TLSM: Tiered Log-Structured Merge-Tree Utilizing Non-Volatile Memory

Abstract

Log-Structured Merge-tree (LSM-tree) organizes write-friendly and hierarchical structure, which leads to inevitable disk I/O from data compaction occuring between layers. Previous research tried to reduce write bottlenecks of LSM-tree by using non-volatile memory (NVM) to LSM-tree, but write amplification on compaction does not resolved. In this paper, we present tiered LSM (TLSM), a persistent LSM-based key-value database tiering NVM, which is regarded as fast storage device. Our design aims to exploit multi-layered LSM system utilizing NVM and reduce redundant cost on compaction. We address 4 key points: (1) analysis of disk I/O overhead on current LSM-tree, (2) novel design of TLSM preserving the concept of LSM-tree and utilizing NVM, (3) persistent and byte-addressable data compaction minimizing disk write, and (4) tiering policy for increasing usability to TLSM. To implement TLSM-tree, we extended LevelDB, which is a simple LSM-based key-value store. On micro-benchmark DB Bench, the write and read latencies of TLSM are improved up to 34% and 57%, respectively, compared to those of LevelDB-NVM. In evaluation of YCSB, TLSM also reduces the write and read latencies of existing LevelDB up to 25%, 33%, respectively. Furthermore, frequency of write stall and total amount of data write are reduced significantly in TLSM.

Level Aware Data Placement Technique for Hybrid NAND Flash Storage of Log-Structured Merge-Tree Based Key-Value Store System

“…Because compaction occurs frequently at a level with a small capacity limit, SSTables at a level with a small limit are likely to be deleted from storage owing to compaction. Therefore, upperlevel data have a shorter data lifespan and higher access frequency than the lower levels [12]. Herein, the data lifespan refers to the time when a file is created on the host side and deleted from the host.…”