T
- The type of the elements in the Stream@Public public class DataStreamSink<T> extends Object
Modifier | Constructor and Description |
---|---|
protected |
DataStreamSink(DataStream<T> inputStream,
StreamSink<T> operator) |
Modifier and Type | Method and Description |
---|---|
DataStreamSink<T> |
disableChaining()
Turns off chaining for this operator so thread co-location will not be
used as an optimization.
|
SinkTransformation<T> |
getTransformation()
Returns the transformation that contains the actual sink operator of this sink.
|
DataStreamSink<T> |
name(String name)
Sets the name of this sink.
|
DataStreamSink<T> |
setParallelism(int parallelism)
Sets the parallelism for this sink.
|
DataStreamSink<T> |
setUidHash(String uidHash)
Sets an user provided hash for this operator.
|
DataStreamSink<T> |
slotSharingGroup(String slotSharingGroup)
Sets the slot sharing group of this operation.
|
DataStreamSink<T> |
uid(String uid)
Sets an ID for this operator.
|
protected DataStreamSink(DataStream<T> inputStream, StreamSink<T> operator)
@Internal public SinkTransformation<T> getTransformation()
public DataStreamSink<T> name(String name)
@PublicEvolving public DataStreamSink<T> uid(String uid)
The specified ID is used to assign the same operator ID across job submissions (for example when starting a job from a savepoint).
Important: this ID needs to be unique per transformation and job. Otherwise, job submission will fail.
uid
- The unique user-specified ID of this transformation.@PublicEvolving public DataStreamSink<T> setUidHash(String uidHash)
The user provided hash is an alternative to the generated hashes, that is considered when identifying an operator through the default hash mechanics fails (e.g. because of changes between Flink versions).
Important: this should be used as a workaround or for trouble shooting. The provided hash needs to be unique per transformation and job. Otherwise, job submission will fail. Furthermore, you cannot assign user-specified hash to intermediate nodes in an operator chain and trying so will let your job fail.
A use case for this is in migration between Flink versions or changing the jobs in a way that changes the automatically generated hashes. In this case, providing the previous hashes directly through this method (e.g. obtained from old logs) can help to reestablish a lost mapping from states to their target operator.
uidHash
- The user provided hash for this operator. This will become the JobVertexID, which is shown in the
logs and web ui.public DataStreamSink<T> setParallelism(int parallelism)
parallelism
- The parallelism for this sink.@PublicEvolving public DataStreamSink<T> disableChaining()
Chaining can be turned off for the whole
job by StreamExecutionEnvironment.disableOperatorChaining()
however it is not advised for performance considerations.
@PublicEvolving public DataStreamSink<T> slotSharingGroup(String slotSharingGroup)
Operations inherit the slot sharing group of input operations if all input operations are in the same slot sharing group and no slot sharing group was explicitly specified.
Initially an operation is in the default slot sharing group. An operation can be put into
the default group explicitly by setting the slot sharing group to "default"
.
slotSharingGroup
- The slot sharing group name.Copyright © 2014–2020 The Apache Software Foundation. All rights reserved.