DataStream API Integration

DataStream API Integration #

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.

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.

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Converting between DataStream and Table #

Flink provides a specialized StreamTableEnvironment 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.

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.

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]

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]

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment
from pyflink.common.typeinfo import Types

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# create a DataStream
ds = env.from_collection(["Alice", "Bob", "John"], Types.STRING())

# interpret the insert-only DataStream as a Table
t = t_env.from_data_stream(ds)

# register the Table object as a view and query it
t_env.create_temporary_view("InputTable", t)
res_table = t_env.sql_query("SELECT UPPER(f0) FROM InputTable")

# interpret the insert-only Table as a DataStream again
res_ds = t_env.to_data_stream(res_table)

# add a printing sink and execute in DataStream API
res_ds.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 converting 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.

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]

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]

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment
from pyflink.common.typeinfo import Types

# create environments of both APIs
env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# create a DataStream
ds = env.from_collection([("Alice", 12), ("Bob", 10), ("Alice", 100)],
                          type_info=Types.ROW_NAMED(
                          ["a", "b"],
                          [Types.STRING(), Types.INT()]))

input_table = t_env.from_data_stream(ds).alias("name", "score")

# register the Table object as a view and query it
# the query contains an aggregation that produces updates
t_env.create_temporary_view("InputTable", input_table)
res_table = t_env.sql_query("SELECT name, SUM(score) FROM InputTable GROUP BY name")

# interpret the updating Table as a changelog DataStream
res_stream = t_env.to_changelog_stream(res_table)

# add a printing sink and execute in DataStream API
res_stream.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.

The example above shows how the final result is computed incrementally by continuously emitting row-wise updates for each incoming record. However, in cases where the input streams are finite (i.e. bounded), a result can be computed more efficiently by leveraging batch processing principles.

In batch processing, operators can be executed in successive stages that consume the entire input table before emitting results. For example, a join operator can sort both bounded inputs before performing the actual joining (i.e. sort-merge join algorithm), or build a hash table from one input before consuming the other (i.e. build/probe phase of the hash join algorithm).

Both DataStream API and Table API offer a specialized batch runtime mode.

The following example illustrates that the unified pipeline is able to process both batch and streaming data by just switching a flag.

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

// setup DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

// set the batch runtime mode
env.setRuntimeMode(RuntimeExecutionMode.BATCH);

// uncomment this for streaming mode
// env.setRuntimeMode(RuntimeExecutionMode.STREAMING);

// setup Table API
// the table environment adopts the runtime mode during initialization
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// define the same pipeline as above

// prints in BATCH mode:
// +I[Bob, 10]
// +I[Alice, 112]

// prints in STREAMING mode:
// +I[Alice, 12]
// +I[Bob, 10]
// -U[Alice, 12]
// +U[Alice, 112]

import org.apache.flink.api.common.RuntimeExecutionMode
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

// setup DataStream API
val env = StreamExecutionEnvironment.getExecutionEnvironment()

// set the batch runtime mode
env.setRuntimeMode(RuntimeExecutionMode.BATCH)

// uncomment this for streaming mode
// env.setRuntimeMode(RuntimeExecutionMode.STREAMING)

// setup Table API
// the table environment adopts the runtime mode during initialization
val tableEnv = StreamTableEnvironment.create(env)

// define the same pipeline as above

// prints in BATCH mode:
// +I[Bob, 10]
// +I[Alice, 112]

// prints in STREAMING mode:
// +I[Alice, 12]
// +I[Bob, 10]
// -U[Alice, 12]
// +U[Alice, 112]

from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode
from pyflink.table import StreamTableEnvironment

# setup DataStream API
env = StreamExecutionEnvironment.get_execution_environment()

# set the batch runtime mode
env.set_runtime_mode(RuntimeExecutionMode.BATCH)

# uncomment this for streaming mode
# env.set_runtime_mode(RuntimeExecutionMode.STREAMING)

# setup Table API
# the table environment adopts the runtime mode during initialization
table_env = StreamTableEnvironment.create(env)

# define the same pipeline as above
# prints in BATCH mode:
# +I[Bob, 10]
# +I[Alice, 112]

# prints in STREAMING mode:
# +I[Alice, 12]
# +I[Bob, 10]
# -U[Alice, 12]
# +U[Alice, 112]

Once the changelog is applied to an external system (e.g. a key-value store), one can see that both modes are able to produce exactly the same output table. By consuming all input data before emitting results, the changelog of the batch mode consists solely of insert-only changes. See also the dedicated batch mode section below for more insights.

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.

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-table-api-java-bridge_2.12</artifactId>
  <version>2.0-preview1</version>
  <scope>provided</scope>
</dependency>

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-table-api-scala-bridge_2.12</artifactId>
  <version>2.0-preview1</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.

// 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.*;

// 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._

# imports for Python DataStream API
from pyflink.datastream import *

# imports for Table API to Python DataStream API
from pyflink.table import *

Please refer to the configuration section for more information.

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. The propagation of options from Table API to DataStream API happens during planning.

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

import java.time.ZoneId;
import org.apache.flink.core.execution.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().setCheckpointingConsistencyMode(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...

import java.time.ZoneId
import org.apache.flink.core.execution.CheckpointingMode
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
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.setCheckpointingConsistencyMode(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...

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment
from pyflink.datastream.checkpointing_mode import CheckpointingMode


# create Python DataStream API
env = StreamExecutionEnvironment.get_execution_environment()

# set various configuration early
env.set_max_parallelism(256)

env.get_checkpoint_config().set_checkpointing_mode(CheckpointingMode.EXACTLY_ONCE)

# then switch to Python Table API
t_env = StreamTableEnvironment.create(env)

# set configuration early
t_env.get_config().set_local_timezone("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 uses a builder pattern to construct a complex pipeline. The pipeline possibly splits into multiple branches that might or might not end with a sink. The environment buffers all these defined branches until it comes to job submission.

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.

