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<script id="implementation-template" type="text/x-handlebars-template">
    <h3><a id="networklayer" href="#networklayer">5.1 Network Layer</a></h3>
    <p>
    The network layer is a fairly straight-forward NIO server, and will not be described in great detail. The sendfile implementation is done by giving the <code>MessageSet</code> interface a <code>writeTo</code> method. This allows the file-backed message set to use the more efficient <code>transferTo</code> implementation instead of an in-process buffered write. The threading model is a single acceptor thread and <i>N</i> processor threads which handle a fixed number of connections each. This design has been pretty thoroughly tested <a href="http://sna-projects.com/blog/2009/08/introducing-the-nio-socketserver-implementation">elsewhere</a> and found to be simple to implement and fast. The protocol is kept quite simple to allow for future implementation of clients in other languages.
    </p>
    <h3><a id="messages" href="#messages">5.2 Messages</a></h3>
    <p>
    Messages consist of a variable-length header, a variable length opaque key byte array and a variable length opaque value byte array. The format of the header is described in the following section.
    Leaving the key and value opaque is the right decision: there is a great deal of progress being made on serialization libraries right now, and any particular choice is unlikely to be right for all uses. Needless to say a particular application using Kafka would likely mandate a particular serialization type as part of its usage. The <code>RecordBatch</code> interface is simply an iterator over messages with specialized methods for bulk reading and writing to an NIO <code>Channel</code>.</p>

    <h3><a id="messageformat" href="#messageformat">5.3 Message Format</a></h3>
    <p>
    Messages (aka Records) are always written in batches. The technical term for a batch of messages is a record batch, and a record batch contains one or more records. In the degenerate case, we could have a record batch containing a single record.
    Record batches and records have their own headers. The format of each is described below for Kafka version 0.11.0 and later (message format version v2, or magic=2). <a href="https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-Messagesets">Click here</a> for details about message formats 0 and 1.</p>

    <h4><a id="recordbatch" href="#recordbatch">5.3.1 Record Batch</a></h4>
	<p> The following is the on-disk format of a RecordBatch. </p>
	<p><pre class="brush: java;">
		baseOffset: int64
		batchLength: int32
		partitionLeaderEpoch: int32
		magic: int8 (current magic value is 2)
		crc: int32
		attributes: int16
			bit 0~2:
				0: no compression
				1: gzip
				2: snappy
				3: lz4
			bit 3: timestampType
			bit 4: isTransactional (0 means not transactional)
			bit 5: isControlBatch (0 means not a control batch)
			bit 6~15: unused
		lastOffsetDelta: int32
		firstTimestamp: int64
		maxTimestamp: int64
		producerId: int64
		producerEpoch: int16
		baseSequence: int32
		records: [Record]
	</pre></p>
    <p> Note that when compression is enabled, the compressed record data is serialized directly following the count of the number of records. </p>

    <p>The CRC covers the data from the attributes to the end of the batch (i.e. all the bytes that follow the CRC). It is located after the magic byte, which
    means that clients must parse the magic byte before deciding how to interpret the bytes between the batch length and the magic byte. The partition leader
    epoch field is not included in the CRC computation to avoid the need to recompute the CRC when this field is assigned for every batch that is received by
    the broker. The CRC-32C (Castagnoli) polynomial is used for the computation.</p>

    <p>On compaction: unlike the older message formats, magic v2 and above preserves the first and last offset/sequence numbers from the original batch when the log is cleaned. This is required in order to be able to restore the
    producer's state when the log is reloaded. If we did not retain the last sequence number, for example, then after a partition leader failure, the producer might see an OutOfSequence error. The base sequence number must
    be preserved for duplicate checking (the broker checks incoming Produce requests for duplicates by verifying that the first and last sequence numbers of the incoming batch match the last from that producer). As a result,
    it is possible to have empty batches in the log when all the records in the batch are cleaned but batch is still retained in order to preserve a producer's last sequence number. One oddity here is that the baseTimestamp
    field is not preserved during compaction, so it will change if the first record in the batch is compacted away.</p>

