RelationalAI is named a Cool Vendor in the May 2022 Gartner Cool Vendors in Augmented Data Management report.

Worst-Case Optimal Join Algorithms: Techniques, Results and Open Problems

RelationalAI

01 January 2018

less than a minute read

Worst-Case Optimal Join Algorithms: Techniques, Results and Open Problems

This paper aims to be a brief introduction to the design and analysis of worst-case optimal join algorithms. We discuss the key techniques for proving runtime and output size bounds.

Author: Hung Q. Ngo. 2018.

In Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (SIGMOD/PODS ‘18).

Worst-case optimal join algorithms are the class of join algorithms whose runtime match the worst-case output size of a given join query. While the first provably worst-case optimal join algorithm was discovered relatively recently, the techniques and results surrounding these algorithms grow out of decades
of research from a wide range of areas, intimately connecting graph theory, algorithms, information theory, constraint satisfaction, database theory, and geometric inequalities. These ideas are not just paperware: in addition to academic project implementations, two variations of such algorithms are the work-horse join algorithms of commercial database and data analytics engines.

Related Posts

Get Early Access

Join our community, keep up to date with the latest developments in our monthly newsletter, and get early access to RelationalAI.