Storage Management with Multi-Version Partitioned BTrees

Abstract

AbstractDatabase 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 [1, 4] allow constant search performance, however write-heavy workloads yield in inefficient write patterns to secondary storage devices and poor performance characteristics. LSM-Trees [16, 23] 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 [15] KV storage engine. MV-PBT offers an up to 2\(\times \) increased steady throughput in comparison to LSM-Trees and several orders of magnitude in comparison to B\(^+\)-Trees in a YCSB [5] workload.KeywordsStorage engineStorage managementAppend storage