Yes, this all makes sense. The option untilWallClockTimeLimit would solve the
problem for us. I can see the difficulty with delivering the "final" result.
The problem here is assuming that something will be *complete* at some point in
time and a final result can be delivered. The time for a window is not
synchronized with the "event" that we want to detect. Hence, the application
*has* to deal with state that may not be *complete* i.e some sort of cross
window aggregation has to be implemented outside the application. I am
wondering how you can possibly avoid that.
Let me know how I can help.
Thanks
-mohan
On 6/28/19, 1:21 PM, "John Roesler" <j...@confluent.io> wrote:
Ok, good, that's what I was hoping. I think that's a good strategy, at
the end of the "real" data, just write a dummy record with the same
keys with a high timestamp to flush everything else through.
For the most part, I'd expect a production program to get a steady
stream of traffic with increasing timestamps, so windows would be
constantly be getting flushed out as stream time moves forward.
Some folks have reported that they don't get enough traffic through
their program to flush out the suppressed results on a regular basis,
though. Right now, the best solution is to have the same Producer that
writes data to the input topics also write "heartbeat/dummy" records
periodically when there is no data to send, just to keep stream time
moving forward. But this isn't a perfect solution, as you have pointed
out in this thread; you really want the heartbeat records to go to all
partitions, and also go to all re-partitions if there are repartition
topics in the topology.
I agree that there seems to be a need for some first-class support for
keeping stream time moving reliably. I think that the ideal would be
to allow Producers to automatically send the heartbeats and to
implement a variant of the Chandy-Lamport distributed snapshot
algorithm to push them though the whole topology while skipping any
actual computations (so your business logic wouldn't have to see
them). I'd really love to see this feature in Streams; I just haven't
written it up yet because I haven't had time.
During the design for Suppress, we did consider having some kind of
timer, but the problem is that it's not possible for this to be
deterministic. If you want the "until window closes" version, you're
supposed to get a guarantee that you'll really only see one, final,
result for each window/key. If we were to use the system clock on the
Streams machine to decide it's probably been long enough and emit the
"final" result, but it turns out that we actually had just stalled
(maybe waiting for quorum during a broker upgrade or something, or
just a run-of-the-mill networking problem) and the next record we poll
was supposed to be in the window, what can we do? We already emitted
the "final" result, so we can say "oops, that wasn't the final result,
_this_ one is the final result", but that seems to render the words
"final result" kind of meaningless. On the other hand, we can just
drop that record and ignore it, but that's a bummer because the only
reason we couldn't include it in the result was some ephemeral
environmental problem. If we run the same data through the same
program again, we'd get a different result.
So for "until window closes" mode, where we guarantee you _only_ see
the final results, we only offer stream time expiration. Anything else
would violate correctness one way or another. On the other hand, you
have "until time limit" mode. In that case, it's just "buffer for a
while, but multiple results are still ok" semantics. For that case, we
have
https://cwiki.apache.org/confluence/display/KAFKA/KIP-424%3A+Allow+suppression+of+intermediate+events+based+on+wall+clock+time
, to indeed use a timer to emit events if too much time passes on the
Streams side. It's just not implemented yet.
Does this all seem about right to you?
-john
On Wed, Jun 26, 2019 at 12:57 PM Parthasarathy, Mohan <mpart...@hpe.com>
wrote:
>
> Initially it started in the testing. QA reported problems where "events"
were not detected after they finished their testing. After this discussion, my
proposal was to send a few more records to cause the windows to flush so that
the suppressed event would show up. Now it looks to me, these few dummy records
have to match the "key" of the pending windows. Then it would be flushed.
>
> In practice, it may not be a problem always. But then the real time
nature of the problem might require us that there is not a huge delay between
the processing of the event and the flush. How does one solve this issue in
production ? I am wondering why the design did not accommodate a timer to flush
the windows ?
>
> Thanks
> Mohan
>
>
> On 6/26/19, 8:18 AM, "John Roesler" <j...@confluent.io> wrote:
>
> Hi Mohan,
>
> I see where you're going with this, and it might indeed be a
> challenge. Even if you send a "dummy" message on all input topics, you
> won't have a guarantee that after the repartition, the dummy message
> is propagated to all partitions of the repartition topics. So it might
> be difficult to force the suppression buffer to flush if it's after a
> repartition.
>
> Can we take a step back and discuss the motivation for forcing the
> records to flush out? Is this for testing your app, or is it to drive
> some production logic?
>
> Thanks,
> -John
>
>
> On Mon, Jun 24, 2019 at 7:26 PM Parthasarathy, Mohan
<mpart...@hpe.com> wrote:
> >
> > John,
> >
> > Thanks for the nice explanation. When the repartitioning happens,
does the window get associated with the new partition i.e., now does a message
with new timestamp has to appear on the repartition topic for the window to
expire ? It is possible that there is new stream of messages coming in but
post-map operation, the partitions in the repartitioned topic does not see the
same thing.
