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Posted to issues@spark.apache.org by "Rajat Goel (Jira)" <ji...@apache.org> on 2021/01/13 04:08:00 UTC
[jira] [Commented] (SPARK-24815) Structured Streaming should
support dynamic allocation
[ https://issues.apache.org/jira/browse/SPARK-24815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17263876#comment-17263876 ]
Rajat Goel commented on SPARK-24815:
------------------------------------
Is there any updates/progress on this feature especially for cases where Kafka is input 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, Spark Core, Structured Streaming
> Affects Versions: 2.3.1
> Reporter: Karthik Palaniappan
> Priority: Minor
>
> 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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