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) >>>> >>> >>> >> >
