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From David Capwell <dcapw...@gmail.com>
Subject Re: SizeEstimator
Date Tue, 27 Feb 2018 19:25:26 GMT
Thanks for the reply and sorry for my delayed response, had to go find the
profile data to lookup the class again.


https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala

That class extends SizeEstimator and has a field "map" which buffers the
rows.  In my case the buffer was > 1 million rows so became costly every
time it was checked.


This can be reproduced, create a random data set of (string, long), then
group by string (I believe this is what the code did first, there was a
sort later but should have been a different stage).  Make sure number of
executors is small (for example only one) else you are reducing the size of
M for each executor.

On Mon, Feb 26, 2018, 10:04 PM 叶先进 <advancedxy@gmail.com> wrote:

> What type is for the buffer you mentioned?
>
>
> On 27 Feb 2018, at 11:46 AM, David Capwell <dcapwell@gmail.com> wrote:
>
> advancedxy <advancedxy@gmail.com>, I don't remember the code as well
> anymore but what we hit was a very simple schema (string, long). The issue
> is the buffer had a million of these so SizeEstimator of the buffer had to
> keep recalculating the same elements over and over again.  SizeEstimator
> was on-cpu about 30% of the time, bounding the buffer got it to be < 5%
> (going off memory so may be off).
>
> The class info(size of fields lay on heap) is cached for every occurred
> class, so the size info of the same elements would not be recalculated.
> However, for Collection class (or similar) SizeEstimator will scan all the
> elements in the container (`next` field in LinkedList for example).
>
> And the array is a special case: SizeEstimator will sample array if
> array.length > ARRAY_SIZE_FOR_SAMPLING(400).
>
> The cost is really (assuming memory is O(1) which is not true) O(N × M)
> where N is number of rows in buffer and M is size of schema.  My case could
> be solved by not recomputing which would bring the cost to O(M) since
> bookkeeping should be consistent time. There was logic to delay
> recalculating bases off a change in frequency, but that didn't really do
> much for us, bounding and spilling was the bigger win in our case.
>
> On Mon, Feb 26, 2018, 7:24 PM Xin Liu <xin.e.liu@gmail.com> wrote:
>
>> Thanks David. Another solution is to convert the protobuf object to byte
>> array, It does speed up SizeEstimator
>>
>> On Mon, Feb 26, 2018 at 5:34 PM, David Capwell <dcapwell@gmail.com>
>> wrote:
>>
>>> This is used to predict the current cost of memory so spark knows to
>>> flush or not. This is very costly for us so we use a flag marked in the
>>> code as private to lower the cost
>>>
>>> spark.shuffle.spill.numElementsForceSpillThreshold (on phone hope no
>>> typo) - how many records before flush
>>>
>>> This lowers the cost because it let's us leave data in young, if we
>>> don't bound we get everyone promoted to old and GC becomes a issue.  This
>>> doesn't solve the fact that the walk is slow, but lowers the cost of GC.
>>> For us we make sure to have spare memory on the system for page cache so
>>> spilling to disk for us is a memory write 99% of the time.  If your host
>>> has less free memory spilling may become more expensive.
>>>
>>>
>>> If the walk is your bottleneck and not GC then I would recommend JOL and
>>> guessing to better predict memory.
>>>
>>> On Mon, Feb 26, 2018, 4:47 PM Xin Liu <xin.e.liu@gmail.com> wrote:
>>>
>>>> Hi folks,
>>>>
>>>> We have a situation where, shuffled data is protobuf based, and
>>>> SizeEstimator is taking a lot of time.
>>>>
>>>> We have tried to override SizeEstimator to return a constant value,
>>>> which speeds up things a lot.
>>>>
>>>> My questions, what is the side effect of disabling SizeEstimator? Is it
>>>> just spark do memory reallocation, or there is more severe consequences?
>>>>
>>>> Thanks!
>>>>
>>>
>>
>

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