This makes me think that trigger behavior across transforms should actually be part of the model. Or at least be standardized for SDK authors. This would also imply that it is documented for end users.
In the end, users do not care about whether it's part of the model or not, but they like having no surprises :)
On 29.04.19 09:20, Robert Bradshaw wrote:
I would say that the triggering done in stacked GBKs, with windowings in between, is part of the model (at least in the sense that it's not something that we'd want different SDKs to do separately.) OTOH, I'm not sure the continuation trigger should be part of the model. Much easier to either let WindowInto with no trigger specified either keep the existing one or reset it to the default. A runner can mutate this to a continuation trigger under the hood, which should be strictly looser (triggers are a promise about the earliest possible firing, they don't force firings to happen). On Mon, Apr 29, 2019 at 4:34 AM Kenneth Knowles <k...@apache.org> wrote:It is accurate to say that the "continuation trigger" is not documented in the general programming guide. It shows up in the javadoc only, as far as I can tell [1]. Technically, this is accurate. It is not part of the core of Beam - each language SDK is required to explicitly specify a trigger for every GroupByKey when they submit a pipeline to a runner. But, of course, this is pretty much an implementation detail. Kenn [1] https://www.google.com/search?q="continuation+trigger"+site%3Abeam.apache.org On Sun, Apr 28, 2019 at 7:08 PM Reza Rokni <r...@google.com> wrote:+1 I recall a fun afternoon a few years ago figuring this out ... On Mon, 11 Mar 2019 at 18:36, Maximilian Michels <m...@apache.org> 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 <k...@apache.org> 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 <d...@debrunners.com> 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 <d...@debrunners.com> 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 <rober...@google.com> 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 <d...@debrunners.com> 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 <k...@apache.org> 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 <d...@debrunners.com> 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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