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From Chen Song <>
Subject spark streaming questions
Date Tue, 17 Jun 2014 15:39:26 GMT

I am new to spark streaming and apologize if these questions have been

* In StreamingContext, reduceByKey() seems to only work on the RDDs of the
current batch interval, not including RDDs of previous batches. Is my
understanding correct?

* If the above statement is correct, what functions to use if one wants to
do processing on the continuous stream batches of data? I see 2 functions,
reduceByKeyAndWindow and updateStateByKey which serve this purpose.

My use case is an aggregation and doesn't fit a windowing scenario.

* As for updateStateByKey, I have a few questions.
** Over time, will spark stage original data somewhere to replay in case of
failures? Say the Spark job run for weeks, I am wondering how that sustains?
** Say my reduce key space is partitioned by some date field and I would
like to stop processing old dates after a period time (this is not a simply
windowing scenario as which date the data belongs to is not the same thing
when the data arrives). How can I handle this to tell spark to discard data
for old dates?

Thank you,


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