@Internal public final class StreamTableEnvironmentImpl extends AbstractStreamTableEnvironmentImpl implements StreamTableEnvironment
StreamTableEnvironment. This enables conversions from/to
DataStream.
It binds to a given StreamExecutionEnvironment.
executionEnvironmentexecEnv, functionCatalog, planner, resourceManager, tableConfig| Constructor and Description |
|---|
StreamTableEnvironmentImpl(CatalogManager catalogManager,
ModuleManager moduleManager,
ResourceManager resourceManager,
FunctionCatalog functionCatalog,
TableConfig tableConfig,
StreamExecutionEnvironment executionEnvironment,
Planner planner,
Executor executor,
boolean isStreamingMode) |
| Modifier and Type | Method and Description |
|---|---|
static StreamTableEnvironment |
create(StreamExecutionEnvironment executionEnvironment,
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 accepts pipelines defined by DML statements or Table objects. |
<T> void |
createTemporaryView(String path,
DataStream<T> dataStream)
Creates a view from the given
DataStream in a given path. |
<T> void |
createTemporaryView(String path,
DataStream<T> dataStream,
Expression... fields)
Creates a view from the given
DataStream in a given path with specified field names. |
<T> void |
createTemporaryView(String path,
DataStream<T> dataStream,
Schema schema)
Creates a view from the given
DataStream in a given path. |
Table |
fromChangelogStream(DataStream<Row> dataStream)
Converts the given
DataStream of changelog entries into a Table. |
Table |
fromChangelogStream(DataStream<Row> dataStream,
Schema schema)
Converts the given
DataStream of changelog entries into a Table. |
Table |
fromChangelogStream(DataStream<Row> dataStream,
Schema schema,
ChangelogMode changelogMode)
Converts the given
DataStream of changelog entries into a Table. |
<T> Table |
fromDataStream(DataStream<T> dataStream)
Converts the given
DataStream into a Table. |
<T> Table |
fromDataStream(DataStream<T> dataStream,
Expression... fields)
Converts the given
DataStream into a Table with specified field names. |
<T> Table |
fromDataStream(DataStream<T> dataStream,
Schema schema)
Converts the given
DataStream into a Table. |
<T> void |
registerDataStream(String name,
DataStream<T> dataStream)
Creates a view from the given
DataStream. |
<T,ACC> void |
registerFunction(String name,
AggregateFunction<T,ACC> aggregateFunction)
Registers an
AggregateFunction under a unique name in the TableEnvironment's catalog. |
<T,ACC> void |
registerFunction(String name,
TableAggregateFunction<T,ACC> tableAggregateFunction)
Registers an
TableAggregateFunction under a unique name in the TableEnvironment's
catalog. |
<T> void |
registerFunction(String name,
TableFunction<T> tableFunction)
Registers a
TableFunction under a unique name in the TableEnvironment's catalog. |
<T> DataStream<T> |
toAppendStream(Table table,
Class<T> clazz)
Converts the given
Table into an append DataStream of a specified type. |
<T> DataStream<T> |
toAppendStream(Table table,
TypeInformation<T> typeInfo)
Converts the given
Table into an append DataStream of a specified type. |
DataStream<Row> |
toChangelogStream(Table table)
Converts the given
Table into a DataStream of changelog entries. |
DataStream<Row> |
toChangelogStream(Table table,
Schema targetSchema)
Converts the given
Table into a DataStream of changelog entries. |
DataStream<Row> |
toChangelogStream(Table table,
Schema targetSchema,
ChangelogMode changelogMode)
Converts the given
Table into a DataStream of changelog entries. |
DataStream<Row> |
toDataStream(Table table)
Converts the given
Table into a DataStream. |
<T> DataStream<T> |
toDataStream(Table table,
AbstractDataType<?> targetDataType)
|
<T> DataStream<T> |
toDataStream(Table table,
Class<T> targetClass)
|
<T> DataStream<Tuple2<Boolean,T>> |
toRetractStream(Table table,
Class<T> clazz)
Converts the given
Table into a DataStream of add and retract messages. |
<T> DataStream<Tuple2<Boolean,T>> |
toRetractStream(Table table,
TypeInformation<T> typeInfo)
Converts the given
Table into a DataStream of add and retract messages. |
protected void |
validateTableSource(TableSource<?> tableSource)
Subclasses can override this method to add additional checks.
