> Can windows only be used for aggregations, or can they also be used for > foreach(), and such?
As of today, you can use windows only in aggregations. > And is it possible to get metadata on the message, such as whether or not its late, its index/position within the other messages, etc? If you use the Processor API of Kafka Streams, you can have access to an incoming record's topic, partition, offset, etc. via the so-called ProcessorContext (which is updated for every new incoming record): http://docs.confluent.io/current/streams/javadocs/org/apache/kafka/streams/processor/Processor.html - You can get/store a reference to the ProcessorContext from `Processor#init()`. http://docs.confluent.io/current/streams/javadocs/org/apache/kafka/streams/processor/ProcessorContext.html - The context can then be used within `Processor#process()` when you process a new record. As I said, the context is updated behind the scenes to match the record that is currently being processed. Best, Michael On Mon, Mar 20, 2017 at 5:59 PM, Ali Akhtar <ali.rac...@gmail.com> wrote: > Can windows only be used for aggregations, or can they also be used for > foreach(), and such? > > And is it possible to get metadata on the message, such as whether or not > its late, its index/position within the other messages, etc? > > On Mon, Mar 20, 2017 at 9:44 PM, Michael Noll <mich...@confluent.io> > wrote: > > > And since you asked for a pointer, Ali: > > http://docs.confluent.io/current/streams/concepts.html#windowing > > > > > > On Mon, Mar 20, 2017 at 5:43 PM, Michael Noll <mich...@confluent.io> > > wrote: > > > > > Late-arriving and out-of-order data is only treated specially for > > windowed > > > aggregations. > > > > > > For stateless operations such as `KStream#foreach()` or > `KStream#map()`, > > > records are processed in the order they arrive (per partition). > > > > > > -Michael > > > > > > > > > > > > > > > On Sat, Mar 18, 2017 at 10:47 PM, Ali Akhtar <ali.rac...@gmail.com> > > wrote: > > > > > >> > 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. > > >> > >>> > > >> > >> > > >> > > > >> > > > > > > > > > > > >
