Thanks you all for advice including (1) using CMS GC (2) use multiple worker instance and (3) use Tachyon.
I will try (1) and (2) first and report back what I found. I will also try JDK 7 with G1 GC. Best regards, Wei --------------------------------- Wei Tan, PhD Research Staff Member IBM T. J. Watson Research Center http://researcher.ibm.com/person/us-wtan From: Aaron Davidson <ilike...@gmail.com> To: user@spark.apache.org, Date: 06/15/2014 09:06 PM Subject: Re: long GC pause during file.cache() 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 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 -- 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.hira...@velos.io W: www.velos.io
