kafka-users mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Guozhang Wang <wangg...@gmail.com>
Subject Re: [DISCUSS] Streams DSL/StateStore Refactoring
Date Fri, 07 Jul 2017 08:30:13 GMT
Hi Damian / Kyle,

I think I agree with you guys about the pros / cons of using the builder
pattern v.s. using some "secondary classes". And I'm thinking if we can
take a "mid" manner between these two. I spent some time with a slight
different approach from Damian's current proposal:

https://github.com/guozhangwang/kafka/blob/dsl-refactor/streams/src/main/java/org/apache/kafka/streams/RefactoredAPIs.java

The key idea is to tolerate the final "table()" or "stream()" function to
"upgrade" from the secondary classes to the first citizen classes, while
having all the specs inside this function. Also this proposal includes some
other refactoring that people have been discussed about for the builder to
reduce the overloaded functions as well. WDYT?


Guozhang


On Tue, Jul 4, 2017 at 1:40 AM, Damian Guy <damian.guy@gmail.com> wrote:

> Hi Jan,
>
> Thanks very much for the input.
>
> On Tue, 4 Jul 2017 at 08:54 Jan Filipiak <Jan.Filipiak@trivago.com> wrote:
>
> > Hi Damian,
> >
> > I do see your point of something needs to change. But I fully agree with
> > Gouzhang when he says.
> > ---
> >
> > But since this is a incompatibility change, and we are going to remove
> the
> > compatibility annotations soon it means we only have one chance and we
> > really have to make it right.
> > ----
> >
> >
> I think we all agree on this one! Hence the discussion.
>
>
> > I fear all suggestions do not go far enough to become something that will
> > carry on for very much longer.
> > I am currently working on KAFKA-3705 and try to find the most easy way
> for
> > the user to give me all the required functionality. The easiest
> interface I
> > could come up so far can be looked at here.
> >
> >
> > https://github.com/Kaiserchen/kafka/blob/3da2b8f787a5d30dee2de71cf0f125
> ab3e57d89b/streams/src/main/java/org/apache/kafka/streams/
> kstream/internals/KTableImpl.java#L622
> >
> >
> And its already horribly complicated. I am currently unable to find the
> > right abstraction level to have everything falling into place naturally.
> To
> > be honest I already think introducing
> >
> >
> To be fair that is not a particularly easy problem to solve!
>
>
> >
> > https://github.com/Kaiserchen/kafka/blob/3da2b8f787a5d30dee2de71cf0f125
> ab3e57d89b/streams/src/main/java/org/apache/kafka/streams/
> kstream/internals/KTableImpl.java#L493
> >
> > was unideal and makes everything a mess.
>
>
> I'm not sure i agree that it makes everything a mess, but It could have
> been done differently.
>
> The JoinType:Whatever is also not really flexible. 2 things come to my
> mind:
> >
> > 1. I don't think we should rule out config based decisions say configs
> like
> >         streams.$applicationID.joins.$joinname.conf = value
> >
>
> Is this just for config? Or are you suggesting that we could somehow "code"
> the join in a config file?
>
>
> > This can allow for tremendous changes without single API change and IMO
> it
> > was not considered enough yet.
> >
> > 2. Push logic from the DSL to the Callback classes. A ValueJoiner for
> > example can be used to implement different join types as the user wishes.
> >
>
> Do you have an example of how this might look?
>
>
> > As Gouzhang said: stopping to break users is very important.
>
>
> Of course. We want to make it as easy as possible for people to use
> streams.
>
>
> especially with this changes + All the plans I sadly only have in my head
> > but hopefully the first link can give a glimpse.
> >
> > Thanks for preparing the examples made it way clearer to me what exactly
> > we are talking about. I would argue to go a bit slower and more carefull
> on
> > this one. At some point we need to get it right. Peeking over to the
> hadoop
> > guys with their hughe userbase. Config files really work well for them.
> >
> > Best Jan
> >
> >
> >
> >
> >
> > On 30.06.2017 09:31, Damian Guy wrote:
> > > Thanks Matthias
> > >
> > > On Fri, 30 Jun 2017 at 08:05 Matthias J. Sax <matthias@confluent.io>
> > wrote:
> > >
> > >> I am just catching up on this thread, so sorry for the long email in
> > >> advance... Also, it's to some extend a dump of thoughts and not
> always a
> > >> clear proposal. Still need to think about this in more detail. But
> maybe
> > >> it helps other to get new ideas :)
> > >>
> > >>
> > >>>> However, I don't understand your argument about putting aggregate()
> > >>>> after the withXX() -- all the calls to withXX() set optional
> > parameters
> > >>>> for aggregate() and not for groupBy() -- but a groupBy().withXX()
> > >>>> indicates that the withXX() belongs to the groupBy(). IMHO, this
> might
> > >>>> be quite confusion for developers.
