DataStream API Integration

This page only discusses the integration with DataStream API in JVM languages such as Java or Scala. For Python, see the Python API area.

Both Table API and DataStream API are equally important when it comes to defining a data processing pipeline.

The DataStream API offers the primitives of stream processing (namely time, state, and dataflow management) in a relatively low-level imperative programming API. The Table API abstracts away many internals and provides a structured and declarative API.

Both APIs can work with bounded and unbounded streams.

Bounded streams need to be managed when processing historical data. Unbounded streams occur in real-time processing scenarios that might be initialized with historical data first.

For efficient execution, both APIs offer processing bounded streams in an optimized batch execution mode. However, since batch is just a special case of streaming, it is also possible to run pipelines of bounded streams in regular streaming execution mode.

Both DataStream API and Table API provide their own way of enabling the batch execution mode at the moment. In the near future, this will be further unified.

Pipelines in one API can be defined end-to-end without dependencies on the other API. However, it might be useful to mix both APIs for various reasons:

• Use the table ecosystem for accessing catalogs or connecting to external systems easily, before implementing the main pipeline in DataStream API.
• Access some of the SQL functions for stateless data normalization and cleansing, before implementing the main pipeline in DataStream API.
• Switch to DataStream API every now and then if a more low-level operation (e.g. custom timer handling) is not present in Table API.

Flink provides special bridging functionalities to make the integration with DataStream API as smooth as possible.

Switching between DataStream and Table API adds some conversion overhead. For example, internal data structures of the table runtime (i.e. RowData) that partially work on binary data need to be converted to more user-friendly data structures (i.e. Row). Usually, this overhead can be neglected but is mentioned here for completeness.

Converting between DataStream and Table

Flink provides a specialized StreamTableEnvironment in Java and Scala for integrating with the DataStream API. Those environments extend the regular TableEnvironment with additional methods and take the StreamExecutionEnvironment used in the DataStream API as a parameter.

Currently, the StreamTableEnvironment does not support enabling the batch execution mode yet. Nevertheless, bounded streams can be processed there using the streaming execution mode but with lower efficiency.

Note, however, that the general TableEnvironment can work in both streaming execution or optimized batch execution mode.

The following code shows an example of how to go back and forth between the two APIs. Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. Since the DataStream API does not support changelog processing natively, the code assumes append-only/insert-only semantics during the stream-to-table and table-to-stream conversion.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
// create environments of both APIs
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// create a DataStream
DataStream<String> dataStream = env.fromElements("Alice", "Bob", "John");
// interpret the insert-only DataStream as a Table
Table inputTable = tableEnv.fromDataStream(dataStream);
// register the Table object as a view and query it
tableEnv.createTemporaryView("InputTable", inputTable);
Table resultTable = tableEnv.sqlQuery("SELECT UPPER(f0) FROM InputTable");
// interpret the insert-only Table as a DataStream again
DataStream<Row> resultStream = tableEnv.toDataStream(resultTable);
// add a printing sink and execute in DataStream API
resultStream.print();
env.execute();
// prints:
// +I[Alice]
// +I[Bob]
// +I[John]

Scala

import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
// create environments of both APIs
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)
// create a DataStream
val dataStream = env.fromElements("Alice", "Bob", "John")
// interpret the insert-only DataStream as a Table
val inputTable = tableEnv.fromDataStream(dataStream)
// register the Table object as a view and query it
tableEnv.createTemporaryView("InputTable", inputTable)
val resultTable = tableEnv.sqlQuery("SELECT UPPER(f0) FROM InputTable")
// interpret the insert-only Table as a DataStream again
val resultStream = tableEnv.toDataStream(resultTable)
// add a printing sink and execute in DataStream API
resultStream.print()
env.execute()
// prints:
// +I[Alice]
// +I[Bob]
// +I[John]

The complete semantics of fromDataStream and toDataStream can be found in the dedicated section below. In particular, the section discusses how to influence the schema derivation with more complex and nested types. It also covers working with event-time and watermarks.

Depending on the kind of query, in many cases the resulting dynamic table is a pipeline that does not only produce insert-only changes when coverting the Table to a DataStream but also produces retractions and other kinds of updates. During table-to-stream conversion, this could lead to an exception similar to

Table sink 'Unregistered_DataStream_Sink_1' doesn't support consuming update changes [...].

in which case one needs to revise the query again or switch to toChangelogStream.

The following example shows how updating tables can be converted. Every result row represents an entry in a changelog with a change flag that can be queried by calling row.getKind() on it. In the example, the second score for Alice creates an update before (-U) and update after (+U) change.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
// create environments of both APIs
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// create a DataStream
DataStream<Row> dataStream = env.fromElements(
    Row.of("Alice", 12),
    Row.of("Bob", 10),
    Row.of("Alice", 100));
// interpret the insert-only DataStream as a Table
Table inputTable = tableEnv.fromDataStream(dataStream).as("name", "score");
// register the Table object as a view and query it
// the query contains an aggregation that produces updates
tableEnv.createTemporaryView("InputTable", inputTable);
Table resultTable = tableEnv.sqlQuery(
    "SELECT name, SUM(score) FROM InputTable GROUP BY name");
// interpret the updating Table as a changelog DataStream
DataStream<Row> resultStream = tableEnv.toChangelogStream(resultTable);
// add a printing sink and execute in DataStream API
resultStream.print();
env.execute();
// prints:
// +I[Alice, 12]
// +I[Bob, 10]
// -U[Alice, 12]
// +U[Alice, 112]

Scala

import org.apache.flink.api.scala.typeutils.Types
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
import org.apache.flink.types.Row
// create environments of both APIs
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)
// create a DataStream
val dataStream = env.fromElements(
  Row.of("Alice", Int.box(12)),
  Row.of("Bob", Int.box(10)),
  Row.of("Alice", Int.box(100))
)(Types.ROW(Types.STRING, Types.INT))
// interpret the insert-only DataStream as a Table
val inputTable = tableEnv.fromDataStream(dataStream).as("name", "score")
// register the Table object as a view and query it
// the query contains an aggregation that produces updates
tableEnv.createTemporaryView("InputTable", inputTable)
val resultTable = tableEnv.sqlQuery("SELECT name, SUM(score) FROM InputTable GROUP BY name")
// interpret the updating Table as a changelog DataStream
val resultStream = tableEnv.toChangelogStream(resultTable)
// add a printing sink and execute in DataStream API
resultStream.print()
env.execute()
// prints:
// +I[Alice, 12]
// +I[Bob, 10]
// -U[Alice, 12]
// +U[Alice, 112]

The complete semantics of fromChangelogStream and toChangelogStream can be found in the dedicated section below. In particular, the section discusses how to influence the schema derivation with more complex and nested types. It covers working with event-time and watermarks. It discusses how to declare a primary key and changelog mode for the input and output streams.

