User-defined Sources & Sinks #
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 connector section for more information about built-in table sources and sinks.
This page focuses on how to develop a custom, user-defined connector.
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 byADD JAR
orCREATE FUNCTION .. USING JAR ..
statements. User-defined connectors should replaceThread.currentThread().getContextClassLoader()
with the user class loader to load classes. Otherwise,ClassNotFoundException
maybe thrown. The user class loader can be accessed viaDynamicTableFactory.Context
.
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.
Metadata #
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.
Planning #
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 DynamicTableSink
. 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 Service Provider Interfaces (SPI). 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.
Runtime #
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.
Project Configuration #
If you want to implement a custom connector or a custom format, the following dependency is usually sufficient:
pom.xml
file in your project directory and add the following in the dependencies block.
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-common</artifactId>
<version>1.19.1</version>
<scope>provided</scope>
</dependency>
build.gradle
file in your project directory and add the following in the dependencies block.
runtime "org.apache.flink:flink-table-common:1.19.1"
Check out Project configuration for more details.
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:
pom.xml
file in your project directory and add the following in the dependencies block.
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-java-bridge</artifactId>
<version>1.19.1</version>
<scope>provided</scope>
</dependency>
build.gradle
file in your project directory and add the following in the dependencies block.
runtime "org.apache.flink:flink-table-api-java-bridge:1.19.1"
Check out Project configuration for more details.
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.
You should not depend onflink-table-planner_2.12
in production code. With the new moduleflink-table-planner-loader
introduced in Flink 1.15, the application’s classpath will not have direct access toorg.apache.flink.table.planner
classes anymore. If you need a feature available only internally within theorg.apache.flink.table.planner
package and subpackages, please open an issue. To learn more, check out Anatomy of Table Dependencies.
Extension Points #
This section explains the available interfaces for extending Flink’s table connectors.
Dynamic Table Factories #
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
.
Dynamic Table Source #
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
interface. - 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.
Scan Table Source #
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 source abilities table.
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.
Lookup Table Source #
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.
Source Abilities #
Interface | Description |
---|---|
SupportsFilterPushDown | Enables to push down the filter into the DynamicTableSource . For efficiency, a source can
push filters further down in order to be close to the actual data generation. |
SupportsLimitPushDown | Enables to push down a limit (the expected maximum number of produced records) into a DynamicTableSource . |
SupportsPartitionPushDown | Enables to pass available partitions to the planner and push down partitions into a DynamicTableSource .
During the runtime, the source will only read data from the passed partition list for efficiency. |
SupportsProjectionPushDown | Enables to push down a (possibly nested) projection into a DynamicTableSource . 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 SupportsReadingMetadata , the source will also read the required metadata only.
|
SupportsReadingMetadata | Enables to read metadata columns from a DynamicTableSource . 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. |
SupportsWatermarkPushDown | Enables to push down a watermark strategy into a DynamicTableSource . 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. |
SupportsSourceWatermark | Enables to fully rely on the watermark strategy provided by the ScanTableSource
itself. Thus, a CREATE TABLE DDL is able to use SOURCE_WATERMARK() which
is a built-in marker function that will be detected by the planner and translated into a call
to this interface if available. |
SupportsRowLevelModificationScan | Enables to pass a scan context described by RowLevelModificationScanContext from ScanTableSource
to sink which implements SupportsRowLevelDelete , SupportsRowLevelUpdate .
|
Attention The interfaces above are currently only available for
ScanTableSource
, not for LookupTableSource
.
Dynamic Table Sink #
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 sink abilities table.
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.
Sink Abilities #
Interface | Description |
---|---|
SupportsOverwrite | Enables to overwrite existing data in a DynamicTableSink . By default, if
this interface is not implemented, existing tables or partitions cannot be overwritten using
e.g. the SQL INSERT OVERWRITE clause. |
SupportsPartitioning | Enables to write partitioned data in a DynamicTableSink . |
SupportsWritingMetadata | Enables to write metadata columns into a DynamicTableSink . 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. |
SupportsDeletePushDown | Enables to push down filters decomposed from the WHERE clause in DELETE statement to DynamicTableSink .
The table sink can delete existing data directly according to the filters.
|
SupportsRowLevelDelete | Enables to delete existing data according to row-level changes in a DynamicTableSink . 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.
|
SupportsRowLevelUpdate | Enables to update existing data according to row-level changes in a DynamicTableSink . 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.
|
SupportsStaging | Enables to support atomic semantic for CTAS(CREATE TABLE AS SELECT) or RTAS([CREATE OR] REPLACE TABLE AS SELECT) in a DynamicTableSink . The table sink is responsible for returning StagedTable object that provides atomic semantics.
|
Encoding / Decoding Formats #
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 table factories. 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.
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:
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:
SELECT name, SUM(score) FROM UserScores GROUP BY name;
Use the following command to ingest data in a terminal:
> nc -lk 9999
INSERT|Alice|12
INSERT|Bob|5
DELETE|Alice|12
INSERT|Alice|18
Factories #
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.
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.
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);
}
}
Table Source and Decoding Format #
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
).
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.
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 org.apache.flink.types.RowKind;
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();
}
}
Runtime #
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.
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.
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
}
}
}