On Wed, Jan 22, 2020 at 11:37 PM Jan Lukavský <je...@seznam.cz> wrote:
> Hi Kenn, > > I do not agree with the last part. We are talking about definition of > semantics. If GBK can be implemented on top of stateful dofn, then stateful > dofn is the more generic transform. Therefore, semantics should be defined > on this transform, and _derived_ (or transferred) to the less generic ones. > Your first statement is partially true, but the second statement doesn't follow from that. Stateful DoFn is in some sense a more general transform, yes. However that doesn't mean that semantics should be defined in terms of stateful DoFn. There are other ways of implementing GroupByKey, and it's far from clear that stateful DoFn is always the best way. For example, batch runners never implement GroupByKey on top of state. Even in streaming, the current released Beam does not have sufficient functionality in Stateful DoFn to properly implement GroupByKey. You would need watermark holds for instance (now added to Beam, but not yet released). To implement things somewhat efficiently you would also need dynamic states, and Beam currently supports only static state tags (hopefully dynamic is coming soon) > If you execute GBK as a stateful dofn or not (probably not) is just a > runtime optimization (these optimizations are possible due to discrete - > and predictable - movements of time defined by triggers). But semantics > should adhere to the generic definition and not be affected by runtime > optimizations. > > Last remark, yes, if we disallow moving element's timestamp to the past, > then we don't need window.minTimestamp, because the minTimestamp is the > defined implicitly by window open time. It opens a question if a droppable > element should or should not be dropped not only when arriving too late > after window close, but if arriving too late after window open. > > But disallowing timestamp to move back in time seems impractical, because > I can imagine source assigning elements ingestion time timestamps (e.g. > kafka by default), which are later remapped to event time in user code. > That will necessarily mean moving time backwards. > This is a valid point, but also problematic. The watermark cannot work when element times move backwards (partially because the watermark is defined to be monotonic). Usually such pipelines end up being restricted to using non-watermark techniques for aggregation - i.e. processing-time triggers or state+timers. Jan > On 1/22/20 11:53 PM, Kenneth Knowles wrote: > > Had a lunch chat about this issue. > > Moving elements back in time can make them late or droppable. You just > can't really do it safely. > > Moving elements into the future is fine up to the end of the window. It is > not safe to move further. The watermark for a PCollection is based on the > element timestamps. If an element's timestamp is in the future, the > watermark can advance to that point in the future. This may cause the > watermark to expire the window. So this can also make data late or > droppable. > > It is actually not true that GBK is based on stateful DoFn. That is one > way to implement it, but not the only way nor always the best way. They are > qualitatively different. > > Kenn > > On Wed, Jan 22, 2020 at 1:52 AM Jan Lukavský <je...@seznam.cz> wrote: > >> I sense this discussion might be (remotely) related to [1] (and >> especially [2]). The common ground here is that we need a sound definition >> of window. I think people might be currently having different definitions, >> which leads to this sort of misunderstandings. The definition should be >> created in terms of stateful dofn (not GBK, which might probably be the >> case today), because that is the most low level transform, all the others >> are being built upon it. Looking at this with this optics, it seems that >> window actually scopes state of stateful dofn. The scope can be: >> >> (a) one sided (having only defined max timestamp) >> >> (b) both sided (having minimum and maximum) >> >> We have currently approach (a), which results in ability to move >> timestamp *arbitrarily far to the past*, which moving timestamp to future >> is limited by window's maxTimestamp. If we extend this to (b), then >> windowFn starts to create something like universe (actually multiverse, >> because it can return multiple windows). It should be invalid for element >> to escape its universe, that would be counter intuitive. If we disallow >> emission of data elements that are _late even when created_ (i.e. are >> emitted with timestamp less than output watermark) and we disallow setting >> timers with timestamp higher than window.maxTimestamp (which we currently >> do), then we have disallowed any element to escape its window (universe, >> range of validity). It would also require the output watermark of stateful >> dofn to be keyed and set to at least window.minTimestamp when window is >> opened. This would remove a sort of asymmetry (why to know maxTimestamp and >> not minTimestamp?). Also note that (a) is equal to (b) if and only if we >> disallow shifting time to past. >> Jan >> >> [1] >> https://lists.apache.org/thread.html/c37dfb6c545fba7d794a13c507dccebb654bbd8b317dab748a6775dc%40%3Cdev.beam.apache.org%3E >> >> [2] >> https://lists.apache.org/thread.html/r7f38860557d6571869e8e0989275f6ed610cf8c99b2f56fc6418a1d1%40%3Cdev.beam.apache.org%3E >> On 1/21/20 10:08 PM, Ankur Goenka wrote: >> >> >> >> 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 >>>> >>>