Dependencies and Imports

Projects that combine Table API with DataStream API need to add one of the following bridging modules. They include transitive dependencies to flink-table-api-java or flink-table-api-scala and the corresponding language-specific DataStream API module.

Java

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-table-api-java-bridge_2.11</artifactId>
  <version>1.13.0</version>
  <scope>provided</scope>
</dependency>

Scala

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-table-api-scala-bridge_2.11</artifactId>
  <version>1.13.0</version>
  <scope>provided</scope>
</dependency>

The following imports are required to declare common pipelines using either the Java or Scala version of both DataStream API and Table API.

Java

// imports for Java DataStream API
import org.apache.flink.streaming.api.*;
import org.apache.flink.streaming.api.environment.*;
// imports for Table API with bridging to Java DataStream API
import org.apache.flink.table.api.*;
import org.apache.flink.table.api.bridge.java.*;

Scala

// imports for Scala DataStream API
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala._
// imports for Table API with bridging to Scala DataStream API
import org.apache.flink.table.api._
import org.apache.flink.table.api.bridge.scala._

Configuration

The TableEnvironment will adopt all configuration options from the passed StreamExecutionEnvironment. However, it cannot be guaranteed that further changes to the configuration of StreamExecutionEnvironment are propagated to the StreamTableEnvironment after its instantiation. Also, the reverse propagation of options from Table API to DataStream API is not supported.

We recommend setting all configuration options in DataStream API early before switching to Table API.

Java

import java.time.ZoneId;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
// create Java DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// set various configuration early
env.setMaxParallelism(256);
env.getConfig().addDefaultKryoSerializer(MyCustomType.class, CustomKryoSerializer.class);
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
// then switch to Java Table API
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// set configuration early
tableEnv.getConfig().setLocalTimeZone(ZoneId.of("Europe/Berlin"));
// start defining your pipelines in both APIs...

Scala

import java.time.ZoneId
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.CheckpointingMode
import org.apache.flink.table.api.bridge.scala._
// create Scala DataStream API
val env = StreamExecutionEnvironment.getExecutionEnvironment
// set various configuration early
env.setMaxParallelism(256)
env.getConfig.addDefaultKryoSerializer(classOf[MyCustomType], classOf[CustomKryoSerializer])
env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
// then switch to Scala Table API
val tableEnv = StreamTableEnvironment.create(env)
// set configuration early
tableEnv.getConfig.setLocalTimeZone(ZoneId.of("Europe/Berlin"))
// start defining your pipelines in both APIs...

Execution Behavior

Both APIs provide methods to execute pipelines. In other words: if requested, they compile a job graph that will be submitted to the cluster and triggered for execution. Results will be streamed to the declared sinks.

Usually, both APIs mark such behavior with the term execute in method names. However, the execution behavior is slightly different between Table API and DataStream API.

DataStream API

The DataStream API’s StreamExecutionEnvironment acts as a builder pattern to construct a complex pipeline. The pipeline possibly splits into multiple branches that might or might not end with a sink.

At least one sink must be defined. Otherwise, the following exception is thrown:

java.lang.IllegalStateException: No operators defined in streaming topology. Cannot execute.

StreamExecutionEnvironment.execute() submits the entire constructed pipeline and clears the builder afterward. In other words: no sources and sinks are declared anymore, and a new pipeline can be added to the builder. Thus, every DataStream program usually ends with a call to StreamExecutionEnvironment.execute(). Alternatively, DataStream.executeAndCollect() implicitly defines a sink for streaming the results to the local client and only executes the current branch.

Table API

In the Table API, branching pipelines is only supported within a StatementSet where each branch must declare a final sink. Both TableEnvironment and also StreamTableEnvironment do not offer a dedicated general execute() method. Instead, they offer methods for submitting a single source-to-sink pipeline or a statement set:

// execute with explicit sink
tableEnv.from("InputTable").executeInsert("OutputTable")
tableEnv.executeSql("INSERT INTO OutputTable SELECT * FROM InputTable")
tableEnv.createStatementSet()
    .addInsert("OutputTable", tableEnv.from("InputTable"))
    .addInsert("OutputTable2", tableEnv.from("InputTable"))
    .execute()
tableEnv.createStatementSet()
    .addInsertSql("INSERT INTO OutputTable SELECT * FROM InputTable")
    .addInsertSql("INSERT INTO OutputTable2 SELECT * FROM InputTable")
    .execute()
// execute with implicit local sink
tableEnv.from("InputTable").execute().print()
tableEnv.executeSql("SELECT * FROM InputTable").print()

To combine both execution behaviors, every call to StreamTableEnvironment.toDataStream or StreamTableEnvironment.toChangelogStream will materialize (i.e. compile) the Table API sub-pipeline and insert it into the DataStream API pipeline builder. This means that StreamExecutionEnvironment.execute() or DataStream.executeAndCollect must be called afterwards. An execution in Table API will not trigger these “external parts”.

// (1)
// adds a branch with a printing sink to the StreamExecutionEnvironment
tableEnv.toDataStream(table).print()
// (2)
// executes a Table API end-to-end pipeline as a Flink job and prints locally,
// thus (1) has still not been executed
table.execute().print()
// executes the DataStream API pipeline with the sink defined in (1) as a
// Flink job, (2) was already running before
env.execute()

Handling of (Insert-Only) Streams

A StreamTableEnvironment offers the following methods to convert from and to DataStream API:

• fromDataStream(DataStream): Interprets a stream of insert-only changes and arbitrary type as a table. Event-time and watermarks are not propagated by default.

