I have a 1 liner solution for this in KTableFilter.java and about 5-6 lines changes to existing unit test KTableFilterTest.testSendingOldValue. I included those lines with context in the JIRA. I am struggling a bit with github being new to it and how to do a proper pull request so hopefully that can be followed up by you? I had the streams test suite pass aside for a few cases that pertain specifically to this JIRA as assumptions have now changed.
On Sat, Jun 25, 2016 at 1:14 AM, Guozhang Wang <wangg...@gmail.com> wrote: > Hi Philippe, > > Great, since you agree with my reasonings, I have created a JIRA ticket for > optimizing KTableFilter (feel free to pick it up if you are interested in > contributing): > > https://issues.apache.org/jira/browse/KAFKA-3902 > > About case 3-c-1), what I meant is that since "predicate return true on > both", > the resulted pair would just be the same as the original pair. > > About KIP-63, itself is a rather big story, but it has one correspondence > to this JIRA: with caching you can dedup some records with the same key, > for example in the input records to the KTable is: > > <a: 1>, <a: 2>, <a: 3>, <a: 4>, <a: 5>, <a: 6> ... > > And the KTable is materialized into a state store with cache on top of it, > then the resulted downstream could be: > > <a: {null -> 1}>, <a: {1 -> 6}> ... > > Instead of > > <a: {null -> 1}>, <a: {1 -> 2}>, <a: {2 -> 3}>, ... <a: {5 -> 6}> ... > > So if it is piped to a filter() operator, then even less data will be > produced. > > > Guozhang > > > On Fri, Jun 24, 2016 at 5:58 PM, Philippe Derome <phder...@gmail.com> > wrote: > > > Yes, it looks very good. Your detailed explanation appears compelling > > enough to reveal that some of the details of the complexity of a streams > > system are probably inherent complexity (not that I dared assume it was > > "easy" but I could afford to be conveniently unaware). It took me 30 > > minutes to grasp this latest response. > > > > There might be a typo in your email for case 3.c.1) as I would think we > > should send the most recent pair as opposed to original, in any event it > > does not materially impact your presentation. > > > > Your case 3a) is really what triggered my line of questioning and I found > > the current behaviour vexing as it may lead to some undesirable and > > necessary filter (see Michael G. Noll's fix in UserRegionLambdaExample at > > the very end trying to weed out null) used to output to topic to console. > > Without looking at design, it seemed self-evident to me that the 3a) > > behaviour had to be implemented ( from my point of view with the code > > example I was looking at, it simply means never say to delete a key that > > was never created, simply don't "create a deleted" key). > > > > Likewise cases 3 b,c look very reasonable. > > > > Just out of curiosity, did you effectively just restate the essence of > > KIP-63 in a more approachable language I could understand or is KIP-63 > > really a different beast? > > > > > > > > On Fri, Jun 24, 2016 at 5:45 PM, Guozhang Wang <wangg...@gmail.com> > wrote: > > > > > Hello Philippe, > > > > > > Very good points, let me dump my thoughts about "KTable.filter" > > > specifically and how we can improve on that: > > > > > > 1. Some context: when a KTable participates in a downstream operators > > (e.g. > > > if that operator is an aggregation), then we need to materialize this > > > KTable and send both its old value as well as new value as a pair {old > -> > > > new} to the downstream operator. In practice it usually needs to send > the > > > pair. > > > > > > So let's discuss about them separately, take the following example > source > > > stream for your KTable > > > > > > <a: 1>, <b: 2>, <a: 3> ... > > > > > > When the KTable needs to be materialized, it will transform the source > > > messages into the pairs of: > > > > > > <a: {null -> 1}>, <b: {nul -> 2}>, <a: {1 -> 3}> > > > > > > 2. If "send old value" is not enabled, then when the filter predicate > > > returns false, we MUST send a <key: null> to the downstream operator to > > > indicate that this key is being filtered in the table. Otherwise, for > > > example if your filter is "value < 2", then the updated value <a: 3> > will > > > just be filtered, resulting in incorrect semantics. > > > > > > If it returns true we should still send the original <key: value> to > > > downstream operators. > > > > > > 