> > >>>>
> > >>>>
> > >>> I see what you are saying, but the grouped stream is effectively a
> > no-op
> > >>> until you call one of the aggregate/count/reduce etc functions. So
> the
> > >>> optional params are ones that are applicable to any of the operations
> > you
> > >>> can perform on this grouped stream. Then the final
> > >>> count()/reduce()/aggregate() call has any of the params that are
> > >>> required/specific to that function.
> > >>>
> > >> I understand your argument, but you don't share the conclusion. If we
> > >> need a "final/terminal" call, the better way might be
> > >>
> > >> .groupBy().count().withXX().build()
> > >>
> > >> (with a better name for build() though)
> > >>
> > >>
> > > The point is that all the other calls, i.e,withBlah, windowed, etc
> apply
> > > too all the aggregate functions. The terminal call being the actual
> type
> > of
> > > aggregation you want to do. I personally find this more natural than
> > > groupBy().count().withBlah().build()
> > >
> > >
> > >>> groupedStream.count(/** non windowed count**/)
> > >>> groupedStream.windowed(TimeWindows.of(10L)).count(...)
> > >>> groupedStream.sessionWindowed(SessionWindows.of(10L)).count(...)
> > >>
> > >> I like this. However, I don't see a reason to have windowed() and
> > >> sessionWindowed(). We should have one top-level `Windows` interface
> that
> > >> both `TimeWindows` and `SessionWindows` implement and just have a
> single
> > >> windowed() method that accepts all `Windows`. (I did not like the
> > >> separation of `SessionWindows` in the first place, and this seems to
> be
> > >> an opportunity to clean this up. It was hard to change when we
> > >> introduced session windows)
> > >>
> > > Yes - true we should look into that.
> > >
> > >
> > >> Btw: we do you the imperative groupBy() and groupByKey(), and thus we
> > >> might also want to use windowBy() (instead of windowed()). Not sure
> how
> > >> important this is, but it seems to be inconsistent otherwise.
> > >>
> > >>
> > > Makes sense
> > >
> > >
> > >> About joins:  I don't like .withJoinType(JoinType.LEFT) at all. I
> think,
> > >> defining an inner/left/outer join is not an optional argument but a
> > >> first class concept and should have a proper representation in the API
> > >> (like the current methods join(), leftJoin, outerJoin()).
> > >>
> > >>
> > > Yep, i did originally have it as a required param and maybe that is
> what
> > we
> > > go with. It could have a default, but maybe that is confusing.
> > >
> > >
> > >
> > >> About the two join API proposals, the second one has too much boiler
> > >> plate code for my taste. Also, the actual join() operator has only one
> > >> argument what is weird to me, as in my thinking process, the main
> > >> operator call, should have one parameter per mandatory argument but
> your
> > >> proposal put the mandatory arguments into Joins.streamStreamJoin()
> call.
> > >> This is far from intuitive IMHO.
> > >>
> > >>
> > > This is the builder pattern, you only need one param as the builder has
> > > captured all of the required and optional arguments.
> > >
> > >
> > >> The first join proposal also seems to align better with the pattern
> > >> suggested for aggregations and having the same pattern for all
> operators
> > >> is important (as you stated already).
> > >>
> > >>
> > > This is why i offered two alternatives as i started out with. 1 is the
> > > builder pattern, the other is the more fluent pattern.
> > >
> > >
> > >>
> > >> Coming back to the config vs optional parameter. What about having a
> > >> method withConfig[s](...) that allow to put in the configuration?
> > >>
> > >>
> > > Sure, it is currently called withLogConfig() as that is the only thing
> > that
> > > is really config.
> > >
> > >
> > >> This also raises the question if until() is a windows property?
> > >> Actually, until() seems to be a configuration parameter and thus,
> should
> > >> not not have it's own method.
> > >>
> > >>
> > > Hmmm, i don't agree. Until is a property of the window. It is going to
> be
> > > potentially different for every window operation you do in a streams
> app.
> > >
> > >
> > >>
> > >> Browsing throw your example DSL branch, I also saw this one:
> > >>
> > >>> final KTable<Windowed<String>, Long> windowed>
> > >>   groupedStream.counting()
> > >>>                   .windowed(TimeWindows.of(10L).until(10))
> > >>>                   .table();
> > >> This is an interesting idea, and it remind my on some feedback about
> "I
> > >> wanted to count a stream, but there was no count() method -- I first
> > >> needed to figure out, that I need to group the stream first to be able
> > >> to count it. It does make sense in hindsight but was not obvious in
> the
> > >> beginning". Thus, carrying out this thought, we could also do the
> > >> following:
> > >>
> > >> stream.count().groupedBy().windowedBy().table();
> > >>
> > >> -> Note, I use "grouped" and "windowed" instead of imperative here, as
> > >> it comes after the count()
> > >>
> > >> This would be more consistent than your proposal (that has grouping
> > >> before but windowing after count()). It might even allow us to enrich
> > >> the API with a some syntactic sugar like `stream.count().table()` to
> get
> > >> the overall count of all records (this would obviously not scale, but
> we
> > >> could support it -- if not now, maybe later).