    <h5><a id="controlbatch" href="#controlbatch">5.3.1.1 Control Batches</a></h5>
    <p>A control batch contains a single record called the control record. Control records should not be passed on to applications. Instead, they are used by consumers to filter out aborted transactional messages.</p>
    <p> The key of a control record conforms to the following schema: </p>
    <p><pre class="brush: java">
       version: int16 (current version is 0)
       type: int16 (0 indicates an abort marker, 1 indicates a commit)
    </pre></p>
    <p>The schema for the value of a control record is dependent on the type. The value is opaque to clients.</p>

	<h4><a id="record" href="#record">5.3.2 Record</a></h4>
	<p>Record level headers were introduced in Kafka 0.11.0. The on-disk format of a record with Headers is delineated below. </p>
	<p><pre class="brush: java;">
		length: varint
		attributes: int8
			bit 0~7: unused
		timestampDelta: varint
		offsetDelta: varint
		keyLength: varint
		key: byte[]
		valueLen: varint
		value: byte[]
		Headers => [Header]
	</pre></p>
	<h5><a id="recordheader" href="#recordheader">5.4.2.1 Record Header</a></h5>
	<p><pre class="brush: java;">
		headerKeyLength: varint
		headerKey: String
		headerValueLength: varint
		Value: byte[]
	</pre></p>
    <p>We use the same varint encoding as Protobuf. More information on the latter can be found <a href="https://developers.google.com/protocol-buffers/docs/encoding#varints">here</a>. The count of headers in a record
    is also encoded as a varint.</p>

    <h3><a id="log" href="#log">5.4 Log</a></h3>
    <p>
    A log for a topic named "my_topic" with two partitions consists of two directories (namely <code>my_topic_0</code> and <code>my_topic_1</code>) populated with data files containing the messages for that topic. The format of the log files is a sequence of "log entries""; each log entry is a 4 byte integer <i>N</i> storing the message length which is followed by the <i>N</i> message bytes. Each message is uniquely identified by a 64-bit integer <i>offset</i> giving the byte position of the start of this message in the stream of all messages ever sent to that topic on that partition. The on-disk format of each message is given below. Each log file is named with the offset of the first message it contains. So the first file created will be 00000000000.kafka, and each additional file will have an integer name roughly <i>S</i> bytes from the previous file where <i>S</i> is the max log file size given in the configuration.
    </p>
    <p>
    The exact binary format for records is versioned and maintained as a standard interface so record batches can be transferred between producer, broker, and client without recopying or conversion when desirable. The previous section included details about the on-disk format of records.</p>
    </p>
   <p>
    The use of the message offset as the message id is unusual. Our original idea was to use a GUID generated by the producer, and maintain a mapping from GUID to offset on each broker. But since a consumer must maintain an ID for each server, the global uniqueness of the GUID provides no value. Furthermore, the complexity of maintaining the mapping from a random id to an offset requires a heavy weight index structure which must be synchronized with disk, essentially requiring a full persistent random-access data structure. Thus to simplify the lookup structure we decided to use a simple per-partition atomic counter which could be coupled with the partition id and node id to uniquely identify a message; this makes the lookup structure simpler, though multiple seeks per consumer request are still likely. However once we settled on a counter, the jump to directly using the offset seemed natural&mdash;both after all are monotonically increasing integers unique to a partition. Since the offset is hidden from the consumer API this decision is ultimately an implementation detail and we went with the more efficient approach.
    </p>
    <img class="centered" src="/{{version}}/images/kafka_log.png">
    <h4><a id="impl_writes" href="#impl_writes">Writes</a></h4>
    <p>
    The log allows serial appends which always go to the last file. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). The log takes two configuration parameters: <i>M</i>, which gives the number of messages to write before forcing the OS to flush the file to disk, and <i>S</i>, which gives a number of seconds after which a flush is forced. This gives a durability guarantee of losing at most <i>M</i> messages or <i>S</i> seconds of data in the event of a system crash.
    </p>
    <h4><a id="impl_reads" href="#impl_reads">Reads</a></h4>
    <p>
    Reads are done by giving the 64-bit logical offset of a message and an <i>S</i>-byte max chunk size. This will return an iterator over the messages contained in the <i>S</i>-byte buffer. <i>S</i> is intended to be larger than any single message, but in the event of an abnormally large message, the read can be retried multiple times, each time doubling the buffer size, until the message is read successfully. A maximum message and buffer size can be specified to make the server reject messages larger than some size, and to give a bound to the client on the maximum it needs to ever read to get a complete message. It is likely that the read buffer ends with a partial message, this is easily detected by the size delimiting.
    </p>
    <p>
    The actual process of reading from an offset requires first locating the log segment file in which the data is stored, calculating the file-specific offset from the global offset value, and then reading from that file offset. The search is done as a simple binary search variation against an in-memory range maintained for each file.
    </p>
    <p>
    The log provides the capability of getting the most recently written message to allow clients to start subscribing as of "right now". This is also useful in the case the consumer fails to consume its data within its SLA-specified number of days. In this case when the client attempts to consume a non-existent offset it is given an OutOfRangeException and can either reset itself or fail as appropriate to the use case.
    </p>

