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
