org.apache.flink.table.api.bridge.java

Interface StreamTableEnvironment

All Superinterfaces:

org.apache.flink.table.api.TableEnvironment

All Known Implementing Classes:

StreamTableEnvironmentImpl

@PublicEvolving
public interface StreamTableEnvironment
extends org.apache.flink.table.api.TableEnvironment

This table environment is the entry point and central context for creating Table and SQL API programs that integrate with the Java-specific DataStream API.

It is unified for bounded and unbounded data processing.

A stream table environment is responsible for:

• Convert a DataStream into Table and vice-versa.
• Connecting to external systems.
• Registering and retrieving Tables and other meta objects from a catalog.
• Executing SQL statements.
• Offering further configuration options.

Note: If you don't intend to use the DataStream API, TableEnvironment is meant for pure table programs.

Method Summary

Modifier and Type	Method and Description
static StreamTableEnvironment	create(org.apache.flink.streaming.api.environment.StreamExecutionEnvironment executionEnvironment) Creates a table environment that is the entry point and central context for creating Table and SQL API programs that integrate with the Java-specific DataStream API.
static StreamTableEnvironment	create(org.apache.flink.streaming.api.environment.StreamExecutionEnvironment executionEnvironment, org.apache.flink.table.api.EnvironmentSettings settings) Creates a table environment that is the entry point and central context for creating Table and SQL API programs that integrate with the Java-specific DataStream API.
StreamStatementSet	createStatementSet() Returns a StatementSet that integrates with the Java-specific DataStream API.
<T> void	createTemporaryView(String path, org.apache.flink.streaming.api.datastream.DataStream<T> dataStream) Creates a view from the given DataStream in a given path.
<T> void	createTemporaryView(String path, org.apache.flink.streaming.api.datastream.DataStream<T> dataStream, org.apache.flink.table.expressions.Expression... fields) Deprecated. Use createTemporaryView(String, DataStream, Schema) instead. In most cases, createTemporaryView(String, DataStream) should already be sufficient. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can consume. The semantics might be slightly different for raw and structured types.
<T> void	createTemporaryView(String path, org.apache.flink.streaming.api.datastream.DataStream<T> dataStream, org.apache.flink.table.api.Schema schema) Creates a view from the given DataStream in a given path.
org.apache.flink.table.api.Table	fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream) Converts the given DataStream of changelog entries into a Table.
org.apache.flink.table.api.Table	fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream, org.apache.flink.table.api.Schema schema) Converts the given DataStream of changelog entries into a Table.
org.apache.flink.table.api.Table	fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream, org.apache.flink.table.api.Schema schema, org.apache.flink.table.connector.ChangelogMode changelogMode) Converts the given DataStream of changelog entries into a Table.
<T> org.apache.flink.table.api.Table	fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream) Converts the given DataStream into a Table.
<T> org.apache.flink.table.api.Table	fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream, org.apache.flink.table.expressions.Expression... fields) Deprecated. Use fromDataStream(DataStream, Schema) instead. In most cases, fromDataStream(DataStream) should already be sufficient. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can consume. The semantics might be slightly different for raw and structured types.
<T> org.apache.flink.table.api.Table	fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream, org.apache.flink.table.api.Schema schema) Converts the given DataStream into a Table.
<T> void	registerDataStream(String name, org.apache.flink.streaming.api.datastream.DataStream<T> dataStream) Deprecated. use createTemporaryView(String, DataStream)
<T,ACC> void	registerFunction(String name, org.apache.flink.table.functions.AggregateFunction<T,ACC> aggregateFunction) Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of AggregateFunction for more information on the new function design.
<T,ACC> void	registerFunction(String name, org.apache.flink.table.functions.TableAggregateFunction<T,ACC> tableAggregateFunction) Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of TableAggregateFunction for more information on the new function design.
<T> void	registerFunction(String name, org.apache.flink.table.functions.TableFunction<T> tableFunction) Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of TableFunction for more information on the new function design.
<T> org.apache.flink.streaming.api.datastream.DataStream<T>	toAppendStream(org.apache.flink.table.api.Table table, Class<T> clazz) Deprecated. Use toDataStream(Table, Class) instead. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can produce. The semantics might be slightly different for raw and structured types. Use toDataStream(DataTypes.of(TypeInformation.of(Class))) if TypeInformation should be used as source of truth.
<T> org.apache.flink.streaming.api.datastream.DataStream<T>	toAppendStream(org.apache.flink.table.api.Table table, org.apache.flink.api.common.typeinfo.TypeInformation<T> typeInfo) Deprecated. Use toDataStream(Table, Class) instead. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can produce. The semantics might be slightly different for raw and structured types. Use toDataStream(DataTypes.of(TypeInformation.of(Class))) if TypeInformation should be used as source of truth.
org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>	toChangelogStream(org.apache.flink.table.api.Table table) Converts the given Table into a DataStream of changelog entries.
org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>	toChangelogStream(org.apache.flink.table.api.Table table, org.apache.flink.table.api.Schema targetSchema) Converts the given Table into a DataStream of changelog entries.
org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>	toChangelogStream(org.apache.flink.table.api.Table table, org.apache.flink.table.api.Schema targetSchema, org.apache.flink.table.connector.ChangelogMode changelogMode) Converts the given Table into a DataStream of changelog entries.
org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>	toDataStream(org.apache.flink.table.api.Table table) Converts the given Table into a DataStream.
<T> org.apache.flink.streaming.api.datastream.DataStream<T>	toDataStream(org.apache.flink.table.api.Table table, org.apache.flink.table.types.AbstractDataType<?> targetDataType) Converts the given Table into a DataStream of the given DataType.
<T> org.apache.flink.streaming.api.datastream.DataStream<T>	toDataStream(org.apache.flink.table.api.Table table, Class<T> targetClass) Converts the given Table into a DataStream of the given Class.
<T> org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.api.java.tuple.Tuple2<Boolean,T>>	toRetractStream(org.apache.flink.table.api.Table table, Class<T> clazz) Deprecated. Use toChangelogStream(Table, Schema) instead. It integrates with the new type system and supports all kinds of DataTypes and every ChangelogMode that the table runtime can produce.
<T> org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.api.java.tuple.Tuple2<Boolean,T>>	toRetractStream(org.apache.flink.table.api.Table table, org.apache.flink.api.common.typeinfo.TypeInformation<T> typeInfo) Deprecated. Use toChangelogStream(Table, Schema) instead. It integrates with the new type system and supports all kinds of DataTypes and every ChangelogMode that the table runtime can produce.