    <p> The following is the format of the results sent to the consumer.

    <pre class="brush: text;">
    MessageSetSend (fetch result)

    total length     : 4 bytes
    error code       : 2 bytes
    message 1        : x bytes
    ...
    message n        : x bytes
    </pre>

    <pre class="brush: text;">
    MultiMessageSetSend (multiFetch result)

    total length       : 4 bytes
    error code         : 2 bytes
    messageSetSend 1
    ...
    messageSetSend n
    </pre>
    <h4><a id="impl_deletes" href="#impl_deletes">Deletes</a></h4>
    <p>
    Data is deleted one log segment at a time. The log manager allows pluggable delete policies to choose which files are eligible for deletion. The current policy deletes any log with a modification time of more than <i>N</i> days ago, though a policy which retained the last <i>N</i> GB could also be useful. To avoid locking reads while still allowing deletes that modify the segment list we use a copy-on-write style segment list implementation that provides consistent views to allow a binary search to proceed on an immutable static snapshot view of the log segments while deletes are progressing.
    </p>
    <h4><a id="impl_guarantees" href="#impl_guarantees">Guarantees</a></h4>
    <p>
    The log provides a configuration parameter <i>M</i> which controls the maximum number of messages that are written before forcing a flush to disk. On startup a log recovery process is run that iterates over all messages in the newest log segment and verifies that each message entry is valid. A message entry is valid if the sum of its size and offset are less than the length of the file AND the CRC32 of the message payload matches the CRC stored with the message. In the event corruption is detected the log is truncated to the last valid offset.
    </p>
    <p>
    Note that two kinds of corruption must be handled: truncation in which an unwritten block is lost due to a crash, and corruption in which a nonsense block is ADDED to the file. The reason for this is that in general the OS makes no guarantee of the write order between the file inode and the actual block data so in addition to losing written data the file can gain nonsense data if the inode is updated with a new size but a crash occurs before the block containing that data is written. The CRC detects this corner case, and prevents it from corrupting the log (though the unwritten messages are, of course, lost).
    </p>

