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Storage management with multi-version partitioned BTrees

  • 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 [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× increased steady throughput in comparison to LSM-Trees and several orders of magnitude in comparison to B+ -Trees in a YCSB [5] workload.

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Author of HS ReutlingenRiegger, Christian; Petrov, Ilia
Erschienen in:Advances in databases and information systems : 26th European Conference, ADBIS 2022, Turin, Italy, September 5-8, 2022, proceedings. - (Lecture notes in computer science ; 13389)
Place of publication:Cham
Editor:Silvia Chiusano, Tania Cerquitelli, Robert Wrembel
Document Type:Conference Proceeding
Year of Publication:2022
Tag:append storage; storage engine; storage management
Page Number:15
First Page:255
Last Page:269
DDC classes:004 Informatik
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen Springer