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From Nathan Kronenfeld <nkronenf...@oculusinfo.com>
Subject Re: data locality, task distribution
Date Thu, 13 Nov 2014 17:08:54 GMT
I am seeing skewed execution times.  As far as I can tell, they are
attributable to differences in data locality - tasks with locality
PROCESS_LOCAL run fast, NODE_LOCAL, slower, and ANY, slowest.

This seems entirely as it should be - the question is, why the different
locality levels?

I am seeing skewed caching, as I mentioned before - in the case I isolated,
with 4 nodes, they were distributed at about 42%, 31%, 20%, and 6%.
However, the total amount was significantly less than the memory of any
single node, so I don't think they could have overpopulated their cache.  I
am occasionally seeing task failures, but the re-execute themselves, and
work fine the next time.  Yet I'm still seeing incomplete caching (from 65%
cached up to 100%, depending on the run).

I shouldn't have much variance in task time - this is simply a foreach over
the data, adding to an accumulator, and the data is completely randomly
distributed, so should be pretty even overall.

I am seeing GC regressions occasionally - they slow a request from about 2
seconds to about 5 seconds.  They 8 minute slowdown seems to be solely
attributable to the data locality issue, as far as I can tell.  There was
some further confusion though in the times I mentioned - the list I gave
(3.1 min, 2 seconds, ... 8 min) were not different runs with different
cache %s, they were iterations within a single run with 100% caching.

                       -Nathan



On Thu, Nov 13, 2014 at 1:45 AM, Aaron Davidson <ilikerps@gmail.com> wrote:

> Spark's scheduling is pretty simple: it will allocate tasks to open cores
> on executors, preferring ones where the data is local. It even performs
> "delay scheduling", which means waiting a bit to see if an executor where
> the data resides locally becomes available.
>
> Are yours tasks seeing very skewed execution times? If some tasks are
> taking a very long time and using all the resources on a node, perhaps the
> other nodes are quickly finishing many tasks, and actually overpopulating
> their caches. If a particular machine were not overpopulating its cache,
> and there are no failures, then you should see 100% cached after the first
> run.
>
> It's also strange that running totally uncached takes 3.1 minutes, but
> running 80-90% cached may take 8 minutes. Does your workload produce
> nondeterministic variance in task times? Was it a single straggler, or many
> tasks, that was keeping the job from finishing? It's not too uncommon to
> see occasional performance regressions while caching due to GC, though 2
> seconds to 8 minutes is a bit extreme.
>
> On Wed, Nov 12, 2014 at 9:01 PM, Nathan Kronenfeld <
> nkronenfeld@oculusinfo.com> wrote:
>
>> Sorry, I think I was not clear in what I meant.
>> I didn't mean it went down within a run, with the same instance.
>>
>> I meant I'd run the whole app, and one time, it would cache 100%, and the
>> next run, it might cache only 83%
>>
>> Within a run, it doesn't change.
>>
>> On Wed, Nov 12, 2014 at 11:31 PM, Aaron Davidson <ilikerps@gmail.com>
>> wrote:
>>
>>> The fact that the caching percentage went down is highly suspicious. It
>>> should generally not decrease unless other cached data took its place, or
>>> if unless executors were dying. Do you know if either of these were the
>>> case?
>>>
>>> On Tue, Nov 11, 2014 at 8:58 AM, Nathan Kronenfeld <
>>> nkronenfeld@oculusinfo.com> wrote:
>>>
>>>> Can anyone point me to a good primer on how spark decides where to send
>>>> what task, how it distributes them, and how it determines data locality?
>>>>
>>>> I'm trying a pretty simple task - it's doing a foreach over cached
>>>> data, accumulating some (relatively complex) values.
>>>>
>>>> So I see several inconsistencies I don't understand:
>>>>
>>>> (1) If I run it a couple times, as separate applications (i.e.,
>>>> reloading, recaching, etc), I will get different %'s cached each time.
>>>> I've got about 5x as much memory as I need overall, so it isn't running
>>>> out.  But one time, 100% of the data will be cached; the next, 83%, the
>>>> next, 92%, etc.
>>>>
>>>> (2) Also, the data is very unevenly distributed. I've got 400
>>>> partitions, and 4 workers (with, I believe, 3x replication), and on my last
>>>> run, my distribution was 165/139/25/71.  Is there any way to get spark to
>>>> distribute the tasks more evenly?
>>>>
>>>> (3) If I run the problem several times in the same execution (to take
>>>> advantage of caching etc.), I get very inconsistent results.  My latest
>>>> try, I get:
>>>>
>>>>    - 1st run: 3.1 min
>>>>    - 2nd run: 2 seconds
>>>>    - 3rd run: 8 minutes
>>>>    - 4th run: 2 seconds
>>>>    - 5th run: 2 seconds
>>>>    - 6th run: 6.9 minutes
>>>>    - 7th run: 2 seconds
>>>>    - 8th run: 2 seconds
>>>>    - 9th run: 3.9 minuts
>>>>    - 10th run: 8 seconds
>>>>
>>>> I understand the difference for the first run; it was caching that
>>>> time.  Later times, when it manages to work in 2 seconds, it's because all
>>>> the tasks were PROCESS_LOCAL; when it takes longer, the last 10-20% of the
>>>> tasks end up with locality level ANY.  Why would that change when running
>>>> the exact same task twice in a row on cached data?
>>>>
>>>> Any help or pointers that I could get would be much appreciated.
>>>>
>>>>
>>>> Thanks,
>>>>
>>>>                  -Nathan
>>>>
>>>>
>>>>
>>>> --
>>>> Nathan Kronenfeld
>>>> Senior Visualization Developer
>>>> Oculus Info Inc
>>>> 2 Berkeley Street, Suite 600,
>>>> Toronto, Ontario M5A 4J5
>>>> Phone:  +1-416-203-3003 x 238
>>>> Email:  nkronenfeld@oculusinfo.com
>>>>
>>>
>>>
>>
>>
>> --
>> Nathan Kronenfeld
>> Senior Visualization Developer
>> Oculus Info Inc
>> 2 Berkeley Street, Suite 600,
>> Toronto, Ontario M5A 4J5
>> Phone:  +1-416-203-3003 x 238
>> Email:  nkronenfeld@oculusinfo.com
>>
>
>


-- 
Nathan Kronenfeld
Senior Visualization Developer
Oculus Info Inc
2 Berkeley Street, Suite 600,
Toronto, Ontario M5A 4J5
Phone:  +1-416-203-3003 x 238
Email:  nkronenfeld@oculusinfo.com

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