry in sending read requests (full, not digest) to replicas if a full data response is not received, whether in the initial full read request or a full data read request during read repair. With speculative retry if it looks like a response may not be received from the initial set of replicas Cassandra sent messages to, to satisfy the consistency level, it speculatively sends additional read request to un-contacted replica/s. Cassandra 4.0 will also speculatively send a repair mutation to a minority of nodes not involved in the read repair data read / write cycle with the combined contents of all un-acknowledged mutations if it looks like one may not respond. Cassandra accepts acks from them in lieu of acks from the initial mutations sent out, so long as it receives the same number of acks as repair mutations transmitted. Only blocks on Full Data Responses to satisfy the Consistency Level. Cassandra 4.0 only blocks for what is needed for resolving the digest mismatch and wait for enough full data responses to meet the consistency level, no matter whether it’s speculative retry or read repair chance. As an example, if it looks like Cassandra might not receive full data requests from everyone in time, it sends additional requests to additional replicas not contacted in the initial full data read. If the collection of nodes that end up responding in time end up agreeing on the data, the response from the disagreeing replica that started the read repair is not considered, and won’t be included in the response to the client, preserving the expectation of monotonic quorum reads. Cassandra 4.0 adds diagnostic events for read repair (CASSANDRA-14668) that can be used for exposing information such as: Contacted endpoints Digest responses by endpoint Affected partition keys Speculated reads / writes Update oversized Background read repair, which was configured using read_repair_chance and dclocal_read_repair_chance settings in cassandra.yaml is removed Cassandra 4.0 (CASSANDRA-13910). Read repair is not an alternative for other kind of repairs such as full repairs or replacing a node that keeps failing. The data returned even after a read repair has been performed may not be the most up-to-date data if consistency level is other than one requiring response from all replicas.","title":"Read repair","component":"Cassandra","version":"4.1","name":"read_repair","url":"/Cassandra/4.1/cassandra/operating/read_repair.html","titles":[{"text":"Expectation of Monotonic Quorum Reads","id":"expectation-of-monotonic-quorum-reads"},{"text":"Table level configuration of monotonic reads","id":"table-level-configuration-of-monotonic-reads"},{"text":"Blocking","id":"blocking"},{"text":"None","id":"none"},{"text":"Read Repair Example","id":"read-repair-example"},{"text":"Read Consistency Level and Read Repair","id":"read-consistency-level-and-read-repair"},{"text":"Improved Read Repair Blocking Behavior in Cassandra 4.0","id":"improved-read-repair-blocking-behavior-in-cassandra-4-0"},{"text":"Diagnostic Events for Read Repairs","id":"diagnostic-events-for-read-repairs"},{"text":"Background Read Repair","id":"background-read-repair"}]},"/Cassandra/4.1/cassandra/operating/repair.html":{"text":"Cassandra is designed to remain available if one of it’s nodes is down or unreachable. However, when a node is down or unreachable, it needs to eventually discover the writes it missed. Hints attempt to inform a node of missed writes, but are a best effort, and aren’t guaranteed to inform a node of 100% of the writes it missed. These inconsistencies can eventually result in data loss as nodes are replaced or tombstones expire. These inconsistencies are fixed with the repair process. Repair synchronizes the data between nodes by comparing their respective datasets for their common token ranges, and streaming the differences for any out of sync sections between the nodes. It compares the data with merkle trees, which are a hierarchy of hashes. There are 2 types of repairs: full repairs, and incremental repairs. Full repairs operate over all of the data in the token range being repaired. Incremental repairs only repair data that’s been written since the previous incremental repair. Incremental repairs are the default repair type, and if run regularly, can significantly reduce the time and io cost of performing a repair. However, it’s important to understand that once an incremental repair marks data as repaired, it won’t try to repair it again. This is fine for syncing up missed writes, but it doesn’t protect against things like disk corruption, data loss by operator error, or bugs in Cassandra. For this reason, full repairs should still be run occasionally. Since repair can result in a lot of disk and network io, it’s not run automatically by Cassandra. It is run by the operator via nodetool. Incremental repair is the default and is run with the following command: nodetool repair A full repair can be run with the following command: nodetool repair --full Additionally, repair can be run on a single keyspace: nodetool repair [options] Or even on specific tables: nodetool repair [options] The repair command only repairs token ranges on the node being repaired, it doesn’t repair the whole cluster. By default, repair will operate on all token ranges replicated by the node you’re running repair on, which will cause duplicate work if you run it on every node. The -pr flag will only repair the \"primary\" ranges on a node, so you can repair your entire cluster by running nodetool repair -pr on each node in a single datacenter. The specific frequency of repair that’s right for your cluster, of course, depends on several factors. However, if you’re just starting out and looking for somewhere to start, running an incremental repair every 1-3 days, and a full repair every 1-3 weeks is probably reasonable. If you don’t want to run incremental repairs, a full repair every 5 days is a good place to start. At a minimum, repair should be run often enough that the gc grace period never expires on unrepaired data. Otherwise, deleted data could reappear. With a default gc grace period of 10 days, repairing every node in your cluster at least once every 7 days will prevent this, while providing enough slack to allow for delays. -pr, --partitioner-range Restricts repair to the 'primary' token ranges of the node being repaired. A primary range is just a token range for which a node is the first replica in the ring. -prv, --preview Estimates the amount of streaming that would occur for the given repair command. This builds the merkle trees, and prints the expected streaming activity, but does not actually do any streaming. By default, incremental repairs are estimated, add the --full flag to estimate a full repair. -vd, --validate Verifies that the repaired data is the same across all nodes. Similiar to --preview, this builds and compares merkle trees of repaired data, but doesn’t do any streaming. This is useful for troubleshooting. If this shows that the repaired data is out of sync, a full repair should be run. nodetool repair docs Full repair is typically needed to redistribute data after increasing the replication factor of a keyspace or after adding a node to the cluster. Full repair involves streaming SSTables. To demonstrate full repair start with a three node cluster. [ec2-user@ip-10-0-2-238 ~]$ nodetool status Datacenter: us-east-1 ===================== Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns Host ID Rack UN 10.0.1.115 547 KiB 256 ? b64cb32a-b32a-46b4-9eeb-e123fa8fc287 us-east-1b UN 10.0.3.206 617.91 KiB 256 ? 74863177-684b-45f4-99f7-d1006625dc9e us-east-1d UN 10.0.2.238 670.26 KiB 256 ? 4dcdadd2-41f9-4f34-9892-1f20868b27c7 us-east-1c Create a keyspace with replication factor 3: cqlsh> DROP KEYSPACE cqlkeyspace; cqlsh> CREATE KEYSPACE CQLKeyspace ... WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 3}; Add a table to the keyspace: cqlsh> use cqlkeyspace; cqlsh:cqlkeyspace> CREATE TABLE t ( ... id int, ... k int, ... v text, ... PRIMARY KEY (id) ... ); Add table data: cqlsh:cqlkeyspace> INSERT INTO t (id, k, v) VALUES (0, 0, 'val0'); cqlsh:cqlkeyspace> INSERT INTO t (id, k, v) VALUES (1, 1, 'val1'); cqlsh:cqlkeyspace> INSERT INTO t (id, k, v) VALUES (2, 2, 'val2'); A query lists the data added: cqlsh:cqlkeyspace> SELECT * FROM t; id | k | v ----+---+------ 1 | 1 | val1 0 | 0 | val0 2 | 2 | val2 (3 rows) Make the following changes to a three node cluster: Increase the replication factor from 3 to 4. Add a 4th node to the cluster When the replication factor is increased the following message gets output indicating that a full repair is needed as per (CASSANDRA-13079): cqlsh:cqlkeyspace> ALTER KEYSPACE CQLKeyspace ... WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 4}; Warnings : When increasing replication factor you need to run a full (-full) repair to distribute the data. Perform a full repair on the keyspace cqlkeyspace table t with following command: nodetool repair -full cqlkeyspace t Full repair completes in about a second as indicated by the output: [ec2-user@ip-10-0-2-238 ~]$ nodetool repair -full cqlkeyspace t [2019-08-17 03:06:21,445] Starting repair command #1 (fd576da0-c09b-11e9-b00c-1520e8c38f00), repairing keyspace cqlkeyspace with repair options (parallelism: parallel, primary range: false, incremental: false, job threads: 1, ColumnFamilies: [t], dataCenters: [], hosts: [], previewKind: NONE, # of ranges: 1024, pull repair: false, force repair: false, optimise streams: false) [2019-08-17 03:06:23,059] Repair session fd8e5c20-c09b-11e9-b00c-1520e8c38f00 for range [(-8792657144775336505,-8786320730900698730], (-5454146041421260303,-5439402053041523135], (4288357893651763201,4324309707046452322], ... , (4350676211955643098,4351706629422088296]] finished (progress: 0%) [2019-08-17 03:06:23,077] Repair completed successfully [2019-08-17 03:06:23,077] Repair command #1 finished in 1 second [ec2-user@ip-10-0-2-238 ~]$ The nodetool tpstats command should list a repair having been completed as Repair-Task > Completed column value of 1: [ec2-user@ip-10-0-2-238 ~]$ nodetool tpstats Pool Name Active Pending Completed Blocked All time blocked ReadStage 0 0 99 0 0 … Repair-Task 0 0 1 0 0 RequestResponseStage 0 0 2078 0 0","title":"Repair","component":"Cassandra","version":"4.1","name":"repair","url":"/Cassandra/4.1/cassandra/operating/repair.html","titles":[{"text":"Incremental and Full Repairs","id":"incremental-and-full-repairs"},{"text":"Usage and Best Practices","id":"usage-and-best-practices"},{"text":"Other Options","id":"other-options"},{"text":"Full Repair Example","id":"full-repair-example"}]},"/Cassandra/4.1/cassandra/operating/security.html":{"text":"There are three main components to the security features provided by Cassandra: TLS/SSL encryption for client and inter-node communication Client authentication Authorization By default, these features are disabled as Cassandra is configured to easily find and be found by other members of a cluster. In other words, an out-of-the-box Cassandra installation presents a large attack surface for a bad actor. Enabling authentication for clients using the binary protocol is not sufficient to protect a cluster. Malicious users able to access internode communication and JMX ports can still: Craft internode messages to insert users into authentication schema Craft internode messages to truncate or drop schema Use tools such as sstableloader to overwrite system_auth tables Attach to the cluster directly to capture write traffic Correct configuration of all three security components should negate theses vectors. Therefore, understanding Cassandra’s security features is crucial to configuring your cluster to meet your security needs. Cassandra provides secure communication between a client machine and a database cluster and between nodes within a cluster. Enabling encryption ensures that data in flight is not compromised and is transferred securely. The options for client-to-node and node-to-node encryption are managed separately and may be configured independently. In both cases, the JVM defaults for supported protocols and cipher suites are used when encryption is enabled. These can be overidden using the settings in cassandra.yaml, but this is not recommended unless there are policies in place which dictate certain settings or a need to disable vulnerable ciphers or protocols in cases where the JVM cannot be updated. FIPS compliant settings can be configured at the JVM level and should not involve changing encryption settings in cassandra.yaml. See the java document on FIPS for more details. Cassandra provides flexibility of using Java based key material or completely customizing the SSL context. You can choose any keystore format supported by Java (JKS, PKCS12 etc) as well as other standards like PEM. You can even customize the SSL context creation to use Cloud Native technologies like Kuberenetes Secrets for storing the key material or to integrate with your in-house Key Management System. For information on generating the keystore and truststore files required with the Java supported keystores used in SSL communications, see the java documentation on creating keystores. For customizing the SSL context creation you can implement ISslContextCreationFactory interface or extend one of its public subclasses appropriately. You can then use the ssl_context_factory setting for server_encryption_options or client_encryption_options sections appropriately. See ssl-factory examples for details. Refer to the below class diagram to understand the class hierarchy. You can use the in-built class PEMBasedSSLContextFactory as the ssl_context_factory setting for the PEM based key material. You can configure this factory with either inline PEM data or with the files having the required PEM data as shown below, Configuration: PEM keys/certs defined in-line (mind the spaces in the YAML!) client/server_encryption_options: ssl_context_factory: class_name: org.apache.cassandra.security.PEMBasedSslContextFactory parameters: private_key: | -----BEGIN ENCRYPTED PRIVATE KEY----- OR -----BEGIN PRIVATE KEY----- -----END ENCRYPTED PRIVATE KEY----- OR -----END PRIVATE KEY----- -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- private_key_password: \"\" trusted_certificates: | -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- Configuration: PEM keys/certs defined in files client/server_encryption_options: ssl_context_factory: class_name: org.apache.cassandra.security.PEMBasedSslContextFactory keystore: keystore_password: \"\" truststore: Beginning with Cassandra 4, Cassandra supports hot reloading of SSL Certificates. If SSL/TLS support is enabled in Cassandra and you are using default file based key material, the node periodically (every 10 minutes) polls the Trust and Key Stores specified in cassandra.yaml. When the files are updated, Cassandra will reload them and use them for subsequent connections. Please note that the Trust & Key Store passwords are part of the yaml so the updated files should also use the same passwords. If you are customizing the SSL configuration via ssl_context_factory setting, Cassandra polls (at the same periodic interval mentioned above) your implementation to check if the SSL certificates need to be reloaded. See the ISslContextFactory documentation for more details. If you are using one of the Cassandra’s in-built SSL context factory class (example: PEMBasedSslContextFactory) with file based key material, it supports the hot reloading of the SSL certificates like mentioned above. Certificate Hot reloading may also be triggered using the nodetool reloadssl command. Use this if you want to Cassandra to immediately notice the changed certificates. The settings for managing inter-node encryption are found in cassandra.yaml in the server_encryption_options section. To enable inter-node encryption, change the internode_encryption setting from its default value of none to one value from: rack, dc or all. The settings for managing client to node encryption are found in cassandra.yaml in the client_encryption_options section. There are two primary toggles here for enabling encryption, enabled and optional. If neither is set to true, client connections are entirely unencrypted. If enabled is set to true and optional is set to false, all client connections must be secured. If both options are set to true, both encrypted and unencrypted connections are supported using the same port. Client connections using encryption with this configuration will be automatically detected and handled by the server. As an alternative to the optional setting, separate ports can also be configured for secure and unsecure connections where operational requirements demand it. To do so, set optional to false and use the native_transport_port_ssl setting in cassandra.yaml to specify the port to be used for secure client communication. Cassandra uses database roles, which may represent either a single user or a group of users, in both authentication and permissions management. Role management is an extension point in Cassandra and may be configured using the role_manager setting in cassandra.yaml. The default setting uses CassandraRoleManager, an implementation which stores role information in the tables of the system_auth keyspace. See also the CQL documentation on roles. Authentication is pluggable in Cassandra and is configured using the authenticator setting in cassandra.yaml. Cassandra ships with two options included in the default distribution. By default, Cassandra is configured with AllowAllAuthenticator which performs no authentication checks and therefore requires no credentials. It is used to disable authentication completely. Note that authentication is a necessary condition of Cassandra’s permissions subsystem, so if authentication is disabled, effectively so are permissions. The default distribution also includes PasswordAuthenticator, which stores encrypted credentials in a system table. This can be used to enable simple username/password authentication. Before enabling client authentication on the cluster, client applications should be pre-configured with their intended credentials. When a connection is initiated, the server will only ask for credentials once authentication is enabled, so setting up the client side config in advance is safe. In contrast, as soon as a server has authentication enabled, any connection attempt without proper credentials will be rejected which may cause availability problems for client applications. Once clients are setup and ready for authentication to be enabled, follow this procedure to enable it on the cluster. Pick a single node in the cluster on which to perform the initial configuration. Ideally, no clients should connect to this node during the setup process, so you may want to remove it from client config, block it at the network level or possibly add a new temporary node to the cluster for this purpose. On that node, perform the following steps: Open a cqlsh session and change the replication factor of the system_auth keyspace. By default, this keyspace uses SimpleReplicationStrategy and a replication_factor of 1. It is recommended to change this for any non-trivial deployment to ensure that should nodes become unavailable, login is still possible. Best practice is to configure a replication factor of 3 to 5 per-DC. ALTER KEYSPACE system_auth WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1': 3, 'DC2': 3}; Edit cassandra.yaml to change the authenticator option like so: authenticator: PasswordAuthenticator Restart the node. Open a new cqlsh session using the credentials of the default superuser: $ cqlsh -u cassandra -p cassandra During login, the credentials for the default superuser are read with a consistency level of QUORUM, whereas those for all other users (including superusers) are read at LOCAL_ONE. In the interests of performance and availability, as well as security, operators should create another superuser and disable the default one. This step is optional, but highly recommended. While logged in as the default superuser, create another superuser role which can be used to bootstrap further configuration. # create a new superuser CREATE ROLE dba WITH SUPERUSER = true AND LOGIN = true AND PASSWORD = 'super'; Start a new cqlsh session, this time logging in as the new_superuser and disable the default superuser. ALTER ROLE cassandra WITH SUPERUSER = false AND LOGIN = false; Finally, set up the roles and credentials for your application users with CREATE ROLE statements. At the end of these steps, the one node is configured to use password authentication. To roll that out across the cluster, repeat steps 2 and 3 on each node in the cluster. Once all nodes have been restarted, authentication will be fully enabled throughout the cluster. Note that using PasswordAuthenticator also requires the use of CassandraRoleManager. See also: setting-credentials-for-internal-authentication, CREATE ROLE, ALTER ROLE, ALTER KEYSPACE and GRANT PERMISSION. Authorization is pluggable in Cassandra and is configured using the authorizer setting in cassandra.yaml. Cassandra ships with two options included in the default distribution. By default, Cassandra is configured with AllowAllAuthorizer which performs no checking and so effectively grants all permissions to all roles. This must be used if AllowAllAuthenticator is the configured authenticator. The default distribution also includes CassandraAuthorizer, which does implement full permissions management functionality and stores its data in Cassandra system tables. Permissions are modelled as a whitelist, with the default assumption that a given role has no access to any database resources. The implication of this is that once authorization is enabled on a node, all requests will be rejected until the required permissions have been granted. For this reason, it is strongly recommended to perform the initial setup on a node which is not processing client requests. The following assumes that authentication has already been enabled via the process outlined in password-authentication. Perform these steps to enable internal authorization across the cluster: On the selected node, edit cassandra.yaml to change the authorizer option like so: authorizer: CassandraAuthorizer Restart the node. Open a new cqlsh session using the credentials of a role with superuser credentials: $ cqlsh -u dba -p super Configure the appropriate access privileges for your clients using GRANT PERMISSION statements. On the other nodes, until configuration is updated and the node restarted, this will have no effect so disruption to clients is avoided. GRANT SELECT ON ks.t1 TO db_user; Once all the necessary permissions have been granted, repeat steps 1 and 2 for each node in turn. As each node restarts and clients reconnect, the enforcement of the granted permissions will begin. See also: GRANT PERMISSION, GRANT ALL and REVOKE PERMISSION. Enabling authentication and authorization places additional load on the cluster by frequently reading from the system_auth tables. Furthermore, these reads are in the critical paths of many client operations, and so has the potential to severely impact quality of service. To mitigate this, auth data such as credentials, permissions and role details are cached for a configurable period. The caching can be configured (and even disabled) from cassandra.yaml or using a JMX client. The JMX interface also supports invalidation of the various caches, but any changes made via JMX are not persistent and will be re-read from cassandra.yaml when the node is restarted. Each cache has 3 options which can be set: Validity Period Controls the expiration of cache entries. After this period, entries are invalidated and removed from the cache. Refresh Rate Controls the rate at which background reads are performed to pick up any changes to the underlying data. While these async refreshes are performed, caches will continue to serve (possibly) stale data. Typically, this will be set to a shorter time than the validity period. Max Entries Controls the upper bound on cache size. The naming for these options in cassandra.yaml follows the convention: _validity_in_ms _update_interval_in_ms _cache_max_entries Where is one of credentials, permissions, or roles. As mentioned, these are also