3. If "send old value" is enabled, then there are a couple of cases we > > can > > > consider: > > > > > > a. If old value is <key: null> and new value is <key: not-null>, > and > > > the filter predicate return false for the new value, then in this case > it > > > is safe to optimize and not returning anything to the downstream > > operator, > > > since in this case we know there is no value for the key previously > > > anyways; otherwise we send the original pair. > > > > > > b. If old value is <key: not-null> and new value is <key: null>, > > > indicating to delete this key, and the filter predicate return false > for > > > the old value, then in this case it is safe to optimize and not > returning > > > anything to the downstream operator, since we know that the old value > has > > > already been filtered in a previous message; otherwise we send the > > original > > > pair. > > > > > > c. If both old and new values are not null, and: > > > > > > > > > 1) predicate return true on both, send the original pair; > > > > > > 2) predicate return false on both, we can optimize and do not send > > > anything; > > > > > > 3) predicate return true on old and false on new, send the key: {old > -> > > > null}; > > > > > > 4) predicate return false on old and true on new, send the key: {null > > -> > > > new}; > > > > > > Does this sounds good to you? > > > > > > > > > Guozhang > > > > > > > > > On Thu, Jun 23, 2016 at 6:17 PM, Philippe Derome <phder...@gmail.com> > > > wrote: > > > > > > > Thanks a lot for the detailed feedback, its clarity and the reference > > to > > > > KIP-63, which however is for the most part above my head for now. > > > > > > > > Having said that, I still hold the view that the behaviour I > presented > > is > > > > undesirable and hardly defensible and we may have no choice but to > > agree > > > to > > > > disagree and it could be a sterile discussion to keep at it and > > > addressing > > > > KIP-63 and other issues are more important than my brief observation. > > > > > > > > What follows supports my point of view that the filter method is not > > > > behaving as expected and I'd still think it's a defect, however I am > > > > guarded with my observation admitting my status of "total newbie" at > > > stream > > > > processing and Kafka. > > > > > > > > if we rewrite the code snippet I provided from > > > > KTable<String, *String*> regionCounts = userRegions > > > > .groupBy((userId, region) -> KeyValue.pair(region, region)) > > > > .count("CountsByRegion") > > > > .filter((regionName, count) -> false) > > > > .mapValues(count -> count.toString()); > > > > > > > > to > > > > > > > > > > > > KTable<String, Long> regionCounts1 = userRegions > > > > .groupBy((userId, region) -> KeyValue.pair(region, region)) > > > > .count("CountsByRegion"); > > > > > > > > KTable<String, String> regionCounts = regionCounts1 > > > > .filter((regionName, count) -> false) > > > > .mapValues(count -> count.toString()); > > > > > > > > > > > > It becomes clear that regionCounts1 could build up plenty of keys > with > > > > valid Long counts, normal behaviour > > > > > > > > (I think you call this a node in the topology in KIP-63 and > > > > regionCounts is a successor node). > > > > > > > > These regionCounts1 keys are then exposed to evaluation of KTable > > > > regionCounts as an input. But why should there be any key created in > > > > KTable regionCounts that has a false filter? In other words, the > > > > "optimization" > > > > > > > > seems really compelling here: do not create a key before that key > > > > becomes relevant. The key with a null value is valid and relevant in > > > > regionCounts1 but not regionCounts. By a programming composition > > > > argument, the original block > > > > > > > > of code I presented should be equivalent to the broken down one in > two > > > > blocks here (and I guess that's saying 1 unified node in the topology > > > > should be equivalent to a chain of 2 nodes represented below if I > > > > understand the terminology right). > > > > > > > > The contents of regionCounts should not change depending on the set > of > > > > keys present in regionCounts1 if we view this > > > > > > > > from a functional programming point of view (it's as if we are > > > > carrying garbage collected objects into regionCounts), which seems > > > > natural considering the method filter that is pervasive in FP. > > > > > > > > Here regionCounts is totally oblivious that aggregation took place > > > > previously in regionCounts1 and that's fine (KIP-63 talks much about > > > > aggregation but I don't really care about, I care about the 2nd node > > > > and the behaviour of filter). > > > > > > > > > > > > On Thu, Jun 23, 2016 at 6:13 PM, Guozhang Wang <wangg...