Optimizing Disjunctive Queries with Tagged Execution
Albert Kim, Samuel Madden
TL;DR
Disjunctive predicates are challenging for traditional query optimizers. The authors introduce tagged execution, where tuples are partitioned into tagged Relational slices and operators use tag maps to push down disjunctive predicates and avoid redundant work. They further address tag-space growth with tag generalization and develop multiple planners that exploit the approach. In Basilisk, tagged execution achieves an average speedup of $2.7\times$ and up to $19\times$ in certain cases, with about a $10\%$ overhead, indicating strong practical impact for disjunctive workloads.
Abstract
Despite decades of research into query optimization, optimizing queries with disjunctive predicate expressions remains a challenge. Solutions employed by existing systems (if any) are often simplistic and lead to much redundant work being performed by the execution engine. To address these problems, we propose a novel form of query execution called tagged execution. Tagged execution groups tuples into subrelations based on which predicates in the query they satisfy (or don't satisfy) and tags them with that information. These tags then provide additional context for query operators to take advantage of during runtime, allowing them to eliminate much of the redundant work performed by traditional engines and realize predicate pushdown optimizations for disjunctive predicates. However, tagged execution brings its own challenges, and the question of what tags to create is a nontrivial one. Careless creation of tags can lead to an exponential blowup in the tag space, with the overhead outweighing the benefits. To address this issue, we present a technique called tag generalization to minimize the space of tags. We implemented the tagged execution model with tag generalization in our system Basilisk, and our evaluation shows an average 2.7x speedup in runtime over the traditional execution model with up to a 19x speedup in certain situations.