• fromDataStream(DataStream, Schema): Interprets a stream of insert-only changes and arbitrary type as a table. The optional schema allows to enrich column data types and add time attributes, watermarks strategies, other computed columns, or primary keys.

• createTemporaryView(String, DataStream): Registers the stream under a name to access it in SQL. It is a shortcut for createTemporaryView(String, fromDataStream(DataStream)).

• createTemporaryView(String, DataStream, Schema): Registers the stream under a name to access it in SQL. It is a shortcut for createTemporaryView(String, fromDataStream(DataStream, Schema)).

• toDataStream(DataStream): Converts a table into a stream of insert-only changes. The default stream record type is org.apache.flink.types.Row. A single rowtime attribute column is written back into the DataStream API’s record. Watermarks are propagated as well.

• toDataStream(DataStream, AbstractDataType): Converts a table into a stream of insert-only changes. This method accepts a data type to express the desired stream record type. The planner might insert implicit casts and reorders columns to map columns to fields of the (possibly nested) data type.

• toDataStream(DataStream, Class): A shortcut for toDataStream(DataStream, DataTypes.of(Class)) to quickly create the desired data type reflectively.

From a Table API’s perspective, converting from and to DataStream API is similar to reading from or writing to a virtual table connector that has been defined using a CREATE TABLE DDL in SQL.

The schema part in the virtual CREATE TABLE name (schema) WITH (options) statement can be automatically derived from the DataStream’s type information, enriched, or entirely defined manually using org.apache.flink.table.api.Schema.

The virtual DataStream table connector exposes the following metadata for every row:

The virtual DataStream table source implements SupportsSourceWatermark and thus allows calling the SOURCE_WATERMARK() built-in function as a watermark strategy to adopt watermarks from the DataStream API.

Examples for fromDataStream

The following code shows how to use fromDataStream for different scenarios.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import java.time.Instant;
// some example POJO
public static class User {
  public String name;
  public Integer score;
  public Instant event_time;
  // default constructor for DataStream API
  public User() {}
  // fully assigning constructor for Table API
  public User(String name, Integer score, Instant event_time) {
    this.name = name;
    this.score = score;
    this.event_time = event_time;
  }
}
// create a DataStream
DataStream<User> dataStream =
    env.fromElements(
        new User("Alice", 4, Instant.ofEpochMilli(1000)),
        new User("Bob", 6, Instant.ofEpochMilli(1001)),
        new User("Alice", 10, Instant.ofEpochMilli(1002)));
// === EXAMPLE 1 ===
// derive all physical columns automatically
Table table = tableEnv.fromDataStream(dataStream);
table.printSchema();
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9)
// )
// === EXAMPLE 2 ===
// derive all physical columns automatically
// but add computed columns (in this case for creating a proctime attribute column)
Table table = tableEnv.fromDataStream(
    dataStream,
    Schema.newBuilder()
        .columnByExpression("proc_time", "PROCTIME()")
        .build());
table.printSchema();
// prints:
// (
//  `name` STRING,
//  `score` INT NOT NULL,
//  `event_time` TIMESTAMP_LTZ(9),
//  `proc_time` TIMESTAMP_LTZ(3) NOT NULL *PROCTIME* AS PROCTIME()
//)
// === EXAMPLE 3 ===
// derive all physical columns automatically
// but add computed columns (in this case for creating a rowtime attribute column)
// and a custom watermark strategy
Table table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .columnByExpression("rowtime", "CAST(event_time AS TIMESTAMP_LTZ(3))")
            .watermark("rowtime", "rowtime - INTERVAL '10' SECOND")
            .build());
table.printSchema();
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9),
//  `rowtime` TIMESTAMP_LTZ(3) *ROWTIME* AS CAST(event_time AS TIMESTAMP_LTZ(3)),
//  WATERMARK FOR `rowtime`: TIMESTAMP_LTZ(3) AS rowtime - INTERVAL '10' SECOND
// )
// === EXAMPLE 4 ===
// derive all physical columns automatically
// but access the stream record's timestamp for creating a rowtime attribute column
// also rely on the watermarks generated in the DataStream API
// we assume that a watermark strategy has been defined for `dataStream` before
// (not part of this example)
Table table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
            .watermark("rowtime", "SOURCE_WATERMARK()")
            .build());
table.printSchema();
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9),
//  `rowtime` TIMESTAMP_LTZ(3) *ROWTIME* METADATA,
//  WATERMARK FOR `rowtime`: TIMESTAMP_LTZ(3) AS SOURCE_WATERMARK()
// )
// === EXAMPLE 5 ===
// define physical columns manually
// in this example,
//   - we can reduce the default precision of timestamps from 9 to 3
//   - we also project the columns and put `event_time` to the beginning
Table table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .column("event_time", "TIMESTAMP_LTZ(3)")
            .column("name", "STRING")
            .column("score", "INT")
            .watermark("event_time", "SOURCE_WATERMARK()")
            .build());
table.printSchema();
// prints:
// (
//  `event_time` TIMESTAMP_LTZ(3) *ROWTIME*,
//  `name` VARCHAR(200),
//  `score` INT
// )
// note: the watermark strategy is not shown due to the inserted column reordering projection

