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Posted to issues@spark.apache.org by "Matei Zaharia (JIRA)" <ji...@apache.org> on 2014/11/06 00:45:35 UTC
[jira] [Updated] (SPARK-4040) Update spark documentation for local
mode and spark-streaming.
[ https://issues.apache.org/jira/browse/SPARK-4040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Matei Zaharia updated SPARK-4040:
---------------------------------
Assignee: jay vyas
> 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: Documentation
> Components: Documentation
> Reporter: jay vyas
> Assignee: jay vyas
> Fix For: 1.2.0
>
>
> *Note: this JIRA has changed since its inception - its not a bug, but something which can be tricky to surmise from existing docs. So the attached patch is a doc improvement.*
> Below is the original JIRA which was filed:
> 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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