can you try:
spark.shuffle.reduceLocality.enabled=false

On Mon, Apr 4, 2016 at 8:17 PM, Mike Hynes <91mbbh@gmail.com> wrote:
Dear all,

Thank you for your responses.

Michael Slavitch:
> Just to be sure:  Has spark-env.sh and spark-defaults.conf been correctly propagated to all nodes?  Are they identical?
Yes; these files are stored on a shared memory directory accessible to
all nodes.

Koert Kuipers:
> we ran into similar issues and it seems related to the new memory
> management. can you try:
> spark.memory.useLegacyMode = true
I reran the exact same code with a restarted cluster using this
modification, and did not observe any difference. The partitioning is
still imbalanced.

Ted Yu:
> If the changes can be ported over to 1.6.1, do you mind reproducing the issue there ?
Since the spark.memory.useLegacyMode setting did not impact my code
execution, I will have to change the Spark dependency back to earlier
versions to see if the issue persists and get back to you.

Meanwhile, if anyone else has any other ideas or experience, please let me know.

Mike

On 4/4/16, Koert Kuipers <koert@tresata.com> wrote:
> we ran into similar issues and it seems related to the new memory
> management. can you try:
> spark.memory.useLegacyMode = true
>
> On Mon, Apr 4, 2016 at 9:12 AM, Mike Hynes <91mbbh@gmail.com> wrote:
>
>> [ CC'ing dev list since nearly identical questions have occurred in
>> user list recently w/o resolution;
>> c.f.:
>>
>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-work-distribution-among-execs-tt26502.html
>>
>> http://apache-spark-user-list.1001560.n3.nabble.com/Partitions-are-get-placed-on-the-single-node-tt26597.html
>> ]
>>
>> Hello,
>>
>> In short, I'm reporting a problem concerning load imbalance of RDD
>> partitions across a standalone cluster. Though there are 16 cores
>> available per node, certain nodes will have >16 partitions, and some
>> will correspondingly have <16 (and even 0).
>>
>> In more detail: I am running some scalability/performance tests for
>> vector-type operations. The RDDs I'm considering are simple block
>> vectors of type RDD[(Int,Vector)] for a Breeze vector type. The RDDs
>> are generated with a fixed number of elements given by some multiple
>> of the available cores, and subsequently hash-partitioned by their
>> integer block index.
>>
>> I have verified that the hash partitioning key distribution, as well
>> as the keys themselves, are both correct; the problem is truly that
>> the partitions are *not* evenly distributed across the nodes.
>>
>> For instance, here is a representative output for some stages and
>> tasks in an iterative program. This is a very simple test with 2
>> nodes, 64 partitions, 32 cores (16 per node), and 2 executors. Two
>> examples stages from the stderr log are stages 7 and 9:
>> 7,mapPartitions at DummyVector.scala:113,64,1459771364404,1459771365272
>> 9,mapPartitions at DummyVector.scala:113,64,1459771364431,1459771365639
>>
>> When counting the location of the partitions on the compute nodes from
>> the stderr logs, however, you can clearly see the imbalance. Examples
>> lines are:
>> 13627&INFO&TaskSetManager&Starting task 0.0 in stage 7.0 (TID 196,
>> himrod-2, partition 0,PROCESS_LOCAL, 3987 bytes)&
>> 13628&INFO&TaskSetManager&Starting task 1.0 in stage 7.0 (TID 197,
>> himrod-2, partition 1,PROCESS_LOCAL, 3987 bytes)&
>> 13629&INFO&TaskSetManager&Starting task 2.0 in stage 7.0 (TID 198,
>> himrod-2, partition 2,PROCESS_LOCAL, 3987 bytes)&
>>
>> Grep'ing the full set of above lines for each hostname, himrod-?,
>> shows the problem occurs in each stage. Below is the output, where the
>> number of partitions stored on each node is given alongside its
>> hostname as in (himrod-?,num_partitions):
>> Stage 7: (himrod-1,0) (himrod-2,64)
>> Stage 9: (himrod-1,16) (himrod-2,48)
>> Stage 12: (himrod-1,0) (himrod-2,64)
>> Stage 14: (himrod-1,16) (himrod-2,48)
>> The imbalance is also visible when the executor ID is used to count
>> the partitions operated on by executors.
>>
>> I am working off a fairly recent modification of 2.0.0-SNAPSHOT branch
>> (but the modifications do not touch the scheduler, and are irrelevant
>> for these particular tests). Has something changed radically in 1.6+
>> that would make a previously (<=1.5) correct configuration go haywire?
>> Have new configuration settings been added of which I'm unaware that
>> could lead to this problem?
>>
>> Please let me know if others in the community have observed this, and
>> thank you for your time,
>> Mike
>>
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>


--
Thanks,
Mike