sorry, can't disclose info about my prod cluster nothing jumps into my mind regarding your config we don't use lz4 compression, don't know what is spark.deploy.spreadOut(there is no documentation regarding this)
If you are sure that you don't have memory leak in your business logic I would try to reset each property to default(or just remove it from your config) and try to run your job to see if it's not somehow connected my config(nothing special really) spark.shuffle.consolidateFiles true spark.speculation false spark.executor.extraJavaOptions -XX:+UseStringCache -XX:+UseCompressedStrings -XX:+PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:gc.log -verbose:gc spark.executor.logs.rolling.maxRetainedFiles 1000 spark.executor.logs.rolling.strategy time spark.worker.cleanup.enabled true spark.logConf true spark.rdd.compress true On 4 August 2015 at 12:59, Sea <261810...@qq.com> wrote: > How much machines are there in your standalone cluster? > I am not using tachyon. > > GC can not help me... Can anyone help ? > > my configuration: > > spark.deploy.spreadOut false > spark.eventLog.enabled true > spark.executor.cores 24 > > spark.ui.retainedJobs 10 > spark.ui.retainedStages 10 > spark.history.retainedApplications 5 > spark.deploy.retainedApplications 10 > spark.deploy.retainedDrivers 10 > spark.streaming.ui.retainedBatches 10 > spark.sql.thriftserver.ui.retainedSessions 10 > spark.sql.thriftserver.ui.retainedStatements 100 > > spark.file.transferTo false > spark.driver.maxResultSize 4g > spark.sql.hive.metastore.jars=/spark/spark-1.4.1/hive/* > > spark.eventLog.dir hdfs://mycluster/user/spark/historylog > spark.history.fs.logDirectory hdfs://mycluster/user/spark/historylog > > spark.driver.extraClassPath=/spark/spark-1.4.1/extlib/* > spark.executor.extraClassPath=/spark/spark-1.4.1/extlib/* > > spark.sql.parquet.binaryAsString true > spark.serializer org.apache.spark.serializer.KryoSerializer > spark.kryoserializer.buffer 32 > spark.kryoserializer.buffer.max 256 > spark.shuffle.consolidateFiles true > spark.io.compression.codec org.apache.spark.io.LZ4CompressionCodec > > > > > > ------------------ 原始邮件 ------------------ > *发件人:* "Igor Berman";<igor.ber...@gmail.com>; > *发送时间:* 2015年8月3日(星期一) 晚上7:56 > *收件人:* "Sea"<261810...@qq.com>; > *抄送:* "Barak Gitsis"<bar...@similarweb.com>; "Ted Yu"<yuzhih...@gmail.com>; > "user@spark.apache.org"<user@spark.apache.org>; "rxin"<r...@databricks.com>; > "joshrosen"<joshro...@databricks.com>; "davies"<dav...@databricks.com>; > *主题:* Re: About memory leak in spark 1.4.1 > > in general, what is your configuration? use --conf "spark.logConf=true" > > we have 1.4.1 in production standalone cluster and haven't experienced > what you are describing > can you verify in web-ui that indeed spark got your 50g per executor > limit? I mean in configuration page.. > > might be you are using offheap storage(Tachyon)? > > > On 3 August 2015 at 04:58, Sea <261810...@qq.com> wrote: > >> "spark uses a lot more than heap memory, it is the expected behavior." >> It didn't exist in spark 1.3.x >> What does "a lot more than" means? It means that I lose control of it! >> I try to apply 31g, but it still grows to 55g and continues to grow!!! >> That is the point! >> I have tried set memoryFraction to 0.2，but it didn't help. >> I don't know whether it will still exist in the next release 1.5, I wish >> not. >> >> >> >> ------------------ 原始邮件 ------------------ >> *发件人:* "Barak Gitsis";<bar...