Flink's Table API & SQL programs can be connected to other external systems for reading and writing both batch and streaming tables. A table source provides access to data which is stored in external systems (such as a database, key-value store, message queue, or file system). A table sink emits a table to an external storage system. Depending on the type of source and sink, they support different formats such as CSV, Parquet, or ORC.
This page describes how to declare built-in table sources and/or table sinks and register them in Flink. After a source or sink has been registered, it can be accessed by Table API & SQL statements.
Attention If you want to implement your own custom table source or sink, have a look at the user-defined sources & sinks page.
The following tables list all available connectors and formats. Their mutual compatibility is tagged in the corresponding sections for table connectors and table formats. The following tables provide dependency information for both projects using a build automation tool (such as Maven or SBT) and SQL Client with SQL JAR bundles.
{% if site.is_stable %}
| Name | Version | Maven dependency | SQL Client JAR |
|---|---|---|---|
| Filesystem | Built-in | Built-in | |
| Elasticsearch | 6 | flink-connector-elasticsearch6 | Download |
| Apache Kafka | 0.8 | flink-connector-kafka-0.8 | Not available |
| Apache Kafka | 0.9 | flink-connector-kafka-0.9 | Download |
| Apache Kafka | 0.10 | flink-connector-kafka-0.10 | Download |
| Apache Kafka | 0.11 | flink-connector-kafka-0.11 | Download |
| Apache Kafka | 0.11+ (universal) | flink-connector-kafka | Download |
| Name | Maven dependency | SQL Client JAR |
|---|---|---|
| CSV | Built-in | Built-in |
| JSON | flink-json | Download |
| Apache Avro | flink-avro | Download |
{% else %}
These tables are only available for stable releases.
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Beginning from Flink 1.6, the declaration of a connection to an external system is separated from the actual implementation.
Connections can be specified either
Descriptor under org.apache.flink.table.descriptors for Table & SQL APIThis allows not only for better unification of APIs and SQL Client but also for better extensibility in case of custom implementations without changing the actual declaration.
Every declaration is similar to a SQL CREATE TABLE statement. One can define the name of the table, the schema of the table, a connector, and a data format upfront for connecting to an external system.
The connector describes the external system that stores the data of a table. Storage systems such as Apacha Kafka or a regular file system can be declared here. The connector might already provide a fixed format with fields and schema.
Some systems support different data formats. For example, a table that is stored in Kafka or in files can encode its rows with CSV, JSON, or Avro. A database connector might need the table schema here. Whether or not a storage system requires the definition of a format, is documented for every connector. Different systems also require different types of formats (e.g., column-oriented formats vs. row-oriented formats). The documentation states which format types and connectors are compatible.
The table schema defines the schema of a table that is exposed to SQL queries. It describes how a source maps the data format to the table schema and a sink vice versa. The schema has access to fields defined by the connector or format. It can use one or more fields for extracting or inserting time attributes. If input fields have no deterministic field order, the schema clearly defines column names, their order, and origin.
The subsequent sections will cover each definition part (connector, format, and schema) in more detail. The following example shows how to pass them:
The table's type (source, sink, or both) determines how a table is registered. In case of table type both, both a table source and table sink are registered under the same name. Logically, this means that we can both read and write to such a table similarly to a table in a regular DBMS.
For streaming queries, an update mode declares how to communicate between a dynamic table and the storage system for continuous queries.
The following code shows a full example of how to connect to Kafka for reading Avro records.
