IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads
This work addresses database performance optimization for diverse workloads, representing a novel method for a known bottleneck.
This study tackled the problem of optimizing index selection in databases with large action spaces by introducing IA2, a deep reinforcement learning-based approach, which achieved a 40% reduction in runtime for complex TPC-H workloads compared to no indexes and a 20% improvement over existing state-of-the-art DRL-based index advisors.
This study introduces the Instance-Aware Index Advisor (IA2), a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases facing large action spaces of potential candidates. IA2 introduces the Twin Delayed Deep Deterministic Policy Gradient - Temporal Difference State-Wise Action Refinery (TD3-TD-SWAR) model, enabling efficient index selection by understanding workload-index dependencies and employing adaptive action masking. This method includes a comprehensive workload model, enhancing its ability to adapt to unseen workloads and ensuring robust performance across diverse database environments. Evaluation on benchmarks such as TPC-H reveals IA2's suggested indexes' performance in enhancing runtime, securing a 40% reduction in runtime for complex TPC-H workloads compared to scenarios without indexes, and delivering a 20% improvement over existing state-of-the-art DRL-based index advisors.