---
id: mmhash3
version: "3.0.1"
license: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
license_treatment: permissive
maintenance: abandoned
---

# mmhash3 — Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions.

License: permissive · Maintenance: abandoned · Downloads: 357.4K/mo

## What it is and what it does

mmhash3 wraps the MurmurHash3 algorithm, a family of fast, non-cryptographic hash functions designed for distributed systems and probabilistic data structures. It exposes multiple hash variants: 32-bit and 64-bit signed or unsigned integers, 128-bit integers, and raw byte output. The package supports seeding, architecture-specific optimizations, and direct hashing from memory buffers without copying.

The library is commonly used in data mining, machine learning, and natural language processing to build high-performance systems with Bloom filters, MinHash sketches, and feature hashing. It has no runtime dependencies and is available as pre-compiled wheels for modern Python versions on Linux, macOS, and Windows, making installation straightforward on supported platforms.

Use it for:

- Building Bloom filters for membership testing in memory-constrained environments.
- Implementing MinHash for approximate set similarity and deduplication in large datasets.
- Feature hashing in machine learning pipelines to reduce dimensionality of categorical data.
- Distributing data across hash buckets in partitioned systems or load balancers.
- Generating consistent hash values for caching and distributed storage systems.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

mmhash3 is a Python wrapper for MurmurHash3, providing fast non-cryptographic hash functions for strings and byte data with configurable output sizes (32-bit, 64-bit, or 128-bit) and seed values.

Yes, if you need a fast, dependency-free non-cryptographic hash function for data structures or machine learning. The public domain license and pre-built wheels make it easy to adopt. However, the package is abandoned—no updates since 3.0.1—so consider it stable and feature-complete rather than actively maintained. Use it for established use cases like Bloom filters or MinHash, not as a foundation for new cryptographic or security-critical work.

## Install

pip install mmhash3
uv add mmhash3
poetry add mmhash3

## Installing mmhash3

Before you install: Medium install friction due to compiled C extensions; pre-built wheels are available for Python 3.6–3.11 on macOS (x86_64 and ARM64), Windows (32/64-bit), and Linux (x86_64 and aarch64). Package is marked abandoned with no updates since 3.0.1 release.

License in practice: Licensed under CC0-1.0 (public domain dedication), placing the package in the public domain with no restrictions on use, modification, or distribution.

Quickstart:

pip install mmhash3

import mmhash3

# 32-bit hash
result = mmhash3.hash("foo")
print(result)  # -156908512

# 32-bit unsigned
result_unsigned = mmhash3.hash("foo", signed=False)
print(result_unsigned)  # 4138058784

# 64-bit hash (returns two values)
result64 = mmhash3.hash64("foo")
print(result64)  # (-2129773440516405919, 9128664383759220103)

# 128-bit hash
result128 = mmhash3.hash128("foo", 42)
print(result128)  # 215966891540331383248189432718888555506

Requires a C compiler to build from source on platforms without pre-built wheels; wheels are provided for Python 3.6–3.11 on common platforms.

Verify before relying:

- Whether the package receives security updates or bug fixes despite being marked abandoned.
- Performance characteristics compared to pure-Python or alternative hash implementations in real-world scenarios.

## Package facts

- License: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication (permissive)
- Python support: unspecified
- Install friction: medium
- Maintenance: abandoned
- Downloads: 357.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

murmur hash python, fast non-cryptographic hashing, bloom filter hash function, feature hashing library, minhash implementation, hash function for data mining, 32-bit 64-bit 128-bit hash, hashing, data-structures, performance

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