    <h3><a id="distributionimpl" href="#distributionimpl">5.5 Distribution</a></h3>
    <h4><a id="impl_offsettracking" href="#impl_offsettracking">Consumer Offset Tracking</a></h4>
    <p>
    The high-level consumer tracks the maximum offset it has consumed in each partition and periodically commits its offset vector so that it can resume from those offsets in the event of a restart. Kafka provides the option to store all the offsets for a given consumer group in a designated broker (for that group) called the <i>offset manager</i>. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). The high-level consumer handles this automatically. If you use the simple consumer you will need to manage offsets manually. This is currently unsupported in the Java simple consumer which can only commit or fetch offsets in ZooKeeper. If you use the Scala simple consumer you can discover the offset manager and explicitly commit or fetch offsets to the offset manager. A consumer can look up its offset manager by issuing a GroupCoordinatorRequest to any Kafka broker and reading the GroupCoordinatorResponse which will contain the offset manager. The consumer can then proceed to commit or fetch offsets from the offsets manager broker. In case the offset manager moves, the consumer will need to rediscover the offset manager. If you wish to manage your offsets manually, you can take a look at these <a href="https://cwiki.apache.org/confluence/display/KAFKA/Committing+and+fetching+consumer+offsets+in+Kafka">code samples that explain how to issue OffsetCommitRequest and OffsetFetchRequest</a>.
    </p>

    <p>
    When the offset manager receives an OffsetCommitRequest, it appends the request to a special <a href="#compaction">compacted</a> Kafka topic named <i>__consumer_offsets</i>. The offset manager sends a successful offset commit response to the consumer only after all the replicas of the offsets topic receive the offsets. In case the offsets fail to replicate within a configurable timeout, the offset commit will fail and the consumer may retry the commit after backing off. (This is done automatically by the high-level consumer.) The brokers periodically compact the offsets topic since it only needs to maintain the most recent offset commit per partition. The offset manager also caches the offsets in an in-memory table in order to serve offset fetches quickly.
    </p>

    <p>
    When the offset manager receives an offset fetch request, it simply returns the last committed offset vector from the offsets cache. In case the offset manager was just started or if it just became the offset manager for a new set of consumer groups (by becoming a leader for a partition of the offsets topic), it may need to load the offsets topic partition into the cache. In this case, the offset fetch will fail with an OffsetsLoadInProgress exception and the consumer may retry the OffsetFetchRequest after backing off. (This is done automatically by the high-level consumer.)
    </p>

    <h5><a id="offsetmigration" href="#offsetmigration">Migrating offsets from ZooKeeper to Kafka</a></h5>
    <p>
    Kafka consumers in earlier releases store their offsets by default in ZooKeeper. It is possible to migrate these consumers to commit offsets into Kafka by following these steps:
    <ol>
    <li>Set <code>offsets.storage=kafka</code> and <code>dual.commit.enabled=true</code> in your consumer config.
    </li>
    <li>Do a rolling bounce of your consumers and then verify that your consumers are healthy.
    </li>
    <li>Set <code>dual.commit.enabled=false</code> in your consumer config.
    </li>
    <li>Do a rolling bounce of your consumers and then verify that your consumers are healthy.
    </li>
    </ol>
    A roll-back (i.e., migrating from Kafka back to ZooKeeper) can also be performed using the above steps if you set <code>offsets.storage=zookeeper</code>.
    </p>

    <h4><a id="impl_zookeeper" href="#impl_zookeeper">ZooKeeper Directories</a></h4>
    <p>
    The following gives the ZooKeeper structures and algorithms used for co-ordination between consumers and brokers.
    </p>

    <h4><a id="impl_zknotation" href="#impl_zknotation">Notation</a></h4>
    <p>
    When an element in a path is denoted [xyz], that means that the value of xyz is not fixed and there is in fact a ZooKeeper znode for each possible value of xyz. For example /topics/[topic] would be a directory named /topics containing a sub-directory for each topic name. Numerical ranges are also given such as [0...5] to indicate the subdirectories 0, 1, 2, 3, 4. An arrow -> is used to indicate the contents of a znode. For example /hello -> world would indicate a znode /hello containing the value "world".
    </p>