Methods inherited from interface org.apache.flink.table.api.TableEnvironment

compilePlanSql, create, create, createFunction, createFunction, createTable, createTemporaryFunction, createTemporaryFunction, createTemporarySystemFunction, createTemporarySystemFunction, createTemporaryTable, createTemporaryView, dropFunction, dropTemporaryFunction, dropTemporarySystemFunction, dropTemporaryTable, dropTemporaryView, executePlan, executeSql, explainSql, from, from, fromValues, fromValues, fromValues, fromValues, fromValues, fromValues, getCatalog, getCompletionHints, getConfig, getCurrentCatalog, getCurrentDatabase, listCatalogs, listDatabases, listFullModules, listFunctions, listModules, listTables, listTables, listTemporaryTables, listTemporaryViews, listUserDefinedFunctions, listViews, loadModule, loadPlan, registerCatalog, registerFunction, registerTable, scan, sqlQuery, unloadModule, useCatalog, useDatabase, useModules

Method Detail

create

static StreamTableEnvironment create(org.apache.flink.streaming.api.environment.StreamExecutionEnvironment executionEnvironment)

Creates a table environment that is the entry point and central context for creating Table and SQL API programs that integrate with the Java-specific DataStream API.

It is unified for bounded and unbounded data processing.

A stream table environment is responsible for:

• Convert a DataStream into Table and vice-versa.
• Connecting to external systems.
• Registering and retrieving Tables and other meta objects from a catalog.
• Executing SQL statements.
• Offering further configuration options.

Note: If you don't intend to use the DataStream API, TableEnvironment is meant for pure table programs.

Parameters:

executionEnvironment - The Java StreamExecutionEnvironment of the TableEnvironment.

create

static StreamTableEnvironment create(org.apache.flink.streaming.api.environment.StreamExecutionEnvironment executionEnvironment,
                                     org.apache.flink.table.api.EnvironmentSettings settings)

Creates a table environment that is the entry point and central context for creating Table and SQL API programs that integrate with the Java-specific DataStream API.

It is unified for bounded and unbounded data processing.

A stream table environment is responsible for:

• Convert a DataStream into Table and vice-versa.
• Connecting to external systems.
• Registering and retrieving Tables and other meta objects from a catalog.
• Executing SQL statements.
• Offering further configuration options.

Note: If you don't intend to use the DataStream API, TableEnvironment is meant for pure table programs.

Parameters:

executionEnvironment - The Java StreamExecutionEnvironment of the TableEnvironment.

settings - The environment settings used to instantiate the TableEnvironment.

registerFunction

@Deprecated
<T> void registerFunction(String name,
                                      org.apache.flink.table.functions.TableFunction<T> tableFunction)

Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of TableFunction for more information on the new function design.

Registers a TableFunction under a unique name in the TableEnvironment's catalog. Registered functions can be referenced in Table API and SQL queries.

