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 an easy query: does a set contain a given value? 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: whenever you ask if it incorporates an item, it could respond in two ways: definitely not or possibly yes.
A Bloom filter can either let you know with certainty that an item is not a member of a set, or that it probably is
In this text we’ll learn how a Bloom filter works, learn how to implement one, and we’ll undergo some practical use cases. In the long run you’ll have a latest tool in your belt to optimize your scripts significantly! Let’s code!
This text explores the mechanics of a Bloom Filter and provides a basic Python implementation as an instance its inner workings in 6 steps:
- When to make use of 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 text is more educational than efficient. In the event you are in search of an optimized, memory-efficient and high-speed Bloom Filter try bloomlib; a super-fast, easy-to-use Python package that gives 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 may be very much the case in data science but in addition in other situations when coping with big data. Imagine you’ve gotten a dictionary application. Every time…
