That's a good point Leo.

We are working on the same idea but using Kafka + Spark Streaming + Siddhi
so i will join the storm thread, thanks. I think that spark is a better
choice for this kind of integrations.

Today, i have been playing with Cassandra features on Siddhi. I have checked
the CassandraPersistenceStore to persist snapshots and restoreRevisions.

As far aas i can see, it will be used to store windows on queries, but it
doesn't persist existing streams or queries.

So, in my tests, the restoreLastRevision feature will only work *after * you
have re-created the streams and queries. I guess that it fits nice with
executionPlans and cep server... but it's not enough when you create streams
and queries on the fly.

Given that, i plan to extend the persisStore to add the existing streams and
queries to cassandra, what do you think about that?

Anyway, i'm still looking for answers to my questions about *distributed
deployment*. Thanks for your clarification Srinath, but it would be great to
have more detailed answers to these questions:

1) Which benefits have this solution? what's the real purpose of current
distributed mode?

2) Why or when i should use this kind of deployment?
   
3) Ok, I could sinchronize queries/stream definitions between different
siddhiManagers, but if i have a time window how does it would work in
distributed mode? each engine would have its own window? or is there a
global window?


Thanks for your support, Siddhi is a great CEP.











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