Type Parameters:

T - The type of the output row.

Parameters:

name - The name under which the function is registered.

tableFunction - The TableFunction to register.

registerFunction

@Deprecated
<T,ACC> void registerFunction(String name,
                                          org.apache.flink.table.functions.AggregateFunction<T,ACC> aggregateFunction)

Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of AggregateFunction for more information on the new function design.

Registers an AggregateFunction under a unique name in the TableEnvironment's catalog. Registered functions can be referenced in Table API and SQL queries.

Type Parameters:

T - The type of the output value.

ACC - The type of aggregate accumulator.

Parameters:

name - The name under which the function is registered.

aggregateFunction - The AggregateFunction to register.

registerFunction

@Deprecated
<T,ACC> void registerFunction(String name,
                                          org.apache.flink.table.functions.TableAggregateFunction<T,ACC> tableAggregateFunction)

Deprecated. Use TableEnvironment.createTemporarySystemFunction(String, UserDefinedFunction) instead. Please note that the new method also uses the new type system and reflective extraction logic. It might be necessary to update the function implementation as well. See the documentation of TableAggregateFunction for more information on the new function design.

Registers an TableAggregateFunction under a unique name in the TableEnvironment's catalog. Registered functions can only be referenced in Table API.

Type Parameters:

T - The type of the output value.

ACC - The type of aggregate accumulator.

Parameters:

name - The name under which the function is registered.

tableAggregateFunction - The TableAggregateFunction to register.

fromDataStream

<T> org.apache.flink.table.api.Table fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream)

Converts the given DataStream into a Table.

Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. If the outermost record's TypeInformation is a CompositeType, it will be flattened in the first level. TypeInformation that cannot be represented as one of the listed DataTypes will be treated as a black-box DataTypes.RAW(Class, TypeSerializer) type. Thus, composite nested fields will not be accessible.

Since the DataStream API does not support changelog processing natively, this method assumes append-only/insert-only semantics during the stream-to-table conversion. Records of type Row must describe RowKind.INSERT changes.

By default, the stream record's timestamp and watermarks are not propagated unless explicitly declared via fromDataStream(DataStream, Schema).

Type Parameters:

T - The external type of the DataStream.

Parameters:

dataStream - The DataStream to be converted.

Returns:

The converted Table.

See Also:

fromChangelogStream(DataStream)

fromDataStream

<T> org.apache.flink.table.api.Table fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream,
                                                    org.apache.flink.table.api.Schema schema)

Converts the given DataStream into a Table.

Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. If the outermost record's TypeInformation is a CompositeType, it will be flattened in the first level. TypeInformation that cannot be represented as one of the listed DataTypes will be treated as a black-box DataTypes.RAW(Class, TypeSerializer) type. Thus, composite nested fields will not be accessible.

Since the DataStream API does not support changelog processing natively, this method assumes append-only/insert-only semantics during the stream-to-table conversion. Records of class Row must describe RowKind.INSERT changes.

By default, the stream record's timestamp and watermarks are not propagated unless explicitly declared.

This method allows to declare a Schema for the resulting table. The declaration is similar to a CREATE TABLE DDL in SQL and allows to:

• enrich or overwrite automatically derived columns with a custom DataType
• reorder columns
• add computed or metadata columns next to the physical columns
• access a stream record's timestamp
• declare a watermark strategy or propagate the DataStream watermarks

It is possible to declare a schema without physical/regular columns. In this case, those columns will be automatically derived and implicitly put at the beginning of the schema declaration.

The following examples illustrate common schema declarations and their semantics:

     // given a DataStream of Tuple2 < String , BigDecimal >

     // === EXAMPLE 1 ===

     // no physical columns defined, they will be derived automatically,
     // e.g. BigDecimal becomes DECIMAL(38, 18)

     Schema.newBuilder()
         .columnByExpression("c1", "f1 + 42")
         .columnByExpression("c2", "f1 - 1")
         .build()

     // equal to: CREATE TABLE (f0 STRING, f1 DECIMAL(38, 18), c1 AS f1 + 42, c2 AS f1 - 1)

     // === EXAMPLE 2 ===

     // physical columns defined, input fields and columns will be mapped by name,
     // columns are reordered and their data type overwritten,
     // all columns must be defined to show up in the final table's schema

     Schema.newBuilder()
         .column("f1", "DECIMAL(10, 2)")
         .columnByExpression("c", "f1 - 1")
         .column("f0", "STRING")
         .build()

     // equal to: CREATE TABLE (f1 DECIMAL(10, 2), c AS f1 - 1, f0 STRING)

     // === EXAMPLE 3 ===

     // timestamp and watermarks can be added from the DataStream API,
     // physical columns will be derived automatically

     Schema.newBuilder()
         .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)") // extract timestamp into a column
         .watermark("rowtime", "SOURCE_WATERMARK()")  // declare watermarks propagation
         .build()

     // equal to:
     //     CREATE TABLE (
     //        f0 STRING,
     //        f1 DECIMAL(38, 18),
     //        rowtime TIMESTAMP(3) METADATA,
     //        WATERMARK FOR rowtime AS SOURCE_WATERMARK()
     //     )
 

Type Parameters:

T - The external type of the DataStream.