@gmail.com> > > > wrote: > > > > > > > > > Hello Philippe, > > > > > > > > > > I think your question is really in two-folds: > > > > > > > > > > 1. What is the semantic difference between a KTable and a KStream, > > and > > > > more > > > > > specifically how should we interpret (key, null) in KTable? > > > > > > > > > > You can find some explanations in this documentation: > > > > > > > > > > > > > > > > > > > > http://docs.confluent.io/3.0.0/streams/concepts.html#ktable-changelog-stream > > > > > > > > > > Note that KTable itself is still a stream behind the scene, > although > > it > > > > may > > > > > be materialized when necessary. And specifically to your question, > > > (key, > > > > > null) can be treated as a tombstone on the specified key, and when > > this > > > > > KTable stream is materialized, it will result in a "delete" on > > > > materialized > > > > > view. > > > > > > > > > > > > > > > 2. As for the "filter" operator, yes it will generate a large > amount > > of > > > > > (key, null) records which indicates "delete" in the resulted > KTable, > > > and > > > > > hence large traffic to the piped topic. But we are working on > KIP-63 > > > > which > > > > > unifies the caching mechanism in the `KTable.to` operator as well > so > > > that > > > > > de-duping can be done in this operator and hence the outgoing > traffic > > > can > > > > > be largely reduced: > > > > > > > > > > > > > > > > > > > > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-63:+Unify+store+and+downstream+caching+in+streams > > > > > > > > > > > > > > > Guozhang > > > > > > > > > > > > > > > On Thu, Jun 23, 2016 at 5:50 AM, Philippe Derome < > phder...@gmail.com > > > > > > > > wrote: > > > > > > > > > > > I made a modification of latest Confluent's example > > > > > > UserRegionLambdaExample. See relevant code at end of email. > > > > > > > > > > > > Am I correct in understanding that KTable semantics should be > > similar > > > > to > > > > > a > > > > > > store-backed cache of a view as (per wikipedia on materialized > > views) > > > > or > > > > > > similar to Oracle's materialized views and indexed views? More > > > > > > specifically, I am looking at when a (key, null value) pair can > > make > > > it > > > > > > into KTable on generating table from a valid KStream with a false > > > > filter. > > > > > > > > > > > > Here's relevant code modified from example for which I observed > > that > > > > all > > > > > > keys within userRegions are sent out to topic LargeRegions with a > > > null > > > > > > value. I would think that both regionCounts KTable and topic > > > > LargeRegions > > > > > > should be empty so that the cached view agrees with the intended > > > query > > > > (a > > > > > > query with an intentional empty result set as the filter is > > > > intentionally > > > > > > false as 1 >= 2). > > > > > > > > > > > > I am not sure I understand implications properly as I am new but > it > > > > seems > > > > > > possible that a highly selective filter from a large incoming > > stream > > > > > would > > > > > > result in high memory usage for regionCounts and hence the stream > > > > > > application. > > > > > > > > > > > > KTable<String, *String*> regionCounts = userRegions > > > > > > // Count by region > > > > > > // We do not need to specify any explicit serdes because the > > key > > > > > > and value types do not change > > > > > > .groupBy((userId, region) -> KeyValue.pair(region, region)) > > > > > > .count("CountsByRegion") > > > > > > // discard any regions FOR SAKE OF EXAMPLE > > > > > > .filter((regionName, count) -> *1 >= 2*) > > > > > > .mapValues(count -> count.toString()); > > > > > > > > > > > > > > > > > > KStream<String, *String*> regionCountsForConsole = > > > > > regionCounts.toStream(); > > > > > > > > > > > > regionCountsForConsole.to(stringSerde, *stringSerde*, > > > "LargeRegions"); > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > > > > -- Guozhang > > > > > > > > > > > > > > > > > > > > > -- > > > -- Guozhang > > > > > > > > > -- > -- Guozhang >
