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https://issues.apache.org/jira/browse/SPARK-6847?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15008908#comment-15008908
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Yunjie Qiu commented on SPARK-6847:
-----------------------------------
Hi Glyton Camilleri & Jack Hu,
We also ran into the same StackOverflow issue in our application, where we
wrote like
val dStream1 =
context.union(kafkaStreams).updateStateByKey(updateFunc).checkpoint(Seconds(50))
I've read about your comments, does it mean that I can get rid of this issue
by simply add an extra meaningless map() step to dStream1? Or I should do
something like
val workaroundStream =
dStream1.map(...).checkpoint(Seconds(some_value_other_than_50)) ?
I was confused by what certainConditionsAreMet refers to, or what kind of the
content should be filled _.foreachPartition { ... } that I had to ask here for
detail.
Best Regards.
> Stack overflow on updateStateByKey which followed by a dstream with
> checkpoint set
> ----------------------------------------------------------------------------------
>
> Key: SPARK-6847
> URL: https://issues.apache.org/jira/browse/SPARK-6847
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.3.0
> Reporter: Jack Hu
> Labels: StackOverflowError, Streaming
>
> The issue happens with the following sample code: uses {{updateStateByKey}}
> followed by a {{map}} with checkpoint interval 10 seconds
> {code}
> val sparkConf = new SparkConf().setAppName("test")
> val streamingContext = new StreamingContext(sparkConf, Seconds(10))
> streamingContext.checkpoint("""checkpoint""")
> val source = streamingContext.socketTextStream("localhost", 9999)
> val updatedResult = source.map(
> (1,_)).updateStateByKey(
> (newlist : Seq[String], oldstate : Option[String]) =>
> newlist.headOption.orElse(oldstate))
> updatedResult.map(_._2)
> .checkpoint(Seconds(10))
> .foreachRDD((rdd, t) => {
> println("Deep: " + rdd.toDebugString.split("\n").length)
> println(t.toString() + ": " + rdd.collect.length)
> })
> streamingContext.start()
> streamingContext.awaitTermination()
> {code}
> From the output, we can see that the dependency will be increasing time over
> time, the {{updateStateByKey}} never get check-pointed, and finally, the
> stack overflow will happen.
> Note:
> * The rdd in {{updatedResult.map(_._2)}} get check-pointed in this case, but
> not the {{updateStateByKey}}
> * If remove the {{checkpoint(Seconds(10))}} from the map result (
> {{updatedResult.map(_._2)}} ), the stack overflow will not happen
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