> > >>
> > >>
> > > I guess i'd prefer
> > > stream.groupBy().windowBy().count()
> > > stream.groupBy().windowBy().reduce()
> > > stream.groupBy().count()
> > >
> > > As i said above, everything that happens before the final aggregate
> call
> > > can be applied to any of them. So it makes sense to me to do those
> things
> > > ahead of the final aggregate call.
> > >
> > >
> > >> Last about builder pattern. I am convinced that we need some
> "terminal"
> > >> operator/method that tells us when to add the processor to the
> topology.
> > >> But I don't see the need for a plain builder pattern that feels alien
> to
> > >> me (see my argument about the second join proposal). Using .stream() /
> > >> .table() as use in many examples might work. But maybe a more generic
> > >> name that we can use in all places like build() or apply() might also
> be
> > >> an option.
> > >>
> > >>
> > > Sure, a generic name might be ok.
> > >
> > >
> > >
> > >
> > >> -Matthias
> > >>
> > >>
> > >>
> > >> On 6/29/17 7:37 AM, Damian Guy wrote:
> > >>> Thanks Kyle.
> > >>>
> > >>> On Thu, 29 Jun 2017 at 15:11 Kyle Winkelman <
> winkelman.kyle@gmail.com>
> > >>> wrote:
> > >>>
> > >>>> Hi Damian,
> > >>>>
> > >>>>>>>> When trying to program in the fluent API that has been discussed
> > >> most
> > >>>> it
> > >>>>>>>> feels difficult to know when you will actually get an object you
> > can
> > >>>> reuse.
> > >>>>>>>> What if I make one KGroupedStream that I want to reuse, is it
> > legal
> > >> to
> > >>>>>>>> reuse it or does this approach expect you to call grouped each
> > time?
> > >>>>>> I'd anticipate that once you have a KGroupedStream you can re-use
> it
> > >> as
> > >>>> you
> > >>>>>> can today.
> > >>>> You said it yourself in another post that the grouped stream is
> > >>>> effectively a no-op until a count, reduce, or aggregate. The way I
> see
> > >> it
> > >>>> you wouldn’t be able to reuse anything except KStreams and KTables,
> > >> because
> > >>>> most of this fluent api would continue returning this (this being
> the
> > >>>> builder object currently being manipulated).
> > >>> So, if you ever store a reference to anything but KStreams and
> KTables
> > >> and
> > >>>> you use it in two different ways then its possible you make
> > conflicting
> > >>>> withXXX() calls on the same builder.
> > >>>>
> > >>>>
> > >>> No necessarily true. It could return a new instance of the builder,
> > i.e.,
> > >>> the builders being immutable. So if you held a reference to the
> builder
> > >> it
> > >>> would always be the same as it was when it was created.
> > >>>
> > >>>
> > >>>> GroupedStream<K,V> groupedStreamWithDefaultSerdes =
> kStream.grouped();
> > >>>> GroupedStream<K,V> groupedStreamWithDeclaredSerdes =
> > >>>> groupedStreamsWithDefaultSerdes.withKeySerde(…).withValueSerde(…);
> > >>>>
> > >>>> I’ll admit that this shouldn’t happen but some user is going to do
> it
> > >>>> eventually…
> > >>>> Depending on implementation uses of groupedStreamWithDefaultSerdes
> > would
> > >>>> most likely be equivalent to the version withDeclaredSerdes. One
> work
> > >>>> around would be to always make copies of the config objects you are
> > >>>> building, but this approach has its own problem because now we have
> to
> > >>>> identify which configs are equivalent so we don’t create repeated
> > >>>> processors.
> > >>>>
> > >>>> The point of this long winded example is that we always have to be
> > >>>> thinking about all of the possible ways it could be misused by a
> user
> > >>>> (causing them to see hard to diagnose problems).
> > >>>>
> > >>> Exactly! That is the point of the discussion really.
> > >>>
> > >>>
> > >>>> In my attempt at a couple methods with builders I feel that I could
> > >>>> confidently say the user couldn’t really mess it up.
> > >>>>> // Count
> > >>>>> KTable<String, Long> count =
> > >>>>>
> > kGroupedStream.count(Count.count().withQueryableStoreName("my-store"));
> > >>>> The kGroupedStream is reusable and if they attempted to reuse the
> > Count
> > >>>> for some reason it would throw an error message saying that a store
> > >> named
> > >>>> “my-store” already exists.
> > >>>>
> > >>>>
> > >>> Yes i agree and i think using builders is my preferred pattern.
