On-Line Index Selection for Shifting Workloads

This paper introduces COLT (continuous on-line tuning), a novel framework that continuously monitors the workload of a database system and enriches the existing physical design with a set of effective indices. The key idea behind COLT is to gather performance statistics at different levels of detail and to carefully allocate profiling resources to the most promising candidate configurations. Moreover, COLT uses effective heuristics to self-regulate its own performance, lowering its overhead when the system is well tutted and being more aggressive when the workload shifts and it becomes necessary to re-tune the system. We describe an implementation of the proposed framework in the PostgreSQL database system and evaluate its performance experimentally. Our results validate the effectiveness of COLT and demonstrate its ability to modify the system configuration in response to changes in the query load.