Table API

In the Table API, branching pipelines are 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").insertInto("OutputTable").execute();

tableEnv.executeSql("INSERT INTO OutputTable SELECT * FROM InputTable");

tableEnv.createStatementSet()
    .add(tableEnv.from("InputTable").insertInto("OutputTable"))
    .add(tableEnv.from("InputTable").insertInto("OutputTable2"))
    .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();

# execute with explicit sink
table_env.from_path("input_table").execute_insert("output_table")

table_env.execute_sql("INSERT INTO output_table SELECT * FROM input_table")

table_env.create_statement_set() \
    .add_insert("output_table", input_table) \
    .add_insert("output_table2", input_table) \
    .execute()

table_env.create_statement_set() \
    .add_insert_sql("INSERT INTO output_table SELECT * FROM input_table") \
    .add_insert_sql("INSERT INTO output_table2 SELECT * FROM input_table") \
    .execute()

# execute with implicit local sink
table_env.from_path("input_table").execute().print()

table_env.execute_sql("SELECT * FROM input_table").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();

# (1)

# adds a branch with a printing sink to the StreamExecutionEnvironment
table_env.to_data_stream(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()

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Batch Runtime Mode #

The batch runtime mode is a specialized execution mode for bounded Flink programs.

Generally speaking, boundedness is a property of a data source that tells us whether all the records coming from that source are known before execution or whether new data will show up, potentially indefinitely. A job, in turn, is bounded if all its sources are bounded, and unbounded otherwise.

Streaming runtime mode, on the other hand, can be used for both bounded and unbounded jobs.

For more information on the different execution modes, see also the corresponding DataStream API section.

The Table API & SQL planner provides a set of specialized optimizer rules and runtime operators for either of the two modes.

Currently, the runtime mode is not derived automatically from sources, thus, it must be set explicitly or will be adopted from StreamExecutionEnvironment when instantiating a StreamTableEnvironment:

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;

// adopt mode from StreamExecutionEnvironment
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.BATCH);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// or

// set mode explicitly for StreamTableEnvironment
// it will be propagated to StreamExecutionEnvironment during planning
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, EnvironmentSettings.inBatchMode());

import org.apache.flink.api.common.RuntimeExecutionMode
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
import org.apache.flink.table.api.EnvironmentSettings

// adopt mode from StreamExecutionEnvironment
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setRuntimeMode(RuntimeExecutionMode.BATCH)
val tableEnv = StreamTableEnvironment.create(env)

// or

// set mode explicitly for StreamTableEnvironment
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env, EnvironmentSettings.inBatchMode)

from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode
from pyflink.table import EnvironmentSettings, StreamTableEnvironment

# adopt mode from StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()
env.set_runtime_mode(RuntimeExecutionMode.BATCH)
table_env = StreamTableEnvironment.create(env)

# or

# set mode explicitly for StreamTableEnvironment
# it will be propagated to StreamExecutionEnvironment during planning
env = StreamExecutionEnvironment.get_execution_environment()
table_env = StreamTableEnvironment.create(env, EnvironmentSettings.in_batch_mode())

One must meet the following prerequisites before setting the runtime mode to BATCH:

  • All sources must declare themselves as bounded.

  • Currently, table sources must emit insert-only changes.

  • Operators need a sufficient amount of off-heap memory for sorting and other intermediate results.

  • All table operations must be available in batch mode. Currently, some of them are only available in streaming mode. Please check the corresponding Table API & SQL pages.

A batch execution has the following implications (among others):

  • Progressive watermarks are neither generated nor used in operators. However, sources emit a maximum watermark before shutting down.

  • Exchanges between tasks might be blocking according to the execution.batch-shuffle-mode. This also means potentially less resource requirements compared to executing the same pipeline in streaming mode.

  • Checkpointing is disabled. Artificial state backends are inserted.

  • Table operations don’t produce incremental updates but only a complete final result which converts to an insert-only changelog stream.

Since batch processing can be considered as a special case of stream processing, we recommend implementing a streaming pipeline first as it is the most general implementation for both bounded and unbounded data.

In theory, a streaming pipeline can execute all operators. However, in practice, some operations might not make much sense as they would lead to ever-growing state and are therefore not supported. A global sort would be an example that is only available in batch mode. Simply put: it should be possible to run a working streaming pipeline in batch mode but not necessarily vice versa.

The following example shows how to play around with batch mode using the DataGen table source. Many sources offer options that implicitly make the connector bounded, for example, by defining a terminating offset or timestamp. In our example, we limit the number of rows with the number-of-rows option.

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.api.TableDescriptor;

Table table =
    tableEnv.from(
        TableDescriptor.forConnector("datagen")
            .option("number-of-rows", "10") // make the source bounded
            .schema(
                Schema.newBuilder()
                    .column("uid", DataTypes.TINYINT())
                    .column("payload", DataTypes.STRING())
                    .build())
            .build());

// convert the Table to a DataStream and further transform the pipeline
tableEnv.toDataStream(table)
    .keyBy(r -> r.<Byte>getFieldAs("uid"))
    .map(r -> "My custom operator: " + r.<String>getFieldAs("payload"))
    .executeAndCollect()
    .forEachRemaining(System.out::println);

// prints:
// My custom operator: 9660912d30a43c7b035e15bd...
// My custom operator: 29f5f706d2144f4a4f9f52a0...
// ...

import org.apache.flink.api.scala._
import org.apache.flink.table.api._

val table =
    tableEnv.from(
        TableDescriptor.forConnector("datagen")
            .option("number-of-rows", "10") // make the source bounded
            .schema(
                Schema.newBuilder()
                    .column("uid", DataTypes.TINYINT())
                    .column("payload", DataTypes.STRING())
                    .build())
            .build())

// convert the Table to a DataStream and further transform the pipeline
tableEnv.toDataStream(table)
    .keyBy(r => r.getFieldAs[Byte]("uid"))
    .map(r => "My custom operator: " + r.getFieldAs[String]("payload"))
    .executeAndCollect()
    .foreach(println)

// prints:
// My custom operator: 9660912d30a43c7b035e15bd...
// My custom operator: 29f5f706d2144f4a4f9f52a0...
// ...

from pyflink.table import TableDescriptor, Schema, DataTypes

table = table_env.from_descriptor(
    TableDescriptor.for_connector("datagen")
        .option("number-of-rows", "10")
        .schema(
        Schema.new_builder()
            .column("uid", DataTypes.TINYINT())
            .column("payload", DataTypes.STRING())
            .build())
        .build())

# convert the Table to a DataStream and further transform the pipeline
collect = table_env.to_data_stream(table) \
    .key_by(lambda r: r[0]) \
    .map(lambda r: "My custom operator: " + r[1]) \
    .execute_and_collect()

for c in collect:
    print(c)

# prints:
# My custom operator: 9660912d30a43c7b035e15bd...
# My custom operator: 29f5f706d2144f4a4f9f52a0...
# ...

Changelog Unification #

In most cases, the pipeline definition itself can remain constant in both Table API and DataStream API when switching from streaming to batch mode and vice versa. However, as mentioned before, the resulting changelog streams might differ due to the avoidance of incremental operations in batch mode.