> > >>>
> > >>> Cheers,
> > >>> Damian
> > >>>
> > >>>
> > >>>> Thanks,
> > >>>> Kyle
> > >>>>
> > >>>> From: Damian Guy
> > >>>> Sent: Thursday, June 29, 2017 3:59 AM
> > >>>> To: dev@kafka.apache.org
> > >>>> Subject: Re: [DISCUSS] Streams DSL/StateStore Refactoring
> > >>>>
> > >>>> Hi Kyle,
> > >>>>
> > >>>> Thanks for your input. Really appreciated.
> > >>>>
> > >>>> On Thu, 29 Jun 2017 at 06:09 Kyle Winkelman <
> winkelman.kyle@gmail.com
> > >
> > >>>> wrote:
> > >>>>
> > >>>>> I like more of a builder pattern even though others have voiced
> > against
> > >>>>> it. The reason I like it is because it makes it clear to the user
> > that
> > >> a
> > >>>>> call to KGroupedStream#count will return a KTable not some
> > intermediate
> > >>>>> class that I need to undetstand.
> > >>>>>
> > >>>> Yes, that makes sense.
> > >>>>
> > >>>>
> > >>>>> When trying to program in the fluent API that has been discussed
> most
> > >> it
> > >>>>> feels difficult to know when you will actually get an object you
> can
> > >>>> reuse.
> > >>>>> What if I make one KGroupedStream that I want to reuse, is it legal
> > to
> > >>>>> reuse it or does this approach expect you to call grouped each
> time?
> > >>>>
> > >>>> I'd anticipate that once you have a KGroupedStream you can re-use it
> > as
> > >> you
> > >>>> can today.
> > >>>>
> > >>>>
> > >>>>> This question doesn’t pop into my head at all in the builder
> pattern
> > I
> > >>>>> assume I can reuse everything.
> > >>>>> Finally, I like .groupByKey and .groupBy(KeyValueMapper) not a big
> > fan
> > >> of
> > >>>>> the grouped.
> > >>>>>
> > >>>>> Yes, grouped() was more for demonstration and because groupBy() and
> > >>>> groupByKey() were taken! So i'd imagine the api would actually want
> to
> > >> be
> > >>>> groupByKey(/** no required args***/).withOptionalArg() and
> > >>>> groupBy(KeyValueMapper m).withOpitionalArg(...)  of course this all
> > >> depends
> > >>>> on maintaining backward compatibility.
> > >>>>
> > >>>>
> > >>>>> Unfortunately, the below approach would require atleast 2 (probably
> > 3)
> > >>>>> overloads (one for returning a KTable and one for returning a
> KTable
> > >> with
> > >>>>> Windowed Key, probably would want to split windowed and
> > sessionwindowed
> > >>>> for
> > >>>>> ease of implementation) of each count, reduce, and aggregate.
> > >>>>> Obviously not exhaustive but enough for you to get the picture.
> > Count,
> > >>>>> Reduce, and Aggregate supply 3 static methods to initialize the
> > >> builder:
> > >>>>> // Count
> > >>>>> KTable<String, Long> count =
> > >>>>>
> > groupedStream.count(Count.count().withQueryableStoreName("my-store"));
> > >>>>>
> > >>>>> // Windowed Count
> > >>>>> KTable<Windowed<String>, Long> windowedCount =
> > >>>>>
> > >>
> > groupedStream.count(Count.windowed(TimeWindows.of(10L).until(10)).
> withQueryableStoreName("my-windowed-store"));
> > >>>>> // Session Count
> > >>>>> KTable<Windowed<String>, Long> sessionCount =
> > >>>>>
> > >>
> > groupedStream.count(Count.sessionWindowed(SessionWindows.with(10L)).
> withQueryableStoreName("my-session-windowed-store"));
> > >>>>>
> > >>>> Above and below, i think i'd prefer it to be:
> > >>>> groupedStream.count(/** non windowed count**/)
> > >>>> groupedStream.windowed(TimeWindows.of(10L)).count(...)
> > >>>> groupedStream.sessionWindowed(SessionWindows.of(10L)).count(...)
> > >>>>
> > >>>>
> > >>>>
> > >>>>
> > >>>>> // Reduce
> > >>>>> Reducer<Long> reducer;
> > >>>>> KTable<String, Long> reduce = groupedStream.reduce(reducer,
> > >>>>> Reduce.reduce().withQueryableStoreName("my-store"));
> > >>>>>
> > >>>>> // Aggregate Windowed with Custom Store
> > >>>>> Initializer<String> initializer;
> > >>>>> Aggregator<String, Long, String> aggregator;
> > >>>>> KTable<Windowed<String>, String> aggregate =
> > >>>>> groupedStream.aggregate(initializer, aggregator,
> > >>>>>
> > >>
> > Aggregate.windowed(TimeWindows.of(10L).until(10))
> .withStateStoreSupplier(stateStoreSupplier)));
> > >>>>> // Cogroup SessionWindowed
> > >>>>> KTable<String, String> cogrouped =
> > groupedStream1.cogroup(aggregator1)
> > >>>>>          .cogroup(groupedStream2, aggregator2)
> > >>>>>          .aggregate(initializer, aggregator,
> > >>>>> Aggregate.sessionWindowed(SessionWindows.with(10L),
> > >>>>> sessionMerger).withQueryableStoreName("my-store"));
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> public class Count {
> > >>>>>
> > >>>>>      public static class Windowed extends Count {
> > >>>>>          private Windows windows;
> > >>>>>      }
> > >>>>>      public static class SessionWindowed extends Count {
> > >>>>>          private SessionWindows sessionWindows;
> > >>>>>      }
> > >>>>>
> > >>>>>      public static Count count();
> > >>>>>      public static Windowed windowed(Windows windows);
> > >>>>>      public static SessionWindowed sessionWindowed(SessionWindows
> > >>>>> sessionWindows);
> > >>>>>
> > >>>>>      // All withXXX(...) methods.
