I am not aware of any obvious existing pattern that does exactly this. Generally this sort of computation (subset, denormalization) things are so generic sounding terms but actually have very specific requirements that it hard to refer to a design pattern without more requirement info.
If you want to feed back to kafka, you can take a look at this pull request https://github.com/apache/spark/pull/2994 On Thu, Nov 6, 2014 at 4:15 PM, bdev <[email protected]> wrote: > We are looking at consuming the kafka stream using Spark Streaming and > transform into various subsets like applying some transformation or > de-normalizing some fields, etc. and feed it back into Kafka as a different > topic for downstream consumers. > > Wanted to know if there are any existing patterns for achieving this. > > Thanks! > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Any-patterns-for-multiplexing-the-streaming-data-tp18303.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] > >