Time-based operations that rely on event-time and leverage watermarks as a completeness marker are able to produce an insert-only changelog stream that is independent of the runtime mode.

The following Java example illustrates a Flink program that is not only unified on an API level but also in the resulting changelog stream. The example joins two tables in SQL (UserTable and OrderTable) using an interval join based on the time attributes in both tables (ts). It uses DataStream API to implement a custom operator that deduplicates the user name using a KeyedProcessFunction and value state.

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
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.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.Collector;
import java.time.LocalDateTime;

// setup DataStream API
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

// use BATCH or STREAMING mode
env.setRuntimeMode(RuntimeExecutionMode.BATCH);

// setup Table API
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

// create a user stream
DataStream<Row> userStream = env
    .fromElements(
        Row.of(LocalDateTime.parse("2021-08-21T13:00:00"), 1, "Alice"),
        Row.of(LocalDateTime.parse("2021-08-21T13:05:00"), 2, "Bob"),
        Row.of(LocalDateTime.parse("2021-08-21T13:10:00"), 2, "Bob"))
    .returns(
        Types.ROW_NAMED(
            new String[] {"ts", "uid", "name"},
            Types.LOCAL_DATE_TIME, Types.INT, Types.STRING));

// create an order stream
DataStream<Row> orderStream = env
    .fromElements(
        Row.of(LocalDateTime.parse("2021-08-21T13:02:00"), 1, 122),
        Row.of(LocalDateTime.parse("2021-08-21T13:07:00"), 2, 239),
        Row.of(LocalDateTime.parse("2021-08-21T13:11:00"), 2, 999))
    .returns(
        Types.ROW_NAMED(
            new String[] {"ts", "uid", "amount"},
            Types.LOCAL_DATE_TIME, Types.INT, Types.INT));

// create corresponding tables
tableEnv.createTemporaryView(
    "UserTable",
    userStream,
    Schema.newBuilder()
        .column("ts", DataTypes.TIMESTAMP(3))
        .column("uid", DataTypes.INT())
        .column("name", DataTypes.STRING())
        .watermark("ts", "ts - INTERVAL '1' SECOND")
        .build());

tableEnv.createTemporaryView(
    "OrderTable",
    orderStream,
    Schema.newBuilder()
        .column("ts", DataTypes.TIMESTAMP(3))
        .column("uid", DataTypes.INT())
        .column("amount", DataTypes.INT())
        .watermark("ts", "ts - INTERVAL '1' SECOND")
        .build());

// perform interval join
Table joinedTable =
    tableEnv.sqlQuery(
        "SELECT U.name, O.amount " +
        "FROM UserTable U, OrderTable O " +
        "WHERE U.uid = O.uid AND O.ts BETWEEN U.ts AND U.ts + INTERVAL '5' MINUTES");

DataStream<Row> joinedStream = tableEnv.toDataStream(joinedTable);

joinedStream.print();

// implement a custom operator using ProcessFunction and value state
joinedStream
    .keyBy(r -> r.<String>getFieldAs("name"))
    .process(
        new KeyedProcessFunction<String, Row, String>() {

          ValueState<String> seen;

          @Override
          public void open(OpenContext openContext) {
              seen = getRuntimeContext().getState(
                  new ValueStateDescriptor<>("seen", String.class));
          }

          @Override
          public void processElement(Row row, Context ctx, Collector<String> out)
                  throws Exception {
              String name = row.getFieldAs("name");
              if (seen.value() == null) {
                  seen.update(name);
                  out.collect(name);
              }
          }
        })
    .print();

// execute unified pipeline
env.execute();

// prints (in both BATCH and STREAMING mode):
// +I[Bob, 239]
// +I[Alice, 122]
// +I[Bob, 999]
//
// Bob
// Alice

from datetime import datetime
from pyflink.common import Row, Types
from pyflink.datastream import StreamExecutionEnvironment, RuntimeExecutionMode,
    KeyedProcessFunction, RuntimeContext
from pyflink.datastream.state import ValueStateDescriptor
from pyflink.table import StreamTableEnvironment, Schema, DataTypes

# setup DataStream API
env = StreamExecutionEnvironment.get_execution_environment()

# use BATCH or STREAMING mode
env.set_runtime_mode(RuntimeExecutionMode.BATCH)

# setup Table API
table_env = StreamTableEnvironment.create(env)

# create a user stream
t_format = "%Y-%m-%dT%H:%M:%S"
user_stream = env.from_collection(
    [Row(datetime.strptime("2021-08-21T13:00:00", t_format), 1, "Alice"),
     Row(datetime.strptime("2021-08-21T13:05:00", t_format), 2, "Bob"),
     Row(datetime.strptime("2021-08-21T13:10:00", t_format), 2, "Bob")],
    type_info=Types.ROW_NAMED(["ts1", "uid", "name"],
                              [Types.SQL_TIMESTAMP(), Types.INT(), Types.STRING()]))

# create an order stream
order_stream = env.from_collection(
    [Row(datetime.strptime("2021-08-21T13:02:00", t_format), 1, 122),
     Row(datetime.strptime("2021-08-21T13:07:00", t_format), 2, 239),
     Row(datetime.strptime("2021-08-21T13:11:00", t_format), 2, 999)],
    type_info=Types.ROW_NAMED(["ts1", "uid", "amount"],
                        [Types.SQL_TIMESTAMP(), Types.INT(), Types.INT()]))

