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https://issues.apache.org/jira/browse/SPARK-13747?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15982949#comment-15982949
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Dmitry Naumenko commented on SPARK-13747:
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[~zsxwing] I did a similar test with join and have the same error in 2.2.0
(actual query here -
https://github.com/dnaumenko/spark-realtime-analytics-sample/blob/master/samples/src/main/scala/com/github/sparksample/httpapp/SimpleServer.scala).
My test setup is a simple akka-http long-running application and separate
Gatling script that spawns multiple requests for join query
(https://github.com/dnaumenko/spark-realtime-analytics-sample/blob/master/loadtool/src/main/scala/com/github/sparksample/SimpleServerSimulation.scala
is test simulation script).
[~barrybecker4] I've managed to fix the problem by switching akka's default
executor to thread-pool. But I guess the root cause is that Spark is relying on
ThreadLocal variables and manages them incorrectly.
> Concurrent execution in SQL doesn't work with Scala ForkJoinPool
> ----------------------------------------------------------------
>
> Key: SPARK-13747
> URL: https://issues.apache.org/jira/browse/SPARK-13747
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.0.1
> Reporter: Shixiong Zhu
> Assignee: Shixiong Zhu
> Fix For: 2.2.0
>
>
> Run the following codes may fail
> {code}
> (1 to 100).par.foreach { _ =>
> println(sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count())
> }
> java.lang.IllegalArgumentException: spark.sql.execution.id is already set
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:87)
>
> at
> org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:1904)
> at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1385)
> {code}
> This is because SparkContext.runJob can be suspended when using a
> ForkJoinPool (e.g.,scala.concurrent.ExecutionContext.Implicits.global) as it
> calls Await.ready (introduced by https://github.com/apache/spark/pull/9264).
> So when SparkContext.runJob is suspended, ForkJoinPool will run another task
> in the same thread, however, the local properties has been polluted.
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