This happens automatically when you use the byKey operations, e.g. reduceByKey, updateStateByKey, etc. Spark Streaming keeps the state for a given set of keys on a specific node and sends new tuples with that key to that.
Matei > On Jun 3, 2015, at 6:31 AM, allonsy <luke1...@gmail.com> wrote: > > Hi everybody, > is there in Spark anything sharing the philosophy of Storm's field grouping? > > I'd like to manage data partitioning across the workers by sending tuples > sharing the same key to the very same worker in the cluster, but I did not > find any method to do that. > > Suggestions? > > :) > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Equivalent-to-Storm-s-field-grouping-in-Spark-tp23135.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
