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https://issues.apache.org/jira/browse/SPARK-4040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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jay vyas updated SPARK-4040:
----------------------------
Component/s: (was: Streaming)
Documentation
> Update spark documentation for local mode and spark-streaming.
> ---------------------------------------------------------------
>
> Key: SPARK-4040
> URL: https://issues.apache.org/jira/browse/SPARK-4040
> Project: Spark
> Issue Type: Bug
> Components: Documentation
> Reporter: jay vyas
> Fix For: 1.2.0
>
>
> 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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