exposed via JMX in the mbeans under the org.apache.cassandra.auth domain. Access control for JMX clients is configured separately to that for CQL. For both authentication and authorization, two providers are available; the first based on standard JMX security and the second which integrates more closely with Cassandra’s own auth subsystem. The default settings for Cassandra make JMX accessible only from localhost. To enable remote JMX connections, edit cassandra-env.sh to change the LOCAL_JMX setting to no. Under the standard configuration, when remote JMX connections are enabled, standard JMX authentication is also switched on. Note that by default, local-only connections are not subject to authentication, but this can be enabled. If enabling remote connections, it is recommended to also use SSL connections. Finally, after enabling auth and/or SSL, ensure that tools which use JMX, such as nodetool are correctly configured and working as expected. Users permitted to connect to the JMX server are specified in a simple text file. The location of this file is set in cassandra-env.sh by the line: JVM_OPTS=\"$JVM_OPTS -Dcom.sun.management.jmxremote.password.file=/etc/cassandra/jmxremote.password\" Edit the password file to add username/password pairs: jmx_user jmx_password Secure the credentials file so that only the user running the Cassandra process can read it : $ chown cassandra:cassandra /etc/cassandra/jmxremote.password $ chmod 400 /etc/cassandra/jmxremote.password Optionally, enable access control to limit the scope of what defined users can do via JMX. Note that this is a fairly blunt instrument in this context as most operational tools in Cassandra require full read/write access. To configure a simple access file, uncomment this line in cassandra-env.sh: #JVM_OPTS=\"$JVM_OPTS -Dcom.sun.management.jmxremote.access.file=/etc/cassandra/jmxremote.access\" Then edit the access file to grant your JMX user readwrite permission: jmx_user readwrite Cassandra must be restarted to pick up the new settings. See also : Using File-Based Password Authentication In JMX An alternative to the out-of-the-box JMX auth is to useeCassandra’s own authentication and/or authorization providers for JMX clients. This is potentially more flexible and secure but it come with one major caveat. Namely that it is not available until after a node has joined the ring, because the auth subsystem is not fully configured until that point However, it is often critical for monitoring purposes to have JMX access particularly during bootstrap. So it is recommended, where possible, to use local only JMX auth during bootstrap and then, if remote connectivity is required, to switch to integrated auth once the node has joined the ring and initial setup is complete. With this option, the same database roles used for CQL authentication can be used to control access to JMX, so updates can be managed centrally using just cqlsh. Furthermore, fine grained control over exactly which operations are permitted on particular MBeans can be acheived via GRANT PERMISSION. To enable integrated authentication, edit cassandra-env.sh to uncomment these lines: #JVM_OPTS=\"$JVM_OPTS -Dcassandra.jmx.remote.login.config=CassandraLogin\" #JVM_OPTS=\"$JVM_OPTS -Djava.security.auth.login.config=$CASSANDRA_HOME/conf/cassandra-jaas.config\" And disable the JMX standard auth by commenting this line: JVM_OPTS=\"$JVM_OPTS -Dcom.sun.management.jmxremote.password.file=/etc/cassandra/jmxremote.password\" To enable integrated authorization, uncomment this line: #JVM_OPTS=\"$JVM_OPTS -Dcassandra.jmx.authorizer=org.apache.cassandra.auth.jmx.AuthorizationProxy\" Check standard access control is off by ensuring this line is commented out: #JVM_OPTS=\"$JVM_OPTS -Dcom.sun.management.jmxremote.access.file=/etc/cassandra/jmxremote.access\" With integrated authentication and authorization enabled, operators can define specific roles and grant them access to the particular JMX resources that they need. For example, a role with the necessary permissions to use tools such as jconsole or jmc in read-only mode would be defined as: CREATE ROLE jmx WITH LOGIN = false; GRANT SELECT ON ALL MBEANS TO jmx; GRANT DESCRIBE ON ALL MBEANS TO jmx; GRANT EXECUTE ON MBEAN 'java.lang:type=Threading' TO jmx; GRANT EXECUTE ON MBEAN 'com.sun.management:type=HotSpotDiagnostic' TO jmx; # Grant the role with necessary permissions to use nodetool commands (including nodetool status) in read-only mode GRANT EXECUTE ON MBEAN 'org.apache.cassandra.db:type=EndpointSnitchInfo' TO jmx; GRANT EXECUTE ON MBEAN 'org.apache.cassandra.db:type=StorageService' TO jmx; # Grant the jmx role to one with login permissions so that it can access the JMX tooling CREATE ROLE ks_user WITH PASSWORD = 'password' AND LOGIN = true AND SUPERUSER = false; GRANT jmx TO ks_user; Fine grained access control to individual MBeans is also supported: GRANT EXECUTE ON MBEAN 'org.apache.cassandra.db:type=Tables,keyspace=test_keyspace,table=t1' TO ks_user; GRANT EXECUTE ON MBEAN 'org.apache.cassandra.db:type=Tables,keyspace=test_keyspace,table=*' TO ks_owner; This permits the ks_user role to invoke methods on the MBean representing a single table in test_keyspace, while granting the same permission for all table level MBeans in that keyspace to the ks_owner role. Adding/removing roles and granting/revoking of permissions is handled dynamically once the initial setup is complete, so no further restarts are required if permissions are altered. See also: Permissions. JMX SSL configuration is controlled by a number of system properties, some of which are optional. To turn on SSL, edit the relevant lines in cassandra-env.sh to uncomment and set the values of these properties as required: com.sun.management.jmxremote.ssl set to true to enable SSL com.sun.management.jmxremote.ssl.need.client.auth set to true to enable validation of client certificates com.sun.management.jmxremote.registry.ssl enables SSL sockets for the RMI registry from which clients obtain the JMX connector stub com.sun.management.jmxremote.ssl.enabled.protocols by default, the protocols supported by the JVM will be used, override with a comma-separated list. Note that this is not usually necessary and using the defaults is the preferred option. com.sun.management.jmxremote.ssl.enabled.cipher.suites by default, the cipher suites supported by the JVM will be used, override with a comma-separated list. Note that this is not usually necessary and using the defaults is the preferred option. javax.net.ssl.keyStore set the path on the local filesystem of the keystore containing server private keys and public certificates javax.net.ssl.keyStorePassword set the password of the keystore file javax.net.ssl.trustStore if validation of client certificates is required, use this property to specify the path of the truststore containing the public certificates of trusted clients javax.net.ssl.trustStorePassword set the password of the truststore file See also: Oracle Java7 Docs, Monitor Java with JMX","title":"Security","component":"Cassandra","version":"4.1","name":"security","url":"/Cassandra/4.1/cassandra/operating/security.html","titles":[{"text":"TLS/SSL Encryption","id":"tlsssl-encryption"},{"text":"Using PEM based key material","id":"using-pem-based-key-material"},{"text":"SSL Certificate Hot Reloading","id":"ssl-certificate-hot-reloading"},{"text":"Inter-node Encryption","id":"inter-node-encryption"},{"text":"Client to Node Encryption","id":"client-to-node-encryption"},{"text":"Roles","id":"operation-roles"},{"text":"Authentication","id":"authentication"},{"text":"Enabling Password Authentication","id":"password-authentication"},{"text":"Authorization","id":"authorization"},{"text":"Enabling Internal Authorization","id":"enabling-internal-authorization"},{"text":"Caching","id":"auth-caching"},{"text":"JMX access","id":"jmx-access"},{"text":"Standard JMX Auth","id":"standard-jmx-auth"},{"text":"Cassandra Integrated Auth","id":"cassandra-integrated-auth"},{"text":"JMX With SSL","id":"jmx-with-ssl"}]},"/Cassandra/4.1/cassandra/operating/topo_changes.html":{"text":"Adding new nodes is called \"bootstrapping\". The num_tokens parameter will define the amount of virtual nodes (tokens) the joining node will be assigned during bootstrap. The tokens define the sections of the ring (token ranges) the node will become responsible for. With the default token allocation algorithm the new node will pick num_tokens random tokens to become responsible for. Since tokens are distributed randomly, load distribution improves with a higher amount of virtual nodes, but it also increases token management overhead. The default of 256 virtual nodes should provide a reasonable load balance with acceptable overhead. On 3.0+ a new token allocation algorithm was introduced to allocate tokens based on the load of existing virtual nodes for a given keyspace, and thus yield an improved load distribution with a lower number of tokens. To use this approach, the new node must be started with the JVM option -Dcassandra.allocate_tokens_for_keyspace=, where is the keyspace from which the algorithm can find the load information to optimize token assignment for. You may specify a comma-separated list of tokens manually with the initial_token cassandra.yaml parameter, and if that is specified Cassandra will skip the token allocation process. This may be useful when doing token assignment with an external tool or when restoring a node with its previous tokens. After the tokens are allocated, the joining node will pick current replicas of the token ranges it will become responsible for to stream data from. By default it will stream from the primary replica of each token range in order to guarantee data in the new node will be consistent with the current state. In the case of any unavailable replica, the consistent bootstrap process will fail. To override this behavior and potentially miss data from an unavailable replica, set the JVM flag -Dcassandra.consistent.rangemovement=false. On 2.2+, if the bootstrap process fails, it’s possible to resume bootstrap from the previous saved state by calling nodetool bootstrap resume. If for some reason the bootstrap hangs or stalls, it may also be resumed by simply restarting the node. In order to cleanup bootstrap state and start fresh, you may set the JVM startup flag -Dcassandra.reset_bootstrap_progress=true. On lower versions, when the bootstrap proces fails it is recommended to wipe the node (remove all the data), and restart the bootstrap process again. It’s possible to skip the bootstrapping process entirely and join the ring straight away by setting the hidden parameter auto_bootstrap: false. This may be useful when restoring a node from a backup or creating a new data-center. You can take a node out of the cluster with nodetool decommission to a live node, or nodetool removenode (to any other machine) to remove a dead one. This will assign the ranges the old node was responsible for to other nodes, and replicate the appropriate data there. If decommission is used, the data will stream from the decommissioned node. If removenode is used, the data will stream from the remaining replicas. No data is removed automatically from the node being decommissioned, so if you want to put the node back into service at a different token on the ring, it should be removed manually. When num_tokens: 1 it’s possible to move the node position in the ring with nodetool move. Moving is both a convenience over and more efficient than decommission + bootstrap. After moving a node, nodetool cleanup should be run to remove any unnecessary data. In order to replace a dead node, start cassandra with the JVM startup flag -Dcassandra.replace_address_first_boot=. Once this property is enabled the node starts in a hibernate state, during which all the other nodes will see this node to be DOWN (DN), however this node will see itself as UP (UN). Accurate replacement state can be found in nodetool netstats. The replacing node will now start to bootstrap the data from the rest of the nodes in the cluster. A replacing node will only receive writes during the bootstrapping phase if it has a different ip address to the node that is being replaced. (See CASSANDRA-8523 and CASSANDRA-12344) Once the bootstrapping is complete the node will be marked \"UP\". Note If any of the following cases apply, you MUST run repair to make the replaced node consistent again, since it missed ongoing writes during/prior to bootstrapping. The replacement timeframe refers to the period from when the node initially dies to when a new node completes the replacement process. The node is down for longer than max_hint_window before being replaced. You are replacing using the same IP address as the dead node and replacement takes longer than max_hint_window. Bootstrap, replace, move and remove progress can be monitored using nodetool netstats which will show the progress of the streaming operations. As a safety measure, Cassandra does not automatically remove data from nodes that \"lose\" part of their token range due to a range movement operation (bootstrap, move, replace). Run nodetool cleanup on the nodes that lost ranges to the joining node when you are satisfied the new node is up and working. If you do not do this the old data will still be counted against the load on that node.","title":"Adding, replacing, moving and removing nodes","component":"Cassandra","version":"4.1","name":"topo_changes","url":"/Cassandra/4.1/cassandra/operating/topo_changes.html","titles":[{"text":"Bootstrap","id":"bootstrap"},{"text":"Token allocation","id":"token-allocation"},{"text":"Manual token assignment","id":"manual-token-assignment"},{"text":"Range streaming","id":"range-streaming"},{"text":"Resuming failed/hanged bootstrap","id":"resuming-failedhanged-bootstrap"},{"text":"Manual bootstrapping","id":"manual-bootstrapping"},{"text":"Removing nodes","id":"removing-nodes"},{"text":"Moving nodes","id":"moving-nodes"},{"text":"Replacing a dead node","id":"replacing-a-dead-node"},{"text":"Monitoring progress","id":"monitoring-progress"},{"text":"Cleanup data after range movements","id":"cleanup-data-after-range-movements"}]},"/Cassandra/4.1/cassandra/operating/transientreplication.html":{"text":"Note: Transient Replication (CASSANDRA-14404) is an experimental feature designed for expert Apache Cassandra users who are able to validate every aspect of the database for their application and deployment. That means being able to check that operations like reads, writes, decommission, remove, rebuild, repair, and replace all work with your queries, data, configuration, operational practices, and availability requirements. Apache Cassandra 4.0 has the initial implementation of transient replication. Future releases of Cassandra will make this feature suitable for a wider audience. It is anticipated that a future version will support monotonic reads with transient replication as well as LWT, logged batches, and counters. Being experimental, Transient replication is not recommended for production use. The objective of transient replication is to decouple storage requirements from data redundancy (or consensus group size) using incremental repair, in order to reduce storage overhead. Certain nodes act as full replicas (storing all the data for a given token range), and some nodes act as transient replicas, storing only unrepaired data for the same token ranges. The optimization that is made possible with transient replication is called \"Cheap quorums\", which implies that data redundancy is increased without corresponding increase in storage usage. Transient replication is useful when sufficient full replicas are unavailable to receive and store all the data. Transient replication allows you to configure a subset of replicas to only replicate data that hasn’t been incrementally repaired. As an optimization, we can avoid writing data to a transient replica if we have successfully written data to the full replicas. After incremental repair, transient data stored on transient replicas can be discarded. Transient replication is not enabled by default. Transient replication must be enabled on each node in a cluster separately by setting the following configuration property in cassandra.yaml. transient_replication_enabled: true Transient replication may be configured with both SimpleStrategy and NetworkTopologyStrategy. Transient replication is configured by setting replication factor as /. As an example, create a keyspace with replication factor (RF) 3. CREATE KEYSPACE CassandraKeyspaceSimple WITH replication = {'class': 'SimpleStrategy', 'replication_factor' : 4/1}; As another example, some_keysopace keyspace will have 3 replicas in DC1, 1 of which is transient, and 5 replicas in DC2, 2 of which are transient: CREATE KEYSPACE some_keysopace WITH replication = {'class': 'NetworkTopologyStrategy', 'DC1' : '3/1'', 'DC2' : '5/2'}; Transiently replicated keyspaces only support tables with read_repair set to NONE. Important Restrictions: RF cannot be altered while some endpoints are not in a normal state (no range movements). You can’t add full replicas if there are any transient replicas. You must first remove all transient replicas, then change the # of full replicas, then add back the transient replicas. You can only safely increase number of transients one at a time with incremental repair run in between each time. Additionally, transient replication cannot be used for: Monotonic Reads Lightweight Transactions (LWTs) Logged Batches Counters Keyspaces using materialized views Secondary indexes (2i) Cheap quorums are a set of optimizations on the write path to avoid writing to transient replicas unless sufficient full replicas are not available to satisfy the requested consistency level. Hints are never written for transient replicas. Optimizations on the read path prefer reading from transient replicas. When writing at quorum to a table configured to use transient replication the quorum will always prefer available full replicas over transient replicas so that transient replicas don’t have to process writes. Tail latency is reduced by rapid write protection (similar to rapid read protection) when full replicas are slow or unavailable by sending writes to transient replicas. Transient replicas can serve reads faster as they don’t have to do anything beyond bloom filter checks if they have no data. With vnodes and large cluster sizes they will not have a large quantity of data even for failure of one or more full replicas where transient replicas start to serve a steady amount of write traffic for some of their transiently replicated ranges. The CREATE TABLE adds an option speculative_write_threshold for use with transient replicas. The option is of type simple with default value as 99PERCENTILE. When replicas are slow or unresponsive speculative_write_threshold specifies the threshold at which a cheap quorum write will be upgraded to include transient replicas. Pending ranges refers to the movement of token ranges between transient replicas. When a transient range is moved, there will be a period of time where both transient replicas would need to receive any write intended for the logical transient replica so that after the movement takes effect a read quorum is able to return a response. Nodes are not temporarily transient replicas during expansion. They stream data like a full replica for the transient range before they can serve reads. A pending state is incurred similar to how there is a pending state for full replicas. Transient replicas also always receive writes when they are pending. Pending transient ranges are sent a bit more data and reading from them is avoided. Read repair never attempts to repair a transient replica. Reads will always include at least one full replica. They should also prefer transient replicas where possible. Range scans ensure the entire scanned range performs replica selection that satisfies the requirement that every range scanned includes one full replica. During incremental & validation repair handling, at transient replicas anti-compaction does not output any data for transient ranges as the data will be dropped after repair, and transient replicas never have data streamed to them. The additional state transitions that transient replication introduces requires streaming and nodetool cleanup to behave differently. When data is streamed it is ensured that it is streamed from a full replica and not a transient replica. Transitioning from not replicated to transiently replicated means that a node must stay pending until the next incremental repair completes at which point the data for that range is known to be available at full replicas. Transitioning from transiently replicated to fully replicated requires streaming from a full replica and is identical to how data is streamed when transitioning from not replicated to replicated. The transition is managed so the transient replica is not read from as a full replica until streaming completes. It can be used immediately for a write quorum. Transitioning from fully replicated to transiently replicated requires cleanup to remove repaired data from the transiently replicated range to reclaim space. It can be used immediately for a write quorum. Transitioning from transiently replicated to not replicated requires cleanup to be run to remove the formerly transiently replicated data. When transient replication is in use ring changes are supported including add/remove node, change RF, add/remove DC. (CASSANDRA-14727) adds support for Transient Replication support for EACH_QUORUM. Per (CASSANDRA-14768), we ensure we write to at least a QUORUM of nodes in every DC, regardless of how many responses we need to wait for and our requested consistency level. This is to minimally surprise users with transient replication; with normal writes, we soft-ensure that we reach QUORUM in all DCs we are able to, by writing to every node; even if we don’t wait for ACK, we have in both cases sent sufficient messages.","title":"Transient Replication","component":"Cassandra","version":"4.1","name":"transientreplication","url":"/Cassandra/4.1/cassandra/operating/transientreplication.html","titles":[{"text":"Objective","id":"objective"},{"text":"Enabling Transient Replication","id":"enabling-transient-replication"},{"text":"Cheap Quorums","id":"cheap-quorums"},{"text":"Speculative Write Option","id":"speculative-write-option"},{"text":"Pending Ranges and Transient Replicas","id":"pending-ranges-and-transient-replicas"},{"text":"Read Repair and Transient Replicas","id":"read-repair-and-transient-replicas"},{"text":"Transitioning between Full Replicas and Transient Replicas","id":"transitioning-between-full-replicas-and-transient-replicas"},{"text":"Transient Replication supports EACH_QUORUM","id":"transient-replication-supports-each_quorum"}]},"/Cassandra/4.1/cassandra/operating/virtualtables.html":{"text":"Apache Cassandra 4.0 implements virtual tables (CASSANDRA-7622). Virtual tables are tables backed by an API instead of data explicitly managed and stored as SSTables. Apache Cassandra 4.0 implements a virtual keyspace interface for virtual tables. Virtual tables are specific to each node. Some of the features of virtual tables are the ability to: expose metrics through CQL expose YAML configuration information Virtual keyspaces and tables are quite different from regular tables and keyspaces: Virtual tables are created in special keyspaces and not just any keyspace. Virtual tables are managed by Cassandra. Users cannot run DDL to create new virtual tables or DML to modify existing virtual tables. Virtual tables are currently read-only, although that may change in a later version. Virtual tables are local only, non-distributed, and thus not replicated. Virtual tables have no associated SSTables. Consistency level of the queries sent to virtual tables are ignored. All existing virtual tables use LocalPartitioner. Since a virtual table is not replicated the partitioner sorts in order of partition keys instead of by their hash. Making advanced queries using ALLOW FILTERING and aggregation functions can be executed in virtual tables, even though in normal tables we dont recommend it. Apache Cassandra 4.0 has added two new keyspaces for virtual tables: system_virtual_schema system_views. The