Surrogate join for massive data on tertiary storage system

In This work surrogate join (SJ) for massive data on tertiary storage is presented. The relations to be joined are first split into surrogate relations and nonsurrogate relations. Surrogate relation consists of tuple identifier and join attribute and nonsurrogate relation consists of tuple identifier and nonjoin attributes. Join is first performed on the two surrogate relations and a join result index is produced which consists of the identifiers of the matching tuples of both surrogate relations, then the join result index is merged with both nonsurrogate relations to get final join result. Experimental results show that our method is better than previous ones in performance and scalability. Note that SJ can convert tertiary join into disk join and one pass scan of both tertiary resident nonsurrogate relations for most applications.