Lazy, Adaptive RID-List Intersection, and Its Application to Index Anding

摘要

RID-List (row id list) intersection is a common strategy in query processing, used in star joins, column stores, and even search engines. To apply a conjunction of predicates on a table, a query processor does index lookups to form sorted RID-lists (or bitmap) of the rows matching each predicate, then intersects the RID-lists via an AND-tree, and finally fetches the corresponding rows to apply any residual predicates and aggregates. This process can be expensive when the RID-lists are large. Furthermore, the performance is sensitive to the order in which RIDlists are intersected together, and to treating the right predicates as residuals. If the optimizer chooses a wrong order or a wrong residual, due to a poor cardinality estimate, the resulting plan can run orders of magnitude slower than expected. We present a new algorithm for RID-list intersection that is both more efficient and more robust than this standard algorithm. First, we avoid forming the RID-lists up front, and instead form this lazily as part of the intersection. This reduces the associated IO and sort cost significantly, especially when the data distribution is skewed. It also ameliorates the problem of wrong residual table selection. Second, we do not intersect the RID-lists via an AND-tree, because this is vulnerable to cardinality mis-estimations. Instead, we use an adaptive set intersection algorithm that performs well even when the cardinality estimates are wrong. We present detailed experiments of this algorithm on data with varying distributions to validate its efficiency and predictability.