And this is with spark version 1.2.0. On Mon, Dec 29, 2014 at 11:43 PM, Mukesh Jha <me.mukesh....@gmail.com> wrote:
> Sorry Sandy, The command is just for reference but I can confirm that > there are 4 executors and a driver as shown in the spark UI page. > > Each of these machines is a 8 core box with ~15G of ram. > > On Mon, Dec 29, 2014 at 11:23 PM, Sandy Ryza <sandy.r...@cloudera.com> > wrote: > >> Hi Mukesh, >> >> Based on your spark-submit command, it looks like you're only running >> with 2 executors on YARN. Also, how many cores does each machine have? >> >> -Sandy >> >> On Mon, Dec 29, 2014 at 4:36 AM, Mukesh Jha <me.mukesh....@gmail.com> >> wrote: >> >>> Hello Experts, >>> I'm bench-marking Spark on YARN ( >>> https://spark.apache.org/docs/latest/running-on-yarn.html) vs a >>> standalone spark cluster ( >>> https://spark.apache.org/docs/latest/spark-standalone.html). >>> I have a standalone cluster with 3 executors, and a spark app running on >>> yarn with 4 executors as shown below. >>> >>> The spark job running inside yarn is 10x slower than the one running on >>> the standalone cluster (even though the yarn has more number of workers), >>> also in both the case all the executors are in the same datacenter so there >>> shouldn't be any latency. On YARN each 5sec batch is reading data from >>> kafka and processing it in 5sec & on the standalone cluster each 5sec batch >>> is getting processed in 0.4sec. >>> Also, In YARN mode all the executors are not getting used up evenly as >>> vm-13 & vm-14 are running most of the tasks whereas in the standalone mode >>> all the executors are running the tasks. >>> >>> Do I need to set up some configuration to evenly distribute the tasks? >>> Also do you have any pointers on the reasons the yarn job is 10x slower >>> than the standalone job? >>> Any suggestion is greatly appreciated, Thanks in advance. >>> >>> YARN(5 workers + driver) >>> ======================== >>> Executor ID Address RDD Blocks Memory Used DU AT FT CT TT TT Input >>> ShuffleRead >>> ShuffleWrite Thread Dump >>> 1 vm-18.cloud.com:51796 0 0.0B/530.3MB 0.0 B 1 0 16 17 634 ms 0.0 B 2047.0 >>> B 1710.0 B Thread Dump >>> 2 vm-13.cloud.com:57264 0 0.0B/530.3MB 0.0 B 0 0 1427 1427 5.5 m 0.0 B 0.0 >>> B 0.0 B Thread Dump >>> 3 vm-14.cloud.com:54570 0 0.0B/530.3MB 0.0 B 0 0 1379 1379 5.2 m 0.0 B >>> 1368.0 >>> B 2.8 KB Thread Dump >>> 4 vm-11.cloud.com:56201 0 0.0B/530.3MB 0.0 B 0 0 10 10 625 ms 0.0 B 1368.0 >>> B 1026.0 B Thread Dump >>> 5 vm-5.cloud.com:42958 0 0.0B/530.3MB 0.0 B 0 0 22 22 632 ms 0.0 B 1881.0 >>> B 2.8 KB Thread Dump >>> <driver> vm.cloud.com:51847 0 0.0B/530.0MB 0.0 B 0 0 0 0 0 ms 0.0 B 0.0 >>> B 0.0 B Thread Dump >>> >>> /homext/spark/bin/spark-submit >>> --master yarn-cluster --num-executors 2 --driver-memory 512m >>> --executor-memory 512m --executor-cores 2 >>> --class com.oracle.ci.CmsgK2H /homext/lib/MJ-ci-k2h.jar >>> vm.cloud.com:2181/kafka spark-yarn avro 1 5000 >>> >>> STANDALONE(3 workers + driver) >>> ============================== >>> Executor ID Address RDD Blocks Memory Used DU AT FT CT TT TT Input >>> ShuffleRead >>> ShuffleWrite Thread Dump >>> 0 vm-71.cloud.com:55912 0 0.0B/265.0MB 0.0 B 0 0 1069 1069 6.0 m 0.0 B >>> 1534.0 >>> B 3.0 KB Thread Dump >>> 1 vm-72.cloud.com:40897 0 0.0B/265.0MB 0.0 B 0 0 1057 1057 5.9 m 0.0 B >>> 1368.0 >>> B 4.0 KB Thread Dump >>> 2 vm-73.cloud.com:37621 0 0.0B/265.0MB 0.0 B 1 0 1059 1060 5.9 m 0.0 B 2.0 >>> KB 1368.0 B Thread Dump >>> <driver> vm.cloud.com:58299 0 0.0B/265.0MB 0.0 B 0 0 0 0 0 ms 0.0 B 0.0 >>> B 0.0 B Thread Dump >>> >>> /homext/spark/bin/spark-submit >>> --master spark://chsnmvproc71vm3.usdc2.oraclecloud.com:7077 >>> --class com.oracle.ci.CmsgK2H /homext/lib/MJ-ci-k2h.jar >>> vm.cloud.com:2181/kafka spark-standalone avro 1 5000 >>> >>> PS: I did go through the spark website and >>> http://www.virdata.com/tuning-spark/, but was out of any luck. >>> >>> -- >>> Cheers, >>> Mukesh Jha >>> >> >> > > > -- > > > Thanks & Regards, > > *Mukesh Jha <me.mukesh....@gmail.com>* > -- Thanks & Regards, *Mukesh Jha <me.mukesh....@gmail.com>*