    <h4><a id="impl_zkbroker" href="#impl_zkbroker">Broker Node Registry</a></h4>
    <pre class="brush: json;">
    /brokers/ids/[0...N] --> {"jmx_port":...,"timestamp":...,"endpoints":[...],"host":...,"version":...,"port":...} (ephemeral node)
    </pre>
    <p>
    This is a list of all present broker nodes, each of which provides a unique logical broker id which identifies it to consumers (which must be given as part of its configuration). On startup, a broker node registers itself by creating a znode with the logical broker id under /brokers/ids. The purpose of the logical broker id is to allow a broker to be moved to a different physical machine without affecting consumers. An attempt to register a broker id that is already in use (say because two servers are configured with the same broker id) results in an error.
    </p>
    <p>
    Since the broker registers itself in ZooKeeper using ephemeral znodes, this registration is dynamic and will disappear if the broker is shutdown or dies (thus notifying consumers it is no longer available).
    </p>
    <h4><a id="impl_zktopic" href="#impl_zktopic">Broker Topic Registry</a></h4>
    <pre class="brush: json;">
    /brokers/topics/[topic]/partitions/[0...N]/state --> {"controller_epoch":...,"leader":...,"version":...,"leader_epoch":...,"isr":[...]} (ephemeral node)
    </pre>

    <p>
    Each broker registers itself under the topics it maintains and stores the number of partitions for that topic.
    </p>

    <h4><a id="impl_zkconsumers" href="#impl_zkconsumers">Consumers and Consumer Groups</a></h4>
    <p>
    Consumers of topics also register themselves in ZooKeeper, in order to coordinate with each other and balance the consumption of data. Consumers can also store their offsets in ZooKeeper by setting <code>offsets.storage=zookeeper</code>. However, this offset storage mechanism will be deprecated in a future release. Therefore, it is recommended to <a href="#offsetmigration">migrate offsets storage to Kafka</a>.
    </p>

    <p>
    Multiple consumers can form a group and jointly consume a single topic. Each consumer in the same group is given a shared group_id.
    For example if one consumer is your foobar process, which is run across three machines, then you might assign this group of consumers the id "foobar". This group id is provided in the configuration of the consumer, and is your way to tell the consumer which group it belongs to.
    </p>

    <p>
    The consumers in a group divide up the partitions as fairly as possible, each partition is consumed by exactly one consumer in a consumer group.
    </p>

    <h4><a id="impl_zkconsumerid" href="#impl_zkconsumerid">Consumer Id Registry</a></h4>
    <p>
    In addition to the group_id which is shared by all consumers in a group, each consumer is given a transient, unique consumer_id (of the form hostname:uuid) for identification purposes. Consumer ids are registered in the following directory.
    <pre class="brush: json;">
    /consumers/[group_id]/ids/[consumer_id] --> {"version":...,"subscription":{...:...},"pattern":...,"timestamp":...} (ephemeral node)
    </pre>
    Each of the consumers in the group registers under its group and creates a znode with its consumer_id. The value of the znode contains a map of &lt;topic, #streams&gt;. This id is simply used to identify each of the consumers which is currently active within a group. This is an ephemeral node so it will disappear if the consumer process dies.
    </p>

    <h4><a id="impl_zkconsumeroffsets" href="#impl_zkconsumeroffsets">Consumer Offsets</a></h4>
    <p>
    Consumers track the maximum offset they have consumed in each partition. This value is stored in a ZooKeeper directory if <code>offsets.storage=zookeeper</code>.
    </p>
    <pre class="brush: json;">
    /consumers/[group_id]/offsets/[topic]/[partition_id] --> offset_counter_value (persistent node)
    </pre>

    <h4><a id="impl_zkowner" href="#impl_zkowner">Partition Owner registry</a></h4>

    <p>
    Each broker partition is consumed by a single consumer within a given consumer group. The consumer must establish its ownership of a given partition before any consumption can begin. To establish its ownership, a consumer writes its own id in an ephemeral node under the particular broker partition it is claiming.
    </p>

    <pre class="brush: json;">
    /consumers/[group_id]/owners/[topic]/[partition_id] --> consumer_node_id (ephemeral node)
    </pre>

