Project: Flink (App Definition and Development / Streaming & Messaging)
File: sourcesSinks.md

title: "User-defined Sources & Sinks" weight: 131 type: docs aliases: /dev/table/sourceSinks.html <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> Dynamic tables are the core concept of Flink's Table & SQL API for processing both bounded and unbounded data in a unified fashion. Because dynamic tables are only a logical concept, Flink does not own the data itself. Instead, the content of a dynamic table is stored in external systems (such as databases, key-value stores, message queues) or files. Dynamic sources and dynamic sinks can be used to read and write data from and to an external system. In the documentation, sources and sinks are often summarized under the term connector. Flink provides pre-defined connectors for Kafka, Hive, and different file systems. See the for more information about built-in table sources and sinks. This page focuses on how to develop a custom, user-defined connector. {{< hint warning >}}Since Flink v1.16, TableEnvironment introduces a user class loader to have a consistent class loading behavior in table programs, SQL Client and SQL Gateway. The user classloader manages all user jars such as jar added by `ADD JAR` or `CREATE FUNCTION .. USING JAR ..` statements. User-defined connectors should replace `Thread.currentThread().getContextClassLoader()` with the user class loader to load classes. Otherwise, `ClassNotFoundException` maybe thrown. The user class loader can be accessed via `DynamicTableFactory.Context`. {{< /hint >}} Overview -- In many cases, implementers don't need to create a new connector from scratch but would like to slightly modify existing connectors or hook into the existing stack. In other cases, implementers would like to create specialized connectors. This section helps for both kinds of use cases. It explains the general architecture of table connectors from pure declaration in the API to runtime code that will be executed on the cluster. The filled arrows show how objects are transformed to other objects from one stage to the next stage during the translation process. {{< img width="90%" src="/fig/table_connectors.svg" alt="Translation of table connectors" >}} Both Table API and SQL are declarative APIs. This includes the declaration of tables. Thus, executing a `CREATE TABLE` statement results in updated metadata in the target catalog. For most catalog implementations, physical data in the external system is not modified for such an operation. Connector-specific dependencies don't have to be present in the classpath yet. The options declared in the `WITH` clause are neither validated nor otherwise interpreted. The metadata for dynamic tables (created via DDL or provided by the catalog) is represented as instances of `CatalogTable`. A table name will be resolved into a `CatalogTable` internally when necessary. When it comes to planning and optimization of the table program, a `CatalogTable` needs to be resolved into a `DynamicTableSource` (for reading in a `SELECT` query) and `DynamicTableSink` (for writing in an `INSERT INTO` statement). `DynamicTableSourceFactory` and `DynamicTableSinkFactory` provide connector-specific logic for translating the metadata of a `CatalogTable` into instances of `DynamicTableSource` and

In most of the cases, a factory's purpose is to validate options (such as `'port' = '5022'` in the example), configure encoding/decoding formats (if required), and create a parameterized instance of the table connector. By default, instances of `DynamicTableSourceFactory` and `DynamicTableSinkFactory` are discovered using Java's . The `connector` option (such as `'connector' = 'custom'` in the example) must correspond to a valid factory identifier. Although it might not be apparent in the class naming, `DynamicTableSource` and `DynamicTableSink` can also be seen as stateful factories that eventually produce concrete runtime implementation for reading/writing the actual data. The planner uses the source and sink instances to perform connector-specific bidirectional communication until an optimal logical plan could be found. Depending on the optionally declared ability interfaces (e.g. `SupportsProjectionPushDown` or `SupportsOverwrite`), the planner might apply changes to an instance and thus mutate the produced runtime implementation. Once the logical planning is complete, the planner will obtain the runtime implementation from the table connector. Runtime logic is implemented in Flink's core connector interfaces such as `InputFormat` or `SourceFunction`. Those interfaces are grouped by another level of abstraction as subclasses of `ScanRuntimeProvider`, `LookupRuntimeProvider`, and `SinkRuntimeProvider`. For example, both `OutputFormatProvider` (providing `org.apache.flink.api.common.io.OutputFormat`) and `SinkFunctionProvider` (providing `org.apache.flink.streaming.api.functions.sink.SinkFunction`) are concrete instances of `SinkRuntimeProvider` that the planner can handle. {{< top >}} Project