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 <zhunanmcg...@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 <zhunanmcg...@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 <http://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 > Email:wh.s...@gmail.com > > > On Sun, Jun 15, 2014 at 10:24 AM, Wei Tan <w...@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* > <http://researcher.ibm.com/person/us-wtan> > > > > > > > -- > > SUREN HIRAMAN, VP TECHNOLOGY > Velos > Accelerating Machine Learning > > 440 NINTH AVENUE, 11TH FLOOR > NEW YORK, NY 10001 > O: (917) 525-2466 ext. 105 > F: 646.349.4063 > E: suren.hiraman@v <suren.hira...@sociocast.com>elos.io > W: www.velos.io > > >
