[
https://issues.apache.org/jira/browse/SPARK-8062?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen updated SPARK-8062:
------------------------------
Comment: was deleted
(was: Alright, I've filed https://issues.apache.org/jira/browse/SPARK-8086 to
follow up on hardening InputOutputMetricsSuite.)
> NullPointerException in SparkHadoopUtil.getFileSystemThreadStatistics
> ---------------------------------------------------------------------
>
> Key: SPARK-8062
> URL: https://issues.apache.org/jira/browse/SPARK-8062
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.2.1
> Environment: MapR 4.0.1, Hadoop 2.4.1, Yarn
> Reporter: Josh Rosen
> Assignee: Josh Rosen
> Fix For: 1.2.3
>
>
> I received the following error report from a user:
> While running a Spark Streaming job that reads from MapRfs and writes to
> HBase using Spark 1.2.1, the job intermittently experiences a total job
> failure due to the following errors:
> {code}
> 15/05/28 10:35:50 ERROR executor.Executor: Exception in task 1.1 in stage 6.0
> (TID 24)
> java.lang.NullPointerException
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$4.apply(SparkHadoopUtil.scala:178)
>
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$4.apply(SparkHadoopUtil.scala:178)
>
> at
> scala.collection.TraversableLike$$anonfun$filter$1.apply(TraversableLike.scala:264)
>
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
> at scala.collection.TraversableLike$class.filter(TraversableLike.scala:263)
> at scala.collection.AbstractTraversable.filter(Traversable.scala:105)
> at
> org.apache.spark.deploy.SparkHadoopUtil.getFileSystemThreadStatistics(SparkHadoopUtil.scala:178)
>
> at
> org.apache.spark.deploy.SparkHadoopUtil.getFSBytesReadOnThreadCallback(SparkHadoopUtil.scala:139)
>
> at org.apache.spark.rdd.NewHadoopRDD$$anon$1.<init>(NewHadoopRDD.scala:116)
> at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:107)
> at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:69)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
> at org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
> at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:56)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200)
> 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:744)
> 15/05/28 10:35:50 INFO executor.CoarseGrainedExecutorBackend: Got assigned
> task 25
> 15/05/28 10:35:50 INFO executor.Executor: Running task 2.1 in stage 6.0 (TID
> 25)
> 15/05/28 10:35:50 INFO rdd.NewHadoopRDD: Input split: hdfs:/[REDACTED]
> 15/05/28 10:35:50 ERROR executor.Executor: Exception in task 2.1 in stage 6.0
> (TID 25)
> java.lang.NullPointerException
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$4.apply(SparkHadoopUtil.scala:178)
>
> at
> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$4.apply(SparkHadoopUtil.scala:178)
>
> at
> scala.collection.TraversableLike$$anonfun$filter$1.apply(TraversableLike.scala:264)
>
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> {code}
> Diving into the code here:
> The NPE is occurring on this line of SparkHadoopUtil (in 1.2.1.):
> https://github.com/apache/spark/blob/v1.2.1/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala#L178
> Here's that block of code from 1.2.1 (it's the same in 1.2.2):
> {code}
> private def getFileSystemThreadStatistics(path: Path, conf: Configuration):
> Seq[AnyRef] = {
> val qualifiedPath = path.getFileSystem(conf).makeQualified(path)
> val scheme = qualifiedPath.toUri().getScheme()
> val stats =
> FileSystem.getAllStatistics().filter(_.getScheme().equals(scheme)) // <---
> exception occurs at this line
> stats.map(Utils.invoke(classOf[Statistics], _, "getThreadStatistics"))
> }
> {code}
> Since the top call on the stack was
> {{org.apache.spark.deploy.SparkHadoopUtil$$anonfun$4}}, I'm assuming that the
> _.getScheme().equals(scheme) call here is failing because
> FileSystem.getAllStatistics() is returning a collection that has a null
> element or that _.getScheme() is null.
> Diving into the Hadoop source, it looks like FileSystem.getAllStatistics()
> accesses some synchronized static state to return statistics for all Hadoop
> filesystems created within the JVM. I wonder if it's possible that some code
> is nondeterministically creating a new FIleSystem instance for a FileSystem
> that lacks a scheme, causing entires to be stored in the statistics map that
> will return null when we call getScheme() on them.
> I am unable to reproduce this issue myself, but I think that we can fix it
> for the user by adding try-catch blocks to prevent errors in metrics
> collection from leading to task failures.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