// declare a format for this system .withFormat( new Avro() .avroSchema( “{” + " "namespace": "org.myorganization"," + " "type": "record"," + " "name": "UserMessage"," + " "fields": [" + " {"name": "timestamp", "type": "string"}," + " {"name": "user", "type": "long"}," + " {"name": "message", "type": ["string", "null"]}" + " ]" + “}” ) )
// declare the schema of the table .withSchema( new Schema() .field(“rowtime”, Types.SQL_TIMESTAMP) .rowtime(new Rowtime() .timestampsFromField(“timestamp”) .watermarksPeriodicBounded(60000) ) .field(“user”, Types.LONG) .field(“message”, Types.STRING) )
// specify the update-mode for streaming tables .inAppendMode()
// register as source, sink, or both and under a name .registerTableSource(“MyUserTable”); {% endhighlight %}
# declare the external system to connect to
connector:
type: kafka
version: "0.10"
topic: test-input
startup-mode: earliest-offset
properties:
- key: zookeeper.connect
value: localhost:2181
- key: bootstrap.servers
value: localhost:9092
# declare a format for this system
format:
type: avro
avro-schema: >
{
"namespace": "org.myorganization",
"type": "record",
"name": "UserMessage",
"fields": [
{"name": "ts", "type": "string"},
{"name": "user", "type": "long"},
{"name": "message", "type": ["string", "null"]}
]
}
# declare the schema of the table
schema:
- name: rowtime
type: TIMESTAMP
rowtime:
timestamps:
type: from-field
from: ts
watermarks:
type: periodic-bounded
delay: "60000"
- name: user
type: BIGINT
- name: message
type: VARCHAR
{% endhighlight %}
In both ways the desired connection properties are converted into normalized, string-based key-value pairs. So-called table factories create configured table sources, table sinks, and corresponding formats from the key-value pairs. All table factories that can be found via Java's Service Provider Interfaces (SPI) are taken into account when searching for exactly-one matching table factory.
If no factory can be found or multiple factories match for the given properties, an exception will be thrown with additional information about considered factories and supported properties.
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The table schema defines the names and types of columns similar to the column definitions of a SQL CREATE TABLE statement. In addition, one can specify how columns are mapped from and to fields of the format in which the table data is encoded. The origin of a field might be important if the name of the column should differ from the input/output format. For instance, a column user_name should reference the field $$-user-name from a JSON format. Additionally, the schema is needed to map types from an external system to Flink's representation. In case of a table sink, it ensures that only data with valid schema is written to an external system.
The following example shows a simple schema without time attributes and one-to-one field mapping of input/output to table columns.
For each field, the following properties can be declared in addition to the column's name and type:
Time attributes are essential when working with unbounded streaming tables. Therefore both processing-time and event-time (also known as “rowtime”) attributes can be defined as part of the schema.
For more information about time handling in Flink and especially event-time, we recommend the general event-time section.
In order to control the event-time behavior for tables, Flink provides predefined timestamp extractors and watermark strategies.
The following timestamp extractors are supported:
// Converts the assigned timestamps from a DataStream API record into the rowtime attribute // and thus preserves the assigned timestamps from the source. // This requires a source that assigns timestamps (e.g., Kafka 0.10+). .rowtime( new Rowtime() .timestampsFromSource() )
// Sets a custom timestamp extractor to be used for the rowtime attribute. // The extractor must extend org.apache.flink.table.sources.tsextractors.TimestampExtractor. .rowtime( new Rowtime() .timestampsFromExtractor(...) ) {% endhighlight %}
rowtime: timestamps: type: from-source {% endhighlight %}
The following watermark strategies are supported:
// Sets a built-in watermark strategy for rowtime attributes which are out-of-order by a bounded time interval. // Emits watermarks which are the maximum observed timestamp minus the specified delay. .rowtime( new Rowtime() .watermarksPeriodicBounded(2000) // delay in milliseconds )
// Sets a built-in watermark strategy which indicates the watermarks should be preserved from the // underlying DataStream API and thus preserves the assigned watermarks from the source. .rowtime( new Rowtime() .watermarksFromSource() ) {% endhighlight %}
rowtime: watermarks: type: periodic-bounded delay: ... # required: delay in milliseconds
rowtime: watermarks: type: from-source {% endhighlight %}
Make sure to always declare both timestamps and watermarks. Watermarks are required for triggering time-based operations.
