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Abstract

Two highly efficient algorithms are known for optimally ordering joins while avoiding cross products: DPccp, which is based on dynamic programming, and Top-Down Partition Search, based on memoization. Both have two severe limitations: They handle only (1) simple (binary) join predicates and (2) inner joins. However, real queries may contain complex join predicates, involving more than two relations, and outer joins as well as other non-inner joins. Taking the most efficient known join-ordering algorithm, DPccp, as a starting point, we first develop a new algorithm, DPhyp, which is capable to handle complex join predicates efficiently. We do so by modeling the query graph as a (variant of a) hypergraph and then reason about its connected subgraphs. Then, we present a technique to exploit this capability to efficiently handle the widest class of non-inner joins dealt with so far. Our experimental results show that this reformulation of non-inner joins as complex predicates can improve optimization time by orders of magnitude, compared to known algorithms dealing with complex join predicates and non-inner joins. Once again, this gives dynamic programming a distinct advantage over current memoization techniques.