Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/21718
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/21718
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Github user HeartSaVioR commented on the issue:
https://github.com/apache/spark/pull/21718
@bjkonglu @bethunebtj @wguangliang
Update: I thought about splitting execution tasks and data partitions
(`spark.sql.shuffle.partitions`), and turned out it can be achieved by calling
Github user bjkonglu commented on the issue:
https://github.com/apache/spark/pull/21718
Thx @HeartSaVioR .I am looking forward to your solution.
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Github user HeartSaVioR commented on the issue:
https://github.com/apache/spark/pull/21718
I'm aware of this issue and have it in my backlog, but for now it doesn't
look like easy to address in efficient way. I'll propose an approach for
rescaling state when I get one.
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Github user HeartSaVioR commented on the issue:
https://github.com/apache/spark/pull/21718
It has been fairly easy to rescale partitions before stateful operators
came into play. For structured streaming, it is now not a trivial thing, cause
rescaling partitions should also handle
Github user wguangliang commented on the issue:
https://github.com/apache/spark/pull/21718
it is neccessary that user can restart the spark app and change the
configuration without deleting the checkpoint
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Github user bethunebtj commented on the issue:
https://github.com/apache/spark/pull/21718
Follow.
I meet this problem too.
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Github user AmplabJenkins commented on the issue:
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Github user AmplabJenkins commented on the issue:
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Github user AmplabJenkins commented on the issue:
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