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https://issues.apache.org/jira/browse/SPARK-46798?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-46798:
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Labels: pull-request-available (was: )
> Kafka custom partition location assignment in Spark Structured Streaming
> (rack awareness)
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-46798
> URL: https://issues.apache.org/jira/browse/SPARK-46798
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
> Affects Versions: 3.1.0, 3.2.0, 3.3.0, 3.4.0, 3.5.0
> Reporter: Randall Schwager
> Priority: Major
> Labels: pull-request-available
>
> I'd like to propose, and implement if approved, support for custom partition
> location assignment. [Please find the design doc for SPARK-46798 describing
> the change
> here.|https://docs.google.com/document/d/1RoEk_mt8AUh9sTQZ1NfzIuuYKf1zx6BP1K3IlJ2b8iM/edit#heading=h.pbt6pdb2jt5c]
> SPARK-15406 Added Kafka consumer support to Spark Structured Streaming, but
> it did not add custom partition location assignment as a feature. The
> Structured Streaming Kafka consumer as it exists today evenly allocates Kafka
> topic partitions to executors without regard to Kafka broker rack information
> or executor location. This behavior can drive large cross-AZ networking costs
> in large deployments.
> [The design doc for
> SPARK-15406|https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit#heading=h.k36c6oyz89xw]
> described the ability to assign Kafka partitions to particular executors (a
> feature which would enable rack awareness), but it seems that feature was
> never implemented.
> For DStreams users, there does seem to be a way to assign Kafka partitions to
> Spark executors in a custom fashion with
> [LocationStrategies.PreferFixed|https://github.com/apache/spark/blob/master/connector/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/LocationStrategy.scala#L69],
> so this sort of functionality has a precedent.
>
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