Configuration If you want to implement a custom connector or a custom format, the following dependency is usually sufficient: {{< artifact_tabs flink-table-common withProvidedScope >}} If you want to develop a connector that needs to bridge with DataStream APIs (i.e. if you want to adapt a DataStream connector to the Table API), you need to add this dependency: {{< artifact_tabs flink-table-api-java-bridge withProvidedScope >}} When developing the connector/format, we suggest shipping both a thin JAR and an uber JAR, so users can easily load the uber JAR in the SQL client or in the Flink distribution and start using it. The uber JAR should include all the third-party dependencies of the connector, excluding the table dependencies listed above. {{< hint warning >}} You should not depend on `flink-table-planner{{< scala_version >}}` in production code. With the new module `flink-table-planner-loader` introduced in Flink 1.15, the application's classpath will not have direct access to `org.apache.flink.table.planner` classes anymore. If you need a feature available only internally within the `org.apache.flink.table.planner` package and subpackages, please open an issue. To learn more, check out . {{< /hint >}} Extension Points This section explains the available interfaces for extending Flink's table connectors. Dynamic table factories are used to configure a dynamic table connector for an external storage system from catalog and session information. `org.apache.flink.table.factories.DynamicTableSourceFactory` can be implemented to construct a `DynamicTableSource`. `org.apache.flink.table.factories.DynamicTableSinkFactory` can be implemented to construct a `DynamicTableSink`. By default, the factory is discovered using the value of the `connector` option as the factory identifier and Java's Service Provider Interface. In JAR files, references to new implementations can be added to the service file: `META-INF/services/org.apache.flink.table.factories.Factory` The framework will check for a single matching factory that is uniquely identified by factory identifier and requested base class (e.g. `DynamicTableSourceFactory`). The factory discovery process can be bypassed by the catalog implementation if necessary. For this, a catalog needs to return an instance that implements the requested base class in `org.apache.flink.table.catalog.Catalog#getFactory`. By definition, a dynamic table can change over time. When reading a dynamic table, the content can either be considered as: A changelog (finite or infinite) for which all changes are consumed continuously until the changelog is exhausted. This is represented by the `ScanTableSource`

A continuously changing or very large external table whose content is usually never read entirely but queried for individual values when necessary. This is represented by the `LookupTableSource` interface. A class can implement both of these interfaces at the same time. The planner decides about their usage depending on the specified query. A `ScanTableSource` scans all rows from an external storage system during runtime. The scanned rows don't have to contain only insertions but can also contain updates and deletions. Thus, the table source can be used to read a (finite or infinite) changelog. The returned changelog mode indicates the set of changes that the planner can expect during runtime. For regular batch scenarios, the source can emit a bounded stream of insert-only rows. For regular streaming scenarios, the source can emit an unbounded stream of insert-only rows. For change data capture (CDC) scenarios, the source can emit bounded or unbounded streams with insert, update, and delete rows. A table source can implement further ability interfaces such as `SupportsProjectionPushDown` that might mutate an instance during planning. All abilities can be found in the `org.apache.flink.table.connector.source.abilities` package and are listed in the . The returned scan runtime provider provides the runtime implementation for reading the data. There are different interfaces for runtime implementation, among which `SourceProvider` is the recommended core interface. Independent of the provider interface, the source runtime implementation must produce internal data structures. Thus, records must be emitted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such that a source can still work on common data structures and perform a conversion at the end. To support parallelism setting, the dynamic table factory should support the optional `scan.parallelism` option defined in `org.apache.flink.table.factories.FactoryUtil` and pass its value to a provider that also implements the `ParallelismProvider` interface. A `LookupTableSource` looks up rows of an external storage system by one or more keys during runtime. Compared to `ScanTableSource`, the source does not have to read the entire table and can lazily fetch individual values from a (possibly continuously changing) external table when necessary. Compared to `ScanTableSource`, a `LookupTableSource` does only support emitting insert-only changes currently. Further abilities are not supported. See the documentation of `org.apache.flink.table.connector.source.LookupTableSource` for more information. The runtime implementation of a `LookupTableSource` is a `TableFunction` or `AsyncTableFunction`. The function will be called with values for the given lookup keys during runtime. <table class="table table-bordered"> <thead> <tr> <th class="text-left" style="width: 25%">Interface</th> <th class="text-center" style="width: 75%">Description</th> </tr> </thead> <tbody> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsFilterPushDown.java" name="SupportsFilterPushDown" >}}</td> <td>Enables to push down the filter into the <code>DynamicTableSource</code>. For efficiency, a source can push filters further down in order to be close to the actual data generation.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsLimitPushDown.java" name="SupportsLimitPushDown" >}}</td> <td>Enables to push down a limit (the expected maximum number of produced records) into a <code>DynamicTableSource</code>.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsPartitionPushDown.java" name="SupportsPartitionPushDown" >}}</td> <td>Enables to pass available partitions to the planner and push down partitions into a <code>DynamicTableSource</code>. During the runtime, the source will only read data from the passed partition list for efficiency.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsProjectionPushDown.java" name="SupportsProjectionPushDown" >}}</td> <td>Enables to push down a (possibly nested) projection into a <code>DynamicTableSource</code>. For efficiency, a source can push a projection further down in order to be close to the actual data generation. If the source also implements <code>SupportsReadingMetadata</code>, the source will also read the required metadata only. </td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsReadingMetadata.java" name="SupportsReadingMetadata" >}}</td> <td>Enables to read metadata columns from a <code>DynamicTableSource</code>. The source is responsible to add the required metadata at the end of the produced rows. This includes potentially forwarding metadata column from contained formats.</td> </tr> <tr> <td>{{< gh_link

name="SupportsWatermarkPushDown" >}}</td> <td>Enables to push down a watermark strategy into a <code>DynamicTableSource</code>. The watermark strategy is a builder/factory for timestamp extraction and watermark generation. During the runtime, the watermark generator is located inside the source and is able to generate per-partition watermarks.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsSourceWatermark.java" name="SupportsSourceWatermark" >}}</td> <td>Enables to fully rely on the watermark strategy provided by the <code>ScanTableSource</code> itself. Thus, a <code>CREATE TABLE</code> DDL is able to use <code>SOURCE_WATERMARK()</code> which is a built-in marker function that will be detected by the planner and translated into a call to this interface if available.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/source/abilities/SupportsRowLevelModificationScan.java" name="SupportsRowLevelModificationScan" >}}</td> <td>Enables to pass a scan context described by <code>RowLevelModificationScanContext</code> from <code>ScanTableSource</code> to sink which implements <code>SupportsRowLevelDelete</code>, <code>SupportsRowLevelUpdate</code>. </tr> </tbody> </table> <span class="label label-danger">Attention</span> The interfaces above are currently only available for `ScanTableSource`, not for `LookupTableSource`. By definition, a dynamic table can change over time. When writing a dynamic table, the content can always be considered as a changelog (finite or infinite) for which all changes are written out continuously until the changelog is exhausted. The returned changelog mode indicates the set of changes that the sink accepts during runtime. For regular batch scenarios, the sink can solely accept insert-only rows and write out bounded streams. For regular streaming scenarios, the sink can solely accept insert-only rows and can write out unbounded streams. For change data capture (CDC) scenarios, the sink can write out bounded or unbounded streams with insert, update, and delete rows. A table sink can implement further ability interfaces such as `SupportsOverwrite` that might mutate an instance during planning. All abilities can be found in the `org.apache.flink.table.connector.sink.abilities` package and are listed in the . The returned sink runtime provider provides the runtime implementation for writing the data. There are different interfaces for runtime implementation, among which `SinkV2Provider` is the recommended core interface. Independent of the provider interface, the sink runtime implementation must consume internal data structures. Thus, records must be accepted as `org.apache.flink.table.data.RowData`. The framework provides runtime converters such that a sink can still work on common