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commit 1651d9a8a8365b4b2e8dd5a9279cc3fc28231f69 Author: Timo Walther <[email protected]> AuthorDate: Mon Jun 22 13:56:08 2020 +0200 [FLINK-18066] Add new Table source/sink documentation This closes #12724. --- docs/dev/table/sourceSinks.md | 741 ++++++++++++++++++++++++++++++++++++++++++ docs/fig/table_connectors.svg | 666 +++++++++++++++++++++++++++++++++++++ 2 files changed, 1407 insertions(+) diff --git a/docs/dev/table/sourceSinks.md b/docs/dev/table/sourceSinks.md new file mode 100644 index 0000000..6b1df8a --- /dev/null +++ b/docs/dev/table/sourceSinks.md @@ -0,0 +1,741 @@ +--- +title: "User-defined Sources & Sinks" +nav-parent_id: tableapi +nav-pos: 130 +--- +<!-- +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 [connector section]({% link dev/table/connectors/index.md %}) +for more information about built-in table sources and sinks. + +This page focuses on how to develop a custom, user-defined connector. + +<span class="label label-danger">Attention</span> New table source and table sink interfaces have been +introduced in Flink 1.11 as part of [FLIP-95](https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces). +Also the factory interfaces have been reworked. FLIP-95 is not fully implemented yet. Many ability interfaces +are not supported yet (e.g. for filter or partition push down). If necessary, please also have a look +at the [old table sources and sinks page]({% link dev/table/legacySourceSinks.md %}). Those interfaces +are still supported for backwards compatibility. + +* This will be replaced by the TOC +{:toc} + +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. + +<div style="text-align: center"> + <img width="90%" src="{% link fig/table_connectors.svg %}" alt="Translation of table connectors" /> +</div> + +### 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)](https://docs.oracle.com/javase/tutorial/sound/SPI-intro.html). 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 comunication +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. + +{% top %} + +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 abilitiy interfaces such as `SupportsProjectionPushDown` that might +mutate an instance during planning. All abilities are listed in the `org.apache.flink.table.connector.source.abilities` +package and in the documentation of `org.apache.flink.table.connector.source.ScanTableSource`. + +The runtime implementation of a `ScanTableSource` 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. + +#### 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. + +### 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 abilitiy interfaces such as `SupportsOverwrite` that might mutate an +instance during planning. All abilities are listed in the `org.apache.flink.table.connector.sink.abilities` +package and in the documentation of `org.apache.flink.table.connector.sink.DynamicTableSink`. + +The runtime implementation of a `DynamicTableSink` 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. + +### 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](#dynamic-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. + +{% 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 metioned above to enable the following DDL: + +{% highlight 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' = '|' +); +{% endhighlight %} + +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: + +{% highlight sql %} +SELECT name, SUM(score) FROM UserScores GROUP BY name; +{% endhighlight %} + +Use the following command to ingest data in a terminal: +{% highlight text %} +> nc -lk 9999 +INSERT|Alice|12 +INSERT|Bob|5 +DELETE|Alice|12 +INSERT|Alice|18 +{% endhighlight %} + +### 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. + +{% highlight 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().getSchema().toPhysicalRowDataType(); + + // create and return dynamic table source + return new SocketDynamicTableSource(hostname, port, byteDelimiter, decodingFormat, producedDataType); + } +} +{% endhighlight %} + +**`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. + +{% highlight 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.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) { + // get the validated options + final String columnDelimiter = formatOptions.get(COLUMN_DELIMITER); + + // create and return the format + return new ChangelogCsvFormat(columnDelimiter); + } +} +{% endhighlight %} + +### 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`). + +{% highlight 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"; + } +} +{% endhighlight %} + +**`ChangelogCsvFormat`** + +The `ChangelogCsvFormat` is a decoding format that uses a `DeserializationSchema` during runtime. It +supports emitting `INSERT` and `DELETE` changes. + +{% highlight 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 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(); + } +} +{% endhighlight %} + +### 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. + +{% highlight 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 opened + 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; + } +} +{% endhighlight %} + +**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. + +{% highlight 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(Configuration parameters) 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 + } + } +} +{% endhighlight %} + +{% top %} diff --git a/docs/fig/table_connectors.svg b/docs/fig/table_connectors.svg new file mode 100644 index 0000000..f41d0cb --- /dev/null +++ b/docs/fig/table_connectors.svg @@ -0,0 +1,666 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- +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. 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