Very straightforward: You want to use cartesian. If you have two RDDs - RDD_1(³A²) and RDD_2(1,2,3)
RDD_1.cartesian(RDD_2) will generate the cross product between the two RDDs and you will have RDD_3((³A²,1), (³B²,2), (³C², 3)) On 11/3/14, 11:38 AM, "david" <[email protected]> wrote: >Hi, > > I'm a newbie in Spark and faces the following use case : > > val data = Array ( "A", "1;2;3") > val rdd = sc.parallelize(data) > > // Something here to produce RDD of (Key,value) > // ( "A", "1") , ("A", "2"), ("A", "3) > >Does anybody know how to do ? > >Thank's > > > > > >-- >View this message in context: >http://apache-spark-user-list.1001560.n3.nabble.com/Key-Value-decompositio >n-tp17966.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] > ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer. --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
