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