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>* >
