You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Ian (JIRA)" <ji...@apache.org> on 2017/02/05 10:28:41 UTC
[jira] [Comment Edited] (SPARK-19462) when
spark.sql.adaptive.enabled is enabled, RDD is not resilient to node
container failure
[ https://issues.apache.org/jira/browse/SPARK-19462?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15853188#comment-15853188 ]
Ian edited comment on SPARK-19462 at 2/5/17 10:28 AM:
------------------------------------------------------
In fact, the "Exchange not implemented for UnknownPartitioning(1)" can be observed even without any node failures
{code}
val df1 = sc.parallelize( 1 to 1000, 2).toDF("number")
df1.registerTempTable("test")
val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY number")
data2.collect
scala> data2.rdd.toDebugString
res4: String =
(1) MapPartitionsRDD[9] at rdd at <console>:26 []
| MapPartitionsRDD[8] at rdd at <console>:26 []
| ShuffledRowRDD[5] at collect at <console>:26 []
+-(2) MapPartitionsRDD[4] at collect at <console>:26 []
| MapPartitionsRDD[3] at collect at <console>:26 []
| MapPartitionsRDD[2] at collect at <console>:26 []
| MapPartitionsRDD[1] at intRddToDataFrameHolder at <console>:25 []
| ParallelCollectionRDD[0] at parallelize at <console>:25 []
// collect on ShuffledRowRDD[5], working ok
data2.rdd.dependencies(0).rdd.dependencies(0).rdd.collect
// collect on MapPartitionsRDD[4], working not !!!!
data2.rdd.dependencies(0).rdd.dependencies(0).rdd.dependencies(0).rdd.collect
// stacktrace
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 4 times, most recent failure: Lost task 0.3 in stage 8.0 (TID 19, ip-10-1-1-150.dev.clearstory.com): 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.ResultTask.runTask(ResultTask.scala:66)
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 is saying that, given the above lineage, MapPartitionsRDD[4] is not re-calcubable.
was (Author: ianlcsd):
In fact, the "Exchange not implemented for UnknownPartitioning(1)" can be observed even without any node failures
{code}
val df1 = sc.parallelize( 1 to 1000, 2).toDF("number")
df1.registerTempTable("test")
val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY number")
data2.collect
scala> data2.rdd.toDebugString
res4: String =
(1) MapPartitionsRDD[9] at rdd at <console>:26 []
| MapPartitionsRDD[8] at rdd at <console>:26 []
| ShuffledRowRDD[5] at collect at <console>:26 []
+-(2) MapPartitionsRDD[4] at collect at <console>:26 []
| MapPartitionsRDD[3] at collect at <console>:26 []
| MapPartitionsRDD[2] at collect at <console>:26 []
| MapPartitionsRDD[1] at intRddToDataFrameHolder at <console>:25 []
| ParallelCollectionRDD[0] at parallelize at <console>:25 []
// collect on ShuffledRowRDD[5], working ok
data2.rdd.dependencies(0).rdd.dependencies(0).rdd.collect
// collect on MapPartitionsRDD[4], working not !!!!
data2.rdd.dependencies(0).rdd.dependencies(0).rdd.dependencies(0).rdd.collect
// stacktrace
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 4 times, most recent failure: Lost task 0.3 in stage 8.0 (TID 19, ip-10-1-1-150.dev.clearstory.com): 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$21.apply(RDD.scala:728)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
This is saying that, given the above lineage, MapPartitionsRDD[4] is not re-calculatable.
> when spark.sql.adaptive.enabled is enabled, RDD is not resilient to node container failure
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-19462
> URL: https://issues.apache.org/jira/browse/SPARK-19462
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.3
> Reporter: Ian
>
> property spark.sql.adaptive.enabled needs to be set "true"
> reproducible steps using spark-shell
> 0. we use yarn as cluster manager, spark-shell runs in client mode
> 1. launch spark-shell
> 2.
> {code}
> val df1 = sc.parallelize( 1 to 1000, 2).toDF("number")
> df1.registerTempTable("test")
> val data1 = sqlContext.sql("SELECT * FROM test WHERE number > 50")
> data1.collect
> val data2 = sqlContext.sql("SELECT number, count(*) cnt FROM test GROUP BY number")
> data2.collect
> // everything is fine up to this point
> // manually kill both the AM and all the NMs of the spark-shell app
> // re-run data1.collect, the result is returned successfully
> data1.collect
> // but data2.collect will fail
> data2.collect
> // stacktrace
> Caused by: java.lang.RuntimeException: Exchange not implemented for UnknownPartitioning(1)
> at scala.sys.package$.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}
> The difference between data1 and data2 is whether ShuffledRowRDD is present in lineage.
> 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}
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org