> >
> > Thanks
> > Mohan
> >
> > On 6/24/19, 7:49 AM, "John Roesler" <j...@confluent.io> wrote:
> >
> > Hey, this is a very apt question.
> >
> > GroupByKey isn't a great example because it doesn't actually
change
> > the key, so all the aggregation results are actually on records
from
> > the same partition. But let's say you do a groupBy or a map (or
any
> > operation that can change the key), followed by an aggregation.
Now
> > it's possible that the aggregation would need to process
records from
> > two different partitions. In such a case (key-changing operation
> > followed by a stateful operation), Streams actually round-trips
the
> > data through an intermediate topic, called a repartition topic,
before
> > the aggregation. This has the effect, similar to the "shuffle"
phase
> > of map-reduce, of putting all the data into its *new* right
partition,
> > so then the aggregation can still process each of its partitions
> > independently.
> >
> > Regarding the latter statement, even though you only have one
> > instance, Streams _still_ processes each partition
independently. The
> > "unit of work" responsible for processing a partition is called
a
> > "task". So if you have 4 partitions, then your one instance
actually
> > has 4 state stores, one for each task, where each task only gets
> > records from a single partition. The tasks can't see anything
about
> > each other, not their state nor other metadata like their
current
> > stream time. Otherwise, the results would depend on which tasks
happen
> > to be co-located with which other tasks. So, having to send your
> > "purge" event to all partitions is a pain, but in the end, it
buys you
> > a lot, as you can add another instance to your cluster at any
time,
> > and Streams will scale up, and you'll know that the program is
> > executing exactly the same way the whole time.
> >
> > -John
> >
> > On Sat, Jun 22, 2019 at 4:37 PM Parthasarathy, Mohan
<mpart...@hpe.com> wrote:
> > >
> > > I can see the issue. But it raised other questions. Pardon my
ignorance. Even though partitions are processed independently, windows can be
aggregating state from records read from many partitions. Let us say there is a
groupByKey followed by aggregate. In this case how is the state reconciled
across all the application instances ? Is there a designated instance for a
particular key ?
> > >
> > > In my case, there was only one instance processing records
from all partitions and it is kind of odd that windows did not expire even
though I understand why now.
> > >
> > > Thanks
> > > Mohan
> > >
> > >
> > > On 6/21/19, 2:25 PM, "John Roesler" <j...@confluent.io> wrote:
> > >
> > > No problem. It's definitely a subtlety. It occurs because
each
> > > partition is processed completely independently of the
others, so
> > > "stream time" is tracked per partition, and there's no
way to look
> > > across at the other partitions to find out what stream
time they have.
> > >
> > > In general, it's not a problem because you'd expect all
partitions to
> > > receive updates over time, but if you're specifically
trying to send
> > > events that cause stuff to get flushed from the buffers,
it can mess
> > > with you. It's especially notable in tests. So, for most
tests, I just
> > > configure the topics to have one partition.
> > >
> > > -John
> > >
> > > On Fri, Jun 21, 2019 at 3:56 PM Parthasarathy, Mohan
<mpart...@hpe.com> wrote:
> > > >
> > > > That change "In the same partition" must explain what
we are seeing. Unless you see one message per partition, all windows will not
expire. That is an interesting twist. Thanks for the correction ( I will go
back and confirm this.
> > > >
> > > > -mohan
> > > >
> > > >
> > > > On 6/21/19, 12:40 PM, "John Roesler"
<j...@confluent.io> wrote:
> > > >
> > > > Sure, the record cache attempts to save downstream
operators from
> > > > unnecessary updates by also buffering for a short
amount of time
> > > > before forwarding. It forwards results whenever the
cache fills up or
> > > > whenever there is a commit. If you're happy to wait
at least "commit
> > > > interval" amount of time for updates, then you
don't need to do
> > > > anything, but if you're on the edge of your seat,
waiting for these
> > > > results, you can set cache.max.bytes.buffering to 0
to disable the
> > > > record cache entirely. Note that this would hurt
throughput in
> > > > general, though.
> > > >
> > > > Just a slight modification:
> > > > * a new record with new timestamp > (all the
previous timestamps +
> > > > grace period) will cause all the old windows *in
the same partition*
> > > > to close
> > > > * yes, expiry of the window depends only on the
event time
> > > >
> > > > Hope this helps!
> > > > -John
> > > >
> > > > On Thu, Jun 20, 2019 at 11:42 AM Parthasarathy,
Mohan <mpart...@hpe.com> wrote:
> > > > >
> > > > > Could you tell me a little more about the delays
about the record caches and how I can disable it ?
> > > > >
> > > > > If I could summarize my problem:
> > > > >
> > > > > -A new record with a new timestamp > all records
sent before, I expect *all* of the old windows to close
> > > > > -Expiry of the windows depends only on the event
time and not on the key
> > > > >
> > > > > Are these two statements correct ?
> > > > >
> > > > > Thanks
> > > > > Mohan
> > > > >
> > > > > On 6/20/19, 9:17 AM, "John Roesler"
<j...@confluent.io> wrote:
> > > > >
> > > > > Hi!
