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Posted to issues@spark.apache.org by "Xiao Li (JIRA)" <ji...@apache.org> on 2016/10/07 22:15:20 UTC
[jira] [Resolved] (SPARK-7875) Exception when using CLUSTER BY or
ORDER BY
[ https://issues.apache.org/jira/browse/SPARK-7875?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiao Li resolved SPARK-7875.
----------------------------
Resolution: Fixed
> Exception when using CLUSTER BY or ORDER BY
> -------------------------------------------
>
> Key: SPARK-7875
> URL: https://issues.apache.org/jira/browse/SPARK-7875
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.3.1
> Environment: Mesos scheduler with fine-grained mode
> Reporter: Thomas Dudziak
>
> Under certain circumstances that I haven't yet been able to isolate, I get the following error when doing a HQL query using HiveContext:
> org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
> at org.apache.spark.rdd.RDD$$anonfun$zip$1$$anon$1.hasNext(RDD.scala:746)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at org.apache.spark.util.random.SamplingUtils$.reservoirSampleAndCount(SamplingUtils.scala:56)
> at org.apache.spark.RangePartitioner$$anonfun$8.apply(Partitioner.scala:259)
> at org.apache.spark.RangePartitioner$$anonfun$8.apply(Partitioner.scala:257)
> at org.apache.spark.rdd.RDD$$anonfun$15.apply(RDD.scala:647)
> at org.apache.spark.rdd.RDD$$anonfun$15.apply(RDD.scala:647)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
> at org.apache.spark.scheduler.Task.run(Task.scala:64)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
> 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)
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