    <h4><a id="impl_clusterid" href="#impl_clusterid">Cluster Id</a></h4>

    <p>
        The cluster id is a unique and immutable identifier assigned to a Kafka cluster. The cluster id can have a maximum of 22 characters and the allowed characters are defined by the regular expression [a-zA-Z0-9_\-]+, which corresponds to the characters used by the URL-safe Base64 variant with no padding. Conceptually, it is auto-generated when a cluster is started for the first time.
    </p>
    <p>
        Implementation-wise, it is generated when a broker with version 0.10.1 or later is successfully started for the first time. The broker tries to get the cluster id from the <code>/cluster/id</code> znode during startup. If the znode does not exist, the broker generates a new cluster id and creates the znode with this cluster id.
    </p>

    <h4><a id="impl_brokerregistration" href="#impl_brokerregistration">Broker node registration</a></h4>

    <p>
    The broker nodes are basically independent, so they only publish information about what they have. When a broker joins, it registers itself under the broker node registry directory and writes information about its host name and port. The broker also register the list of existing topics and their logical partitions in the broker topic registry. New topics are registered dynamically when they are created on the broker.
    </p>

    <h4><a id="impl_consumerregistration" href="#impl_consumerregistration">Consumer registration algorithm</a></h4>

    <p>
    When a consumer starts, it does the following:
    <ol>
    <li> Register itself in the consumer id registry under its group.
    </li>
    <li> Register a watch on changes (new consumers joining or any existing consumers leaving) under the consumer id registry. (Each change triggers rebalancing among all consumers within the group to which the changed consumer belongs.)
    </li>
    <li> Register a watch on changes (new brokers joining or any existing brokers leaving) under the broker id registry. (Each change triggers rebalancing among all consumers in all consumer groups.) </li>
    <li> If the consumer creates a message stream using a topic filter, it also registers a watch on changes (new topics being added) under the broker topic registry. (Each change will trigger re-evaluation of the available topics to determine which topics are allowed by the topic filter. A new allowed topic will trigger rebalancing among all consumers within the consumer group.)</li>
    <li> Force itself to rebalance within in its consumer group.
    </li>
    </ol>
    </p>

    <h4><a id="impl_consumerrebalance" href="#impl_consumerrebalance">Consumer rebalancing algorithm</a></h4>
    <p>
    The consumer rebalancing algorithms allows all the consumers in a group to come into consensus on which consumer is consuming which partitions. Consumer rebalancing is triggered on each addition or removal of both broker nodes and other consumers within the same group. For a given topic and a given consumer group, broker partitions are divided evenly among consumers within the group. A partition is always consumed by a single consumer. This design simplifies the implementation. Had we allowed a partition to be concurrently consumed by multiple consumers, there would be contention on the partition and some kind of locking would be required. If there are more consumers than partitions, some consumers won't get any data at all. During rebalancing, we try to assign partitions to consumers in such a way that reduces the number of broker nodes each consumer has to connect to.
    </p>
    <p>
    Each consumer does the following during rebalancing:
    </p>
    <pre class="brush: text;">
    1. For each topic T that C<sub>i</sub> subscribes to
    2.   let P<sub>T</sub> be all partitions producing topic T
    3.   let C<sub>G</sub> be all consumers in the same group as C<sub>i</sub> that consume topic T
    4.   sort P<sub>T</sub> (so partitions on the same broker are clustered together)
    5.   sort C<sub>G</sub>
    6.   let i be the index position of C<sub>i</sub> in C<sub>G</sub> and let N = size(P<sub>T</sub>)/size(C<sub>G</sub>)
    7.   assign partitions from i*N to (i+1)*N - 1 to consumer C<sub>i</sub>
    8.   remove current entries owned by C<sub>i</sub> from the partition owner registry
    9.   add newly assigned partitions to the partition owner registry
            (we may need to re-try this until the original partition owner releases its ownership)
    </pre>
    <p>
    When rebalancing is triggered at one consumer, rebalancing should be triggered in other consumers within the same group about the same time.
    </p>
</script>

<div class="p-implementation"></div>