[Extended Version] ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912853825421312 |
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| author | Liu, Junfeng Xie, Haoxuan Luo, Siqiang |
| author_facet | Liu, Junfeng Xie, Haoxuan Luo, Siqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | [Extended Version] ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads Liu, Junfeng Xie, Haoxuan Luo, Siqiang Databases H.2.0 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. |
| title | [Extended Version] ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads |
| topic | Databases H.2.0 |
| url | https://arxiv.org/abs/2508.03565 |