|
asQueryOperation, attachAsDataStream, execEnv, extractTypeInformation, fromStreamInternal, lookupExecutor, qualifyQueryOperation, toStreamInternal, toStreamInternal, validateTimeCharacteristic, wrapWithChangeFlagcompilePlan, compilePlanSql, create, create, createCatalog, createFunction, createFunction, createFunction, createFunction, createTable, createTable, createTemporaryFunction, createTemporaryFunction, createTemporaryFunction, createTemporarySystemFunction, createTemporarySystemFunction, createTemporarySystemFunction, createTemporaryTable, createTemporaryView, dropFunction, dropTemporaryFunction, dropTemporarySystemFunction, dropTemporaryTable, dropTemporaryView, executeCachedPlanInternal, executeInternal, executeInternal, executePlan, executeSql, explainInternal, explainPlan, explainSql, from, from, fromTableSource, fromValues, fromValues, fromValues, fromValues, fromValues, fromValues, generatePipelineFromQueryOperation, getCatalog, getCatalogManager, getCompletionHints, getConfig, getCurrentCatalog, getCurrentDatabase, getOperationTreeBuilder, getParser, getPlanner, listCatalogs, listDatabases, listFullModules, listFunctions, listModules, listTables, listTables, listTemporaryTables, listTemporaryViews, listUserDefinedFunctions, listViews, loadModule, loadPlan, registerCatalog, registerFunction, registerTable, registerTableSinkInternal, registerTableSourceInternal, scan, sqlQuery, translate, unloadModule, useCatalog, useDatabase, useModulesclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreatecompilePlanSql, create, create, createCatalog, createFunction, createFunction, createFunction, createFunction, createTable, createTemporaryFunction, createTemporaryFunction, createTemporaryFunction, createTemporarySystemFunction, createTemporarySystemFunction, createTemporarySystemFunction, createTemporaryTable, createTemporaryView, dropFunction, dropTemporaryFunction, dropTemporarySystemFunction, dropTemporaryTable, dropTemporaryView, executePlan, executeSql, explainSql, 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, useModulesexplainInternalpublic StreamTableEnvironmentImpl(CatalogManager catalogManager, ModuleManager moduleManager, ResourceManager resourceManager, FunctionCatalog functionCatalog, TableConfig tableConfig, StreamExecutionEnvironment executionEnvironment, Planner planner, Executor executor, boolean isStreamingMode)
public static StreamTableEnvironment create(StreamExecutionEnvironment executionEnvironment, EnvironmentSettings settings)
StreamTableEnvironmentDataStream API.
It is unified for bounded and unbounded data processing.
A stream table environment is responsible for:
DataStream into Table and vice-versa.
Tables and other meta objects from a catalog.
Note: If you don't intend to use the DataStream API, TableEnvironment is
meant for pure table programs.
create in interface StreamTableEnvironmentexecutionEnvironment - The Java StreamExecutionEnvironment of the TableEnvironment.settings - The environment settings used to instantiate the TableEnvironment.public <T> void registerFunction(String name, TableFunction<T> tableFunction)
StreamTableEnvironmentTableFunction under a unique name in the TableEnvironment's catalog.
Registered functions can be referenced in Table API and SQL queries.registerFunction in interface StreamTableEnvironmentT - The type of the output row.name - The name under which the function is registered.tableFunction - The TableFunction to register.public <T,ACC> void registerFunction(String name, AggregateFunction<T,ACC> aggregateFunction)
StreamTableEnvironmentAggregateFunction under a unique name in the TableEnvironment's catalog.
Registered functions can be referenced in Table API and SQL queries.registerFunction in interface StreamTableEnvironmentT - The type of the output value.ACC - The type of aggregate accumulator.name - The name under which the function is registered.aggregateFunction - The AggregateFunction to register.public <T,ACC> void registerFunction(String name, TableAggregateFunction<T,ACC> tableAggregateFunction)
StreamTableEnvironmentTableAggregateFunction under a unique name in the TableEnvironment's
catalog. Registered functions can only be referenced in Table API.registerFunction in interface StreamTableEnvironmentT - The type of the output value.ACC - The type of aggregate accumulator.name - The name under which the function is registered.tableAggregateFunction - The TableAggregateFunction to register.public <T> Table fromDataStream(DataStream<T> dataStream)
StreamTableEnvironmentDataStream 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 to downstream
table operations unless explicitly declared via StreamTableEnvironment.fromDataStream(DataStream, Schema).
