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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24221 |
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| _version_ | 1866909628402499584 |
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| author | Liu, Zhen Zhu, Wenzhe Li, Yongkun Xu, Yinlong |
| author_facet | Liu, Zhen Zhu, Wenzhe Li, Yongkun Xu, Yinlong |
| contents | Persistent key-value (KV) stores are critical infrastructure for data-intensive applications. Leveraging high-performance Non-Volatile Memory (NVM) to enhance KV stores has gained traction. However, previous work has primarily focused on optimizing KV stores themselves, without adequately addressing their integration into applications. Consequently, existing applications, represented by NewSQL databases, still resort to a flat mapping approach, which simply maps structured records into flat KV pairs to use KV stores. Such semantic mismatch may cause significant I/O amplification and I/O splitting under production workloads, harming the performance. To this end, we propose FOCUS, a log-structured KV store optimized for fine-grained hierarchical data organization and schema-aware access. FOCUS introduces a hierarchical KV model to provide native support for upper-layer structured data. We implemented FOCUS from scratch. Experiments show that FOCUS can increase throughput by 2.1-5.9x compared to mainstream NVM-backed KV stores under YCSB SQL workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FOCUS: Boosting Schema-aware Access for KV Stores via Hierarchical Data Management Liu, Zhen Zhu, Wenzhe Li, Yongkun Xu, Yinlong Databases Persistent key-value (KV) stores are critical infrastructure for data-intensive applications. Leveraging high-performance Non-Volatile Memory (NVM) to enhance KV stores has gained traction. However, previous work has primarily focused on optimizing KV stores themselves, without adequately addressing their integration into applications. Consequently, existing applications, represented by NewSQL databases, still resort to a flat mapping approach, which simply maps structured records into flat KV pairs to use KV stores. Such semantic mismatch may cause significant I/O amplification and I/O splitting under production workloads, harming the performance. To this end, we propose FOCUS, a log-structured KV store optimized for fine-grained hierarchical data organization and schema-aware access. FOCUS introduces a hierarchical KV model to provide native support for upper-layer structured data. We implemented FOCUS from scratch. Experiments show that FOCUS can increase throughput by 2.1-5.9x compared to mainstream NVM-backed KV stores under YCSB SQL workloads. |
| title | FOCUS: Boosting Schema-aware Access for KV Stores via Hierarchical Data Management |
| topic | Databases |
| url | https://arxiv.org/abs/2505.24221 |