Hi all, Thanks for the feedback on the KIP. I've updated the KIP page <https://cwiki.apache.org/confluence/display/KAFKA/KIP-450%3A+Sliding+Window+Aggregations+in+the+DSL> to address these points and have created a child page <https://cwiki.apache.org/confluence/display/KAFKA/Aggregation+details+for+KIP-450> to go more in depth on certain implementation details.
*Grace Period:* I think Sophie raises a good point that the default grace period of 24 hours is often too long and was chosen when retention time and grace period were the same. For SlidingWindows, I propose we make the grace period mandatory. To keep formatting consistent with other types of windows, grace period won't be an additional parameter in the #of method, but will still look like it does in other use cases: .windowedBy(SlidingWindows.of(twentySeconds).grace(fiftySeconds). If grace period isn't properly initialized, an error will be thrown through the process method. *Storage Layer + Aggregation:* SlidingWindows will use a WindowStore because computation can be done with the information stored in a WindowStore (window timestamp and value). Using the WindowStore also simplifies computation as SlidingWindows can leverage existing processes. Because we are using a WindowStore, the aggregation process will be similar to that of a hopping window. As records come in their value is added to the aggregation that already exists, following the same procedure as hopping windows. The aggregation difference between SlidingWindows and HoppingWindows comes in creating new windows for a SlidingWindow, where you need to find the existing records that belong to the new window. This computation is similar to the aggregation in SessionWindows and requires a scan to the WindowStore to find the window with the aggregation needed, which will always be pre-computed. The scan requires creating an iterator, but should have minimal performance effects as this strategy is already implemented in SessionWindows. More details on finding the aggregation that needs to be put in a new window can be found on the implementation page <https://cwiki.apache.org/confluence/display/KAFKA/Aggregation+details+for+KIP-450> . *Extending Windows<TimeWindow>, Windows<Window> or nothing* Because SlidingWindows are still defined by a windowSize (whereas SessionWindows are defined purely by data), I think it makes sense to leverage the existing Window processes instead of creating a new store type that would be very similar to the WindowStore. While it's true that the #windowsFor method isn't necessary for SlidingWindows, JoinWindows also extends Windows<Window> and throws an UnsupportedOperationException in the #windowsFor method, which is what SlidingWindows can do. The difference between extending Windows<TimeWindow> or Windows<Window> is minimal, as both are ways to pass window parameters. Extending Windows<TimeWindow> will give us more leverage in utilizing existing processes. *Emit Strategy* I would argue that emitting for every update is still the best way to go for SlidingWindows because it mimics the other types of windows, and suppression can be leveraged to limit what SlidingWindows emits. While some users may only want to see the last value, others may want to see more, and leaving the emit strategy to emit partial results allows both users to access what they want. *Additional Features* Supporting sliding windows inherently, and shifting inefficient hopping windows to sliding windows, is an interesting idea and could be built on top of SlidingWindows when they are finished, but right now seems out of scope for the needs of this KIP. Similarly, including a `subtraction` feature could have performance improvements, but doesn't seem necessary for the implementation of this KIP. Let me know what you think of the updates, Leah On Thu, Jul 16, 2020 at 11:57 AM John Roesler <vvcep...@apache.org> wrote: > Hello all, > > Thanks for the KIP, Leah! > > Regarding (1): I'd go farther actually. Making Windows an abstract > class was a mistake from the beginning that led to us not being > able to fix a very confusing situation for users around retention times, > final results emitting, etc. Thus, I would not suggest extending > TimeWindows for sure, but would also not suggest extending Windows. > > The very simplest thing to do is follow the example of SessionWindows, > which is just a completely self-contained class. If we don't mess with > class inheritance, we won't ever have any of the problems related to > class inheritance. This is my preferred solution. > > Still, Sliding windows has a lot in common with TimeWindows and other > fixed-size windows, namely that the windows are fixed in size. If we want > to preserve the current two-part windowing API in which you can window > by either "fixed" or "data driven" modes, I'd suggest we avoid increasing > the blast radius of Windows by taking the opportunity to replace it with > a proper interface and implement that interface instead. > > For example: > https://github.com/apache/kafka/pull/9031 > > Then, SlidingWindows would just implement FixedSizeWindowDefinition > > ====== > > Regarding (2), it seems more straightforward as a user of Streams > to just have one mental model. _All_ of our aggregation operations > follow an eager emission model, in which we just emit an update whenever > an update is available. We already provided Suppression to explicitly apply > different update semantics in the case it's required. Why should we define > a snowflake operation with completely different semantics from everything > else? I.e., systems are generally easier to use when they follow a few > simple, composable rules than when they have a lot of different, specific > rules. > > > ====== > > New point: (4): > It would be nice to include some examples of user code that would use the > new API, which should include: > 1. using the DSL with the sliding window definition > 2. accessing the stored results of a sliding window aggregation via IQ > 3. defining a custom processor to access sliding windows in a store > > It generally helps reviewers wrap their heads around the proposal, as well > as shaking out any design issues that would otherwise only come up during > implementation/testing/review. > > Thanks again for the awesome proposal! > -John > > > On Tue, Jul 14, 2020, at 12:31, Guozhang Wang wrote: > > Hello Leah, > > > > Thanks for the nice written KIP. A few thoughts: > > > > 1) I echo the other reviewer's comments regarding the typing: why > extending > > TimeWindow instead of just extending Window? > > > > 2) I also feel that emitting policy for this type of windowing > aggregation > > may be different from the existing ones. Existing emitting policy is very > > simple: emit every time when window get updates, and emit every time on > > out-of-ordering data within grace period, this is because for > time-windows > > the window close time is strictly depend on the window start time which > is > > fixed, while for session-windows although the window open/close time is > > also data-dependent it is relatively infrequent compared to the > > sliding-windows. For this KIP, since each new data would cause a > > new sliding-window, the num. windows maintained logically could be much > > larger and hence emitting on each update may be too aggressive. > > > > 3) Although KIP itself should be focusing on user face interfaces, I'd > > suggest we create a children page of KIP-450 discussing about its > > implementations as well, since some of that may drive the interface > design. > > E.g. personally I think having a combiner interface in addition to > > aggregator would be useful but that's based on my 2cents about the > > implementation design (I once created a child page describing it: > > > https://cwiki.apache.org/confluence/display/KAFKA/Optimize+Windowed+Aggregation > > ). > > > > > > Guozhang > > > > > > > > > > On Tue, Jul 14, 2020 at 5:31 AM Bruno Cadonna <br...@confluent.io> > wrote: > > > > > 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 > > > > >>>> > > > > >>> > > > > >>> > > > > >> > > > > > > > > > > > > > > > > > > -- > > -- Guozhang > > >
