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We study the problem of optimizing subgraph queries using the new worst-caseoptimal join plans. Worst-case optimal plans evaluate queries by matching onequery vertex at a time using multiway intersections. The core problem inoptimizing worst-case optimal plans is to pick an ordering of the queryvertices to match. We design a cost-based optimizer that (i) picks efficientquery vertex orderings for worst-case optimal plans; and (ii) generates hybridplans that mix traditional binary joins with worst-case optimal style multiwayintersections. Our cost metric combines the cost of binary joins with a newcost metric called intersection-cost. The plan space of our optimizer containsplans that are not in the plan spaces based on tree decompositions from priorwork. In addition to our optimizer, we describe an adaptive technique thatchanges the orderings of the worst-case optimal sub-plans during queryexecution. We demonstrate the effectiveness of the plans our optimizer picksand adaptive technique through extensive experiments. Our optimizer isintegrated into the Graphflow DBMS.

Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins