[ https://issues.apache.org/jira/browse/BEAM-2095?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Stas Levin reassigned BEAM-2095: -------------------------------- Assignee: Stas Levin (was: Amit Sela) > 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(). -- This message was sent by Atlassian JIRA (v6.3.15#6346)