I saw that note, thanks for commenting. I are cutting the next 0.10.0.0 RC next week, so I am not certain if it will make it for 0.10.0.0. But we can push it to be in 0.10.0.1.
Guozhang On Wed, Apr 20, 2016 at 4:57 PM, Henry Cai <h...@pinterest.com.invalid> wrote: > 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 > > > -- -- Guozhang