# # create corresponding tables
table_env.create_temporary_view(
    "user_table",
    user_stream,
    Schema.new_builder()
        .column_by_expression("ts", "CAST(ts1 AS TIMESTAMP(3))")
        .column("uid", DataTypes.INT())
        .column("name", DataTypes.STRING())
        .watermark("ts", "ts - INTERVAL '1' SECOND")
        .build())

table_env.create_temporary_view(
    "order_table",
    order_stream,
    Schema.new_builder()
        .column_by_expression("ts", "CAST(ts1 AS TIMESTAMP(3))")
        .column("uid", DataTypes.INT())
        .column("amount", DataTypes.INT())
        .watermark("ts", "ts - INTERVAL '1' SECOND")
        .build())

# perform interval join
joined_table = table_env.sql_query(
    "SELECT U.name, O.amount " +
    "FROM user_table U, order_table O " +
    "WHERE U.uid = O.uid AND O.ts BETWEEN U.ts AND U.ts + INTERVAL '5' MINUTES")

joined_stream = table_env.to_data_stream(joined_table)

joined_stream.print()

# implement a custom operator using ProcessFunction and value state
class MyProcessFunction(KeyedProcessFunction):

    def __init__(self):
        self.seen = None

    def open(self, runtime_context: RuntimeContext):
        state_descriptor = ValueStateDescriptor("seen", Types.STRING())
        self.seen = runtime_context.get_state(state_descriptor)

    def process_element(self, value, ctx):
        name = value[0]
        if self.seen.value() is None:
            self.seen.update(name)
            yield name

joined_stream \
    .key_by(lambda r: r[0]) \
    .process(MyProcessFunction()) \
    .print()

# execute unified pipeline
env.execute()

# prints (in both BATCH and STREAMING mode):
# +I[Bob, 239]
# +I[Alice, 122]
# +I[Bob, 999]
#
# Bob
# Alice

Back to top

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(Table): 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(Table, 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(Table, Class): A shortcut for toDataStream(Table, 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:

Key Data Type Description R/W
rowtime TIMESTAMP_LTZ(3) NOT NULL Stream record's timestamp. R/W

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.

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

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

from pyflink.common.time import Instant
from pyflink.common.types import Row
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, Schema

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)
ds = env.from_collection([
    Row("Alice", 12, Instant.of_epoch_milli(1000)),
    Row("Bob", 5, Instant.of_epoch_milli(1001)),
    Row("Alice", 10, Instant.of_epoch_milli(1002))],
    type_info=Types.ROW_NAMED(['name', 'score', 'event_time'], [Types.STRING(), Types.INT(), Types.INSTANT()]))

# === EXAMPLE 1 ===

# derive all physical columns automatically

table = t_env.from_data_stream(ds)
table.print_schema()

# 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 = t_env.from_data_stream(
    ds,
    Schema.new_builder()
        .column_by_expression("proc_time", "PROCTIME()")
        .build())
table.print_schema()

# prints:
# (
#  `name` STRING,
#  `score` INT,
#  `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 = t_env.from_data_stream(
          ds,
          Schema.new_builder()
              .column_by_expression("rowtime", "CAST(event_time AS TIMESTAMP_LTZ(3))")
              .watermark("rowtime", "rowtime - INTERVAL '10' SECOND")
              .build())
table.print_schema()

# 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 = t_env.from_data_stream(
        ds,
        Schema.new_builder()
            .column_by_metadata("rowtime", "TIMESTAMP_LTZ(3)")
            .watermark("rowtime", "SOURCE_WATERMARK()")
            .build())
table.print_schema()

# 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 = t_env.from_data_stream(
        ds,
        Schema.new_builder()
            .column("event_time", "TIMESTAMP_LTZ(3)")
            .column("name", "STRING")
            .column("score", "INT")
            .watermark("event_time", "SOURCE_WATERMARK()")
            .build())
table.print_schema()

# prints:
# (
#  `event_time` TIMESTAMP_LTZ(3) *ROWTIME*,
#  `name` STRING,
#  `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 table = 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>*
// )

Custom PoJo Class is unsupported in PyFlink now.

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.

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Schema;

// 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
// )

// 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
// )

from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import DataTypes, StreamTableEnvironment, Schema

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)
ds = env.from_collection([(12, "Alice"), (0, "Bob")], type_info=Types.TUPLE([Types.LONG(), Types.STRING()]))

# === EXAMPLE 1 ===

# register the DataStream as view "MyView" in the current session
# all columns are derived automatically

t_env.create_temporary_view("MyView", ds)

t_env.from_path("MyView").print_schema()

# 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

t_env.create_temporary_view(
    "MyView",
    ds,
    Schema.new_builder()
        .column("f0", "BIGINT")
        .column("f1", "STRING")
        .build())

t_env.from_path("MyView").print_schema()

# prints:
# (
#  `f0` BIGINT,
#  `f1` STRING
# )


# === EXAMPLE 3 ===

# use the Table API before creating the view if it is only about renaming columns

t_env.create_temporary_view(
    "MyView",
    t_env.from_data_stream(ds).alias("id", "name"))

t_env.from_path("MyView").print_schema()

# prints:
# (
#  `id` BIGINT NOT NULL,
#  `name` STRING
# )

Back to top

Examples for toDataStream #

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

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);

// === EXAMPLE 3 ===

// 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))));

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, classOf[User])

// === EXAMPLE 3 ===

// data types can be extracted reflectively as above or explicitly defined

val dataStream: DataStream[User] =
    tableEnv.toDataStream(
        table,
        DataTypes.STRUCTURED(
            classOf[User],
            DataTypes.FIELD("name", DataTypes.STRING()),
            DataTypes.FIELD("score", DataTypes.INT()),
            DataTypes.FIELD("event_time", DataTypes.TIMESTAMP_LTZ(3))))

t_env.execute_sql(
    "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 = t_env.from_path("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

ds = t_env.to_data_stream(table)

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.

Back to top

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.