Because type information is only available in a programming language, the following type strings are supported for being defined in a YAML file:
{% highlight yaml %} VARCHAR BOOLEAN TINYINT SMALLINT INT BIGINT FLOAT DOUBLE DECIMAL DATE TIME TIMESTAMP MAP<fieldtype, fieldtype> # generic map; e.g. MAP<VARCHAR, INT> that is mapped to Flink‘s MapTypeInfo MULTISET # multiset; e.g. MULTISET that is mapped to Flink’s MultisetTypeInfo PRIMITIVE_ARRAY # primitive array; e.g. PRIMITIVE_ARRAY that is mapped to Flink‘s PrimitiveArrayTypeInfo OBJECT_ARRAY # object array; e.g. OBJECT_ARRAY<POJO(org.mycompany.MyPojoClass)> that is mapped to # Flink’s ObjectArrayTypeInfo ROW<fieldtype, ...> # unnamed row; e.g. ROW<VARCHAR, INT> that is mapped to Flink‘s RowTypeInfo # with indexed fields names f0, f1, ... ROW<fieldname fieldtype, ...> # named row; e.g., ROW<myField VARCHAR, myOtherField INT> that # is mapped to Flink’s RowTypeInfo POJO # e.g., POJO<org.mycompany.MyPojoClass> that is mapped to Flink‘s PojoTypeInfo ANY # e.g., ANY<org.mycompany.MyClass> that is mapped to Flink’s GenericTypeInfo ANY<class, serialized> # used for type information that is not supported by Flink's Table & SQL API {% endhighlight %}
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For streaming queries, it is required to declare how to perform the conversion between a dynamic table and an external connector. The update mode specifies which kind of messages should be exchanged with the external system:
Append Mode: In append mode, a dynamic table and an external connector only exchange INSERT messages.
Retract Mode: In retract mode, a dynamic table and an external connector exchange ADD and RETRACT messages. An INSERT change is encoded as an ADD message, a DELETE change as a RETRACT message, and an UPDATE change as a RETRACT message for the updated (previous) row and an ADD message for the updating (new) row. In this mode, a key must not be defined as opposed to upsert mode. However, every update consists of two messages which is less efficient.
Upsert Mode: In upsert mode, a dynamic table and an external connector exchange UPSERT and DELETE messages. This mode requires a (possibly composite) unique key by which updates can be propagated. The external connector needs to be aware of the unique key attribute in order to apply messages correctly. INSERT and UPDATE changes are encoded as UPSERT messages. DELETE changes as DELETE messages. The main difference to a retract stream is that UPDATE changes are encoded with a single message and are therefore more efficient.
Attention The documentation of each connector states which update modes are supported.
See also the general streaming concepts documentation for more information.
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Flink provides a set of connectors for connecting to external systems.
Please note that not all connectors are available in both batch and streaming yet. Furthermore, not every streaming connector supports every streaming mode. Therefore, each connector is tagged accordingly. A format tag indicates that the connector requires a certain type of format.
Source: Batch Source: Streaming Append Mode Sink: Batch Sink: Streaming Append Mode Format: CSV-only
The file system connector allows for reading and writing from a local or distributed filesystem. A filesystem can be defined as:
The file system connector itself is included in Flink and does not require an additional dependency. A corresponding format needs to be specified for reading and writing rows from and to a file system.
Attention Make sure to include [Flink File System specific dependencies]({{ site.baseurl }}/ops/filesystems.html).
Attention File system sources and sinks for streaming are only experimental. In the future, we will support actual streaming use cases, i.e., directory monitoring and bucket output.
Source: Streaming Append Mode Sink: Streaming Append Mode Format: Serialization Schema Format: Deserialization Schema
The Kafka connector allows for reading and writing from and to an Apache Kafka topic. It can be defined as follows:
// optional: connector specific properties
.property("zookeeper.connect", "localhost:2181")
.property("bootstrap.servers", "localhost:9092")
.property("group.id", "testGroup")
// optional: select a startup mode for Kafka offsets
.startFromEarliest()
.startFromLatest()
.startFromSpecificOffsets(...)
