Note also that Java does not work well with very large JVMs due to this exact issue. There are two commonly used workarounds:

1) Spawn multiple (smaller) executors on the same machine. This can be done by creating multiple Workers (via SPARK_WORKER_INSTANCES in standalone mode[1]).
2) Use Tachyon for off-heap caching of RDDs, allowing Spark executors to be smaller and avoid GC pauses

[1] See standalone documentation here: http://spark.apache.org/docs/latest/spark-standalone.html#cluster-launch-scripts


On Sun, Jun 15, 2014 at 3:50 PM, Nan Zhu <zhunanmcgill@gmail.com> wrote:
Yes, I think in the spark-env.sh.template, it is listed in the comments (didn’t check….)

Best,

-- 
Nan Zhu

On Sunday, June 15, 2014 at 5:21 PM, Surendranauth Hiraman wrote:

Is SPARK_DAEMON_JAVA_OPTS valid in 1.0.0?



On Sun, Jun 15, 2014 at 4:59 PM, Nan Zhu <zhunanmcgill@gmail.com> wrote:
SPARK_JAVA_OPTS is deprecated in 1.0, though it works fine if you don’t mind the WARNING in the logs

you can set spark.executor.extraJavaOpts in your SparkConf obj

Best,

-- 
Nan Zhu

On Sunday, June 15, 2014 at 12:13 PM, Hao Wang wrote:

Hi, Wei

You may try to set JVM opts in spark-env.sh as follow to prevent or mitigate GC pause:

export SPARK_JAVA_OPTS="-XX:-UseGCOverheadLimit -XX:+UseConcMarkSweepGC -Xmx2g -XX:MaxPermSize=256m"

There are more options you could add, please just Google :) 


Regards,
Wang Hao(王灏)

CloudTeam | School of Software Engineering
Shanghai Jiao Tong University
Address:800 Dongchuan Road, Minhang District, Shanghai, 200240


On Sun, Jun 15, 2014 at 10:24 AM, Wei Tan <wtan@us.ibm.com> wrote:
Hi,

  I have a single node (192G RAM) stand-alone spark, with memory configuration like this in spark-env.sh

SPARK_WORKER_MEMORY=180g
SPARK_MEM=180g


 In spark-shell I have a program like this:

val file = sc.textFile("/localpath") //file size is 40G
file.cache()


val output = file.map(line => extract something from line)

output.saveAsTextFile (...)


When I run this program again and again, or keep trying file.unpersist() --> file.cache() --> output.saveAsTextFile(), the run time varies a lot, from 1 min to 3 min to 50+ min. Whenever the run-time is more than 1 min, from the stage monitoring GUI I observe big GC pause (some can be 10+ min). Of course when run-time is "normal", say ~1 min, no significant GC is observed. The behavior seems somewhat random.

Is there any JVM tuning I should do to prevent this long GC pause from happening?



I used java-1.6.0-openjdk.x86_64, and my spark-shell process is something like this:

root     10994  1.7  0.6 196378000 1361496 pts/51 Sl+ 22:06   0:12 /usr/lib/jvm/java-1.6.0-openjdk.x86_64/bin/java -cp ::/home/wtan/scala/spark-1.0.0-bin-hadoop1/conf:/home/wtan/scala/spark-1.0.0-bin-hadoop1/lib/spark-assembly-1.0.0-hadoop1.0.4.jar:/home/wtan/scala/spark-1.0.0-bin-hadoop1/lib/datanucleus-core-3.2.2.jar:/home/wtan/scala/spark-1.0.0-bin-hadoop1/lib/datanucleus-rdbms-3.2.1.jar:/home/wtan/scala/spark-1.0.0-bin-hadoop1/lib/datanucleus-api-jdo-3.2.1.jar -XX:MaxPermSize=128m -Djava.library.path= -Xms180g -Xmx180g org.apache.spark.deploy.SparkSubmit spark-shell --class org.apache.spark.repl.Main

Best regards,
Wei

---------------------------------
Wei Tan, PhD
Research Staff Member
IBM T. J. Watson Research Center
http://researcher.ibm.com/person/us-wtan





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