On Thu, Jan 16, 2020 at 9:52 PM Kenneth Knowles <k...@apache.org> wrote:
> > > On Thu, Jan 16, 2020 at 11:38 AM Robert Bradshaw <rober...@google.com> > wrote: > >> On Thu, Jan 16, 2020 at 11:00 AM Kenneth Knowles <k...@apache.org> wrote: >> > >> > IIRC in Java it is forbidden to output an element with a timestamp >> outside its current window. >> >> I don't think this is checked anywhere. (Not sure how you would check >> it, as there's not generic window containment function--I suppose you >> could check if it's past the end of the window (and of course skew >> limits how far you can go back). I suppose you could try re-windowing >> and then fail if it didn't agree with what was already there. >> > > I think you are right. This is governed by how a runner invoked utilities > from runners-core (output ultimately reaches this point without validation: > https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java#L258 > ) > > >> > An exception is outputs from @FinishBundle, where the output timestamp >> is required and the window is applied. TBH it seems more of an artifact of >> a mismatch between the pre-windowing and post-windowing worlds. >> >> Elements are always in some window, even if just the global window. >> > > I mean that the existence of a window-unaware @FinishBundle method is an > artifact of the method existing prior to windowing as a concept. The idea > that a user can use a DoFn's local variables to buffer stuff and then > output in @FinishBundle predates the existence of windowing. > > > Most of the time, mixing processing across windows is simply wrong. But >> there are fears that calling @FinishBundle once per window would be a >> performance problem. On the other hand, don't most correct implementations >> have to separate processing for each window anyhow? >> >> Processing needs to be done per window iff the result depends on the >> window or if there are side effects. >> >> > Anyhow I think the Java behavior is better, so window assignment >> happens exactly and only at window transforms. >> >> But then one ends up with timestamps that are unrelated to the windows, >> right? >> > > As far as the model goes, I think windows provide an upper bound but not a > lower bound. If we take the approach that windows are a "secondary key with > a max timestamp" then the timestamps should be related to the window in the > sense that they are <= the window's max timestamp. > A window only makes sense when a trigger or timer is fired. And the timestamp of the elements in the window should be within the window's time range when a trigger is set. For consistency, I think element timestamp should remain within the corresponding time range at every stage of the graph. IIUC based on the discussion, users can violate this requirement easily in the pipeline code which might give inconsistent behavior across runners. I think we should stick to a consistent behavior across languages and runners. We have multiple options here like 1. Don't have any promised correlation between element timestamp and window. Window will just behave like a secondary key for the element. 2. Making it explicit that the last window function can be applied out of order anytime on the elements. 3. Not letting users change the timestamp without applying a windowing function after the changed timestamp and before a trigger. Though, this can only be validated at the runtime in python. 4. Revalidating the window after changing the timestamp. Also provide additional methods to explicitly change the timestamp and window in oneshot. 5. etc.... > Kenn > > > >> > Kenn >> > >> > On Wed, Jan 15, 2020 at 4:59 PM Ankur Goenka <goe...@google.com> wrote: >> >> >> >> The case where a plan vanilla value or a windowed value is emitted >> seems as expected as the user intent is honored without any surprises. >> >> >> >> If I understand correctly in the case when timestamp is changed then >> applying window function again can have unintended behavior in following >> cases >> >> * Custom windows: User code can be executed in unintended order. >> >> * User emit a windowed value in a previous transform: Timestamping the >> value in this case would overwrite the user assigned window in earlier step >> even when the actual timestamp is the same. Semantically, emitting an >> element or a timestamped value with the same timestamp should have the same >> behaviour. >> >> >> >> What do you think? >> >> >> >> >> >> On Wed, Jan 15, 2020 at 4:04 PM Robert Bradshaw <rober...@google.com> >> wrote: >> >>> >> >>> If an element is emitted with a timestamp, the window assignment is >> >>> re-applied at that time. At least that's how it is in Python. You can >> >>> emit the full windowed value (accepted without checking...), a >> >>> timestamped value (in which case the window will be computed), or a >> >>> plain old element (in which case the window and timestamp will be >> >>> computed (really, propagated)). >> >>> >> >>> On Wed, Jan 15, 2020 at 3:51 PM Ankur Goenka <goe...@google.com> >> wrote: >> >>> > >> >>> > Yup, This might result in unintended behavior as timestamp is >> changed after the window assignment as elements in windows do not have >> timestamp in the window time range. >> >>> > >> >>> > Shall we start validating atleast one window assignment between >> timestamp assignment and GBK/triggers to avoid unintended behaviors >> mentioned above? >> >>> > >> >>> > On Wed, Jan 15, 2020 at 1:24 PM Luke Cwik <lc...@google.com> wrote: >> >>> >> >> >>> >> Window assignment happens at the point in the pipeline the >> WindowInto transform was applied. So in this case the window would have >> been assigned using the original timestamp. >> >>> >> >> >>> >> Grouping is by key and window. >> >>> >> >> >>> >> On Tue, Jan 14, 2020 at 7:30 PM Ankur Goenka <goe...@google.com> >> wrote: >> >>> >>> >> >>> >>> Hi, >> >>> >>> >> >>> >>> I am not sure about the effect of the order of element timestamp >> change and window association has on a group by key. >> >>> >>> More specifically, what would be the behavior if we apply window >> -> change element timestamp -> Group By key. >> >>> >>> I think we should always apply window function after changing the >> timestamp of elements. Though this is neither checked nor a recommended >> practice in Beam. >> >>> >>> >> >>> >>> Example pipeline would look like this: >> >>> >>> >> >>> >>> def applyTimestamp(value): >> >>> >>> return window.TimestampedValue((key, value), >> int(time.time()) >> >>> >>> >> >>> >>> p \ >> >>> >>> | 'Create' >> beam.Create(range(0, 10)) \ >> >>> >>> | 'Fixed Window' >> >> beam.WindowInto(window.FixedWindows(5)) \ >> >>> >>> | 'Apply Timestamp' >> beam.Map(applyTimestamp) \ # >> Timestamp is changed after windowing and before GBK >> >>> >>> | 'Group By Key' >> beam.GroupByKey() \ >> >>> >>> | 'Print' >> beam.Map(print) >> >>> >>> >> >>> >>> Thanks, >> >>> >>> Ankur >> >