data structures and perform a conversion at the beginning. To support parallelism setting, the dynamic table factory should support the optional `sink.parallelism` option defined in `org.apache.flink.table.factories.FactoryUtil` and pass its value to a provider that also implements the `ParallelismProvider` interface. <table class="table table-bordered"> <thead> <tr> <th class="text-left" style="width: 25%">Interface</th> <th class="text-center" style="width: 75%">Description</th> </tr> </thead> <tbody> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsOverwrite.java" name="SupportsOverwrite" >}}</td> <td>Enables to overwrite existing data in a <code>DynamicTableSink</code>. By default, if this interface is not implemented, existing tables or partitions cannot be overwritten using e.g. the SQL <code>INSERT OVERWRITE</code> clause.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsPartitioning.java" name="SupportsPartitioning" >}}</td> <td>Enables to write partitioned data in a <code>DynamicTableSink</code>.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsWritingMetadata.java" name="SupportsWritingMetadata" >}}</td> <td>Enables to write metadata columns into a <code>DynamicTableSink</code>. A table sink is responsible for accepting requested metadata columns at the end of consumed rows and persist them. This includes potentially forwarding metadata columns to contained formats.</td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsDeletePushDown.java" name="SupportsDeletePushDown" >}}</td> <td>Enables to push down filters decomposed from the <code>WHERE</code> clause in <code>DELETE</code> statement to <code>DynamicTableSink</code>. The table sink can delete existing data directly according to the filters. </td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsRowLevelDelete.java" name="SupportsRowLevelDelete" >}}</td> <td>Enables to delete existing data according to row-level changes in a <code>DynamicTableSink</code>. The table sink is responsible for telling planner how to produce the row changes, and consuming them to achieve the purpose of row(s) deletion. </td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsRowLevelUpdate.java" name="SupportsRowLevelUpdate" >}}</td> <td>Enables to update existing data according to row-level changes in a

The table sink is responsible for telling planner how to produce the row changes, and consuming them to achieve the purpose of row(s) update. </td> </tr> <tr> <td>{{< gh_link file="flink-table/flink-table-common/src/main/java/org/apache/flink/table/connector/sink/abilities/SupportsStaging.java" name="SupportsStaging" >}}</td> <td>Enables to support atomic semantic for CTAS(CREATE TABLE AS SELECT) or RTAS([CREATE OR] REPLACE TABLE AS SELECT) in a <code>DynamicTableSink</code>. The table sink is responsible for returning <code>StagedTable</code> object that provides atomic semantics. </td> </tr> </tbody> </table> Some table connectors accept different formats that encode and decode keys and/or values. Formats work similar to the pattern `DynamicTableSourceFactory -> DynamicTableSource -> ScanRuntimeProvider`, where the factory is responsible for translating options and the source is responsible for creating runtime logic. Because formats might be located in different modules, they are discovered using Java's Service Provider Interface similar to . In order to discover a format factory, the dynamic table factory searches for a factory that corresponds to a factory identifier and connector-specific base class. For example, the Kafka table source requires a `DeserializationSchema` as runtime interface for a decoding format. Therefore, the Kafka table source factory uses the value of the `value.format` option to discover a `DeserializationFormatFactory`. The following format factories are currently supported: ``` org.apache.flink.table.factories.DeserializationFormatFactory org.apache.flink.table.factories.SerializationFormatFactory ``` The format factory translates the options into an `EncodingFormat` or a `DecodingFormat`. Those interfaces are another kind of factory that produce specialized format runtime logic for the given data type. For example, for a Kafka table source factory, the `DeserializationFormatFactory` would return an `EncodingFormat<DeserializationSchema>` that can be passed into the Kafka table source. {{< top >}} Full Stack Example This section sketches how to implement a scan table source with a decoding format that supports changelog semantics. The example illustrates how all of the mentioned components play together. It can serve as a reference implementation. In particular, it shows how to create factories that parse and validate options, implement table connectors, implement and discover custom formats, and use provided utilities such as data structure converters and the `FactoryUtil`. The table source uses a simple single-threaded `SourceFunction` to open a socket that listens for incoming bytes. The raw bytes are decoded into rows by a pluggable format. The format expects a changelog