[
https://issues.apache.org/jira/browse/SPARK-21999?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16192394#comment-16192394
]
Michael N edited comment on SPARK-21999 at 10/5/17 2:38 AM:
------------------------------------------------------------
Committers: The actual issues are with Seam Owen himself where
1. Sean Owen does not completely understand the issues in the tickets.
2. For the posted questions in the ticket to help with understanding the issue,
he does not know the answers.
3. Instead of either find the correct answers to the questions or ask people
who know the correct answers, he'd blindly close the tickets.
For instance, for the issue with ticket
https://issues.apache.org/jira/browse/SPARK-22163, it occurs in both slaves and
driver. Further, while Spark uses multiple threads to run the Stream processing
asynchronously, there is still synchronization from batch to batch. For
instance, say batch interval is 5 seconds. But batch #3 takes 1 minute to
complete, Spark does not start batch #4 until batch #3 is completelty done. So
in aspect, batch processing between each interval is synchronous. Yet Sean
Owen assumes everything in Spark Stream is asynchronous.
Another instance is Sean Owen does not understand the difference between design
flaw and coding bugs. Code may be perfect and match with the design. However,
if the design is flawed, then it is the design that needs to be changed. An
example is in the earlier Spark API around 1.x, Spark's map interface passes in
only one object at a time. So that is a design flaw because it causes massive
overhead for billions of objects. The newer Spark map interface passes in a
list of objects via an Iterator.
Therefore, the broader issue is with Sean Owen acting more a blocker and
closing the tickets when he does not understand them. When such cases come up,
he should have either learn about the issues or ask someone else to do that.
was (Author: michaeln_apache):
The actual issues are with Seam Owen himself where
1. Sean Owen does not completely understand the issues in the tickets.
2. For the posted questions in the ticket to help with understanding the issue,
he does not know the answers.
3. Instead of either find the correct answers to the questions or ask people
who know the correct answers, he'd blindly close the tickets.
For instance, for the issue with ticket
https://issues.apache.org/jira/browse/SPARK-22163, it occurs in both slaves and
driver. Further, while Spark uses multiple threads to run the Stream processing
asynchronously, there is still synchronization from batch to batch. For
instance, say batch interval is 5 seconds. But batch #3 takes 1 minute to
complete, Spark does not start batch #4 until batch #3 is completelty done. So
in aspect, batch processing between each interval is synchronous. Yet Sean
Owen assumes everything in Spark Stream is asynchronous.
Another instance is Sean Owen does not understand the difference between design
flaw and coding bugs. Code may be perfect and match with the design. However,
if the design is flawed, then it is the design that needs to be changed. An
example is in the earlier Spark API around 1.x, Spark's map interface passes in
only one object at a time. So that is a design flaw because it causes massive
overhead for billions of objects. The newer Spark map interface passes in a
list of objects via an Iterator.
Therefore, the broader issue is with Sean Owen acting more a blocker and
closing the tickets when he does not understand them. When such cases come up,
he should have either learn about the issues or ask someone else to do that.
> 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 1505224930000 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
> 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$$anonfun$1.apply(DStream.scala:340)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
> at
> org.apache.spark.streaming.dstream.TransformedDStream.createRDDWithLocalProperties(TransformedDStream.scala:65)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
> at scala.Option.orElse(Option.scala:289)
> at
> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
> at
> org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
> at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
> at
> org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
> at scala.util.Try$.apply(Try.scala:192)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> Caused by: java.util.ConcurrentModificationException
> at java.util.ArrayList.writeObject(ArrayList.java:766)
> at sun.reflect.GeneratedMethodAccessor21.invoke(Unknown Source)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1028)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:295)
> ... 60 more
> 2017-09-12 07:02:10.029 ERROR
> 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$$anonfun$apply$7.apply(DStream.scala:341)
> at
> org.apache.spark.streaming.dstream.DStream$$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.TransformedDStream.createRDDWithLocalProperties(TransformedDStream.scala:65)
> 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.ForEachDStream.generateJob(ForEachDStream.scala:48)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)
> at
> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
> at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
> at
> org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
> at scala.util.Try$.apply(Try.scala:192)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
> at
> org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
> at
> org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> Caused by: java.util.ConcurrentModificationException
> at java.util.ArrayList.writeObject(ArrayList.java:766)
> at sun.reflect.GeneratedMethodAccessor21.invoke(Unknown Source)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1028)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:295)
> ... 60 more
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