Make sure you are setting num executors correctly
M
> On Jul 17, 2015, at 9:16 PM, Charles Menguy <menguy.char...@gmail.com> wrote:
>
> I am trying to use PySpark on EMR to analyze some data stored as
> SequenceFiles on S3, but running into performance issues due to data
> locality. Here is a very simple sample that doesn't work well:
>
> seqRDD =
> sc.sequenceFile("s3n://<access>:<secret>@<bucket>/<table>/day=2015-07-04/hour=*/*")
> seqRDD.count()
>
> The issue is with the count action, it works fine but distribution of the
> tasks is very poor. For some reason in the Spark logs I only see 2 IPs of the
> cluster doing any actual work while the rest sits idle. I tried with a 5 node
> cluster and 50 nodes cluster and it's always only 2 IPs appearing in the logs.
>
> Also very strange is that these 2 IPs have a locality of RACK_LOCAL. I'm
> presuming it's because data is in S3 so it's not local, but how can I make
> Spark use the whole cluster instead of just 2 instances?
>
> I didn't do anything specific for Spark configuration on EMR, simply
> installing it on EMR via native app and I believe it takes care automatically
> of optimizing the configs. I ran PySpark with --master yarn-client
>
> I saw this in the logs, the allowLocal=false could be an issue but I couldn't
> find anything on that:
>
> 15/07/17 23:55:27 INFO spark.SparkContext: Starting job: count at :1
> 15/07/17 23:55:27 INFO scheduler.DAGScheduler: Got job 1 (count at :1) with
> 1354 output partitions (allowLocal=false)
> 15/07/17 23:55:27 INFO scheduler.DAGScheduler: Final stage: Stage 1(count at
> :1)
>
> Some logs that follow when running the count, showing only 2 IPs:
>
> 15/07/17 23:55:28 INFO scheduler.DAGScheduler: Submitting 1354 missing tasks
> from Stage 1 (PythonRDD[3] at count at :1)
> 15/07/17 23:55:28 INFO cluster.YarnScheduler: Adding task set 1.0 with 1354
> tasks
> 15/07/17 23:55:28 INFO scheduler.TaskSetManager: Starting task 0.0 in stage
> 1.0 (TID 1, ip-172-31-41-210.ec2.internal, RACK_LOCAL, 1418 bytes)
> 15/07/17 23:55:28 INFO scheduler.TaskSetManager: Starting task 1.0 in stage
> 1.0 (TID 2, ip-172-31-36-179.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:28 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in
> memory on ip-172-31-36-179.ec2.internal:39998 (size: 3.7 KB, free: 535.0 MB)
> 15/07/17 23:55:28 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in
> memory on ip-172-31-41-210.ec2.internal:36847 (size: 3.7 KB, free: 535.0 MB)
> 15/07/17 23:55:29 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in
> memory on ip-172-31-41-210.ec2.internal:36847 (size: 18.8 KB, free: 535.0 MB)
> 15/07/17 23:55:31 INFO scheduler.TaskSetManager: Starting task 2.0 in stage
> 1.0 (TID 3, ip-172-31-41-210.ec2.internal, RACK_LOCAL, 1421 bytes)
> 15/07/17 23:55:31 INFO scheduler.TaskSetManager: Finished task 0.0 in stage
> 1.0 (TID 1) in 3501 ms on ip-172-31-41-210.ec2.internal (1/1354)
> 15/07/17 23:55:31 INFO scheduler.TaskSetManager: Starting task 3.0 in stage
> 1.0 (TID 4, ip-172-31-41-210.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:31 INFO scheduler.TaskSetManager: Finished task 2.0 in stage
> 1.0 (TID 3) in 99 ms on ip-172-31-41-210.ec2.internal (2/1354)
> 15/07/17 23:55:33 INFO scheduler.TaskSetManager: Starting task 4.0 in stage
> 1.0 (TID 5, ip-172-31-36-179.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:33 INFO scheduler.TaskSetManager: Finished task 1.0 in stage
> 1.0 (TID 2) in 5190 ms on ip-172-31-36-179.ec2.internal (3/1354)
> 15/07/17 23:55:36 INFO scheduler.TaskSetManager: Starting task 5.0 in stage
> 1.0 (TID 6, ip-172-31-41-210.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:36 INFO scheduler.TaskSetManager: Finished task 3.0 in stage
> 1.0 (TID 4) in 4471 ms on ip-172-31-41-210.ec2.internal (4/1354)
> 15/07/17 23:55:37 INFO scheduler.TaskSetManager: Starting task 6.0 in stage
> 1.0 (TID 7, ip-172-31-36-179.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:37 INFO scheduler.TaskSetManager: Finished task 4.0 in stage
> 1.0 (TID 5) in 3676 ms on ip-172-31-36-179.ec2.internal (5/1354)
> 15/07/17 23:55:40 INFO scheduler.TaskSetManager: Starting task 7.0 in stage
> 1.0 (TID 8, ip-172-31-41-210.ec2.internal, RACK_LOCAL, 1420 bytes)
> 15/07/17 23:55:40 INFO scheduler.TaskSetManager: Finished task 5.0 in stage
> 1.0 (TID 6) in 3895 ms on ip-172-31-41-210.ec2.internal (6/1354)
> 15/07/17 23:55:40 INFO scheduler.TaskSetManager: Starting task 8.0 in stage
> 1.0 (TID 9, ip-1
>
> I also tried eliminating S3 by distcp'ing the S3 data first into HDFS in the
> EMR cluster and then running a count() on that, but it doesn't make much
> difference, there are still only 2 IPs processing, they initially start as
> NODE_LOCAL but eventually switch to RACK_LOCAL.
>
> I'm at a loss at what I have misconfigured, any help would be appreciated.
>
> Thanks !
>
> Charles
