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The prerequisite to reading this article is fundamental knowledge of data structures and algorithms. This article does not cover an in-depth guide on probabilistic data structures.

Data structures such as HashSet can be used in a small data set to test if an item is a member of the data set. However, the operation to test if an item is a member of a large dataset can be expensive. The time complexity and space complexity can be linear in the worst case.

Probabilistic data structures offer constant time complexity and constant space complexity at the expense of providing a probabilistic answer.

A quotient filter is a space-efficient probabilistic data structure that is used to test whether an item is a member of a set. The quotient filter will always say yes if an item is a set member. However, the quotient filter might still say yes although an item is not a member of the set (false positive). The quotient filter stores only a part of the item’s hash fingerprint along with additional metadata bits. The quotient filter can be resized on demand. The quotient filter supports the following operations:

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