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https://issues.apache.org/jira/browse/SPARK-24815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16850235#comment-16850235
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Jungtaek Lim commented on SPARK-24815:
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I'm also interested on the design doc, as I'd like to see whether the fact is
considered as well: there're some points which dynamic allocation could hurt
the performance of streaming query. Executors running structured streaming
query is stateful, at least for stateful queries, and query leveraging Kafka
source.
> Structured Streaming should support dynamic allocation
> ------------------------------------------------------
>
> Key: SPARK-24815
> URL: https://issues.apache.org/jira/browse/SPARK-24815
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Structured Streaming
> Affects Versions: 2.3.1
> Reporter: Karthik Palaniappan
> Priority: Minor
>
> Dynamic allocation is very useful for adding and removing containers to match
> the actual workload. On multi-tenant clusters, it ensures that a Spark job is
> taking no more resources than necessary. In cloud environments, it enables
> autoscaling.
> However, if you set spark.dynamicAllocation.enabled=true and run a structured
> streaming job, Core's dynamic allocation algorithm kicks in. It requests
> executors if the task backlog is a certain size, and remove executors if they
> idle for a certain period of time.
> This does not make sense for streaming jobs, as outlined in
> https://issues.apache.org/jira/browse/SPARK-12133, which introduced dynamic
> allocation for the old streaming API.
> First, Spark should print a warning if you run a structured streaming job
> when Core's dynamic allocation is enabled
> Second, structured streaming should have support for dynamic allocation. It
> would be convenient if it were the same set of properties as Core's dynamic
> allocation, but I don't have a strong opinion on that.
> If somebody can give me pointers on how to add dynamic allocation support,
> I'd be happy to take a stab.
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