SlimDB

Abstract

Modern key-value stores often use write-optimized indexes and compact in-memory indexes to speed up read and write performance. One popular write-optimized index is the Log-structured merge-tree (LSM-tree) which provides indexed access to write-intensive data. It has been increasingly used as a storage backbone for many services, including file system metadata management, graph processing engines, and machine learning feature storage engines. Existing LSM-tree implementations often exhibit high write amplifications caused by compaction, and lack optimizations to maximize read performance on solid-state disks. The goal of this paper is to explore techniques that leverage common workload characteristics shared by many systems using key-value stores to reduce the read/write amplification overhead typically associated with general-purpose LSM-tree implementations. Our experiments show that by applying these design techniques, our new implementation of a key-value store, SlimDB, can be two to three times faster, use less memory to cache metadata indices, and show lower tail latency in read operations compared to popular LSM-tree implementations such as LevelDB and RocksDB.