This post here has a bit information
http://blog.cloudera.com/blog/2015/05/working-with-apache-spark-or-how-i-learned-to-stop-worrying-and-love-the-shuffle/
Thanks
Best Regards
On Wed, Sep 9, 2015 at 6:44 AM, mark <manwoodv...@googlemail.com> wrote:
> As I understand things (maybe naively), my input data are stored in equal
> sized blocks in HDFS, and each block represents a partition within Spark
> when read from HDFS, therefore each block should hold roughly the same
> number of records.
>
> So something is missing in my understanding - what can cause some
> partitions to have zero records and others to have roughly equal sized
> chunks (~50k in this case)?
>
> Before writing a custom partitioner, I would like to understand why has
> the default partitioner failed in my case?
> On 8 Sep 2015 3:00 pm, "Akhil Das" <ak...@sigmoidanalytics.com> wrote:
>
>> Try using a custom partitioner for the keys so that they will get evenly
>> distributed across tasks
>>
>> Thanks
>> Best Regards
>>
>> On Fri, Sep 4, 2015 at 7:19 PM, mark <manwoodv...@googlemail.com> wrote:
>>
>>> I am trying to tune a Spark job and have noticed some strange behavior -
>>> tasks in a stage vary in execution time, ranging from 2 seconds to 20
>>> seconds. I assume tasks should all run in roughly the same amount of time
>>> in a well tuned job.
>>>
>>> So I did some investigation - the fast tasks appear to have no records,
>>> whilst the slow tasks do. I need help understanding why this is happening.
>>>
>>> The code in the stage is pretty simple. All it does is:
>>>
>>> - filters records
>>> - maps records to a (key, record) tuple
>>> - reduces by key
>>>
>>> The data are Avro objects stored in Parquet files in 16MB blocks in HDFS.
>>>
>>> To establish how many records in each partition I added this snippet:
>>>
>>> val counts = rdd.mapPartitions(iter => {
>>> val ctx = TaskContext.get
>>> val stageId = ctx.stageId
>>> val partId = ctx.partitionId
>>> val attemptid = ctx.taskAttemptId()
>>> Array(Array(stageId, partId, attemptid, iter.size)).iterator }
>>> , true).collect()
>>>
>>> Which produces the following:
>>>
>>> 1 1 0 0
>>> 1 2 1 50489
>>> 1 3 2 0
>>> 1 4 3 0
>>> 1 5 4 0
>>> 1 6 5 53200
>>> 1 7 6 0
>>> 1 8 7 0
>>> 1 9 8 0
>>> 1 10 9 56946
>>> 1 11 10 0
>>> 1 12 11 0
>>> 1 13 12 0
>>> 1 14 13 59209
>>> 1 15 14 0
>>> 1 16 15 0
>>> 1 17 16 0
>>> 1 18 17 50202
>>> 1 19 18 0
>>> 1 20 19 0
>>> 1 21 20 0
>>> 1 22 21 54613
>>> 1 23 22 0
>>> 1 24 23 0
>>> 1 25 24 54157
>>> 1 26 25 0
>>> 1 27 26 0
>>> 1 28 27 0
>>> 1 29 28 53595
>>> 1 30 29 0
>>> 1 31 30 0
>>> 1 32 31 10750
>>>
>>>
>>> Looking at the logs, you can see the tasks that contain records have the
>>> longest run time:
>>>
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 25.0 in stage 1.0
>>> (TID 26) in 2782 ms on DG1322 (6/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 7.0 in stage 1.0
>>> (TID 8) in 2815 ms on DG1322 (7/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 19.0 in stage 1.0
>>> (TID 20) in 2815 ms on DG1322 (8/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 23.0 in stage 1.0
>>> (TID 24) in 2840 ms on DG1321 (9/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 29.0 in stage 1.0
>>> (TID 30) in 2839 ms on DG1321 (10/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 11.0 in stage 1.0
>>> (TID 12) in 2878 ms on DG1321 (11/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 30.0 in stage 1.0
>>> (TID 31) in 2870 ms on DG1321 (12/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 18.0 in stage 1.0
>>> (TID 19) in 2892 ms on DG1321 (13/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 0.0 in stage 1.0
