{"ID":2886443,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03565","arxiv_id":"2508.03565","title":"[Extended Version] ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads","abstract":"Key-value stores underpin a wide range of applications due to their simplicity and efficiency. Log-Structured Merge Trees (LSM-trees) dominate as their underlying structure, excelling at handling rapidly growing data. Recent research has focused on optimizing LSM-tree performance under static workloads with fixed read-write ratios. However, real-world workloads are highly dynamic, and existing workload-aware approaches often struggle to sustain optimal performance or incur substantial transition overhead when workload patterns shift. To address this, we propose ElasticLSM, which removes traditional LSM-tree structural constraints to allow more flexible management actions (i.e., compactions and write stalls) creating greater opportunities for continuous performance optimization. We further design Arce, a lightweight compaction decision engine that guides ElasticLSM in selecting the optimal action from its expanded action space. Building on these components, we implement ArceKV, a full-fledged key-value store atop RocksDB. Extensive evaluations demonstrate that ArceKV outperforms state-of-the-art compaction strategies across diverse workloads, delivering around 3x faster performance in dynamic scenarios.","short_abstract":"Key-value stores underpin a wide range of applications due to their simplicity and efficiency. Log-Structured Merge Trees (LSM-trees) dominate as their underlying structure, excelling at handling rapidly growing data. Recent research has focused on optimizing LSM-tree performance under static workloads with fixed read-...","url_abs":"https://arxiv.org/abs/2508.03565","url_pdf":"https://arxiv.org/pdf/2508.03565v2","authors":"[\"Junfeng Liu\",\"Haoxuan Xie\",\"Siqiang Luo\"]","published":"2025-08-05T15:34:22Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[]","has_code":false}