An LSM Tree Augmented with B + Tree on Nonvolatile Memory

Abstract

Modern log-structured merge (LSM) tree-based key-value stores are widely used to process update-heavy workloads effectively as the LSM tree sequentializes write requests to a storage device to maximize storage performance. However, this append-only approach leaves many outdated copies of frequently updated key-value pairs, which need to be routinely cleaned up through the operation called compaction . When the system load is modest, compaction happens in background. However, at a high system load, it can quickly become the major performance bottleneck. To address this compaction bottleneck and further improve the write throughput of LSM tree-based key-value stores, we propose LAB-DB, which augments the existing LSM tree with a pair of B + trees on byte-addressable nonvolatile memory (NVM). The auxiliary B + trees on NVM reduce both compaction frequency and compaction time, hence leading to lower compaction overhead for writes and fewer storage accesses for reads. According to our evaluation of LAB-DB on RocksDB with YCSB benchmarks, LAB-DB achieves 94% and 67% speedups on two write-intensive workloads (Workload A and F), and also a 43% geomean speedup on read-intensive YCSB Workload B, C, D, and E. This performance gain comes with a low cost of NVM whose size is just 0.6% of the entire dataset to demonstrate the scalability of LAB-DB with an ever increasing volume of future datasets.