> > >>>>> }
> > >>>>>
> > >>>>> public class KGroupedStream {
> > >>>>>      public KTable<K, Long> count(Count count);
> > >>>>>      public KTable<Windowed<K>, Long> count(Count.Windowed count);
> > >>>>>      public KTable<Windowed<K>, Long> count(Count.SessionWindowed
> > >> count);
> > >>>>> …
> > >>>>> }
> > >>>>>
> > >>>>>
> > >>>>> Thanks,
> > >>>>> Kyle
> > >>>>>
> > >>>>> From: Guozhang Wang
> > >>>>> Sent: Wednesday, June 28, 2017 7:45 PM
> > >>>>> To: dev@kafka.apache.org
> > >>>>> Subject: Re: [DISCUSS] Streams DSL/StateStore Refactoring
> > >>>>>
> > >>>>> I played the current proposal a bit with
> > >> https://github.com/dguy/kafka/
> > >>>>> tree/dsl-experiment <
> > https://github.com/dguy/kafka/tree/dsl-experiment
> > >>> ,
> > >>>>> and here are my observations:
> > >>>>>
> > >>>>> 1. Personally I prefer
> > >>>>>
> > >>>>>      "stream.group(mapper) / stream.groupByKey()"
> > >>>>>
> > >>>>> than
> > >>>>>
> > >>>>>      "stream.group().withKeyMapper(mapper) / stream.group()"
> > >>>>>
> > >>>>> Since 1) withKeyMapper is not enforced programmatically though it
> is
> > >> not
> > >>>>> "really" optional like others, 2) syntax-wise it reads more
> natural.
> > >>>>>
> > >>>>> I think it is okay to add the APIs in (
> > >>>>>
> > >>>>>
> > >>
> > https://github.com/dguy/kafka/blob/dsl-experiment/streams/
> src/main/java/org/apache/kafka/streams/kstream/GroupedStream.java
> > >>>>> )
> > >>>>> in KGroupedStream.
> > >>>>>
> > >>>>>
> > >>>>> 2. For the "withStateStoreSupplier" API, are the user supposed to
> > pass
> > >> in
> > >>>>> the most-inner state store supplier (e.g. then one whose get()
> return
> > >>>>> RocksDBStore), or it is supposed to return the most-outer supplier
> > with
> > >>>>> logging / metrics / etc? I think it would be more useful to only
> > >> require
> > >>>>> users pass in the inner state store supplier while specifying
> > caching /
> > >>>>> logging through other APIs.
> > >>>>>
> > >>>>> In addition, the "GroupedWithCustomStore" is a bit suspicious to
> me:
> > we
> > >>>> are
> > >>>>> allowing users to call other APIs like "withQueryableName" multiple
> > >> time,
> > >>>>> but only call "withStateStoreSupplier" only once in the end. Why is
> > >> that?
> > >>>>>
> > >>>>> 3. The current DSL seems to be only for aggregations, what about
> > joins?
> > >>>>>
> > >>>>>
> > >>>>> 4. I think it is okay to keep the "withLogConfig": for the
> > >>>>> StateStoreSupplier it will still be user code specifying the
> topology
> > >> so
> > >>>> I
> > >>>>> do not see there is a big difference.
> > >>>>>
> > >>>>>
> > >>>>> 5. "WindowedGroupedStream" 's withStateStoreSupplier should take
> the
> > >>>>> windowed state store supplier to enforce typing?
> > >>>>>
> > >>>>>
> > >>>>> Below are minor ones:
> > >>>>>
> > >>>>> 6. "withQueryableName": maybe better "withQueryableStateName"?
> > >>>>>
> > >>>>> 7. "withLogConfig": maybe better "withLoggingTopicConfig()"?
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> Guozhang
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> On Wed, Jun 28, 2017 at 3:59 PM, Matthias J. Sax <
> > >> matthias@confluent.io>
> > >>>>> wrote:
> > >>>>>
> > >>>>>> I see your point about "when to add the processor to the
> topology".
