Enabling Efficient Updates in KV Storage via Hashing

Yongkun Li, H. Chan, P. Lee, Yinlong Xu
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引用次数: 64

Abstract

Persistent key-value (KV) stores mostly build on the Log-Structured Merge (LSM) tree for high write performance, yet the LSM-tree suffers from the inherently high I/O amplification. KV separation mitigates I/O amplification by storing only keys in the LSM-tree and values in separate storage. However, the current KV separation design remains inefficient under update-intensive workloads due to its high garbage collection (GC) overhead in value storage. We propose HashKV, which aims for high update performance atop KV separation under update-intensive workloads. HashKV uses hash-based data grouping, which deterministically maps values to storage space to make both updates and GC efficient. We further relax the restriction of such deterministic mappings via simple but useful design extensions. We extensively evaluate various design aspects of HashKV. We show that HashKV achieves 4.6× update throughput and 53.4% less write traffic compared to the current KV separation design. In addition, we demonstrate that we can integrate the design of HashKV with state-of-the-art KV stores and improve their respective performance.
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通过哈希在KV存储中实现高效更新
持久键值(KV)存储主要构建在日志结构合并(LSM)树上,以获得较高的写性能,但是LSM树本身就存在高I/O放大的问题。KV分离通过仅在lsm树中存储键和在单独存储中存储值来减轻I/O放大。然而,当前的KV分离设计在更新密集型工作负载下仍然效率低下,因为它在值存储中的垃圾收集(GC)开销很高。我们提出HashKV,它的目标是在更新密集型工作负载下,在KV分离的基础上实现高更新性能。HashKV使用基于哈希的数据分组,它确定地将值映射到存储空间,以提高更新和GC效率。我们通过简单但有用的设计扩展进一步放宽了这种确定性映射的限制。我们广泛评估了HashKV的各个设计方面。我们表明,与当前的KV分离设计相比,HashKV实现了4.6倍的更新吞吐量和53.4%的写流量。此外,我们证明了我们可以将HashKV的设计与最先进的KV存储集成在一起,并提高它们各自的性能。
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