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Greg Hogan commented on FLINK-4855: ----------------------------------- Hi and thanks for submitting a ticket for this idea! Has this been discussed on the dev mailing list (if yes, please add a link)? It looks like a major addition to the DataStream API and should be fully vetted before starting. > Add partitionedKeyBy to DataStream > ---------------------------------- > > Key: FLINK-4855 > URL: https://issues.apache.org/jira/browse/FLINK-4855 > Project: Flink > Issue Type: Improvement > Components: DataStream API > Reporter: Xiaowei Jiang > Assignee: MaGuowei > Original Estimate: 168h > Remaining Estimate: 168h > > After we do any interesting operations (e.g. reduce) on KeyedStream, the > result becomes DataStream. In a lot of cases, the output still has the same > or compatible keys with the KeyedStream (logically). But to do further > operations on these keys, we are forced to use keyby again. This works > semantically, but is costly in two aspects. First, it destroys the > possibility of chaining, which is one of the most important optimization > technique. Second, keyby will greatly expand the connected components of > tasks, which has implications in failover optimization. > To address this shortcoming, we propose a new operator partitionedKeyBy. > DataStream { > public <K> KeyedStream<T, K> partitionedKeyBy(KeySelector<T, K> key) > } > Semantically, DataStream.partitionedKeyBy(key) is equivalent to > DataStream.keyBy(partitionedKey) where partitionedKey is key plus the taskid > as an extra field. This guarantees that records from different tasks will > never produce the same keys. > With this, it's possible to do > ds.keyBy(key1).reduce(func1) > .partitionedKeyBy(key1).reduce(func2) > .partitionedKeyBy(key2).reduce(func3); > Most importantly, in certain cases, we will be able to chains these into a > single vertex. -- This message was sent by Atlassian JIRA (v6.3.4#6332)