If the returned table is converted back to DataStream via StreamTableEnvironment.toDataStream(Table),
the input DataStream of this method would be returned.
fromDataStream in interface StreamTableEnvironmentT - The external type of the DataStream.dataStream - The DataStream to be converted.Table.StreamTableEnvironment.fromChangelogStream(DataStream)public <T> Table fromDataStream(DataStream<T> dataStream, Schema schema)
StreamTableEnvironmentDataStream 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 to downstream table operations unless explicitly declared in the input schema.
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:
DataType
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()
// )
fromDataStream in interface StreamTableEnvironmentT - The external type of the DataStream.dataStream - The DataStream to be converted.schema - The customized schema for the final table.Table.StreamTableEnvironment.fromChangelogStream(DataStream, Schema)public Table fromChangelogStream(DataStream<Row> dataStream)
StreamTableEnvironmentDataStream of changelog entries into a Table.
Compared to StreamTableEnvironment.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 StreamTableEnvironment.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 to downstream
table operations unless explicitly declared via StreamTableEnvironment.fromChangelogStream(DataStream,
Schema).
fromChangelogStream in interface StreamTableEnvironmentdataStream - The changelog stream of Row.Table.public Table fromChangelogStream(DataStream<Row> dataStream, Schema schema)
StreamTableEnvironmentDataStream of changelog entries into a Table.
Compared to StreamTableEnvironment.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 StreamTableEnvironment.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 to downstream table operations unless explicitly declared in the input schema.
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:
DataType
DataStream watermarks
See StreamTableEnvironment.fromDataStream(DataStream, Schema) for more information and examples on how
to declare a Schema.
fromChangelogStream in interface StreamTableEnvironmentdataStream - The changelog stream of Row.schema - The customized schema for the final table.Table.public Table fromChangelogStream(DataStream<Row> dataStream, Schema schema, ChangelogMode changelogMode)
StreamTableEnvironmentDataStream of changelog entries into a Table.
Compared to StreamTableEnvironment.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 to downstream table operations unless explicitly declared in the input schema.
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:
DataType
DataStream watermarks
See StreamTableEnvironment.fromDataStream(DataStream, Schema) for more information and examples of how
to declare a Schema.
fromChangelogStream in interface StreamTableEnvironmentdataStream - The changelog stream of Row.schema - The customized schema for the final table.changelogMode - The expected kinds of changes in the incoming changelog.Table.public <T> void createTemporaryView(String path, DataStream<T> dataStream)
StreamTableEnvironmentDataStream in a given path. Registered views can be
referenced in SQL queries.
See StreamTableEnvironment.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.
createTemporaryView in interface StreamTableEnvironmentT - The type of the DataStream.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.public <T> void createTemporaryView(String path, DataStream<T> dataStream, Schema schema)
StreamTableEnvironmentDataStream in a given path. Registered views can be
referenced in SQL queries.
See StreamTableEnvironment.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.
createTemporaryView in interface StreamTableEnvironmentT - The type of the DataStream.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.schema - The customized schema for the final table.public DataStream<Row> toDataStream(Table table)
StreamTableEnvironmentTable 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 StreamTableEnvironment.toDataStream(Table, Class) or StreamTableEnvironment.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.
toDataStream in interface StreamTableEnvironmenttable - The Table to convert. It must be insert-only.DataStream.StreamTableEnvironment.toDataStream(Table, AbstractDataType),
StreamTableEnvironment.toChangelogStream(Table)public <T> DataStream<T> toDataStream(Table table, Class<T> targetClass)
StreamTableEnvironmentTable into a DataStream of the given Class.
See StreamTableEnvironment.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 StreamTableEnvironment.toDataStream(Table).
toDataStream in interface StreamTableEnvironmentT - External record.table - The Table to convert. It must be insert-only.targetClass - The Class that decides about the final external representation in
DataStream records.DataStream.StreamTableEnvironment.toChangelogStream(Table, Schema)public <T> DataStream<T> toDataStream(Table table, AbstractDataType<?> targetDataType)
StreamTableEnvironmentTable 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.
toDataStream in interface StreamTableEnvironmentT - External record.table - The Table to convert. It must be insert-only.targetDataType - The DataType that decides about the final external
representation in DataStream records.DataStream.StreamTableEnvironment.toDataStream(Table),
StreamTableEnvironment.toChangelogStream(Table, Schema)public DataStream<Row> toChangelogStream(Table table)
StreamTableEnvironmentTable into a DataStream of changelog entries.
