Experimental Features #
This section describes experimental features in the DataStream API. Experimental features are still evolving and can be either unstable, incomplete, or subject to heavy change in future versions.
Reinterpreting a pre-partitioned data stream as keyed stream #
We can re-interpret a pre-partitioned data stream as a keyed stream to avoid shuffling.
WARNING: The re-interpreted data stream MUST already be pre-partitioned in EXACTLY the same way Flink’s keyBy would partition the data in a shuffle w.r.t. key-group assignment.
One use-case for this could be a materialized shuffle between two jobs: the first job performs a keyBy shuffle and materializes each output into a partition. A second job has sources that, for each parallel instance, reads from the corresponding partitions created by the first job. Those sources can now be re-interpreted as keyed streams, e.g. to apply windowing. Notice that this trick makes the second job embarrassingly parallel, which can be helpful for a fine-grained recovery scheme.
This re-interpretation functionality is exposed through
static <T, K> KeyedStream<T, K> reinterpretAsKeyedStream( DataStream<T> stream, KeySelector<T, K> keySelector, TypeInformation<K> typeInfo)
Given a base stream, a key selector, and type information, the method creates a keyed stream from the base stream.
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStreamSource<Integer> source = ... DataStreamUtils.reinterpretAsKeyedStream(source, (in) -> in, TypeInformation.of(Integer.class)) .window(TumblingEventTimeWindows.of(Time.seconds(1))) .reduce((a, b) -> a + b) .addSink(new DiscardingSink<>()); env.execute();
val env = StreamExecutionEnvironment.getExecutionEnvironment env.setParallelism(1) val source = ... new DataStreamUtils(source).reinterpretAsKeyedStream((in) => in) .window(TumblingEventTimeWindows.of(Time.seconds(1))) .reduce((a, b) => a + b) .addSink(new DiscardingSink[Int]) env.execute()