Scala

import org.apache.flink.api.scala._
import java.time.Instant;
// some example case class
case class User(name: String, score: java.lang.Integer, event_time: java.time.Instant)
// create a DataStream
val dataStream = env.fromElements(
    User("Alice", 4, Instant.ofEpochMilli(1000)),
    User("Bob", 6, Instant.ofEpochMilli(1001)),
    User("Alice", 10, Instant.ofEpochMilli(1002)))
// === EXAMPLE 1 ===
// derive all physical columns automatically
val table = tableEnv.fromDataStream(dataStream)
table.printSchema()
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9)
// )
// === EXAMPLE 2 ===
// derive all physical columns automatically
// but add computed columns (in this case for creating a proctime attribute column)
val table = tableEnv.fromDataStream(
    dataStream,
    Schema.newBuilder()
        .columnByExpression("proc_time", "PROCTIME()")
        .build())
table.printSchema()
// prints:
// (
//  `name` STRING,
//  `score` INT NOT NULL,
//  `event_time` TIMESTAMP_LTZ(9),
//  `proc_time` TIMESTAMP_LTZ(3) NOT NULL *PROCTIME* AS PROCTIME()
//)
// === EXAMPLE 3 ===
// derive all physical columns automatically
// but add computed columns (in this case for creating a rowtime attribute column)
// and a custom watermark strategy
val table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .columnByExpression("rowtime", "CAST(event_time AS TIMESTAMP_LTZ(3))")
            .watermark("rowtime", "rowtime - INTERVAL '10' SECOND")
            .build())
table.printSchema()
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9),
//  `rowtime` TIMESTAMP_LTZ(3) *ROWTIME* AS CAST(event_time AS TIMESTAMP_LTZ(3)),
//  WATERMARK FOR `rowtime`: TIMESTAMP_LTZ(3) AS rowtime - INTERVAL '10' SECOND
// )
// === EXAMPLE 4 ===
// derive all physical columns automatically
// but access the stream record's timestamp for creating a rowtime attribute column
// also rely on the watermarks generated in the DataStream API
// we assume that a watermark strategy has been defined for `dataStream` before
// (not part of this example)
val table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
            .watermark("rowtime", "SOURCE_WATERMARK()")
            .build())
table.printSchema()
// prints:
// (
//  `name` STRING,
//  `score` INT,
//  `event_time` TIMESTAMP_LTZ(9),
//  `rowtime` TIMESTAMP_LTZ(3) *ROWTIME* METADATA,
//  WATERMARK FOR `rowtime`: TIMESTAMP_LTZ(3) AS SOURCE_WATERMARK()
// )
// === EXAMPLE 5 ===
// define physical columns manually
// in this example,
//   - we can reduce the default precision of timestamps from 9 to 3
//   - we also project the columns and put `event_time` to the beginning
val table =
    tableEnv.fromDataStream(
        dataStream,
        Schema.newBuilder()
            .column("event_time", "TIMESTAMP_LTZ(3)")
            .column("name", "STRING")
            .column("score", "INT")
            .watermark("event_time", "SOURCE_WATERMARK()")
            .build())
table.printSchema()
// prints:
// (
//  `event_time` TIMESTAMP_LTZ(3) *ROWTIME*,
//  `name` VARCHAR(200),
//  `score` INT
// )
// note: the watermark strategy is not shown due to the inserted column reordering projection

Example 1 illustrates a simple use case when no time-based operations are needed.

Example 4 is the most common use case when time-based operations such as windows or interval joins should be part of the pipeline. Example 2 is the most common use case when these time-based operations should work in processing time.

Example 5 entirely relies on the declaration of the user. This can be useful to replace generic types from the DataStream API (which would be RAW in the Table API) with proper data types.

Since DataType is richer than TypeInformation, we can easily enable immutable POJOs and other complex data structures. The following example in Java shows what is possible. Check also the Data Types & Serialization page of the DataStream API for more information about the supported types there.

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
// the DataStream API does not support immutable POJOs yet,
// the class will result in a generic type that is a RAW type in Table API by default
public static class User {
    public final String name;
    public final Integer score;
    public User(String name, Integer score) {
        this.name = name;
        this.score = score;
    }
}
// create a DataStream
DataStream<User> dataStream = env.fromElements(
    new User("Alice", 4),
    new User("Bob", 6),
    new User("Alice", 10));
// since fields of a RAW type cannot be accessed, every stream record is treated as an atomic type
// leading to a table with a single column `f0`
Table table = tableEnv.fromDataStream(dataStream);
table.printSchema();
// prints:
// (
//  `f0` RAW('User', '...')
// )
// instead, declare a more useful data type for columns using the Table API's type system
// in a custom schema and rename the columns in a following `as` projection
Table table = tableEnv
    .fromDataStream(
        dataStream,
        Schema.newBuilder()
            .column("f0", DataTypes.of(User.class))
            .build())
    .as("user");
table.printSchema();
// prints:
// (
//  `user` *User<`name` STRING,`score` INT>*
// )
// data types can be extracted reflectively as above or explicitly defined
Table table3 = tableEnv
    .fromDataStream(
        dataStream,
        Schema.newBuilder()
            .column(
                "f0",
                DataTypes.STRUCTURED(
                    User.class,
                    DataTypes.FIELD("name", DataTypes.STRING()),
                    DataTypes.FIELD("score", DataTypes.INT())))
            .build())
    .as("user");
table.printSchema();
// prints:
// (
//  `user` *User<`name` STRING,`score` INT>*
// )

Examples for createTemporaryView

A DataStream can be registered directly as a view (possibly enriched with a schema).

Views created from a DataStream can only be registered as temporary views. Due to their inline/anonymous nature, it is not possible to register them in a permanent catalog.

The following code shows how to use createTemporaryView for different scenarios.

Java

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
// create some DataStream
DataStream<Tuple2<Long, String>> dataStream = env.fromElements(
    Tuple2.of(12L, "Alice"),
    Tuple2.of(0L, "Bob"));
// === EXAMPLE 1 ===
// register the DataStream as view "MyView" in the current session
// all columns are derived automatically
tableEnv.createTemporaryView("MyView", dataStream);
tableEnv.from("MyView").printSchema();
// prints:
// (
//  `f0` BIGINT NOT NULL,
//  `f1` STRING
// )
// === EXAMPLE 2 ===
// register the DataStream as view "MyView" in the current session,
// provide a schema to adjust the columns similar to `fromDataStream`
// in this example, the derived NOT NULL information has been removed
tableEnv.createTemporaryView(
    "MyView",
    dataStream,
    Schema.newBuilder()
        .column("f0", "BIGINT")
        .column("f1", "STRING")
        .build());
tableEnv.from("MyView").printSchema();
// prints:
// (
//  `f0` BIGINT,
//  `f1` STRING
// )
// === EXAMPLE 3 ===
// use the Table API before creating the view if it is only about renaming columns
tableEnv.createTemporaryView(
    "MyView",
    tableEnv.fromDataStream(dataStream).as("id", "name"));
tableEnv.from("MyView").printSchema();
// prints:
// (
//  `id` BIGINT NOT NULL,
//  `name` STRING
// )

