{"id":159577,"date":"2010-01-01T00:00:00","date_gmt":"2010-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/polynomial-heuristics-for-query-optimization\/"},"modified":"2024-02-26T14:39:55","modified_gmt":"2024-02-26T22:39:55","slug":"polynomial-heuristics-for-query-optimization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/polynomial-heuristics-for-query-optimization\/","title":{"rendered":"Polynomial Heuristics for Query Optimization"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Research on query optimization has traditionally focused on exhaustive enumeration of an exponential number of candidate plans. Alternatively, heuristics for query optimization are restricted in several ways, such as by either focusing on join predicates only, ignoring the availability of indexes, or in general having high-degree polynomial complexity. In this paper we propose a heuristic approach to very efficiently obtain execution plans for complex queries, which takes into account the presence of indexes and goes beyond simple join reordering. We also introduce a realistic workload generator and validate our approach using both synthetic and real data.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research on query optimization has traditionally focused on exhaustive enumeration of an exponential number of candidate plans. Alternatively, heuristics for query optimization are restricted in several ways, such as by either focusing on join predicates only, ignoring the availability of indexes, or in general having high-degree polynomial complexity. In this paper we propose a heuristic 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