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Posted to issues@spark.apache.org by "Michael Armbrust (JIRA)" <ji...@apache.org> on 2015/12/01 06:04:11 UTC
[jira] [Updated] (SPARK-12031) Integer overflow when do sampling.
[ https://issues.apache.org/jira/browse/SPARK-12031?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Armbrust updated SPARK-12031:
-------------------------------------
Description:
In my case, some partitions contain too much items. When do range partition, exception thrown as:
{code}
java.lang.IllegalArgumentException: n must be positive
at java.util.Random.nextInt(Random.java:300)
at org.apache.spark.util.random.SamplingUtils$.reservoirSampleAndCount(SamplingUtils.scala:58)
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$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{code}
was:
In my case, some partitions contain too much items. When do range partition, exception thrown as:
java.lang.IllegalArgumentException: n must be positive
at java.util.Random.nextInt(Random.java:300)
at org.apache.spark.util.random.SamplingUtils$.reservoirSampleAndCount(SamplingUtils.scala:58)
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$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
> Integer overflow when do sampling.
> ----------------------------------
>
> Key: SPARK-12031
> URL: https://issues.apache.org/jira/browse/SPARK-12031
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.5.1, 1.5.2
> Reporter: uncleGen
>
> In my case, some partitions contain too much items. When do range partition, exception thrown as:
> {code}
> java.lang.IllegalArgumentException: n must be positive
> at java.util.Random.nextInt(Random.java:300)
> at org.apache.spark.util.random.SamplingUtils$.reservoirSampleAndCount(SamplingUtils.scala:58)
> 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$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$18.apply(RDD.scala:703)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
> at org.apache.spark.scheduler.Task.run(Task.scala:70)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {code}
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