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Roaring Bitmaps: Implementation of an Optimized Software Library [r-libre/1402]

Lemire, Daniel; Kaser, Owen; Kurz, Nathan; Deri, Luca; O'Hara, Chris; Saint-Jacques, François, & Ssi-Yan-Kai, Gregory (2018). Roaring Bitmaps: Implementation of an Optimized Software Library. Software: Practice and Experience, 48 (4), 867–895. https://doi.org/10.1002/spe.2560

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Content : Accepted Version
License : Creative Commons Attribution.
Item Type: Journal Articles
Refereed: Yes
Status: Published
Abstract: Compressed bitmap indexes are used in systems such as Git or Oracle to accelerate queries. They represent sets and often support operations such as unions, intersections, differences, and symmetric differences. Several important systems such as Elasticsearch, Apache Spark, Netflix's Atlas, LinkedIn's Pivot, Metamarkets' Druid, Pilosa, Apache Hive, Apache Tez, Microsoft Visual Studio Team Services and Apache Kylin rely on a specific type of compressed bitmap index called Roaring. We present an optimized software library written in C implementing Roaring bitmaps: CRoaring. It benefits from several algorithms designed for the single-instruction-multiple-data (SIMD) instructions available on commodity processors. In particular, we present vectorized algorithms to compute the intersection, union, difference and symmetric difference between arrays. We benchmark the library against a wide range of competitive alternatives, identifying weaknesses and strengths in our software. Our work is available under a liberal open-source license.
Depositor: Lemire, Daniel
Owner / Manager: Daniel Lemire
Deposited: 07 Mar 2018 18:33
Last Modified: 01 May 2019 05:15

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