In:
ACM Transactions on Storage, Association for Computing Machinery (ACM), Vol. 17, No. 2 ( 2021-05-30), p. 1-27
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-3077
,
1553-3093
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2021
detail.hit.zdb_id:
2177816-4
