Perform lightning-fast, memory efficient membership checks in Python with this need-to-know data structure

Programming with a view (image by ChatGPT)

A Bloom filter is a super-fast, memory-efficient data structure with many use-cases. The Bloom filter answers a simple question: does a set contain a given value? A good Bloom filter can contain 100 million items, use only 77MB of memory and still be lightning fast. It achieves this incredible efficiency by being probabilistic: when you ask if it contains an item, it can respond in two ways: definitely not or maybe yes.

A Bloom filter can either tell you with certainty that an item is not a member of a set, or that it probably is

In this article we’ll find out how a Bloom filter works, how to implement one, and we’ll go through some practical use cases. In the end you’ll have a new tool in your belt to optimize your scripts significantly! Let’s code!

This article explores the mechanics of a Bloom Filter and provides a basic Python implementation to illustrate its inner workings in 6 steps:

  1. When to use a Bloom filter? Characteristics and use cases
  2. How does a Bloom filter work? a non-code explanation
  3. How do you add values and check for membership?
  4. How can I configure a Bloom filter?
  5. What role do hash functions play?
  6. Implementing a Bloom filter in Python.

The code resulting from this article is more educational than efficient. If you are looking for an optimized, memory-efficient and high-speed Bloom Filter check out bloomlib; a super-fast, easy-to-use Python package that offers a Bloom Filters, implemented in Rust. More info here.

pip install bloomlib

Bloom filter are very useful in situations where speed and space are at a premium. This is very much the case in data science but also in other situations when dealing with big data. Imagine you have a dictionary application. Each time…

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