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https://issues.apache.org/jira/browse/SPARK-2629?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tathagata Das updated SPARK-2629:
---------------------------------
Description:
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 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. Please take a look and provide feedback as
comments.
https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em
was:
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 - (i) timeout of idle
data, (ii) returning items other than state
The high level idea that I am proposing is
- Introduce a new API trackStateByKey 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. Please take a look and provide feedback as
comments.
https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em
> 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 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. Please take a look and provide feedback as
> comments.
> https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em
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