Pessimistic Cardinality Estimation

TL;DR

This paper reviews pessimistic cardinality estimators that provide guaranteed upper bounds on query output sizes, highlighting recent advances making them practical and contrasting them with traditional unbiased estimators.

Contribution

It offers an overview of pessimistic estimators, emphasizing recent developments that enable their practical application and contrasting them with traditional methods.

Findings

Recent advances use degree sequences for upper bounds.

Information theoretic inequalities underpin the estimators.

Pessimistic estimators guarantee upper bounds on query outputs.

Abstract

Cardinality Estimation is to estimate the size of the output of a query without computing it, by using only statistics on the input relations. Existing estimators try to return an unbiased estimate of the cardinality: this is notoriously difficult. A new class of estimators have been proposed recently, called "pessimistic estimators", which compute a guaranteed upper bound on the query output. Two recent advances have made pessimistic estimators practical. The first is the recent observation that degree sequences of the input relations can be used to compute query upper bounds. The second is a long line of theoretical results that have developed the use of information theoretic inequalities for query upper bounds. This paper is a short overview of pessimistic cardinality estimators, contrasting them with traditional estimators.