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https://issues.apache.org/jira/browse/SPARK-24947?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-24947:
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Assignee: (was: Apache Spark)
> aggregateAsync and foldAsync for RDD
> ------------------------------------
>
> Key: SPARK-24947
> URL: https://issues.apache.org/jira/browse/SPARK-24947
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Affects Versions: 2.3.1
> Reporter: Cody Allen
> Priority: Minor
>
> {{AsyncRDDActions}} contains {{collectAsync}}, {{countAsync}},
> {{foreachAsync}}, etc; but it doesn't provide general mechanisms for reducing
> datasets asynchronously. If I want to aggregate some statistics on a large
> dataset and it's going to take an hour, I shouldn't need to completely block
> a thread for the hour to wait for the result.
>
> I propose the following methods be added to {{AsyncRDDActions}}:
>
> {{def aggregateAsync[U](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) =>
> U): FutureAction[U]}}
> {{def foldAsync(zeroValue: T)(op: (T, T) => T): FutureAction[T]}}
>
> Locally I have a version of {{aggregateAsync}} implemented based on
> {{submitJob}} (similar to how {{countAsync}} is implemented), and a
> {{foldAsync}} implementation that simply delegates through to
> {{aggregateAsync}}. I haven't yet written unit tests for these, but I can do
> so if this is a contribution that would be accepted. Please let me know.
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