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 <ko...@tresata.com>: > > 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 <cloud0...@gmail.com> 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 <ko...@tresata.com> 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 <cloud0...@gmail.com> 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 <ko...@tresata.com> wrote: >>>>> 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 <cloud0...@gmail.com> wrote: >>>>>> 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 <szh.s...@gmail.com> >>>>>>> wrote: >>>>>>> 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 <ko...@tresata.com> wrote: >>>>>>>> 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 design. >>>>>>>> >>>>>>>> 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 >>>>>>>>> <szh.s...@gmail.com> wrote: >>>>>>>>> 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 by OOM RDDs >>>>>>>>> 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 dataset. >>>>>>>>> But I don't need such a huge amount of files as the number of >>>>>>>>> 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, >>>>>>>>> true).toDebugString >>>>>>>>> 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 additional >>>>>>>>> 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 without >>>>>>>>> reshuffling itself? >>>>>>>>> >>>>>>>>> --------------------------------------------------------------------- >>>>>>>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>>>>>>>>
