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From Shivani Rao <raoshiv...@gmail.com>
Subject Re: Spark 0.9.1 java.lang.outOfMemoryError: Java Heap Space
Date Mon, 23 Jun 2014 23:39:46 GMT
Hello Eugene,

Thanks for your patience and answers. The issue was that one of the third
party libraries was not build with "sbt assembly" but just packaged as "sbt
package". So it did not contain all the source dependencies.

Thanks for all your help

Shivani


On Fri, Jun 20, 2014 at 1:46 PM, Eugen Cepoi <cepoi.eugen@gmail.com> wrote:

> In short, ADD_JARS will add the jar to your driver classpath and also send
> it to the workers (similar to what you are doing when you do sc.addJars).
>
> ex: MASTER=master/url ADD_JARS=/path/to/myJob.jar ./bin/spark-shell
>
>
> You also have SPARK_CLASSPATH var but it does not distribute the code, it
> is only used to compute the driver classpath.
>
>
> BTW, you are not supposed to change the compute_classpath.script
>
>
> 2014-06-20 19:45 GMT+02:00 Shivani Rao <raoshivani@gmail.com>:
>
> Hello Eugene,
>>
>> You are right about this. I did encounter the "pergmgenspace" in the
>> spark shell. Can you tell me a little more about "ADD_JARS". In order to
>> ensure my spark_shell has all required jars, I added the jars to the
>> "$CLASSPATH" in the compute_classpath.sh script. is there another way of
>> doing it?
>>
>> Shivani
>>
>>
>> On Fri, Jun 20, 2014 at 9:47 AM, Eugen Cepoi <cepoi.eugen@gmail.com>
>> wrote:
>>
>>> In my case it was due to a case class I was defining in the spark-shell
>>> and not being available on the workers. So packaging it in a jar and adding
>>> it with ADD_JARS solved the problem. Note that I don't exactly remember if
>>> it was an out of heap space exception or pergmen space. Make sure your
>>> jarsPath is correct.
>>>
>>> Usually to debug this kind of problems I am using the spark-shell (you
>>> can do the same in your job but its more time constuming to repackage,
>>> deploy, run, iterate). Try for example
>>> 1) read the lines (without any processing) and count them
>>> 2) apply processing and count
>>>
>>>
>>>
>>> 2014-06-20 17:15 GMT+02:00 Shivani Rao <raoshivani@gmail.com>:
>>>
>>> Hello Abhi, I did try that and it did not work
>>>>
>>>> And Eugene, Yes I am assembling the argonaut libraries in the fat jar.
>>>> So how did you overcome this problem?
>>>>
>>>> Shivani
>>>>
>>>>
>>>> On Fri, Jun 20, 2014 at 1:59 AM, Eugen Cepoi <cepoi.eugen@gmail.com>
>>>> wrote:
>>>>
>>>>>
>>>>> Le 20 juin 2014 01:46, "Shivani Rao" <raoshivani@gmail.com> a écrit
:
>>>>>
>>>>> >
>>>>> > Hello Andrew,
>>>>> >
>>>>> > i wish I could share the code, but for proprietary reasons I can't.
>>>>> But I can give some idea though of what i am trying to do. The job reads
a
>>>>> file and for each line of that file and processors these lines. I am
not
>>>>> doing anything intense in the "processLogs" function
>>>>> >
>>>>> > import argonaut._
>>>>> > import argonaut.Argonaut._
>>>>> >
>>>>> >
>>>>> > /* all of these case classes are created from json strings extracted
>>>>> from the line in the processLogs() function
>>>>> > *
>>>>> > */
>>>>> > case class struct1…
>>>>> > case class struct2…
>>>>> > case class value1(struct1, struct2)
>>>>> >
>>>>> > def processLogs(line:String): Option[(key1, value1)] {…
>>>>> > }
>>>>> >
>>>>> > def run(sparkMaster, appName, executorMemory, jarsPath) {
>>>>> >   val sparkConf = new SparkConf()
>>>>> >    sparkConf.setMaster(sparkMaster)
>>>>> >    sparkConf.setAppName(appName)
>>>>> >    sparkConf.set("spark.executor.memory", executorMemory)
>>>>> >     sparkConf.setJars(jarsPath) // This includes all the jars
>>>>> relevant jars..
>>>>> >    val sc = new SparkContext(sparkConf)
>>>>> >   val rawLogs =
>>>>> sc.textFile("hdfs://<my-hadoop-namenode:8020:myfile.txt")
>>>>> >
>>>>> rawLogs.saveAsTextFile("hdfs://<my-hadoop-namenode:8020:writebackForTesting")
>>>>> >
>>>>> rawLogs.flatMap(processLogs).saveAsTextFile("hdfs://<my-hadoop-namenode:8020:outfile.txt")
>>>>> > }
>>>>> >
>>>>> > If I switch to "local" mode, the code runs just fine, it fails with
>>>>> the error I pasted above. In the cluster mode, even writing back the
file
>>>>> we just read fails
>>>>> (rawLogs.saveAsTextFile("hdfs://<my-hadoop-namenode:8020:writebackForTesting")
>>>>> >
>>>>> > I still believe this is a classNotFound error in disguise
>>>>> >
>>>>>
>>>>> Indeed you are right, this can be the reason. I had similar errors
>>>>> when defining case classes in the shell and trying to use them in the
RDDs.
>>>>> Are you shading argonaut in the fat jar ?
>>>>>
>>>>> > Thanks
>>>>> > Shivani
>>>>> >
>>>>> >
>>>>> >
>>>>> > On Wed, Jun 18, 2014 at 2:49 PM, Andrew Ash <andrew@andrewash.com>
>>>>> wrote:
>>>>> >>
>>>>> >> Wait, so the file only has four lines and the job running out
of
>>>>> heap space?  Can you share the code you're running that does the
>>>>> processing?  I'd guess that you're doing some intense processing on every
>>>>> line but just writing parsed case classes back to disk sounds very
>>>>> lightweight.
>>>>> >>
>>>>> >> I
>>>>> >>
>>>>> >>
>>>>> >> On Wed, Jun 18, 2014 at 5:17 PM, Shivani Rao <raoshivani@gmail.com>
>>>>> wrote:
>>>>> >>>