flag as the first column. We will use most of the interfaces mentioned above to enable the following DDL: ```sql CREATE TABLE UserScores (name STRING, score INT) WITH ( 'connector' = 'socket', 'hostname' = 'localhost', 'port' = '9999', 'byte-delimiter' = '10', 'format' = 'changelog-csv', 'changelog-csv.column-delimiter' = '|' ); ``` Because the format supports changelog semantics, we are able to ingest updates during runtime and create an updating view that can continuously evaluate changing data: ```sql SELECT name, SUM(score) FROM UserScores GROUP BY name; ``` Use the following command to ingest data in a terminal: ```text nc -lk 9999 INSERT|Alice|12 INSERT|Bob|5 DELETE|Alice|12 INSERT|Alice|18 ``` This section illustrates how to translate metadata coming from the catalog to concrete connector instances. Both factories have been added to the `META-INF/services` directory. `SocketDynamicTableFactory` The `SocketDynamicTableFactory` translates the catalog table to a table source. Because the table source requires a decoding format, we are discovering the format using the provided `FactoryUtil` for convenience. ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.configuration.ConfigOption; import org.apache.flink.configuration.ConfigOptions; import org.apache.flink.configuration.ReadableConfig; import org.apache.flink.table.connector.format.DecodingFormat; import org.apache.flink.table.connector.source.DynamicTableSource; import org.apache.flink.table.data.RowData; import org.apache.flink.table.factories.DeserializationFormatFactory; import org.apache.flink.table.factories.DynamicTableSourceFactory; import org.apache.flink.table.factories.FactoryUtil; import org.apache.flink.table.types.DataType; public class SocketDynamicTableFactory implements DynamicTableSourceFactory { // define all options statically public static final ConfigOption<String> HOSTNAME = ConfigOptions.key("hostname") .stringType() .noDefaultValue(); public static final ConfigOption<Integer> PORT = ConfigOptions.key("port") .intType() .noDefaultValue(); public static final ConfigOption<Integer> BYTE_DELIMITER = ConfigOptions.key("byte-delimiter") .intType() .defaultValue(10); // corresponds to '\n' @Override public String factoryIdentifier() { return "socket"; // used for matching to `connector =

} @Override public Set<ConfigOption<?>> requiredOptions() { final Set<ConfigOption<?>> options = new HashSet<>(); options.add(HOSTNAME); options.add(PORT); options.add(FactoryUtil.FORMAT); // use pre-defined option for format return options; } @Override public Set<ConfigOption<?>> optionalOptions() { final Set<ConfigOption<?>> options = new HashSet<>(); options.add(BYTE_DELIMITER); return options; } @Override public DynamicTableSource createDynamicTableSource(Context context) { // either implement your custom validation logic here ... // or use the provided helper utility final FactoryUtil.TableFactoryHelper helper = FactoryUtil.createTableFactoryHelper(this, context); // discover a suitable decoding format final DecodingFormat<DeserializationSchema<RowData>> decodingFormat = helper.discoverDecodingFormat( DeserializationFormatFactory.class, FactoryUtil.FORMAT); // validate all options helper.validate(); // get the validated options final ReadableConfig options = helper.getOptions(); final String hostname = options.get(HOSTNAME); final int port = options.get(PORT); final byte byteDelimiter = (byte) (int) options.get(BYTE_DELIMITER); // derive the produced data type (excluding computed columns) from the catalog table final DataType producedDataType = context.getCatalogTable().getResolvedSchema().toPhysicalRowDataType(); // create and return dynamic table source return new SocketDynamicTableSource(hostname, port, byteDelimiter, decodingFormat, producedDataType); } } ``` `ChangelogCsvFormatFactory` The `ChangelogCsvFormatFactory` translates format-specific options to a format. The `FactoryUtil` in `SocketDynamicTableFactory` takes care of adapting the option keys accordingly and handles the prefixing like `changelog-csv.column-delimiter`. Because this factory implements `DeserializationFormatFactory`, it could also be used for other connectors that support deserialization formats such as the Kafka connector. ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.configuration.ConfigOption; import org.apache.flink.configuration.ConfigOptions; import org.apache.flink.configuration.ReadableConfig; import org.apache.flink.table.connector.format.DecodingFormat; import org.apache.flink.table.data.RowData; import org.apache.flink.table.factories.FactoryUtil; import org.apache.flink.table.factories.DeserializationFormatFactory; import org.apache.flink.table.factories.DynamicTableFactory; public class ChangelogCsvFormatFactory implements DeserializationFormatFactory { // define all options statically public static final ConfigOption<String> COLUMN_DELIMITER = ConfigOptions.key("column-delimiter") .stringType() .defaultValue("|"); @Override public String factoryIdentifier() { return "changelog-csv"; } @Override public Set<ConfigOption<?