Scala

// create some DataStream
val dataStream: DataStream[(Long, String)] = env.fromElements(
    (12L, "Alice"),
    (0L, "Bob"))
// === EXAMPLE 1 ===
// register the DataStream as view "MyView" in the current session
// all columns are derived automatically
tableEnv.createTemporaryView("MyView", dataStream)
tableEnv.from("MyView").printSchema()
// prints:
// (
//  `_1` BIGINT NOT NULL,
//  `_2` STRING
// )
// === EXAMPLE 2 ===
// register the DataStream as view "MyView" in the current session,
// provide a schema to adjust the columns similar to `fromDataStream`
// in this example, the derived NOT NULL information has been removed
tableEnv.createTemporaryView(
    "MyView",
    dataStream,
    Schema.newBuilder()
        .column("_1", "BIGINT")
        .column("_2", "STRING")
        .build())
tableEnv.from("MyView").printSchema()
// prints:
// (
//  `_1` BIGINT,
//  `_2` STRING
// )
// === EXAMPLE 3 ===
// use the Table API before creating the view if it is only about renaming columns
tableEnv.createTemporaryView(
    "MyView",
    tableEnv.fromDataStream(dataStream).as("id", "name"))
tableEnv.from("MyView").printSchema()
// prints:
// (
//  `id` BIGINT NOT NULL,
//  `name` STRING
// )

Examples for toDataStream

The following code shows how to use toDataStream for different scenarios.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Table;
import org.apache.flink.types.Row;
import java.time.Instant;
// POJO with mutable fields
// since no fully assigning constructor is defined, the field order
// is alphabetical [event_time, name, score]
public static class User {
    public String name;
    public Integer score;
    public Instant event_time;
}
tableEnv.executeSql(
    "CREATE TABLE GeneratedTable "
    + "("
    + "  name STRING,"
    + "  score INT,"
    + "  event_time TIMESTAMP_LTZ(3),"
    + "  WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
    + ")"
    + "WITH ('connector'='datagen')");
Table table = tableEnv.from("GeneratedTable");
// === EXAMPLE 1 ===
// use the default conversion to instances of Row
// since `event_time` is a single rowtime attribute, it is inserted into the DataStream
// metadata and watermarks are propagated
DataStream<Row> dataStream = tableEnv.toDataStream(table);
// === EXAMPLE 2 ===
// a data type is extracted from class `User`,
// the planner reorders fields and inserts implicit casts where possible to convert internal
// data structures to the desired structured type
// since `event_time` is a single rowtime attribute, it is inserted into the DataStream
// metadata and watermarks are propagated
DataStream<User> dataStream = tableEnv.toDataStream(table, User.class);
// data types can be extracted reflectively as above or explicitly defined
DataStream<User> dataStream =
    tableEnv.toDataStream(
        table,
        DataTypes.STRUCTURED(
            User.class,
            DataTypes.FIELD("name", DataTypes.STRING()),
            DataTypes.FIELD("score", DataTypes.INT()),
            DataTypes.FIELD("event_time", DataTypes.TIMESTAMP_LTZ(3))));

Scala

import org.apache.flink.streaming.api.scala.DataStream
import org.apache.flink.table.api.DataTypes
case class User(name: String, score: java.lang.Integer, event_time: java.time.Instant)
tableEnv.executeSql(
    "CREATE TABLE GeneratedTable "
    + "("
    + "  name STRING,"
    + "  score INT,"
    + "  event_time TIMESTAMP_LTZ(3),"
    + "  WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
    + ")"
    + "WITH ('connector'='datagen')")
val table = tableEnv.from("GeneratedTable")
// === EXAMPLE 1 ===
// use the default conversion to instances of Row
// since `event_time` is a single rowtime attribute, it is inserted into the DataStream
// metadata and watermarks are propagated
val dataStream: DataStream[Row] = tableEnv.toDataStream(table)
// === EXAMPLE 2 ===
// a data type is extracted from class `User`,
// the planner reorders fields and inserts implicit casts where possible to convert internal
// data structures to the desired structured type
// since `event_time` is a single rowtime attribute, it is inserted into the DataStream
// metadata and watermarks are propagated
val dataStream: DataStream[User] = tableEnv.toDataStream(table, User.class)
// data types can be extracted reflectively as above or explicitly defined
val dataStream: DataStream[User] =
    tableEnv.toDataStream(
        table,
        DataTypes.STRUCTURED(
            User.class,
            DataTypes.FIELD("name", DataTypes.STRING()),
            DataTypes.FIELD("score", DataTypes.INT()),
            DataTypes.FIELD("event_time", DataTypes.TIMESTAMP_LTZ(3))))

Note that only non-updating tables are supported by toDataStream. Usually, time-based operations such as windows, interval joins, or the MATCH_RECOGNIZE clause are a good fit for insert-only pipelines next to simple operations like projections and filters. Pipelines with operations that produce updates can use toChangelogStream.

Handling of Changelog Streams

Internally, Flink’s table runtime is a changelog processor. The concepts page describes how dynamic tables and streams relate to each other.

A StreamTableEnvironment offers the following methods to expose these change data capture (CDC) functionalities:

• fromChangelogStream(DataStream): Interprets a stream of changelog entries as a table. The stream record type must be org.apache.flink.types.Row since its RowKind flag is evaluated during runtime. Event-time and watermarks are not propagated by default. This method expects a changelog containing all kinds of changes (enumerated in org.apache.flink.types.RowKind) as the default ChangelogMode.

• fromChangelogStream(DataStream, Schema): Allows to define a schema for the DataStream similar to fromDataStream(DataStream, Schema). Otherwise the semantics are equal to fromChangelogStream(DataStream).

