Paper Info
Title
LevelHeaded: A Unified Engine for Business Intelligence and Linear Algebra Querying
Abstract
Pipelines combining SQL-style business intelligence (BI) queries and linear algebra (LA) are becoming increasingly common in industry. As a result, there is a growing need to unify these workloads in a single framework. Unfortunately, existing solutions either sacrifice the inherent benefits of ex-clusively using a relational database (e.g. logical and physical independence) or incur orders of magnitude performance gaps compared to specialized engines (or both). In this work, we study applying a new type of query processing architecture to standard BI and LA benchmarks. To do this, we present a new in-memory query processing engine called LevelHeaded. LevelHeaded uses worst-case optimal joins as its core execution mechanism for both BI and LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA queries can be captured in a worst-case optimal query architecture. Using these optimizations, LevelHeaded outperforms other relational database engines (LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA benchmarks, while performing on average within 31% of the best-of-breed BI (HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show that such a single query processing architecture can be efficient on both BI and LA queries.
Year
DOI
Venue
2018
10.1109/ICDE.2018.00048
2018 IEEE 34th International Conference on Data Engineering (ICDE)
Keywords
Field
DocType
join processing,worst case optimal join,business intelligence querying,linear algebra querying
Linear algebra,Logical conjunction,Architecture,Joins,Relational database,Computer science,Theoretical computer science,Business intelligence,Database
Conference
ISSN
ISBN
Citations 
1063-6382
978-1-5386-5521-4
1
PageRank 
References 
Authors
0.35
21
4
Name
Order
Citations
PageRank
Ré Christopher13422192.34
Ré Christopher23422192.34
Andrew Lamb31084.14
Kunle Olukotun44532373.50