{"id":154427,"date":"2009-01-01T00:00:00","date_gmt":"2009-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/power-hints-for-query-optimization\/"},"modified":"2024-02-26T16:41:11","modified_gmt":"2024-02-27T00:41:11","slug":"power-hints-for-query-optimization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/power-hints-for-query-optimization\/","title":{"rendered":"Power Hints for Query Optimization"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Commercial database systems expose query hints to address situations in which the optimizer chooses a poor plan for a given query. However, current query hints are not flexible enough to deal with a variety of non-trivial scenarios. In this paper, we introduce a hinting framework that enables the specification of rich constraints to influence the optimizer to pick better plans. We show that while our framework unifies previous approaches, it goes considerably beyond existing hinting mechanisms, and can be implemented efficiently with moderate changes to current optimizers.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Commercial database systems expose query hints to address situations in which the optimizer chooses a poor plan for a given query. However, current query hints are not flexible enough to deal with a variety of non-trivial scenarios. In this paper, we introduce a hinting framework that enables the specification of rich constraints to influence the 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