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From Jörn Franke <>
Subject Re: Coalesce behaviour
Date Mon, 15 Oct 2018 06:49:16 GMT
This is not fully correct. If you have less files then you need to move some data to some other
nodes, because not all the data is there for writing (even the case for the same node, but
then it is easier from a network perspective). Hence a shuffling is needed.

> Am 15.10.2018 um 05:04 schrieb Koert Kuipers <>:
> sure, i understand currently the workaround is to add a shuffle. but that's just a workaround,
not a satisfactory solution: we shouldn't have to introduce another shuffle (an expensive
operation) just to reduce the number of files.
> logically all you need is a map-phase with less tasks after the reduce phase with many
tasks to reduce the number of files, but there is currently no way to express this in spark.
it seems the map operation always gets tagged on to the end of the previous reduce operation,
which is generally a reasonable optimization, but not here since it causes the tasks for the
reduce to go down which is unacceptable.
>> On Sun, Oct 14, 2018 at 10:06 PM Wenchen Fan <> wrote:
>> You have a heavy workload, you want to run it with many tasks for better performance
and stability(no OMM), but you also want to run it with few tasks to avoid too many small
files. The reality is, mostly you can't reach these 2 goals together, they conflict with each
other. The solution I can think of is to sacrifice performance a little: run the workload
with many tasks at first, and then merge the many small files. Generally this is how `coalesce(n,
shuffle = true)` does.
>>> On Sat, Oct 13, 2018 at 10:05 PM Koert Kuipers <> wrote:
>>> we have a collection of programs in dataframe api that all do big shuffles for
which we use 2048+ partitions. this works fine but it produces a lot of (small) output files,
which put pressure on the memory of the drivers programs of any spark program that reads this
data in again.
>>> so one of our developers stuck in a .coalesce at the end of every program just
before writing to disk to reduce the output files thinking this would solve the many files
issue. to his surprise the coalesce caused the existing shuffles to run with less tasks, leading
to unacceptable slowdowns and OOMs. so this is not a solution.
>>> how can we insert a coalesce as a new map-phase (new job on application manager
with narrow dependency) instead of modifying the existing reduce phase? i am saying map-phase
because it should not introduce a new shuffle: this is wasteful and unnecessary.
>>>> On Sat, Oct 13, 2018 at 1:39 AM Wenchen Fan <> wrote:
>>>> In your first example, the root RDD has 1000 partitions, then you do a shuffle
(with repartitionAndSortWithinPartitions), and shuffles data to 1000 reducers. Then you do
coalesce, which asks Spark to launch only 20 reducers to process the data which were prepared
for 10000 reducers. since the reducers have heavy work(sorting), so you OOM. In general, your
work flow is: 1000 mappers -> 20 reducers.
>>>> In your second example, the coalesce introduces shuffle, so your work flow
is: 1000 mappers -> 1000 reducers(also mappers) -> 20 reducers. The sorting is done
by 1000 tasks so no OOM.
>>>> BTW have you tried DataFrame API? With Spark SQL, the memory management is
more precise, so even we only have 20 tasks to do the heavy sorting, the system should just
have more disk spills instead of OOM.
>>>>> On Sat, Oct 13, 2018 at 11:35 AM Koert Kuipers <>
>>>>> how can i get a shuffle with 2048 partitions and 2048 tasks and then
a map phase with 10 partitions and 10 tasks that writes to hdfs?
>>>>> every time i try to do this using coalesce the shuffle ends up having
10 tasks which is unacceptable due to OOM. this makes coalesce somewhat useless.
>>>>>> On Wed, Oct 10, 2018 at 9:06 AM Wenchen Fan <>
>>>>>> Note that, RDD partitions and Spark tasks are not always 1-1 mapping.
>>>>>> Assuming `rdd1` has 100 partitions, and `rdd2 = rdd1.coalesce(10)`.
Then `rdd2` has 10 partitions, and there is no shuffle between `rdd1` and `rdd2`. During scheduling,
`rdd1` and `rdd2` are in the same stage, and this stage has 10 tasks (decided by the last
RDD). This means, each Spark task will process 10 partitions of `rdd1`.
>>>>>> Looking at your example, I don't see where is the problem. Can you
describe what is not expected?
>>>>>>> On Wed, Oct 10, 2018 at 2:11 PM Sergey Zhemzhitsky <>
>>>>>>> Well, it seems that I can still extend the CoalesceRDD to make
it preserve the total number of partitions from the parent RDD, reduce some partitons in the
same way as the original coalesce does for map-only jobs and fill the gaps (partitions which
should reside on the positions of the coalesced ones) with just a special kind of partitions
which do not have any parent dependencies and always return an empty iterator.
