[ https://issues.apache.org/jira/browse/SPARK-19462?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ian updated SPARK-19462: ------------------------ Description: 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$$anonfun$apply$21.apply(RDD.scala:728) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:89) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) {code} was: property spark.sql.adaptive.enabled needs to be set "true" reproducible steps using spark-shell 0. we use yarn as cluster manager, spark-shell runs in client mode 1. launch spark-shell 2. {code} val df1 = sc.parallelize( 1 to 1000, 2).toDF("number") df1.registerTempTable("test") val data1 = sqlContext.sql("SELECT * FROM test WHERE number > 50") data1.collect val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY number") data2.collect // everything is fine up to this point // manually kill all both the AM and all the NM of the spark-shell app // rerun data1.collect the result is returned successfully data1.collect // but data2.collect will fail data2.collect // stacktrace Caused by: java.lang.RuntimeException: Exchange not implemented for UnknownPartitioning(1) at scala.sys.package$.error(package.scala:27) at org.apache.spark.sql.execution.Exchange.org$apache$spark$sql$execution$Exchange$$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$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) {code} > when spark.sql.adaptive.enabled is enabled, RDD is not resilient to node > container failure > ------------------------------------------------------------------------------------------ > > Key: SPARK-19462 > URL: https://issues.apache.org/jira/browse/SPARK-19462 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.6.3 > Reporter: Ian > > property spark.sql.adaptive.enabled needs to be set "true" > reproducible steps using spark-shell > 0. we use yarn as cluster manager, spark-shell runs in client mode > 1. launch spark-shell > 2. > {code} > val df1 = sc.parallelize( 1 to 1000, 2).toDF("number") > df1.registerTempTable("test") > val data1 = sqlContext.sql("SELECT * FROM test WHERE number > 50") > data1.collect > val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY > number") > data2.collect > // everything is fine up to this point > // manually kill both the AM and all the NMs of the spark-shell app > // re-run data1.collect, the result is returned successfully > data1.collect > // but data2.collect will fail > data2.collect > // stacktrace > Caused by: java.lang.RuntimeException: Exchange not implemented for > UnknownPartitioning(1) > at scala.sys.package$.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} -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: 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