// optional: output partitioning from Flink's partitions into Kafka's partitions
.sinkPartitionerFixed() // each Flink partition ends up in at-most one Kafka partition (default)
.sinkPartitionerRoundRobin() // a Flink partition is distributed to Kafka partitions round-robin
.sinkPartitionerCustom(MyCustom.class) // use a custom FlinkKafkaPartitioner subclass
) {% endhighlight %}
properties: # optional: connector specific properties - key: zookeeper.connect value: localhost:2181 - key: bootstrap.servers value: localhost:9092 - key: group.id value: testGroup
startup-mode: ... # optional: valid modes are “earliest-offset”, “latest-offset”, # “group-offsets”, or “specific-offsets” specific-offsets: # optional: used in case of startup mode with specific offsets - partition: 0 offset: 42 - partition: 1 offset: 300
sink-partitioner: ... # optional: output partitioning from Flink‘s partitions into Kafka’s partitions # valid are “fixed” (each Flink partition ends up in at most one Kafka partition), # “round-robin” (a Flink partition is distributed to Kafka partitions round-robin) # “custom” (use a custom FlinkKafkaPartitioner subclass) sink-partitioner-class: org.mycompany.MyPartitioner # optional: used in case of sink partitioner custom {% endhighlight %}
Specify the start reading position: By default, the Kafka source will start reading data from the committed group offsets in Zookeeper or Kafka brokers. You can specify other start positions, which correspond to the configurations in section [Kafka Consumers Start Position Configuration]({{ site.baseurl }}/dev/connectors/kafka.html#kafka-consumers-start-position-configuration).
Flink-Kafka Sink Partitioning: By default, a Kafka sink writes to at most as many partitions as its own parallelism (each parallel instance of the sink writes to exactly one partition). In order to distribute the writes to more partitions or control the routing of rows into partitions, a custom sink partitioner can be provided. The round-robin partitioner is useful to avoid an unbalanced partitioning. However, it will cause a lot of network connections between all the Flink instances and all the Kafka brokers.
Consistency guarantees: By default, a Kafka sink ingests data with at-least-once guarantees into a Kafka topic if the query is executed with [checkpointing enabled]({{ site.baseurl }}/dev/stream/state/checkpointing.html#enabling-and-configuring-checkpointing).
Kafka 0.10+ Timestamps: Since Kafka 0.10, Kafka messages have a timestamp as metadata that specifies when the record was written into the Kafka topic. These timestamps can be used for a rowtime attribute by selecting timestamps: from-source in YAML and timestampsFromSource() in Java/Scala respectively.
Kafka 0.11+ Versioning: Since Flink 1.7, the Kafka connector definition should be independent of a hard-coded Kafka version. Use the connector version universal as a wildcard for Flink's Kafka connector that is compatible with all Kafka versions starting from 0.11.
Make sure to add the version-specific Kafka dependency. In addition, a corresponding format needs to be specified for reading and writing rows from and to Kafka.
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Sink: Streaming Append Mode Sink: Streaming Upsert Mode Format: JSON-only
The Elasticsearch connector allows for writing into an index of the Elasticsearch search engine.
The connector can operate in upsert mode for exchanging UPSERT/DELETE messages with the external system using a key defined by the query.
For append-only queries, the connector can also operate in append mode for exchanging only INSERT messages with the external system. If no key is defined by the query, a key is automatically generated by Elasticsearch.