Parameters:

dataStream - The DataStream to be converted.

schema - The customized schema for the final table.

Returns:

The converted Table.

See Also:

fromChangelogStream(DataStream, Schema)

fromChangelogStream

org.apache.flink.table.api.Table fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream)

Converts the given DataStream of changelog entries into a Table.

Compared to fromDataStream(DataStream), this method consumes instances of Row and evaluates the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSource.

This method expects a changelog containing all kinds of changes (enumerated in RowKind) as the default ChangelogMode. Use fromChangelogStream(DataStream, Schema, ChangelogMode) to limit the kinds of changes (e.g. for upsert mode).

Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. If the outermost record's TypeInformation is a CompositeType, it will be flattened in the first level. TypeInformation that cannot be represented as one of the listed DataTypes will be treated as a black-box DataTypes.RAW(Class, TypeSerializer) type. Thus, composite nested fields will not be accessible.

By default, the stream record's timestamp and watermarks are not propagated unless explicitly declared via fromChangelogStream(DataStream, Schema).

Parameters:

dataStream - The changelog stream of Row.

Returns:

The converted Table.

fromChangelogStream

org.apache.flink.table.api.Table fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream,
                                                     org.apache.flink.table.api.Schema schema)

Converts the given DataStream of changelog entries into a Table.

Compared to fromDataStream(DataStream), this method consumes instances of Row and evaluates the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSource.

This method expects a changelog containing all kinds of changes (enumerated in RowKind) as the default ChangelogMode. Use fromChangelogStream(DataStream, Schema, ChangelogMode) to limit the kinds of changes (e.g. for upsert mode).

Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. If the outermost record's TypeInformation is a CompositeType, it will be flattened in the first level. TypeInformation that cannot be represented as one of the listed DataTypes will be treated as a black-box DataTypes.RAW(Class, TypeSerializer) type. Thus, composite nested fields will not be accessible.

By default, the stream record's timestamp and watermarks are not propagated unless explicitly declared.

This method allows to declare a Schema for the resulting table. The declaration is similar to a CREATE TABLE DDL in SQL and allows to:

• enrich or overwrite automatically derived columns with a custom DataType
• reorder columns
• add computed or metadata columns next to the physical columns
• access a stream record's timestamp
• declare a watermark strategy or propagate the DataStream watermarks
• declare a primary key

See fromDataStream(DataStream, Schema) for more information and examples on how to declare a Schema.

Parameters:

dataStream - The changelog stream of Row.

schema - The customized schema for the final table.

Returns:

The converted Table.

fromChangelogStream

org.apache.flink.table.api.Table fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> dataStream,
                                                     org.apache.flink.table.api.Schema schema,
                                                     org.apache.flink.table.connector.ChangelogMode changelogMode)

Converts the given DataStream of changelog entries into a Table.

Compared to fromDataStream(DataStream), this method consumes instances of Row and evaluates the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSource.

This method requires an explicitly declared ChangelogMode. For example, use ChangelogMode.upsert() if the stream will not contain RowKind.UPDATE_BEFORE, or ChangelogMode.insertOnly() for non-updating streams.

Column names and types of the Table are automatically derived from the TypeInformation of the DataStream. If the outermost record's TypeInformation is a CompositeType, it will be flattened in the first level. TypeInformation that cannot be represented as one of the listed DataTypes will be treated as a black-box DataTypes.RAW(Class, TypeSerializer) type. Thus, composite nested fields will not be accessible.

By default, the stream record's timestamp and watermarks are not propagated unless explicitly declared.

This method allows to declare a Schema for the resulting table. The declaration is similar to a CREATE TABLE DDL in SQL and allows to:

• enrich or overwrite automatically derived columns with a custom DataType
• reorder columns
• add computed or metadata columns next to the physical columns
• access a stream record's timestamp
• declare a watermark strategy or propagate the DataStream watermarks
• declare a primary key

See fromDataStream(DataStream, Schema) for more information and examples of how to declare a Schema.

Parameters:

dataStream - The changelog stream of Row.

schema - The customized schema for the final table.

changelogMode - The expected kinds of changes in the incoming changelog.

Returns:

The converted Table.

createTemporaryView

<T> void createTemporaryView(String path,
                             org.apache.flink.streaming.api.datastream.DataStream<T> dataStream)

Creates a view from the given DataStream in a given path. Registered views can be referenced in SQL queries.

