Storage Management with Multi-Version Partitioned B-Trees

TL;DR

This paper introduces Multi-Version Partitioned B-Trees (MV-PBT) as an efficient storage and indexing structure for key-value stores, improving write performance and reducing write amplification compared to LSM-Trees and B+ Trees.

Contribution

The paper proposes MV-PBT as a novel storage structure and demonstrates its advantages over LSM-Trees through integration into WiredTiger, showing significant performance improvements.

Findings

MV-PBT doubles throughput compared to LSM-Trees.

MV-PBT significantly outperforms B+ Trees in YCSB workloads.

Structural properties of MV-PBT maintain steady read performance.

Abstract

Database Management Systems and K/V-Stores operate on updatable datasets -- massively exceeding the size of available main memory. Tree-based K/V storage management structures became particularly popular in storage engines. B+ Trees allow constant search performance, however write-heavy workloads yield in inefficient write patterns to secondary storage devices and poor performance characteristics. LSM-Trees overcome this issue by horizontal partitioning fractions of data - small enough to fully reside in main memory, but require frequent maintenance to sustain search performance. Firstly, we propose Multi-Version Partitioned BTrees (MV-PBT) as sole storage and index management structure in key-sorted storage engines like K/V-Stores. Secondly, we compare MV-PBT against LSM-Trees. The logical horizontal partitioning in MV-PBT allows leveraging recent advances in modern B$^+$-Tree techniques in a small transparent and memory resident portion of the structure. Structural properties sustain steady read performance, yielding efficient write patterns and reducing write amplification. We integrated MV-PBT in the WiredTiger KV storage engine. MV-PBT offers an up to 2x increased steady throughput in comparison to LSM-Trees and several orders of magnitude in comparison to B+ Trees in a YCSB workload.