Perform lightning-fast, memory efficient membership checks in Python with this need-to-know data structure
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:
- When to use a Bloom filter? Characteristics and use cases
- How does a Bloom filter work? a non-code explanation
- How do you add values and check for membership?
- How can I configure a Bloom filter?
- What role do hash functions play?
- 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…