The connector can be defined as follows:
.keyDelimiter("$") // optional: delimiter for composite keys ("_" by default)
// e.g., "$" would result in IDs "KEY1$KEY2$KEY3"
.keyNullLiteral("n/a") // optional: representation for null fields in keys ("null" by default)
// optional: failure handling strategy in case a request to Elasticsearch fails (fail by default)
.failureHandlerFail() // optional: throws an exception if a request fails and causes a job failure
.failureHandlerIgnore() // or ignores failures and drops the request
.failureHandlerRetryRejected() // or re-adds requests that have failed due to queue capacity saturation
.failureHandlerCustom(...) // or custom failure handling with a ActionRequestFailureHandler subclass
// optional: configure how to buffer elements before sending them in bulk to the cluster for efficiency
.disableFlushOnCheckpoint() // optional: disables flushing on checkpoint (see notes below!)
.bulkFlushMaxActions(42) // optional: maximum number of actions to buffer for each bulk request
.bulkFlushMaxSize("42 mb") // optional: maximum size of buffered actions in bytes per bulk request
// (only MB granularity is supported)
.bulkFlushInterval(60000L) // optional: bulk flush interval (in milliseconds)
.bulkFlushBackoffConstant() // optional: use a constant backoff type
.bulkFlushBackoffExponential() // or use an exponential backoff type
.bulkFlushBackoffMaxRetries(3) // optional: maximum number of retries
.bulkFlushBackoffDelay(30000L) // optional: delay between each backoff attempt (in milliseconds)
// optional: connection properties to be used during REST communication to Elasticsearch
.connectionMaxRetryTimeout(3) // optional: maximum timeout (in milliseconds) between retries
.connectionPathPrefix("/v1") // optional: prefix string to be added to every REST communication
) {% endhighlight %}
key-delimiter: "$" # optional: delimiter for composite keys ("_" by default)
# e.g., "$" would result in IDs "KEY1$KEY2$KEY3"
key-null-literal: "n/a" # optional: representation for null fields in keys ("null" by default)
# optional: failure handling strategy in case a request to Elasticsearch fails ("fail" by default)
failure-handler: ... # valid strategies are "fail" (throws an exception if a request fails and
# thus causes a job failure), "ignore" (ignores failures and drops the request),
# "retry-rejected" (re-adds requests that have failed due to queue capacity
# saturation), or "custom" for failure handling with a
# ActionRequestFailureHandler subclass
# optional: configure how to buffer elements before sending them in bulk to the cluster for efficiency
flush-on-checkpoint: true # optional: disables flushing on checkpoint (see notes below!) ("true" by default)
bulk-flush:
max-actions: 42 # optional: maximum number of actions to buffer for each bulk request
max-size: 42 mb # optional: maximum size of buffered actions in bytes per bulk request
# (only MB granularity is supported)
interval: 60000 # optional: bulk flush interval (in milliseconds)
back-off: # optional: backoff strategy ("disabled" by default)
type: ... # valid strategies are "disabled", "constant", or "exponential"
max-retries: 3 # optional: maximum number of retries
delay: 30000 # optional: delay between each backoff attempt (in milliseconds)
# optional: connection properties to be used during REST communication to Elasticsearch
connection-max-retry-timeout: 3 # optional: maximum timeout (in milliseconds) between retries
connection-path-prefix: "/v1" # optional: prefix string to be added to every REST communication
{% endhighlight %}
Bulk flushing: For more information about characteristics of the optional flushing parameters see the [corresponding low-level documentation]({{ site.baseurl }}/dev/connectors/elasticsearch.html).
Disabling flushing on checkpoint: When disabled, a sink will not wait for all pending action requests to be acknowledged by Elasticsearch on checkpoints. Thus, a sink does NOT provide any strong guarantees for at-least-once delivery of action requests.
Key extraction: Flink automatically extracts valid keys from a query. For example, a query SELECT a, b, c FROM t GROUP BY a, b defines a composite key of the fields a and b. The Elasticsearch connector generates a document ID string for every row by concatenating all key fields in the order defined in the query using a key delimiter. A custom representation of null literals for key fields can be defined.
Attention A JSON format defines how to encode documents for the external system, therefore, it must be added as a dependency.
