Re: [DISCUSS] KIP-655: Windowed "Distinct" Operation for KStream

[Ivan Ponomarev]

Hello all,

I have rewritten the KIP-655 summarizing what was agreed upon during 
this discussion (now the proposal is much simpler and less invasive).

I have also created KIP-759 (cancelRepartition operation) and started a 
discussion for it.

Regards,

Ivan.



04.06.2021 8:15, Matthias J. Sax пишет:
Just skimmed over the thread -- first of all, I am glad that we could
merge KIP-418 and ship it :)

About the re-partitioning concerns, there are already two tickets for it:

  - https://issues.apache.org/jira/browse/KAFKA-4835
  - https://issues.apache.org/jira/browse/KAFKA-10844

Thus, it seems best to exclude this topic from this KIP, and do a
separate KIP for it (if necessary, we can "pause" this KIP until the
repartition KIP is done). It's a long standing "issue" and we should
resolve it in a general way I guess.

(Did not yet ready all responses in detail yet, so keeping this comment
short.)


-Matthias

On 6/2/21 6:35 AM, John Roesler wrote:
Thanks, Ivan!

That sounds like a great plan to me. Two smaller KIPs are easier to agree on 
than one big one.

I agree hopping and sliding windows will actually have a duplicating effect. We 
can avoid adding distinct() to the sliding window interface, but hopping 
windows are just a different parameterization of epoch-aligned windows. It 
seems we can’t do much about that except document the issue.

Thanks,
John

On Wed, May 26, 2021, at 10:14, Ivan Ponomarev wrote:
Hi John!

I think that your proposal is just fantastic, it simplifies things a lot!

I also felt uncomfortable due to the fact that the proposed `distinct()`
is not somewhere near `count()` and `reduce(..)`. But
`selectKey(..).groupByKey().windowedBy(..).distinct()` didn't look like
a correct option for  me because of the issue with the unneeded
repartitioning.

The bold idea that we can just CANCEL the repartitioning didn't came to
my mind.

What seemed to me a single problem is in fact two unrelated problems:
`distinct` operation and cancelling the unneeded repartitioning.

  > what if we introduce a parameter to `selectKey()` that specifies that
the caller asserts that the new key does _not_ change the data partitioning?

I think a more elegant solution would be not to add a new parameter to
`selectKey` and all the other key-changing operations (`map`,
`transform`, `flatMap`, ...), but add a new operator
`KStream#cancelRepartitioning()` that resets `keyChangingOperation` flag
for the upstream node. Of course, "use it only if you know what you're
doing" warning is to be added. Well, it's a topic for a separate KIP!

Concerning `distinct()`. If we use `XXXWindowedKStream` facilities, then
changes to the API are minimally invasive: we're just adding
`distinct()` to TimeWindowedKStream and SessionWindowedKStream, and
that's all.

We can now define `distinct` as an operation that returns only a first
record that falls into a new window, and filters out all the other
records that fall into an already existing window. BTW, we can mock the
behaviour of such an operation with `TopologyTestDriver` using
`reduce((l, r) -> STOP)`.filterNot((k, v)->STOP.equals(v)).  ;-)

Consider the following example (record times are in seconds):

//three bursts of variously ordered records
4, 5, 6
23, 22, 24
34, 33, 32
//'late arrivals'
7, 22, 35


1. 'Epoch-aligned deduplication' using tumbling windows:

.groupByKey().windowedBy(TimeWindows.of(Duration.ofSeconds(10))).distinct()

produces

(key@[00000/10000], 4)
(key@[20000/30000], 23)
(key@[30000/40000], 34)

-- that is, one record per epoch-aligned window.

2. Hopping and sliding windows do not make much sense here, because they
produce multiple intersected windows, so that one record can be
multiplied, but we want deduplication.

3. SessionWindows work for 'data-aligned deduplication'.

.groupByKey().windowedBy(SessionWindows.with(Duration.ofSeconds(10))).distinct()


produces only

([key@4000/4000], 4)
([key@23000/23000], 23)

because all the records bigger than 7 are stuck together in one session.
Setting inactivity gap to 9 seconds will return three records:

([key@4000/4000], 4)
([key@23000/23000], 23)
([key@34000/34000], 34)

WDYT? If you like this variant, I will re-write KIP-655 and propose a
separate KIP for `cancelRepartitioning` (or whatever name we will choose
for it).

