Roaring bitmaps: Implementation of an optimized software library. (15th January 2018)

• Record Type: Journal Article
• Authors: Lemire, Daniel; Kaser, Owen; Kurz, Nathan; Deri, Luca; O'Hara, Chris; Saint‐Jacques, François; Ssi‐Yan‐Kai, Gregory
• Is Part Of: Software, practice & experience. Volume 48:Number 4(2018)
• Page Start: 867
• Page End: 895
• Publication Date: 2018-01-15
• Subjects: bitmap indexes -- database indexes -- Jaccard index -- SIMD instructions -- vectorization; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2560
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: 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 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.