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