Re: scala.NotImplementedError: put() should not be called on an EmptyStateMap while doing stateful computation on spark streaming

2016-06-21 Thread Ted Yu 
Are you using 1.6.1 ?

If not, does the problem persist when you use 1.6.1 ?

Thanks

> On Jun 20, 2016, at 11:16 PM, umanga  wrote:
> 
> I am getting following warning while running stateful computation. The state
> consists of BloomFilter (stream-lib) as Value and Integer as key.
> 
> The program runs smoothly for few minutes and after that, i am getting this
> warning, and streaming app becomes unstable (processing time increases
> exponentially), and ultimately job fails.
> 
> 
> WARN TaskSetManager: Lost task 0.0 in stage 144.0 (TID 326, mesos-slave-02):
> scala.NotImplementedError: put() should not be called on an EmptyStateMap
>at org.apache.spark.streaming.util.EmptyStateMap.put(StateMap.scala:73)
>at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:62)
>at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
>at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
>at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
>at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:155)
>at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
>at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
>at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>at org.apache.spark.scheduler.Task.run(Task.scala:89)
>at org.apache.spark.executor.Executor$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)
> 
> I am using kryo serialization. From somewhere in internet, I am getting hint
> that this may be due to kryo serialization error for
> OpenHashMapBasedStateMap. But, I have no idea how to fix this.
> 
> 
> 
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Re: scala.NotImplementedError: put() should not be called on an EmptyStateMap while doing stateful computation on spark streaming

2016-06-21 Thread umanga 
further descriptions:

Environment: Spark cluster running in standalone mode with 1 master, 5
slaves, each has 4 vCPUS, 8GB RAM
data is being streamed from 3 node kafka cluster (managed by 3 node zk
cluster). 

Checkpointing is being done at hadoop-cluster, 
plus we are also saving state in HBase (on top of hadoop-cluster) and
recovering it while starting the streaming appplication

Please find the screen shot

 



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scala.NotImplementedError: put() should not be called on an EmptyStateMap while doing stateful computation on spark streaming

2016-06-20 Thread umanga 
I am getting following warning while running stateful computation. The state
consists of BloomFilter (stream-lib) as Value and Integer as key.

The program runs smoothly for few minutes and after that, i am getting this
warning, and streaming app becomes unstable (processing time increases
exponentially), and ultimately job fails.


WARN TaskSetManager: Lost task 0.0 in stage 144.0 (TID 326, mesos-slave-02):
scala.NotImplementedError: put() should not be called on an EmptyStateMap
at org.apache.spark.streaming.util.EmptyStateMap.put(StateMap.scala:73)
at
org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:62)
at
org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at
org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at
org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
at
org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:155)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$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)

I am using kryo serialization. From somewhere in internet, I am getting hint
that this may be due to kryo serialization error for
OpenHashMapBasedStateMap. But, I have no idea how to fix this.



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