Marcelo Vanzin created SPARK-27094:
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Summary: Thread interrupt being swallowed while launching
executors in YarnAllocator
Key: SPARK-27094
URL: https://issues.apache.org/jira/browse/SPARK-27094
Project: Spark
Issue Type: Bug
Components: YARN
Affects Versions: 2.4.0
Reporter: Marcelo Vanzin
When shutting down a SparkContext, the YarnAllocator thread is interrupted. If
the interrupt happens just at the wrong time, you'll see something like this:
{noformat}
19/03/05 07:04:20 WARN ScriptBasedMapping: Exception running blah
java.io.IOException: java.lang.InterruptedException
at org.apache.hadoop.util.Shell.runCommand(Shell.java:578)
at org.apache.hadoop.util.Shell.run(Shell.java:478)
at
org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:766)
at
org.apache.hadoop.net.ScriptBasedMapping$RawScriptBasedMapping.runResolveCommand(ScriptBasedMapping.java:251)
at
org.apache.hadoop.net.ScriptBasedMapping$RawScriptBasedMapping.resolve(ScriptBasedMapping.java:188)
at
org.apache.hadoop.net.CachedDNSToSwitchMapping.resolve(CachedDNSToSwitchMapping.java:119)
at
org.apache.hadoop.yarn.util.RackResolver.coreResolve(RackResolver.java:101)
at
org.apache.hadoop.yarn.util.RackResolver.resolve(RackResolver.java:81)
at
org.apache.spark.deploy.yarn.SparkRackResolver.resolve(SparkRackResolver.scala:37)
at
org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$handleAllocatedContainers$2.apply(YarnAllocator.scala:431)
at
org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$handleAllocatedContainers$2.apply(YarnAllocator.scala:430)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.deploy.yarn.YarnAllocator.handleAllocatedContainers(YarnAllocator.scala:430)
at
org.apache.spark.deploy.yarn.YarnAllocator.allocateResources(YarnAllocator.scala:281)
at
org.apache.spark.deploy.yarn.ApplicationMaster$$anon$3.run(ApplicationMaster.scala:556)
{noformat}
That means the YARN code being called ({{RackResolver}}) is swallowing the
interrupt , so the Spark allocator thread never exits. In this particular app,
the allocator was in the middle of allocating a very large number of executors,
so it seemed like the application was hung, and there were a lot of executor
coming up even though the context was being shut down.
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