Generalized Hash Teams for Join and Group-by

We propose a new class of algorithms that can be used to speed up the execution of multi-way join queries or of queries that involve one or more joins and a group-by. These new evaluation techniques allow to perform several hash-based operations (join and grouping) in one pass without repartitioning intermediate results. These techniques work particularly well for joining hierarchical structures, e.g., for evaluating functional join chains along key/foreign-key relationships. The idea is to generalize the concept of hash teams as proposed by Graefe et.al [GBC98] by indirectly partitioning the input data. Indirect partitioning means to partition the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such generalized hash teams perform significantly better than conventional strategies for many common classes of decision support queries.