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From Nicolas Phung <nicolas.ph...@gmail.com>
Subject Re: Resume checkpoint failed with Spark Streaming Kafka via createDirectStream under heavy reprocessing
Date Tue, 28 Jul 2015 14:30:40 GMT
Hi,

After using KafkaUtils.createDirectStream[Object, Object, KafkaAvroDecoder,
KafkaAvroDecoder, Option[AnalyticEventEnriched]](ssc, kafkaParams, map,
messageHandler), I'm encountering the following issue:

15/07/28 00:29:57 ERROR actor.ActorSystemImpl: Uncaught fatal error from
thread [sparkDriver-akka.actor.default-dispatcher-24] shutting down
ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: Java heap space
    at
java.io.ObjectOutputStream$HandleTable.growEntries(ObjectOutputStream.java:2351)
    at
java.io.ObjectOutputStream$HandleTable.assign(ObjectOutputStream.java:2276)
    at
java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1428)
    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.writeArray(ObjectOutputStream.java:1378)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174)
    at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
    at
scala.collection.mutable.HashMap$$anonfun$writeObject$1.apply(HashMap.scala:137)
    at
scala.collection.mutable.HashMap$$anonfun$writeObject$1.apply(HashMap.scala:135)
    at
scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:226)
    at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39)
    at
scala.collection.mutable.HashTable$class.serializeTo(HashTable.scala:124)
    at scala.collection.mutable.HashMap.serializeTo(HashMap.scala:39)
    at scala.collection.mutable.HashMap.writeObject(HashMap.scala:135)
    at sun.reflect.GeneratedMethodAccessor22.invoke(Unknown Source)
    at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:483)
    at
java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
    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.defaultWriteObject(ObjectOutputStream.java:441)
    at
org.apache.spark.streaming.dstream.DStreamCheckpointData$$anonfun$writeObject$1.apply$mcV$sp(DStreamCheckpointData.scala:128)
    at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1137)
    at
org.apache.spark.streaming.dstream.DStreamCheckpointData.writeObject(DStreamCheckpointData.scala:123)
    at sun.reflect.GeneratedMethodAccessor21.invoke(Unknown Source)
    at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

I don't know why, after that, it's eating all the CPU on one of the node
till the entire job stopped. It tries to resume from checkpoint several
times but failed with this error too. I think I have enough spared memory
with 4 nodes with 24 Gb per nodes. It has processed successfully around 40
gb before that and looking into storage in Spark UI, I don't have a big rdd
stored in memory/disk. I notice on this node, there's an increase in
connection to kafka that are not closed too.

Regards,
Nicolas P.

On Fri, Jul 24, 2015 at 3:32 PM, Cody Koeninger <cody@koeninger.org> wrote:

> It's really a question of whether you need access to the
> MessageAndMetadata, or just the key / value from the message.
>
> If you just need the key/value, dstream map is fine.
>
> In your case, since you need to be able to control a possible failure when
> deserializing the message from the MessageAndMetadata, I'd just go ahead
> and do the work in the messageHandler.
>
> On Fri, Jul 24, 2015 at 2:46 AM, Nicolas Phung <nicolas.phung@gmail.com>
> wrote:
>
>> Hello,
>>
>> I manage to read all my data back with skipping offset that contains a
>> corrupt message. I have one more question regarding messageHandler method
>> vs dstream.foreachRDD.map vs dstream.map.foreachRDD best practices. I'm
>> using a function to read the serialized message from kafka and convert it
>> into my appropriate object with some enrichments and sometimes add filter
>> after that. Where's the best spot to put this logic inside messageHandler
>> method (convert each message within this handler) or dstream.foreachRDD.map
>> (map rdd) or dstream.map.foreachRDD (map dstream) ?
>>
>> Thank you for your help Cody.
>> Regards,
>> Nicolas PHUNG
>>
>> On Tue, Jul 21, 2015 at 4:53 PM, Cody Koeninger <cody@koeninger.org>
>> wrote:
>>
>>> Yeah, I'm referring to that api.
>>>
>>> If you want to filter messages in addition to catching that exception,
>>> have your mesageHandler return an option, so the type R would end up being
>>> Option[WhateverYourClassIs], then filter out None before doing the rest of
>>> your processing.
>>>
>>> If you aren't already recording offsets somewhere, and need to find the
>>> offsets at the beginning of the topic, you can take a look at this
>>>
>>>
>>> https://github.com/apache/spark/blob/branch-1.3/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala#L143
>>>
>>> as an example of querying offsets from Kafka.
>>>
>>> That code is private, but you can either use it as an example, or remove
>>> the private[spark] and recompile just the spark-streaming-kafka package.
>>> That artifact is included in your job assembly, so you won't have to
>>> redeploy spark if you go that route.
>>>
>>>
>>> On Tue, Jul 21, 2015 at 6:42 AM, Nicolas Phung <nicolas.phung@gmail.com>
>>> wrote:
>>>
>>>> Hi Cody,
>>>>
>>>> Thanks for your answer. I'm with Spark 1.3.0. I don't quite understand
>>>> how to use the messageHandler parameter/function in the createDirectStream
>>>> method. You are referring to this, aren't you ?
>>>>
>>>> def createDirectStream[ K: ClassTag, V: ClassTag, KD <: Decoder[K]:
>>>> ClassTag, VD <: Decoder[V]: ClassTag, R: ClassTag] ( ssc:
>>>> StreamingContext, kafkaParams: Map[String, String], fromOffsets: Map[
>>>> TopicAndPartition, Long], messageHandler: MessageAndMetadata[K, V] =>
R
>>>> ): InputDStream[R] = { new DirectKafkaInputDStream[K, V, KD, VD, R](
>>>> ssc, kafkaParams, fromOffsets, messageHandler) }
>>>>
>>>> So, I must supply the fromOffsets parameter too, but how do I tell this
>>>> method to read from the beginning of my topic ?
>>>>
>>>> If I have a filter (e.g. a R.date field) on my R class, I can put a
>>>> filter in the messageHandler function too ?
>>>>
>>>> Regards,
>>>> Nicolas P.
>>>>
>>>> On Mon, Jul 20, 2015 at 7:08 PM, Cody Koeninger <cody@koeninger.org>
>>>> wrote:
>>>>
>>>>> Yeah, in the function you supply for the messageHandler parameter to
>>>>> createDirectStream, catch the exception and do whatever makes sense for
>>>>> your application.
>>>>>
>>>>> On Mon, Jul 20, 2015 at 11:58 AM, Nicolas Phung <
>>>>> nicolas.phung@gmail.com> wrote:
>>>>>
>>>>>> Hello,
>>>>>>
>>>>>> Using the old Spark Streaming Kafka API, I got the following around
>>>>>> the same offset:
>>>>>>
>>>>>> kafka.message.InvalidMessageException: Message is corrupt (stored
crc
>>>>>> = 3561357254, computed crc = 171652633)
>>>>>>         at kafka.message.Message.ensureValid(Message.scala:166)
>>>>>>         at
>>>>>> kafka.consumer.ConsumerIterator.makeNext(ConsumerIterator.scala:102)
>>>>>>         at
>>>>>> kafka.consumer.ConsumerIterator.makeNext(ConsumerIterator.scala:33)
>>>>>>         at
>>>>>> kafka.utils.IteratorTemplate.maybeComputeNext(IteratorTemplate.scala:66)
>>>>>>         at
>>>>>> kafka.utils.IteratorTemplate.hasNext(IteratorTemplate.scala:58)
>>>>>>         at
>>>>>> org.apache.spark.streaming.kafka.ReliableKafkaReceiver$MessageHandler.run(ReliableKafkaReceiver.scala:265)
>>>>>>         at
>>>>>> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
>>>>>>         at java.util.concurrent.FutureTask.run(FutureTask.java:266)
>>>>>>         at
>>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>>         at
>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>>         at java.lang.Thread.run(Thread.java:745)
>>>>>> 15/07/20 15:56:57 INFO BlockManager: Removing broadcast 4641
>>>>>> 15/07/20 15:56:57 ERROR ReliableKafkaReceiver: Error handling message
>>>>>> java.lang.IllegalStateException: Iterator is in failed state
>>>>>>         at
>>>>>> kafka.utils.IteratorTemplate.hasNext(IteratorTemplate.scala:54)
>>>>>>         at
>>>>>> org.apache.spark.streaming.kafka.ReliableKafkaReceiver$MessageHandler.run(ReliableKafkaReceiver.scala:265)
>>>>>>         at
>>>>>> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
>>>>>>         at java.util.concurrent.FutureTask.run(FutureTask.java:266)
>>>>>>         at
>>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>>         at
>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>>         at java.lang.Thread.run(Thread.java:745)
>>>>>>
>>>>>> I found some old topic about some possible corrupt Kafka message
>>>>>> produced by the new producer API with Snappy compression on. My question
>>>>>> is, is it possible to skip/ignore those offsets when full processing