> > >>>> That
> > >>>>>> is indeed an issue. Not sure it we could allow "updates" to the
> > >>>>> topology...
> > >>>>>> I don't see any problem with having all the withXX() in KTable
> > >>>> interface
> > >>>>>> -- but this might be subjective.
> > >>>>>>
> > >>>>>>
> > >>>>>> However, I don't understand your argument about putting
> aggregate()
> > >>>>>> after the withXX() -- all the calls to withXX() set optional
> > >> parameters
> > >>>>>> for aggregate() and not for groupBy() -- but a groupBy().withXX()
> > >>>>>> indicates that the withXX() belongs to the groupBy(). IMHO, this
> > might
> > >>>>>> be quite confusion for developers.
> > >>>>>>
> > >>>>>>
> > >>>>>> -Matthias
> > >>>>>>
> > >>>>>> On 6/28/17 2:55 AM, Damian Guy wrote:
> > >>>>>>>> I also think that mixing optional parameters with configs is a
> bad
> > >>>>> idea.
> > >>>>>>>> Have not proposal for this atm but just wanted to mention it.
> Hope
> > >>>> to
> > >>>>>>>> find some time to come up with something.
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>> Yes, i don't like the mix of config either. But the only real
> > config
> > >>>>> here
> > >>>>>>> is the logging config - which we don't really need as it can
> > already
> > >>>> be
> > >>>>>>> done via a custom StateStoreSupplier.
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>> What I don't like in the current proposal is the
> > >>>>>>>> .grouped().withKeyMapper() -- the current solution with
> > >>>> .groupBy(...)
> > >>>>>>>> and .groupByKey() seems better. For clarity, we could rename to
> > >>>>>>>> .groupByNewKey(...) and .groupByCurrentKey() (even if we should
> > find
> > >>>>>>>> some better names).
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>> it could be groupByKey(), groupBy() or something different bt
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>> The proposed pattern "chains" grouping and aggregation too close
> > >>>>>>>> together. I would rather separate both more than less, ie, do
> into
> > >>>> the
> > >>>>>>>> opposite direction.
> > >>>>>>>>
> > >>>>>>>> I am also wondering, if we could so something more "fluent". The
> > >>>>> initial
> > >>>>>>>> proposal was like:
> > >>>>>>>>
> > >>>>>>>>>> groupedStream.count()
> > >>>>>>>>>>     .withStoreName("name")
> > >>>>>>>>>>     .withCachingEnabled(false)
> > >>>>>>>>>>     .withLoggingEnabled(config)
> > >>>>>>>>>>     .table()
> > >>>>>>>> The .table() statement in the end was kinda alien.
> > >>>>>>>>
> > >>>>>>> I agree, but then all of the withXXX methods need to be on KTable
> > >>>> which
> > >>>>>> is
> > >>>>>>> worse in my opinion. You also need something that is going to
> > "build"
> > >>>>> the
> > >>>>>>> internal processors and add them to the topology.
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>> The current proposal put the count() into the end -- ie, the
> > >>>> optional
> > >>>>>>>> parameter for count() have to specified on the .grouped() call
> --
> > >>>> this
> > >>>>>>>> does not seems to be the best way either.
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>> I actually prefer this method as you are building a grouped
> stream
> > >>>> that
> > >>>>>> you
> > >>>>>>> will aggregate. So
> > >>>> table.grouped(...).withOptionalStuff().aggregate(..)
> > >>>>>> etc
> > >>>>>>> seems natural to me.
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>> I did not think this through in detail, but can't we just do the
> > >>>>> initial
> > >>>>>>>> proposal with the .table() ?
> > >>>>>>>>
> > >>>>>>>> groupedStream.count().withStoreName("name").mapValues(...)
> > >>>>>>>>
> > >>>>>>>> Each .withXXX(...) return the current KTable and all the
> > .withXXX()
> > >>>>> are
> > >>>>>>>> just added to the KTable interface. Or do I miss anything why
> this
> > >>>>> wont'
> > >>>>>>>> work or any obvious disadvantage?
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>> See above.
> > >>>>>>>
> > >>>>>>>
> > >>>>>>>> -Matthias
> > >>>>>>>>
> > >>>>>>>> On 6/22/17 4:06 AM, Damian Guy wrote:
> > >>>>>>>>> Thanks everyone. My latest attempt is below. It builds on the
> > >>>> fluent
> > >>>>>>>>> approach, but i think it is slightly nicer.
> > >>>>>>>>> I agree with some of what Eno said about mixing configy stuff
> in
> > >>>> the
> > >>>>>> DSL,
> > >>>>>>>>> but i think that enabling caching and enabling logging are
> things
> > >>>>> that
> > >>>>>>>>> aren't actually config. I'd probably not add withLogConfig(...)
