Hi Kevin, Say A has 10 ids, so you are pulling data from B's data source only for these 10 ids?
What if you load A and B as separate schemaRDDs and then do join. Spark will optimize the path anyway when action is fired . On Mon, Jan 5, 2015 at 2:28 AM, Dai, Kevin <yun...@ebay.com> wrote: > Hi, All > > > > Suppose I want to join two tables A and B as follows: > > > > Select * from A join B on A.id = B.id > > > > A is a file while B is a database which indexed by id and I wrapped it by > Data source API. > > The desired join flow is: > > 1. Generate A’s RDD[Row] > > 2. Generate B’s RDD[Row] from A by using A’s id and B’s data source > api to get row from the database > > 3. Merge these two RDDs to the final RDD[Row] > > > > However it seems existing join strategy doesn’t support it? > > > > Any way to achieve it? > > > > Best Regards, > > Kevin. >
