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) >