See fromDataStream(DataStream) for more information on how a DataStream is translated into a table.

Temporary objects can shadow permanent ones. If a permanent object in a given path exists, it will be inaccessible in the current session. To make the permanent object available again you can drop the corresponding temporary object.

Type Parameters:

T - The type of the DataStream.

Parameters:

path - The path under which the DataStream is created. See also the TableEnvironment class description for the format of the path.

dataStream - The DataStream out of which to create the view.

createTemporaryView

<T> void createTemporaryView(String path,
                             org.apache.flink.streaming.api.datastream.DataStream<T> dataStream,
                             org.apache.flink.table.api.Schema schema)

Creates a view from the given DataStream in a given path. Registered views can be referenced in SQL queries.

See fromDataStream(DataStream, Schema) for more information on how a DataStream is translated into a table.

Temporary objects can shadow permanent ones. If a permanent object in a given path exists, it will be inaccessible in the current session. To make the permanent object available again you can drop the corresponding temporary object.

Type Parameters:

T - The type of the DataStream.

Parameters:

path - The path under which the DataStream is created. See also the TableEnvironment class description for the format of the path.

schema - The customized schema for the final table.

dataStream - The DataStream out of which to create the view.

toDataStream

org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> toDataStream(org.apache.flink.table.api.Table table)

Converts the given Table into a DataStream.

Since the DataStream API does not support changelog processing natively, this method assumes append-only/insert-only semantics during the table-to-stream conversion. The records of class Row will always describe RowKind.INSERT changes. Updating tables are not supported by this method and will produce an exception.

If you want to convert the Table to a specific class or data type, use toDataStream(Table, Class) or toDataStream(Table, AbstractDataType) instead.

Note that the type system of the table ecosystem is richer than the one of the DataStream API. The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API. Afterwards, the Types semantics of the DataStream API need to be considered.

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.

Parameters:

table - The Table to convert. It must be insert-only.

Returns:

The converted DataStream.

See Also:

toDataStream(Table, AbstractDataType), toChangelogStream(Table)

toDataStream

<T> org.apache.flink.streaming.api.datastream.DataStream<T> toDataStream(org.apache.flink.table.api.Table table,
                                                                         Class<T> targetClass)

Converts the given Table into a DataStream of the given Class.

See toDataStream(Table, AbstractDataType) for more information on how a Table is translated into a DataStream.

This method is a shortcut for:

     tableEnv.toDataStream(table, DataTypes.of(targetClass))
 

Calling this method with a class of Row will redirect to toDataStream(Table).

Type Parameters:

T - External record.

Parameters:

table - The Table to convert. It must be insert-only.

targetClass - The Class that decides about the final external representation in DataStream records.

Returns:

The converted DataStream.

See Also:

toChangelogStream(Table, Schema)

toDataStream

<T> org.apache.flink.streaming.api.datastream.DataStream<T> toDataStream(org.apache.flink.table.api.Table table,
                                                                         org.apache.flink.table.types.AbstractDataType<?> targetDataType)

Converts the given Table into a DataStream of the given DataType.

The given DataType is used to configure the table runtime to convert columns and internal data structures to the desired representation. The following example shows how to convert the table columns into the fields of a POJO type.

     // given a Table of (name STRING, age INT)

     public static class MyPojo {
         public String name;
         public Integer age;

         // default constructor for DataStream API
         public MyPojo() {}

         // fully assigning constructor for field order in Table API
         public MyPojo(String name, Integer age) {
             this.name = name;
             this.age = age;
         }
     }

     tableEnv.toDataStream(table, DataTypes.of(MyPojo.class));
 

Since the DataStream API does not support changelog processing natively, this method assumes append-only/insert-only semantics during the table-to-stream conversion. Updating tables are not supported by this method and will produce an exception.

Note that the type system of the table ecosystem is richer than the one of the DataStream API. The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API. Afterwards, the Types semantics of the DataStream API need to be considered.

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.

Type Parameters:

T - External record.

Parameters:

table - The Table to convert. It must be insert-only.

targetDataType - The DataType that decides about the final external representation in DataStream records.

Returns:

The converted DataStream.

See Also:

toDataStream(Table), toChangelogStream(Table, Schema)

toChangelogStream

org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> toChangelogStream(org.apache.flink.table.api.Table table)

Converts the given Table into a DataStream of changelog entries.

Compared to toDataStream(Table), this method produces instances of Row and sets the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSink.

This method can emit a changelog containing all kinds of changes (enumerated in RowKind) that the given updating table requires as the default ChangelogMode. Use toChangelogStream(Table, Schema, ChangelogMode) to limit the kinds of changes (e.g. for upsert mode).