Regards,

Ivan


24.05.2021 22:32, John Roesler пишет:
Hey there, Ivan!

In typical fashion, I'm going to make a somewhat outlandish
proposal. I'm hoping that we can side-step some of the
complications that have arisen. Please bear with me.

It seems like `distinct()` is not fundamentally unlike other windowed
"aggregation" operations. Your concern about unnecessary
repartitioning seems to apply just as well to `count()` as to `distinct()`.
This has come up before, but I don't remember when: what if we
introduce a parameter to `selectKey()` that specifies that the caller
asserts that the new key does _not_ change the data partitioning?
The docs on that parameter would of course spell out all the "rights
and responsibilities" of setting it.

In that case, we could indeed get back to
`selectKey(A).windowBy(B).distinct(...)`, where we get to compose the
key mapper and the windowing function without having to carve out
a separate domain just for `distinct()`. All the rest of the KStream
operations would also benefit.

What do you think?

Thanks,
John

On Sun, May 23, 2021, at 08:09, Ivan Ponomarev wrote:
Hello everyone,

let me revive the discussion for KIP-655. Now I have some time again and
I'm eager to finalize this.

Based on what was already discussed, I think that we can split the
discussion into three topics for our convenience.

The three topics are:

- idExtractor  (how should we extract the deduplication key for the record)

- timeWindows (what time windows should we use)

- miscellaneous (naming etc.)

---- idExtractor ----

Original proposal: use (k, v) -> f(k, v) mapper, defaulting to (k, v) ->
k.  The drawback here is that we must warn the user to choose such a
function that sets different IDs for records from different partitions,
otherwise same IDs might be not co-partitioned (and not deduplicated as
a result). Additional concern: what should we do when this function
returns null?

Matthias proposed key-only deduplication: that is, no idExtractor at
all, and if we want to use `distinct` for a particular identifier, we
must `selectKey()` before. The drawback of this approach is that we will
always have repartitioning after the key selection, while in practice
repartitioning will not always be necessary (for example, when the data
stream is such that different values infer different keys).

So here we have a 'safety vs. performance' trade-off. But 'safe' variant
is also not very convenient for developers, since we're forcing them to
change the structure of their records.

A 'golden mean' here might be using composite ID with its first
component equals to k and its second component equals to some f(v) (f
defaults to v -> null, and null value returned by f(v) means
'deduplicate by the key only'). The nuance here is that we will have
serializers only for types of k and f(v), and we must correctly
serialize a tuple (k, f(v)), but of course this is doable.

What do you think?

---- timeWindows ----

Originally I proposed TimeWindows only just because they solved my
particular case :-) but agree with Matthias' and Sophie's objections.

I like the Sophie's point: we need both epoch-aligned and data-aligned
windows. IMO this is absolutely correct: "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."

I just cannot agree right away with Sophie's
.groupByKey().windowedBy(...).distinct() proposal, as it implies  the
key-only deduplication -- see the previous topic.

Epoch-aligned windows are very simple: they should forward only one
record per enumerated time window. TimeWindows are exactly what we want
here. I mentioned in the KIP both tumbling and hopping windows just
because both are possible for TimeWindows, but indeed I don't see any
real use case for hopping windows, only tumbling windows make sence IMO.

For data-aligned windows SlidingWindow interface seems to be a nearly
valid choice. Nearly. It 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.  However, we cannot reuse SlidingWindow as is, because
just as Matthias noted, SlidingWindows go backward in time, while we
need a windows that go forward in time, and are not opened while records
fall into an already existing window. We definitely should make our own
implementation, maybe we should call it ExpirationWindow? WDYT?


---- miscellaneous ----

Persistent/in-memory stores. Matthias proposed to pass Materialized
parameter next to DistinctParameters (and this is necessary, because we
will need to provide a serializer for extracted id). This is absolutely
valid point, I agree and I will fix it in the KIP.

Naming. Sophie noted that the Streams DSL operators are typically named
as verbs, so she proposes `deduplicate` in favour of `distinct`. I think
that while it's important to stick to the naming conventions, it is also
important to think of the experience of those who come from different
stacks/technologies. People who are familiar with SQL and Java Streams
API must know for sure what does 'distinct' mean, while data
deduplication in general is a more complex task and thus `deduplicate`
might be misleading. But I'm ready to be convinced if the majority
thinks otherwise.


Regards,

Ivan



14.09.2020 21:31, Sophie Blee-Goldman пишет:
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