It doesn't make sense to me. Because in the another cluster process all data in less than a second. Anyway, I'm going to set that parameter.
2015-07-31 0:36 GMT+02:00 Tathagata Das <t...@databricks.com>: > Yes, and that is indeed the problem. It is trying to process all the data > in Kafka, and therefore taking 60 seconds. You need to set the rate limits > for that. > > On Thu, Jul 30, 2015 at 8:51 AM, Cody Koeninger <c...@koeninger.org> > wrote: > >> If you don't set it, there is no maximum rate, it will get everything >> from the end of the last batch to the maximum available offset >> >> On Thu, Jul 30, 2015 at 10:46 AM, Guillermo Ortiz <konstt2...@gmail.com> >> wrote: >> >>> The difference is that one recives more data than the others two. I can >>> pass thought parameters the topics, so, I could execute the code trying >>> with one topic and figure out with one is the topic, although I guess that >>> it's the topics which gets more data. >>> >>> Anyway it's pretty weird those delays in just one of the cluster even if >>> the another one is not running. >>> I have seen the parameter "spark.streaming.kafka.maxRatePerPartition", >>> I haven't set any value for this parameter, how does it work if this >>> parameter doesn't have a value? >>> >>> 2015-07-30 16:32 GMT+02:00 Cody Koeninger <c...@koeninger.org>: >>> >>>> If the jobs are running on different topicpartitions, what's different >>>> about them? Is one of them 120x the throughput of the other, for >>>> instance? You should be able to eliminate cluster config as a difference >>>> by running the same topic partition on the different clusters and comparing >>>> the results. >>>> >>>> On Thu, Jul 30, 2015 at 9:29 AM, Guillermo Ortiz <konstt2...@gmail.com> >>>> wrote: >>>> >>>>> I have three topics with one partition each topic. So each jobs run >>>>> about one topics. >>>>> >>>>> 2015-07-30 16:20 GMT+02:00 Cody Koeninger <c...@koeninger.org>: >>>>> >>>>>> Just so I'm clear, the difference in timing you're talking about is >>>>>> this: >>>>>> >>>>>> 15/07/30 14:33:59 INFO DAGScheduler: Job 24 finished: foreachRDD at >>>>>> MetricsSpark.scala:67, took 60.391761 s >>>>>> >>>>>> 15/07/30 14:37:35 INFO DAGScheduler: Job 93 finished: foreachRDD at >>>>>> MetricsSpark.scala:67, took 0.531323 s >>>>>> >>>>>> >>>>>> Are those jobs running on the same topicpartition? >>>>>> >>>>>> >>>>>> On Thu, Jul 30, 2015 at 8:03 AM, Guillermo Ortiz < >>>>>> konstt2...@gmail.com> wrote: >>>>>> >>>>>>> I read about maxRatePerPartition parameter, I haven't set this >>>>>>> parameter. Could it be the problem?? Although this wouldn't explain why >>>>>>> it >>>>>>> doesn't work in one of the clusters. >>>>>>> >>>>>>> 2015-07-30 14:47 GMT+02:00 Guillermo Ortiz <konstt2...@gmail.com>: >>>>>>> >>>>>>>> They just share the kafka, the rest of resources are independents. >>>>>>>> I tried to stop one cluster and execute just the cluster isn't working >>>>>>>> but >>>>>>>> it happens the same. >>>>>>>> >>>>>>>> 2015-07-30 14:41 GMT+02:00 Guillermo Ortiz <konstt2...@gmail.com>: >>>>>>>> >>>>>>>>> I have some problem with the JobScheduler. I have executed same >>>>>>>>> code in two cluster. I read from three topics in Kafka with >>>>>>>>> DirectStream so >>>>>>>>> I have three tasks. >>>>>>>>> >>>>>>>>> I have check YARN and there aren't more jobs launched. >>>>>>>>> >>>>>>>>> The cluster where I have troubles I got this logs: >>>>>>>>> >>>>>>>>> 15/07/30 14:32:58 INFO TaskSetManager: Starting task 0.0 in stage >>>>>>>>> 24.0 (TID 72, xxxxxxxxx, RACK_LOCAL, 14856 bytes) >>>>>>>>> 15/07/30 14:32:58 INFO TaskSetManager: Starting task 1.0 in stage >>>>>>>>> 24.0 (TID 73, xxxxxxxxxxxxxxx, RACK_LOCAL, 14852 bytes) >>>>>>>>> 15/07/30 14:32:58 INFO BlockManagerInfo: Added broadcast_24_piece0 >>>>>>>>> in memory on xxxxxxxxxxx:44909 (size: 1802.0 B, free: 530.3 MB) >>>>>>>>> 15/07/30 14:32:58 INFO BlockManagerInfo: Added broadcast_24_piece0 >>>>>>>>> in memory on xxxxxxxxx:43477 (size: 1802.0 B, free: 530.3 MB) >>>>>>>>> 15/07/30 14:32:59 INFO TaskSetManager: Starting task 2.0 in stage >>>>>>>>> 24.0 (TID 74, xxxxxxxxx, RACK_LOCAL, 14860 bytes) >>>>>>>>> 15/07/30 14:32:59 INFO TaskSetManager: Finished task 0.0 in stage >>>>>>>>> 24.0 (TID 72) in 208 ms on xxxxxxxxx (1/3) >>>>>>>>> 15/07/30 14:32:59 INFO TaskSetManager: Finished task 2.0 in stage >>>>>>>>> 24.0 (TID 74) in 49 ms on xxxxxxxxx (2/3) >>>>>>>>> *15/07/30 14:33:00 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259580000 ms* >>>>>>>>> *15/07/30 14:33:05 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259585000 