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Flink provides a set of table formats that can be used with table connectors.
A format tag indicates the format type for matching with a connector.
The CSV format allows to read and write comma-separated rows.
The CSV format is included in Flink and does not require additional dependencies.
Attention The CSV format for writing rows is limited at the moment. Only a custom field delimiter is supported as optional parameter.
Format: Serialization Schema Format: Deserialization Schema
The JSON format allows to read and write JSON data that corresponds to a given format schema. The format schema can be defined either as a Flink type, as a JSON schema, or derived from the desired table schema. A Flink type enables a more SQL-like definition and mapping to the corresponding SQL data types. The JSON schema allows for more complex and nested structures.
If the format schema is equal to the table schema, the schema can also be automatically derived. This allows for defining schema information only once. The names, types, and field order of the format are determined by the table's schema. Time attributes are ignored if their origin is not a field. A from definition in the table schema is interpreted as a field renaming in the format.
// required: define the schema either by using type information which parses numbers to corresponding types
.schema(Type.ROW(...))
// or by using a JSON schema which parses to DECIMAL and TIMESTAMP
.jsonSchema(
"{" +
" type: 'object'," +
" properties: {" +
" lon: {" +
" type: 'number'" +
" }," +
" rideTime: {" +
" type: 'string'," +
" format: 'date-time'" +
" }" +
" }" +
"}"
)
// or use the table's schema
.deriveSchema()
) {% endhighlight %}
schema: “ROW(lon FLOAT, rideTime TIMESTAMP)”
json-schema: > { type: ‘object’, properties: { lon: { type: ‘number’ }, rideTime: { type: ‘string’, format: ‘date-time’ } } }
derive-schema: true {% endhighlight %}
The following table shows the mapping of JSON schema types to Flink SQL types:
| JSON schema | Flink SQL |
|---|---|
object | ROW |
boolean | BOOLEAN |
array | ARRAY[_] |
number | DECIMAL |
integer | DECIMAL |
string | VARCHAR |
string with format: date-time | TIMESTAMP |
string with format: date | DATE |
string with format: time | TIME |
string with encoding: base64 | ARRAY[TINYINT] |
null | NULL (unsupported yet) |
Currently, Flink supports only a subset of the JSON schema specification draft-07. Union types (as well as allOf, anyOf, not) are not supported yet. oneOf and arrays of types are only supported for specifying nullability.
Simple references that link to a common definition in the document are supported as shown in the more complex example below:
{% highlight json %} { “definitions”: { “address”: { “type”: “object”, “properties”: { “street_address”: { “type”: “string” }, “city”: { “type”: “string” }, “state”: { “type”: “string” } }, “required”: [ “street_address”, “city”, “state” ] } }, “type”: “object”, “properties”: { “billing_address”: { “$ref”: “#/definitions/address” }, “shipping_address”: { “$ref”: “#/definitions/address” }, “optional_address”: { “oneOf”: [ { “type”: “null” }, { “$ref”: “#/definitions/address” } ] } } } {% endhighlight %}
Missing Field Handling: By default, a missing JSON field is set to null. You can enable strict JSON parsing that will cancel the source (and query) if a field is missing.
Make sure to add the JSON format as a dependency.
Format: Serialization Schema Format: Deserialization Schema
The Apache Avro format allows to read and write Avro data that corresponds to a given format schema. The format schema can be defined either as a fully qualified class name of an Avro specific record or as an Avro schema string. If a class name is used, the class must be available in the classpath during runtime.