Note that the type system of the table ecosystem is richer than the one of the DataStream API. The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API. Afterwards, the Types semantics of the DataStream API need to be considered.

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.

Parameters:

table - The Table to convert. It can be updating or insert-only.

Returns:

The converted changelog stream of Row.

toChangelogStream

org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> toChangelogStream(org.apache.flink.table.api.Table table,
                                                                                                   org.apache.flink.table.api.Schema targetSchema)

Converts the given Table into a DataStream of changelog entries.

Compared to toDataStream(Table), this method produces instances of Row and sets the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSink.

This method can emit a changelog containing all kinds of changes (enumerated in RowKind) that the given updating table requires as the default ChangelogMode. Use toChangelogStream(Table, Schema, ChangelogMode) to limit the kinds of changes (e.g. for upsert mode).

The given Schema is used to configure the table runtime to convert columns and internal data structures to the desired representation. The following example shows how to convert a table column into a POJO type.

     // given a Table of (id BIGINT, payload ROW < name STRING , age INT >)

     public static class MyPojo {
         public String name;
         public Integer age;

         // default constructor for DataStream API
         public MyPojo() {}

         // fully assigning constructor for field order in Table API
         public MyPojo(String name, Integer age) {
             this.name = name;
             this.age = age;
         }
     }

     tableEnv.toChangelogStream(
         table,
         Schema.newBuilder()
             .column("id", DataTypes.BIGINT())
             .column("payload", DataTypes.of(MyPojo.class)) // force an implicit conversion
             .build());
 

Note that the type system of the table ecosystem is richer than the one of the DataStream API. The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API. Afterwards, the Types semantics of the DataStream API need to be considered.

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.

If the rowtime should not be a concrete field in the final Row anymore, or the schema should be symmetrical for both fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>) and toChangelogStream(org.apache.flink.table.api.Table), the rowtime can also be declared as a metadata column that will be propagated into a stream record's timestamp. It is possible to declare a schema without physical/regular columns. In this case, those columns will be automatically derived and implicitly put at the beginning of the schema declaration.

The following examples illustrate common schema declarations and their semantics:

     // given a Table of (id INT, name STRING, my_rowtime TIMESTAMP_LTZ(3))

     // === EXAMPLE 1 ===

     // no physical columns defined, they will be derived automatically,
     // the last derived physical column will be skipped in favor of the metadata column

     Schema.newBuilder()
         .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
         .build()

     // equal to: CREATE TABLE (id INT, name STRING, rowtime TIMESTAMP_LTZ(3) METADATA)

     // === EXAMPLE 2 ===

     // physical columns defined, all columns must be defined

     Schema.newBuilder()
         .column("id", "INT")
         .column("name", "STRING")
         .columnByMetadata("rowtime", "TIMESTAMP_LTZ(3)")
         .build()

     // equal to: CREATE TABLE (id INT, name STRING, rowtime TIMESTAMP_LTZ(3) METADATA)
 

Parameters:

table - The Table to convert. It can be updating or insert-only.

targetSchema - The Schema that decides about the final external representation in DataStream records.

Returns:

The converted changelog stream of Row.

toChangelogStream

org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row> toChangelogStream(org.apache.flink.table.api.Table table,
                                                                                                   org.apache.flink.table.api.Schema targetSchema,
                                                                                                   org.apache.flink.table.connector.ChangelogMode changelogMode)

Converts the given Table into a DataStream of changelog entries.

Compared to toDataStream(Table), this method produces instances of Row and sets the RowKind flag that is contained in every record during runtime. The runtime behavior is similar to that of a DynamicTableSink.

This method requires an explicitly declared ChangelogMode. For example, use ChangelogMode.upsert() if the stream will not contain RowKind.UPDATE_BEFORE, or ChangelogMode.insertOnly() for non-updating streams.

Note that the type system of the table ecosystem is richer than the one of the DataStream API. The table runtime will make sure to properly serialize the output records to the first operator of the DataStream API. Afterwards, the Types semantics of the DataStream API need to be considered.

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. However, it is also possible to write out the rowtime as a metadata column. See toChangelogStream(Table, Schema) for more information and examples on how to declare a Schema.

Parameters:

table - The Table to convert. It can be updating or insert-only.

targetSchema - The Schema that decides about the final external representation in DataStream records.

changelogMode - The required kinds of changes in the result changelog. An exception will be thrown if the given updating table cannot be represented in this changelog mode.

Returns:

The converted changelog stream of Row.

createStatementSet

StreamStatementSet createStatementSet()

Returns a StatementSet that integrates with the Java-specific DataStream API.

It accepts pipelines defined by DML statements or Table objects. The planner can optimize all added statements together and then either submit them as one job or attach them to the underlying StreamExecutionEnvironment.

