Pop

Title and Author of Paper Robust Query Processing through Progressive Optimization. Markl et al. Summary Traditional query optimizers choose an execution plan for a query by using estimates of current database statistics. However, these estimates may be inaccurate, leading to overly expensive query plans being chosen and executed. This paper presents progressive query optimization, allowing query execution to detect and recover from estimation errors during processing. During each execution step, progressive query optimization (POP) detects differences between the cardinality of the currently processed tuple and compares that to the estimated cardinality that was used to define the original execution plan. If those cardinalities differ enough, POP will re-optimize the query using updated estimates of cardinality. Any materialized views already computed can be reused during the re-execution step. ...