// required: define the schema either by using an Avro specific record class
.recordClass(User.class)
// or by using an Avro schema
.avroSchema(
"{" +
" \"type\": \"record\"," +
" \"name\": \"test\"," +
" \"fields\" : [" +
" {\"name\": \"a\", \"type\": \"long\"}," +
" {\"name\": \"b\", \"type\": \"string\"}" +
" ]" +
"}"
)
) {% endhighlight %}
record-class: “org.organization.types.User”
avro-schema: > { “type”: “record”, “name”: “test”, “fields” : [ {“name”: “a”, “type”: “long”}, {“name”: “b”, “type”: “string”} ] } {% endhighlight %}
Avro types are mapped to the corresponding SQL data types. Union types are only supported for specifying nullability otherwise they are converted to an ANY type. The following table shows the mapping:
| Avro schema | Flink SQL |
|---|---|
record | ROW |
enum | VARCHAR |
array | ARRAY[_] |
map | MAP[VARCHAR, _] |
union | non-null type or ANY |
fixed | ARRAY[TINYINT] |
string | VARCHAR |
bytes | ARRAY[TINYINT] |
int | INT |
long | BIGINT |
float | FLOAT |
double | DOUBLE |
boolean | BOOLEAN |
int with logicalType: date | DATE |
int with logicalType: time-millis | TIME |
int with logicalType: time-micros | INT |
long with logicalType: timestamp-millis | TIMESTAMP |
long with logicalType: timestamp-micros | BIGINT |
bytes with logicalType: decimal | DECIMAL |
fixed with logicalType: decimal | DECIMAL |
null | NULL (unsupported yet) |
Avro uses Joda-Time for representing logical date and time types in specific record classes. The Joda-Time dependency is not part of Flink's distribution. Therefore, make sure that Joda-Time is in your classpath together with your specific record class during runtime. Avro formats specified via a schema string do not require Joda-Time to be present.
Make sure to add the Apache Avro dependency.
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The following table sources and sinks have not yet been migrated (or have not been migrated entirely) to the new unified interfaces.
These are the additional TableSources which are provided with Flink:
| Class name | Maven dependency | Batch? | Streaming? | Description | OrcTableSource | flink-orc | Y | N | A TableSource for ORC files.
These are the additional TableSinks which are provided with Flink:
| Class name | Maven dependency | Batch? | Streaming? | Description | CsvTableSink | flink-table | Y | Append | A simple sink for CSV files. | JDBCAppendTableSink | flink-jdbc | Y | Append | Writes a Table to a JDBC table. | CassandraAppendTableSink | flink-connector-cassandra | N | Append | Writes a Table to a Cassandra table.
The OrcTableSource reads ORC files. ORC is a file format for structured data and stores the data in a compressed, columnar representation. ORC is very storage efficient and supports projection and filter push-down.
An OrcTableSource is created as shown below:
// create Hadoop Configuration Configuration config = new Configuration();
OrcTableSource orcTableSource = OrcTableSource.builder() // path to ORC file(s). NOTE: By default, directories are recursively scanned. .path(“file:///path/to/data”) // schema of ORC files .forOrcSchema(“struct<name:string,addresses:array<structstreet:string,zip:smallint>>”) // Hadoop configuration .withConfiguration(config) // build OrcTableSource .build(); {% endhighlight %}
// create Hadoop Configuration val config = new Configuration()
val orcTableSource = OrcTableSource.builder() // path to ORC file(s). NOTE: By default, directories are recursively scanned. .path(“file:///path/to/data”) // schema of ORC files .forOrcSchema(“struct<name:string,addresses:array<structstreet:string,zip:smallint>>”) // Hadoop configuration .withConfiguration(config) // build OrcTableSource .build() {% endhighlight %}
Note: The OrcTableSource does not support ORC's Union type yet.
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The CsvTableSink emits a Table to one or more CSV files.
The sink only supports append-only streaming tables. It cannot be used to emit a Table that is continuously updated. See the documentation on Table to Stream conversions for details. When emitting a streaming table, rows are written at least once (if checkpointing is enabled) and the CsvTableSink does not split output files into bucket files but continuously writes to the same files.