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 |
// | -U |                          Alice |          12 |
// | +U |                          Alice |         100 |
// +----+--------------------------------+-------------+

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 |
// | -U |                          Alice |          12 |
// | +U |                          Alice |         100 |
// +----+--------------------------------+-------------+

from pyflink.common import Row, RowKind
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import DataTypes, StreamTableEnvironment, Schema

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# === EXAMPLE 1 ===

# create a changelog DataStream

ds = env.from_collection([
        Row.of_kind(RowKind.INSERT, "Alice", 12),
        Row.of_kind(RowKind.INSERT, "Bob", 5),
        Row.of_kind(RowKind.UPDATE_BEFORE, "Alice", 12),
        Row.of_kind(RowKind.UPDATE_AFTER, "Alice", 100)],
        type_info=Types.ROW([Types.STRING(),Types.INT()]))

# interpret the DataStream as a Table
table = t_env.from_changelog_stream(ds)


# register the table under a name and perform an aggregation
t_env.create_temporary_view("InputTable", table)
t_env.execute_sql("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
ds = env.from_collection([
        Row.of_kind(RowKind.INSERT, "Alice", 12),
        Row.of_kind(RowKind.INSERT, "Bob", 5),
        Row.of_kind(RowKind.UPDATE_AFTER, "Alice", 100)],
        type_info=Types.ROW([Types.STRING(),Types.INT()]))

# interpret the DataStream as a Table
table = t_env.from_changelog_stream(
        ds,
        Schema.new_builder().primary_key("f0").build(),
        ChangelogMode.upsert())

# register the table under a name and perform an aggregation
t_env.create_temporary_view("InputTable", table)
t_env.execute_sql("SELECT f0 AS name, SUM(f1) AS score FROM InputTable GROUP BY f0").print()

# prints:
# +----+--------------------------------+-------------+
# | op |                           name |       score |
# +----+--------------------------------+-------------+
# | +I |                            Bob |           5 |
# | +I |                          Alice |          12 |
# | -U |                          Alice |          12 |
# | +U |                          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% using upsert mode. The number of result messages can be reduced by defining a primary key and upsert changelog mode for toChangelogStream.

Examples for toChangelogStream #

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

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)

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)

from pyflink.common import Row
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.functions import ProcessFunction
from pyflink.table import DataTypes, StreamTableEnvironment, Schema
from pyflink.table.expressions import col

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# create Table with event-time
t_env.execute_sql(
    "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 = t_env.from_path("GeneratedTable")

# === EXAMPLE 1 ===

# convert to DataStream in the simplest and most general way possible (no event-time)
simple_table = t_env.from_elements([Row("Alice", 12), Row("Alice", 2), Row("Bob", 12)],
                                  DataTypes.ROW([DataTypes.FIELD("name", DataTypes.STRING()),
                                                DataTypes.FIELD("score", DataTypes.INT())]))

simple_table = simple_table.group_by(col('name')).select(col('name'), col('score').sum)

t_env.to_changelog_stream(simple_table).print()

env.execute()

# 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)

ds = t_env.to_changelog_stream(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

class MyProcessFunction(ProcessFunction):
    def process_element(self, row, ctx):
        print(row)
        assert ctx.timestamp() == row.event_time.to_epoch_milli()

ds.process(MyProcessFunction())

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

ds = t_env.to_changelog_stream(
    table,
    Schema.new_builder()
        .column("name", "STRING")
        .column("score", "INT")
        .column_by_metadata("rowtime", "TIMESTAMP_LTZ(3)")
        .build())

class MyProcessFunction(ProcessFunction):
    def process_element(self, row, ctx):
        print(row)
        print(ctx.timestamp())

ds.process(MyProcessFunction())

env.execute()

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.

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Adding Table API Pipelines to DataStream API #

A single Flink job can consist of multiple disconnected pipelines that run next to each other.

Source-to-sink pipelines defined in Table API can be attached as a whole to the StreamExecutionEnvironment and will be submitted when calling one of the execute methods in the DataStream API.

However, a source does not necessarily have to be a table source but can also be another DataStream pipeline that was converted to Table API before. Thus, it is possible to use table sinks for DataStream API programs.

The functionality is available through a specialized StreamStatementSet instance created with StreamTableEnvironment.createStatementSet(). By using a statement set, the planner can optimize all added statements together and come up with one or more end-to-end pipelines that are added to the StreamExecutionEnvironment when calling StreamStatementSet.attachAsDataStream().

The following example shows how to add table programs to a DataStream API program within one job.

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.v2.DiscardingSink;
import org.apache.flink.table.api.*;
import org.apache.flink.table.api.bridge.java.*;

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

StreamStatementSet statementSet = tableEnv.createStatementSet();

// create some source
TableDescriptor sourceDescriptor =
    TableDescriptor.forConnector("datagen")
        .option("number-of-rows", "3")
        .schema(
            Schema.newBuilder()
                .column("myCol", DataTypes.INT())
                .column("myOtherCol", DataTypes.BOOLEAN())
                .build())
        .build();

// create some sink
TableDescriptor sinkDescriptor = TableDescriptor.forConnector("print").build();

// add a pure Table API pipeline
Table tableFromSource = tableEnv.from(sourceDescriptor);
statementSet.add(tableFromSource.insertInto(sinkDescriptor));

// use table sinks for the DataStream API pipeline
DataStream<Integer> dataStream = env.fromElements(1, 2, 3);
Table tableFromStream = tableEnv.fromDataStream(dataStream);
statementSet.add(tableFromStream.insertInto(sinkDescriptor));

// attach both pipelines to StreamExecutionEnvironment
// (the statement set will be cleared after calling this method)
statementSet.attachAsDataStream();

// define other DataStream API parts
env.fromElements(4, 5, 6).sinkTo(new DiscardingSink<>());

// use DataStream API to submit the pipelines
env.execute();

// prints similar to:
// +I[1618440447, false]
// +I[1259693645, true]
// +I[158588930, false]
// +I[1]
// +I[2]
// +I[3]

import org.apache.flink.streaming.api.functions.sink.v2.DiscardingSink
import org.apache.flink.streaming.api.scala._
import org.apache.flink.table.api._
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val statementSet = tableEnv.createStatementSet()

// create some source
val sourceDescriptor = TableDescriptor.forConnector("datagen")
    .option("number-of-rows", "3")
    .schema(Schema.newBuilder
        .column("myCol", DataTypes.INT)
        .column("myOtherCol", DataTypes.BOOLEAN).build)
    .build

// create some sink
val sinkDescriptor = TableDescriptor.forConnector("print").build

// add a pure Table API pipeline
val tableFromSource = tableEnv.from(sourceDescriptor)
statementSet.add(tableFromSource.insertInto(sinkDescriptor))

// use table sinks for the DataStream API pipeline
val dataStream = env.fromElements(1, 2, 3)
val tableFromStream = tableEnv.fromDataStream(dataStream)
statementSet.add(tableFromStream.insertInto(sinkDescriptor))

