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.
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