[jira] [Resolved] (SPARK-21999) ConcurrentModificationException - Spark Streaming
[ https://issues.apache.org/jira/browse/SPARK-21999?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Steve Loughran resolved SPARK-21999. Resolution: Won't Fix > ConcurrentModificationException - Spark Streaming > - > > Key: SPARK-21999 > URL: https://issues.apache.org/jira/browse/SPARK-21999 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.1.0 >Reporter: Michael N >Priority: Critical > > Hi, > I am using Spark Streaming v2.1.0 with Kafka 0.8. I am getting > ConcurrentModificationException intermittently. When it occurs, Spark does > not honor the specified value of spark.task.maxFailures. So Spark aborts the > current batch and fetch the next batch, so it results in lost data. Its > exception stack is listed below. > This instance of ConcurrentModificationException is similar to the issue at > https://issues.apache.org/jira/browse/SPARK-17463, which was about > Serialization of accumulators in heartbeats. However, my Spark stream app > does not use accumulators. > The stack trace listed below occurred on the Spark master in Spark streaming > driver at the time of data loss. > From the line of code in the first stack trace, can you tell which object > Spark was trying to serialize ? What is the root cause for this issue ? > Because this issue results in lost data as described above, could you have > this issue fixed ASAP ? > Thanks. > Michael N., > > Stack trace of Spark Streaming driver > ERROR JobScheduler:91: Error generating jobs for time 150522493 ms > org.apache.spark.SparkException: Task not serializable > at > org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108) > at org.apache.spark.SparkContext.clean(SparkContext.scala:2094) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:792) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) > at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:792) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at scala.Option.map(Option.scala:146) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream.compute(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333) > at scala.Option.orElse(Option.scala:289) > at > org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at scala.collection.immutable.List.foreach(List.scala:381) > at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) > at scala.collection.immutable.List.map(List.scala:285) > at > org.apache.spark.streaming.dstream.TransformedDStream.compute(TransformedDStream.scala:42) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at
[jira] [Resolved] (SPARK-21999) ConcurrentModificationException - Spark Streaming
[ https://issues.apache.org/jira/browse/SPARK-21999?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-21999. --- Resolution: Not A Problem I am not sure what you mean by bold-faced asynchronously. Your app is modifying a collection asynchronously w.r.t. Spark. Right? You said as much. This is why I'm asking you to back up and present a specific example, on the mailing list. The map function is not a flaw, in Spark or Scala or any other functional language. That is not the reason flatMap exists. If that's the same misunderstanding going on here, then, no, this isn't a problem. > ConcurrentModificationException - Spark Streaming > - > > Key: SPARK-21999 > URL: https://issues.apache.org/jira/browse/SPARK-21999 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.1.0 >Reporter: Michael N >Priority: Critical > > Hi, > I am using Spark Streaming v2.1.0 with Kafka 0.8. I am getting > ConcurrentModificationException intermittently. When it occurs, Spark does > not honor the specified value of spark.task.maxFailures. So Spark aborts the > current batch and fetch the next batch, so it results in lost data. Its > exception stack is listed below. > This instance of ConcurrentModificationException is similar to the issue at > https://issues.apache.org/jira/browse/SPARK-17463, which was about > Serialization of accumulators in heartbeats. However, my Spark stream app > does not use accumulators. > The stack trace listed below occurred on the Spark master in Spark streaming > driver at the time of data loss. > From the line of code in the first stack trace, can you tell which object > Spark was trying to serialize ? What is the root cause for this issue ? > Because this issue results in lost data as described above, could you have > this issue fixed ASAP ? > Thanks. > Michael N., > > Stack trace of Spark Streaming driver > ERROR JobScheduler:91: Error generating jobs for time 150522493 ms > org.apache.spark.SparkException: Task not serializable > at > org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108) > at org.apache.spark.SparkContext.clean(SparkContext.scala:2094) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:792) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) > at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:792) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at scala.Option.map(Option.scala:146) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream.compute(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333) > at scala.Option.orElse(Option.scala:289) > at > org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at
[jira] [Resolved] (SPARK-21999) ConcurrentModificationException - Spark Streaming
[ https://issues.apache.org/jira/browse/SPARK-21999?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-21999. --- Resolution: Not A Problem > ConcurrentModificationException - Spark Streaming > - > > Key: SPARK-21999 > URL: https://issues.apache.org/jira/browse/SPARK-21999 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.1.0 >Reporter: Michael N >Priority: Critical > > Hi, > I am using Spark Streaming v2.1.0 with Kafka 0.8. I am getting > ConcurrentModificationException intermittently. When it occurs, Spark does > not honor the specified value of spark.task.maxFailures. So Spark aborts the > current batch and fetch the next batch, so it results in lost data. Its > exception stack is listed below. > This instance of ConcurrentModificationException is similar to the issue at > https://issues.apache.org/jira/browse/SPARK-17463, which was about > Serialization of accumulators in heartbeats. However, my Spark stream app > does not use accumulators. > The stack trace listed below occurred on the Spark master in Spark streaming > driver at the time of data loss. > From the line of code in the first stack trace, can you tell which object > Spark was trying to serialize ? What is the root cause for this issue ? > Because this issue results in lost data as described above, could you have > this issue fixed ASAP ? > Thanks. > Michael N., > > Stack trace of Spark Streaming driver > ERROR JobScheduler:91: Error generating jobs for time 150522493 ms > org.apache.spark.SparkException: Task not serializable > at > org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108) > at org.apache.spark.SparkContext.clean(SparkContext.scala:2094) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793) > at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:792) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) > at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:792) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream$$anonfun$compute$1.apply(MapPartitionedDStream.scala:37) > at scala.Option.map(Option.scala:146) > at > org.apache.spark.streaming.dstream.MapPartitionedDStream.compute(MapPartitionedDStream.scala:37) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340) > at > org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333) > at scala.Option.orElse(Option.scala:289) > at > org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > org.apache.spark.streaming.dstream.TransformedDStream$$anonfun$6.apply(TransformedDStream.scala:42) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) > at scala.collection.immutable.List.foreach(List.scala:381) > at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) > at scala.collection.immutable.List.map(List.scala:285) > at > org.apache.spark.streaming.dstream.TransformedDStream.compute(TransformedDStream.scala:42) > at > org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341) > at >
