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From yash datta <sau...@gmail.com>
Subject Re: Dataframe Partitioning
Date Wed, 02 Mar 2016 04:30:46 GMT
+1
This is  one of the most common problems we encounter in our flow. Mark, I
am happy to help if you would like to share some of the workload.

Best
Yash

On Wednesday 2 March 2016, Mark Hamstra <mark@clearstorydata.com> wrote:

> I don't entirely agree.  You're best off picking the right size :).
> That's almost impossible, though, since at the input end of the query
> processing you often want a large number of partitions to get sufficient
> parallelism for both performance and to avoid spilling or OOM, while at the
> output end of the query processing (after all the pruning and filtering)
> you often have only a few result rows, which means that splitting those few
> rows across many partitions in order to do a sort or similar is actually
> pretty silly and inefficient. I'll frequently see sorts where the
> per-partition sorts have only one or two records and it would have been
> quicker and more efficient to sort using a small number of partitions
> rather than using RangePartitioning to split the few rows across many
> partitions, then doing a degenerate/trivial form of sort on each of those
> partitions with their one or two rows, and finally merging all those tiny
> partitions back in order to produce the final results.
>
> Since the optimum number of shuffle partitions is different at different
> points in the query processing flow, it's really impossible to pick a
> static best number of shuffle partitions.  Using spark.sql.adaptive.enabled
> to turn on ExchangeCoordinator and dynamically set the number of shuffle
> partitions mostly works pretty well, but it still has at least a couple of
> issues.  One is that it makes things worse in the case of data skew since
> it doesn't stop coalescing partitions until after the coalesced partition
> size exceeds a target value; so if you've got some big ugly partitions that
> exceed the target size all on their own, they'll often be even bigger and
> uglier after the ExchangeCoordinator is done merging them with a few
> smaller partitions.  The other issue is that adaptive partitioning doesn't
> even try to do anything currently with any partitioning other than
> HashPartitioning, so you've still got the sorting problem using
> RangePartitioning that I just got done describing.
>
> I've actually started working on addressing each of those problems.
>
> On Tue, Mar 1, 2016 at 3:43 PM, Michael Armbrust <michael@databricks.com
> <javascript:_e(%7B%7D,'cvml','michael@databricks.com');>> wrote:
>
>> If you have to pick a number, its better to over estimate than
>> underestimate since task launching in spark is relatively cheap compared to
>> spilling to disk or OOMing (now much less likely due to Tungsten).
>> Eventually, we plan to make this dynamic, but you should tune for your
>> particular workload.
>>
>> On Tue, Mar 1, 2016 at 3:19 PM, Teng Liao <tliao@palantir.com
>> <javascript:_e(%7B%7D,'cvml','tliao@palantir.com');>> wrote:
>>
>>> Hi,
>>>
>>> I was wondering what the rationale behind defaulting all repartitioning
>>> to spark.sql.shuffle.partitions is. I’m seeing a huge overhead when running
>>> a job whose input partitions is 2 and, using the default value for
>>> spark.sql.shuffle.partitions, this is now 200. Thanks.
>>>
>>> -Teng Fei Liao
>>>
>>
>>
>

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