ms* >>>>>>>>> *15/07/30 14:33:10 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259590000 ms* >>>>>>>>> *15/07/30 14:33:15 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259595000 ms* >>>>>>>>> *15/07/30 14:33:20 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259600000 ms* >>>>>>>>> *15/07/30 14:33:25 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259605000 ms* >>>>>>>>> *15/07/30 14:33:30 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259610000 ms* >>>>>>>>> *15/07/30 14:33:35 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259615000 ms* >>>>>>>>> *15/07/30 14:33:40 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259620000 ms* >>>>>>>>> *15/07/30 14:33:45 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259625000 ms* >>>>>>>>> *15/07/30 14:33:50 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259630000 ms* >>>>>>>>> *15/07/30 14:33:55 INFO JobScheduler: Added jobs for time >>>>>>>>> 1438259635000 ms* >>>>>>>>> 15/07/30 14:33:59 INFO TaskSetManager: Finished task 1.0 in stage >>>>>>>>> 24.0 (TID 73) in 60373 ms onxxxxxxxxxxxxxxxx (3/3) >>>>>>>>> 15/07/30 14:33:59 INFO YarnScheduler: Removed TaskSet 24.0, whose >>>>>>>>> tasks have all completed, from pool >>>>>>>>> 15/07/30 14:33:59 INFO DAGScheduler: Stage 24 (foreachRDD at >>>>>>>>> MetricsSpark.scala:67) finished in 60.379 s >>>>>>>>> 15/07/30 14:33:59 INFO DAGScheduler: Job 24 finished: foreachRDD >>>>>>>>> at MetricsSpark.scala:67, took 60.391761 s >>>>>>>>> 15/07/30 14:33:59 INFO JobScheduler: Finished job streaming job >>>>>>>>> 1438258210000 ms.0 from job set of time 1438258210000 ms >>>>>>>>> 15/07/30 14:33:59 INFO JobScheduler: Total delay: 1429.249 s for >>>>>>>>> time 1438258210000 ms (execution: 60.399 s) >>>>>>>>> 15/07/30 14:33:59 INFO JobScheduler: Starting job streaming job >>>>>>>>> 1438258215000 ms.0 from job set of time 1438258215000 ms >>>>>>>>> >>>>>>>>> There are *always *a minute of delay in the third task, when I >>>>>>>>> have executed same code in another cluster there isn't this delay in >>>>>>>>> the >>>>>>>>> JobScheduler. I checked the configuration in YARN in both clusters >>>>>>>>> and it >>>>>>>>> seems the same. >>>>>>>>> >>>>>>>>> The log in the cluster is working good is >>>>>>>>> >>>>>>>>> 15/07/30 14:37:35 INFO YarnScheduler: Adding task set 93.0 with 3 >>>>>>>>> tasks >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Starting task 0.0 in stage >>>>>>>>> 93.0 (TID 279, xxxxxxxxxxxxxxxxxx, RACK_LOCAL, 14643 bytes) >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Starting task 1.0 in stage >>>>>>>>> 93.0 (TID 280, xxxxxxxxx, RACK_LOCAL, 14639 bytes) >>>>>>>>> 15/07/30 14:37:35 INFO BlockManagerInfo: Added broadcast_93_piece0 >>>>>>>>> in memory on xxxxxxxxxxxxxxxxx:45132 (size: 1801.0 B, free: 530.3 MB) >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Starting task 2.0 in stage >>>>>>>>> 93.0 (TID 281, xxxxxxxxxxxxxxxxxxx, RACK_LOCAL, 14647 bytes) >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Finished task 0.0 in stage >>>>>>>>> 93.0 (TID 279) in 121 ms on xxxxxxxxxxxxxxxxxxxx (1/3) >>>>>>>>> 15/07/30 14:37:35 INFO BlockManagerInfo: Added broadcast_93_piece0 >>>>>>>>> in memory on xxxxxxxxx:49886 (size: 1801.0 B, free: 530.3 MB) >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Finished task 2.0 in stage >>>>>>>>> 93.0 (TID 281) in 261 ms on xxxxxxxxxxxxxxxxxx (2/3) >>>>>>>>> 15/07/30 14:37:35 INFO TaskSetManager: Finished task 1.0 in stage >>>>>>>>> 93.0 (TID 280) in 519 ms on xxxxxxxxx (3/3) >>>>>>>>> 15/07/30 14:37:35 INFO DAGScheduler: Stage 93 (foreachRDD at >>>>>>>>> MetricsSpark.scala:67) finished in 0.522 s >>>>>>>>> 15/07/30 14:37:35 INFO YarnScheduler: Removed TaskSet 93.0, whose >>>>>>>>> tasks have all completed, from pool >>>>>>>>> 15/07/30 14:37:35 INFO DAGScheduler: Job 93 finished: foreachRDD >>>>>>>>> at MetricsSpark.scala:67, took 0.531323 s >>>>>>>>> 15/07/30 14:37:35 INFO JobScheduler: Finished job streaming job >>>>>>>>> 1438259855000 ms.0 from job set of time 1438259855000 ms >>>>>>>>> 15/07/30 14:37:35 INFO JobScheduler: Total delay: 0.548 s for time >>>>>>>>> 1438259855000 ms (execution: 0.540 s) >>>>>>>>> 15/07/30 14:37:35 INFO KafkaRDD: Removing RDD 184 from persistence >>>>>>>>> list >>>>>>>>> >>>>>>>>> Any clue about where I could take a look? Number of cpus in YARN >>>>>>>>> is enough. I executing YARN with same options (--master yarn-server >>>>>>>>> with 1g >>>>>>>>> of memory in both) >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