Specified by:

createStatementSet in interface org.apache.flink.table.api.TableEnvironment

Returns:

statement set builder for the Java-specific DataStream API

fromDataStream

@Deprecated
<T> org.apache.flink.table.api.Table fromDataStream(org.apache.flink.streaming.api.datastream.DataStream<T> dataStream,
                                                                org.apache.flink.table.expressions.Expression... fields)

Deprecated. Use fromDataStream(DataStream, Schema) instead. In most cases, fromDataStream(DataStream) should already be sufficient. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can consume. The semantics might be slightly different for raw and structured types.

Converts the given DataStream into a Table with specified field names.

There are two modes for mapping original fields to the fields of the Table:

1. Reference input fields by name: All fields in the schema definition are referenced by name (and possibly renamed using an alias (as). Moreover, we can define proctime and rowtime attributes at arbitrary positions using arbitrary names (except those that exist in the result schema). In this mode, fields can be reordered and projected out. This mode can be used for any input type, including POJOs.

Example:

 DataStream<Tuple2<String, Long>> stream = ...
 Table table = tableEnv.fromDataStream(
    stream,
    $("f1"), // reorder and use the original field
    $("rowtime").rowtime(), // extract the internally attached timestamp into an event-time
                            // attribute named 'rowtime'
    $("f0").as("name") // reorder and give the original field a better name
 );
 

2. Reference input fields by position: In this mode, fields are simply renamed. Event-time attributes can replace the field on their position in the input data (if it is of correct type) or be appended at the end. Proctime attributes must be appended at the end. This mode can only be used if the input type has a defined field order (tuple, case class, Row) and none of the fields references a field of the input type.

Example:

 DataStream<Tuple2<String, Long>> stream = ...
 Table table = tableEnv.fromDataStream(
    stream,
    $("a"), // rename the first field to 'a'
    $("b"), // rename the second field to 'b'
    $("rowtime").rowtime() // extract the internally attached timestamp into an event-time
                           // attribute named 'rowtime'
 );
 

Type Parameters:

T - The type of the DataStream.

Parameters:

dataStream - The DataStream to be converted.

fields - The fields expressions to map original fields of the DataStream to the fields of the Table.

Returns:

The converted Table.

registerDataStream

@Deprecated
<T> void registerDataStream(String name,
                                        org.apache.flink.streaming.api.datastream.DataStream<T> dataStream)

Deprecated. use createTemporaryView(String, DataStream)

Creates a view from the given DataStream. Registered views can be referenced in SQL queries.

The field names of the Table are automatically derived from the type of the DataStream.

The view is registered in the namespace of the current catalog and database. To register the view in a different catalog use createTemporaryView(String, DataStream).

Temporary objects can shadow permanent ones. If a permanent object in a given path exists, it will be inaccessible in the current session. To make the permanent object available again you can drop the corresponding temporary object.

Type Parameters:

T - The type of the DataStream to register.

Parameters:

name - The name under which the DataStream is registered in the catalog.

dataStream - The DataStream to register.

createTemporaryView

@Deprecated
<T> void createTemporaryView(String path,
                                         org.apache.flink.streaming.api.datastream.DataStream<T> dataStream,
                                         org.apache.flink.table.expressions.Expression... fields)

Deprecated. Use createTemporaryView(String, DataStream, Schema) instead. In most cases, createTemporaryView(String, DataStream) should already be sufficient. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can consume. The semantics might be slightly different for raw and structured types.

Creates a view from the given DataStream in a given path with specified field names. Registered views can be referenced in SQL queries.

There are two modes for mapping original fields to the fields of the View:

1. Reference input fields by name: All fields in the schema definition are referenced by name (and possibly renamed using an alias (as). Moreover, we can define proctime and rowtime attributes at arbitrary positions using arbitrary names (except those that exist in the result schema). In this mode, fields can be reordered and projected out. This mode can be used for any input type, including POJOs.

Example:

 DataStream<Tuple2<String, Long>> stream = ...
 tableEnv.createTemporaryView(
    "cat.db.myTable",
    stream,
    $("f1"), // reorder and use the original field
    $("rowtime").rowtime(), // extract the internally attached timestamp into an event-time
                            // attribute named 'rowtime'
    $("f0").as("name") // reorder and give the original field a better name
 );
 

2. Reference input fields by position: In this mode, fields are simply renamed. Event-time attributes can replace the field on their position in the input data (if it is of correct type) or be appended at the end. Proctime attributes must be appended at the end. This mode can only be used if the input type has a defined field order (tuple, case class, Row) and none of the fields references a field of the input type.

