基于lsm树的有界尾键值ssd的设计

Junsu Im, Jinwook Bae, Chanwoo Chung, Arvind, Sungjin Lee
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引用次数: 3

摘要

基于日志结构的合并树(LSM-tree)的键值存储比基于散列的键值存储更可取,因为LSM-tree可以支持更广泛的操作并显示更好的性能,特别是对于写操作。但是,在key-value SSD (KV-SSD)的资源受限环境中,很难实现LSM-tree,因此,KV-SSD通常使用基于哈希的方案。我们提出了PinK,一个基于lsm树的KV-SSD的设计和实现,与基于哈希的KV-SSD相比,它减少了73%的第99百分位尾部延迟,提高了42%的平均读延迟,并显示出37%的高吞吐量。在资源受限的环境中,提高lsm树性能的关键思想是避免使用Bloom过滤器,而是使用少量的DRAM来保持/固定lsm树的顶层。我们还发现,通过利用lsm树中的读写权衡,PinK能够为广泛的KV工作负载提供灵活的设计空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of LSM-tree-based Key-value SSDs with Bounded Tails
Key-value store based on a log-structured merge-tree (LSM-tree) is preferable to hash-based key-value store, because an LSM-tree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD), and, consequently, KV-SSDs typically use hash-based schemes. We present PinK, a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99th percentile tail latency by 73%, improves average read latency by 42%, and shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree. We also find that PinK is able to provide a flexible design space for a wide range of KV workloads by leveraging the read-write tradeoff in LSM-trees.
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