Hey all, I'm not convinced either epoch-aligned or data-aligned will fit all possible use cases. Both seem totally reasonable to me: data-aligned is useful for example when you know that a large number of updates to a single key will occur in short bursts, and epoch- aligned when you specifically want to get just a single update per discrete time interval.
Going a step further, though, what if you want just a single update per calendar month, or per year with accounting for leap years? Neither of those are serviced that well by the existing Windows specification to windowed aggregations, a well-known limitation of the current API. There is actually a KIP <https://cwiki.apache.org/confluence/display/KAFKA/KIP-645%3A+Replace+Windows+with+a+proper+interface> going on in parallel to fix this exact issue and make the windowing interface much more flexible. Maybe instead of re-implementing this windowing interface in a similarly limited fashion for the Distinct operator, we could leverage it here and get all the benefits coming with KIP-645. Specifically, I'm proposing to remove the TimeWindows/etc config from the DistinctParameters class, and move the distinct() method from the KStream interface to the TimeWindowedKStream interface. Since it's semantically similar to a kind of windowed aggregation, it makes sense to align it with the existing windowing framework, ie: inputStream .groupKyKey() .windowedBy() .distinct() Then we could use data-aligned windows if SlidingWindows is specified in the windowedBy(), and epoch-aligned (or some other kind of enumerable window) if a Windows is specified in windowedBy() (or an EnumerableWindowDefinition once KIP-645 is implemented to replace Windows). *SlidingWindows*: should forward a record once when it's first seen, and then not again for any identical records that fall into the next N timeUnits. This includes out-of-order records, ie if you have a SlidingWindows of size 10s and process records at time 15s, 20s, 14s then you would just forward the one at 15s. Presumably, if you're using SlidingWindows, you don't care about what falls into exact time boxes, you just want to deduplicate. If you do care about exact time boxing then you should use... *EnumerableWindowDefinition* (eg *TimeWindows*): should forward only one record per enumerated time window. If you get a records at 15s, 20s,14s where the windows are enumerated at [5,14], [15, 24], etc then you forward the record at 15s and also the record at 14s Just an idea: not sure if the impedance mismatch would throw users off since the semantics of the distinct windows are slightly different than in the aggregations. But if we don't fit this into the existing windowed framework, then we shouldn't use any existing Windows-type classes at all, imo. ie we should create a new DistinctWindows config class, similar to how stream-stream joins get their own JoinWindows class I also think that non-windowed deduplication could be useful, in which case we would want to also have the distinct() operator on the KStream interface. One quick note regarding the naming: it seems like the Streams DSL operators are typically named as verbs rather than adjectives, for example. #suppress or #aggregate. I get that there's some precedent for 'distinct' specifically, but maybe something like 'deduplicate' would be more appropriate for the Streams API. WDYT? On Mon, Sep 14, 2020 at 10:04 AM Ivan Ponomarev <iponoma...@mail.ru.invalid> wrote: > Hi Matthias, > > Thanks for your review! It made me think deeper, and indeed I understood > that I was missing some important details. > > To simplify, let me explain my particular use case first so I can refer > to it later. > > We have a system that collects information about ongoing live sporting > events from different sources. The information sources have their IDs > and these IDs are keys of the stream. Each source emits messages > concerning sporting events, and we can have many messages about each > sporing event from each source. Event ID is extracted from the message. > > We need a database of event IDs that were reported at least once by each > source (important: events from different sources are considered to be > different entities). The requirements are: > > 1) each new event ID should be written to the database as soon as possible > > 2) although it's ok and sometimes even desired to repeat the > notification about already known event ID, but we wouldn’t like our > database to be bothered by the same event ID more often than once in a > given period of time (say, 15 minutes). > > With this example in mind let me answer your questions > > > (1) Using the `idExtractor` has the issue that data might not be > > co-partitioned as you mentioned in the KIP. Thus, I am wondering if it > > might be better to do deduplication only on the key? If one sets a new > > key upstream (ie, extracts the deduplication id into the key), the > > `distinct` operator could automatically repartition the data and thus we > > would avoid user errors. > > Of course with 'key-only' deduplication + autorepartitioning we will > never cause problems with co-partitioning. But in practice, we often > don't need repartitioning even if 'dedup ID' is different from the key, > like in my example above. So here we have a sort of 'performance vs > security' tradeoff. > > The 'golden middle way' here can be the following: we can form a > deduplication ID as KEY + separator + idExtractor(VALUE). In case > idExtractor is not provided, we deduplicate by key only (as in original > proposal). Then idExtractor transforms only the value (and not the key) > and its result is appended to the key. Records from different partitions > will inherently have different deduplication IDs and all the data will > be co-partitioned. As with any stateful operation, we will repartition > the topic in case the key was changed upstream, but only in this case, > thus avoiding unnecessary repartitioning. My example above fits this > perfectly. > > > (2) What is the motivation for allowing the `idExtractor` to return > > `null`? Might be good to have some use-case examples for this feature. > > Can't think of any use-cases. As it often happens, it's just came with a > copy-paste from StackOverflow -- see Michael Noll's answer here: > > https://stackoverflow.com/questions/55803210/how-to-handle-duplicate-messages-using-kafka-streaming-dsl-functions > > But, jokes aside, we'll have to decide what to do with nulls. If we > accept the above proposal of having deduplication ID as KEY + postfix, > then null can be treated as no postfix at all. If we don't accept this > approach, then treating nulls as 'no-deduplication' seems to be a > reasonable assumption (we can't get or put null as a key to a KV store, > so a record with null ID is always going to look 'new' for us). > > > > (2) Is using a `TimeWindow` really what we want? I was wondering if a > > `SlidingWindow` might be better? Or maybe we need a new type of window? > > Agree. It's probably not what we want. Once I thought that reusing > TimeWindow is a clever idea, now I don't. > > Do we need epoch alignment in our use case? No, we don't, and I don't > know if anyone going to need this. Epoch alignment is good for > aggregation, but deduplication is a different story. > > Let me describe the semantic the way I see it now and tell me what you > think: > > - the only parameter that defines the deduplication logic is 'expiration > period' > > - when a deduplication ID arrives and we cannot find it in the store, we > forward the message downstream and store the ID + its timestamp. > > - when an out-of-order ID arrives with an older timestamp and we find a > 'fresher' record, we do nothing and don't forward the message (??? OR > NOT? In what case would we want to forward an out-of-order message?) > > - when an ID with fresher timestamp arrives we check if it falls into > the expiration period and either forward it or not, but in both cases we > update the timestamp of the message in the store > > - the WindowStore retention mechanism should clean up very old records > in order not to run out of space. > > > (3) `isPersistent` -- instead of using this flag, it seems better to > > allow users to pass in a `Materialized` parameter next to > > `DistinctParameters` to configure the state store? > > Fully agree! Users might also want to change the retention time. > > > (4) I am wondering if we should really have 4 overloads for > > `DistinctParameters.with()`? It might be better to have one overload > > with all require parameters, and add optional parameters using the > > builder pattern? This seems to follow the DSL Grammer proposal. > > Oh, I can explain. We can't fully rely on the builder pattern because of > Java type inference limitations. We have to provide type parameters to > the builder methods or the code won't compile: see e. g. this > https://twitter.com/inponomarev/status/1265053286933159938 and following > discussion with Tagir Valeev. > > When we came across the similar difficulties in KIP-418, we finally > decided to add all the necessary overloads to parameter class. So I just > reproduced that approach here. > > > (5) Even if it might be an implementation detail (and maybe the KIP > > itself does not need to mention it), can you give a high level overview > > how you intent to implement it (that would be easier to grog, compared > > to reading the PR). > > Well as with any operation on KStreamImpl level I'm building a store and > a processor node. > > KStreamDistinct class is going to be the ProcessorSupplier, with the > logic regarding the forwarding/muting of the records located in > KStreamDistinct.KStreamDistinctProcessor#process > > ---- > > Matthias, if you are still reading this :-) a gentle reminder: my PR for > already accepted KIP-418 is still waiting for your review. I think it's > better for me to finalize at least one KIP before proceeding to a new > one :-) > > Regards, > > Ivan > > 03.09.2020 4:20, Matthias J. Sax пишет: > > Thanks for the KIP Ivan. Having a built-in deduplication operator is for > > sure a good addition. > > > > Couple of questions: > > > > (1) Using the `idExtractor` has the issue that data might not be > > co-partitioned as you mentioned in the KIP. Thus, I am wondering if it > > might be better to do deduplication only on the key? If one sets a new > > key upstream (ie, extracts the deduplication id into the key), the > > `distinct` operator could automatically repartition the data and thus we > > would avoid user errors. > > > > (2) What is the motivation for allowing the `idExtractor` to return > > `null`? Might be good to have some use-case examples for this feature. > > > > (2) Is using a `TimeWindow` really what we want? I was wondering if a > > `SlidingWindow` might be better? Or maybe we need a new type of window? > > > > It would be helpful if you could describe potential use cases in more > > detail. -- I am mainly wondering about hopping window? Each record would > > always falls into multiple window and thus would be emitted multiple > > times, ie, each time the window closes. Is this really a valid use case? > > > > It seems that for de-duplication, one wants to have some "expiration > > time", ie, for each ID, deduplicate all consecutive records with the > > same ID and emit the first record after the "expiration time" passed. In > > terms of a window, this would mean that the window starts at `r.ts` and > > ends at `r.ts + windowSize`, ie, the window is aligned to the data. > > TimeWindows are aligned to the epoch though. While `SlidingWindows` also > > align to the data, for the aggregation use-case they go backward in > > time, while we need a window that goes forward in time. It's an open > > question if we can re-purpose `SlidingWindows` -- it might be ok the > > make the alignment (into the past vs into the future) an operator > > dependent behavior? > > > > (3) `isPersistent` -- instead of using this flag, it seems better to > > allow users to pass in a `Materialized` parameter next to > > `DistinctParameters` to configure the state store? > > > > (4) I am wondering if we should really have 4 overloads for > > `DistinctParameters.with()`? It might be better to have one overload > > with all require parameters, and add optional parameters using the > > builder pattern? This seems to follow the DSL Grammer proposal. > > > > (5) Even if it might be an implementation detail (and maybe the KIP > > itself does not need to mention it), can you give a high level overview > > how you intent to implement it (that would be easier to grog, compared > > to reading the PR). > > > > > > > > -Matthias > > > > On 8/23/20 4:29 PM, Ivan Ponomarev wrote: > >> Sorry, I forgot to add [DISCUSS] tag to the topic > >> > >> 24.08.2020 2:27, Ivan Ponomarev пишет: > >>> Hello, > >>> > >>> I'd like to start a discussion for KIP-655. > >>> > >>> KIP-655: > >>> > https://cwiki.apache.org/confluence/display/KAFKA/KIP-655%3A+Windowed+Distinct+Operation+for+Kafka+Streams+API > >>> > >>> > >>> I also opened a proof-of-concept PR for you to experiment with the API: > >>> > >>> PR#9210: https://github.com/apache/kafka/pull/9210 > >>> > >>> Regards, > >>> > >>> Ivan Ponomarev > >> > > > >