>> requiredOptions() { return Collections.emptySet(); } @Override public Set<ConfigOption<?>> optionalOptions() { final Set<ConfigOption<?>> options = new HashSet<>(); options.add(COLUMN_DELIMITER); return options; } @Override public DecodingFormat<DeserializationSchema<RowData>> createDecodingFormat( DynamicTableFactory.Context context, ReadableConfig formatOptions) { // either implement your custom validation logic here ... // or use the provided helper method FactoryUtil.validateFactoryOptions(this, formatOptions); // get the validated options final String columnDelimiter = formatOptions.get(COLUMN_DELIMITER); // create and return the format return new ChangelogCsvFormat(columnDelimiter); } } ``` This section illustrates how to translate from instances of the planning layer to runtime instances that are shipped to the cluster. `SocketDynamicTableSource` The `SocketDynamicTableSource` is used during planning. In our example, we don't implement any of the available ability interfaces. Therefore, the main logic can be found in `getScanRuntimeProvider(...)` where we instantiate the required `SourceFunction` and its `DeserializationSchema` for runtime. Both instances are parameterized to return internal data structures (i.e. `RowData`). ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.streaming.api.functions.source.SourceFunction; import org.apache.flink.table.connector.ChangelogMode; import org.apache.flink.table.connector.format.DecodingFormat; import org.apache.flink.table.connector.source.DynamicTableSource; import org.apache.flink.table.connector.source.ScanTableSource; import org.apache.flink.table.connector.source.SourceFunctionProvider; import org.apache.flink.table.data.RowData; import org.apache.flink.table.types.DataType; public class SocketDynamicTableSource implements ScanTableSource { private final String hostname; private final int port; private final byte byteDelimiter; private final DecodingFormat<DeserializationSchema<RowData>> decodingFormat; private final DataType producedDataType; public SocketDynamicTableSource( String hostname, int port, byte byteDelimiter, DecodingFormat<DeserializationSchema<RowData>> decodingFormat, DataType producedDataType) { this.hostname = hostname; this.port = port; this.byteDelimiter = byteDelimiter; this.decodingFormat = decodingFormat; this.producedDataType = producedDataType; } @Override public ChangelogMode getChangelogMode() { // in our example the format decides about the changelog mode // but it could also be the source itself return decodingFormat.getChangelogMode(); } @Override public ScanRuntimeProvider getScanRuntimeProvider(ScanContext runtimeProviderContext) { // create runtime classes that are shipped to the cluster final DeserializationSchema<RowData> deserializer = decodingFormat.createRuntimeDecoder( runtimeProviderContext, producedDataType); final SourceFunction<RowData> sourceFunction = new SocketSourceFunction( hostname, port, byteDelimiter, deserializer); return SourceFunctionProvider.of(sourceFunction, false); } @Override public DynamicTableSource copy() { return new SocketDynamicTableSource(hostname, port, byteDelimiter, decodingFormat, producedDataType); } @Override public String asSummaryString() { return "Socket Table Source"; } } ``` `ChangelogCsvFormat` The `ChangelogCsvFormat` is a decoding format that uses a `DeserializationSchema` during runtime. It supports emitting `INSERT` and `DELETE` changes. ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.api.common.typeinfo.TypeInformation; import org.apache.flink.table.connector.ChangelogMode; import org.apache.flink.table.connector.format.DecodingFormat; import org.apache.flink.table.connector.source.DynamicTableSource; import org.apache.flink.table.connector.source.DynamicTableSource.DataStructureConverter; import org.apache.flink.table.data.RowData; import org.apache.flink.table.types.DataType; import org.apache.flink.table.types.logical.LogicalType; import

public class ChangelogCsvFormat implements DecodingFormat<DeserializationSchema<RowData>> { private final String columnDelimiter; public ChangelogCsvFormat(String columnDelimiter) { this.columnDelimiter = columnDelimiter; } @Override @SuppressWarnings("unchecked") public DeserializationSchema<RowData> createRuntimeDecoder( DynamicTableSource.Context context, DataType producedDataType) { // create type information for the DeserializationSchema final TypeInformation<RowData> producedTypeInfo = (TypeInformation<RowData>) context.createTypeInformation( producedDataType); // most of the code in DeserializationSchema will not work on internal data structures // create a converter for conversion at the end final DataStructureConverter converter = context.createDataStructureConverter(producedDataType); // use logical types during runtime for parsing final List<LogicalType> parsingTypes = producedDataType.getLogicalType().getChildren(); // create runtime class return new ChangelogCsvDeserializer(parsingTypes, converter, producedTypeInfo, columnDelimiter); } @Override public ChangelogMode getChangelogMode() { // define that this format can produce INSERT and DELETE rows return ChangelogMode.newBuilder() .addContainedKind(RowKind.INSERT) .addContainedKind(RowKind.DELETE) .build(); } } ``` For completeness, this section illustrates the runtime logic for both `SourceFunction` and `DeserializationSchema`. ChangelogCsvDeserializer The `ChangelogCsvDeserializer` contains a simple parsing logic for converting bytes into `Row` of `Integer` and `String` with a row kind. The final conversion step converts those into internal data structures. ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.api.common.typeinfo.TypeInformation; import org.apache.flink.table.connector.RuntimeConverter.Context; import org.apache.flink.table.connector.source.DynamicTableSource.DataStructureConverter; import org.apache.flink.table.data.RowData; import org.apache.flink.table.types.logical.LogicalType; import org.apache.flink.table.types.logical.LogicalTypeRoot; import org.apache.flink.types.Row; import org.apache.flink.types.RowKind; public class ChangelogCsvDeserializer implements DeserializationSchema<RowData> { private final List<LogicalType> parsingTypes; private final DataStructureConverter converter; private final TypeInformation<RowData> producedTypeInfo; private final String columnDelimiter; public ChangelogCsvDeserializer( List<LogicalType> parsingTypes, DataStructureConverter converter, TypeInformation<RowData> producedTypeInfo, String columnDelimiter) { this.parsingTypes = parsingTypes; this.converter = converter; this.producedTypeInfo = producedTypeInfo; this.columnDelimiter = columnDelimiter; } @Override public TypeInformation<RowData> getProducedType() { // return the type information required by Flink's core interfaces return producedTypeInfo; } @Override public void open(InitializationContext context) { // converters must be open converter.open(Context.create(ChangelogCsvDeserializer.class.getClassLoader())); } @Override public RowData deserialize(byte[] message) { // parse the columns including a changelog flag final String[] columns = new String(message).split(Pattern.quote(columnDelimiter)); final RowKind kind = RowKind.valueOf(columns[0]); final Row row = new Row(kind, parsingTypes.size()); for (int i = 0; i < parsingTypes.size(); i++) { row.setField(i, parse(parsingTypes.get(i).getTypeRoot(), columns[i + 1])); } // convert to internal data structure return (RowData) converter.toInternal(row); } private static Object parse(LogicalTypeRoot root, String value) { switch (root) { case INTEGER: return Integer.parseInt(value); case VARCHAR: return value; default: throw new IllegalArgumentException(); } } @Override public boolean isEndOfStream(RowData nextElement) { return false; } } ``` SocketSourceFunction The `SocketSourceFunction` opens a socket and consumes bytes. It splits records by the given byte delimiter (`\n` by default) and delegates the decoding to a pluggable `DeserializationSchema`. The source function can only work with a parallelism of 1. ```java import org.apache.flink.api.common.serialization.DeserializationSchema; import org.apache.flink.api.common.typeinfo.TypeInformation; import org.apache.flink.api.java.typeutils.ResultTypeQueryable; import org.apache.flink.configuration.Configuration; import org.apache.flink.streaming.api.functions.source.RichSourceFunction; import org.apache.flink.table.data.RowData; public class SocketSourceFunction extends RichSourceFunction<RowData> implements ResultTypeQueryable<RowData> { private final String hostname; private final int port; private final byte byteDelimiter; private final DeserializationSchema<RowData> deserializer; private volatile boolean isRunning = true; private Socket currentSocket; public SocketSourceFunction(String hostname, int port, byte byteDelimiter, DeserializationSchema<RowData> deserializer) { this.hostname = hostname; this.port = port; this.byteDelimiter = byteDelimiter; this.deserializer = deserializer; } @Override public TypeInformation<RowData> getProducedType() { return deserializer.getProducedType(); } @Override public void open(OpenContext openContext) throws Exception { deserializer.open(() -> getRuntimeContext().getMetricGroup()); } @Override public void run(SourceContext<RowData> ctx) throws Exception { while (isRunning) { // open and consume from socket try (final Socket socket = new Socket()) { currentSocket = socket; socket.connect(new InetSocketAddress(hostname, port), 0); try (InputStream stream = socket.getInputStream()) { ByteArrayOutputStream buffer = new ByteArrayOutputStream(); int b; while ((b = stream.read()) >= 0) { // buffer until delimiter if (b != byteDelimiter) { buffer.write(b); } // decode and emit record else { ctx.collect(deserializer.deserialize(buffer.toByteArray())); buffer.reset(); } } } } catch (Throwable t) { t.printStackTrace(); // print and continue } Thread.sleep(1000); } } @Override public void cancel() { isRunning = false; try { currentSocket.close(); } catch (Throwable t) { // ignore } } } ``` {{< top >}}