Re: Coalesce behaviour
unch only 20 >>>>> reducers to process the data which were prepared for 1 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 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 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 >>>>>>> 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 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,
Re: Coalesce behaviour
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 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 :25 [] >>>>>>>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>>>>>>> | CoalescedRDD[13] at repartition at :25 [] >>>>>>>>> | ShuffledRDD[12] at repartition at :25 [] >>>>>>>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>>>>>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>>>>>>> | CoalescedRDD[23] at coalesce at :25 [] >>>>>>>>> | ShuffledRDD[22] at coalesce at :25 [] >>>>>>>>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>>>>>>>> | MapPartitionsRDD[20] at repartition at :25 [] >>>>>>>>> | CoalescedRDD[19] at repartition at :25 [] >>>>>>>>> | ShuffledRDD[18] at repartition at :25 [] >>>>>>>>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>>>>>>>> | ParallelCollectionRDD[16] at makeRDD at :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 >>>>>>>>> >>>>>>>>>
Re: Coalesce behaviour
gt;>>> 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 >>>>>>> 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 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 >>>>>>>>> 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 :25 [] >>>>>>>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>>>>>>> | CoalescedRDD[13] at repartition at :25 [] >>>>>>>>> | ShuffledRDD[12] at repartition at :25 [] >>>>>>>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>>>>>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>>>>>>> | CoalescedRDD[23] at coalesce at :25 [] >>>>>>>>> | ShuffledRDD[22] at coalesce at :25 [] >>>>>>>>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>>>>>>>> | MapPartitionsRDD[20] at repartition at :25 [] >>>>>>>>> | CoalescedRDD[19] at repartition at :25 [] >>>>>>>>> | ShuffledRDD[18] at repartition at :25 [] >>>>>>>>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>>>>>>>> | ParallelCollectionRDD[16] at makeRDD at :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 >>>>>>>>>
Re: Coalesce behaviour
rent 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 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 :25 [] >>>>>>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>>>>>> | CoalescedRDD[13] at repartition at :25 [] >>>>>>>> | ShuffledRDD[12] at repartition at :25 [] >>>>>>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>>>>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>>>>>> | CoalescedRDD[23] at coalesce at :25 [] >>>>>>>> | ShuffledRDD[22] at coalesce at :25 [] >>>>>>>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>>>>>>> | MapPartitionsRDD[20] at repartition at :25 [] >>>>>>>> | CoalescedRDD[19] at repartition at :25 [] >>>>>>>> | ShuffledRDD[18] at repartition at :25 [] >>>>>>>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>>>>>>> | ParallelCollectionRDD[16] at makeRDD at :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 >>>>>>>> >>>>>>>>
Re: Coalesce behaviour
t; >>>>> >>>>> On Mon, Oct 8, 2018, 23:57 Koert Kuipers 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 >>>>>> 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 :25 [] >>>>>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>>>>> | CoalescedRDD[13] at repartition at :25 [] >>>>>>> | ShuffledRDD[12] at repartition at :25 [] >>>>>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>>>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>>>>> | CoalescedRDD[23] at coalesce at :25 [] >>>>>>> | ShuffledRDD[22] at coalesce at :25 [] >>>>>>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>>>>>> | MapPartitionsRDD[20] at repartition at :25 [] >>>>>>> | CoalescedRDD[19] at repartition at :25 [] >>>>>>> | ShuffledRDD[18] at repartition at :25 [] >>>>>>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>>>>>> | ParallelCollectionRDD[16] at makeRDD at :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 >>>>>>> >>>>>>>
Re: Coalesce behaviour
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 1 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 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 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 >>> 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 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, bu
Re: Coalesce behaviour
