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https://issues.apache.org/jira/browse/SPARK-24815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17926273#comment-17926273
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Pavan Kotikalapudi commented on SPARK-24815:
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Hi Mich,
All the work has completed a year ago, We need apache spark committers to
review/support the work to get it merged.
Due to inactivity the PR is marked to close, will wait for the committers
attention before requesting to re-open again.
> 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, Spark Core, Structured Streaming
> Affects Versions: 2.3.1
> Reporter: Karthik Palaniappan
> Priority: Minor
> Labels: pull-request-available
>
> For batch jobs, 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, the batch dynamic allocation algorithm kicks in. It requests
> more executors if the task backlog is a certain size, and removes executors
> if they idle for a certain period of time.
> Quick thoughts:
> 1) Dynamic allocation should be pluggable, rather than hardcoded to a
> particular implementation in SparkContext.scala (this should be a separate
> JIRA).
> 2) We should make a structured streaming algorithm that's separate from the
> batch algorithm. Eventually, continuous processing might need its own
> algorithm.
> 3) Spark should print a warning if you run a structured streaming job when
> Core's dynamic allocation is enabled
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