>>>>> >>> I am trying to process a file that contains 4 log lines
(not very
>>>>> long) and then write my parsed out case classes to a destination folder,
>>>>> and I get the following error:
>>>>> >>>
>>>>> >>>
>>>>> >>> java.lang.OutOfMemoryError: Java heap space
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.hadoop.io.WritableUtils.readCompressedStringArray(WritableUtils.java:183)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.hadoop.conf.Configuration.readFields(Configuration.java:2244)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.hadoop.io.ObjectWritable.readObject(ObjectWritable.java:280)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.hadoop.io.ObjectWritable.readFields(ObjectWritable.java:75)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.spark.SerializableWritable.readObject(SerializableWritable.scala:39)
>>>>> >>>
>>>>> >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>>>> >>>
>>>>> >>> at
>>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
>>>>> >>>
>>>>> >>> at
>>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
>>>>> >>>
>>>>> >>> at java.lang.reflect.Method.invoke(Method.java:597)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:974)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1848)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1752)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1328)
>>>>> >>>
>>>>> >>> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:350)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.spark.broadcast.HttpBroadcast$.read(HttpBroadcast.scala:165)
>>>>> >>>
>>>>> >>> at
>>>>> org.apache.spark.broadcast.HttpBroadcast.readObject(HttpBroadcast.scala:56)
>>>>> >>>
>>>>> >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>>>> >>>
>>>>> >>> at
>>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
>>>>> >>>
>>>>> >>> at
>>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
>>>>> >>>
>>>>> >>> at java.lang.reflect.Method.invoke(Method.java:597)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:974)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1848)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1752)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1328)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1946)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1870)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1752)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1328)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1946)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1870)
>>>>> >>>
>>>>> >>> at
>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1752)
>>>>> >>>
>>>>> >>>
>>>>> >>> Sadly, there are several folks that have faced this error
while
>>>>> trying to execute Spark jobs and there are various solutions, none of
which
>>>>> work for me
>>>>> >>>
>>>>> >>>
>>>>> >>> a) I tried (
>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-1-0-0-java-lang-outOfMemoryError-Java-Heap-Space-td7735.html#a7736)
>>>>> changing the number of partitions in my RDD by using coalesce(8) and
the
>>>>> error persisted
>>>>> >>>
>>>>> >>> b)  I tried changing SPARK_WORKER_MEM=2g,
>>>>> SPARK_EXECUTOR_MEMORY=10g, and both did not work
>>>>> >>>
>>>>> >>> c) I strongly suspect there is a class path error (
>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/how-to-set-spark-executor-memory-and-heap-size-td4719.html)
>>>>> Mainly because the call stack is repetitive. Maybe the OOM error is a
>>>>> disguise ?
>>>>> >>>
>>>>> >>> d) I checked that i am not out of disk space and that i
do not
>>>>> have too many open files (ulimit -u << sudo ls
>>>>> /proc/<spark_master_process_id>/fd | wc -l)
>>>>> >>>
>>>>> >>>
>>>>> >>> I am also noticing multiple reflections happening to find
the
>>>>> right "class" i guess, so it could be "class Not Found: error disguising
>>>>> itself as a memory error.
>>>>> >>>
>>>>> >>>
>>>>> >>> Here are other threads that are encountering same situation
.. but
>>>>> have not been resolved in any way so far..
>>>>> >>>
>>>>> >>>
>>>>> >>>
>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/no-response-in-spark-web-UI-td4633.html
>>>>> >>>
>>>>> >>>
>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-program-thows-OutOfMemoryError-td4268.html
>>>>> >>>
>>>>> >>>
>>>>> >>> Any help is greatly appreciated. I am especially calling
out on
>>>>> creators of Spark and Databrick folks. This seems like a "known bug"
>>>>> waiting to happen.
>>>>> >>>
>>>>> >>>
>>>>> >>> Thanks,
>>>>> >>>
>>>>> >>> Shivani
>>>>> >>>
>>>>> >>>
>>>>> >>> --
>>>>> >>> Software Engineer
>>>>> >>> Analytics Engineering Team@ Box
>>>>> >>> Mountain View, CA
>>>>> >>
>>>>> >>
>>>>> >
>>>>> >
>>>>> >
>>>>> > --
>>>>> > Software Engineer
>>>>> > Analytics Engineering Team@ Box
>>>>> > Mountain View, CA
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Software Engineer
>>>> Analytics Engineering Team@ Box
>>>> Mountain View, CA
>>>>
>>>
>>>
>>
>>
>> --
>> Software Engineer
>> Analytics Engineering Team@ Box
>> Mountain View, CA
>>
>
>


-- 
Software Engineer
Analytics Engineering Team@ Box
Mountain View, CA

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