with
>>>>>> KafkaUtils.createStream or KafkaUtils.createDirectStream ?
>>>>>>
>>>>>> Regards,
>>>>>> Nicolas PHUNG
>>>>>>
>>>>>> On Mon, Jul 20, 2015 at 3:46 PM, Cody Koeninger <cody@koeninger.org>
>>>>>> wrote:
>>>>>>
>>>>>>> I'd try logging the offsets for each message, see where problems
>>>>>>> start, then try using the console consumer starting at those
offsets and
>>>>>>> see if you can reproduce the problem.
>>>>>>>
>>>>>>> On Mon, Jul 20, 2015 at 2:15 AM, Nicolas Phung <
>>>>>>> nicolas.phung@gmail.com> wrote:
>>>>>>>
>>>>>>>> Hi Cody,
>>>>>>>>
>>>>>>>> Thanks for you help. It seems there's something wrong with
some
>>>>>>>> messages within my Kafka topics then. I don't understand
how, I can get
>>>>>>>> bigger or incomplete message since I use default configuration
to accept
>>>>>>>> only 1Mb message in my Kafka topic. If you have any others
informations or
>>>>>>>> suggestions, please tell me.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Nicolas PHUNG
>>>>>>>>
>>>>>>>> On Thu, Jul 16, 2015 at 7:08 PM, Cody Koeninger <cody@koeninger.org
>>>>>>>> > wrote:
>>>>>>>>
>>>>>>>>> Not exactly the same issue, but possibly related:
>>>>>>>>>
>>>>>>>>> https://issues.apache.org/jira/browse/KAFKA-1196
>>>>>>>>>
>>>>>>>>> On Thu, Jul 16, 2015 at 12:03 PM, Cody Koeninger <
>>>>>>>>> cody@koeninger.org> wrote:
>>>>>>>>>
>>>>>>>>>> Well, working backwards down the stack trace...
>>>>>>>>>>
>>>>>>>>>> at java.nio.Buffer.limit(Buffer.java:275)
>>>>>>>>>>
>>>>>>>>>> That exception gets thrown if the limit is negative
or greater than the buffer's capacity
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> at kafka.message.Message.sliceDelimited(Message.scala:236)
>>>>>>>>>>
>>>>>>>>>> If size had been negative, it would have just returned
null, so
>>>>>>>>>> we know the exception got thrown because the size
was greater than the
>>>>>>>>>> buffer's capacity
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I haven't seen that before... maybe a corrupted message
of some
>>>>>>>>>> kind?
>>>>>>>>>>
>>>>>>>>>> If that problem is reproducible, try providing an
explicit
>>>>>>>>>> argument for messageHandler, with a function that
logs the message offset.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Thu, Jul 16, 2015 at 11:28 AM, Nicolas Phung <
>>>>>>>>>> nicolas.phung@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hello,
>>>>>>>>>>>
>>>>>>>>>>> When I'm reprocessing the data from kafka (about
40 Gb) with the new Spark Streaming Kafka method createDirectStream, everything is fine till
a driver error happened (driver is killed, connection lost...). When the driver pops up again,
it resumes the processing with the checkpoint in HDFS. Except, I got this:
>>>>>>>>>>>
>>>>>>>>>>> 15/07/16 15:23:41 ERROR TaskSetManager: Task
4 in stage 4.0 failed 4 times; aborting job
>>>>>>>>>>> 15/07/16 15:23:41 ERROR JobScheduler: Error running
job streaming job 1437032118000 ms.0
>>>>>>>>>>> org.apache.spark.SparkException: Job aborted
due to stage failure: Task 4 in stage 4.0 failed 4 times, most recent failure: Lost task 4.3
in stage 4.0 (TID 16, slave05.local): java.lang.IllegalArgumentException
>>>>>>>>>>> 	at java.nio.Buffer.limit(Buffer.java:275)
>>>>>>>>>>> 	at kafka.message.Message.sliceDelimited(Message.scala:236)
>>>>>>>>>>> 	at kafka.message.Message.payload(Message.scala:218)
>>>>>>>>>>> 	at kafka.message.MessageAndMetadata.message(MessageAndMetadata.scala:32)
>>>>>>>>>>> 	at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$6.apply(KafkaUtils.scala:395)
>>>>>>>>>>> 	at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$6.apply(KafkaUtils.scala:395)
>>>>>>>>>>> 	at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator.getNext(KafkaRDD.scala:176)
>>>>>>>>>>> 	at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
>>>>>>>>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>> 	at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:248)
>>>>>>>>>>> 	at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:172)
>>>>>>>>>>> 	at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:79)
>>>>>>>>>>> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
>>>>>>>>>>> 	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
>>>>>>>>>>> 	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
>>>>>>>>>>> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
>>>>>>>>>>> 	at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:93)
>>>>>>>>>>> 	at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:92)
>>>>>>>>>>> 	at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>>>>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>>>>>>>>> 	at org.elasticsearch.spark.rdd.EsRDDWriter.write(EsRDDWriter.scala:48)
>>>>>>>>>>> 	at org.elasticsearch.spark.rdd.EsSpark$$anonfun$saveToEs$1.apply(EsSpark.scala:67)
>>>>>>>>>>> 	at org.elasticsearch.spark.rdd.EsSpark$$anonfun$saveToEs$1.apply(EsSpark.scala:67)
>>>>>>>>>>> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
>>>>>>>>>>> 	at org.apache.spark.scheduler.Task.run(Task.scala:64)
>>>>>>>>>>> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
>>>>>>>>>>> 	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)
>>>>>>>>>>>
>>>>>>>>>>> This is happening only when I'm doing a full
data processing
>>>>>>>>>>> from Kafka. If there's no load, when you killed
the driver and then
>>>>>>>>>>> restart, it resumes the checkpoint as expected
without missing data. Did
>>>>>>>>>>> someone encounters something similar ? How did
you solve this ?
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>>
>>>>>>>>>>> Nicolas PHUNG
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>

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