Makes sense, I've also tries it in standalone mode where all 3 workers & driver were running on the same 8 core box and the results were similar.
Anyways I will share the results in YARN mode with 8 core yarn containers. On Mon, Dec 29, 2014 at 11:58 PM, Sandy Ryza <sandy.r...@cloudera.com> wrote: > When running in standalone mode, each executor will be able to use all 8 > cores on the box. When running on YARN, each executor will only have > access to 2 cores. So the comparison doesn't seem fair, no? > > -Sandy > > On Mon, Dec 29, 2014 at 10:22 AM, Mukesh Jha <me.mukesh....@gmail.com> > wrote: > >> Nope, I am setting 5 executors with 2 cores each. Below is the command >> that I'm using to submit in YARN mode. This starts up 5 executor nodes and >> a drives as per the spark application master UI. >> >> spark-submit --master yarn-cluster --num-executors 5 --driver-memory >> 1024m --executor-memory 1024m --executor-cores 2 --class >> com.oracle.ci.CmsgK2H /homext/lib/MJ-ci-k2h.jar vm.cloud.com:2181/kafka >> spark-yarn avro 1 5000 >> >> On Mon, Dec 29, 2014 at 11:45 PM, Sandy Ryza <sandy.r...@cloudera.com> >> wrote: >> >>> *oops, I mean are you setting --executor-cores to 8 >>> >>> On Mon, Dec 29, 2014 at 10:15 AM, Sandy Ryza <sandy.r...@cloudera.com> >>> wrote: >>> >>>> Are you setting --num-executors to 8? >>>> >>>> On Mon, Dec 29, 2014 at 10:13 AM, 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>* >> > > -- Thanks & Regards, *Mukesh Jha <me.mukesh....@gmail.com>*