CsvTableSink sink = new CsvTableSink( path, // output path “|”, // optional: delimit files by ‘|’ 1, // optional: write to a single file WriteMode.OVERWRITE); // optional: override existing files
tableEnv.registerTableSink( “csvOutputTable”, // specify table schema new String[]{“f0”, “f1”}, new TypeInformation[]{Types.STRING, Types.INT}, sink);
Table table = ... table.insertInto(“csvOutputTable”); {% endhighlight %}
val sink: CsvTableSink = new CsvTableSink( path, // output path fieldDelim = “|”, // optional: delimit files by ‘|’ numFiles = 1, // optional: write to a single file writeMode = WriteMode.OVERWRITE) // optional: override existing files
tableEnv.registerTableSink( “csvOutputTable”, // specify table schema ArrayString, Array[TypeInformation[_]](Types.STRING, Types.INT), sink)
val table: Table = ??? table.insertInto(“csvOutputTable”) {% endhighlight %}
The JDBCAppendTableSink emits a Table to a JDBC connection. The sink only supports append-only streaming tables. It cannot be used to emit a Table that is continuously updated. See the documentation on Table to Stream conversions for details.
The JDBCAppendTableSink inserts each Table row at least once into the database table (if checkpointing is enabled). However, you can specify the insertion query using REPLACE or INSERT OVERWRITE to perform upsert writes to the database.
To use the JDBC sink, you have to add the JDBC connector dependency (flink-jdbc) to your project. Then you can create the sink using JDBCAppendSinkBuilder:
JDBCAppendTableSink sink = JDBCAppendTableSink.builder() .setDrivername(“org.apache.derby.jdbc.EmbeddedDriver”) .setDBUrl(“jdbc:derby:memory:ebookshop”) .setQuery(“INSERT INTO books (id) VALUES (?)”) .setParameterTypes(INT_TYPE_INFO) .build();
tableEnv.registerTableSink( “jdbcOutputTable”, // specify table schema new String[]{“id”}, new TypeInformation[]{Types.INT}, sink);
Table table = ... table.insertInto(“jdbcOutputTable”); {% endhighlight %}
tableEnv.registerTableSink( “jdbcOutputTable”, // specify table schema ArrayString, ArrayTypeInformation[_], sink)
val table: Table = ??? table.insertInto(“jdbcOutputTable”) {% endhighlight %}
Similar to using JDBCOutputFormat, you have to explicitly specify the name of the JDBC driver, the JDBC URL, the query to be executed, and the field types of the JDBC table.
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The CassandraAppendTableSink emits a Table to a Cassandra table. The sink only supports append-only streaming tables. It cannot be used to emit a Table that is continuously updated. See the documentation on Table to Stream conversions for details.
The CassandraAppendTableSink inserts all rows at least once into the Cassandra table if checkpointing is enabled. However, you can specify the query as upsert query.
To use the CassandraAppendTableSink, you have to add the Cassandra connector dependency (flink-connector-cassandra) to your project. The example below shows how to use the CassandraAppendTableSink.
ClusterBuilder builder = ... // configure Cassandra cluster connection
CassandraAppendTableSink sink = new CassandraAppendTableSink( builder, // the query must match the schema of the table “INSERT INTO flink.myTable (id, name, value) VALUES (?, ?, ?)”);
tableEnv.registerTableSink( “cassandraOutputTable”, // specify table schema new String[]{“id”, “name”, “value”}, new TypeInformation[]{Types.INT, Types.STRING, Types.DOUBLE}, sink);
Table table = ... table.insertInto(cassandraOutputTable); {% endhighlight %}
val sink: CassandraAppendTableSink = new CassandraAppendTableSink( builder, // the query must match the schema of the table “INSERT INTO flink.myTable (id, name, value) VALUES (?, ?, ?)”)
tableEnv.registerTableSink( “cassandraOutputTable”, // specify table schema Array[String](“id”, “name”, “value”), Array[TypeInformation[_]](Types.INT, Types.STRING, Types.DOUBLE), sink)
val table: Table = ??? table.insertInto(cassandraOutputTable) {% endhighlight %}
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