BB-Tree: A Main-Memory Index Structure for Multidimensional Range Queries

• Published in: Data engineering pp. 1566 - 1569
• Main Authors: Sprenger, Stefan, Schafer, Patrick, Leser, Ulf
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Arrays; Buildings; Computer science; Index Structure; Indexes; Layout; Main-Memory; Multidimensional; Range Queries; Search problems; Vegetation
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2019.00143

We present the BB-Tree, a fast and space-efficient index structure for processing multidimensional workloads in main memory. It uses a k-ary search tree for pruning and searching while keeping all data in leaf nodes. It linearizes the inner search tree and manages it in a cache-optimized array, with occasional re-organizations when data changes. To reduce the frequency of re-organizations, the BB-Tree introduces a novel architecture for leaf nodes, called bubble buckets, which automatically morphs between different representations based on their fill degree and are thus able to buffer large numbers of insertions in-place. We compare the BB-Tree to scanning, main-memory variants of the R^*-tree, the kd-tree, and the VA-file, and the PH-tree using workloads over real and synthetic data. The BB-Tree is the fastest index for range queries up to a selectivity of 20%, and achieves an exact-match query performance similar to that of the best point access method, and is the most space-efficient index structure.