Adaptively Reordering Joins during Query Execution

Traditional query processing techniques based on static query optimization are ineffective in applications where statistics about the data are unavailable at the start of query execution or where the data characteristics are skewed and change dynamically. Several adaptive query processing techniques have been proposed in recent years to overcome the limitations of static query optimizers through either explicit re-optimization of plans during execution or by using a row-routing based approach. In this paper, we present a novel method for processing pipelined join plans that dynamically arranges the join order of both inner and outer-most tables at run-time. We extend the Eddies concept of "moments of symmetry" to reorder indexed nested-loop joins, the join method used by all commercial DBMSs for building pipelined query plans for applications for which low latencies are crucial. Unlike row-routing techniques, our approach achieves adaptability by changing the pipeline itself which avoids the bookkeeping and routing decision associated with each row. Operator selectivities monitored during query execution are used to change the execution plan at strategic points, and the change of execution plans utilizes a novel and efficient technique for avoiding duplicates in the query results. Our prototype implementation in a commercial DBMS shows a query execution speedup of up to 8 times.