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https://issues.apache.org/jira/browse/FLINK-19383?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17327633#comment-17327633
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Flink Jira Bot commented on FLINK-19383:
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This major issue is unassigned and itself and all of its Sub-Tasks have not
been updated for 30 days. So, it has been labeled "stale-major". If this ticket
is indeed "major", please either assign yourself or give an update. Afterwards,
please remove the label. In 7 days the issue will be deprioritized.
> Per Partition State
> -------------------
>
> Key: FLINK-19383
> URL: https://issues.apache.org/jira/browse/FLINK-19383
> Project: Flink
> Issue Type: Improvement
> Components: API / DataStream
> Reporter: Theo Diefenthal
> Priority: Major
> Labels: stale-major
>
> With Kafka possibly being the mostly used data source in Flink, I'd like to
> propse a new "per-partition-state".
> Right now, Flink only knows about OperatorState (evenly distributed or union)
> or keyedState.
> With Kafka having multiple partitions per topic, Flink already exploits that
> nicely. Most widely used is the feature that one can produce data with
> ascending timestamps per kafka partition. (e.g. server logs with one server
> sending data to one partition). In Flink, this results in a huge optimization
> namingly that in that case, one can use an
> AscendingTimestampWatermarkAssigner and windows can be closed quickly.
> Making use of the performance optimization leads me to thinking that we could
> go a step further and introduce a per-kafka-partition state. In my current
> scenario, I need to buffer the data per server (and thus per kafka partition)
> for 1 minute in event time, waiting if during that time certain other events
> arrive or not.
> A state per kafka partition is currently hard to implement. The best to do is
> keyby the datastream by kafka-partition. However, the KafkaAssigner has
> different assignment characteristics then the KeyGroupRangeAssignment leading
> to an unnecessary shuffle step. Even worse, the KeyGroupRangeAssignment is
> kind of random whereas the kafka-partition assignment from source works round
> robing. Having similarly loaded kafka-partitions, after keying, the load can
> be skewed on the taskmanagers. For a simple pipeline with parallelism 3 and 3
> partitions, this can lead to e.g. one taskManager processing 2 partitions,
> one taskmanager 1 partition and one taskManager being idle.
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