> > sure, but then my values are not sorted per key, right?
It does do a partition local sort. Look at the query plan in my example <https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1023043053387187/1828840559545742/2840265927289860/latest.html>. The code here will also take care of finding the boundaries and is pretty careful to spill / avoid materializing unnecessarily. I think you are correct though that we are not pushing any of the sort into the shuffle. I'm not sure how much that buys you. If its a lot we could extend the planner to look for Exchange->Sort pairs and change the exchange. On Fri, Nov 4, 2016 at 7:06 AM, Koert Kuipers <ko...@tresata.com> wrote: > i just noticed Sort for Dataset has a global flag. and Dataset also has > sortWithinPartitions. > > how about: > repartition + sortWithinPartitions + mapPartitions? > > the plan looks ok, but it is not clear to me if the sort is done as part > of the shuffle (which is the important optimization). > > scala> val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", > "value") > > scala> df.repartition(2, col("key")).sortWithinPartitions("value").as[(String, > String)].mapPartitions{ (x: Iterator[(String, String)]) => x }.explain > == Physical Plan == > *SerializeFromObject [staticinvoke(class > org.apache.spark.unsafe.types.UTF8String, > StringType, fromString, assertnotnull(input[0, scala.Tuple2, true], top > level non-flat input object)._1, true) AS _1#39, staticinvoke(class > org.apache.spark.unsafe.types.UTF8String, StringType, fromString, > assertnotnull(input[0, scala.Tuple2, true], top level non-flat input > object)._2, true) AS _2#40] > +- MapPartitions <function1>, obj#38: scala.Tuple2 > +- DeserializeToObject newInstance(class scala.Tuple2), obj#37: > scala.Tuple2 > +- *Sort [value#6 ASC], false, 0 > +- Exchange hashpartitioning(key#5, 2) > +- LocalTableScan [key#5, value#6] > > > > > On Fri, Nov 4, 2016 at 9:18 AM, Koert Kuipers <ko...@tresata.com> wrote: > >> sure, but then my values are not sorted per key, right? >> >> so a group by key with values sorted according to to some ordering is an >> operation that can be done efficiently in a single shuffle without first >> figuring out range boundaries. and it is needed for quite a few algos, >> including Window and lots of timeseries stuff. but it seems there is no way >> to express i want to do this yet (at least not in an efficient way). >> >> which makes me wonder, what does Window do? >> >> >> On Fri, Nov 4, 2016 at 12:59 AM, Michael Armbrust <mich...@databricks.com >> > wrote: >> >>> Thinking out loud is good :) >>> >>> You are right in that anytime you ask for a global ordering from Spark >>> you will pay the cost of figuring out the range boundaries for partitions. >>> If you say orderBy, though, we aren't sure that you aren't expecting a >>> global order. >>> >>> If you only want to make sure that items are colocated, it is cheaper to >>> do a groupByKey followed by a flatMapGroups >>> <https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1023043053387187/1828840559545742/2840265927289860/latest.html> >>> . >>> >>> >>> >>> On Thu, Nov 3, 2016 at 7:31 PM, Koert Kuipers <ko...@tresata.com> wrote: >>> >>>> i guess i could sort by (hashcode(key), key, secondarySortColumn) and >>>> then do mapPartitions? >>>> >>>> sorry thinking out loud a bit here. ok i think that could work. thanks >>>> >>>> On Thu, Nov 3, 2016 at 10:25 PM, Koert Kuipers <ko...@tresata.com> >>>> wrote: >>>> >>>>> thats an interesting thought about orderBy and mapPartitions. i guess >>>>> i could emulate a groupBy with secondary sort using those two. however >>>>> isn't using an orderBy expensive since it is a total sort? i mean a >>>>> groupBy >>>>> with secondary sort is also a total sort under the hood, but its on >>>>> (hashCode(key), secondarySortColumn) which is easier to distribute and >>>>> therefore can be implemented more efficiently. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Thu, Nov 3, 2016 at 8:59 PM, Michael Armbrust < >>>>> mich...@databricks.com> wrote: >>>>> >>>>>> It is still unclear to me why we should remember all these tricks (or >>>>>>> add lots of extra little functions) when this elegantly can be >>>>>>> expressed in >>>>>>> a reduce operation with a simple one line lamba function. >>>>>>> >>>>>> I think you can do that too. KeyValueGroupedDataset has a >>>>>> reduceGroups function. This probably won't be as fast though because you >>>>>> end up creating objects where as the version I gave will get codgened to >>>>>> operate on binary data the whole way though. >>>>>> >>>>>>> The same applies to these Window functions. I had to read it 3 times >>>>>>> to understand what it all means. Maybe it makes sense for someone who >>>>>>> has >>>>>>> been forced to use such limited tools in sql for many years but that's >>>>>>> not >>>>>>> necessary what we should aim for. Why can I not just have the sortBy and >>>>>>> then an Iterator[X] => Iterator[Y] to express what I want to do? >>>>>>> >>>>>> We also have orderBy and mapPartitions. >>>>>> >>>>>>> All these functions (rank etc.) can be trivially expressed in this, >>>>>>> plus I can add other operations if needed, instead of being locked in >>>>>>> like >>>>>>> this Window framework. >>>>>>> >>>>>> I agree that window functions would probably not be my first choice >>>>>> for many problems, but for people coming from SQL it was a very popular >>>>>> feature. My real goal is to give as many paradigms as possible in a >>>>>> single >>>>>> unified framework. Let people pick the right mode of expression for any >>>>>> given job :) >>>>>> >>>>> >>>>> >>>> >>> >> >