>>>>>>> I believe this should work as desired (at least the previous
ShuffleMapStage will think that the number of partitons in the next stage, it generates shuffle
output for, is not changed).
>>>>>>> There are few issues though - existence of empty partitions which
can be evaluated almost for free and empty output files from these empty partitons which can
be beaten by means of LazyOutputFormat in case of RDDs.
>>>>>>>> On Mon, Oct 8, 2018, 23:57 Koert Kuipers <>
>>>>>>>> although i personally would describe this as a bug the answer
will be that this is the intended behavior. the coalesce "infects" the shuffle before it,
making a coalesce useless for reducing output files after a shuffle with many partitions b
>>>>>>>> your only option left is a repartition for which you pay
the price in that it introduces another expensive shuffle.
>>>>>>>> interestingly if you do a coalesce on a map-only job it knows
how to reduce the partitions and output files without introducing a shuffle, so clearly it
is possible, but i dont know how to get this behavior after a shuffle in an existing job.
>>>>>>>>> On Fri, Oct 5, 2018 at 6:34 PM Sergey Zhemzhitsky <>
>>>>>>>>> Hello guys,
>>>>>>>>> Currently I'm a little bit confused with coalesce behaviour.
>>>>>>>>> Consider the following usecase - I'd like to join two
pretty big RDDs.
>>>>>>>>> To make a join more stable and to prevent it from failures
>>>>>>>>> are usually repartitioned to redistribute data more evenly
and to
>>>>>>>>> prevent every partition from hitting 2GB limit. Then
after join with a
>>>>>>>>> lot of partitions.
>>>>>>>>> Then after successful join I'd like to save the resulting
>>>>>>>>> But I don't need such a huge amount of files as the number
>>>>>>>>> partitions/tasks during joining. Actually I'm fine with
such number of
>>>>>>>>> files as the total number of executor cores allocated
to the job. So
>>>>>>>>> I've considered using a coalesce.
>>>>>>>>> The problem is that coalesce with shuffling disabled
prevents join
>>>>>>>>> from using the specified number of partitions and instead
forces join
>>>>>>>>> to use the number of partitions provided to coalesce
>>>>>>>>> scala> sc.makeRDD(1 to 100, 20).repartition(100).coalesce(5,
>>>>>>>>> false).toDebugString
>>>>>>>>> res5: String =
>>>>>>>>> (5) CoalescedRDD[15] at coalesce at <console>:25
>>>>>>>>>  |  MapPartitionsRDD[14] at repartition at <console>:25
>>>>>>>>>  |  CoalescedRDD[13] at repartition at <console>:25
>>>>>>>>>  |  ShuffledRDD[12] at repartition at <console>:25
>>>>>>>>>  +-(20) MapPartitionsRDD[11] at repartition at <console>:25
>>>>>>>>>     |   ParallelCollectionRDD[10] at makeRDD at <console>:25
>>>>>>>>> With shuffling enabled everything is ok, e.g.
>>>>>>>>> scala> sc.makeRDD(1 to 100, 20).repartition(100).coalesce(5,
>>>>>>>>> res6: String =
>>>>>>>>> (5) MapPartitionsRDD[24] at coalesce at <console>:25
>>>>>>>>>  |  CoalescedRDD[23] at coalesce at <console>:25
>>>>>>>>>  |  ShuffledRDD[22] at coalesce at <console>:25
>>>>>>>>>  +-(100) MapPartitionsRDD[21] at coalesce at <console>:25
>>>>>>>>>      |   MapPartitionsRDD[20] at repartition at <console>:25
>>>>>>>>>      |   CoalescedRDD[19] at repartition at <console>:25
>>>>>>>>>      |   ShuffledRDD[18] at repartition at <console>:25
>>>>>>>>>      +-(20) MapPartitionsRDD[17] at repartition at <console>:25
>>>>>>>>>         |   ParallelCollectionRDD[16] at makeRDD at <console>:25
>>>>>>>>> In that case the problem is that for pretty huge datasets
>>>>>>>>> reshuffling can take hours or at least comparable amount
of time as
>>>>>>>>> for the join itself.
>>>>>>>>> So I'd like to understand whether it is a bug or just
an expected behaviour?
>>>>>>>>> In case it is expected is there any way to insert additional
>>>>>>>>> ShuffleMapStage into an appropriate position of DAG but
>>>>>>>>> reshuffling itself?
>>>>>>>>> ---------------------------------------------------------------------
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