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https://issues.apache.org/jira/browse/BEAM-2095?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15994352#comment-15994352
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ASF GitHub Bot commented on BEAM-2095:
--------------------------------------
GitHub user staslev opened a pull request:
https://github.com/apache/beam/pull/2854
[BEAM-2095] Made SourceRDD hasNext idempotent
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- [ ] Make sure tests pass via `mvn clean verify`.
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---
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/staslev/beam
BEAM-2095-SourceRDD-hasNext-not-idempotent
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/beam/pull/2854.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #2854
----
commit 0cc5b42b4df24261ded37066a3e6e11a4103a938
Author: Stas Levin <[email protected]>
Date: 2017-05-01T04:30:49Z
[BEAM-2095] Made SourceRDD hasNext idempotent
----
> SourceRDD hasNext not idempotent
> --------------------------------
>
> Key: BEAM-2095
> URL: https://issues.apache.org/jira/browse/BEAM-2095
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Affects Versions: 0.6.0
> Reporter: Arvid Heise
> Assignee: Stas Levin
>
> When reading an Avro from HDFS with the new HDFSFileSource, we experience the
> following exceptions:
> {code}
> 17/04/27 11:48:38 ERROR executor.Executor: Exception in task 2.0 in stage 1.0
> (TID 32)
> java.util.NoSuchElementException
> at
> com.gfk.hyperlane.engine.target_group_evaluation.dataset.HDFSFileSource$HDFSFileReader.getCurrent(HDFSFileSource.java:498)
> at
> org.apache.beam.runners.spark.io.SourceRDD$Bounded$1.next(SourceRDD.java:142)
> at
> org.apache.beam.runners.spark.io.SourceRDD$Bounded$1.next(SourceRDD.java:111)
> at
> scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:42)
> at
> org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
> at scala.collection.Iterator$$anon$12.next(Iterator.scala:357)
> at
> org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:30)
> at
> org.apache.beam.runners.spark.translation.SparkProcessContext$ProcCtxtIterator.computeNext(SparkProcessContext.java:165)
> at
> org.apache.beam.spark.repackaged.com.google.common.collect.AbstractIterator.tryToComputeNext(AbstractIterator.java:145)
> at
> org.apache.beam.spark.repackaged.com.google.common.collect.AbstractIterator.hasNext(AbstractIterator.java:140)
> at
> org.apache.beam.runners.spark.translation.SparkProcessContext$ProcCtxtIterator.computeNext(SparkProcessContext.java:162)
> at
> org.apache.beam.spark.repackaged.com.google.common.collect.AbstractIterator.tryToComputeNext(AbstractIterator.java:145)
> at
> org.apache.beam.spark.repackaged.com.google.common.collect.AbstractIterator.hasNext(AbstractIterator.java:140)
> at
> org.apache.beam.runners.spark.translation.SparkProcessContext.processPartition(SparkProcessContext.java:64)
> at
> org.apache.beam.runners.spark.translation.DoFnFunction.call(DoFnFunction.java:105)
> at
> org.apache.beam.runners.spark.translation.DoFnFunction.call(DoFnFunction.java:48)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:159)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:159)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 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 error comes from a call to BoundedReader#getCurrent after it has been
> closed.
> We logged the following call patterns:
> (for data)
> advance
> getCurrent
> (when drained)
> advance
> close
> getCurrent
> The issue probably comes from the implementation in SourceRDD
> https://github.com/apache/beam/blob/3101e69c438d5c42577fc7d3476d623f6e551837/runners/spark/src/main/java/org/apache/beam/runners/spark/io/SourceRDD.java#L145
> A repeated call to hasNext will result in repeated calls of advance. This
> results in a data loss and may return different results. In particular, it
> may cause the issue as observed.
> The usual solution is to use hasNext() to already retrieve and cache the next
> element if cache empty and return and reset the cache in next().
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