> > > > >
> > > > > In addition to setting the grace period to
zero (or some small
> > > > > number), you should also consider the delays
introduced by record
> > > > > caches upstream of the suppression. If you're
closely watching the
> > > > > timing of records going into and coming out
of the topology, this
> > > > > might also spoil your expectations. You could
always disable the
> > > > > record cache to make the system more
predictable (although this would
> > > > > hurt throughput in production).
> > > > >
> > > > > Thanks,
> > > > > -John
> > > > >
> > > > > On Wed, Jun 19, 2019 at 3:01 PM
Parthasarathy, Mohan <mpart...@hpe.com> wrote:
> > > > > >
> > > > > > We do explicitly set the grace period to
zero. I am going to try the new version
> > > > > >
> > > > > > -mohan
> > > > > >
> > > > > >
> > > > > > On 6/19/19, 12:50 PM, "Parthasarathy,
Mohan" <mpart...@hpe.com> wrote:
> > > > > >
> > > > > > Thanks. We will give it a shot.
> > > > > >
> > > > > > On 6/19/19, 12:42 PM, "Bruno Cadonna"
<br...@confluent.io> wrote:
> > > > > >
> > > > > > Hi Mohan,
> > > > > >
> > > > > > I realized that my previous
statement was not clear. With a grace
> > > > > > period of 12 hour, suppress would
wait for late events until stream
> > > > > > time has advanced 12 hours before a
result would be emitted.
> > > > > >
> > > > > > Best,
> > > > > > Bruno
> > > > > >
> > > > > > On Wed, Jun 19, 2019 at 9:21 PM
Bruno Cadonna <br...@confluent.io> wrote:
> > > > > > >
> > > > > > > Hi Mohan,
> > > > > > >
> > > > > > > if you do not set a grace period,
the grace period defaults to 12
> > > > > > > hours. Hence, suppress would wait
for an event that occurs 12 hour
> > > > > > > later before it outputs a result.
Try to explicitly set the grace
> > > > > > > period to 0 and let us know if it
worked.
> > > > > > >
> > > > > > > If it still does not work,
upgrade to version 2.2.1 if it is possible
> > > > > > > for you. We had a couple of bugs
in suppress recently that are fixed
> > > > > > > in that version.
> > > > > > >
> > > > > > > Best,
> > > > > > > Bruno
> > > > > > >
> > > > > > > On Wed, Jun 19, 2019 at 8:37 PM
Parthasarathy, Mohan <mpart...@hpe.com> wrote:
> > > > > > > >
> > > > > > > > No, I have not set any grace
period. Is that mandatory ? Have you seen problems with suppress and windows
expiring ?
> > > > > > > >
> > > > > > > > Thanks
> > > > > > > > Mohan
> > > > > > > >
> > > > > > > > On 6/19/19, 12:41 AM, "Bruno
Cadonna" <br...@confluent.io> wrote:
> > > > > > > >
> > > > > > > > Hi Mohan,
> > > > > > > >
> > > > > > > > Did you set a grace period
on the window?
> > > > > > > >
> > > > > > > > Best,
> > > > > > > > Bruno
> > > > > > > >
> > > > > > > > On Tue, Jun 18, 2019 at
2:04 AM Parthasarathy, Mohan <mpart...@hpe.com> wrote:
> > > > > > > > >
> > > > > > > > > On further debugging,
what we are seeing is that windows are expiring rather randomly as new messages
are being processed. . We tested with new key for every new message. We waited
for the window time before replaying new messages. Sometimes a new message
would come in and create state. It takes several messages to make some of the
old windows to be closed (go past suppress to the next stage). We have also
seen where one of them never closed even but several other older ones expired.
Then we explicitly sent a message with the same old key and then it showed up.
Also, for every new message, only one of the previous window expires even
though there are several pending.
> > > > > > > > >
> > > > > > > > > If we don't use suppress,
then there is never an issue. With suppress, the behavior we are seeing is
weird. We are using 2.1.0 version in DSL mode. Any clues on what we could be
missing ? Why isn't there an order in the way windows are closed ? As event
time progresses by the new messages arriving, the older ones should expire. Is
that right understanding or not ?
> > > > > > > > >
> > > > > > > > > Thanks
> > > > > > > > > Mohan
> > > > > > > > >
> > > > > > > > > On 6/17/19, 3:43 PM,
"Parthasarathy, Mohan" <mpart...@hpe.com> wrote:
> > > > > > > > >
> > > > > > > > > Hi,
> > > > > > > > >
> > > > > > > > > We are using suppress
in the application. We see some state being created at some point in time. Now
there is no new data for a day or two. We send new data but the old window of
data (where we see the state being created) is not closing i.e not seeing it go
through suppress and on to the next stage. It is as though the state created
earlier was purged. Is this possible ?
> > > > > > > > >
> > > > > > > > > Thanks
> > > > > > > > > Mohan
> > > > > > > > >
> > > > > > > > >
> > > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > >
> > > > >
> > > >
> > > >
> > >
> > >
> >
> >
>
>
