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https://issues.apache.org/jira/browse/SPARK-12864?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15107859#comment-15107859
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iward commented on SPARK-12864:
-------------------------------
The important point of the idea is to fix this conflict executor id. As the
task log show, if the data of current task to shuffle is not found, it will
throw a FetchFailedException. So, I think the mechanism in AM restarted case is
continue to run, it won't recomputed, if the data computed is not found, it
will throw a FetchFailedException. And I have run a test that it will normally
continue to run.
> initialize executorIdCounter after ApplicationMaster killed for max number
> of executor failures reached
> --------------------------------------------------------------------------------------------------------
>
> Key: SPARK-12864
> URL: https://issues.apache.org/jira/browse/SPARK-12864
> Project: Spark
> Issue Type: Bug
> Components: YARN
> Affects Versions: 1.3.1, 1.4.1, 1.5.2
> Reporter: iward
>
> Currently, when max number of executor failures reached the
> *maxNumExecutorFailures*, *ApplicationMaster* will be killed and re-register
> another one.This time, *YarnAllocator* will be created a new instance.
> But, the value of property *executorIdCounter* in *YarnAllocator* will reset
> to *0*. Then the *Id* of new executor will starting from 1. This will confuse
> with the executor has already created before, which will cause
> FetchFailedException.
> For example, the following is the task log:
> {noformat}
> 2015-12-22 02:33:15 INFO 15/12/22 02:33:15 WARN
> YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster has
> disassociated: 172.22.92.14:45125
> 2015-12-22 02:33:26 INFO 15/12/22 02:33:26 INFO
> YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as
> AkkaRpcEndpointRef(Actor[akka.tcp://sparkYarnAM@172.22.168.72:54040/user/YarnAM#-1290854604])
> {noformat}
> {noformat}
> 2015-12-22 02:35:02 INFO 15/12/22 02:35:02 INFO YarnClientSchedulerBackend:
> Registered executor:
> AkkaRpcEndpointRef(Actor[akka.tcp://sparkexecu...@bjhc-hera-16217.hadoop.jd.local:46538/user/Executor#-790726793])
> with ID 1
> {noformat}
> {noformat}
> Lost task 3.0 in stage 102.0 (TID 1963, BJHC-HERA-16217.hadoop.jd.local):
> FetchFailed(BlockManagerId(1, BJHC-HERA-17030.hadoop.jd.local, 7337
> ), shuffleId=5, mapId=2, reduceId=3, message=
> 2015-12-22 02:43:20 INFO org.apache.spark.shuffle.FetchFailedException:
> /data3/yarn1/local/usercache/dd_edw/appcache/application_1450438154359_206399/blockmgr-b1fd0363-6d53-4d09-8086-adc4a13f4dc4/0f/shuffl
> e_5_2_0.index (No such file or directory)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:84)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:84)
> 2015-12-22 02:43:20 INFO at
> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> 2015-12-22 02:43:20 INFO at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> 2015-12-22 02:43:20 INFO at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:154)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.sql.execution.Aggregate$$anonfun$execute$1$$anonfun$7.apply(Aggregate.scala:149)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:640)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:640)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> 2015-12-22 02:43:20 INFO at
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> {noformat}
> As the task log show, the executor id of *BJHC-HERA-16217.hadoop.jd.local*
> is the same as *BJHC-HERA-17030.hadoop.jd.local*. So, it is confusion and
> cause FetchFailedException.
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