gh 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 >>>>> 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 :25 [] >>>>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>>>> | CoalescedRDD[13] at repartition at :25 [] >>>>>> | ShuffledRDD[12] at repartition at :25 [] >>>>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>>>> | CoalescedRDD[23] at coalesce at :25 [] >>>>>> | ShuffledRDD[22] at coalesce at :25 [] >>>>>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>>>>> | MapPartitionsRDD[20] at repartition at :25 [] >>>>>> | CoalescedRDD[19] at repartition at :25 [] >>>>>> | ShuffledRDD[18] at repartition at :25 [] >>>>>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>>>>> | ParallelCollectionRDD[16] at makeRDD at :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 >>>>>> - To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
Re: Coalesce behaviour
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 1 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 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 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 >> 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 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 >>>> 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. &g
Re: Coalesce behaviour
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 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 > 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 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 >>> 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 :25 [] >>>> | MapPartitionsRDD[14] at repartition at :25 [] >>>> | CoalescedRDD[13] at repartition at :25 [] >>>> | ShuffledRDD[12] at repartition at :25 [] >>>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>>> | CoalescedRDD[23]
Re: Coalesce behaviour
... sorry for that, but there is a mistake in the second sample, here
is the right one
// fails with either OOM or 'Container killed by YARN for exceeding
memory limits ... spark.yarn.executor.memoryOverhead'
rdd
.map(item => item._1.toString -> item._2.toString)
.repartitionAndSortWithinPartitions(new HashPartitioner(1000))
.coalesce(20,false)
.count
// works as expected
rdd
.map(item => item._1.toString -> item._2.toString)
.repartitionAndSortWithinPartitions(new HashPartitioner(1000))
.coalesce(20,true)
.count
On Fri, Oct 12, 2018 at 7:20 PM Sergey Zhemzhitsky wrote:
>
> I'd like to reduce the number of files written to hdfs without
> introducing additional shuffles and at the same time to preserve the
> stability of the job, and also I'd like to understand why the samples
> below work in one case and fail in another one.
>
> Consider the following example which does the same thing using the
> same resources, but fails in one case and works without issues in
> another one if there is an additional shuffle introduced:
>
> spark-shell \
> --num-executors=5 \
> --executor-cores=2 \
> --master=yarn-client \
> --conf spark.executor.memory=4g \
> --conf spark.executor.memoryOverhead=1024 \
> --conf spark.dynamicAllocation.enabled=false
>
> import org.apache.hadoop.io._
> import org.apache.hadoop.io.compress._
> import org.apache.commons.lang._
> import org.apache.spark._
>
> // generate 100M records of sample data
> sc.makeRDD(1 to 1000, 1000)
> .flatMap(item => (1 to 10)
> .map(i => new
> Text(RandomStringUtils.randomAlphanumeric(3).toLowerCase) -> new
> Text(RandomStringUtils.randomAlphanumeric(1024)))
> )
> .saveAsSequenceFile("/tmp/random-strings", Some(classOf[GzipCodec]))
> val rdd = sc.sequenceFile("/tmp/random-strings", classOf[Text], classOf[Text])
>
> // count unique keys
> rdd.keys.map(_.toString).distinct.count
> // in my case it's equal to 46656
>
> // fails with either OOM or 'Container killed by YARN for exceeding
> memory limits ... spark.yarn.executor.memoryOverhead'
> rdd
> .map(item => item._1.toString -> item._2.toString)
> .repartitionAndSortWithinPartitions(new HashPartitioner(1000))
> .coalesce(20,false)
> .count
>
> // works as expected
> rdd
> .map(item => item._1.toString -> item._2.toString)
> .repartitionAndSortWithinPartitions(new HashPartitioner(1000))
> .coalesce(20,false)
> .count
> On Wed, Oct 10, 2018 at 4:06 PM Wenchen Fan 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
> > 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 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
> >&g
Re: Coalesce behaviour
I'd like to reduce the number of files written to hdfs without
introducing additional shuffles and at the same time to preserve the
stability of the job, and also I'd like to understand why the samples
below work in one case and fail in another one.