// attach both pipelines to StreamExecutionEnvironment
// (the statement set will be cleared calling this method)
statementSet.attachAsDataStream()

// define other DataStream API parts
env.fromElements(4, 5, 6).sinkTo(new DiscardingSink[Int]())

// now use DataStream API to submit the pipelines
env.execute()

// prints similar to:
// +I[1618440447, false]
// +I[1259693645, true]
// +I[158588930, false]
// +I[1]
// +I[2]
// +I[3]

from pyflink.common import Encoder
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.connectors.file_system import FileSink
from pyflink.table import StreamTableEnvironment, TableDescriptor, Schema, DataTypes

env = StreamExecutionEnvironment.get_execution_environment()
table_env = StreamTableEnvironment.create(env)

statement_set = table_env.create_statement_set()

# create some source
source_descriptor = TableDescriptor.for_connector("datagen") \
    .option("number-of-rows", "3") \
    .schema(
    Schema.new_builder()
        .column("my_col", DataTypes.INT())
        .column("my_other_col", DataTypes.BOOLEAN())
        .build()) \
    .build()

# create some sink
sink_descriptor = TableDescriptor.for_connector("print").build()

# add a pure Table API pipeline
table_from_source = table_env.from_descriptor(source_descriptor)
statement_set.add_insert(sink_descriptor, table_from_source)


# use table sinks for the DataStream API pipeline
data_stream = env.from_collection([1, 2, 3])
table_from_stream = table_env.from_data_stream(data_stream)
statement_set.add_insert(sink_descriptor, table_from_stream)

# attach both pipelines to StreamExecutionEnvironment
# (the statement set will be cleared after calling this method)
statement_set.attach_as_datastream()

# define other DataStream API parts
env.from_collection([4, 5, 6]) \
   .add_sink(FileSink
             .for_row_format('/tmp/output', Encoder.simple_string_encoder())
             .build())

# use DataStream API to submit the pipelines
env.execute()

# prints similar to:
# +I[1618440447, false]
# +I[1259693645, true]
# +I[158588930, false]
# +I[1]
# +I[2]
# +I[3]

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Implicit Conversions in Scala #

Users of the Scala API can use all the conversion methods above in a more fluent way by leveraging Scala’s implicit feature.

Those implicits are available in the API when importing the package object via org.apache.flink.table.api.bridge.scala._.

If enabled, methods such as toTable or toChangelogTable can be called directly on a DataStream object. Similarly, toDataStream and toChangelogStream are available on Table objects. Furthermore, Table objects will be converted to a changelog stream when requesting a DataStream API specific method for DataStream[Row].

The use of an implicit conversion should always be a conscious decision. One should pay attention whether the IDE proposes an actual Table API method, or a DataStream API method via implicits.

For example, a table.execute().collect() stays in Table API whereas table.executeAndCollect() implicitly uses the DataStream API’s executeAndCollect() method and therefore forces an API conversion.

import org.apache.flink.streaming.api.scala._
import org.apache.flink.table.api.bridge.scala._
import org.apache.flink.types.Row

val env = StreamExecutionEnvironment.getExecutionEnvironment
val tableEnv = StreamTableEnvironment.create(env)

val dataStream: DataStream[(Int, String)] = env.fromElements((42, "hello"))

// call toChangelogTable() implicitly on the DataStream object
val table: Table = dataStream.toChangelogTable(tableEnv)

// force implicit conversion
val dataStreamAgain1: DataStream[Row] = table

// call toChangelogStream() implicitly on the Table object
val dataStreamAgain2: DataStream[Row] = table.toChangelogStream

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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.

The DataStream API is not always able to extract a more specific TypeInformation based on reflection. This often happens silently and leads to GenericTypeInfo that is backed by the generic Kryo serializer.

For example, the Row class cannot be analyzed reflectively and always needs an explicit type information declaration. If no proper type information is declared in DataStream API, the row will show as RAW data type and the Table API is unable to access its fields. Use .map(...).returns(TypeInformation) in Java or .map(...)(TypeInformation) in Scala to declare type information explicitly.

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.

Back to top

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.

StreamTableEnvironment tableEnv = ...; 
DataStream<Tuple2<Long, String>> stream = ...;

Table table2 = tableEnv.fromDataStream(stream, $("myLong"), $("myString"));

val tableEnv: StreamTableEnvironment = ???
val stream: DataStream[(Long, String)] = ???

val table2: Table = tableEnv.fromDataStream(stream, $"myLong", $"myString")

t_env = ... # type: StreamTableEnvironment

stream = ... # type: DataStream of Types.TUPLE([Types.LONG(), Types.STRING()])

table2 = t_env.from_data_stream(stream, col('my_long'), col('my_stream'))

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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.
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);

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)

from pyflink.table import DataTypes
from pyflink.common.typeinfo import Types

t_env = ...

table = t_env.from_elements([("john", 35), ("sarah", 32)], 
              DataTypes.ROW([DataTypes.FIELD("name", DataTypes.STRING()),
                             DataTypes.FIELD("age", DataTypes.INT())]))

# Convert the Table into an append DataStream of Row by specifying the type information
ds_row = t_env.to_append_stream(table, Types.ROW([Types.STRING(), Types.INT()]))

# Convert the Table into an append DataStream of Tuple[str, int] with TypeInformation
ds_tuple = t_env.to_append_stream(table, Types.TUPLE([Types.STRING(), Types.INT()]))

# Convert the Table into a retract DataStream of Row by specifying the type information
# A retract stream of type X is a DataStream of Tuple[bool, X]. 
# The boolean field indicates the type of the change. 
# True is INSERT, false is DELETE.
retract_stream = t_env.to_retract_stream(table, Types.ROW([Types.STRING(), Types.INT()]))

Note: A detailed discussion about dynamic tables and their properties is given in the Dynamic Tables document.

Once the Table is converted to a DataStream, please use the StreamExecutionEnvironment.execute() method to execute the DataStream program.

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Mapping of Data Types to Table Schema #

Flink’s DataStream API supports many diverse types. Composite types such as Tuples (built-in Scala , Flink Java tuples and Python tuples), POJOs, Scala case classes, and Flink’s Row type allow for nested data structures with multiple fields that can be accessed in table expressions. Other types are treated as atomic types. In the following, we describe how the Table API converts these types into an internal row representation and show examples of converting a DataStream into a Table.

