+1 I recall a fun afternoon a few years ago figuring this out ... On Mon, 11 Mar 2019 at 18:36, Maximilian Michels <[email protected]> wrote:
> Hi, > > I have seen several users including myself get confused by the "default" > triggering behavior. I think it would be worthwhile to update the docs. > > In fact, Window.into(windowFn) does not override the existing > windowing/triggering. It merges the previous input WindowStrategy with > the new one. > > So your w1trigger will still be set when you do not set w2trigger. The > default `AfterWatermark.pastEndOfWindow()` trigger will only be used > when windowing for the first time, or when you set it explicitly. > > Thanks, > Max > > On 06.03.19 00:28, Daniel Debrunner wrote: > > Thanks Kenn,. > > > > Is it fair to say that this continuation trigger functionality is not > > documented? > > > > In the Javadoc it has a similar sentence to the programming guide: > > > >> triggering(Trigger) allows specifying a trigger to control when (in > processing time) results for the given window can be produced. If > unspecified, the default behavior is to trigger first when the watermark > passes the end of the window, and then trigger again every time there is > late arriving data. > > > > Thanks, > > Dan. > > > > On Tue, Mar 5, 2019 at 1:46 PM Kenneth Knowles <[email protected]> wrote: > >> > >> The Window.into transform does not reset the trigger to the default. So > where you have w2trigger, if you leave it off, then the triggering is left > as the "continuation trigger" from w1trigger. Basically it tries to let any > output caused by w1trigger to flow all the way through the pipeline without > delay. > >> > >> Kenn > >> > >> On Tue, Mar 5, 2019 at 1:27 PM Daniel Debrunner <[email protected]> > wrote: > >>> > >>> I discover how to fix my issue but not sure I understand why it does. > >>> > >>> I created a complete sample here: > >>> > https://gist.github.com/ddebrunner/5d4ef21c255c1d40a4517a0060ff8b99#file-cascadewindows-java-L104 > >>> Link points to the area of interest. > >>> > >>> With the second window I was originally not specifying a trigger so > >>> using the default trigger which lead to multiple triggers of the > >>> combine on the second window. > >>> > >>> However changing the trigger to be AfterWatermark.pastEndOfWindow() > >>> produced the output I expected, a single combine across all the > >>> elements in the window. > >>> The gist has comments showing the output and the two code variations. > >>> > >>> I don't understand why, since according to 8.1.1 [1] I thought > >>> AfterWatermark.pastEndOfWindow() was the default. Maybe its due to > >>> late data in some way but I'm not sure I understand how the data could > >>> be late in this case. > >>> > >>> This is with Beam 2.7 direct runner btw. > >>> > >>> Thanks again for your help, > >>> Dan. > >>> [1] > https://beam.apache.org/documentation/programming-guide/#event-time-triggers > >>> > >>> On Tue, Mar 5, 2019 at 11:48 AM Daniel Debrunner <[email protected]> > wrote: > >>>> > >>>> Thanks Robert, your description is what I'm expecting, I'm working on > >>>> a simple example to see if what I'm seeing is different and then > >>>> hopefully use that to clarify my misunderstanding. > >>>> > >>>> Thanks, > >>>> Dan. > >>>> > >>>> On Tue, Mar 5, 2019 at 11:31 AM Robert Bradshaw <[email protected]> > wrote: > >>>>> > >>>>> Windows are assigned to elements via the Window.into transform. They > >>>>> influence grouping operations such as GroupByKey, Combine.perKey, and > >>>>> Combine.globally. Looking at your example, you start with > >>>>> > >>>>> PCollection<KV<A,B>> > >>>>> > >>>>> Presumably via a Read or a Create. These KVs are in a global window, > >>>>> so the elements are really triples (ignoring PaneInfo) of the form > >>>>> > >>>>> (KV<A, B>, GlobalWindow, timestamp) > >>>>> > >>>>> From what I gather, the next step you do is a > >>>>> Window.into(FixedWindows.of(...)), yielding a PCollection<KV<A,B>> > >>>>> whose elements are, implicitly > >>>>> > >>>>> (KV<A, B>, IntervalWindow, timestamp) > >>>>> > >>>>> Now you apply a GroupByKey to get elements of the form > >>>>> > >>>>> (KV<A, Iterable<B>>, IntervalWindow, timestamp) > >>>>> > >>>>> where there is one Iterable for each distinct key and window. You > >>>>> apply a ParDo to get PCollection<X> which is of the form > >>>>> > >>>>> (X, IntervalWindow, timestamp) > >>>>> > >>>>> It looks like your next step is another > >>>>> Window.into(FixedWindows.of(...)), yielding > >>>>> > >>>>> (X, IntervalWindow, timestamp) > >>>>> > >>>>> where the IntervalWindow here may be different if the parameters to > >>>>> FixedWindows were different (e.g. the first was by minute, the second > >>>>> by hours). If it's the same, this is a no-op. Now you apply > >>>>> Combine.globally(CombineFn<X, R>) to get a PCollection<R> whose > >>>>> elements are of the form > >>>>> > >>>>> (R, IntervalWindow, timestamp) > >>>>> > >>>>> where there is now one R per window (the elements in the same window > >>>>> being combined, the elements across windows not). > >>>>> > >>>>> FWIW, internally, Combine.globally is implemented as PariWithNullKey > + > >>>>> CombinePerKey + StripNullKey. > >>>>> > >>>>> Does this help? > >>>>> > >>>>> > >>>>> On Tue, Mar 5, 2019 at 8:09 PM Daniel Debrunner <[email protected]> > wrote: > >>>>>> > >>>>>> Thanks for the reply. > >>>>>> > >>>>>> As for every element is always associated with a window, when a > >>>>>> element is produced due to a window trigger (e.g. the GroupByKey) > what > >>>>>> window is it associated with? The window it was produced from? Maybe > >>>>>> the question is when is a window assigned to an element? > >>>>>> > >>>>>> I'll see if I can come up with an example, > >>>>>> > >>>>>> Thanks, > >>>>>> Dan. > >>>>>> > >>>>>> On Tue, Mar 5, 2019 at 10:47 AM Kenneth Knowles <[email protected]> > wrote: > >>>>>>> > >>>>>>> Two pieces to this: > >>>>>>> > >>>>>>> 1. Every element in a PCollection is always associated with a > window, and GroupByKey (hence CombinePerKey) operates per-key-and-window > (w/ window merging). > >>>>>>> 2. If an element is not explicitly a KV, then there is no key > associated with it. > >>>>>>> > >>>>>>> I'm afraid I don't have any guesses at the problem based on what > you've shared. Can you say more? > >>>>>>> > >>>>>>> Kenn > >>>>>>> > >>>>>>> On Tue, Mar 5, 2019 at 10:29 AM Daniel Debrunner < > [email protected]> wrote: > >>>>>>>> > >>>>>>>> The windowing section of the Beam programming model guide shows a > >>>>>>>> window defined and used in the GropyByKey transform after a ParDo. > >>>>>>>> (section 7.1.1). > >>>>>>>> > >>>>>>>> However I couldn't find any documentation on how long the window > >>>>>>>> remains in scope for subsequent transforms. > >>>>>>>> > >>>>>>>> I have an application with this pipeline: > >>>>>>>> > >>>>>>>> PCollection<KV<A,B>> -> FixedWindow<KV<A,B>> -> GroupByKey -> > >>>>>>>> PCollection<X> -> FixedWindow<X> -> Combine<X,R>.globally -> > >>>>>>>> PCollection<R> > >>>>>>>> > >>>>>>>> The idea is that the first window is aggregating by key but in the > >>>>>>>> second window I need to combine elements across all keys. > >>>>>>>> > >>>>>>>> With my initial app I was seeing some runtime errors in/after the > >>>>>>>> combine where a KV<null,R> was being seen, even though at that > point > >>>>>>>> there should be no key for the PCollection<R>. > >>>>>>>> > >>>>>>>> In a simpler test I can apply FixedWindow<X> -> > Combine<X,R>.globally > >>>>>>>> -> PCollection<R> to a PCollection without an upstream window and > the > >>>>>>>> combine correctly happens once. > >>>>>>>> But then adding the keyed upstream window, the combine occurs > once per > >>>>>>>> key without any final combine across the keys. > >>>>>>>> > >>>>>>>> So it seems somehow the memory of the key exists even with the new > >>>>>>>> window transform, > >>>>>>>> > >>>>>>>> I'm probably misunderstanding some detail of windowing, but I > couldn't > >>>>>>>> find any deeper documentation than the simple examples in the > >>>>>>>> programming model guide. > >>>>>>>> > >>>>>>>> Can anyone point me in the correct direction? > >>>>>>>> > >>>>>>>> Thanks, > >>>>>>>> Dan. > -- This email may be confidential and privileged. 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