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.eu...@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 <raoshiv...@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.eu...@gmail.com>
>> wrote:
>>
>>>
>>> Le 20 juin 2014 01:46, "Shivani Rao" <raoshiv...@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 <and...@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 <raoshiv...@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
