Hi Leah, Thank you for the KIP!
Here is my feedback: 1. The KIP would benefit from some code examples that show how to use sliding windows in aggregations. 2. The different sliding windows in Figure 1 and 2 are really hard to distinguish. Could you please try to make them graphically better distinguishable? You could try to draw the frames of consecutive windows shifted to each other. 3. I agree with Matthias, that extending Windows<TimeWindow> does not seem to be the best approach. What would be the result of windowsFor()? 4. In the section "Public Interfaces" you should remove implementation details like private constructors and private fields. 5. Do we need a new store interface or can we use WindowStore? Some words about that would be informative. 6. @Matthias, if the subtrator is not strictly needed, I would skip it for now and add it later. 7. I also agree that having a section that describes how to handle out-of-order records would be good to understand what is still missing and what we can reuse. Best, Bruno On Sat, Jul 11, 2020 at 9:16 PM Matthias J. Sax <mj...@apache.org> wrote: > > Leah, > > thanks for your update. However, it does not completely answer my question. > > In our current window implementations, we emit a window result update > record (ie, early/partial result) for each input record. When an > out-of-order record arrives, we just update to corresponding old window > and emit another update. > > It's unclear from the KIP if you propose the same emit strategy? -- For > sliding windows it might be worth to consider to use a different emit > strategy and only support emitting the final result only (ie, after the > grace period passed)? > > > > Boyang, also raises a good point that relates to my point from above > about pre-aggregations and storage layout. Our current time windows are > all pre-aggregated and stored in parallel. We can also lookup windows > efficiently, as we can compute the windowed-key given the input record > key and timestamp based on the window definition. > > However, for sliding windows, window boundaries are data dependent and > thus we cannot compute them upfront. Thus, how can we "find" existing > window efficiently? Furthermore, out-of-order data would create new > windows in the past and we need to be able to handle this case. > > Thus, to handle out-of-order data correctly, we need to store all raw > input events. Additionally, we could also store pre-aggregated results > if we thinks it's benfitial. -- If we apply "emit only final results" > strategy, storing pre-aggregated result would not be necessary though. > > > Btw: for sliding windows it might also be useful to consider allowing > users to supply a `Subtractor` -- this subtractor could be applied on > the current window result (in case we store it) if a record drops out of > the window. Of course, not all aggregation functions are subtractable > and we can consider this as a follow up task, too, and not include in > this KIP for now. Thoughts? > > > > I was also thinking about the type hierarchy. I am not sure if extending > TimeWindow is the best approach? For TimeWindows, we can pre-compute > window boundaries (cf `windowsFor()`) while for a sliding window the > boundaries are data dependent. Session windows are also data dependent > and thus they don't inherit from TimeWindow (Maybe check out the KIP > that added session windows? It could provides some good insights.) -- I > believe the same rational applies to sliding windows? > > > > -Matthias > > > > > On 7/10/20 12:47 PM, Boyang Chen wrote: > > Thanks Leah and Sophie for the KIP. > > > > 1. I'm a bit surprised that we don't have an advance time. Could we > > elaborate how the storage layer is structured? > > > > 2. IIUC, there will be extra cost in terms of fetching aggregation results, > > since we couldn't pre-aggregate until the user asks for it. Would be good > > to also discuss it. > > > > 3. We haven't discussed the possibility of supporting sliding windows > > inherently. For a user who actually uses a hopping window, Streams could > > detect such an inefficiency doing a window_size/advance_time ratio to reach > > a conclusion on whether the write amplification is too high compared with > > some configured threshold. The benefit of doing so is that existing Streams > > users don't need to change their code, learn a new API, but only to upgrade > > Streams library to get benefits for their inefficient hopping window > > implementation. There might be some compatibility issues for sure, but > > worth listing them out for trade-off. > > > > Boyang > > > > On Fri, Jul 10, 2020 at 12:40 PM Leah Thomas <ltho...@confluent.io> wrote: > > > >> Hey Matthias, > >> > >> Thanks for pointing that out. I added the following to the Propose Changes > >> section of the KIP: > >> > >> "Records that come out of order will be processed the same way as in-order > >> records, as long as they fall within the grace period. Any new windows > >> created by the late record will still be created, and the existing windows > >> that are changed by the late record will be updated. Any record that falls > >> outside of the grace period (either user defined or default) will be > >> discarded. " > >> > >> All the best, > >> Leah > >> > >> On Thu, Jul 9, 2020 at 9:47 PM Matthias J. Sax <mj...@apache.org> wrote: > >> > >>> Leah, > >>> > >>> thanks a lot for the KIP. Very well written. > >>> > >>> The KIP does not talk about the handling of out-of-order data though. > >>> How do you propose to address this? > >>> > >>> > >>> -Matthias > >>> > >>> On 7/8/20 5:33 PM, Leah Thomas wrote: > >>>> Hi all, > >>>> I'd like to kick-off the discussion for KIP-450, adding sliding window > >>>> aggregation support to Kafka Streams. > >>>> > >>>> > >>> > >> https://cwiki.apache.org/confluence/display/KAFKA/KIP-450%3A+Sliding+Window+Aggregations+in+the+DSL > >>>> > >>>> Let me know what you think, > >>>> Leah > >>>> > >>> > >>> > >> > > >