• fromChangelogStream(DataStream, Schema, ChangelogMode): Gives full control about how to interpret a stream as a changelog. The passed ChangelogMode helps the planner to distinguish between insert-only, upsert, or retract behavior.

• toChangelogStream(Table): Reverse operation of fromChangelogStream(DataStream). It produces a stream with instances of org.apache.flink.types.Row and sets the RowKind flag for every record at runtime. All kinds of updating tables are supported by this method. If the input table contains a single rowtime column, it will be propagated into a stream record’s timestamp. Watermarks will be propagated as well.

• toChangelogStream(Table, Schema): Reverse operation of fromChangelogStream(DataStream, Schema). The method can enrich the produced column data types. The planner might insert implicit casts if necessary. It is possible to write out the rowtime as a metadata column.

• toChangelogStream(Table, Schema, ChangelogMode): Gives full control about how to convert a table to a changelog stream. The passed ChangelogMode helps the planner to distinguish between insert-only, upsert, or retract behavior.

From a Table API’s perspective, converting from and to DataStream API is similar to reading from or writing to a virtual table connector that has been defined using a CREATE TABLE DDL in SQL.

Because fromChangelogStream behaves similar to fromDataStream, we recommend reading the previous section before continuing here.

This virtual connector also supports reading and writing the rowtime metadata of the stream record.

The virtual table source implements SupportsSourceWatermark.

Examples for fromChangelogStream

The following code shows how to use fromChangelogStream for different scenarios.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.connector.ChangelogMode;
import org.apache.flink.types.Row;
import org.apache.flink.types.RowKind;
// === EXAMPLE 1 ===
// interpret the stream as a retract stream
// create a changelog DataStream
DataStream<Row> dataStream =
    env.fromElements(
        Row.ofKind(RowKind.INSERT, "Alice", 12),
        Row.ofKind(RowKind.INSERT, "Bob", 5),
        Row.ofKind(RowKind.UPDATE_BEFORE, "Alice", 12),
        Row.ofKind(RowKind.UPDATE_AFTER, "Alice", 100));
// interpret the DataStream as a Table
Table table = tableEnv.fromChangelogStream(dataStream);
// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table);
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print();
// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+
// === EXAMPLE 2 ===
// interpret the stream as an upsert stream (without a need for UPDATE_BEFORE)
// create a changelog DataStream
DataStream<Row> dataStream =
    env.fromElements(
        Row.ofKind(RowKind.INSERT, "Alice", 12),
        Row.ofKind(RowKind.INSERT, "Bob", 5),
        Row.ofKind(RowKind.UPDATE_AFTER, "Alice", 100));
// interpret the DataStream as a Table
Table table =
    tableEnv.fromChangelogStream(
        dataStream,
        Schema.newBuilder().primaryKey("f0").build(),
        ChangelogMode.upsert());
// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table);
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print();
// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+

Scala

import org.apache.flink.api.scala.typeutils.Types
import org.apache.flink.table.api.Schema
import org.apache.flink.table.connector.ChangelogMode
import org.apache.flink.types.{Row, RowKind}
// === EXAMPLE 1 ===
// interpret the stream as a retract stream
// create a changelog DataStream
val dataStream = env.fromElements(
    Row.ofKind(RowKind.INSERT, "Alice", Int.box(12)),
    Row.ofKind(RowKind.INSERT, "Bob", Int.box(5)),
    Row.ofKind(RowKind.UPDATE_BEFORE, "Alice", Int.box(12)),
    Row.ofKind(RowKind.UPDATE_AFTER, "Alice", Int.box(100))
)(Types.ROW(Types.STRING, Types.INT))
// interpret the DataStream as a Table
val table = tableEnv.fromChangelogStream(dataStream)
// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table)
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print()
// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+
// === EXAMPLE 2 ===
// interpret the stream as an upsert stream (without a need for UPDATE_BEFORE)
// create a changelog DataStream
val dataStream = env.fromElements(
    Row.ofKind(RowKind.INSERT, "Alice", Int.box(12)),
    Row.ofKind(RowKind.INSERT, "Bob", Int.box(5)),
    Row.ofKind(RowKind.UPDATE_AFTER, "Alice", Int.box(100))
)(Types.ROW(Types.STRING, Types.INT))
// interpret the DataStream as a Table
val table =
    tableEnv.fromChangelogStream(
        dataStream,
        Schema.newBuilder().primaryKey("f0").build(),
        ChangelogMode.upsert())
// register the table under a name and perform an aggregation
tableEnv.createTemporaryView("InputTable", table)
tableEnv
    .executeSql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0")
    .print()
// prints:
// +----+--------------------------------+-------------+
// | op |                           name |       score |
// +----+--------------------------------+-------------+
// | +I |                            Bob |           5 |
// | +I |                          Alice |          12 |
// | -D |                          Alice |          12 |
// | +I |                          Alice |         100 |
// +----+--------------------------------+-------------+

The default ChangelogMode shown in example 1 should be sufficient for most use cases as it accepts all kinds of changes.

However, example 2 shows how to limit the kinds of incoming changes for efficiency by reducing the number of update messages by 50%.

Examples for toChangelogStream

The following code shows how to use toChangelogStream for different scenarios.

