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) >> > >
