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From "Ian (JIRA)" <j...@apache.org>
Subject [jira] [Issue Comment Deleted] (SPARK-19462) when spark.sql.adaptive.enabled is enabled, RDD is not resilient to node container failure
Date Sun, 05 Feb 2017 10:24:41 GMT

     [ https://issues.apache.org/jira/browse/SPARK-19462?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ian updated SPARK-19462:
------------------------
    Comment: was deleted

(was: When the RDD lineage contains ShuffledRowRDD, the above mentioned behavior can be observed
when node failures or container loss happens.

{code}
scala> data2.rdd.toDebugString
res6: String =
(1) MapPartitionsRDD[20] at rdd at <console>:26 []
 |  MapPartitionsRDD[19] at rdd at <console>:26 []
 |  ShuffledRowRDD[8] at collect at <console>:26 []
 +-(2) MapPartitionsRDD[7] at collect at <console>:26 []
    |  MapPartitionsRDD[6] at collect at <console>:26 []
    |  MapPartitionsRDD[5] at collect at <console>:26 []
    |  MapPartitionsRDD[1] at intRddToDataFrameHolder at <console>:25 []
    |  ParallelCollectionRDD[0] at parallelize at <console>:25 []

{code}
)

> when spark.sql.adaptive.enabled is enabled, RDD is not resilient to node container failure
> ------------------------------------------------------------------------------------------
>
>                 Key: SPARK-19462
>                 URL: https://issues.apache.org/jira/browse/SPARK-19462
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.3
>            Reporter: Ian
>
> property spark.sql.adaptive.enabled needs to be set "true"
> reproducible steps using spark-shell
> 0. we use yarn as cluster manager, spark-shell runs in client mode 
> 1. launch spark-shell
> 2. 
> {code}
> val df1 = sc.parallelize( 1 to 1000, 2).toDF("number")
> df1.registerTempTable("test")
> val data1 = sqlContext.sql("SELECT * FROM test WHERE number > 50")
> data1.collect
> val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY number")
> data2.collect
> // everything is fine up to this point
> // manually kill both the AM and all the NMs of the spark-shell app
> // re-run data1.collect, the result is returned successfully
> data1.collect
> // but data2.collect will fail
> data2.collect
> // stacktrace
> Caused by: java.lang.RuntimeException: Exchange not implemented for UnknownPartitioning(1)
>   at scala.sys.package$.error(package.scala:27)
>   at org.apache.spark.sql.execution.Exchange.org$apache$spark$sql$execution$Exchange$$getPartitionKeyExtractor$1(Exchange.scala:198)
>   at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:208)
>   at org.apache.spark.sql.execution.Exchange$$anonfun$3.apply(Exchange.scala:207)
>   at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>   at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>   at org.apache.spark.scheduler.Task.run(Task.scala:89)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
>   at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>   at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>   at java.lang.Thread.run(Thread.java:745)
> {code}



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