> > >>>> (even
> > >>>>>>>>> though it is below) as this is actually config and we already
> > have
> > >>>> a
> > >>>>>> way
> > >>>>>>>> of
> > >>>>>>>>> doing that, via the StateStoreSupplier. Arguably we could use
> the
> > >>>>>>>>> StateStoreSupplier for disabling caching etc, but as it stands
> > that
> > >>>>> is
> > >>>>>> a
> > >>>>>>>>> bit of a tedious process for someone that just wants to use the
> > >>>>> default
> > >>>>>>>>> storage engine, but not have caching enabled.
> > >>>>>>>>>
> > >>>>>>>>> There is also an orthogonal concern that Guozhang alluded
> to....
> > If
> > >>>>> you
> > >>>>>>>>> want to plug in a custom storage engine and you want it to be
> > >>>> logged
> > >>>>>> etc,
> > >>>>>>>>> you would currently need to implement that yourself. Ideally we
> > can
> > >>>>>>>> provide
> > >>>>>>>>> a way where we will wrap the custom store with logging,
> metrics,
> > >>>>> etc. I
> > >>>>>>>>> need to think about where this fits, it is probably more
> > >>>> appropriate
> > >>>>> on
> > >>>>>>>> the
> > >>>>>>>>> Stores API.
> > >>>>>>>>>
> > >>>>>>>>> final KeyValueMapper<String, String, Long> keyMapper = null;
> > >>>>>>>>> // count with mapped key
> > >>>>>>>>> final KTable<Long, Long> count = stream.grouped()
> > >>>>>>>>>          .withKeyMapper(keyMapper)
> > >>>>>>>>>          .withKeySerde(Serdes.Long())
> > >>>>>>>>>          .withValueSerde(Serdes.String())
> > >>>>>>>>>          .withQueryableName("my-store")
> > >>>>>>>>>          .count();
> > >>>>>>>>>
> > >>>>>>>>> // windowed count
> > >>>>>>>>> final KTable<Windowed<String>, Long> windowedCount =
> > >>>> stream.grouped()
> > >>>>>>>>>          .withQueryableName("my-window-store")
> > >>>>>>>>>          .windowed(TimeWindows.of(10L).until(10))
> > >>>>>>>>>          .count();
> > >>>>>>>>>
> > >>>>>>>>> // windowed reduce
> > >>>>>>>>> final Reducer<String> windowedReducer = null;
> > >>>>>>>>> final KTable<Windowed<String>, String> windowedReduce =
> > >>>>>> stream.grouped()
> > >>>>>>>>>          .withQueryableName("my-window-store")
> > >>>>>>>>>          .windowed(TimeWindows.of(10L).until(10))
> > >>>>>>>>>          .reduce(windowedReducer);
> > >>>>>>>>>
> > >>>>>>>>> final Aggregator<String, String, Long> aggregator = null;
> > >>>>>>>>> final Initializer<Long> init = null;
> > >>>>>>>>>
> > >>>>>>>>> // aggregate
> > >>>>>>>>> final KTable<String, Long> aggregate = stream.grouped()
> > >>>>>>>>>          .withQueryableName("my-aggregate-store")
> > >>>>>>>>>          .aggregate(aggregator, init, Serdes.Long());
> > >>>>>>>>>
> > >>>>>>>>> final StateStoreSupplier<KeyValueStore<String, Long>>
> > >>>>>> stateStoreSupplier
> > >>>>>>>> = null;
> > >>>>>>>>> // aggregate with custom store
> > >>>>>>>>> final KTable<String, Long> aggWithCustomStore =
> stream.grouped()
> > >>>>>>>>>          .withStateStoreSupplier(stateStoreSupplier)
> > >>>>>>>>>          .aggregate(aggregator, init);
> > >>>>>>>>>
> > >>>>>>>>> // disable caching
> > >>>>>>>>> stream.grouped()
> > >>>>>>>>>          .withQueryableName("name")
> > >>>>>>>>>          .withCachingEnabled(false)
> > >>>>>>>>>          .count();
> > >>>>>>>>>
> > >>>>>>>>> // disable logging
> > >>>>>>>>> stream.grouped()
> > >>>>>>>>>          .withQueryableName("q")
> > >>>>>>>>>          .withLoggingEnabled(false)
> > >>>>>>>>>          .count();
> > >>>>>>>>>
> > >>>>>>>>> // override log config
> > >>>>>>>>> final Reducer<String> reducer = null;
> > >>>>>>>>> stream.grouped()
> > >>>>>>>>>          .withLogConfig(Collections.
> singletonMap("segment.size",
> > >>>>> "10"))
> > >>>>>>>>>          .reduce(reducer);
> > >>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>> If anyone wants to play around with this you can find the code
> > >>>> here:
> > >>>>>>>>> https://github.com/dguy/kafka/tree/dsl-experiment
> > >>>>>>>>>
> > >>>>>>>>> Note: It won't actually work as most of the methods just return
> > >>>> null.
