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https://issues.apache.org/jira/browse/SPARK-3292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14155550#comment-14155550
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Tathagata Das commented on SPARK-3292:
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I mentioned this in the PR but I am adding it here as well. Not returning an
RDD can mess up a lot of the logic and semantics. For example if there is a
transform() followed by updateStateByKey(), the result will be unpredictable.
updateStateByKey expects the previous batch to have a state RDD. If it does not
find any state RDD it will assume that this the start of the streamign
computation and effectively initialize again, forgetting the previous states
from 2 batches ago. So this change is incorrect.
Regarding the original problem of creating too many empty files, you can filter
that out by doing explicitly saving yourself.
dstream.foreachRDD { case (rdd, time) =>
if (rdd.take(1).size == 1) {
rdd.saveAsHadoopFile(....)
}
}
> Shuffle Tasks run incessantly even though there's no inputs
> -----------------------------------------------------------
>
> Key: SPARK-3292
> URL: https://issues.apache.org/jira/browse/SPARK-3292
> Project: Spark
> Issue Type: Improvement
> Components: Streaming
> Affects Versions: 1.0.2
> Reporter: guowei
>
> such as repartition groupby join and cogroup
> for example.
> if i want the shuffle outputs save as hadoop file ,even though there is no
> inputs , many emtpy file generate too.
> it's too expensive ,
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