system_virtual_schema keyspace has three tables: keyspaces, columns and tables for the virtual keyspace, table, and column definitions, respectively. These tables contain schema information for the virtual tables. It is used by Cassandra internally and a user should not access it directly. The system_views keyspace contains the actual virtual tables. Before disccusing virtual keyspaces and tables, note that virtual keyspaces and tables have some limitations. These limitations are subject to change. Virtual keyspaces cannot be altered or dropped. In fact, no operations can be performed against virtual keyspaces. Virtual tables cannot be created in virtual keyspaces. Virtual tables cannot be altered, dropped, or truncated. Secondary indexes, types, functions, aggregates, materialized views, and triggers cannot be created for virtual tables. Expiring time-to-live (TTL) columns cannot be created. Virtual tables do not support conditional updates or deletes. Aggregates may be run in SELECT statements. Conditional batch statements cannot include mutations for virtual tables, nor can a virtual table statement be included in a logged batch. In fact, mutations for virtual and regular tables cannot occur in the same batch table. Each of the virtual tables in the system_views virtual keyspace contain different information. The following table describes the virtual tables: Virtual Table Description caches Displays the general cache information including cache name, capacity_bytes, entry_count, hit_count, hit_ratio double, recent_hit_rate_per_second, recent_request_rate_per_second, request_count, and size_bytes. clients Lists information about all connected clients. coordinator_read_latency Records counts, keyspace_name, table_name, max, median, and per_second for coordinator reads. coordinator_scan Records counts, keyspace_name, table_name, max, median, and per_second for coordinator scans. coordinator_write_latency Records counts, keyspace_name, table_name, max, median, and per_second for coordinator writes. disk_usage Records disk usage including disk_space, keyspace_name, and table_name, sorted by system keyspaces. internode_inbound Lists information about the inbound internode messaging. internode_outbound Information about the outbound internode messaging. local_read_latency Records counts, keyspace_name, table_name, max, median, and per_second for local reads. local_scan Records counts, keyspace_name, table_name, max, median, and per_second for local scans. local_write_latency Records counts, keyspace_name, table_name, max, median, and per_second for local writes. max_partition_size A table metric for maximum partition size. rows_per_read Records counts, keyspace_name, tablek_name, max, and median for rows read. settings Displays configuration settings in cassandra.yaml. sstable_tasks Lists currently running tasks and progress on SSTables, for operations like compaction and upgrade. system_properties Displays environmental system properties set on the node. thread_pools Lists metrics for each thread pool. tombstones_per_read Records counts, keyspace_name, tablek_name, max, and median for tombstones. We shall discuss some of the virtual tables in more detail next. The clients virtual table lists all active connections (connected clients) including their ip address, port, client_options, connection stage, driver name, driver version, hostname, protocol version, request count, ssl enabled, ssl protocol and user name: cqlsh> EXPAND ON ; Now Expanded output is enabled cqlsh> SELECT * FROM system_views.clients; @ Row 1 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50687 client_options | {'CQL_VERSION': '3.4.6', 'DRIVER_NAME': 'DataStax Python Driver', 'DRIVER_VERSION': '3.25.0'} connection_stage | ready driver_name | DataStax Python Driver driver_version | 3.25.0 hostname | localhost protocol_version | 5 request_count | 16 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 2 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50688 client_options | {'CQL_VERSION': '3.4.6', 'DRIVER_NAME': 'DataStax Python Driver', 'DRIVER_VERSION': '3.25.0'} connection_stage | ready driver_name | DataStax Python Driver driver_version | 3.25.0 hostname | localhost protocol_version | 5 request_count | 4 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 3 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50753 client_options | {'APPLICATION_NAME': 'TestApp', 'APPLICATION_VERSION': '1.0.0', 'CLIENT_ID': '55b3efbd-c56b-469d-8cca-016b860b2f03', 'CQL_VERSION': '3.0.0', 'DRIVER_NAME': 'DataStax Java driver for Apache Cassandra(R)', 'DRIVER_VERSION': '4.13.0'} connection_stage | ready driver_name | DataStax Java driver for Apache Cassandra(R) driver_version | 4.13.0 hostname | localhost protocol_version | 5 request_count | 18 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous @ Row 4 ------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- address | 127.0.0.1 port | 50755 client_options | {'APPLICATION_NAME': 'TestApp', 'APPLICATION_VERSION': '1.0.0', 'CLIENT_ID': '55b3efbd-c56b-469d-8cca-016b860b2f03', 'CQL_VERSION': '3.0.0', 'DRIVER_NAME': 'DataStax Java driver for Apache Cassandra(R)', 'DRIVER_VERSION': '4.13.0'} connection_stage | ready driver_name | DataStax Java driver for Apache Cassandra(R) driver_version | 4.13.0 hostname | localhost protocol_version | 5 request_count | 7 ssl_cipher_suite | null ssl_enabled | False ssl_protocol | null username | anonymous (4 rows) Some examples of how clients can be used are: To find applications using old incompatible versions of drivers before upgrading and with nodetool enableoldprotocolversions and nodetool disableoldprotocolversions during upgrades. To identify clients sending too many requests. To find if SSL is enabled during the migration to and from ssl. The virtual tables may be described with DESCRIBE statement. The DDL listed however cannot be run to create a virtual table. As an example describe the system_views.clients virtual table: cqlsh> DESCRIBE TABLE system_views.clients; /* Warning: Table system_views.clients is a virtual table and cannot be recreated with CQL. Structure, for reference: VIRTUAL TABLE system_views.clients ( address inet, port int, client_options frozen>, connection_stage text, driver_name text, driver_version text, hostname text, protocol_version int, request_count bigint, ssl_cipher_suite text, ssl_enabled boolean, ssl_protocol text, username text, PRIMARY KEY (address, port) ) WITH CLUSTERING ORDER BY (port ASC) AND comment = 'currently connected clients'; */ The caches virtual table lists information about the caches. The four caches presently created are chunks, counters, keys and rows. A query on the caches virtual table returns the following details: cqlsh:system_views> SELECT * FROM system_views.caches; name | capacity_bytes | entry_count | hit_count | hit_ratio | recent_hit_rate_per_second | recent_request_rate_per_second | request_count | size_bytes ---------+----------------+-------------+-----------+-----------+----------------------------+--------------------------------+---------------+------------ chunks | 229638144 | 29 | 166 | 0.83 | 5 | 6 | 200 | 475136 counters | 26214400 | 0 | 0 | NaN | 0 | 0 | 0 | 0 keys | 52428800 | 14 | 124 | 0.873239 | 4 | 4 | 142 | 1248 rows | 0 | 0 | 0 | NaN | 0 | 0 | 0 | 0 (4 rows) The settings table is rather useful and lists all the current configuration settings from the cassandra.yaml. The encryption options are overridden to hide the sensitive truststore information or passwords. The configuration settings however cannot be set using DML on the virtual table presently: : cqlsh:system_views> SELECT * FROM system_views.settings; name | value -------------------------------------+-------------------- allocate_tokens_for_keyspace | null audit_logging_options_enabled | false auto_snapshot | true automatic_sstable_upgrade | false cluster_name | Test Cluster enable_transient_replication | false hinted_handoff_enabled | true hints_directory | /home/ec2-user/cassandra/data/hints incremental_backups | false initial_token | null ... ... ... rpc_address | localhost ssl_storage_port | 7001 start_native_transport | true storage_port | 7000 stream_entire_sstables | true (224 rows) The settings table can be really useful if yaml file has been changed since startup and dont know running configuration, or to find if they have been modified via jmx/nodetool or virtual tables. The thread_pools table lists information about all thread pools. Thread pool information includes active tasks, active tasks limit, blocked tasks, blocked tasks all time, completed tasks, and pending tasks. A query on the thread_pools returns following details: cqlsh:system_views> select * from system_views.thread_pools; name | active_tasks | active_tasks_limit | blocked_tasks | blocked_tasks_all_time | completed_tasks | pending_tasks ------------------------------+--------------+--------------------+---------------+------------------------+-----------------+--------------- AntiEntropyStage | 0 | 1 | 0 | 0 | 0 | 0 CacheCleanupExecutor | 0 | 1 | 0 | 0 | 0 | 0 CompactionExecutor | 0 | 2 | 0 | 0 | 881 | 0 CounterMutationStage | 0 | 32 | 0 | 0 | 0 | 0 GossipStage | 0 | 1 | 0 | 0 | 0 | 0 HintsDispatcher | 0 | 2 | 0 | 0 | 0 | 0 InternalResponseStage | 0 | 2 | 0 | 0 | 0 | 0 MemtableFlushWriter | 0 | 2 | 0 | 0 | 1 | 0 MemtablePostFlush | 0 | 1 | 0 | 0 | 2 | 0 MemtableReclaimMemory | 0 | 1 | 0 | 0 | 1 | 0 MigrationStage | 0 | 1 | 0 | 0 | 0 | 0 MiscStage | 0 | 1 | 0 | 0 | 0 | 0 MutationStage | 0 | 32 | 0 | 0 | 0 | 0 Native-Transport-Requests | 1 | 128 | 0 | 0 | 130 | 0 PendingRangeCalculator | 0 | 1 | 0 | 0 | 1 | 0 PerDiskMemtableFlushWriter_0 | 0 | 2 | 0 | 0 | 1 | 0 ReadStage | 0 | 32 | 0 | 0 | 13 | 0 Repair-Task | 0 | 2147483647 | 0 | 0 | 0 | 0 RequestResponseStage | 0 | 2 | 0 | 0 | 0 | 0 Sampler | 0 | 1 | 0 | 0 | 0 | 0 SecondaryIndexManagement | 0 | 1 | 0 | 0 | 0 | 0 ValidationExecutor | 0 | 2147483647 | 0 | 0 | 0 | 0 ViewBuildExecutor | 0 | 1 | 0 | 0 | 0 | 0 ViewMutationStage | 0 | 32 | 0 | 0 | 0 | 0 (24 rows) The internode_inbound virtual table is for the internode inbound messaging. Initially no internode inbound messaging may get listed. In addition to the address, port, datacenter and rack information includes corrupt frames recovered, corrupt frames unrecovered, error bytes, error count, expired bytes, expired count, processed bytes, processed count, received bytes, received count, scheduled bytes, scheduled count, throttled count, throttled nanos, using bytes, using reserve bytes. A query on the internode_inbound returns following details: cqlsh:system_views> SELECT * FROM system_views.internode_inbound; address | port | dc | rack | corrupt_frames_recovered | corrupt_frames_unrecovered | error_bytes | error_count | expired_bytes | expired_count | processed_bytes | processed_count | received_bytes | received_count | scheduled_bytes | scheduled_count | throttled_count | throttled_nanos | using_bytes | using_reserve_bytes ---------+------+----+------+--------------------------+----------------------------+- ---------- (0 rows) The sstable_tasks could be used to get information about running tasks. It lists following columns: cqlsh:system_views> SELECT * FROM sstable_tasks; keyspace_name | table_name | task_id | kind | progress | total | unit ---------------+------------+--------------------------------------+------------+----------+----------+------- basic | wide2 | c3909740-cdf7-11e9-a8ed-0f03de2d9ae1 | compaction | 60418761 | 70882110 | bytes basic | wide2 | c7556770-cdf7-11e9-a8ed-0f03de2d9ae1 | compaction | 2995623 | 40314679 | bytes As another example, to find how much time is remaining for SSTable tasks, use the following query: SELECT total - progress AS remaining FROM system_views.sstable_tasks; Some examples of using other virtual tables are as follows. Find tables with most disk usage: cqlsh> SELECT * FROM disk_usage WHERE mebibytes > 1 ALLOW FILTERING; keyspace_name | table_name | mebibytes ---------------+------------+----------- keyspace1 | standard1 | 288 tlp_stress | keyvalue | 3211 Find queries on table/s with greatest read latency: cqlsh> SELECT * FROM local_read_latency WHERE per_second > 1 ALLOW FILTERING; keyspace_name | table_name | p50th_ms | p99th_ms | count | max_ms | per_second ---------------+------------+----------+----------+----------+---------+------------ tlp_stress | keyvalue | 0.043 | 0.152 | 49785158 | 186.563 | 11418.356 To list the keyspaces, enter cqlsh and run the CQL command DESCRIBE KEYSPACES: cqlsh> DESC KEYSPACES; system_schema system system_distributed system_virtual_schema system_auth system_traces system_views To view the virtual table schema, run the CQL commands USE system_virtual_schema and SELECT * FROM tables: cqlsh> USE system_virtual_schema; cqlsh> SELECT * FROM tables; results in: keyspace_name | table_name | comment -----------------------+---------------------------+-------------------------------------- system_views | caches | system caches system_views | clients | currently connected clients system_views | coordinator_read_latency | system_views | coordinator_scan_latency | system_views | coordinator_write_latency | system_views | disk_usage | system_views | internode_inbound | system_views | internode_outbound | system_views | local_read_latency | system_views | local_scan_latency | system_views | local_write_latency | system_views | max_partition_size | system_views | rows_per_read | system_views | settings | current settings system_views | sstable_tasks | current sstable tasks system_views | system_properties | Cassandra relevant system properties system_views | thread_pools | system_views | tombstones_per_read | system_virtual_schema | columns | virtual column definitions system_virtual_schema | keyspaces | virtual keyspace definitions system_virtual_schema | tables | virtual table definitions (21 rows) To view the virtual tables, run the CQL commands USE system_view and DESCRIBE tables: cqlsh> USE system_view;; cqlsh> DESCRIBE tables; results in: sstable_tasks clients coordinator_write_latency disk_usage local_write_latency tombstones_per_read thread_pools internode_outbound settings local_scan_latency coordinator_scan_latency system_properties internode_inbound coordinator_read_latency max_partition_size local_read_latency rows_per_read caches To look at any table data, run the CQL command SELECT: cqlsh> USE system_view;; cqlsh> SELECT * FROM clients LIMIT 2; results in: address | port | connection_stage | driver_name | driver_version | hostname | protocol_version | request_count | ssl_cipher_suite | ssl_enabled | ssl_protocol | username -----------+-------+------------------+------------------------+----------------+-----------+------------------+---------------+------------------+-------------+--------------+----------- 127.0.0.1 | 37308 | ready | DataStax Python Driver | 3.21.0.post0 | localhost | 4 | 17 | null | False | null | anonymous 127.0.0.1 | 37310 | ready | DataStax Python Driver | 3.21.0.post0 | localhost | 4 | 8 | null | False | null | anonymous (2 rows)","title":"Virtual Tables","component":"Cassandra","version":"4.1","name":"virtualtables","url":"/Cassandra/4.1/cassandra/operating/virtualtables.html","titles":[{"text":"Virtual Keyspaces","id":"virtual-keyspaces"},{"text":"Virtual Table Limitations","id":"virtual-table-limitations"},{"text":"Virtual Tables","id":"virtual-tables"},{"text":"Clients Virtual Table","id":"clients-virtual-table"},{"text":"Caches Virtual Table","id":"caches-virtual-table"},{"text":"Settings Virtual Table","id":"settings-virtual-table"},{"text":"Thread Pools Virtual Table","id":"thread-pools-virtual-table"},{"text":"Internode Inbound Messaging Virtual Table","id":"internode-inbound-messaging-virtual-table"},{"text":"SSTables Tasks Virtual Table","id":"sstables-tasks-virtual-table"},{"text":"Other Virtual Tables","id":"other-virtual-tables"},{"text":"Example","id":"example"}]},"/Cassandra/4.1/cassandra/troubleshooting/finding_nodes.html":{"text":"The first step to troubleshooting a Cassandra issue is to use error messages, metrics and monitoring information to identify if the issue lies with the clients or the server and if it does lie with the server find the problematic nodes in the Cassandra cluster. The goal is to determine if this is a systemic issue (e.g. a query pattern that affects the entire cluster) or isolated to a subset of nodes (e.g. neighbors holding a shared token range or even a single node with bad hardware). There are many sources of information that help determine where the problem lies. Some of the most common are mentioned below. Clients of the cluster often leave the best breadcrumbs to follow. Perhaps client latencies or error rates have increased in a particular datacenter (likely eliminating other datacenter’s nodes), or clients are receiving a particular kind of error code indicating a particular kind of problem. Troubleshooters can often rule out many failure modes just by reading the error messages. In fact, many Cassandra error messages include the last coordinator contacted to help operators find nodes to start with. Some common errors (likely culprit in parenthesis) assuming the client has similar error names as the Datastax drivers : SyntaxError (client). This and other QueryValidationException indicate that the client sent a malformed request. These are rarely server issues and usually indicate bad queries. UnavailableException (server): This means that the Cassandra coordinator node has rejected the query as it believes that insufficent replica nodes are available. If many coordinators are throwing this error it likely means that there really are (typically) multiple nodes down in the cluster and you can identify them using nodetool status If only a single coordinator is throwing this error it may mean that node has been partitioned from the rest. OperationTimedOutException (server): This is the most frequent timeout message raised when clients set timeouts and means that the query took longer than the supplied timeout. This is a client side timeout meaning that it took longer than the client specified timeout. The error message will include the coordinator node that was last tried which is usually a good starting point. This error usually indicates either aggressive client timeout values or latent server coordinators/replicas. ReadTimeoutException or WriteTimeoutException (server): These are raised when clients do not specify lower timeouts and there is a coordinator timeouts based on the values supplied in the cassandra.yaml configuration file. They usually indicate a serious server side problem as the default values are usually multiple seconds. If you have Cassandra metrics reporting to a centralized location such as Graphite or Grafana you can typically use those to narrow down the problem. At this stage narrowing down the issue to a particular datacenter, rack, or even group of nodes is the main goal. Some helpful metrics to look at are: Cassandra refers to internode messaging errors as \"drops\", and provided a number of Dropped Message Metrics to help narrow down errors. If particular nodes are dropping messages actively, they are likely related to the issue. For timeouts or latency related issues you can start with operating/metrics.adoc#table-metrics[table metrics] by comparing Coordinator level metrics e.g. CoordinatorReadLatency or CoordinatorWriteLatency with their associated replica metrics e.g. ReadLatency or WriteLatency. Issues usually show up on the 99th percentile before they show up on the 50th percentile or the mean. While maximum coordinator latencies are not typically very helpful due to the exponentially decaying reservoir used internally to produce metrics, maximum replica latencies that correlate with increased 99th percentiles on coordinators can help narrow down the problem. There are usually three main possibilities: Coordinator latencies are high on all nodes, but only a few node’s local read latencies are high. This points to slow replica nodes and the coordinator’s are just side-effects. This usually happens when clients are not token aware. Coordinator latencies and replica latencies increase at the same time on the a few nodes. If clients are token aware this is almost always what happens and points to slow replicas of a subset of token ranges (only part of the ring). Coordinator and local latencies are high on many nodes. This usually indicates either a tipping point in the cluster capacity (too many writes or reads per second), or a new query pattern. It’s important to remember that depending on the client’s load balancing behavior and consistency levels coordinator and replica metrics may or may not correlate. In particular if you use TokenAware policies the same node’s coordinator and replica latencies will often increase together, but if you just use normal DCAwareRoundRobin coordinator latencies can increase with unrelated replica node’s latencies. For example: TokenAware + LOCAL_ONE: should always have coordinator and replica latencies on the same node rise together TokenAware + LOCAL_QUORUM: should always have coordinator and multiple replica latencies rise together in the same datacenter. TokenAware + QUORUM: replica latencies in other datacenters can affect coordinator latencies. DCAwareRoundRobin + LOCAL_ONE: coordinator latencies and unrelated replica node’s latencies will rise together. DCAwareRoundRobin + LOCAL_QUORUM: different coordinator and replica latencies will rise together with little correlation. Sometimes the table metric query rate metrics can help narrow down load issues as \"small\" increase in coordinator queries per second (QPS) may correlate with a very large increase in replica level QPS. This most often happens with BATCH writes, where a client may send a single BATCH query that might contain 50 statements in it, which if you have 9 copies (RF=3, three datacenters) means that every coordinator BATCH write turns into 450 replica writes! This is why keeping `BATCH’s to the same partition is so critical, otherwise you can exhaust significant CPU capacitity with a \"single\" query. Once you have narrowed down the problem as much as possible (datacenter, rack , node), login to one of the nodes using SSH and proceed to debug using logs, nodetool, and os tools. If you are not able to login you may still have access to logs and nodetool remotely.","title":"Find The Misbehaving Nodes","component":"Cassandra","version":"4.1","name":"finding_nodes","url":"/Cassandra/4.1/cassandra/troubleshooting/finding_nodes.html","titles":[{"text":"Client Logs and Errors","id":"client-logs-and-errors"},{"text":"Metrics","id":"metrics"},{"text":"Errors","id":"errors"},{"text":"Latency","id":"latency"},{"text":"Query Rates","id":"query-rates"},{"text":"Next Step: Investigate the Node(s)","id":"next-step-investigate-the-nodes"}]},"/Cassandra/4.1/cassandra/troubleshooting/index.html":{"text":"As any distributed database does, sometimes Cassandra breaks and you will have to troubleshoot what is going on. Generally speaking you can debug Cassandra like any other distributed Java program, meaning that you have to find which machines in your cluster are misbehaving and then isolate the problem using logs and tools. Luckily Cassandra had a great set of instrospection tools to help you. These pages include a number of command examples demonstrating various debugging and analysis techniques, mostly for Linux/Unix systems. If you don’t have access to the machines running Cassandra, or are running on Windows or another operating system you may not be able to use the exact commands but there are likely equivalent tools you can use. Finding nodes Reading logs Using nodetool Using tools","title":"Troubleshooting","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/troubleshooting/index.html","titles":[]},"/Cassandra/4.1/cassandra/troubleshooting/reading_logs.html":{"text":"Cassandra has rich support for logging and attempts to give operators maximum insight into the database while at the same time limiting noise to the logs. Cassandra has three main logs, the system.log, debug.log and gc.log which hold general logging messages, debugging logging messages, and java garbage collection logs respectively. These logs by default live in ${CASSANDRA_HOME}/logs, but most Linux distributions relocate logs to /var/log/cassandra. Operators can tune this location as well as what levels are logged using the provided logback.xml file. This log is the default Cassandra log and is a good place to start any investigation. Some examples of activities logged to this log: Uncaught exceptions. These can be very useful for debugging errors. GCInspector messages indicating long garbage collector pauses. When long pauses happen Cassandra will print how long and also what was the state of the system (thread state) at the time of that pause. This can help narrow down a capacity issue (either not enough heap or not enough spare CPU). Information about nodes joining and leaving the cluster as well as token metadata (data ownersip) changes. This is useful for debugging network partitions, data movements, and more. Keyspace/Table creation, modification, deletion. StartupChecks that ensure optimal configuration of the operating system to run Cassandra Information about some background operational tasks (e.g. Index Redistribution). As with any application, looking for ERROR or WARN lines can be a great first step: $ # Search for warnings or errors in the latest system.log $ grep 'WARN\\|ERROR' system.log | tail ... $ # Search for warnings or errors in all rotated system.log $ zgrep 'WARN\\|ERROR' system.log.