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 >