Consider the following example which does the same thing using the
same resources, but fails in one case and works without issues in
another one if there is an additional shuffle introduced:
spark-shell \
--num-executors=5 \
--executor-cores=2 \
--master=yarn-client \
--conf spark.executor.memory=4g \
--conf spark.executor.memoryOverhead=1024 \
--conf spark.dynamicAllocation.enabled=false
import org.apache.hadoop.io._
import org.apache.hadoop.io.compress._
import org.apache.commons.lang._
import org.apache.spark._
// generate 100M records of sample data
sc.makeRDD(1 to 1000, 1000)
.flatMap(item => (1 to 10)
.map(i => new
Text(RandomStringUtils.randomAlphanumeric(3).toLowerCase) -> new
Text(RandomStringUtils.randomAlphanumeric(1024)))
)
.saveAsSequenceFile("/tmp/random-strings", Some(classOf[GzipCodec]))
val rdd = sc.sequenceFile("/tmp/random-strings", classOf[Text], classOf[Text])
// count unique keys
rdd.keys.map(_.toString).distinct.count
// in my case it's equal to 46656
// fails with either OOM or 'Container killed by YARN for exceeding
memory limits ... spark.yarn.executor.memoryOverhead'
rdd
.map(item => item._1.toString -> item._2.toString)
.repartitionAndSortWithinPartitions(new HashPartitioner(1000))
.coalesce(20,false)
.count
// works as expected
rdd
.map(item => item._1.toString -> item._2.toString)
.repartitionAndSortWithinPartitions(new HashPartitioner(1000))
.coalesce(20,false)
.count
On Wed, Oct 10, 2018 at 4:06 PM Wenchen Fan 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 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 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
>>> 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
Re: Coalesce behaviour
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 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 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 >> 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 :25 [] >>> | MapPartitionsRDD[14] at repartition at :25 [] >>> | CoalescedRDD[13] at repartition at :25 [] >>> | ShuffledRDD[12] at repartition at :25 [] >>> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >>> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >>> | CoalescedRDD[23] at coalesce at :25 [] >>> | ShuffledRDD[22] at coalesce at :25 [] >>> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >>> | MapPartitionsRDD[20] at repartition at :25 [] >>> | CoalescedRDD[19] at repartition at :25 [] >>> | ShuffledRDD[18] at repartition at :25 [] >>> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >>> | ParallelCollectionRDD[16] at makeRDD at :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 >>> >>>
Re: Coalesce behaviour
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 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 > 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 :25 [] >> | MapPartitionsRDD[14] at repartition at :25 [] >> | CoalescedRDD[13] at repartition at :25 [] >> | ShuffledRDD[12] at repartition at :25 [] >> +-(20) MapPartitionsRDD[11] at repartition at :25 [] >> | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] >> | CoalescedRDD[23] at coalesce at :25 [] >> | ShuffledRDD[22] at coalesce at :25 [] >> +-(100) MapPartitionsRDD[21] at coalesce at :25 [] >> | MapPartitionsRDD[20] at repartition at :25 [] >> | CoalescedRDD[19] at repartition at :25 [] >> | ShuffledRDD[18] at repartition at :25 [] >> +-(20) MapPartitionsRDD[17] at repartition at :25 [] >> | ParallelCollectionRDD[16] at makeRDD at :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 >> >>
Re: Coalesce behaviour
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 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 :25 [] > | MapPartitionsRDD[14] at repartition at :25 [] > | CoalescedRDD[13] at repartition at :25 [] > | ShuffledRDD[12] at repartition at :25 [] > +-(20) MapPartitionsRDD[11] at repartition at :25 [] > | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] > | CoalescedRDD[23] at coalesce at :25 [] > | ShuffledRDD[22] at coalesce at :25 [] > +-(100) MapPartitionsRDD[21] at coalesce at :25 [] > | MapPartitionsRDD[20] at repartition at :25 [] > | CoalescedRDD[19] at repartition at :25 [] > | ShuffledRDD[18] at repartition at :25 [] > +-(20) MapPartitionsRDD[17] at repartition at :25 [] > | ParallelCollectionRDD[16] at makeRDD at :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 > >
Coalesce behaviour
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 :25 [] | MapPartitionsRDD[14] at repartition at :25 [] | CoalescedRDD[13] at repartition at :25 [] | ShuffledRDD[12] at repartition at :25 [] +-(20) MapPartitionsRDD[11] at repartition at :25 [] | ParallelCollectionRDD[10] at makeRDD at :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 :25 [] | CoalescedRDD[23] at coalesce at :25 [] | ShuffledRDD[22] at coalesce at :25 [] +-(100) MapPartitionsRDD[21] at coalesce at :25 [] | MapPartitionsRDD[20] at repartition at :25 [] | CoalescedRDD[19] at repartition at :25 [] | ShuffledRDD[18] at repartition at :25 [] +-(20) MapPartitionsRDD[17] at repartition at :25 [] | ParallelCollectionRDD[16] at makeRDD at :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
