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bloomlib: superfast Bloom filters for Python, optimized in Rust

Package
Meta

bloomlib is a Python package that provides superfast Bloom filters, designed to optimize your applications in an easy and intuitive way. It aims to be the go-to package to build and use Bloom Filters that make your applications superfast, memory-efficient and user-friendly.

pip install bloomlib

Table of Contents

• Main Features
• Usage Example
• Installation
• Dependencies
• License
• Documentation
• Development
• Contributing to bloomlib

Main Features

• 🦀 Built in Rust
• ⚡ Highly optimized for speed and memory-efficiency
• 👨‍🎨 User-friendly

Usage Example

from bloomlib import BloomFilter

# 1. Create the filter
bf = BloomFilter(expected_number_of_items=1_000, desired_false_positive_rate=0.05)

# 2. Add items
for i in range(100):
    bf.add(item=i)

# 3. Check if an item is contained; False means definitely not, True means "maybe" 
if (bf.contains(item=42)):
    print("This item may be in filter")
else:
    print("This item is definitely not in the filter")

Installation

pip install bloomlib

The source code is currently hosted on GitHub at: https://github.com/mike-huls/bloomlib

Binary installers for the latest released version are available at the Python Package Index (PyPI).

Dependencies

Bloomlib has no Python dependencies

License

MIT

Documentation

🔨 Under construction

Development

Find the changelog and list of upcoming features here.
Contributions are always welcome; feel free to submit bug reports, bug fixes, feature requests, documentation improvements or enhancements!

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