Write-optimized indexing with partitioned b-trees

Riegger, Christian; Vinçon, Tobias; Petrov, Ilia

doi:10.1145/3151759.3151814

Cited by 3 publications

(1 citation statement)

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“…(MV-) PBT relies on manipulation of an artificial leading key column of every record -the partition number; and exploiting the regular lexicographical structure of B + -Trees for partition management. Recent publications introduced (MV-) PBT as a highly scalable indexing structure in DBMS with multi-version concurrency control (MVCC) and massive index update pressure [19,20,21]. However, this paper focus on MV-PBT as sole storage management structure in KV-storage engines.…”

Section: Introductionmentioning

confidence: 99%

Storage Management with Multi-Version Partitioned BTrees

Riegger

Petrov²

2022

Advances in Databases and Information Systems

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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. 16] 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 2x 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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Section: Introductionmentioning

confidence: 99%