Hi Sachin,
The KafkaStreams StreamsPartitionAssignor will take care of assigning the
Standby Tasks to the other instances of your Kafka Streams application. The
state store updates are all handled by reading from the change-logs and
updating local copies, there is no communication required between the
individual application instances to do this as all data is flowing through
Kafka, i.e., they are just reading from the topic that backs the state
store.
The State Dir is independent on each machine, but that path must exist.
Kafka Streams doesn't try and keep the directories themselves in sync,
rather it will update local copies of the State Stores that happen to be
under that directory path. The idea being that if the active task fails it
can quickly fail over to one of the standby tasks and not have to spend too
much time catching up to the head of the change-log.
Yes, you should keep the num.standby.replicas value the same on all
instances.
Yes, if you deploy three instances and one crashes, then one of the others
will take over the tasks.
Thanks,
Damian
On Mon, 12 Dec 2016 at 09:15 Sachin Mittal <sjmit...@gmail.com> wrote:
> Hi,
> Thanks for the application. It is not clear that clustering depends on how
> source topics are partitioned.
> In our case I guess num.standby.replicas settings is best suited.
>
> If say I set this to 2 and run two more same application in two different
> machines, how would my original instance know in which two machines it
> needs to be keep the state store up to dated.
>
> My application has a setting
> props.put(StreamsConfig.STATE_DIR_CONFIG, "/data01/kafka-streams");
> props.put(StreamsConfig.APPLICATION_ID_CONFIG, "test";
>
> So when you say it keeps state store up to date, means it will keep the
> dirs /data01/kafka-streams/test in sync in all the three machines?
>
> Also in the other two standby application do I need to keep the same value
> (2) for num.standby.replicas.
>
> So basically need to deploy three instances on same application and if one
> fails and crashes one of the standby takes over.
>
> Please let me know if my understanding is correct so far.
>
> Thanks
> Sachin
>
>
> On Fri, Dec 9, 2016 at 1:34 PM, Damian Guy <damian....@gmail.com> wrote:
>
> > Hi Sachin,
> >
> > What you have suggested will never happen. If there is only 1 partition
> > there will only ever be one consumer of that partition. So if you had 2
> > instances of your streams application, and only a single input partition,
> > only 1 instance would be processing the data.
> > If you are running like this, then you might want to set
> > StreamsConfig.NUM_STANDBY_REPLICAS_CONFIG to 1 - this will mean that the
> > State Store that is generated by the aggregation is kept up to date on
> the
> > instance that is not processing the data. So in the event that the active
> > instance fails, the standby instance should be able to continue without
> too
> > much of a gap in processing time.
> >
> > Thanks,
> > Damian
> >
> > On Fri, 9 Dec 2016 at 04:55 Sachin Mittal <sjmit...@gmail.com> wrote:
> >
> > > Hi,
> > > I followed the document and I have few questions.
> > > Say I have a single partition input key topic and say I run 2 streams
> > > application from machine1 and machine2.
> > > Both the application have same application id are have identical code.
> > > Say topic1 has messages like
> > > (k1, v11)
> > > (k1, v12)
> > > (k1, v13)
> > > (k2, v21)
> > > (k2, v22)
> > > (k2, v23)
> > > When I was running single application I was getting results like
> > > (k1, agg(v11, v12, v13))
> > > (k2, agg(v21, v22, v23))
> > >
> > > Now when 2 applications are run and say messages are read in round
> robin
> > > fashion.
> > > v11 v13 v22 - machine 1
> > > v12 v21 v23 - machine 2
> > >
> > > The aggregation at machine 1 would be
> > > (k1, agg(v11, v13))
> > > (k2, agg(v22))
> > >
> > > The aggregation at machine 2 would be
> > > (k1, agg(v12))
> > > (k2, agg(v21, v23))
> > >
> > > So now where do I join the independent results of these 2 aggregation
> to
> > > get the final result as expected when single instance was running.
> > >
> > > Note my high level dsl is sometime like
> > > srcSTopic.aggragate(...).foreach(key, aggregation) {
> > > //process aggragated value and push it to some external storage
> > > }
> > >
> > > So I want this each to be running against the final set of aggregated
> > > value. Do I need to add another step before foreach to make sure the
> > > different results from 2 machines are joined to get the final one as
> > > expected. If yes what does that step 2.
> > >
> > > Thanks
> > > Sachin
> > >
> > >
> > >
> > >
> > >
> > >
> > > On Fri, Dec 9, 2016 at 9:42 AM, Mathieu Fenniak <
> > > mathieu.fenn...@replicon.com> wrote:
> > >
> > > > Hi Sachin,
> > > >
> > > > Some quick answers, and a link to some documentation to read more:
> > > >
> > > > - If you restart the application, it will start from the point it
> > crashed
> > > > (possibly reprocessing a small window of records).
> > > >
> > > > - You can run more than one instance of the application. They'll
> > > > coordinate by virtue of being part of a Kafka consumer group; if one
> > > > crashes, the partitions that it was reading from will be picked up by
> > > other
> > > > instances.
> > > >
> > > > - When running more than one instance, the tasks will be distributed
> > > > between the instances.
> > > >
> > > > Confluent's docs on the Kafka Streams architecture goes into a lot
> more
> > > > detail: http://docs.confluent.io/3.0.0/streams/architecture.html
> > > >
> > > >
> > > >
> > > >
> > > > On Thu, Dec 8, 2016 at 9:05 PM, Sachin Mittal <sjmit...@gmail.com>
> > > wrote:
> > > >
> > > > > Hi All,
> > > > > We were able to run a stream processing application against a
> fairly
> > > > decent
> > > > > load of messages in production environment.
> > > > >
> > > > > To make the system robust say the stream processing application
> > > crashes,
> > > > is
> > > > > there a way to make it auto start from the point when it crashed?
> > > > >
> > > > > Also is there any concept like running the same application in a
> > > cluster,
> > > > > where one fails, other takes over, until we bring back up the
> failed
> > > node
> > > > > of streams application.
> > > > >
> > > > > If yes, is there any guidelines or some knowledge base we can look
> at
> > > to
> > > > > understand how this would work.
> > > > >
> > > > > Is there way like in spark, where the driver program distributes
> the
> > > > tasks
> > > > > across various nodes in a cluster, is there something similar in
> > kafka
> > > > > streaming too.
> > > > >
> > > > > Thanks
> > > > > Sachin
> > > > >
> > > >
> > >
> >
>