Example:

 DataStream<Tuple2<String, Long>> stream = ...
 tableEnv.createTemporaryView(
    "cat.db.myTable",
    stream,
    $("a"), // rename the first field to 'a'
    $("b"), // rename the second field to 'b'
    $("rowtime").rowtime() // adds an event-time attribute named 'rowtime'
 );
 

Temporary objects can shadow permanent ones. If a permanent object in a given path exists, it will be inaccessible in the current session. To make the permanent object available again you can drop the corresponding temporary object.

Type Parameters:

T - The type of the DataStream.

Parameters:

path - The path under which the DataStream is created. See also the TableEnvironment class description for the format of the path.

dataStream - The DataStream out of which to create the view.

fields - The fields expressions to map original fields of the DataStream to the fields of the View.

toAppendStream

@Deprecated
<T> org.apache.flink.streaming.api.datastream.DataStream<T> toAppendStream(org.apache.flink.table.api.Table table,
                                                                                       Class<T> clazz)

Deprecated. Use toDataStream(Table, Class) instead. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can produce. The semantics might be slightly different for raw and structured types. Use toDataStream(DataTypes.of(TypeInformation.of(Class))) if TypeInformation should be used as source of truth.

Converts the given Table into an append DataStream of a specified type.

The Table must only have insert (append) changes. If the Table is also modified by update or delete changes, the conversion will fail.

The fields of the Table are mapped to DataStream fields as follows:

• Row and Tuple types: Fields are mapped by position, field types must match.
• POJO DataStream types: Fields are mapped by field name, field types must match.

Type Parameters:

T - The type of the resulting DataStream.

Parameters:

table - The Table to convert.

clazz - The class of the type of the resulting DataStream.

Returns:

The converted DataStream.

toAppendStream

@Deprecated
<T> org.apache.flink.streaming.api.datastream.DataStream<T> toAppendStream(org.apache.flink.table.api.Table table,
                                                                                       org.apache.flink.api.common.typeinfo.TypeInformation<T> typeInfo)

Deprecated. Use toDataStream(Table, Class) instead. It integrates with the new type system and supports all kinds of DataTypes that the table runtime can produce. The semantics might be slightly different for raw and structured types. Use toDataStream(DataTypes.of(TypeInformation.of(Class))) if TypeInformation should be used as source of truth.

Converts the given Table into an append DataStream of a specified type.

The Table must only have insert (append) changes. If the Table is also modified by update or delete changes, the conversion will fail.

The fields of the Table are mapped to DataStream fields as follows:

• Row and Tuple types: Fields are mapped by position, field types must match.
• POJO DataStream types: Fields are mapped by field name, field types must match.

Type Parameters:

T - The type of the resulting DataStream.

Parameters:

table - The Table to convert.

typeInfo - The TypeInformation that specifies the type of the DataStream.

Returns:

The converted DataStream.

toRetractStream

@Deprecated
<T> org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.api.java.tuple.Tuple2<Boolean,T>> toRetractStream(org.apache.flink.table.api.Table table,
                                                                                                                                        Class<T> clazz)

Deprecated. Use toChangelogStream(Table, Schema) instead. It integrates with the new type system and supports all kinds of DataTypes and every ChangelogMode that the table runtime can produce.

Converts the given Table into a DataStream of add and retract messages. The message will be encoded as Tuple2. The first field is a Boolean flag, the second field holds the record of the specified type T.

A true Boolean flag indicates an add message, a false flag indicates a retract message.

The fields of the Table are mapped to DataStream fields as follows:

• Row and Tuple types: Fields are mapped by position, field types must match.
• POJO DataStream types: Fields are mapped by field name, field types must match.

Type Parameters:

T - The type of the requested record type.

Parameters:

table - The Table to convert.

clazz - The class of the requested record type.

Returns:

The converted DataStream.

toRetractStream

@Deprecated
<T> org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.api.java.tuple.Tuple2<Boolean,T>> toRetractStream(org.apache.flink.table.api.Table table,
                                                                                                                                        org.apache.flink.api.common.typeinfo.TypeInformation<T> typeInfo)

Deprecated. Use toChangelogStream(Table, Schema) instead. It integrates with the new type system and supports all kinds of DataTypes and every ChangelogMode that the table runtime can produce.

Converts the given Table into a DataStream of add and retract messages. The message will be encoded as Tuple2. The first field is a Boolean flag, the second field holds the record of the specified type T.

A true Boolean flag indicates an add message, a false flag indicates a retract message.

The fields of the Table are mapped to DataStream fields as follows:

• Row and Tuple types: Fields are mapped by position, field types must match.
• POJO DataStream types: Fields are mapped by field name, field types must match.

Type Parameters:

T - The type of the requested record type.

Parameters:

table - The Table to convert.

typeInfo - The TypeInformation of the requested record type.

Returns:

The converted DataStream.