> > >>>>>>>>> Thanks,
> > >>>>>>>>> Damian
> > >>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>> On Thu, 22 Jun 2017 at 11:18 Ismael Juma <ismael@juma.me.uk>
> > >>>> wrote:
> > >>>>>>>>>> Thanks Damian. I think both options have pros and cons. And
> both
> > >>>> are
> > >>>>>>>> better
> > >>>>>>>>>> than overload abuse.
> > >>>>>>>>>>
> > >>>>>>>>>> The fluent API approach reads better, no mention of builder or
> > >>>> build
> > >>>>>>>>>> anywhere. The main downside is that the method signatures are
> a
> > >>>>> little
> > >>>>>>>> less
> > >>>>>>>>>> clear. By reading the method signature, one doesn't
> necessarily
> > >>>>> knows
> > >>>>>>>> what
> > >>>>>>>>>> it returns. Also, one needs to figure out the special method
> > >>>>>> (`table()`
> > >>>>>>>> in
> > >>>>>>>>>> this case) that gives you what you actually care about
> (`KTable`
> > >>>> in
> > >>>>>> this
> > >>>>>>>>>> case). Not major issues, but worth mentioning while doing the
> > >>>>>>>> comparison.
> > >>>>>>>>>> The builder approach avoids the issues mentioned above, but it
> > >>>>> doesn't
> > >>>>>>>> read
> > >>>>>>>>>> as well.
> > >>>>>>>>>>
> > >>>>>>>>>> Ismael
> > >>>>>>>>>>
> > >>>>>>>>>> On Wed, Jun 21, 2017 at 3:37 PM, Damian Guy <
> > damian.guy@gmail.com
> > >>>>>>>> wrote:
> > >>>>>>>>>>> Hi,
> > >>>>>>>>>>>
> > >>>>>>>>>>> I'd like to get a discussion going around some of the API
> > choices
> > >>>>>> we've
> > >>>>>>>>>>> made in the DLS. In particular those that relate to stateful
> > >>>>>> operations
> > >>>>>>>>>>> (though this could expand).
> > >>>>>>>>>>> As it stands we lean heavily on overloaded methods in the
> API,
> > >>>> i.e,
> > >>>>>>>> there
> > >>>>>>>>>>> are 9 overloads for KGroupedStream.count(..)! It is becoming
> > >>>> noisy
> > >>>>>> and
> > >>>>>>>> i
> > >>>>>>>>>>> feel it is only going to get worse as we add more optional
> > >>>> params.
> > >>>>> In
> > >>>>>>>>>>> particular we've had some requests to be able to turn caching
> > >>>> off,
> > >>>>> or
> > >>>>>>>>>>> change log configs,  on a per operator basis (note this can
> be
> > >>>> done
> > >>>>>> now
> > >>>>>>>>>> if
> > >>>>>>>>>>> you pass in a StateStoreSupplier, but this can be a bit
> > >>>>> cumbersome).
> > >>>>>>>>>>> So this is a bit of an open question. How can we change the
> DSL
> > >>>>>>>> overloads
> > >>>>>>>>>>> so that it flows, is simple to use and understand, and is
> > easily
> > >>>>>>>> extended
> > >>>>>>>>>>> in the future?
> > >>>>>>>>>>>
> > >>>>>>>>>>> One option would be to use a fluent API approach for
> providing
> > >>>> the
> > >>>>>>>>>> optional
> > >>>>>>>>>>> params, so something like this:
> > >>>>>>>>>>>
> > >>>>>>>>>>> groupedStream.count()
> > >>>>>>>>>>>     .withStoreName("name")
> > >>>>>>>>>>>     .withCachingEnabled(false)
> > >>>>>>>>>>>     .withLoggingEnabled(config)
> > >>>>>>>>>>>     .table()
> > >>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>>> Another option would be to provide a Builder to the count
> > method,
> > >>>>> so
> > >>>>>> it
> > >>>>>>>>>>> would look something like this:
> > >>>>>>>>>>> groupedStream.count(new
> > >>>>>>>>>>> CountBuilder("storeName").withCachingEnabled(false).build())
> > >>>>>>>>>>>
> > >>>>>>>>>>> Another option is to say: Hey we don't need this, what are
> you
> > on
> > >>>>>>>> about!
> > >>>>>>>>>>> The above has focussed on state store related overloads, but
> > the
> > >>>>> same
> > >>>>>>>>>> ideas
> > >>>>>>>>>>> could  be applied to joins etc, where we presently have many
> > join
> > >>>>>>>> methods
> > >>>>>>>>>>> and many overloads.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Anyway, i look forward to hearing your opinions.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Thanks,
> > >>>>>>>>>>> Damian
> > >>>>>>>>>>>
> > >>>>>>>>
> > >>>>>>
> > >>>>>
> > >>>>> --
> > >>>>> -- Guozhang
> > >>>>>
> > >>>>>
> > >>>>
> > >>
> >
> >
>



-- 
-- Guozhang

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message