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Posted to issues@spark.apache.org by "jay vyas (JIRA)" <ji...@apache.org> on 2014/10/27 22:28:33 UTC
[jira] [Reopened] (SPARK-4040) calling count() on RDD's emitted
from a DStream blocks forEachRDD progress.
[ https://issues.apache.org/jira/browse/SPARK-4040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
jay vyas reopened SPARK-4040:
-----------------------------
Reopening: After thinking some more about this, i think the docs could be clarified a little bit , just submitted a pull request to that effect :) !
> calling count() on RDD's emitted from a DStream blocks forEachRDD progress.
> ---------------------------------------------------------------------------
>
> Key: SPARK-4040
> URL: https://issues.apache.org/jira/browse/SPARK-4040
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Reporter: jay vyas
>
> Please note that Im somewhat new to spark streaming's API, and am not a spark expert - so I've done the best to write up and reproduce this "bug". If its not a bug i hope an expert will help to explain why and promptly close it. However, it appears it could be a bug after discussing with [~rnowling] who is a spark contributor.
> CC [~rnowling] [~willbenton]
>
> It appears that in a DStream context, a call to {{MappedRDD.count()}} blocks progress and prevents emission of RDDs from a stream.
> {noformat}
> tweetStream.foreachRDD((rdd,lent)=> {
> tweetStream.repartition(1)
> //val count = rdd.count() DONT DO THIS !
> checks += 1;
> if (checks > 20) {
> ssc.stop()
> }
> }
> {noformat}
> The above code block should inevitably halt, after 20 intervals of RDDs... However, if we *uncomment the call* to {{rdd.count()}}, it turns out that we get an *infinite stream which emits no RDDs*, and thus our program *runs forever* (ssc.stop is unreachable), because *forEach doesnt receive any more entries*.
> I suspect this is actually because the foreach block never completes, because {{count()}} is winds up calling {{compute}}, which ultimately just reads from the stream.
> I havent put together a minimal reproducer or unit test yet, but I can work on doing so if more info is needed.
> I guess this could be seen as an application bug - but i think spark might be made smarter to throw its hands up when people execute blocking code in a stream processor.
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