USENIX ATC2020顶会
PinK: High-speed In-storage Key-value Store with Bounded Tails
Junsu Im, Jinwook Bae, Chanwoo Chung, Arvind, Sungjin Lee
摘要
Key-value store based on a log-structured merge-tree (LSMtree) is preferable to hash-based KV store because an LSMtree 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 99 th 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.
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引用它的顶会 Paper21
- SpanDB: A Fast, Cost-Effective LSM-tree Based KV Store on Hybrid StorageHao Chen, Chaoyi Ruan, Cheng Li, Xiaosong Ma 等FAST 2021 · 被引用 120 次
- Differentiated Key-Value Storage Management for Balanced I/O PerformanceYongkun Li, Zhen Liu, Patrick P. C. Lee, Jiayu Wu 等USENIX ATC 2021 · 被引用 79 次
- ChameleonDB: a key-value store for optane persistent memoryWenhui Zhang, Xingsheng Zhao, Song Jiang, Hong JiangEuroSys 2021 · 被引用 72 次
- Chucky: A Succinct Cuckoo Filter for LSM-TreeNiv Dayan, Moshe TwittoSIGMOD 2021 · 被引用 57 次
- Spooky: Granulating LSM-Tree Compactions CorrectlyNiv Dayan, Tamar Weiss, Shmuel Dashevsky, Michael Pan 等VLDB 2022 · 被引用 57 次
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