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https://issues.apache.org/jira/browse/SPARK-2629?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15117404#comment-15117404
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Rodrigo Boavida commented on SPARK-2629:
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Hi,
I've experimented the new API method but am struggling to find an option to
have the updateStateByKey behavior which forces all elements to be recomputed
on every batch (this is a requirement for my application as I am calculating
duration fields based on the streaming intervals and updating external store
for every element).
Seems this function does not allow to compute all elements as an option. Seems
by design judging by the design doc.
Could someone please clarify?
tnks,
Rod
> Improved state management for Spark Streaming
> ---------------------------------------------
>
> Key: SPARK-2629
> URL: https://issues.apache.org/jira/browse/SPARK-2629
> Project: Spark
> Issue Type: Epic
> Components: Streaming
> Affects Versions: 0.9.2, 1.0.2, 1.2.2, 1.3.1, 1.4.1, 1.5.1
> Reporter: Tathagata Das
> Assignee: Tathagata Das
>
> Current updateStateByKey provides stateful processing in Spark Streaming. It
> allows the user to maintain per-key state and manage that state using an
> updateFunction. The updateFunction is called for each key, and it uses new
> data and existing state of the key, to generate an updated state. However,
> based on community feedback, we have learnt the following lessons.
> - Need for more optimized state management that does not scan every key
> - Need to make it easier to implement common use cases - (a) timeout of idle
> data, (b) returning items other than state
> The high level idea that I am proposing is
> - Introduce a new API -trackStateByKey- *mapWithState* that, allows the user
> to update per-key state, and emit arbitrary records. The new API is necessary
> as this will have significantly different semantics than the existing
> updateStateByKey API. This API will have direct support for timeouts.
> - Internally, the system will keep the state data as a map/list within the
> partitions of the state RDDs. The new data RDDs will be partitioned
> appropriately, and for all the key-value data, it will lookup the map/list in
> the state RDD partition and create a new list/map of updated state data. The
> new state RDD partition will be created based on the update data and if
> necessary, with old data.
> Here is the detailed design doc (*outdated, to be updated*). Please take a
> look and provide feedback as comments.
> https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em
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