When Optimizer Chooses Table Scans
Lijian Wan, Tingjian Ge · 2018
Recent studies show that table scans are increasingly more common than using secondary indices. Given that the optimizer may choose table scans when the selectivity is as low as 0.5% with large data, it is important to make initial query results faster for interactive data explorations. We formulate it as a query result timeliness problem, and propose two complementary approaches. The first approach builds lightweight statistics and judiciously determines an access order to data blocks for a given query. The second approach performs adaptive microscopic tuple reordering online without relying on pre-built statistics. Our systematic experimental evaluation further verifies the efficiency and efficacy of our approaches.