valid functions can be written for reduce and merge when the zero is null. so not being able to provide null as the initial value is something troublesome.
i guess the proper way to do this is use Option, and have the None be the zero, which is what i assumed you did? unfortunately last time i tried using scala Options with spark Aggregators it didnt work quite well. see: https://issues.apache.org/jira/browse/SPARK-15810 lifting a semigroup into a monoid like this using Option is fairly typical, so either null or None has to work or else this api will be somewhat unpleasant to use for anything practical. for an example of this lifting on a related Aggregator class: https://github.com/twitter/algebird/blob/develop/algebird-core/src/main/scala/com/twitter/algebird/Aggregator.scala#L420 it would be nice to provide a similar convenience method for spark's Aggregator. basically if the user provides no zero the output is Option[OUT] instead of OUT, which spark translates into OUT being nullable. On Fri, Jul 1, 2016 at 5:04 PM, Amit Sela <amitsel...@gmail.com> wrote: > Thanks for pointing that Koert! > > I understand now why zero() and not init(a: IN), though I still don't see > a good reason to skip the aggregation if zero returns null. > If the user did it, it's on him to take care of null cases in > reduce/merge, but it opens-up the possibility to use the input to create > the buffer for the aggregator. > Wouldn't that at least enable the functionality discussed in SPARK-15598 ? > without changing how the Aggregator works. > > I bypassed it by using Optional (Guava) because I'm using the Java API, > but it's a bit cumbersome... > > Thanks, > Amit > > On Thu, Jun 30, 2016 at 1:54 AM Koert Kuipers <ko...@tresata.com> wrote: > >> its the difference between a semigroup and a monoid, and yes max does not >> easily fit into a monoid. >> >> see also discussion here: >> https://issues.apache.org/jira/browse/SPARK-15598 >> >> On Mon, Jun 27, 2016 at 3:19 AM, Amit Sela <amitsel...@gmail.com> wrote: >> >>> OK. I see that, but the current (provided) implementations are very >>> naive - Sum, Count, Average -let's take Max for example: I guess zero() >>> would be set to some value like Long.MIN_VALUE, but what if you trigger (I >>> assume in the future Spark streaming will support time-based triggers) for >>> a result and there are no events ? >>> >>> And like I said, for a more general use case: What if my zero() function >>> depends on my input ? >>> >>> I just don't see the benefit of this behaviour, though I realise this is >>> the implementation. >>> >>> Thanks, >>> Amit >>> >>> On Sun, Jun 26, 2016 at 2:09 PM Takeshi Yamamuro <linguin....@gmail.com> >>> wrote: >>> >>>> No, TypedAggregateExpression that uses Aggregator#zero is different >>>> between v2.0 and v1.6. >>>> v2.0: >>>> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala#L91 >>>> v1.6: >>>> https://github.com/apache/spark/blob/branch-1.6/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TypedAggregateExpression.scala#L115 >>>> >>>> // maropu >>>> >>>> >>>> On Sun, Jun 26, 2016 at 8:03 PM, Amit Sela <amitsel...@gmail.com> >>>> wrote: >>>> >>>>> This "if (value == null)" condition you point to exists in 1.6 branch >>>>> as well, so that's probably not the reason. >>>>> >>>>> On Sun, Jun 26, 2016 at 1:53 PM Takeshi Yamamuro < >>>>> linguin....@gmail.com> wrote: >>>>> >>>>>> Whatever it is, this is expected; if an initial value is null, spark >>>>>> codegen removes all the aggregates. >>>>>> See: >>>>>> https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala#L199 >>>>>> >>>>>> // maropu >>>>>> >>>>>> On Sun, Jun 26, 2016 at 7:46 PM, Amit Sela <amitsel...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Not sure about what's the rule in case of `b + null = null` but the >>>>>>> same code works perfectly in 1.6.1, just tried it.. >>>>>>> >>>>>>> On Sun, Jun 26, 2016 at 1:24 PM Takeshi Yamamuro < >>>>>>> linguin....@gmail.com> wrote: >>>>>>> >>>>>>>> Hi, >>>>>>>> >>>>>>>> This behaviour seems to be expected because you must ensure `b + >>>>>>>> zero() = b` >>>>>>>> The your case `b + null = null` breaks this rule. >>>>>>>> This is the same with v1.6.1. >>>>>>>> See: >>>>>>>> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/expressions/Aggregator.scala#L57 >>>>>>>> >>>>>>>> // maropu >>>>>>>> >>>>>>>> >>>>>>>> On Sun, Jun 26, 2016 at 6:06 PM, Amit Sela <amitsel...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Sometimes, the BUF for the aggregator may depend on the actual >>>>>>>>> input.. and while this passes the responsibility to handle null in >>>>>>>>> merge/reduce to the developer, it sounds fine to me if he is the one >>>>>>>>> who >>>>>>>>> put null in zero() anyway. >>>>>>>>> Now, it seems that the aggregation is skipped entirely when >>>>>>>>> zero() = null. Not sure if that was the behaviour in 1.6 >>>>>>>>> >>>>>>>>> Is this behaviour wanted ? >>>>>>>>> >>>>>>>>> Thanks, >>>>>>>>> Amit >>>>>>>>> >>>>>>>>> Aggregator example: >>>>>>>>> >>>>>>>>> public static class Agg extends Aggregator<Tuple2<String, Integer>, >>>>>>>>> Integer, Integer> { >>>>>>>>> >>>>>>>>> @Override >>>>>>>>> public Integer zero() { >>>>>>>>> return null; >>>>>>>>> } >>>>>>>>> >>>>>>>>> @Override >>>>>>>>> public Integer reduce(Integer b, Tuple2<String, Integer> a) { >>>>>>>>> if (b == null) { >>>>>>>>> b = 0; >>>>>>>>> } >>>>>>>>> return b + a._2(); >>>>>>>>> } >>>>>>>>> >>>>>>>>> @Override >>>>>>>>> public Integer merge(Integer b1, Integer b2) { >>>>>>>>> if (b1 == null) { >>>>>>>>> return b2; >>>>>>>>> } else if (b2 == null) { >>>>>>>>> return b1; >>>>>>>>> } else { >>>>>>>>> return b1 + b2; >>>>>>>>> } >>>>>>>>> } >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> --- >>>>>>>> Takeshi Yamamuro >>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> --- >>>>>> Takeshi Yamamuro >>>>>> >>>>> >>>> >>>> >>>> -- >>>> --- >>>> Takeshi Yamamuro >>>> >>> >>
