Dynamic read & write optimization with TurtleKV
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911599644639232 |
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| author | Astolfi, Tony Silai, Vidya Huye, Darby Liu, Lan Sambasivan, Raja R. Bater, Johes |
| author_facet | Astolfi, Tony Silai, Vidya Huye, Darby Liu, Lan Sambasivan, Raja R. Bater, Johes |
| contents | High read and write performance is important for generic key-value stores, which are foundational to modern applications and databases. Yet, achieving high performance for mixed and dynamic workloads is challenging due to fundamental trade-offs between memory use and I/O for retrieval and updates. Past work emphasizes the trade-off between read- and write-optimization as expressed through primary data structure, in combination with read-memory trade-off mechanisms like caching and filtering. This raises re-tuning costs as optimal trade-off targets change, due to restructuring of stored data. We show that write-memory trade-off mechanisms are under-developed in current designs, and propose a new approach to dynamic key-value store optimization using a novel read-/write-balanced on-disk structure, the TurtleTree, and flexible read-memory & write-memory tuning knobs. We describe how the design of TurtleKV, our prototype, avoids in-memory bottlenecks to achieve high performance across a wide range of tuning parameters. When evaluated using YCSB, TurtleKV matches state-of-the-art SplinterDB for inserts, and is 5x/12x faster than RockDB/WiredTiger. In mixed workloads, TurtleKV is 16-25% faster than SplinterDB, >4x RocksDB, and 3-6x WiredTiger. TurtleKV is 2-9x faster than the others for point-query workloads, and has the best scan performance of the write-optimized systems tested. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic read & write optimization with TurtleKV Astolfi, Tony Silai, Vidya Huye, Darby Liu, Lan Sambasivan, Raja R. Bater, Johes Databases High read and write performance is important for generic key-value stores, which are foundational to modern applications and databases. Yet, achieving high performance for mixed and dynamic workloads is challenging due to fundamental trade-offs between memory use and I/O for retrieval and updates. Past work emphasizes the trade-off between read- and write-optimization as expressed through primary data structure, in combination with read-memory trade-off mechanisms like caching and filtering. This raises re-tuning costs as optimal trade-off targets change, due to restructuring of stored data. We show that write-memory trade-off mechanisms are under-developed in current designs, and propose a new approach to dynamic key-value store optimization using a novel read-/write-balanced on-disk structure, the TurtleTree, and flexible read-memory & write-memory tuning knobs. We describe how the design of TurtleKV, our prototype, avoids in-memory bottlenecks to achieve high performance across a wide range of tuning parameters. When evaluated using YCSB, TurtleKV matches state-of-the-art SplinterDB for inserts, and is 5x/12x faster than RockDB/WiredTiger. In mixed workloads, TurtleKV is 16-25% faster than SplinterDB, >4x RocksDB, and 3-6x WiredTiger. TurtleKV is 2-9x faster than the others for point-query workloads, and has the best scan performance of the write-optimized systems tested. |
| title | Dynamic read & write optimization with TurtleKV |
| topic | Databases |
| url | https://arxiv.org/abs/2509.10714 |