@similarweb.com>; >> *发送时间:* 2015年8月2日(星期天) 晚上9:55 >> *收件人:* "Sea"<261810...@qq.com>; "Ted Yu"<yuzhih...@gmail.com>; >> *抄送:* "user@spark.apache.org"<user@spark.apache.org>; "rxin"< >> r...@databricks.com>; "joshrosen"<joshro...@databricks.com>; "davies"< >> dav...@databricks.com>; >> *主题:* Re: About memory leak in spark 1.4.1 >> >> spark uses a lot more than heap memory, it is the expected behavior. >> in 1.4 off-heap memory usage is supposed to grow in comparison to 1.3 >> >> Better use as little memory as you can for heap, and since you are not >> utilizing it already, it is safe for you to reduce it. >> memoryFraction helps you optimize heap usage for your data/application >> profile while keeping it tight. >> >> >> >> >> >> >> On Sun, Aug 2, 2015 at 12:54 PM Sea <261810...@qq.com> wrote: >> >>> spark.storage.memoryFraction is in heap memory, but my situation is that >>> the memory is more than heap memory ! >>> >>> Anyone else use spark 1.4.1 in production? >>> >>> >>> ------------------ 原始邮件 ------------------ >>> *发件人:* "Ted Yu";<yuzhih...@gmail.com>; >>> *发送时间:* 2015年8月2日(星期天) 下午5:45 >>> *收件人:* "Sea"<261810...@qq.com>; >>> *抄送:* "Barak Gitsis"<bar...@similarweb.com>; "user@spark.apache.org"< >>> user@spark.apache.org>; "rxin"<r...@databricks.com>; "joshrosen"< >>> joshro...@databricks.com>; "davies"<dav...@databricks.com>; >>> *主题:* Re: About memory leak in spark 1.4.1 >>> >>> http://spark.apache.org/docs/latest/tuning.html does mention >>> spark.storage.memoryFraction >>> in two places. >>> One is under Cache Size Tuning section. >>> >>> FYI >>> >>> On Sun, Aug 2, 2015 at 2:16 AM, Sea <261810...@qq.com> wrote: >>> >>>> Hi, Barak >>>> It is ok with spark 1.3.0, the problem is with spark 1.4.1. >>>> I don't think spark.storage.memoryFraction will make any sense, >>>> because it is still in heap memory. >>>> >>>> >>>> ------------------ 原始邮件 ------------------ >>>> *发件人:* "Barak Gitsis";<bar...@similarweb.com>; >>>> *发送时间:* 2015年8月2日(星期天) 下午4:11 >>>> *收件人:* "Sea"<261810...@qq.com>; "user"<user@spark.apache.org>; >>>> *抄送:* "rxin"<r...@databricks.com>; "joshrosen"<joshro...@databricks.com>; >>>> "davies"<dav...@databricks.com>; >>>> *主题:* Re: About memory leak in spark 1.4.1 >>>> >>>> Hi, >>>> reducing spark.storage.memoryFraction did the trick for me. Heap >>>> doesn't get filled because it is reserved.. >>>> My reasoning is: >>>> I give executor all the memory i can give it, so that makes it a >>>> boundary. >>>> From here i try to make the best use of memory I can. >>>> storage.memoryFraction is in a sense user data space. The rest can be used >>>> by the system. >>>> If you don't have so much data that you MUST store in memory for >>>> performance, better give spark more space.. >>>> ended up setting it to 0.3 >>>> >>>> All that said, it is on spark 1.3 on cluster >>>> >>>> hope that helps >>>> >>>> On Sat, Aug 1, 2015 at 5:43 PM Sea <261810...@qq.com> wrote: >>>> >>>>> Hi, all >>>>> I upgrage spark to 1.4.1, many applications failed... I find the heap >>>>> memory is not full , but the process of CoarseGrainedExecutorBackend will >>>>> take more memory than I expect, and it will increase as time goes on, >>>>> finally more than max limited of the server, the worker will die..... >>>>> >>>>> Any can help？ >>>>> >>>>> Mode：standalone >>>>> >>>>> spark.executor.memory 50g >>>>> >>>>> 25583 xiaoju 20 0 75.5g 55g 28m S 1729.3 88.1 2172:52 java >>>>> >>>>> 55g more than 50g I apply >>>>> >>>>> -- >>>> *-Barak* >>>> >>> >>> -- >> *-Barak* >> > >