Java

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.data.StringData;
import org.apache.flink.types.Row;
import org.apache.flink.util.Collector;
import static org.apache.flink.table.api.Expressions.*;
// create Table with event-time
tableEnv.executeSql(
    "CREATE TABLE GeneratedTable "
    + "("
    + "  name STRING,"
    + "  score INT,"
    + "  event_time TIMESTAMP_LTZ(3),"
    + "  WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
    + ")"
    + "WITH ('connector'='datagen')");
Table table = tableEnv.from("GeneratedTable");
// === EXAMPLE 1 ===
// convert to DataStream in the simplest and most general way possible (no event-time)
Table simpleTable = tableEnv
    .fromValues(row("Alice", 12), row("Alice", 2), row("Bob", 12))
    .as("name", "score")
    .groupBy($("name"))
    .select($("name"), $("score").sum());
tableEnv
    .toChangelogStream(simpleTable)
    .executeAndCollect()
    .forEachRemaining(System.out::println);
// prints:
// +I[Bob, 12]
// +I[Alice, 12]
// -U[Alice, 12]
// +U[Alice, 14]
// === EXAMPLE 2 ===
// convert to DataStream in the simplest and most general way possible (with event-time)
DataStream<Row> dataStream = tableEnv.toChangelogStream(table);
// since `event_time` is a single time attribute in the schema, it is set as the
// stream record's timestamp by default; however, at the same time, it remains part of the Row
dataStream.process(
    new ProcessFunction<Row, Void>() {
        @Override
        public void processElement(Row row, Context ctx, Collector<Void> out) {
             // prints: [name, score, event_time]
             System.out.println(row.getFieldNames(true));
             // timestamp exists twice
             assert ctx.timestamp() == row.<Instant>getFieldAs("event_time").toEpochMilli();
        }
    });
env.execute();
// === EXAMPLE 3 ===
// convert to DataStream but write out the time attribute as a metadata column which means
// it is not part of the physical schema anymore
DataStream<Row> dataStream = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column("name", "STRING")
        .column("score", "INT")
        .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
        .build());
// the stream record's timestamp is defined by the metadata; it is not part of the Row
dataStream.process(
    new ProcessFunction<Row, Void>() {
        @Override
        public void processElement(Row row, Context ctx, Collector<Void> out) {
            // prints: [name, score]
            System.out.println(row.getFieldNames(true));
            // timestamp exists once
            System.out.println(ctx.timestamp());
        }
    });
env.execute();
// === EXAMPLE 4 ===
// for advanced users, it is also possible to use more internal data structures for efficiency
// note that this is only mentioned here for completeness because using internal data structures
// adds complexity and additional type handling
// however, converting a TIMESTAMP_LTZ column to `Long` or STRING to `byte[]` might be convenient,
// also structured types can be represented as `Row` if needed
DataStream<Row> dataStream = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column(
            "name",
            DataTypes.STRING().bridgedTo(StringData.class))
        .column(
            "score",
            DataTypes.INT())
        .column(
            "event_time",
            DataTypes.TIMESTAMP_LTZ(3).bridgedTo(Long.class))
        .build());
// leads to a stream of Row(name: StringData, score: Integer, event_time: Long)

Scala

import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.functions.ProcessFunction
import org.apache.flink.streaming.api.scala.DataStream
import org.apache.flink.table.api._
import org.apache.flink.types.Row
import org.apache.flink.util.Collector
import java.time.Instant
// create Table with event-time
tableEnv.executeSql(
    "CREATE TABLE GeneratedTable "
    + "("
    + "  name STRING,"
    + "  score INT,"
    + "  event_time TIMESTAMP_LTZ(3),"
    + "  WATERMARK FOR event_time AS event_time - INTERVAL '10' SECOND"
    + ")"
    + "WITH ('connector'='datagen')")
val table = tableEnv.from("GeneratedTable")
// === EXAMPLE 1 ===
// convert to DataStream in the simplest and most general way possible (no event-time)
val simpleTable = tableEnv
    .fromValues(row("Alice", 12), row("Alice", 2), row("Bob", 12))
    .as("name", "score")
    .groupBy($"name")
    .select($"name", $"score".sum())
tableEnv
    .toChangelogStream(simpleTable)
    .executeAndCollect()
    .foreach(println)
// prints:
// +I[Bob, 12]
// +I[Alice, 12]
// -U[Alice, 12]
// +U[Alice, 14]
// === EXAMPLE 2 ===
// convert to DataStream in the simplest and most general way possible (with event-time)
val dataStream: DataStream[Row] = tableEnv.toChangelogStream(table)
// since `event_time` is a single time attribute in the schema, it is set as the
// stream record's timestamp by default; however, at the same time, it remains part of the Row
dataStream.process(new ProcessFunction[Row, Unit] {
    override def processElement(
        row: Row,
        ctx: ProcessFunction[Row, Unit]#Context,
        out: Collector[Unit]): Unit = {
        // prints: [name, score, event_time]
        println(row.getFieldNames(true))
        // timestamp exists twice
        assert(ctx.timestamp() == row.getFieldAs[Instant]("event_time").toEpochMilli)
    }
})
env.execute()
// === EXAMPLE 3 ===
// convert to DataStream but write out the time attribute as a metadata column which means
// it is not part of the physical schema anymore
val dataStream: DataStream[Row] = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column("name", "STRING")
        .column("score", "INT")
        .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
        .build())
// the stream record's timestamp is defined by the metadata; it is not part of the Row
dataStream.process(new ProcessFunction[Row, Unit] {
    override def processElement(
        row: Row,
        ctx: ProcessFunction[Row, Unit]#Context,
        out: Collector[Unit]): Unit = {
        // prints: [name, score]
        println(row.getFieldNames(true))
        // timestamp exists once
        println(ctx.timestamp())
    }
})
env.execute()
// === EXAMPLE 4 ===
// for advanced users, it is also possible to use more internal data structures for better
// efficiency
// note that this is only mentioned here for completeness because using internal data structures
// adds complexity and additional type handling
// however, converting a TIMESTAMP_LTZ column to `Long` or STRING to `byte[]` might be convenient,
// also structured types can be represented as `Row` if needed
val dataStream: DataStream[Row] = tableEnv.toChangelogStream(
    table,
    Schema.newBuilder()
        .column(
            "name",
            DataTypes.STRING().bridgedTo(classOf[StringData]))
        .column(
            "score",
            DataTypes.INT())
        .column(
            "event_time",
            DataTypes.TIMESTAMP_LTZ(3).bridgedTo(class[Long]))
        .build())
// leads to a stream of Row(name: StringData, score: Integer, event_time: Long)

For more information about which conversions are supported for data types in Example 4, see the Table API’s Data Types page.

The behavior of toChangelogStream(Table).executeAndCollect() is equal to calling Table.execute().collect(). However, toChangelogStream(Table) might be more useful for tests because it allows to access the produced watermarks in a subsequent ProcessFunction in DataStream API.

