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* >