* | less ... This log contains additional debugging information that may be useful when troubleshooting but may be much noiser than the normal system.log. Some examples of activities logged to this log: Information about compactions, including when they start, which sstables they contain, and when they finish. Information about memtable flushes to disk, including when they happened, how large the flushes were, and which commitlog segments the flush impacted. This log can be very noisy, so it is highly recommended to use grep and other log analysis tools to dive deep. For example: # Search for messages involving a CompactionTask with 5 lines of context $ grep CompactionTask debug.log -C 5 # Look at the distribution of flush tasks per keyspace $ grep \"Enqueuing flush\" debug.log | cut -f 10 -d ' ' | sort | uniq -c 6 compaction_history: 1 test_keyspace: 2 local: 17 size_estimates: 17 sstable_activity: The gc log is a standard Java GC log. With the default jvm.options settings you get a lot of valuable information in this log such as application pause times, and why pauses happened. This may help narrow down throughput or latency issues to a mistuned JVM. For example you can view the last few pauses: $ grep stopped gc.log.0.current | tail 2018-08-29T00:19:39.522+0000: 3022663.591: Total time for which application threads were stopped: 0.0332813 seconds, Stopping threads took: 0.0008189 seconds 2018-08-29T00:19:44.369+0000: 3022668.438: Total time for which application threads were stopped: 0.0312507 seconds, Stopping threads took: 0.0007025 seconds 2018-08-29T00:19:49.796+0000: 3022673.865: Total time for which application threads were stopped: 0.0307071 seconds, Stopping threads took: 0.0006662 seconds 2018-08-29T00:19:55.452+0000: 3022679.521: Total time for which application threads were stopped: 0.0309578 seconds, Stopping threads took: 0.0006832 seconds 2018-08-29T00:20:00.127+0000: 3022684.197: Total time for which application threads were stopped: 0.0310082 seconds, Stopping threads took: 0.0007090 seconds 2018-08-29T00:20:06.583+0000: 3022690.653: Total time for which application threads were stopped: 0.0317346 seconds, Stopping threads took: 0.0007106 seconds 2018-08-29T00:20:10.079+0000: 3022694.148: Total time for which application threads were stopped: 0.0299036 seconds, Stopping threads took: 0.0006889 seconds 2018-08-29T00:20:15.739+0000: 3022699.809: Total time for which application threads were stopped: 0.0078283 seconds, Stopping threads took: 0.0006012 seconds 2018-08-29T00:20:15.770+0000: 3022699.839: Total time for which application threads were stopped: 0.0301285 seconds, Stopping threads took: 0.0003789 seconds 2018-08-29T00:20:15.798+0000: 3022699.867: Total time for which application threads were stopped: 0.0279407 seconds, Stopping threads took: 0.0003627 seconds This shows a lot of valuable information including how long the application was paused (meaning zero user queries were being serviced during the e.g. 33ms JVM pause) as well as how long it took to enter the safepoint. You can use this raw data to e.g. get the longest pauses: $ grep stopped gc.log.0.current | cut -f 11 -d ' ' | sort -n | tail | xargs -IX grep X gc.log.0.current | sort -k 1 2018-08-28T17:13:40.520-0700: 1.193: Total time for which application threads were stopped: 0.0157914 seconds, Stopping threads took: 0.0000355 seconds 2018-08-28T17:13:41.206-0700: 1.879: Total time for which application threads were stopped: 0.0249811 seconds, Stopping threads took: 0.0000318 seconds 2018-08-28T17:13:41.638-0700: 2.311: Total time for which application threads were stopped: 0.0561130 seconds, Stopping threads took: 0.0000328 seconds 2018-08-28T17:13:41.677-0700: 2.350: Total time for which application threads were stopped: 0.0362129 seconds, Stopping threads took: 0.0000597 seconds 2018-08-28T17:13:41.781-0700: 2.454: Total time for which application threads were stopped: 0.0442846 seconds, Stopping threads took: 0.0000238 seconds 2018-08-28T17:13:41.976-0700: 2.649: Total time for which application threads were stopped: 0.0377115 seconds, Stopping threads took: 0.0000250 seconds 2018-08-28T17:13:42.172-0700: 2.845: Total time for which application threads were stopped: 0.0475415 seconds, Stopping threads took: 0.0001018 seconds 2018-08-28T17:13:42.825-0700: 3.498: Total time for which application threads were stopped: 0.0379155 seconds, Stopping threads took: 0.0000571 seconds 2018-08-28T17:13:43.574-0700: 4.247: Total time for which application threads were stopped: 0.0323812 seconds, Stopping threads took: 0.0000574 seconds 2018-08-28T17:13:44.602-0700: 5.275: Total time for which application threads were stopped: 0.0238975 seconds, Stopping threads took: 0.0000788 seconds In this case any client waiting on a query would have experienced a 56ms latency at 17:13:41. Note that GC pauses are not _only garbage collection, although generally speaking high pauses with fast safepoints indicate a lack of JVM heap or mistuned JVM GC algorithm. High pauses with slow safepoints typically indicate that the JVM is having trouble entering a safepoint which usually indicates slow disk drives (Cassandra makes heavy use of memory mapped reads which the JVM doesn’t know could have disk latency, so the JVM safepoint logic doesn’t handle a blocking memory mapped read particularly well). Using these logs you can even get a pause distribution with something like histogram.py: $ grep stopped gc.log.0.current | cut -f 11 -d ' ' | sort -n | histogram.py # NumSamples = 410293; Min = 0.00; Max = 11.49 # Mean = 0.035346; Variance = 0.002216; SD = 0.047078; Median 0.036498 # each ∎ represents a count of 5470 0.0001 - 1.1496 [410255]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎ 1.1496 - 2.2991 [ 15]: 2.2991 - 3.4486 [ 5]: 3.4486 - 4.5981 [ 1]: 4.5981 - 5.7475 [ 5]: 5.7475 - 6.8970 [ 9]: 6.8970 - 8.0465 [ 1]: 8.0465 - 9.1960 [ 0]: 9.1960 - 10.3455 [ 0]: 10.3455 - 11.4949 [ 2]: We can see in this case while we have very good average performance something is causing multi second JVM pauses …​ In this case it was mostly safepoint pauses caused by slow disks: $ grep stopped gc.log.0.current | cut -f 11 -d ' ' | sort -n | tail | xargs -IX grep X gc.log.0.current| sort -k 1 2018-07-27T04:52:27.413+0000: 187831.482: Total time for which application threads were stopped: 6.5037022 seconds, Stopping threads took: 0.0005212 seconds 2018-07-30T23:38:18.354+0000: 514582.423: Total time for which application threads were stopped: 6.3262938 seconds, Stopping threads took: 0.0004882 seconds 2018-08-01T02:37:48.380+0000: 611752.450: Total time for which application threads were stopped: 10.3879659 seconds, Stopping threads took: 0.0004475 seconds 2018-08-06T22:04:14.990+0000: 1113739.059: Total time for which application threads were stopped: 6.0917409 seconds, Stopping threads took: 0.0005553 seconds 2018-08-14T00:04:06.091+0000: 1725730.160: Total time for which application threads were stopped: 6.0141054 seconds, Stopping threads took: 0.0004976 seconds 2018-08-17T06:23:06.755+0000: 2007670.824: Total time for which application threads were stopped: 6.0133694 seconds, Stopping threads took: 0.0006011 seconds 2018-08-23T06:35:46.068+0000: 2526830.137: Total time for which application threads were stopped: 6.4767751 seconds, Stopping threads took: 6.4426849 seconds 2018-08-23T06:36:29.018+0000: 2526873.087: Total time for which application threads were stopped: 11.4949489 seconds, Stopping threads took: 11.4638297 seconds 2018-08-23T06:37:12.671+0000: 2526916.741: Total time for which application threads were stopped: 6.3867003 seconds, Stopping threads took: 6.3507166 seconds 2018-08-23T06:37:47.156+0000: 2526951.225: Total time for which application threads were stopped: 7.9528200 seconds, Stopping threads took: 7.9197756 seconds Sometimes reading and understanding java GC logs is hard, but you can take the raw GC files and visualize them using tools such as GCViewer which take the Cassandra GC log as input and show you detailed visual information on your garbage collection performance. This includes pause analysis as well as throughput information. For a stable Cassandra JVM you probably want to aim for pauses less than 200ms and GC throughput greater than 99%. Java GC pauses are one of the leading causes of tail latency in Cassandra (along with drive latency) so sometimes this information can be crucial while debugging tail latency issues. If the default logging levels are insuficient, nodetool can set higher or lower logging levels for various packages and classes using the nodetool setlogginglevel command. Start by viewing the current levels: $ nodetool getlogginglevels Logger Name Log Level ROOT INFO org.apache.cassandra DEBUG Perhaps the Gossiper is acting up and we wish to enable it at TRACE level for even more insight: $ nodetool setlogginglevel org.apache.cassandra.gms.Gossiper TRACE $ nodetool getlogginglevels Logger Name Log Level ROOT INFO org.apache.cassandra DEBUG org.apache.cassandra.gms.Gossiper TRACE $ grep TRACE debug.log | tail -2 TRACE [GossipStage:1] 2018-07-04 17:07:47,879 Gossiper.java:1234 - Updating heartbeat state version to 2344 from 2343 for 127.0.0.2:7000 ... TRACE [GossipStage:1] 2018-07-04 17:07:47,879 Gossiper.java:923 - local heartbeat version 2341 greater than 2340 for 127.0.0.1:7000 Note that any changes made this way are reverted on next Cassandra process restart. To make the changes permanent add the appropriate rule to logback.xml. diff --git a/conf/logback.xml b/conf/logback.xml index b2c5b10..71b0a49 100644 --- a/conf/logback.xml +++ b/conf/logback.xml @@ -98,4 +98,5 @@ appender reference in the root level section below. + Note that if you want more information than this tool provides, there are other live capture options available such as packet-capture.","title":"Cassandra Logs","component":"Cassandra","version":"4.1","name":"reading_logs","url":"/Cassandra/4.1/cassandra/troubleshooting/reading_logs.html","titles":[{"text":"Common Log Files","id":"common-log-files"},{"text":"system.log","id":"system-log"},{"text":"debug.log","id":"debug-log"},{"text":"gc.log","id":"gc-log"},{"text":"Getting More Information","id":"getting-more-information"}]},"/Cassandra/4.1/cassandra/troubleshooting/use_nodetool.html":{"text":"Cassandra’s nodetool allows you to narrow problems from the cluster down to a particular node and gives a lot of insight into the state of the Cassandra process itself. There are dozens of useful commands (see nodetool help for all the commands), but briefly some of the most useful for troubleshooting: You can use nodetool status to assess status of the cluster: $ nodetool status Datacenter: dc1 ======================= Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns (effective) Host ID Rack UN 127.0.1.1 4.69 GiB 1 100.0% 35ea8c9f-b7a2-40a7-b9c5-0ee8b91fdd0e r1 UN 127.0.1.2 4.71 GiB 1 100.0% 752e278f-b7c5-4f58-974b-9328455af73f r2 UN 127.0.1.3 4.69 GiB 1 100.0% 9dc1a293-2cc0-40fa-a6fd-9e6054da04a7 r3 In this case we can see that we have three nodes in one datacenter with about 4.6GB of data each and they are all \"up\". The up/down status of a node is independently determined by every node in the cluster, so you may have to run nodetool status on multiple nodes in a cluster to see the full view. You can use nodetool status plus a little grep to see which nodes are down: $ nodetool status | grep -v '^UN' Datacenter: dc1 =============== Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns (effective) Host ID Rack Datacenter: dc2 =============== Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns (effective) Host ID Rack DN 127.0.0.5 105.73 KiB 1 33.3% df303ac7-61de-46e9-ac79-6e630115fd75 r1 In this case there are two datacenters and there is one node down in datacenter dc2 and rack r1. This may indicate an issue on 127.0.0.5 warranting investigation. You can view latency distributions of coordinator read and write latency to help narrow down latency issues using nodetool proxyhistograms: $ nodetool proxyhistograms Percentile Read Latency Write Latency Range Latency CAS Read Latency CAS Write Latency View Write Latency (micros) (micros) (micros) (micros) (micros) (micros) 50% 454.83 219.34 0.00 0.00 0.00 0.00 75% 545.79 263.21 0.00 0.00 0.00 0.00 95% 654.95 315.85 0.00 0.00 0.00 0.00 98% 785.94 379.02 0.00 0.00 0.00 0.00 99% 3379.39 2346.80 0.00 0.00 0.00 0.00 Min 42.51 105.78 0.00 0.00 0.00 0.00 Max 25109.16 43388.63 0.00 0.00 0.00 0.00 Here you can see the full latency distribution of reads, writes, range requests (e.g. select * from keyspace.table), CAS read (compare phase of CAS) and CAS write (set phase of compare and set). These can be useful for narrowing down high level latency problems, for example in this case if a client had a 20 millisecond timeout on their reads they might experience the occasional timeout from this node but less than 1% (since the 99% read latency is 3.3 milliseconds to determine if this is the case, but often it’s a good idea to check if compactions are even running using nodetool compactionstats: $ nodetool compactionstats pending tasks: 2 - keyspace.table: 2 id compaction type keyspace table completed total unit progress 2062b290-7f3a-11e8-9358-cd941b956e60 Compaction keyspace table 21848273 97867583 bytes 22.32% Active compaction remaining time : 0h00m04s In this case there is a single compaction running on the keyspace.table table, has completed 21.8 megabytes of 97 and Cassandra estimates (based on the configured compaction throughput) that this will take 4 seconds. You can also pass -H to get the units in a human readable format. Generally each running compaction can consume a single core, but the more you do in parallel the faster data compacts. Compaction is crucial to ensuring good read performance so having the right balance of concurrent compactions such that compactions complete quickly but don’t take too many resources away from query threads is very important for performance. If you notice compaction unable to keep up, try tuning Cassandra’s concurrent_compactors or compaction_throughput options. Cassandra is persisting data on disk within the configured directories. Data files are distributed among the directories configured with data_file_directories. Resembling the structure of keyspaces and tables, Cassandra is creating subdirectories within data_file_directories. However, directories aren’t removed even if the tables and keyspaces are dropped. While these directories are kept with the reason of holding snapshots, they are subject to removal. This is where operators need to know which directories are still in use. Running the nodetool datapaths command is an easy way to list in which directories Cassandra is actually storing sstable data on disk. % nodetool datapaths -- system_auth Keyspace: system_auth Table: role_permissions Paths: /var/lib/cassandra/data/system_auth/role_permissions-3afbe79f219431a7add7f5ab90d8ec9c Table: network_permissions Paths: /var/lib/cassandra/data/system_auth/network_permissions-d46780c22f1c3db9b4c1b8d9fbc0cc23 Table: resource_role_permissons_index Paths: /var/lib/cassandra/data/system_auth/resource_role_permissons_index-5f2fbdad91f13946bd25d5da3a5c35ec Table: roles Paths: /var/lib/cassandra/data/system_auth/roles-5bc52802de2535edaeab188eecebb090 Table: role_members Paths: /var/lib/cassandra/data/system_auth/role_members-0ecdaa87f8fb3e6088d174fb36fe5c0d By default all keyspaces and tables are listed, however, a list of keyspace and keyspace.table arguments can be given to query specific data paths. Using the --format option the output can be formatted as YAML or JSON.","title":"Use Nodetool","component":"Cassandra","version":"4.1","name":"use_nodetool","url":"/Cassandra/4.1/cassandra/troubleshooting/use_nodetool.html","titles":[{"text":"Cluster Status","id":"nodetool-status"},{"text":"Coordinator Query Latency","id":"nodetool-proxyhistograms"},{"text":"Local Query Latency","id":"nodetool-tablehistograms"},{"text":"Threadpool State","id":"nodetool-tpstats"},{"text":"Compaction State","id":"nodetool-compactionstats"},{"text":"Paths used for data files","id":"nodetool-datapaths"}]},"/Cassandra/4.1/cassandra/troubleshooting/use_tools.html":{"text":"Machine access allows operators to dive even deeper than logs and nodetool allow. While every Cassandra operator may have their personal favorite toolsets for troubleshooting issues, this page contains some of the most common operator techniques and examples of those tools. Many of these commands work only on Linux, but if you are deploying on a different operating system you may have access to other substantially similar tools that assess similar OS level metrics and processes. The JVM ships with a number of useful tools. Some of them are useful for debugging Cassandra issues, especially related to heap and execution stacks. NOTE: There are two common gotchas with JVM tooling and Cassandra: By default Cassandra ships with -XX:+PerfDisableSharedMem set to prevent long pauses (see CASSANDRA-9242 and CASSANDRA-9483 for details). If you want to use JVM tooling you can instead have /tmp mounted on an in memory tmpfs which also effectively works around CASSANDRA-9242. Make sure you run the tools as the same user as Cassandra is running as, e.g. if the database is running as cassandra the tool also has to be run as cassandra, e.g. via sudo -u cassandra . If you suspect heap pressure you can use jstat to dive deep into the garbage collection state of a Cassandra process. This command is always safe to run and yields detailed heap information including eden heap usage (E), old generation heap usage (O), count of eden collections (YGC), time spend in eden collections (YGCT), old/mixed generation collections (FGC) and time spent in old/mixed generation collections (FGCT): jstat -gcutil 500ms S0 S1 E O M CCS YGC YGCT FGC FGCT GCT 0.00 0.00 81.53 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 82.36 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 82.36 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 83.19 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 83.19 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 84.19 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 84.19 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 85.03 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 85.03 31.16 93.07 88.20 12 0.151 3 0.257 0.408 0.00 0.00 85.94 31.16 93.07 88.20 12 0.151 3 0.257 0.408 In this case we see we have a relatively healthy heap profile, with 31.16% old generation heap usage and 83% eden. If the old generation routinely is above 75% then you probably need more heap (assuming CMS with a 75% occupancy threshold). If you do have such persistently high old gen that often means you either have under-provisioned the old generation heap, or that there is too much live data on heap for Cassandra to collect (e.g. because of memtables). Another thing to watch for is time between young garbage collections (YGC), which indicate how frequently the eden heap is collected. Each young gc pause is about 20-50ms, so if you have a lot of them your clients will notice in their high percentile latencies. To get a point in time snapshot of exactly what Cassandra is doing, run jstack against the Cassandra PID. Note that this does pause the JVM for a very brief period ( > threaddump # display the threaddump $ cat threaddump # look at runnable threads $grep RUNNABLE threaddump -B 1 \"Attach Listener\" #15 daemon prio=9 os_prio=0 tid=0x00007f829c001000 nid=0x3a74 waiting on condition [0x0000000000000000] java.lang.Thread.State: RUNNABLE -- \"DestroyJavaVM\" #13 prio=5 os_prio=0 tid=0x00007f82e800e000 nid=0x2a19 waiting on condition [0x0000000000000000] java.lang.Thread.State: RUNNABLE -- \"JPS thread pool\" #10 prio=5 os_prio=0 tid=0x00007f82e84d0800 nid=0x2a2c runnable [0x00007f82d0856000] java.lang.Thread.State: RUNNABLE -- \"Service Thread\" #9 daemon prio=9 os_prio=0 tid=0x00007f82e80d7000 nid=0x2a2a runnable [0x0000000000000000] java.lang.Thread.State: RUNNABLE -- \"C1 CompilerThread3\" #8 daemon prio=9 os_prio=0 tid=0x00007f82e80cc000 nid=0x2a29 waiting on condition [0x0000000000000000] java.lang.Thread.State: RUNNABLE -- # Note that the nid is the Linux thread id Some of the most important information in the threaddumps are waiting/blocking threads, including what locks or monitors the thread is blocking/waiting on. A great place to start when debugging a Cassandra issue is understanding how Cassandra is interacting with system resources. The following are all resources that Cassandra makes heavy uses of: CPU cores. For executing concurrent user queries CPU processing time. For query activity (data decompression, row merging, etc.) CPU processing time (low priority). For background tasks (compaction, streaming, etc …​) RAM for Java Heap. Used to hold internal data-structures and by default the Cassandra memtables. Heap space is a crucial component of write performance as well as generally. RAM for OS disk cache. Used to cache frequently accessed SSTable blocks. OS disk cache is a crucial component of read performance. Disks. Cassandra cares a lot about disk read latency, disk write throughput, and of course disk space. Network latency. Cassandra makes many internode requests, so network latency between nodes can directly impact performance. Network throughput. Cassandra (as other databases) frequently have the so called \"incast\" problem where a small request (e.g. SELECT * from foo.bar) returns a massively large result set (e.g. the entire dataset). In such situations outgoing bandwidth is crucial. Often troubleshooting Cassandra comes down to troubleshooting what resource the machine or cluster is running out of. Then you create more of that resource or change the query pattern to make less use of that resource. Cassandra makes signifiant use of system resources, and often the very first useful action is to run top or htop (website)to see the state of the machine. Useful things to look at: System load levels. While these numbers can be confusing, generally speaking if the load average is greater than the number of CPU cores, Cassandra probably won’t have very good (sub 100 millisecond) latencies. See Linux Load Averages for more information. CPU utilization. htop in particular can help break down CPU utilization into user (low and normal priority), system (kernel), and io-wait . Cassandra query threads execute as normal priority user threads, while compaction threads execute as low priority user threads. High system time could indicate problems like thread contention, and high io-wait may indicate slow disk drives. This can help you understand what Cassandra is spending processing resources doing. Memory usage. Look for which programs have the most resident memory, it is probably Cassandra. The number for Cassandra is likely inaccurately high due to how Linux (as of 2018) accounts for memory mapped file memory. Use iostat to determine how data drives are faring, including latency distributions, throughput, and utilization: $ sudo iostat -xdm 2 Linux 4.13.0-13-generic (hostname) 07/03/2018 _x86_64_ (8 CPU) Device: rrqm/s wrqm/s r/s w/s rMB/s wMB/s avgrq-sz avgqu-sz await r_await w_await svctm %util sda 0.00 0.28 0.32 5.42 0.01 0.13 48.55 0.01 2.21 0.26 2.32 0.64 0.37 sdb 0.00 0.00 0.00 0.00 0.00 0.00 79.34 0.00 0.20 0.20 0.00 0.16 0.00 sdc 0.34 0.27 0.76 0.36 0.01 0.02 47.56 0.03 26.90 2.98 77.73 9.21 1.03 Device: rrqm/s wrqm/s r/s w/s rMB/s wMB/s avgrq-sz avgqu-sz await r_await w_await svctm %util sda 0.00 0.00 2.00 32.00 0.01 4.04 244.24 0.54 16.00 0.00 17.00 1.06 3.60 sdb 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 sdc 0.00 24.50 0.00 114.00 0.00 11.62 208.70 5.56 48.79 0.00 48.79 1.12 12.80 In this case we can see that /dev/sdc1 is a very slow drive, having an await close to 50 milliseconds and an avgqu-sz close to 5 ios. The drive is not particularly saturated (utilization is only 12.8%), but we should still be concerned about how this would affect our p99 latency since 50ms is quite long for typical Cassandra operations. That being said, in this case most of the latency is present in writes (typically writes are more latent than reads), which due to the LSM nature of Cassandra is often hidden from the user. Important metrics to assess using iostat: Reads and writes per second. These numbers will change with the workload, but generally speaking the more reads Cassandra has to do from disk the slower Cassandra read latencies are. Large numbers of reads per second can be a dead giveaway that the cluster has insufficient memory for OS page caching. Write throughput. Cassandra’s LSM model defers user writes and batches them together, which means that throughput to the underlying medium is the most important write metric for Cassandra. Read latency (r_await). When Cassandra missed the OS page cache and reads from SSTables, the read latency directly determines how fast Cassandra can respond with the data. Write latency. Cassandra is less sensitive to write latency except when it syncs the commit log. This typically enters into the very high percentiles of write latency. Note that to get detailed latency breakdowns you will need a more advanced tool such as bcc-tools. As Cassandra makes heavy use of memory mapped files, the health of the operating system’s Page Cache is crucial to performance. Start by finding how much available cache is in the system: $ free -g total used free shared buff/cache available Mem: 15 9 2 0 3 5 Swap: 0 0 0 In this case 9GB of memory is used by user processes (Cassandra heap) and 8GB is available for OS page cache. Of that, 3GB is actually used to cache files. If most memory is used and unavailable to the page cache, Cassandra performance can suffer significantly. This is why Cassandra starts with a reasonably small amount of memory reserved for the heap. If you suspect that you are missing the OS page cache frequently you can use advanced tools like cachestat or vmtouch to dive deeper. Whenever Cassandra does writes or reads that involve other replicas, LOCAL_QUORUM reads for example, one of the dominant effects on latency is network latency. When trying to debug issues with multi machine operations, the network can be an important resource to investigate. You can determine internode latency using tools like ping and traceroute or most effectively mtr: $ mtr -nr www.google.com Start: Sun Jul 22 13:10:28 2018 HOST: hostname Loss% Snt Last Avg Best Wrst StDev 1.