Mapping between TypeInformation and DataType

The DataStream API uses instances of org.apache.flink.api.common.typeinfo.TypeInformation to describe the record type that travels in the stream. In particular, it defines how to serialize and deserialize records from one DataStream operator to the other. It also helps in serializing state into savepoints and checkpoints.

The Table API uses custom data structures to represent records internally and exposes org.apache.flink.table.types.DataType to users for declaring the external format into which the data structures are converted for easier usage in sources, sinks, UDFs, or DataStream API.

DataType is richer than TypeInformation as it also includes details about the logical SQL type. Therefore, some details will be added implicitly during the conversion.

Column names and types of a Table are automatically derived from the TypeInformation of the DataStream. Use DataStream.getType() to check whether the type information has been detected correctly via the DataStream API’s reflective type extraction facilities. If the outermost record’s TypeInformation is a CompositeType, it will be flattened in the first level when deriving a table’s schema.

TypeInformation to DataType

The following rules apply when converting TypeInformation to a DataType:

• All subclasses of TypeInformation are mapped to logical types, including nullability that is aligned with Flink’s built-in serializers.

• Subclasses of TupleTypeInfoBase are translated into a row (for Row) or structured type (for tuples, POJOs, and case classes).

• BigDecimal is converted to DECIMAL(38, 18) by default.

• The order of PojoTypeInfo fields is determined by a constructor with all fields as its parameters. If that is not found during the conversion, the field order will be alphabetical.

• GenericTypeInfo and other TypeInformation that cannot be represented as one of the listed org.apache.flink.table.api.DataTypes will be treated as a black-box RAW type. The current session configuration is used to materialize the serializer of the raw type. Composite nested fields will not be accessible then.

• See TypeInfoDataTypeConverter for the full translation logic.

Use DataTypes.of(TypeInformation) to call the above logic in custom schema declaration or in UDFs.

DataType to TypeInformation

The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API.

Afterward, the type information semantics of the DataStream API need to be considered.

Legacy Conversion

The following section describes outdated parts of the API that will be removed in future versions.

In particular, these parts might not be well integrated into many recent new features and refactorings (e.g. RowKind is not correctly set, type systems don’t integrate smoothly).

Convert a DataStream into a Table

A DataStream can be directly converted to a Table in a StreamTableEnvironment. The schema of the resulting view depends on the data type of the registered collection.

Java

StreamTableEnvironment tableEnv = ...; 
DataStream<Tuple2<Long, String>> stream = ...
Table table2 = tableEnv.fromDataStream(stream, $("myLong"), $("myString"));

Scala

val tableEnv: StreamTableEnvironment = ???
val stream: DataStream[(Long, String)] = ???
val table2: Table = tableEnv.fromDataStream(stream, $"myLong", $"myString")

Convert a Table into a DataStream

The results of a Table can be converted into a DataStream. In this way, custom DataStream programs can be run on the result of a Table API or SQL query.

When converting a Table into a DataStream you need to specify the data type of the resulting records, i.e., the data type into which the rows of the Table are to be converted. Often the most convenient conversion type is Row. The following list gives an overview of the features of the different options:

• Row: fields are mapped by position, arbitrary number of fields, support for null values, no type-safe access.
• POJO: fields are mapped by name (POJO fields must be named as Table fields), arbitrary number of fields, support for null values, type-safe access.
• Case Class: fields are mapped by position, no support for null values, type-safe access.
• Tuple: fields are mapped by position, limitation to 22 (Scala) or 25 (Java) fields, no support for null values, type-safe access.
• Atomic Type: Table must have a single field, no support for null values, type-safe access.

Convert a Table into a DataStream

A Table that is the result of a streaming query will be updated dynamically, i.e., it is changing as new records arrive on the query’s input streams. Hence, the DataStream into which such a dynamic query is converted needs to encode the updates of the table.

There are two modes to convert a Table into a DataStream:

1. Append Mode: This mode can only be used if the dynamic Table is only modified by INSERT changes, i.e., it is append-only and previously emitted results are never updated.

2. Retract Mode: This mode can always be used. It encodes INSERT and DELETE changes with a boolean flag.

   Java

StreamTableEnvironment tableEnv = ...; 
Table table = tableEnv.fromValues(
    DataTypes.Row(
        DataTypes.FIELD("name", DataTypes.STRING()),
        DataTypes.FIELD("age", DataTypes.INT()),
    row("john", 35),
    row("sarah", 32));
// Convert the Table into an append DataStream of Row by specifying the class
DataStream<Row> dsRow = tableEnv.toAppendStream(table, Row.class);
// Convert the Table into an append DataStream of Tuple2<String, Integer> with TypeInformation
TupleTypeInfo<Tuple2<String, Integer>> tupleType = new TupleTypeInfo<>(Types.STRING(), Types.INT());
DataStream<Tuple2<String, Integer>> dsTuple = tableEnv.toAppendStream(table, tupleType);
// Convert the Table into a retract DataStream of Row.
// A retract stream of type X is a DataStream<Tuple2<Boolean, X>>. 
// The boolean field indicates the type of the change. 
// True is INSERT, false is DELETE.
DataStream<Tuple2<Boolean, Row>> retractStream = tableEnv.toRetractStream(table, Row.class);

Scala

val tableEnv: StreamTableEnvironment = ???
// Table with two fields (String name, Integer age)
val table: Table = tableEnv.fromValues(
    DataTypes.Row(
        DataTypes.FIELD("name", DataTypes.STRING()),
        DataTypes.FIELD("age", DataTypes.INT()),
    row("john", 35),
    row("sarah", 32));
// Convert the Table into an append DataStream of Row by specifying the class
val dsRow: DataStream[Row] = tableEnv.toAppendStream[Row](table)
// Convert the Table into an append DataStream of (String, Integer) with TypeInformation
val dsTuple: DataStream[(String, Int)] dsTuple = 
  tableEnv.toAppendStream[(String, Int)](table)
// Convert the Table into a retract DataStream of Row.
// A retract stream of type X is a DataStream<Tuple2<Boolean, X>>. 
// The boolean field indicates the type of the change. 
// True is INSERT, false is DELETE.
val retractStream: DataStream[(Boolean, Row)] = tableEnv.toRetractStream[Row](table)