Thanks.

Do you know when KAFKA-3101 will be implemented?

I also add a note to that JIRA for a left outer join use case which also
need buffer support.


On Wed, Apr 20, 2016 at 4:42 PM, Guozhang Wang <wangg...@gmail.com> wrote:

> Henry,
>
> I thought you were concerned about consumer memory contention. That's a
> valid point, and yes, you need to keep those buffered records in a
> persistent store.
>
> As I mentioned we are trying to do optimize the aggregation outputs as in
>
> https://issues.apache.org/jira/browse/KAFKA-3101
>
> Its idea is very similar to buffering, while we keep the aggregated values
> in RocksDB, we do not send the updated values for each receiving record but
> only do that based on some policy. More generally we can have a trigger
> mechanism for user to customize when to emit.
>
>
> Guozhang
>
>
> On Wed, Apr 20, 2016 at 4:03 PM, Henry Cai <h...@pinterest.com.invalid>
> wrote:
>
> > I think this scheme still has problems.  If during 'holding' I literally
> > hold (don't return the method call), I will starve the thread.  If I am
> > writing the output to a in-memory buffer and let the method returns, the
> > kafka stream will acknowledge the record to upstream queue as processed,
> so
> > I would lose the record if the node crashed after ack but before 10
> minutes
> > is up.
> >
> > I guess I need to write the buffered result into a persistent store,
> > another kafka queue or K/V store.
> >
> > On Wed, Apr 20, 2016 at 3:49 PM, Guozhang Wang <wangg...@gmail.com>
> wrote:
> >
> > > By "holding the stream", I assume you are still consuming data, but
> just
> > > that you only write data every 10 minutes instead of upon each received
> > > record right?
> > >
> > > Anyways, in either case, consumer should not have severe memory issue
> as
> > > Kafka Streams will pause its consuming when enough data is buffered at
> > the
> > > streams end (note that we have two buffers here, the consumer buffers
> raw
> > > bytes, and the streams library take raw bytes and buffer the
> > de-serialized
> > > objects, and threshold on its own buffer to pause / resume the
> consumer).
> > >
> > >
> > > Guozhang
> > >
> > > On Wed, Apr 20, 2016 at 3:35 PM, Henry Cai <h...@pinterest.com.invalid
> >
> > > wrote:
> > >
> > > > So hold the stream for 15 minutes wouldn't cause too much performance
> > > > problems?
> > > >
> > > > On Wed, Apr 20, 2016 at 3:16 PM, Guozhang Wang <wangg...@gmail.com>
> > > wrote:
> > > >
> > > > > Consumer' buffer does not depend on offset committing, once it is
> > given
> > > > > from the poll() call it is out of the buffer. If offsets are not
> > > > committed,
> > > > > then upon failover it will simply re-consumer these records again
> > from
> > > > > Kafka.
> > > > >
> > > > > Guozhang
> > > > >
> > > > > On Tue, Apr 19, 2016 at 11:34 PM, Henry Cai
> > <h...@pinterest.com.invalid
> > > >
> > > > > wrote:
> > > > >
> > > > > > For the technique of custom Processor of holding call to
> > > > > context.forward(),
> > > > > > if I hold it for 10 minutes, what does that mean for the consumer
> > > > > > acknowledgement on source node?
> > > > > >
> > > > > > I guess if I hold it for 10 minutes, the consumer is not going to
> > ack
> > > > to
> > > > > > the upstream queue, will that impact the consumer performance,
> will
> > > > > > consumer's kafka client message buffer overflow when there is no
> > ack
> > > in
> > > > > 10
> > > > > > minutes?
> > > > > >
> > > > > >
> > > > > > On Tue, Apr 19, 2016 at 6:10 PM, Guozhang Wang <
> wangg...@gmail.com
> > >
> > > > > wrote:
> > > > > >
> > > > > > > Yes we are aware of this behavior and are working on optimizing
> > it:
> > > > > > >
> > > > > > > https://issues.apache.org/jira/browse/KAFKA-3101
> > > > > > >
> > > > > > > More generally, we are considering to add a "trigger" interface
> > > > similar
> > > > > > to
> > > > > > > the Millwheel model where users can customize when they want to
> > > emit
> > > > > > > outputs to the downstream operators. Unfortunately for now
> there
> > > will
> > > > > no
> > > > > > > easy workaround for buffering, and you may want to do this in
> app
> > > > code
> > > > > > (for
> > > > > > > example, in a customized Processor where you can control when
> to
> > > call
> > > > > > > context.forward() ).
> > > > > > >
> > > > > > > Guozhang
> > > > > > >
> > > > > > >
> > > > > > > On Tue, Apr 19, 2016 at 1:40 PM, Jeff Klukas <
> jklu...@simple.com
> > >
> > > > > wrote:
> > > > > > >
> > > > > > > > Is it true that the aggregation and reduction methods of
> > KStream
> > > > will
> > > > > > > emit
> > > > > > > > a new output message for each incoming message?
> > > > > > > >
> > > > > > > > I have an application that's copying a Postgres replication
> > > stream
> > > > > to a
> > > > > > > > Kafka topic, and activity tends to be clustered, with many
> > > updates
> > > > > to a
> > > > > > > > given primary key happening in quick succession. I'd like to
> > > smooth
> > > > > > that
> > > > > > > > out by buffering the messages in tumbling windows, allowing
> the
> > > > > updates
> > > > > > > to
> > > > > > > > overwrite one another, and emitting output messages only at
> the
> > > end
> > > > > of
> > > > > > > the
> > > > > > > > window.
> > > > > > > >
> > > > > > > > Does the Kafka Streams API provide any hooks that I could use
> > to
> > > > > > achieve
> > > > > > > > this kind of windowed "buffering" or "deduplication" of a
> > stream?
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > --
> > > > > > > -- Guozhang
> > > > > > >
> > > > > >
> > > > >
> > > > >
> > > > >
> > > > > --
> > > > > -- Guozhang
> > > > >
> > > >
> > >
> > >
> > >
> > > --
> > > -- Guozhang
> > >
> >
>
>
>
> --
> -- Guozhang
>

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