|-- 192.168.1.1 0.0% 10 2.0 1.9 1.1 3.7 0.7 2.|-- 96.123.29.15 0.0% 10 11.4 11.0 9.0 16.4 1.9 3.|-- 68.86.249.21 0.0% 10 10.6 10.7 9.0 13.7 1.1 4.|-- 162.141.78.129 0.0% 10 11.5 10.6 9.6 12.4 0.7 5.|-- 162.151.78.253 0.0% 10 10.9 12.1 10.4 20.2 2.8 6.|-- 68.86.143.93 0.0% 10 12.4 12.6 9.9 23.1 3.8 7.|-- 96.112.146.18 0.0% 10 11.9 12.4 10.6 15.5 1.6 9.|-- 209.85.252.250 0.0% 10 13.7 13.2 12.5 13.9 0.0 10.|-- 108.170.242.238 0.0% 10 12.7 12.4 11.1 13.0 0.5 11.|-- 74.125.253.149 0.0% 10 13.4 13.7 11.8 19.2 2.1 12.|-- 216.239.62.40 0.0% 10 13.4 14.7 11.5 26.9 4.6 13.|-- 108.170.242.81 0.0% 10 14.4 13.2 10.9 16.0 1.7 14.|-- 72.14.239.43 0.0% 10 12.2 16.1 11.0 32.8 7.1 15.|-- 216.58.195.68 0.0% 10 25.1 15.3 11.1 25.1 4.8 In this example of mtr, we can rapidly assess the path that your packets are taking, as well as what their typical loss and latency are. Packet loss typically leads to between 200ms and 3s of additional latency, so that can be a common cause of latency issues. As Cassandra is sensitive to outgoing bandwidth limitations, sometimes it is useful to determine if network throughput is limited. One handy tool to do this is iftop which shows both bandwidth usage as well as connection information at a glance. An example showing traffic during a stress run against a local ccm cluster: $ # remove the -t for ncurses instead of pure text $ sudo iftop -nNtP -i lo interface: lo IP address is: 127.0.0.1 MAC address is: 00:00:00:00:00:00 Listening on lo # Host name (port/service if enabled) last 2s last 10s last 40s cumulative -------------------------------------------------------------------------------------------- 1 127.0.0.1:58946 => 869Kb 869Kb 869Kb 217KB 127.0.0.3:9042 736Kb 736Kb 736Kb 184KB 127.0.0.1:9042 669Kb 669Kb 669Kb 167KB 127.0.0.2:9042 3.30Kb 3.30Kb 3.30Kb 845B 127.0.0.1:58946 2.79Kb 2.79Kb 2.79Kb 715B 127.0.0.1:54654 2.54Kb 2.54Kb 2.54Kb 650B 127.0.0.1:51186 1.65Kb 1.65Kb 1.65Kb 423B 127.0.0.5:7000 1.50Kb 1.50Kb 1.50Kb 385B 127.0.0.2:7000 1.50Kb 1.50Kb 1.50Kb 383B 127.0.0.1:7000 1.43Kb 1.43Kb 1.43Kb 366B 127.0.0.4:7000 1 : 0 | | 2 -> 3 : 0 | | 4 -> 7 : 0 | | 8 -> 15 : 0 | | 16 -> 31 : 12 |****************************************| 32 -> 63 : 9 |****************************** | 64 -> 127 : 1 |*** | 128 -> 255 : 3 |********** | 256 -> 511 : 7 |*********************** | 512 -> 1023 : 2 |****** | disk = 'sdc' usecs : count distribution 0 -> 1 : 0 | | 2 -> 3 : 0 | | 4 -> 7 : 0 | | 8 -> 15 : 0 | | 16 -> 31 : 0 | | 32 -> 63 : 0 | | 64 -> 127 : 41 |************ | 128 -> 255 : 17 |***** | 256 -> 511 : 13 |*** | 512 -> 1023 : 2 | | 1024 -> 2047 : 0 | | 2048 -> 4095 : 0 | | 4096 -> 8191 : 56 |***************** | 8192 -> 16383 : 131 |****************************************| 16384 -> 32767 : 9 |** | In this case most ios on the data drive (sdc) are fast, but many take between 8 and 16 milliseconds. Finally biosnoop (examples) can be used to dive even deeper and see per IO latencies: $ sudo /usr/share/bcc/tools/biosnoop | grep java | head 0.000000000 java 17427 sdc R 3972458600 4096 13.58 0.000818000 java 17427 sdc R 3972459408 4096 0.35 0.007098000 java 17416 sdc R 3972401824 4096 5.81 0.007896000 java 17416 sdc R 3972489960 4096 0.34 0.008920000 java 17416 sdc R 3972489896 4096 0.34 0.009487000 java 17427 sdc R 3972401880 4096 0.32 0.010238000 java 17416 sdc R 3972488368 4096 0.37 0.010596000 java 17427 sdc R 3972488376 4096 0.34 0.011236000 java 17410 sdc R 3972488424 4096 0.32 0.011825000 java 17427 sdc R 3972488576 16384 0.65 ... time passes 8.032687000 java 18279 sdc R 10899712 122880 3.01 8.033175000 java 18279 sdc R 10899952 8192 0.46 8.073295000 java 18279 sdc R 23384320 122880 3.01 8.073768000 java 18279 sdc R 23384560 8192 0.46 With biosnoop you see every single IO and how long they take. This data can be used to construct the latency distributions in biolatency but can also be used to better understand how disk latency affects performance. For example this particular drive takes ~3ms to service a memory mapped read due to the large default value (128kb) of read_ahead_kb. To improve point read performance you may may want to decrease read_ahead_kb on fast data volumes such as SSDs while keeping the a higher value like 128kb value is probably right for HDs. There are tradeoffs involved, see queue-sysfs docs for more information, but regardless biosnoop is useful for understanding how Cassandra uses drives. Sometimes it’s useful to know how much of the Cassandra data files are being cached by the OS. A great tool for answering this question is vmtouch. First install it: $ git clone https://github.com/hoytech/vmtouch.git $ cd vmtouch $ make Then run it on the Cassandra data directory: $ ./vmtouch /var/lib/cassandra/data/ Files: 312 Directories: 92 Resident Pages: 62503/64308 244M/251M 97.2% Elapsed: 0.005657 seconds In this case almost the entire dataset is hot in OS page Cache. Generally speaking the percentage doesn’t really matter unless reads are missing the cache (per e.g. cachestat in which case having additional memory may help read performance. Cassandra often uses a lot of CPU, but telling what it is doing can prove difficult. One of the best ways to analyze Cassandra on CPU time is to use CPU Flamegraphs which display in a useful way which areas of Cassandra code are using CPU. This may help narrow down a compaction problem to a \"compaction problem dropping tombstones\" or just generally help you narrow down what Cassandra is doing while it is having an issue. To get CPU flamegraphs follow the instructions for Java Flamegraphs. Generally: Enable the -XX:+PreserveFramePointer option in Cassandra’s jvm.options configuation file. This has a negligible performance impact but allows you actually see what Cassandra is doing. Run perf to get some data. Send that data through the relevant scripts in the FlameGraph toolset and convert the data into a pretty flamegraph. View the resulting SVG image in a browser or other image browser. For example just cloning straight off github we first install the perf-map-agent to the location of our JVMs (assumed to be /usr/lib/jvm): $ sudo bash $ export JAVA_HOME=/usr/lib/jvm/java-8-oracle/ $ cd /usr/lib/jvm $ git clone --depth=1 https://github.com/jvm-profiling-tools/perf-map-agent $ cd perf-map-agent $ cmake . $ make Now to get a flamegraph: $ git clone --depth=1 https://github.com/brendangregg/FlameGraph $ sudo bash $ cd FlameGraph $ # Record traces of Cassandra and map symbols for all java processes $ perf record -F 49 -a -g -p -- sleep 30; ./jmaps $ # Translate the data $ perf script > cassandra_stacks $ cat cassandra_stacks | ./stackcollapse-perf.pl | grep -v cpu_idle | \\ ./flamegraph.pl --color=java --hash > cassandra_flames.svg The resulting SVG is searchable, zoomable, and generally easy to introspect using a browser. Sometimes you have to understand what queries a Cassandra node is performing right now to troubleshoot an issue. For these times trusty packet capture tools like tcpdump and Wireshark can be very helpful to dissect packet captures. Wireshark even has native CQL support although it sometimes has compatibility issues with newer Cassandra protocol releases. To get a packet capture first capture some packets: $ sudo tcpdump -U -s0 -i -w cassandra.pcap -n \"tcp port 9042\" Now open it up with wireshark: $ wireshark cassandra.pcap If you don’t see CQL like statements try telling to decode as CQL by right clicking on a packet going to 9042 → Decode as → select CQL from the dropdown for port 9042. If you don’t want to do this manually or use a GUI, you can also use something like cqltrace to ease obtaining and parsing CQL packet captures.","title":"Diving Deep, Use External Tools","component":"Cassandra","version":"4.1","name":"use_tools","url":"/Cassandra/4.1/cassandra/troubleshooting/use_tools.html","titles":[{"text":"JVM Tooling","id":"jvm-tooling"},{"text":"Garbage Collection State (jstat)","id":"garbage-collection-state-jstat"},{"text":"Thread Information (jstack)","id":"thread-information-jstack"},{"text":"Basic OS Tooling","id":"basic-os-tooling"},{"text":"High Level Resource Usage (top/htop)","id":"high-level-resource-usage-tophtop"},{"text":"IO Usage (iostat)","id":"os-iostat"},{"text":"OS page Cache Usage","id":"os-page-cache-usage"},{"text":"Network Latency and Reliability","id":"network-latency-and-reliability"},{"text":"Network Throughput","id":"network-throughput"},{"text":"Advanced tools","id":"advanced-tools"},{"text":"bcc-tools","id":"use-bcc-tools"},{"text":"vmtouch","id":"use-vmtouch"},{"text":"CPU Flamegraphs","id":"cpu-flamegraphs"},{"text":"Packet Capture","id":"packet-capture"}]},"/Cassandra/4.1/cassandra/tools/sstable/index.html":{"text":"This section describes the functionality of the various sstable tools. Cassandra must be stopped before these tools are executed, or unexpected results will occur. Note: the scripts do not verify that Cassandra is stopped. sstabledump sstableexpiredblockers sstablelevelreset sstableloader sstablemetadata sstableofflinerelevel sstablerepairdset sstablescrub sstablesplit sstableupgrade sstableutil sstableverify","title":"SSTable Tools","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/tools/sstable/index.html","titles":[]},"/Cassandra/4.1/cassandra/tools/sstable/sstabledump.html":{"text":"Dump contents of a given SSTable to standard output in JSON format. You must supply exactly one sstable. Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstabledump -d CQL row per line internal representation -e Enumerate partition keys only -k Partition key -x Excluded partition key(s) -t Print raw timestamps instead of iso8601 date strings -l Output each row as a separate JSON object If necessary, use sstableutil first to find out the sstables used by a table. Dump the entire table without any options. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db > eventlog_dump_2018Jul26 cat eventlog_dump_2018Jul26 [ { \"partition\" : { \"key\" : [ \"3578d7de-c60d-4599-aefb-3f22a07b2bc6\" ], \"position\" : 0 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 61, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:23:08.378711Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:23:08.384Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] }, { \"partition\" : { \"key\" : [ \"d18250c0-84fc-4d40-b957-4248dc9d790e\" ], \"position\" : 62 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 123, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:23:07.783522Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:23:07.789Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] }, { \"partition\" : { \"key\" : [ \"cf188983-d85b-48d6-9365-25005289beb2\" ], \"position\" : 124 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 182, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:22:27.028809Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:22:27.055Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] } ] Use the -l option to dump each row as a separate JSON object. This will make the output easier to manipulate for large data sets. ref: issues.apache.org/jira/browse/CASSANDRA-13848 Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -l > eventlog_dump_2018Jul26_justlines cat eventlog_dump_2018Jul26_justlines [ { \"partition\" : { \"key\" : [ \"3578d7de-c60d-4599-aefb-3f22a07b2bc6\" ], \"position\" : 0 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 61, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:23:08.378711Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:23:08.384Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] }, { \"partition\" : { \"key\" : [ \"d18250c0-84fc-4d40-b957-4248dc9d790e\" ], \"position\" : 62 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 123, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:23:07.783522Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:23:07.789Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] }, { \"partition\" : { \"key\" : [ \"cf188983-d85b-48d6-9365-25005289beb2\" ], \"position\" : 124 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 182, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:22:27.028809Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:22:27.055Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] } Dump only the keys by using the -e option. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -e > eventlog_dump_2018Jul26_justkeys cat eventlog_dump_2018Jul26b [ [ \"3578d7de-c60d-4599-aefb-3f22a07b2bc6\" ], [ \"d18250c0-84fc-4d40-b957-4248dc9d790e\" ], [ \"cf188983-d85b-48d6-9365-25005289beb2\" ] Dump a single key using the -k option. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -k 3578d7de-c60d-4599-aefb-3f22a07b2bc6 > eventlog_dump_2018Jul26_singlekey cat eventlog_dump_2018Jul26_singlekey [ { \"partition\" : { \"key\" : [ \"3578d7de-c60d-4599-aefb-3f22a07b2bc6\" ], \"position\" : 0 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 61, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:23:08.378711Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:23:08.384Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] } Dump a table except for the rows excluded with the -x option. Multiple keys can be used. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -x 3578d7de-c60d-4599-aefb-3f22a07b2bc6 d18250c0-84fc-4d40-b957-4248dc9d790e > eventlog_dump_2018Jul26_excludekeys cat eventlog_dump_2018Jul26_excludekeys [ { \"partition\" : { \"key\" : [ \"cf188983-d85b-48d6-9365-25005289beb2\" ], \"position\" : 0 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 182, \"liveness_info\" : { \"tstamp\" : \"2018-07-20T20:22:27.028809Z\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:22:27.055Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] } By default, dates are displayed in iso8601 date format. Using the -t option will dump the data with the raw timestamp. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -t -k cf188983-d85b-48d6-9365-25005289beb2 > eventlog_dump_2018Jul26_times cat eventlog_dump_2018Jul26_times [ { \"partition\" : { \"key\" : [ \"cf188983-d85b-48d6-9365-25005289beb2\" ], \"position\" : 124 }, \"rows\" : [ { \"type\" : \"row\", \"position\" : 182, \"liveness_info\" : { \"tstamp\" : \"1532118147028809\" }, \"cells\" : [ { \"name\" : \"event\", \"value\" : \"party\" }, { \"name\" : \"insertedtimestamp\", \"value\" : \"2018-07-20 20:22:27.055Z\" }, { \"name\" : \"source\", \"value\" : \"asdf\" } ] } ] } Dump the table in a format that reflects the internal structure. Example: sstabledump /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db -d > eventlog_dump_2018Jul26_d cat eventlog_dump_2018Jul26_d [3578d7de-c60d-4599-aefb-3f22a07b2bc6]@0 Row[info=[ts=1532118188378711] ]: | [event=party ts=1532118188378711], [insertedtimestamp=2018-07-20 20:23Z ts=1532118188378711], [source=asdf ts=1532118188378711] [d18250c0-84fc-4d40-b957-4248dc9d790e]@62 Row[info=[ts=1532118187783522] ]: | [event=party ts=1532118187783522], [insertedtimestamp=2018-07-20 20:23Z ts=1532118187783522], [source=asdf ts=1532118187783522] [cf188983-d85b-48d6-9365-25005289beb2]@124 Row[info=[ts=1532118147028809] ]: | [event=party ts=1532118147028809], [insertedtimestamp=2018-07-20 20:22Z ts=1532118147028809], [source=asdf ts=1532118147028809]","title":"sstabledump","component":"Cassandra","version":"4.1","name":"sstabledump","url":"/Cassandra/4.1/cassandra/tools/sstable/sstabledump.html","titles":[{"text":"Usage","id":"usage"},{"text":"Dump entire table","id":"dump-entire-table"},{"text":"Dump table in a more manageable format","id":"dump-table-in-a-more-manageable-format"},{"text":"Dump only keys","id":"dump-only-keys"},{"text":"Dump row for a single key","id":"dump-row-for-a-single-key"},{"text":"Exclude a key or keys in dump of rows","id":"exclude-a-key-or-keys-in-dump-of-rows"},{"text":"Display raw timestamps","id":"display-raw-timestamps"},{"text":"Display internal structure in output","id":"display-internal-structure-in-output"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableexpiredblockers.html":{"text":"During compaction, entire sstables can be dropped if they contain only expired tombstones, and if it is guaranteed that the data is not newer than the data in other sstables. An expired sstable can be blocked from getting dropped if its newest timestamp is newer than the oldest data in another sstable. This tool is used to list all sstables that are blocking other sstables from getting dropped (by having older data than the newest tombstone in an expired sstable) so a user can figure out why certain sstables are still on disk. ref: issues.apache.org/jira/browse/CASSANDRA-10015 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstableexpiredblockers If the sstables exist for the table, but no tables have older data than the newest tombstone in an expired sstable, the script will return nothing. Otherwise, the script will return blocks <> expired sstables from getting dropped# followed by a list of the blocked sstables. Example: sstableexpiredblockers keyspace1 standard1 [BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-0665ae80b2d711e886c66d2c86545d91/mc-2-big-Data.db') (minTS = 5, maxTS = 5, maxLDT = 2147483647)], blocks 1 expired sstables from getting dropped: [BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-0665ae80b2d711e886c66d2c86545d91/mc-3-big-Data.db') (minTS = 1536349775157606, maxTS = 1536349780311159, maxLDT = 1536349780)], [BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-0665ae80b2d711e886c66d2c86545d91/mc-1-big-Data.db') (minTS = 1, maxTS = 10, maxLDT = 2147483647)], blocks 1 expired sstables from getting dropped: [BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-0665ae80b2d711e886c66d2c86545d91/mc-3-big-Data.db') (minTS = 1536349775157606, maxTS = 1536349780311159, maxLDT = 1536349780)],","title":"sstableexpiredblockers","component":"Cassandra","version":"4.1","name":"sstableexpiredblockers","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableexpiredblockers.html","titles":[{"text":"Usage","id":"usage"},{"text":"Output blocked sstables","id":"output-blocked-sstables"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstablelevelreset.html":{"text":"If LeveledCompactionStrategy is set, this script can be used to reset level to 0 on a given set of sstables. This is useful if you want to, for example, change the minimum sstable size, and therefore restart the compaction process using this new configuration. See cassandra.apache.org/doc/latest/operating/compaction.html#leveled-compaction-strategy for information on how levels are used in this compaction strategy. Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. ref: issues.apache.org/jira/browse/CASSANDRA-5271 sstablelevelreset --really-reset The really-reset flag is required, to ensure this intrusive command is not run accidentally. If the keyspace and/or table is not in the schema (e.g., if you misspelled the table name), the script will return an error. Example: ColumnFamily not found: keyspace/evenlog. Example: Found no sstables, did you give the correct keyspace/table? The script will not set the level if it is already set to 0. Example: Skipped /var/lib/cassandra/data/keyspace/eventlog-65c429e08c5a11e8939edf4f403979ef/mc-1-big-Data.db since it is already on level 0 If the level is not already 0, then this will reset it to 0. Example: sstablemetadata /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db | grep -i level SSTable Level: 1 sstablelevelreset --really-reset keyspace eventlog Changing level from 1 to 0 on /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db sstablemetadata /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db | grep -i level SSTable Level: 0","title":"sstablelevelreset","component":"Cassandra","version":"4.1","name":"sstablelevelreset","url":"/Cassandra/4.1/cassandra/tools/sstable/sstablelevelreset.html","titles":[{"text":"Usage","id":"usage"},{"text":"Table not found","id":"table-not-found"},{"text":"Table has no sstables","id":"table-has-no-sstables"},{"text":"Table already at level 0","id":"table-already-at-level-0"},{"text":"Table levels reduced to 0","id":"table-levels-reduced-to-0"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableloader.html":{"text":"Bulk-load the sstables found in the directory to the configured cluster. The parent directories of are used as the target keyspace/table name. For example, to load an sstable named ma-1-big-Data.db into keyspace1/standard1, you will need to have the files ma-1-big-Data.db and ma-1-big-Index.db in a directory /path/to/keyspace1/standard1/. The tool will create new sstables, and does not clean up your copied files. Several of the options listed below don’t work quite as intended, and in those cases, workarounds are mentioned for specific use cases. To avoid having the sstable files to be loaded compacted while reading them, place the files in an alternate keyspace/table path than the data directory. ref: issues.apache.org/jira/browse/CASSANDRA-1278 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstableloader -d, --nodes Required. Try to connect to these hosts (comma-separated) initially for ring information -u, --username username for Cassandra authentication -pw, --password password for Cassandra authentication -p, --port port used for native connection (default 9042) -sp, --storage-port port used for internode communication (default 7000) -ssp, --ssl-storage-port port used for TLS internode communication (default 7001) --no-progress don’t display progress -t, --throttle (deprecated) throttle speed in Mbits (default 0 for unlimited) Use --throttle-mib instead --throttle-mib throttle speed in MiB/s (default 0 for unlimited) -idct, --inter-dc-throttle (deprecated) inter-datacenter throttle speed in Mbits (default 0 for unlimited) Use --inter-dc-throttle-mib instead --inter-dc-throttle-mib inter-datacenter throttle speed in MiB/s (default 0 for unlimited) --entire-sstable-throttle-mib entire SSTable throttle speed in MiB/s (default 0 for unlimited) --entire-sstable-inter-dc-throttle-mib entire SSTable inter-datacenter throttle speed in MiB/s (default 0 for unlimited) -cph, --connections-per-host number of concurrent connections-per-host -i, --ignore don’t stream to this (comma separated) list of nodes -alg, --ssl-alg Client SSL: algorithm (default: SunX509) -ciphers, --ssl-ciphers Client SSL: comma-separated list of encryption suites to use -ks, --keystore Client SSL: full path to keystore -kspw, --keystore-password Client SSL: password of the keystore -st, --store-type Client SSL: type of store -ts, --truststore Client SSL: full path to truststore -tspw, --truststore-password Client SSL: password of the truststore -prtcl, --ssl-protocol Client SSL: connections protocol to use (default: TLS) -ap, --auth-provider custom AuthProvider class name for cassandra authentication -f, --conf-path cassandra.yaml file path for streaming throughput and client/server SSL -v, --verbose verbose output -h, --help display this help message You can provide a cassandra.yaml file with the -f command line option to set up streaming throughput, and client and server encryption options. Only stream_throughput_outbound, server_encryption_options, and client_encryption_options are read from yaml. You can override options read from cassandra.yaml with corresponding command line options. Copy the snapshot sstables into an accessible directory and use sstableloader to restore them. Example: cp snapshots/1535397029191/* /path/to/keyspace1/standard1/ sstableloader --nodes 172.17.0.2 /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ma-3-big-Data.db to [/172.17.0.2] progress: [/172.17.0.2]0:1/1 100% total: 100% 0 MB/s(avg: 1 MB/s) Summary statistics: Connections per host: : 1 Total files transferred: : 1 Total bytes transferred: : 4700000 Total duration (ms): : 4390 Average transfer rate (MB/s): : 1 Peak transfer rate (MB/s): : 1 The -d or --nodes option is required, or the script will not run. Example: sstableloader /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ Initial hosts must be specified (-d) If SSL encryption is enabled in the cluster, use the --conf-path option with sstableloader to point the tool to the cassandra.yaml with the relevant server_encryption_options (e.g., truststore location, algorithm). This will work better than passing individual ssl options shown above to sstableloader on the command line. Example: sstableloader --nodes 172.17.0.2 --conf-path /etc/cassandra/cassandra.yaml /var/lib/cassandra/loadme/keyspace1/standard1-0974e5a0aa5811e8a0a06d2c86545d91/snapshots/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-0974e5a0aa5811e8a0a06d2c86545d91/mc-1-big-Data.db to [/172.17.0.2] progress: [/172.17.0.2]0:0/1 1 % total: 1% 9.165KiB/s (avg: 9.165KiB/s) progress: [/172.17.0.2]0:0/1 2 % total: 2% 5.147MiB/s (avg: 18.299KiB/s) progress: [/172.17.0.2]0:0/1 4 % total: 4% 9.751MiB/s (avg: 27.423KiB/s) progress: [/172.17.0.2]0:0/1 5 % total: 5% 8.203MiB/s (avg: 36.524KiB/s) ... progress: [/172.17.0.2]0:1/1 100% total: 100% 0.000KiB/s (avg: 480.513KiB/s) Summary statistics: Connections per host : 1 Total files transferred : 1 Total bytes transferred : 4.387MiB Total duration : 9356 ms Average transfer rate : 480.105KiB/s Peak transfer rate : 586.410KiB/s To hide the output of progress and the summary statistics (e.g., if you wanted to use this tool in a script), use the --no-progress option. Example: sstableloader --nodes 172.17.0.2 --no-progress /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ma-4-big-Data.db to [/172.17.0.2] Using the --verbose option will provide much more progress output. Example: sstableloader --nodes 172.17.0.2 --verbose /var/lib/cassandra/loadme/keyspace1/standard1-0974e5a0aa5811e8a0a06d2c86545d91/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-0974e5a0aa5811e8a0a06d2c86545d91/mc-1-big-Data.db to [/172.17.0.2] progress: [/172.17.0.2]0:0/1 1 % total: 1% 12.056KiB/s (avg: 12.056KiB/s) progress: [/172.17.0.2]0:0/1 2 % total: 2% 9.092MiB/s (avg: 24.081KiB/s) progress: [/172.17.0.2]0:0/1 4 % total: 4% 18.832MiB/s (avg: 36.099KiB/s) progress: [/172.17.0.2]0:0/1 5 % total: 5% 2.253MiB/s (avg: 47.882KiB/s) progress: [/172.17.0.2]0:0/1 7 % total: 7% 6.388MiB/s (avg: 59.743KiB/s) progress: [/172.17.0.2]0:0/1 8 % total: 8% 14.606MiB/s (avg: 71.635KiB/s) progress: [/172.17.0.2]0:0/1 9 % total: 9% 8.880MiB/s (avg: 83.465KiB/s) progress: [/172.17.0.2]0:0/1 11 % total: 11% 5.217MiB/s (avg: 95.176KiB/s) progress: [/172.17.0.2]0:0/1 12 % total: 12% 12.563MiB/s (avg: 106.975KiB/s) progress: [/172.17.0.2]0:0/1 14 % total: 14% 2.550MiB/s (avg: 118.322KiB/s) progress: [/172.17.0.2]0:0/1 15 % total: 15% 16.638MiB/s (avg: 130.063KiB/s) progress: [/172.17.0.2]0:0/1 17 % total: 17% 17.270MiB/s (avg: 141.793KiB/s) progress: [/172.17.0.2]0:0/1 18 % total: 18% 11.280MiB/s (avg: 153.452KiB/s) progress: [/172.17.0.2]0:0/1 19 % total: 19% 2.903MiB/s (avg: 164.603KiB/s) progress: [/172.17.0.2]0:0/1 21 % total: 21% 6.744MiB/s (avg: 176.061KiB/s) progress: [/172.17.0.2]0:0/1 22 % total: 22% 6.011MiB/s (avg: 187.440KiB/s) progress: [/172.17.0.2]0:0/1 24 % total: 24% 9.690MiB/s (avg: 198.920KiB/s) progress: [/172.17.0.2]0:0/1 25 % total: 25% 11.481MiB/s (avg: 210.412KiB/s) progress: [/172.17.0.2]0:0/1 27 % total: 27% 9.957MiB/s (avg: 221.848KiB/s) progress: [/172.17.0.2]0:0/1 28 % total: 28% 10.270MiB/s (avg: 233.265KiB/s) progress: [/172.17.0.2]0:0/1 29 % total: 29% 7.812MiB/s (avg: 244.571KiB/s) progress: [/172.17.0.2]0:0/1 31 % total: 31% 14.843MiB/s (avg: 256.021KiB/s) progress: [/172.17.0.2]0:0/1 32 % total: 32% 11.457MiB/s (avg: 267.394KiB/s) progress: [/172.17.0.2]0:0/1 34 % total: 34% 6.550MiB/s (avg: 278.536KiB/s) progress: [/172.17.0.2]0:0/1 35 % total: 35% 9.115MiB/s (avg: 289.782KiB/s) progress: [/172.17.0.2]0:0/1 37 % total: 37% 11.054MiB/s (avg: 301.064KiB/s) progress: [/172.17.0.2]0:0/1 38 % total: 38% 10.449MiB/s (avg: 312.307KiB/s) progress: [/172.17.0.2]0:0/1 39 % total: 39% 1.646MiB/s (avg: 321.665KiB/s) progress: [/172.17.0.2]0:0/1 41 % total: 41% 13.300MiB/s (avg: 332.872KiB/s) progress: [/172.17.0.2]0:0/1 42 % total: 42% 14.370MiB/s (avg: 344.082KiB/s) progress: [/172.17.0.2]0:0/1 44 % total: 44% 16.734MiB/s (avg: 355.314KiB/s) progress: [/172.17.0.2]0:0/1 45 % total: 45% 22.245MiB/s (avg: 366.592KiB/s) progress: [/172.17.0.2]0:0/1 47 % total: 47% 25.561MiB/s (avg: 377.882KiB/s) progress: [/172.17.0.2]0:0/1 48 % total: 48% 24.543MiB/s (avg: 389.155KiB/s) progress: [/172.17.0.2]0:0/1 49 % total: 49% 4.894MiB/s (avg: 399.688KiB/s) progress: [/172.17.0.2]0:0/1 51 % total: 51% 8.331MiB/s (avg: 410.559KiB/s) progress: [/172.17.0.2]0:0/1 52 % total: 52% 5.771MiB/s (avg: 421.150KiB/s) progress: [/172.17.0.2]0:0/1 54 % total: 54% 8.738MiB/s (avg: 431.983KiB/s) progress: [/172.17.0.2]0:0/1 55 % total: 55% 3.406MiB/s (avg: 441.911KiB/s) progress: [/172.17.0.2]0:0/1 56 % total: 56% 9.791MiB/s (avg: 452.730KiB/s) progress: [/172.17.0.2]0:0/1 58 % total: 58% 3.401MiB/s (avg: 462.545KiB/s) progress: [/172.17.0.2]0:0/1 59 % total: 59% 5.280MiB/s (avg: 472.840KiB/s) progress: [/172.17.0.2]0:0/1 61 % total: 61% 12.232MiB/s (avg: 483.663KiB/s) progress: [/172.17.0.2]0:0/1 62 % total: 62% 9.258MiB/s (avg: 494.325KiB/s) progress: [/172.17.0.2]0:0/1 64 % total: 64% 2.877MiB/s (avg: 503.640KiB/s) progress: [/172.17.0.2]0:0/1 65 % total: 65% 7.461MiB/s (avg: 514.078KiB/s) progress: [/172.17.0.2]0:0/1 66 % total: 66% 24.247MiB/s (avg: 525.018KiB/s) progress: [/172.17.0.2]0:0/1 68 % total: 68% 9.348MiB/s (avg: 535.563KiB/s) progress: [/172.17.0.2]0:0/1 69 % total: 69% 5.130MiB/s (avg: 545.563KiB/s) progress: [/172.17.0.2]0:0/1 71 % total: 71% 19.861MiB/s (avg: 556.392KiB/s) progress: [/172.17.0.2]0:0/1 72 % total: 72% 15.501MiB/s (avg: 567.122KiB/s) progress: [/172.17.0.2]0:0/1 74 % total: 74% 5.031MiB/s (avg: 576.996KiB/s) progress: [/172.17.0.2]0:0/1 75 % total: 75% 22.771MiB/s (avg: 587.813KiB/s) progress: [/172.17.0.2]0:0/1 76 % total: 76% 22.780MiB/s (avg: 598.619KiB/s) progress: [/172.17.0.2]0:0/1 78 % total: 78% 20.684MiB/s (avg: 609.386KiB/s) progress: [/172.17.0.2]0:0/1 79 % total: 79% 22.920MiB/s (avg: 620.173KiB/s) progress: [/172.17.0.2]0:0/1 81 % total: 81% 7.458MiB/s (avg: 630.333KiB/s) progress: [/172.17.0.2]0:0/1 82 % total: 82% 22.993MiB/s (avg: 641.090KiB/s) progress: [/172.17.0.2]0:0/1 84 % total: 84% 21.392MiB/s (avg: 651.814KiB/s) progress: [/172.17.0.2]0:0/1 85 % total: 85% 7.732MiB/s (avg: 661.938KiB/s) progress: [/172.17.0.2]0:0/1 86 % total: 86% 3.476MiB/s (avg: 670.892KiB/s) progress: [/172.17.0.2]0:0/1 88 % total: 88% 19.889MiB/s (avg: 681.521KiB/s) progress: [/172.17.0.2]0:0/1 89 % total: 89% 21.077MiB/s (avg: 692.162KiB/s) progress: [/172.17.0.2]0:0/1 91 % total: 91% 24.062MiB/s (avg: 702.835KiB/s) progress: [/172.17.0.2]0:0/1 92 % total: 92% 19.798MiB/s (avg: 713.431KiB/s) progress: [/172.17.0.2]0:0/1 94 % total: 94% 17.591MiB/s (avg: 723.965KiB/s) progress: [/172.17.0.2]0:0/1 95 % total: 95% 13.725MiB/s (avg: 734.361KiB/s) progress: [/172.17.0.2]0:0/1 96 % total: 96% 16.737MiB/s (avg: 744.846KiB/s) progress: [/172.17.0.2]0:0/1 98 % total: 98% 22.701MiB/s (avg: 755.443KiB/s) progress: [/172.17.0.2]0:0/1 99 % total: 99% 18.718MiB/s (avg: 765.954KiB/s) progress: [/172.17.0.2]0:1/1 100% total: 100% 6.613MiB/s (avg: 767.802KiB/s) progress: [/172.17.0.2]0:1/1 100% total: 100% 0.000KiB/s (avg: 670.295KiB/s) Summary statistics: Connections per host : 1 Total files transferred : 1 Total bytes transferred : 4.387MiB Total duration : 6706 ms Average transfer rate : 669.835KiB/s Peak transfer rate : 767.802KiB/s To prevent the table loader from overloading the system resources, you can throttle the process with the --throttle option. The default is unlimited (no throttling). Throttle units are in megabits. Note that the total duration is increased in the example below. Example: sstableloader --nodes 172.17.0.2 --throttle 1 /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ma-6-big-Data.db to [/172.17.0.2] progress: [/172.17.0.2]0:1/1 100% total: 100% 0 MB/s(avg: 0 MB/s) Summary statistics: Connections per host: : 1 Total files transferred: : 1 Total bytes transferred: : 4595705 Total duration (ms): : 37634 Average transfer rate (MB/s): : 0 Peak transfer rate (MB/s): : 0 To speed up the load process, the number of connections per host can be increased. Example: sstableloader --nodes 172.17.0.2 --connections-per-host 100 /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ Established connection to initial hosts Opening sstables and calculating sections to stream Streaming relevant part of /var/lib/cassandra/loadme/keyspace1/standard1-f8a4fa30aa2a11e8af27091830ac5256/ma-9-big-Data.db to [/172.17.0.2] progress: [/172.17.0.2]0:1/1 100% total: 100% 0 MB/s(avg: 1 MB/s) Summary statistics: Connections per host: : 100 Total files transferred: : 1 Total bytes transferred: : 4595705 Total duration (ms): : 3486 Average transfer rate (MB/s): : 1 Peak transfer rate (MB/s): : 1 This small data set doesn’t benefit much from the increase in connections per host, but note that the total duration has decreased in this example.","title":"sstableloader","component":"Cassandra","version":"4.1","name":"sstableloader","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableloader.html","titles":[{"text":"Usage","id":"usage"},{"text":"Load sstables from a Snapshot","id":"load-sstables-from-a-snapshot"},{"text":"Use a Config File for SSL Clusters","id":"use-a-config-file-for-ssl-clusters"},{"text":"Hide Progress Output","id":"hide-progress-output"},{"text":"Get More Detail","id":"get-more-detail"},{"text":"Throttling Load","id":"throttling-load"},{"text":"Speeding up Load","id":"speeding-up-load"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstablemetadata.html":{"text":"Print information about an sstable from the related Statistics.db and Summary.db files to standard output. ref: issues.apache.org/jira/browse/CASSANDRA-7159 and issues.apache.org/jira/browse/CASSANDRA-10838 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstablemetadata --gc_grace_seconds The gc_grace_seconds to use when calculating droppable tombstones Run sstablemetadata against the Data.db file(s) related to a table. If necessary, find theData.db file(s) using sstableutil. Example: sstableutil keyspace1 standard1 | grep Data /var/lib/cassandra/data/keyspace1/standard1-f6845640a6cb11e8b6836d2c86545d91/mc-1-big-Data.db sstablemetadata /var/lib/cassandra/data/keyspace1/standard1-f6845640a6cb11e8b6836d2c86545d91/mc-1-big-Data.db SSTable: /var/lib/cassandra/data/keyspace1/standard1-f6845640a6cb11e8b6836d2c86545d91/mc-1-big Partitioner: org.apache.cassandra.dht.Murmur3Partitioner Bloom Filter FP chance: 0.010000 Minimum timestamp: 1535025576141000 Maximum timestamp: 1535025604309000 SSTable min local deletion time: 2147483647 SSTable max local deletion time: 2147483647 Compressor: org.apache.cassandra.io.compress.LZ4Compressor TTL min: 86400 TTL max: 86400 First token: -9223004712949498654 (key=39373333373831303130) Last token: 9222554117157811897 (key=4f3438394e39374d3730) Estimated droppable tombstones: 0.9188263888888889 SSTable Level: 0 Repaired at: 0 Replay positions covered: {CommitLogPosition(segmentId=1535025390651, position=226400)=CommitLogPosition(segmentId=1535025390651, position=6849139)} totalColumnsSet: 100000 totalRows: 20000 Estimated tombstone drop times: 1535039100: 80390 1535039160: 5645 1535039220: 13965 Count Row Size Cell Count 1 0 0 2 0 0 3 0 0 4 0 0 5 0 20000 6 0 0 7 0 0 8 0 0 10 0 0 12 0 0 14 0 0 17 0 0 20 0 0 24 0 0 29 0 0 35 0 0 42 0 0 50 0 0 60 0 0 72 0 0 86 0 0 103 0 0 124 0 0 149 0 0 179 0 0 215 0 0 258 20000 0 310 0 0 372 0 0 446 0 0 535 0 0 642 0 0 770 0 0 924 0 0 1109 0 0 1331 0 0 1597 0 0 1916 0 0 2299 0 0 2759 0 0 3311 0 0 3973 0 0 4768 0 0 5722 0 0 6866 0 0 8239 0 0 9887 0 0 11864 0 0 14237 0 0 17084 0 0 20501 0 0 24601 0 0 29521 0 0 35425 0 0 42510 0 0 51012 0 0 61214 0 0 73457 0 0 88148 0 0 105778 0 0 126934 0 0 152321 0 0 182785 0 0 219342 0 0 263210 0 0 315852 0 0 379022 0 0 454826 0 0 545791 0 0 654949 0 0 785939 0 0 943127 0 0 1131752 0 0 1358102 0 0 1629722 0 0 1955666 0 0 2346799 0 0 2816159 0 0 3379391 0 0 4055269 0 0 4866323 0 0 5839588 0 0 7007506 0 0 8409007 0 0 10090808 0 0 12108970 0 0 14530764 0 0 17436917 0 0 20924300 0 0 25109160 0 0 30130992 0 0 36157190 0 0 43388628 0 0 52066354 0 0 62479625 0 0 74975550 0 0 89970660 0 0 107964792 0 0 129557750 0 0 155469300 0 0 186563160 0 0 223875792 0 0 268650950 0 0 322381140 0 0 386857368 0 0 464228842 0 0 557074610 0 0 668489532 0 0 802187438 0 0 962624926 0 0 1155149911 0 0 1386179893 0 0 1663415872 0 0 1996099046 0 0 2395318855 0 0 2874382626 0 3449259151 0 4139110981 0 4966933177 0 5960319812 0 7152383774 0 8582860529 0 10299432635 0 12359319162 0 14831182994 0 17797419593 0 21356903512 0 25628284214 0 30753941057 0 36904729268 0 44285675122 0 53142810146 0 63771372175 0 76525646610 0 91830775932 0 110196931118 0 132236317342 0 158683580810 0 190420296972 0 228504356366 0 274205227639 0 329046273167 0 394855527800 0 473826633360 0 568591960032 0 682310352038 0 818772422446 0 982526906935 0 1179032288322 0 1414838745986 0 Estimated cardinality: 20196 EncodingStats minTTL: 0 EncodingStats minLocalDeletionTime: 1442880000 EncodingStats minTimestamp: 1535025565275000 KeyType: org.apache.cassandra.db.marshal.BytesType ClusteringTypes: [org.apache.cassandra.db.marshal.UTF8Type] StaticColumns: {C3:org.apache.cassandra.db.marshal.BytesType, C4:org.apache.cassandra.db.marshal.BytesType, C0:org.apache.cassandra.db.marshal.BytesType, C1:org.apache.cassandra.db.marshal.BytesType, C2:org.apache.cassandra.db.marshal.BytesType} RegularColumns: {} To see the ratio of droppable tombstones given a configured gc grace seconds, use the gc_grace_seconds option. Because the sstablemetadata tool doesn’t access the schema directly, this is a way to more accurately estimate droppable tombstones — for example, if you pass in gc_grace_seconds matching what is configured in the schema. The gc_grace_seconds value provided is subtracted from the curent machine time (in seconds). ref: issues.apache.org/jira/browse/CASSANDRA-12208 Example: sstablemetadata /var/lib/cassandra/data/keyspace1/standard1-41b52700b4ed11e896476d2c86545d91/mc-12-big-Data.db | grep \"Estimated tombstone drop times\" -A4 Estimated tombstone drop times: 1536599100: 1 1536599640: 1 1536599700: 2 echo $(date +%s) 1536602005 # if gc_grace_seconds was configured at 100, all of the tombstones would be currently droppable sstablemetadata --gc_grace_seconds 100 /var/lib/cassandra/data/keyspace1/standard1-41b52700b4ed11e896476d2c86545d91/mc-12-big-Data.db | grep \"Estimated droppable tombstones\" Estimated droppable tombstones: 4.0E-5 # if gc_grace_seconds was configured at 4700, some of the tombstones would be currently droppable sstablemetadata --gc_grace_seconds 4700 /var/lib/cassandra/data/keyspace1/standard1-41b52700b4ed11e896476d2c86545d91/mc-12-big-Data.db | grep \"Estimated droppable tombstones\" Estimated droppable tombstones: 9.61111111111111E-6 # if gc_grace_seconds was configured at 100, none of the tombstones would be currently droppable sstablemetadata --gc_grace_seconds 5000 /var/lib/cassandra/data/keyspace1/standard1-41b52700b4ed11e896476d2c86545d91/mc-12-big-Data.db | grep \"Estimated droppable tombstones\" Estimated droppable tombstones: 0.0 Value Explanation SSTable prefix of the sstable filenames related to this sstable Partitioner partitioner type used to distribute data across nodes; defined in cassandra.yaml Bloom Filter FP precision of Bloom filter used in reads; defined in the table definition Minimum timestamp minimum timestamp of any entry in this sstable, in epoch microseconds Maximum timestamp maximum timestamp of any entry in this sstable, in epoch microseconds SSTable min local deletion time minimum timestamp of deletion date, based on TTL, in epoch seconds SSTable max local deletion time maximum timestamp of deletion date, based on TTL, in epoch seconds Compressor blank (-) by default; if not blank, indicates type of compression enabled on the table TTL min time-to-live in seconds; default 0 unless defined in the table definition TTL max time-to-live in seconds; default 0 unless defined in the table definition First token lowest token and related key found in the sstable summary Last token highest token and related key found in the sstable summary Estimated droppable tombstones ratio of tombstones to columns, using configured gc grace seconds if relevant SSTable level compaction level of this sstable, if leveled compaction (LCS) is used Repaired at the timestamp this sstable was marked as repaired via sstablerepairedset, in epoch milliseconds Replay positions covered the interval of time and commitlog positions related to this sstable totalColumnsSet number of cells in the table totalRows number of rows in the table Estimated tombstone drop times approximate number of rows that will expire, ordered by epoch seconds Count Row Size Cell Count two histograms in two columns; one represents distribution of Row Size and the other represents distribution of Cell Count Estimated cardinality an estimate of unique values, used for compaction EncodingStats* minTTL in epoch milliseconds EncodingStats* minLocalDeletionTime in epoch seconds EncodingStats* minTimestamp in epoch microseconds KeyType the type of partition key, useful in reading and writing data from/to storage; defined in the table definition ClusteringTypes the type of clustering key, useful in reading and writing data from/to storage; defined in the table definition StaticColumns a list of the shared columns in the table RegularColumns * For the encoding stats values, the delta of this and the current epoch time is used when encoding and storing data in the most optimal way.","title":"sstablemetadata","component":"Cassandra","version":"4.1","name":"sstablemetadata","url":"/Cassandra/4.1/cassandra/tools/sstable/sstablemetadata.html","titles":[{"text":"Usage","id":"usage"},{"text":"Print all the metadata","id":"print-all-the-metadata"},{"text":"Specify gc grace seconds","id":"specify-gc-grace-seconds"},{"text":"Explanation of each value printed above","id":"explanation-of-each-value-printed-above"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableofflinerelevel.html":{"text":"When using LeveledCompactionStrategy, sstables can get stuck at L0 on a recently bootstrapped node, and compactions may never catch up. This tool is used to bump sstables into the highest level possible. ref: issues.apache.org/jira/browse/CASSANDRA-8301 The way this is done is: sstables are storted by their last token. Given an original leveling like this (note that [ ] indicates token boundaries, not sstable size on disk; all sstables are the same size): L3 [][][][][][][][][][][] L2 [ ][ ][ ][ ] L1 [ ][ ] L0 [ ] Will look like this after being dropped to L0 and sorted by last token (and, to illustrate overlap, the overlapping ones are put on a new line): [][][] [ ][][][] [ ] [ ] ... Then, we start iterating from the smallest last-token and adding all sstables that do not cause an overlap to a level. We will reconstruct the original leveling top-down. Whenever we add an sstable to the level, we remove it from the sorted list. Once we reach the end of the sorted list, we have a full level, and can start over with the level below. If we end up with more levels than expected, we put all levels exceeding the expected in L0, for example, original L0 files will most likely be put in a level of its own since they most often overlap many other sstables. Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstableofflinerelevel [--dry-run] Use the --dry-run option to see the current level distribution and predicted level after the change. Example: sstableofflinerelevel --dry-run keyspace eventlog For sstables in /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753: Current leveling: L0=2 Potential leveling: L0=1 L1=1 Example: sstableofflinerelevel keyspace eventlog For sstables in /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753: Current leveling: L0=2 New leveling: L0=1 L1=1 If an invalid keyspace and/or table is provided, an exception will be thrown. Example: sstableofflinerelevel --dry-run keyspace evenlog Exception in thread \"main\" java.lang.IllegalArgumentException: Unknown keyspace/columnFamily keyspace1.evenlog at org.apache.cassandra.tools.SSTableOfflineRelevel.main(SSTableOfflineRelevel.java:96)","title":"sstableofflinerelevel","component":"Cassandra","version":"4.1","name":"sstableofflinerelevel","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableofflinerelevel.html","titles":[{"text":"Usage","id":"usage"},{"text":"Doing a dry run","id":"doing-a-dry-run"},{"text":"Running a relevel","id":"running-a-relevel"},{"text":"Keyspace or table not found","id":"keyspace-or-table-not-found"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstablerepairedset.html":{"text":"Repairs can take a very long time in some environments, for large sizes of data. Use this tool to set the repairedAt status on a given set of sstables, so that repairs can be run on only un-repaired sstables if desired. Note that running a repair (e.g., via nodetool repair) doesn’t set the status of this metadata. Only setting the status of this metadata via this tool does. ref: issues.apache.org/jira/browse/CASSANDRA-5351 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstablerepairedset --really-set [-f | ] --really-set required if you want to really set the status --is-repaired set the repairedAt status to the last modified time --is-unrepaired set the repairedAt status to 0 -f use a file containing a list of sstables as the input There are many ways to do this programmatically. This way would likely include variables for the keyspace and table. Example: find /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/* -name \"*Data.db\" -print0 | xargs -0 -I % sstablerepairedset --really-set --is-unrepaired % Set the repairedAt status after a repair to mark the sstables as repaired. Again, using variables for the keyspace and table names is a good choice. Example: nodetool repair keyspace1 standard1 find /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/* -name \"*Data.db\" -print0 | xargs -0 -I % sstablerepairedset --really-set --is-repaired % sstablemetadata can be used to view the status set or unset using this command. Example: sstablerepairedset --really-set --is-repaired /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/mc-1-big-Data.db sstablemetadata /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/mc-1-big-Data.db | grep \"Repaired at\" Repaired at: 1534443974000 sstablerepairedset --really-set --is-unrepaired /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/mc-1-big-Data.db sstablemetadata /var/lib/cassandra/data/keyspace1/standard1-d936bd20a17c11e8bc92a55ed562cd82/mc-1-big-Data.db | grep \"Repaired at\" Repaired at: 0 If you know you ran repair 2 weeks ago, you can do something like the following: sstablerepairset --is-repaired -f <(find /var/lib/cassandra/data/.../ -iname \"*Data.db*\" -mtime +14)","title":"sstablerepairedset","component":"Cassandra","version":"4.1","name":"sstablerepairedset","url":"/Cassandra/4.1/cassandra/tools/sstable/sstablerepairedset.html","titles":[{"text":"Usage","id":"usage"},{"text":"Set a lot of sstables to unrepaired status","id":"set-a-lot-of-sstables-to-unrepaired-status"},{"text":"Set one to many sstables to repaired status","id":"set-one-to-many-sstables-to-repaired-status"},{"text":"Print metadata showing repaired status","id":"print-metadata-showing-repaired-status"},{"text":"Using command in a script","id":"using-command-in-a-script"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstablescrub.html":{"text":"Fix a broken sstable. The scrub process rewrites the sstable, skipping any corrupted rows. Because these rows are lost, follow this process with a repair. ref: issues.apache.org/jira/browse/CASSANDRA-4321 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstablescrub --debug display stack traces -h,--help display this help message -m,--manifest-check only check and repair the leveled manifest, without actually scrubbing the sstables -n,--no-validate do not validate columns using column validator -r,--reinsert-overflowed-ttl Rewrites rows with overflowed expiration date affected by CASSANDRA-14092 with the maximum supported expiration date of 2038-01-19T03:14:06+00:00. The rows are rewritten with the original timestamp incremented by one millisecond to override/supersede any potential tombstone that may have been generated during compaction of the affected rows. -s,--skip-corrupted skip corrupt rows in counter tables -v,--verbose verbose output The scrub without options will do a snapshot first, then write all non-corrupted files to a new sstable. Example: sstablescrub keyspace1 standard1 Pre-scrub sstables snapshotted into snapshot pre-scrub-1534424070883 Scrubbing BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-6365332094dd11e88f324f9c503e4753/mc-5-big-Data.db') (17.142MiB) Scrub of BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-6365332094dd11e88f324f9c503e4753/mc-5-big-Data.db') complete: 73367 rows in new sstable and 0 empty (tombstoned) rows dropped Checking leveled manifest ref: issues.apache.org/jira/browse/CASSANDRA-9406 Use the --no-validate option to retain data that may be misrepresented (e.g., an integer stored in a long field) but not corrupt. This data usually doesn not present any errors to the client. Example: sstablescrub --no-validate keyspace1 standard1 Pre-scrub sstables snapshotted into snapshot pre-scrub-1536243158517 Scrubbing BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-bc9cf530b1da11e886c66d2c86545d91/mc-2-big-Data.db') (4.482MiB) Scrub of BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-bc9cf530b1da11e886c66d2c86545d91/mc-2-big-Data.db') complete; looks like all 0 rows were tombstoned ref: issues.apache.org/jira/browse/CASSANDRA-5930 If counter tables are corrupted in a way that prevents sstablescrub from completing, you can use the --skip-corrupted option to skip scrubbing those counter tables. This workaround is not necessary in versions 2.0+. Example: sstablescrub --skip-corrupted keyspace1 counter1 ref: issues.apache.org/jira/browse/CASSANDRA-14092 Using the option --reinsert-overflowed-ttl allows a rewriting of rows that had a max TTL going over the maximum (causing an overflow). Example: sstablescrub --reinsert-overflowed-ttl keyspace1 counter1 As of Cassandra version 2.0, this option is no longer relevant, since level data was moved from a separate manifest into the sstable metadata.","title":"sstablescrub","component":"Cassandra","version":"4.1","name":"sstablescrub","url":"/Cassandra/4.1/cassandra/tools/sstable/sstablescrub.html","titles":[{"text":"Usage","id":"usage"},{"text":"Basic Scrub","id":"basic-scrub"},{"text":"Scrub without Validation","id":"scrub-without-validation"},{"text":"Skip Corrupted Counter Tables","id":"skip-corrupted-counter-tables"},{"text":"Dealing with Overflow Dates","id":"dealing-with-overflow-dates"},{"text":"Manifest Check","id":"manifest-check"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstablesplit.html":{"text":"Big sstable files can take up a lot of disk space. The sstablesplit tool can be used to split those large files into smaller files. It can be thought of as a type of anticompaction. ref: issues.apache.org/jira/browse/CASSANDRA-4766 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstablesplit --debug display stack traces -h, --help display this help message --no-snapshot don’t snapshot the sstables before splitting -s, --size maximum size in MB for the output sstables (default: 50) This command should be run with Cassandra stopped. Note: the script does not verify that Cassandra is stopped. Split a large sstable into smaller sstables. By default, unless the option --no-snapshot is added, a snapshot will be done of the original sstable and placed in the snapshots folder. Example: sstablesplit /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db Pre-split sstables snapshotted into snapshot pre-split-1533144514795 Wildcards can be used in the filename portion of the command to split multiple files. Example: sstablesplit --size 1 /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-1* If the file is already smaller than the split size provided, the sstable will not be split. Example: sstablesplit /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db Skipping /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-8-big-Data.db: it's size (1.442 MB) is less than the split size (50 MB) No sstables needed splitting. The default size used for splitting is 50MB. Specify another size with the --size option. The size is in megabytes (MB). Specify only the number, not the units. For example --size 50 is correct, but --size 50MB is not. Example: sstablesplit --size 1 /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-9-big-Data.db Pre-split sstables snapshotted into snapshot pre-split-1533144996008 By default, sstablesplit will create a snapshot before splitting. If a snapshot is not needed, use the --no-snapshot option to skip it. Example: sstablesplit --size 1 --no-snapshot /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-11-big-Data.db Note: There is no output, but you can see the results in your file system.","title":"sstablesplit","component":"Cassandra","version":"4.1","name":"sstablesplit","url":"/Cassandra/4.1/cassandra/tools/sstable/sstablesplit.html","titles":[{"text":"Usage","id":"usage"},{"text":"Split a File","id":"split-a-file"},{"text":"Split Multiple Files","id":"split-multiple-files"},{"text":"Attempt to Split a Small File","id":"attempt-to-split-a-small-file"},{"text":"Split a File into Specified Size","id":"split-a-file-into-specified-size"},{"text":"Split Without Snapshot","id":"split-without-snapshot"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableupgrade.html":{"text":"Upgrade the sstables in the given table (or snapshot) to the current version of Cassandra. This process is typically done after a Cassandra version upgrade. This operation will rewrite the sstables in the specified table to match the currently installed version of Cassandra. The sstableupgrade command can also be used to downgrade sstables to a previous version. The snapshot option will only upgrade the specified snapshot. Upgrading snapshots is required before attempting to restore a snapshot taken in a major version older than the major version Cassandra is currently running. This will replace the files in the given snapshot as well as break any hard links to live sstables. Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstableupgrade [snapshot_name] --debug display stack traces -h,--help display this help message -k,--keep-source do not delete the source sstables Start with a set of sstables in one version of Cassandra: ls -al /tmp/cassandra/data/keyspace1/standard1-9695b790a63211e8a6fb091830ac5256/ ... -rw-r--r-- 1 user wheel 348 Aug 22 13:45 keyspace1-standard1-ka-1-CRC.db -rw-r--r-- 1 user wheel 5620000 Aug 22 13:45 keyspace1-standard1-ka-1-Data.db -rw-r--r-- 1 user wheel 10 Aug 22 13:45 keyspace1-standard1-ka-1-Digest.sha1 -rw-r--r-- 1 user wheel 25016 Aug 22 13:45 keyspace1-standard1-ka-1-Filter.db -rw-r--r-- 1 user wheel 480000 Aug 22 13:45 keyspace1-standard1-ka-1-Index.db -rw-r--r-- 1 user wheel 9895 Aug 22 13:45 keyspace1-standard1-ka-1-Statistics.db -rw-r--r-- 1 user wheel 3562 Aug 22 13:45 keyspace1-standard1-ka-1-Summary.db -rw-r--r-- 1 user wheel 79 Aug 22 13:45 keyspace1-standard1-ka-1-TOC.txt After upgrading the Cassandra version, upgrade the sstables: sstableupgrade keyspace1 standard1 Found 1 sstables that need upgrading. Upgrading BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-9695b790a63211e8a6fb091830ac5256/keyspace1-standard1-ka-1-Data.db') Upgrade of BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-9695b790a63211e8a6fb091830ac5256/keyspace1-standard1-ka-1-Data.db') complete. ls -al /tmp/cassandra/data/keyspace1/standard1-9695b790a63211e8a6fb091830ac5256/ ... drwxr-xr-x 2 user wheel 64 Aug 22 13:48 backups -rw-r--r-- 1 user wheel 292 Aug 22 13:48 mc-2-big-CRC.db -rw-r--r-- 1 user wheel 4599475 Aug 22 13:48 mc-2-big-Data.db -rw-r--r-- 1 user wheel 10 Aug 22 13:48 mc-2-big-Digest.crc32 -rw-r--r-- 1 user wheel 25256 Aug 22 13:48 mc-2-big-Filter.db -rw-r--r-- 1 user wheel 330807 Aug 22 13:48 mc-2-big-Index.db -rw-r--r-- 1 user wheel 10312 Aug 22 13:48 mc-2-big-Statistics.db -rw-r--r-- 1 user wheel 3506 Aug 22 13:48 mc-2-big-Summary.db -rw-r--r-- 1 user wheel 80 Aug 22 13:48 mc-2-big-TOC.txt Again, starting with a set of sstables in one version: ls -al /tmp/cassandra/data/keyspace1/standard1-db532690a63411e8b4ae091830ac5256/ ... -rw-r--r-- 1 user wheel 348 Aug 22 13:58 keyspace1-standard1-ka-1-CRC.db -rw-r--r-- 1 user wheel 5620000 Aug 22 13:58 keyspace1-standard1-ka-1-Data.db -rw-r--r-- 1 user wheel 10 Aug 22 13:58 keyspace1-standard1-ka-1-Digest.sha1 -rw-r--r-- 1 user wheel 25016 Aug 22 13:58 keyspace1-standard1-ka-1-Filter.db -rw-r--r-- 1 user wheel 480000 Aug 22 13:58 keyspace1-standard1-ka-1-Index.db -rw-r--r-- 1 user wheel 9895 Aug 22 13:58 keyspace1-standard1-ka-1-Statistics.db -rw-r--r-- 1 user wheel 3562 Aug 22 13:58 keyspace1-standard1-ka-1-Summary.db -rw-r--r-- 1 user wheel 79 Aug 22 13:58 keyspace1-standard1-ka-1-TOC.txt After upgrading the Cassandra version, upgrade the sstables, retaining the original sstables: sstableupgrade keyspace1 standard1 -k Found 1 sstables that need upgrading. Upgrading BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-db532690a63411e8b4ae091830ac5256/keyspace1-standard1-ka-1-Data.db') Upgrade of BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-db532690a63411e8b4ae091830ac5256/keyspace1-standard1-ka-1-Data.db') complete. ls -al /tmp/cassandra/data/keyspace1/standard1-db532690a63411e8b4ae091830ac5256/ ... drwxr-xr-x 2 user wheel 64 Aug 22 14:00 backups -rw-r--r--@ 1 user wheel 348 Aug 22 13:58 keyspace1-standard1-ka-1-CRC.db -rw-r--r--@ 1 user wheel 5620000 Aug 22 13:58 keyspace1-standard1-ka-1-Data.db -rw-r--r--@ 1 user wheel 10 Aug 22 13:58 keyspace1-standard1-ka-1-Digest.sha1 -rw-r--r--@ 1 user wheel 25016 Aug 22 13:58 keyspace1-standard1-ka-1-Filter.db -rw-r--r--@ 1 user wheel 480000 Aug 22 13:58 keyspace1-standard1-ka-1-Index.db -rw-r--r--@ 1 user wheel 9895 Aug 22 13:58 keyspace1-standard1-ka-1-Statistics.db -rw-r--r--@ 1 user wheel 3562 Aug 22 13:58 keyspace1-standard1-ka-1-Summary.db -rw-r--r--@ 1 user wheel 79 Aug 22 13:58 keyspace1-standard1-ka-1-TOC.txt -rw-r--r-- 1 user wheel 292 Aug 22 14:01 mc-2-big-CRC.db -rw-r--r-- 1 user wheel 4596370 Aug 22 14:01 mc-2-big-Data.db -rw-r--r-- 1 user wheel 10 Aug 22 14:01 mc-2-big-Digest.crc32 -rw-r--r-- 1 user wheel 25256 Aug 22 14:01 mc-2-big-Filter.db -rw-r--r-- 1 user wheel 330801 Aug 22 14:01 mc-2-big-Index.db -rw-r--r-- 1 user wheel 10312 Aug 22 14:01 mc-2-big-Statistics.db -rw-r--r-- 1 user wheel 3506 Aug 22 14:01 mc-2-big-Summary.db -rw-r--r-- 1 user wheel 80 Aug 22 14:01 mc-2-big-TOC.txt Find the snapshot name: nodetool listsnapshots Snapshot Details: Snapshot name Keyspace name Column family name True size Size on disk ... 1534962986979 keyspace1 standard1 5.85 MB 5.85 MB Then rewrite the snapshot: sstableupgrade keyspace1 standard1 1534962986979 Found 1 sstables that need upgrading. Upgrading BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-5850e9f0a63711e8a5c5091830ac5256/snapshots/1534962986979/keyspace1-standard1-ka-1-Data.db') Upgrade of BigTableReader(path='/var/lib/cassandra/data/keyspace1/standard1-5850e9f0a63711e8a5c5091830ac5256/snapshots/1534962986979/keyspace1-standard1-ka-1-Data.db') complete.","title":"sstableupgrade","component":"Cassandra","version":"4.1","name":"sstableupgrade","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableupgrade.html","titles":[{"text":"Usage","id":"usage"},{"text":"Rewrite tables to the current Cassandra version","id":"rewrite-tables-to-the-current-cassandra-version"},{"text":"Rewrite tables to the current Cassandra version, and keep tables in old version","id":"rewrite-tables-to-the-current-cassandra-version-and-keep-tables-in-old-version"},{"text":"Rewrite a snapshot to the current Cassandra version","id":"rewrite-a-snapshot-to-the-current-cassandra-version"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableutil.html":{"text":"List sstable files for the provided table. ref: issues.apache.org/jira/browse/CASSANDRA-7066 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. sstableutil -c, --cleanup clean up any outstanding transactions -d, --debug display stack traces -h, --help display this help message -o, --oplog include operation logs -t, --type all (list all files, final or temporary), tmp (list temporary files only), final (list final files only), -v, --verbose verbose output The basic command lists the sstables associated with a given keyspace/table. Example: sstableutil keyspace eventlog Listing files... /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-CRC.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Digest.crc32 /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Filter.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Index.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Statistics.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Summary.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-TOC.txt /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-CRC.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Digest.crc32 /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Filter.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Index.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Statistics.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Summary.db /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-TOC.txt Using the -t option followed by tmp will list all temporary sstables, in the format above. Temporary sstables were used in pre-3.0 versions of Cassandra. Using the -t option followed by final will list all final sstables, in the format above. In recent versions of Cassandra, this is the same output as not using the -t option. Using the -o option will include transaction logs in the listing, in the format above. Using the -c option removes any transactions left over from incomplete writes or compactions. From the 3.0 upgrade notes: New transaction log files have been introduced to replace the compactions_in_progress system table, temporary file markers (tmp and tmplink) and sstable ancestors. Therefore, compaction metadata no longer contains ancestors. Transaction log files list sstable descriptors involved in compactions and other operations such as flushing and streaming. Use the sstableutil tool to list any sstable files currently involved in operations not yet completed, which previously would have been marked as temporary. A transaction log file contains one sstable per line, with the prefix \"add:\" or \"remove:\". They also contain a special line \"commit\", only inserted at the end when the transaction is committed. On startup we use these files to cleanup any partial transactions that were in progress when the process exited. If the commit line is found, we keep new sstables (those with the \"add\" prefix) and delete the old sstables (those with the \"remove\" prefix), vice-versa if the commit line is missing. Should you lose or delete these log files, both old and new sstable files will be kept as live files, which will result in duplicated sstables. These files are protected by incremental checksums so you should not manually edit them. When restoring a full backup or moving sstable files, you should clean-up any left over transactions and their temporary files first.","title":"sstableutil","component":"Cassandra","version":"4.1","name":"sstableutil","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableutil.html","titles":[{"text":"Usage","id":"usage"},{"text":"List all sstables","id":"list-all-sstables"},{"text":"List only temporary sstables","id":"list-only-temporary-sstables"},{"text":"List only final sstables","id":"list-only-final-sstables"},{"text":"Include transaction logs","id":"include-transaction-logs"},{"text":"Clean up sstables","id":"clean-up-sstables"}]},"/Cassandra/4.1/cassandra/tools/sstable/sstableverify.html":{"text":"Check sstable(s) for errors or corruption, for the provided table. ref: issues.apache.org/jira/browse/CASSANDRA-5791 Cassandra must be stopped before this tool is executed, or unexpected results will occur. Note: the script does not verify that Cassandra is stopped. See CASSANDRA-9947 and CASSANDRA-17017 for discussion around risks with this tool. Specifically: \"We mark sstables that fail verification as unrepaired, but that’s not going to do what you think. What it means is that the local node will use that sstable in the next repair, but other nodes will not. So all we’ll end up doing is streaming whatever data we can read from it, to the other replicas. If we could magically mark whatever sstables correspond on the remote nodes, to the data in the local sstable, that would work, but we can’t.\" This tool requires the use of a -f or --force flag to indicate that the user understands the risks and would like to attempt its usage anyway. sstableverify --debug display stack traces -e, --extended extended verification -h, --help display this help message -v, --verbose verbose output -f, --force allow use of tool (see CASSANDRA-17017 for risks) This is the basic verification. It is not a very quick process, and uses memory. You might need to increase your memory settings if you have many sstables. Example: sstableverify keyspace eventlog Verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') (7.353MiB) Deserializing sstable metadata for BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') Checking computed hash of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') Verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') (3.775MiB) Deserializing sstable metadata for BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') Checking computed hash of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') During an extended verification, the individual values will be validated for errors or corruption. This of course takes more time. Example: root@DC1C1:/# sstableverify -e keyspace eventlog WARN 14:08:06,255 Only 33.096GiB free across all data volumes. Consider adding more capacity to your cluster or removing obsolete snapshots Verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') (7.353MiB) Deserializing sstable metadata for BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') Checking computed hash of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') Extended Verify requested, proceeding to inspect values Verify of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-32-big-Data.db') succeeded. All 33211 rows read successfully Verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') (3.775MiB) Deserializing sstable metadata for BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') Checking computed hash of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') Extended Verify requested, proceeding to inspect values Verify of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-37-big-Data.db') succeeded. All 17068 rows read successfully Corrupted files are listed if they are detected by the script. Example: sstableverify keyspace eventlog Verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db') (7.416MiB) Deserializing sstable metadata for BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db') Checking computed hash of BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db') Error verifying BigTableReader(path='/var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db'): Corrupted: /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db A similar (but less verbose) tool will show the suggested actions: nodetool verify keyspace eventlog error: Invalid SSTable /var/lib/cassandra/data/keyspace/eventlog-6365332094dd11e88f324f9c503e4753/mc-40-big-Data.db, please force repair","title":"sstableverify","component":"Cassandra","version":"4.1","name":"sstableverify","url":"/Cassandra/4.1/cassandra/tools/sstable/sstableverify.html","titles":[{"text":"WARNING","id":"warning"},{"text":"Usage","id":"usage"},{"text":"Basic Verification","id":"basic-verification"},{"text":"Extended Verification","id":"extended-verification"},{"text":"Corrupted File","id":"corrupted-file"}]},"/Cassandra/4.1/index.html":{"text":"This is the official documentation for Apache Cassandra. If you would like to contribute to this documentation, you are welcome to do so by submitting your contribution like any other patch following these instructions. Getting started Newbie starting point What’s new in 4.1 What’s new in Cassandra 4.1 Architecture Cassandra’s big picture Data modeling Hint: it’s not relational Cassandra Query Language (CQL) CQL reference documentation Configuration Cassandra’s handles and knobs Operation The operator’s corner Tools cqlsh, nodetool, and others Troubleshooting What to look for when you have a problem FAQ Frequently asked questions Plug-ins Third-party plug-ins Native Protocols Native Cassandra protocol specifications Reporting bugs Contact us Contributing code Contributing to the docs Community Download","title":"Welcome to Apache Cassandra’s documentation!","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/index.html","titles":[{"text":"Main documentation","id":"main-documentation"},{"text":"Meta information","id":"meta-information"}]},"/Cassandra/4.1/cassandra/operating/compaction/index.html":{"text":"Picking the right compaction strategy for your workload will ensure the best performance for both querying