The mapping of a data type to a table schema can happen in two ways: based on the field positions or based on the field names.

Position-based Mapping

Position-based mapping can be used to give fields a more meaningful name while keeping the field order. This mapping is available for composite data types with a defined field order and atomic types. Composite data types such as tuples, rows, and case classes have such a field order. However, fields of a POJO must be mapped based on the field names (see next section). Fields can be projected out but can’t be renamed using an alias as(Java and Scala) or alias(Python).

When defining a position-based mapping, the specified names must not exist in the input data type, otherwise, the API will assume that the mapping should happen based on the field names. If no field names are specified, the default field names and field order of the composite type are used or f0 for atomic types.

StreamTableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section;

DataStream<Tuple2<Long, Integer>> stream = ...;

// convert DataStream into Table with field "myLong" only
Table table = tableEnv.fromDataStream(stream, $("myLong"));

// convert DataStream into Table with field names "myLong" and "myInt"
Table table = tableEnv.fromDataStream(stream, $("myLong"), $("myInt"));

// get a TableEnvironment
val tableEnv: StreamTableEnvironment = ... // see "Create a TableEnvironment" section

val stream: DataStream[(Long, Int)] = ...

// convert DataStream into Table with field "myLong" only
val table: Table = tableEnv.fromDataStream(stream, $"myLong")

// convert DataStream into Table with field names "myLong" and "myInt"
val table: Table = tableEnv.fromDataStream(stream, $"myLong", $"myInt")

from pyflink.table.expressions import col

# get a TableEnvironment
t_env = ... # see "Create a TableEnvironment" section

stream = ... # type: DataStream of Types.Tuple([Types.LONG(), Types.INT()])

# convert DataStream into Table with field "my_long" only
table = t_env.from_data_stream(stream, col('my_long'))

# convert DataStream into Table with field names "my_long" and "my_int"
table = t_env.from_data_stream(stream, col('my_long'), col('my_int'))

Name-based Mapping

Name-based mapping can be used for any data type, including POJOs. It is the most flexible way of defining a table schema mapping. All fields in the mapping are referenced by name and can be possibly renamed using an alias as. Fields can be reordered and projected out.

If no field names are specified, the default field names and field order of the composite type are used or f0 for atomic types.

StreamTableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section

DataStream<Tuple2<Long, Integer>> stream = ...;

// convert DataStream into Table with field "f1" only
Table table = tableEnv.fromDataStream(stream, $("f1"));

// convert DataStream into Table with swapped fields
Table table = tableEnv.fromDataStream(stream, $("f1"), $("f0"));

// convert DataStream into Table with swapped fields and field names "myInt" and "myLong"
Table table = tableEnv.fromDataStream(stream, $("f1").as("myInt"), $("f0").as("myLong"));

// get a TableEnvironment
val tableEnv: StreamTableEnvironment = ... // see "Create a TableEnvironment" section

val stream: DataStream[(Long, Int)] = ...

// convert DataStream into Table with field "_2" only
val table: Table = tableEnv.fromDataStream(stream, $"_2")

// convert DataStream into Table with swapped fields
val table: Table = tableEnv.fromDataStream(stream, $"_2", $"_1")

// convert DataStream into Table with swapped fields and field names "myInt" and "myLong"
val table: Table = tableEnv.fromDataStream(stream, $"_2" as "myInt", $"_1" as "myLong")

from pyflink.table.expressions import col

# get a TableEnvironment
t_env = ... # see "Create a TableEnvironment" section

stream = ... # type: DataStream of Types.Tuple([Types.LONG(), Types.INT()])

# convert DataStream into Table with field "f1" only
table = t_env.from_data_stream(stream, col('f1'))

# convert DataStream into Table with swapped fields
table = t_env.from_data_stream(stream, col('f1'), col('f0'))

# convert DataStream into Table with swapped fields and field names "my_int" and "my_long"
table = t_env.from_data_stream(stream, col('f1').alias('my_int'), col('f0').alias('my_long'))

Atomic Types #

Flink treats primitives (Integer, Double, String) or generic types (types that cannot be analyzed and decomposed) as atomic types. A DataStream of an atomic type is converted into a Table with a single column. The type of the column is inferred from the atomic type. The name of the column can be specified.

StreamTableEnvironment tableEnv = ...;

DataStream<Long> stream = ...;

// Convert DataStream into Table with field name "myLong"
Table table = tableEnv.fromDataStream(stream, $("myLong"));

val tableEnv: StreamTableEnvironment = ???

val stream: DataStream[Long] = ...

// Convert DataStream into Table with default field name "f0"
val table: Table = tableEnv.fromDataStream(stream)

// Convert DataStream into Table with field name "myLong"
val table: Table = tableEnv.fromDataStream(stream, $"myLong")

from pyflink.table.expressions import col

t_env = ... 

stream = ... # types: DataStream of Types.Long()

# Convert DataStream into Table with default field name "f0"
table = t_env.from_data_stream(stream)

# Convert DataStream into Table with field name "my_long"
table = t_env.from_data_stream(stream, col('my_long'))

Tuples (Scala, Java, Python) and Case Classes (Scala only) #

Flink provides its own tuple classes for Java. DataStreams of the Java tuple classes can be converted into tables. Fields can be renamed by providing names for all fields (mapping based on position). If no field names are specified, the default field names are used. If the original field names (f0, f1, … for Flink Tuples) are referenced, the API assumes that the mapping is name-based instead of position-based. Name-based mapping allows for reordering fields and projection with alias (as).

StreamTableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section

DataStream<Tuple2<Long, String>> stream = ...;

// convert DataStream into Table with renamed field names "myLong", "myString" (position-based)
Table table = tableEnv.fromDataStream(stream, $("myLong"), $("myString"));

// convert DataStream into Table with reordered fields "f1", "f0" (name-based)
Table table = tableEnv.fromDataStream(stream, $("f1"), $("f0"));

// convert DataStream into Table with projected field "f1" (name-based)
Table table = tableEnv.fromDataStream(stream, $("f1"));

// convert DataStream into Table with reordered and aliased fields "myString", "myLong" (name-based)
Table table = tableEnv.fromDataStream(stream, $("f1").as("myString"), $("f0").as("myLong"));

Flink supports Scala’s built-in tuples. DataStreams of Scala’s built-in tuples can be converted into tables. Fields can be renamed by providing names for all fields (mapping based on position). If no field names are specified, the default field names are used. If the original field names (_1, _2, … for Scala Tuples) are referenced, the API assumes that the mapping is name-based instead of position-based. Name-based mapping allows for reordering fields and projection with alias (as).