Compared to StreamTableEnvironment.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
StreamTableEnvironment.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.
toChangelogStream in interface StreamTableEnvironmenttable - The Table to convert. It can be updating or insert-only.Row.public DataStream<Row> toChangelogStream(Table table, Schema targetSchema)
StreamTableEnvironmentTable into a DataStream of changelog entries.
Compared to StreamTableEnvironment.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
StreamTableEnvironment.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 StreamTableEnvironment.fromChangelogStream(org.apache.flink.streaming.api.datastream.DataStream<org.apache.flink.types.Row>) and StreamTableEnvironment.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)
toChangelogStream in interface StreamTableEnvironmenttable - The Table to convert. It can be updating or insert-only.targetSchema - The Schema that decides about the final external representation
in DataStream records.Row.public DataStream<Row> toChangelogStream(Table table, Schema targetSchema, ChangelogMode changelogMode)
StreamTableEnvironmentTable into a DataStream of changelog entries.
Compared to StreamTableEnvironment.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 StreamTableEnvironment.toChangelogStream(Table, Schema) for
more information and examples on how to declare a Schema.
toChangelogStream in interface StreamTableEnvironmenttable - 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.Row.public StreamStatementSet createStatementSet()
TableEnvironmentStatementSet that accepts pipelines defined by DML statements or Table objects. The planner can optimize all added statements together and then submit them
as one job.createStatementSet in interface StreamTableEnvironmentcreateStatementSet in interface TableEnvironmentcreateStatementSet in class TableEnvironmentImplDataStream APIpublic <T> Table fromDataStream(DataStream<T> dataStream, Expression... fields)
StreamTableEnvironmentDataStream 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'
);
fromDataStream in interface StreamTableEnvironmentT - The type of the DataStream.dataStream - The DataStream to be converted.fields - The fields expressions to map original fields of the DataStream to the fields
of the Table.Table.public <T> void registerDataStream(String name, DataStream<T> dataStream)
StreamTableEnvironmentDataStream. 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 StreamTableEnvironment.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.
registerDataStream in interface StreamTableEnvironmentT - The type of the DataStream to register.name - The name under which the DataStream is registered in the catalog.dataStream - The DataStream to register.public <T> void createTemporaryView(String path, DataStream<T> dataStream, Expression... fields)
StreamTableEnvironmentDataStream 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.
createTemporaryView in interface StreamTableEnvironmentT - The type of the DataStream.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.public <T> DataStream<T> toAppendStream(Table table, Class<T> clazz)
StreamTableEnvironmentTable 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.
DataStream types: Fields are mapped by field name, field types must match.
toAppendStream in interface StreamTableEnvironmentT - The type of the resulting DataStream.table - The Table to convert.clazz - The class of the type of the resulting DataStream.DataStream.public <T> DataStream<T> toAppendStream(Table table, TypeInformation<T> typeInfo)
StreamTableEnvironmentTable 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.
DataStream types: Fields are mapped by field name, field types must match.
toAppendStream in interface StreamTableEnvironmentT - The type of the resulting DataStream.table - The Table to convert.typeInfo - The TypeInformation that specifies the type of the DataStream.DataStream.public <T> DataStream<Tuple2<Boolean,T>> toRetractStream(Table table, Class<T> clazz)
StreamTableEnvironmentTable 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.
DataStream types: Fields are mapped by field name, field types must match.
toRetractStream in interface StreamTableEnvironmentT - The type of the requested record type.table - The Table to convert.clazz - The class of the requested record type.DataStream.public <T> DataStream<Tuple2<Boolean,T>> toRetractStream(Table table, TypeInformation<T> typeInfo)
StreamTableEnvironmentTable 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.
DataStream types: Fields are mapped by field name, field types must match.
toRetractStream in interface StreamTableEnvironmentT - The type of the requested record type.table - The Table to convert.typeInfo - The TypeInformation of the requested record type.DataStream.protected void validateTableSource(TableSource<?> tableSource)
TableEnvironmentImplvalidateTableSource in class TableEnvironmentImpltableSource - tableSource to validateCopyright © 2014–2024 The Apache Software Foundation. All rights reserved.