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https://issues.apache.org/jira/browse/SPARK-43408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Faiz Halde updated SPARK-43408:
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Description:
Does caching benefit a spark job with only a single action in it? Spark IIRC
already optimizes shuffles by persisting them onto the disk
I am unable to find a counter-example where caching would benefit a job with a
single action. In every case I can think of, the shuffle checkpoint acts as a
good enough caching mechanism in itself
FWIW, I am talking specifically in the context of the Dataframe API. The
StorageLevel allowed in my case would only be DISK_ONLY i.e. I am not looking
to speed up by caching data in memory
was:
Does caching benefit a spark job with only a single action in it? Spark IIRC
already optimizes shuffles by persisting them onto the disk
I am unable to find a counter-example where caching would benefit a job with a
single action. In every case I can think of, the shuffle checkpoint acts as a
good enough caching mechanism in itself
FWIW, I am talking specifically in the context of the Dataframe API
> Spark caching in the context of a single job
> --------------------------------------------
>
> Key: SPARK-43408
> URL: https://issues.apache.org/jira/browse/SPARK-43408
> Project: Spark
> Issue Type: Question
> Components: Shuffle
> Affects Versions: 3.3.1
> Reporter: Faiz Halde
> Priority: Trivial
>
> Does caching benefit a spark job with only a single action in it? Spark IIRC
> already optimizes shuffles by persisting them onto the disk
> I am unable to find a counter-example where caching would benefit a job with
> a single action. In every case I can think of, the shuffle checkpoint acts as
> a good enough caching mechanism in itself
> FWIW, I am talking specifically in the context of the Dataframe API. The
> StorageLevel allowed in my case would only be DISK_ONLY i.e. I am not looking
> to speed up by caching data in memory
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