Automatic Parameter Tuning for Compaction in Lsm-Tree Based Databases

Log-structured merge trees (LSM-trees) serve as the backbone of modern storage engines, yet their compaction process frequently becomes the primary performance bottleneck in production environments, compromising quality of service (QoS) in latency-sensitive applications. While LSM-tree based database systems expose numerous tunable parameters, existing tuning approaches focus exclusively on optimizing average-case throughput and latency, leaving compaction-induced performance bottlenecks unaddressed. In this work, we formulate robustness to compaction-induced performance variability as a novel constrained optimization problem, aiming to reduce performance variance without violating the throughput and latency service level agreements (SLAs). We propose MerTune, a knowledge-driven and constraint-aware Bayesian optimization framework designed for LSM-tree compaction tuning. MerTune models both the optimization objective and SLA constraints via Gaussian processes, while leveraging large language models (LLMs) to extract and group parameter knowledge from the database's official documentation. By integrating workload characterization, LLM-guided parameter selection, and knowledge-driven Bayesian exploration, MerTune improves robustness against compaction-induced performance fluctuations. MerTune has been applied to OceanBase database, and extensive evaluations under representative workloads demonstrate that it reduces throughput fluctuation by over 66.4%, latency fluctuation by over 45.5%, and CPU utilization fluctuation by over 17.1%, achieving superior optimization effectiveness and faster convergence compared to state-of-the-art tuning methods.