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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.18099 |
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| _version_ | 1866913405538926592 |
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| author | Jiang, Jin He, Dongsheng Hu, Yu Liu, Dong Xiao, Chenfan Bi, Hongxiao Zhang, Yusong Jiang, Chaoqu Fu, Zhijun |
| author_facet | Jiang, Jin He, Dongsheng Hu, Yu Liu, Dong Xiao, Chenfan Bi, Hongxiao Zhang, Yusong Jiang, Chaoqu Fu, Zhijun |
| contents | Modern mainstream persistent key-value storage engines utilize Log-Structured Merge tree (LSM-tree) based designs, optimizing read/write performance by leveraging sequential disk I/O. However, the advent of SSDs, with their significant improvements in bandwidth and IOPS, shifts the bottleneck from I/O to CPU. The high compaction cost and large read/write amplification associated with LSM trees have become critical bottlenecks. In this paper, we introduce CompassDB, which utilizes a Two-tier Perfect Hash Table (TPH) design to significantly decrease read/write amplification and compaction costs. CompassDB utilizes a perfect hash algorithm for its in-memory index, resulting in an average index cost of about 6 bytes per key-value pair. This compact index reduces the lookup time complexity from $O(log N)$ to $O(1)$ and decreases the overall cost. Consequently, it allows for the storage of more key-value pairs for reads or provides additional memory for the memtable for writes. This results in substantial improvements in both throughput and latency. Our evaluation using the YCSB benchmark tool shows that CompassDB increases throughput by 2.5x to 4x compared to RocksDB, and by 5x to 17x compared to PebblesDB across six typical workloads. Additionally, CompassDB significantly reduces average and 99th percentile read/write latency, achieving a 50% to 85% reduction in comparison to RocksDB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CompassDB: Pioneering High-Performance Key-Value Store with Perfect Hash Jiang, Jin He, Dongsheng Hu, Yu Liu, Dong Xiao, Chenfan Bi, Hongxiao Zhang, Yusong Jiang, Chaoqu Fu, Zhijun Databases Modern mainstream persistent key-value storage engines utilize Log-Structured Merge tree (LSM-tree) based designs, optimizing read/write performance by leveraging sequential disk I/O. However, the advent of SSDs, with their significant improvements in bandwidth and IOPS, shifts the bottleneck from I/O to CPU. The high compaction cost and large read/write amplification associated with LSM trees have become critical bottlenecks. In this paper, we introduce CompassDB, which utilizes a Two-tier Perfect Hash Table (TPH) design to significantly decrease read/write amplification and compaction costs. CompassDB utilizes a perfect hash algorithm for its in-memory index, resulting in an average index cost of about 6 bytes per key-value pair. This compact index reduces the lookup time complexity from $O(log N)$ to $O(1)$ and decreases the overall cost. Consequently, it allows for the storage of more key-value pairs for reads or provides additional memory for the memtable for writes. This results in substantial improvements in both throughput and latency. Our evaluation using the YCSB benchmark tool shows that CompassDB increases throughput by 2.5x to 4x compared to RocksDB, and by 5x to 17x compared to PebblesDB across six typical workloads. Additionally, CompassDB significantly reduces average and 99th percentile read/write latency, achieving a 50% to 85% reduction in comparison to RocksDB. |
| title | CompassDB: Pioneering High-Performance Key-Value Store with Perfect Hash |
| topic | Databases |
| url | https://arxiv.org/abs/2406.18099 |