and for compaction itself. Size Tiered Compaction Strategy (STCS) The default compaction strategy. Useful as a fallback when other strategies don’t fit the workload. Most useful for non pure time series workloads with spinning disks, or when the I/O from LCS is too high. Leveled Compaction Strategy (LCS) Leveled Compaction Strategy (LCS) is optimized for read heavy workloads, or workloads with lots of updates and deletes. It is not a good choice for immutable time series data. Time Window Compaction Strategy (TWCS) Time Window Compaction Strategy is designed for TTL’ed, mostly immutable time series data. The concept of compaction is used for different kinds of operations in Cassandra, the common thing about these operations is that it takes one or more SSTables and output new SSTables. The types of compactions are: Minor compaction triggered automatically in Cassandra. Major compaction a user executes a compaction over all SSTables on the node. User defined compaction a user triggers a compaction on a given set of SSTables. Scrub try to fix any broken SSTables. This can actually remove valid data if that data is corrupted, if that happens you will need to run a full repair on the node. UpgradeSSTables upgrade SSTables to the latest version. Run this after upgrading to a new major version. Cleanup remove any ranges this node does not own anymore, typically triggered on neighbouring nodes after a node has been bootstrapped since that node will take ownership of some ranges from those nodes. Secondary index rebuild rebuild the secondary indexes on the node. Anticompaction after repair the ranges that were actually repaired are split out of the SSTables that existed when repair started. Sub range compaction It is possible to only compact a given sub range - this could be useful if you know a token that has been misbehaving - either gathering many updates or many deletes. (nodetool compact -st x -et y) will pick all SSTables containing the range between x and y and issue a compaction for those SSTables. For STCS this will most likely include all SSTables but with LCS it can issue the compaction for a subset of the SSTables. With LCS the resulting sstable will end up in L0. When an sstable is added to the node through flushing/streaming When autocompaction is enabled after being disabled (nodetool enableautocompaction) When compaction adds new SSTables A check for new minor compactions every 5 minutes Compaction is about merging SSTables, since partitions in SSTables are sorted based on the hash of the partition key it is possible to efficiently merge separate SSTables. Content of each partition is also sorted so each partition can be merged efficiently. When a delete request is received by Cassandra it does not actually remove the data from the underlying store. Instead it writes a special piece of data known as a tombstone. The Tombstone represents the delete and causes all values which occurred before the tombstone to not appear in queries to the database. This approach is used instead of removing values because of the distributed nature of Cassandra. Imagine a three node cluster which has the value [A] replicated to every node.: [A], [A], [A] If one of the nodes fails and and our delete operation only removes existing values we can end up with a cluster that looks like: [], [], [A] Then a repair operation would replace the value of [A] back onto the two nodes which are missing the value.: [A], [A], [A] This would cause our data to be resurrected even though it had been deleted. Starting again with a three node cluster which has the value [A] replicated to every node.: [A], [A], [A] If instead of removing data we add a tombstone record, our single node failure situation will look like this.: [A, Tombstone[A]], [A, Tombstone[A]], [A] Now when we issue a repair the Tombstone will be copied to the replica, rather than the deleted data being resurrected.: [A, Tombstone[A]], [A, Tombstone[A]], [A, Tombstone[A]] Our repair operation will correctly put the state of the system to what we expect with the record [A] marked as deleted on all nodes. This does mean we will end up accruing Tombstones which will permanently accumulate disk space. To avoid keeping tombstones forever we have a parameter known as gc_grace_seconds for every table in Cassandra. The table level gc_grace_seconds parameter controls how long Cassandra will retain tombstones through compaction events before finally removing them. This duration should directly reflect the amount of time a user expects to allow before recovering a failed node. After gc_grace_seconds has expired the tombstone may be removed (meaning there will no longer be any record that a certain piece of data was deleted), but as a tombstone can live in one sstable and the data it covers in another, a compaction must also include both sstable for a tombstone to be removed. More precisely, to be able to drop an actual tombstone the following needs to be true; The tombstone must be older than gc_grace_seconds If partition X contains the tombstone, the sstable containing the partition plus all SSTables containing data older than the tombstone containing X must be included in the same compaction. We don’t need to care if the partition is in an sstable if we can guarantee that all data in that sstable is newer than the tombstone. If the tombstone is older than the data it cannot shadow that data. If the option only_purge_repaired_tombstones is enabled, tombstones are only removed if the data has also been repaired. If a node remains down or disconnected for longer than gc_grace_seconds it’s deleted data will be repaired back to the other nodes and re-appear in the cluster. This is basically the same as in the \"Deletes without Tombstones\" section. Note that tombstones will not be removed until a compaction event even if gc_grace_seconds has elapsed. The default value for gc_grace_seconds is 864000 which is equivalent to 10 days. This can be set when creating or altering a table using WITH gc_grace_seconds. Data in Cassandra can have an additional property called time to live - this is used to automatically drop data that has expired once the time is reached. Once the TTL has expired the data is converted to a tombstone which stays around for at least gc_grace_seconds. Note that if you mix data with TTL and data without TTL (or just different length of the TTL) Cassandra will have a hard time dropping the tombstones created since the partition might span many SSTables and not all are compacted at once. If an sstable contains only tombstones and it is guaranteed that that sstable is not shadowing data in any other sstable compaction can drop that sstable. If you see SSTables with only tombstones (note that TTL:ed data is considered tombstones once the time to live has expired) but it is not being dropped by compaction, it is likely that other SSTables contain older data. There is a tool called sstableexpiredblockers that will list which SSTables are droppable and which are blocking them from being dropped. This is especially useful for time series compaction with TimeWindowCompactionStrategy (and the deprecated DateTieredCompactionStrategy). With TimeWindowCompactionStrategy it is possible to remove the guarantee (not check for shadowing data) by enabling unsafe_aggressive_sstable_expiration. With incremental repairs Cassandra must keep track of what data is repaired and what data is unrepaired. With anticompaction repaired data is split out into repaired and unrepaired SSTables. To avoid mixing up the data again separate compaction strategy instances are run on the two sets of data, each instance only knowing about either the repaired or the unrepaired SSTables. This means that if you only run incremental repair once and then never again, you might have very old data in the repaired SSTables that block compaction from dropping tombstones in the unrepaired (probably newer) SSTables. Since tombstones and data can live in different SSTables it is important to realize that losing an sstable might lead to data becoming live again - the most common way of losing SSTables is to have a hard drive break down. To avoid making data live tombstones and actual data are always in the same data directory. This way, if a disk is lost, all versions of a partition are lost and no data can get undeleted. To achieve this a compaction strategy instance per data directory is run in addition to the compaction strategy instances containing repaired/unrepaired data, this means that if you have 4 data directories there will be 8 compaction strategy instances running. This has a few more benefits than just avoiding data getting undeleted: It is possible to run more compactions in parallel - leveled compaction will have several totally separate levelings and each one can run compactions independently from the others. Users can backup and restore a single data directory. Note though that currently all data directories are considered equal, so if you have a tiny disk and a big disk backing two data directories, the big one will be limited the by the small one. One work around to this is to create more data directories backed by the big disk. When an sstable is written a histogram with the tombstone expiry times is created and this is used to try to find SSTables with very many tombstones and run single sstable compaction on that sstable in hope of being able to drop tombstones in that sstable. Before starting this it is also checked how likely it is that any tombstones will actually will be able to be dropped how much this sstable overlaps with other SSTables. To avoid most of these checks the compaction option unchecked_tombstone_compaction can be enabled. There is a number of common options for all the compaction strategies; enabled (default: true) Whether minor compactions should run. Note that you can have 'enabled': true as a compaction option and then do 'nodetool enableautocompaction' to start running compactions. tombstone_threshold (default: 0.2) How much of the sstable should be tombstones for us to consider doing a single sstable compaction of that sstable. tombstone_compaction_interval (default: 86400s (1 day)) Since it might not be possible to drop any tombstones when doing a single sstable compaction we need to make sure that one sstable is not constantly getting recompacted - this option states how often we should try for a given sstable. log_all (default: false) New detailed compaction logging, see below . unchecked_tombstone_compaction (default: false) The single sstable compaction has quite strict checks for whether it should be started, this option disables those checks and for some usecases this might be needed. Note that this does not change anything for the actual compaction, tombstones are only dropped if it is safe to do so - it might just rewrite an sstable without being able to drop any tombstones. only_purge_repaired_tombstone (default: false) Option to enable the extra safety of making sure that tombstones are only dropped if the data has been repaired. min_threshold (default: 4) Lower limit of number of SSTables before a compaction is triggered. Not used for LeveledCompactionStrategy. max_threshold (default: 32) Upper limit of number of SSTables before a compaction is triggered. Not used for LeveledCompactionStrategy. Further, see the section on each strategy for specific additional options. The nodetool utility provides a number of commands related to compaction: enableautocompaction Enable compaction. disableautocompaction Disable compaction. setcompactionthroughput How fast compaction should run at most - defaults to 64MiB/s. compactionstats Statistics about current and pending compactions. compactionhistory List details about the last compactions. setcompactionthreshold Set the min/max sstable count for when to trigger compaction, defaults to 4/32. It is possible to switch compaction strategies and its options on just a single node using JMX, this is a great way to experiment with settings without affecting the whole cluster. The mbean is: org.apache.cassandra.db:type=ColumnFamilies,keyspace=,columnfamily= and the attribute to change is CompactionParameters or CompactionParametersJson if you use jconsole or jmc. The syntax for the json version is the same as you would use in an ALTER TABLE statement -for example: { 'class': 'LeveledCompactionStrategy', 'sstable_size_in_mb': 123, 'fanout_size': 10} The setting is kept until someone executes an ALTER TABLE that touches the compaction settings or restarts the node. Enable with the compaction option log_all and a more detailed compaction log file will be produced in your log directory.","title":"Compaction","component":"Cassandra","version":"4.1","name":"index","url":"/Cassandra/4.1/cassandra/operating/compaction/index.html","titles":[{"text":"Strategies","id":"strategies"},{"text":"Types of compaction","id":"types-of-compaction"},{"text":"When is a minor compaction triggered?","id":"when-is-a-minor-compaction-triggered"},{"text":"Merging SSTables","id":"merging-sstables"},{"text":"Tombstones and Garbage Collection (GC) Grace","id":"tombstones-and-garbage-collection-gc-grace"},{"text":"Why Tombstones","id":"why-tombstones"},{"text":"Deletes without tombstones","id":"deletes-without-tombstones"},{"text":"Deletes with Tombstones","id":"deletes-with-tombstones"},{"text":"The gc_grace_seconds parameter and Tombstone Removal","id":"the-gc_grace_seconds-parameter-and-tombstone-removal"},{"text":"TTL","id":"ttl"},{"text":"Fully expired SSTables","id":"fully-expired-sstables"},{"text":"Repaired/unrepaired data","id":"repairedunrepaired-data"},{"text":"Data directories","id":"data-directories"},{"text":"Single sstable tombstone compaction","id":"single-sstable-tombstone-compaction"},{"text":"Common options","id":"compaction-options"},{"text":"Compaction nodetool commands","id":"compaction-nodetool-commands"},{"text":"Switching the compaction strategy and options using JMX","id":"switching-the-compaction-strategy-and-options-using-jmx"},{"text":"More detailed compaction logging","id":"detailed-compaction-logging"}]},"/Cassandra/4.1/cassandra/operating/compaction/lcs.html":{"text":"The idea of LeveledCompactionStrategy (LCS) is that all sstables are put into different levels where we guarantee that no overlapping sstables are in the same level. By overlapping we mean that the first/last token of a single sstable are never overlapping with other sstables. This means that for a SELECT we will only have to look for the partition key in a single sstable per level. Each level is 10x the size of the previous one and each sstable is 160MB by default. L0 is where sstables are streamed/flushed - no overlap guarantees are given here. When picking compaction candidates we have to make sure that the compaction does not create overlap in the target level. This is done by always including all overlapping sstables in the next level. For example if we select an sstable in L3, we need to guarantee that we pick all overlapping sstables in L4 and make sure that no currently ongoing compactions will create overlap if we start that compaction. We can start many parallel compactions in a level if we guarantee that we wont create overlap. For L0 → L1 compactions we almost always need to include all L1 sstables since most L0 sstables cover the full range. We also can’t compact all L0 sstables with all L1 sstables in a single compaction since that can use too much memory. When deciding which level to compact LCS checks the higher levels first (with LCS, a \"higher\" level is one with a higher number, L0 being the lowest one) and if the level is behind a compaction will be started in that level. It is possible to do a major compaction with LCS - it will currently start by filling out L1 and then once L1 is full, it continues with L2 etc. This is sub optimal and will change to create all the sstables in a high level instead, CASSANDRA-11817. During bootstrap sstables are streamed from other nodes. The level of the remote sstable is kept to avoid many compactions after the bootstrap is done. During bootstrap the new node also takes writes while it is streaming the data from a remote node - these writes are flushed to L0 like all other writes and to avoid those sstables blocking the remote sstables from going to the correct level, we only do STCS in L0 until the bootstrap is done. If LCS gets very many L0 sstables reads are going to hit all (or most) of the L0 sstables since they are likely to be overlapping. To more quickly remedy this LCS does STCS compactions in L0 if there are more than 32 sstables there. This should improve read performance more quickly compared to letting LCS do its L0 → L1 compactions. If you keep getting too many sstables in L0 it is likely that LCS is not the best fit for your workload and STCS could work out better. If a node ends up with a leveling where there are a few very high level sstables that are not getting compacted they might make it impossible for lower levels to drop tombstones etc. For example, if there are sstables in L6 but there is only enough data to actually get a L4 on the node the left over sstables in L6 will get starved and not compacted. This can happen if a user changes sstable_size_in_mb from 5MB to 160MB for example. To avoid this LCS tries to include those starved high level sstables in other compactions if there has been 25 compaction rounds where the highest level has not been involved. sstable_size_in_mb (default: 160MB) The target compressed (if using compression) sstable size - the sstables can end up being larger if there are very large partitions on the node. fanout_size (default: 10) The target size of levels increases by this fanout_size multiplier. You can reduce the space amplification by tuning this option. LCS also support the cassandra.disable_stcs_in_l0 startup option (-Dcassandra.disable_stcs_in_l0=true) to avoid doing STCS in L0.","title":"Leveled Compaction Strategy","component":"Cassandra","version":"4.1","name":"lcs","url":"/Cassandra/4.1/cassandra/operating/compaction/lcs.html","titles":[{"text":"Major compaction","id":"major-compaction"},{"text":"Bootstrapping","id":"bootstrapping"},{"text":"STCS in L0","id":"stcs-in-l0"},{"text":"Starved sstables","id":"starved-sstables"},{"text":"LCS options","id":"lcs_options"}]},"/Cassandra/4.1/cassandra/operating/compaction/stcs.html":{"text":"The basic idea of SizeTieredCompactionStrategy (STCS) is to merge sstables of approximately the same size. All sstables are put in different buckets depending on their size. An sstable is added to the bucket if size of the sstable is within bucket_low and bucket_high of the current average size of the sstables already in the bucket. This will create several buckets and the most interesting of those buckets will be compacted. The most interesting one is decided by figuring out which bucket’s sstables takes the most reads. When running a major compaction with STCS you will end up with two sstables per data directory (one for repaired data and one for unrepaired data). There is also an option (-s) to do a major compaction that splits the output into several sstables. The sizes of the sstables are approximately 50%, 25%, 12.5%…​ of the total size. min_sstable_size (default: 50MB) Sstables smaller than this are put in the same bucket. bucket_low (default: 0.5) How much smaller than the average size of a bucket a sstable should be before not being included in the bucket. That is, if bucket_low * avg_bucket_size < sstable_size (and the bucket_high condition holds, see below), then the sstable is added to the bucket. bucket_high (default: 1.5) How much bigger than the average size of a bucket a sstable should be before not being included in the bucket. That is, if sstable_size < bucket_high * avg_bucket_size (and the bucket_low condition holds, see above), then the sstable is added to the bucket. Defragmentation is done when many sstables are touched during a read. The result of the read is put in to the memtable so that the next read will not have to touch as many sstables. This can cause writes on a read-only-cluster.","title":"Size Tiered Compaction Strategy","component":"Cassandra","version":"4.1","name":"stcs","url":"/Cassandra/4.1/cassandra/operating/compaction/stcs.html","titles":[{"text":"Major compaction","id":"major-compaction"},{"text":"STCS options","id":"stcs_options"},{"text":"Defragmentation","id":"defragmentation"}]},"/Cassandra/4.1/cassandra/operating/compaction/twcs.html":{"text":"TimeWindowCompactionStrategy (TWCS) is designed specifically for workloads where it’s beneficial to have data on disk grouped by the timestamp of the data, a common goal when the workload is time-series in nature or when all data is written with a TTL. In an expiring/TTL workload, the contents of an entire SSTable likely expire at approximately the same time, allowing them to be dropped completely, and space reclaimed much more reliably than when using SizeTieredCompactionStrategy or LeveledCompactionStrategy. The basic concept is that TimeWindowCompactionStrategy will create one sstable per file for a given window, where a window is simply calculated as the combination of two primary options: compaction_window_unit (default: DAYS) A Java TimeUnit (MINUTES, HOURS, or DAYS). compaction_window_size (default: 1) The number of units that make up a window. unsafe_aggressive_sstable_expiration (default: false) Expired sstables will be dropped without checking its data is shadowing other sstables. This is a potentially risky option that can lead to data loss or deleted data re-appearing, going beyond what unchecked_tombstone_compaction does for single sstable compaction. Due to the risk the jvm must also be started with -Dcassandra.unsafe_aggressive_sstable_expiration=true. Taken together, the operator can specify windows of virtually any size, and TimeWindowCompactionStrategy will work to create a single sstable for writes within that window. For efficiency during writing, the newest window will be compacted using SizeTieredCompactionStrategy. Ideally, operators should select a compaction_window_unit and compaction_window_size pair that produces approximately 20-30 windows - if writing with a 90 day TTL, for example, a 3 Day window would be a reasonable choice ('compaction_window_unit':'DAYS','compaction_window_size':3). The primary motivation for TWCS is to separate data on disk by timestamp and to allow fully expired SSTables to drop more efficiently. One potential way this optimal behavior can be subverted is if data is written to SSTables out of order, with new data and old data in the same SSTable. Out of order data can appear in two ways: If the user mixes old data and new data in the traditional write path, the data will be comingled in the memtables and flushed into the same SSTable, where it will remain comingled. If the user’s read requests for old data cause read repairs that pull old data into the current memtable, that data will be comingled and flushed into the same SSTable. While TWCS tries to minimize the impact of comingled data, users should attempt to avoid this behavior. Specifically, users should avoid queries that explicitly set the timestamp via CQL USING TIMESTAMP. Additionally, users should run frequent repairs (which streams data in such a way that it does not become comingled). Operators wishing to enable TimeWindowCompactionStrategy on existing data should consider running a major compaction first, placing all existing data into a single (old) window. Subsequent newer writes will then create typical SSTables as expected. Operators wishing to change compaction_window_unit or compaction_window_size can do so, but may trigger additional compactions as adjacent windows are joined together. If the window size is decrease d (for example, from 24 hours to 12 hours), then the existing SSTables will not be modified - TWCS can not split existing SSTables into multiple windows.","title":"Time Window CompactionStrategy","component":"Cassandra","version":"4.1","name":"twcs","url":"/Cassandra/4.1/cassandra/operating/compaction/twcs.html","titles":[{"text":"TimeWindowCompactionStrategy Operational Concerns","id":"timewindowcompactionstrategy-operational-concerns"},{"text":"Changing TimeWindowCompactionStrategy Options","id":"changing-timewindowcompactionstrategy-options"}]}}})