>>> (TID 1) in 2930 ms on DG1321 (14/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 6.0 in stage 1.0
>>> (TID 7) in 2934 ms on DG1321 (15/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 12.0 in stage 1.0
>>> (TID 13) in 2931 ms on DG1321 (16/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 3.0 in stage 1.0
>>> (TID 4) in 3246 ms on DG1323 (17/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 27.0 in stage 1.0
>>> (TID 28) in 3226 ms on DG1323 (18/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 15.0 in stage 1.0
>>> (TID 16) in 3249 ms on DG1323 (19/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 10.0 in stage 1.0
>>> (TID 11) in 3669 ms on DG1323 (20/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 16.0 in stage 1.0
>>> (TID 17) in 3666 ms on DG1323 (21/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 22.0 in stage 1.0
>>> (TID 23) in 3664 ms on DG1323 (22/32)
>>> 15/09/03 16:26:36 INFO TaskSetManager: Finished task 4.0 in stage 1.0
>>> (TID 5) in 3692 ms on DG1323 (23/32)
>>> *15/09/03 16:26:39 INFO TaskSetManager: Finished task 31.0 in stage 1.0
>>> (TID 32) in 6668 ms on DG1322 (24/32)*
>>> *15/09/03 16:26:48 INFO TaskSetManager: Finished task 17.0 in stage 1.0
>>> (TID 18) in 15690 ms on DG1321 (25/32)*
>>> *15/09/03 16:26:49 INFO TaskSetManager: Finished task 1.0 in stage 1.0
>>> (TID 2) in 16194 ms on DG1322 (26/32)*
>>> *15/09/03 16:26:49 INFO TaskSetManager: Finished task 5.0 in stage 1.0
>>> (TID 6) in 16384 ms on DG1321 (27/32)*
>>> *15/09/03 16:26:50 INFO TaskSetManager: Finished task 28.0 in stage 1.0
>>> (TID 29) in 17194 ms on DG1323 (28/32)*
>>> *15/09/03 16:26:50 INFO TaskSetManager: Finished task 21.0 in stage 1.0
>>> (TID 22) in 17408 ms on DG1323 (29/32)*
>>> *15/09/03 16:26:50 INFO TaskSetManager: Finished task 13.0 in stage 1.0
>>> (TID 14) in 17711 ms on DG1322 (30/32)*
>>> *15/09/03 16:26:51 INFO TaskSetManager: Finished task 24.0 in stage 1.0
>>> (TID 25) in 17995 ms on DG1321 (31/32)*
>>> *15/09/03 16:26:51 INFO TaskSetManager: Finished task 9.0 in stage 1.0
>>> (TID 10) in 18183 ms on DG1323 (32/32)*
>>>
>>>
>>> The Spark UI for the stage shows this:
>>>
>>> Summary Metrics for 32 Completed Tasks
>>>
>>> Metric Min 25th percentile Median 75th percentile Max
>>>
>>> Duration
>>> 0.9s 2s 2s 16s 18 s
>>>
>>> GC Time
>>> 0ms 0ms 0ms 0.5s 1s
>>>
>>> Input Size / Records
>>> 0.0 B/0 0.0 B/0 0.0 B / 0 0.0 B / 53437 122.0 MB / 59209
>>>
>>> Shuffle Write Size / Records
>>> 0.0 B / 0 0.0 B / 0 0.0 B / 0 3.7 KB / 1 127.8 KB / 35
>>>
>>> Aggregated Metrics by Executor
>>> Executor ID Address Task Time Total Tasks Failed
>>> Tasks Succeeded Tasks Input Size / Records Shuffle Write
>>> Size / Records
>>>
>>> 0 DG1322:41308 35 s 5 0 5 243.9 MB /
>>> 106948 224.3 KB / 63
>>> 1 DG1321:33576 48 s 5 0 5 0.0 B / 111750
>>> 7.4 KB / 2
>>> 2 DG1323:49348 30 s 5 0 5 0.0 B / 53595
>>> 3.7 KB / 1
>>> 3 DG1321:43093 12 s 5 0 5 0.0 B / 0
>>> 0.0 B / 0
>>> 4 DG1323:51009 34 s 6 0 6 0.0 B / 54767
>>> 98.7 KB / 28
>>> 5 DG1322:36787 50 s 6 0 6 0.0 B / 123977
>>> 138.9 KB / 38
>>>
>>>
>>> *** Edited for brevity - included an example of a short and long task ***
>>> Tasks
>>> Index ID Attempt Status Locality Level Executor ID / Host
>>> Duration GC Time Accumulators Input Size / Records
>>> Shuffle Write Size / Records
>>> 0 1 0 SUCCESS NODE_LOCAL 4 / DG1323 2 s
>>> 0.0 B (hadoop) /
>>> 0 0.0 B / 0
>>> 1 2 0 SUCCESS NODE_LOCAL 5 / DG1322 16 s
>>> 0.5 s RecordCount: 54018 0.0 B (hadoop) / 54018 127.8 KB
>>> / 35
>>>
>>>
>>> Why are the tasks in the stage not processing an equal number of
>>> records? What are the empty tasks doing? How can I even the tasks in this
>>> stage out?
>>>
>>> Any insights much appreciated...
>>>
>>>
>>