// get a TableEnvironment
val tableEnv: StreamTableEnvironment = ... // see "Create a TableEnvironment" section

val stream: DataStream[(Long, String)] = ...

// convert DataStream into Table with field names "myLong", "myString" (position-based)
val table: Table = tableEnv.fromDataStream(stream, $"myLong", $"myString")

// convert DataStream into Table with reordered fields "_2", "_1" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"_2", $"_1")

// convert DataStream into Table with projected field "_2" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"_2")

// convert DataStream into Table with reordered and aliased fields "myString", "myLong" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"_2" as "myString", $"_1" as "myLong")

// define case class
case class Person(name: String, age: Int)
val streamCC: DataStream[Person] = ...

// convert DataStream into Table with field names 'myName, 'myAge (position-based)
val table = tableEnv.fromDataStream(streamCC, $"myName", $"myAge")

// convert DataStream into Table with reordered and aliased fields "myAge", "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"age" as "myAge", $"name" as "myName")

Flink supports Python’s built-in Tuples. DataStreams of tuples can be converted into tables. Fields can be renamed by providing names for all fields (mapping based on position). If no field names are specified, the default field names are used. If the original field names (f0, f1, … ) are referenced, the API assumes that the mapping is name-based instead of position-based. Name-based mapping allows for reordering fields and projection with alias (alias).

from pyflink.table.expressions import col

stream = ... # type: DataStream of Types.TUPLE([Types.LONG(), Types.STRING()])

# convert DataStream into Table with renamed field names "my_long", "my_string" (position-based)
table = t_env.from_data_stream(stream, col('my_long'), col('my_string'))

# convert DataStream into Table with reordered fields "f1", "f0" (name-based)
table = t_env.from_data_stream(stream, col('f1'), col('f0'))

# convert DataStream into Table with projected field "f1" (name-based)
table = t_env.from_data_stream(stream, col('f1'))

# convert DataStream into Table with reordered and aliased fields "my_string", "my_long" (name-based)
table = t_env.from_data_stream(stream, col('f1').alias('my_string'), col('f0').alias('my_long'))

POJO (Java and Scala) #

Flink supports POJOs as composite types. The rules for what determines a POJO are documented here.

When converting a POJO DataStream into a Table without specifying field names, the names of the original POJO fields are used. The name mapping requires the original names and cannot be done by positions. Fields can be renamed using an alias (with the as keyword), reordered, and projected.

StreamTableEnvironment tableEnv = ...; // see "Create a TableEnvironment" section

// Person is a POJO with fields "name" and "age"
DataStream<Person> stream = ...;

// convert DataStream into Table with renamed fields "myAge", "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, $("age").as("myAge"), $("name").as("myName"));

// convert DataStream into Table with projected field "name" (name-based)
Table table = tableEnv.fromDataStream(stream, $("name"));

// convert DataStream into Table with projected and renamed field "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, $("name").as("myName"));

// get a TableEnvironment
val tableEnv: StreamTableEnvironment = ... // see "Create a TableEnvironment" section

// Person is a POJO with field names "name" and "age"
val stream: DataStream[Person] = ...

// convert DataStream into Table with renamed fields "myAge", "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"age" as "myAge", $"name" as "myName")

// convert DataStream into Table with projected field "name" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"name")

// convert DataStream into Table with projected and renamed field "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"name" as "myName")
Custom PoJo Class is unsupported in PyFlink now.

Row #

The Row data type supports an arbitrary number of fields and fields with null values. Field names can be specified via a RowTypeInfo or when converting a Row DataStream into a Table. The row type supports mapping of fields by position and by name. Fields can be renamed by providing names for all fields (mapping based on position) or selected individually for projection/ordering/renaming (mapping based on name).

StreamTableEnvironment tableEnv = ...; 

// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo`
DataStream<Row> stream = ...;

// Convert DataStream into Table with renamed field names "myName", "myAge" (position-based)
Table table = tableEnv.fromDataStream(stream, $("myName"), $("myAge"));

// Convert DataStream into Table with renamed fields "myName", "myAge" (name-based)
Table table = tableEnv.fromDataStream(stream, $("name").as("myName"), $("age").as("myAge"));

// Convert DataStream into Table with projected field "name" (name-based)
Table table = tableEnv.fromDataStream(stream, $("name"));

// Convert DataStream into Table with projected and renamed field "myName" (name-based)
Table table = tableEnv.fromDataStream(stream, $("name").as("myName"));

val tableEnv: StreamTableEnvironment = ???

// DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo`
val stream: DataStream[Row] = ...

// Convert DataStream into Table with renamed field names "myName", "myAge" (position-based)
val table: Table = tableEnv.fromDataStream(stream, $"myName", $"myAge")

// Convert DataStream into Table with renamed fields "myName", "myAge" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"name" as "myName", $"age" as "myAge")

// Convert DataStream into Table with projected field "name" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"name")

// Convert DataStream into Table with projected and renamed field "myName" (name-based)
val table: Table = tableEnv.fromDataStream(stream, $"name" as "myName")

from pyflink.table.expressions import col

t_env = ...; 

# DataStream of Row with two fields "name" and "age" specified in `RowTypeInfo`
stream = ...

# Convert DataStream into Table with renamed field names "my_name", "my_age" (position-based)
table = t_env.from_data_stream(stream, col('my_name'), col('my_age'))

# Convert DataStream into Table with renamed fields "my_name", "my_age" (name-based)
table = t_env.from_data_stream(stream, col('name').alias('my_name'), col('age').alias('my_age'))

# Convert DataStream into Table with projected field "name" (name-based)
table = t_env.from_data_stream(stream, col('name'))

# Convert DataStream into Table with projected and renamed field "my_name" (name-based)
table = t_env.from_data_stream(stream, col('name').alias("my_name"))

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