Can you say anything more about what the job is doing? First thing I'd do is try to get some metrics on the time taken by your code on the executors (e.g. when processing the iterator) to see if it's consistent between the two situations.
On Tue, Oct 6, 2015 at 11:45 AM, Gerard Maas <gerard.m...@gmail.com> wrote: > Hi, > > We recently migrated our streaming jobs to the direct kafka receiver. Our > initial migration went quite fine but now we are seeing a weird zig-zag > performance pattern we cannot explain. > In alternating fashion, one task takes about 1 second to finish and the > next takes 7sec for a stable streaming rate. > > Here are comparable metrics for two successive tasks: > *Slow*: > > > > > Executor IDAddressTask TimeTotal TasksFailed TasksSucceeded Tasks > 20151006-044141-2408867082-5050-21047-S0dnode-3.hdfs.private:3686322 s303 > 20151006-044141-2408867082-5050-21047-S1dnode-0.hdfs.private:4381240 s110 > 1120151006-044141-2408867082-5050-21047-S4dnode-5.hdfs.private:5994549 s10 > 010 > *Fast*: > > > > Executor IDAddressTask TimeTotal TasksFailed TasksSucceeded Tasks > 20151006-044141-2408867082-5050-21047-S0dnode-3.hdfs.private:368630.6 s404 > 20151006-044141-2408867082-5050-21047-S1dnode-0.hdfs.private:438121 s909 > 20151006-044141-2408867082-5050-21047-S4dnode-5.hdfs.private:599451 s11011 > We have some custom metrics that measure wall-clock time of execution of > certain blocks of the job, like the time it takes to do the local > computations (RDD.foreachPartition closure) vs total time. > The difference between the slow and fast executing task is on the 'spark > computation time' which is wall-clock for the task scheduling > (DStream.foreachRDD closure) > > e.g. > Slow task: > > local computation time: 347.60968499999996, *spark computation time: 6930*, > metric collection: 70, total process: 7000, total_records: 4297 > > Fast task: > local computation time: 281.539042,* spark computation time: 263*, metric > collection: 138, total process: 401, total_records: 5002 > > We are currently running Spark 1.4.1. The load and the work to be done is > stable -this is on a dev env with that stuff under control. > > Any ideas what this behavior could be? > > thanks in advance, Gerard. > > > > > > >
