> later when message A arrives it will put that message back into > the right temporal context and publish an amended result for the proper > time/session window as if message B were consumed in the timestamp order > before message A.
Does this apply to the aggregation Kafka stream methods then, and not to e.g foreach? On Sun, Mar 19, 2017 at 2:40 AM, Hans Jespersen <h...@confluent.io> wrote: > Yes stream processing and CEP are subtlety different things. > > Kafka Streams helps you write stateful apps and allows that state to be > preserved on disk (a local State store) as well as distributed for HA or > for parallel partitioned processing (via Kafka topic partitions and > consumer groups) as well as in memory (as a performance enhancement). > > However a classical CEP engine with a pre-modeled state machine and > pattern matching rules is something different from stream processing. > > It is on course possible to build a CEP system on top on Kafka Streams and > get the best of both worlds. > > -hans > > > On Mar 18, 2017, at 11:36 AM, Sabarish Sasidharan < > sabarish....@gmail.com> wrote: > > > > Hans > > > > What you state would work for aggregations, but not for state machines > and > > CEP. > > > > Regards > > Sab > > > >> On 19 Mar 2017 12:01 a.m., "Hans Jespersen" <h...@confluent.io> wrote: > >> > >> The only way to make sure A is consumed first would be to delay the > >> consumption of message B for at least 15 minutes which would fly in the > >> face of the principals of a true streaming platform so the short answer > to > >> your question is "no" because that would be batch processing not stream > >> processing. > >> > >> However, Kafka Streams does handle late arriving data. So if you had > some > >> analytics that computes results on a time window or a session window > then > >> Kafka streams will compute on the stream in real time (processing > message > >> B) and then later when message A arrives it will put that message back > into > >> the right temporal context and publish an amended result for the proper > >> time/session window as if message B were consumed in the timestamp order > >> before message A. The end result of this flow is that you eventually get > >> the same results you would get in a batch processing system but with the > >> added benefit of getting intermediary result at much lower latency. > >> > >> -hans > >> > >> /** > >> * Hans Jespersen, Principal Systems Engineer, Confluent Inc. > >> * h...@confluent.io (650)924-2670 > >> */ > >> > >>> On Sat, Mar 18, 2017 at 10:29 AM, Ali Akhtar <ali.rac...@gmail.com> > wrote: > >>> > >>> Is it possible to have Kafka Streams order messages correctly by their > >>> timestamps, even if they arrived out of order? > >>> > >>> E.g, say Message A with a timestamp of 5:00 PM and Message B with a > >>> timestamp of 5:15 PM, are sent. > >>> > >>> Message B arrives sooner than Message A, due to network issues. > >>> > >>> Is it possible to make sure that, across all consumers of Kafka Streams > >>> (even if they are across different servers, but have the same consumer > >>> group), Message A is consumed first, before Message B? > >>> > >>> Thanks. > >>> > >> >
