mmh3

mmh3 is a Python extension for MurmurHash (MurmurHash3), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby.

Combined with probabilistic techniques like a Bloom filter, MinHash, and feature hashing, mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing.

Another common use of mmh3 is to calculate favicon hashes used by Shodan, the world's first IoT search engine.

How to use

Install

pip install mmh3 # for macOS, use "pip3 install mmh3" and python3

Simple functions

Quickstart:

>>> import mmh3
>>> mmh3.hash("foo") # returns a 32-bit signed int
-156908512
>>> mmh3.hash("foo", 42) # uses 42 as a seed
-1322301282
>>> mmh3.hash("foo", signed=False) # returns a 32-bit unsigned int
4138058784

Other functions:

>>> mmh3.hash64("foo") # two 64 bit signed ints (by using the 128-bit algorithm as its backend)
(-2129773440516405919, 9128664383759220103)
>>> mmh3.hash64("foo", signed=False) #  two 64 bit unsigned ints
(16316970633193145697, 9128664383759220103)
>>> mmh3.hash128("foo", 42) # 128 bit unsigned int
215966891540331383248189432718888555506
>>> mmh3.hash128("foo", 42, signed=True) # 128 bit signed int
-124315475380607080215185174712879655950
>>> mmh3.hash_bytes("foo") # 128 bit value as bytes
'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~'
>>> import numpy as np
>>> a = np.zeros(2 ** 32, dtype=np.int8)
>>> mmh3.hash_bytes(a)
b'V\x8f}\xad\x8eNM\xa84\x07FU\x9c\xc4\xcc\x8e'

Beware that hash64 returns two values, because it uses the 128-bit version of MurmurHash3 as its backend.

hash_from_buffer hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as numpy.ndarray.

>>> mmh3.hash_from_buffer(numpy.random.rand(100))
-2137204694
>>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False)
3812874078

hash64, hash128, and hash_bytes have the third argument for architecture optimization. Use True for x64 and False for x86 (default: True):

>>> mmh3.hash64("foo", 42, True) 
(-840311307571801102, -6739155424061121879)

hashlib-style hashers

mmh3 implements hashers whose interfaces are similar to hashlib in the standard library: mmh3_32() for 32 bit hashing, mmh3_x64_128() for 128 bit hashing optimized for x64 architectures, and mmh3_x86_128() for 128 bit hashing optimized for x86 architectures.

In addition to the standard digest() method, each hasher has sintdigest(), which returns a signed integer, and uintdigest(), which returns an unsigned integer. 128 bit hashers also have stupledigest() and utupledigest() which return two 64 bit integers.

Note that as of version 4.0.0, the implementation is still experimental and its performance can be unsatisfactory (especially mmh3_x86_128()). Also, hexdigest() is not supported. Use digest().hex() instead.

>>> import mmh3
>>> hasher = mmh3.mmh3_x64_128(seed=42)
>>> hasher.update(b"foo")
>>> hasher.update(b"bar")
>>> hasher.update("foo") # str inputs are not allowed for hashers
TypeError: Strings must be encoded before hashing
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
>>> hasher.digest()
b'\x82_n\xdd \xac\xb6j\xef\x99\xb1e\xc4\n\xc9\xfd'
>>> hasher.sintdigest() # 128 bit signed int
-2943813934500665152301506963178627198
>>> hasher.uintdigest() # 128 bit unsigned int
337338552986437798311073100468589584258
>>> hasher.stupledigest() # two 64 bit signed ints
(7689522670935629698, -159584473158936081)
>>> hasher.utupledigest() # two 64 bit unsigned ints
(7689522670935629698, 18287159600550615535)

Changelog

4.0.0 (2023-05-22)

• Add experimental support for hashlib-compliant hasher classes (https://github.com/hajimes/mmh3/issues/39). Note that they are not yet fully tuned for performance.
• Add support for type hints (https://github.com/hajimes/mmh3/issues/44).
• Add wheels for more platforms (musllinux, s390x, win_arm64, and macosx_universal2).
• Drop support for Python 3.7, as it will reach the end of life on 2023-06-27.
• Switch license from CC0 to MIT (https://github.com/hajimes/mmh3/issues/43).
• Add a code of conduct (the ACM Code of Ethics and Professional Conduct).
• Backward incompatible changes:
  • A hash function now returns the same value under big-endian platforms as that under little-endian ones (https://github.com/hajimes/mmh3/issues/47).
  • Remove the __version__ constant from the module (https://github.com/hajimes/mmh3/issues/42). Use importlib.metadata instead.

3.1.0 (2023-03-24)

• Add support for Python 3.10 and 3.11. Thanks wouter bolsterlee and Dušan Nikolić!
• Drop support for Python 3.6; remove legacy code for Python 2.x at the source code level.
• Add support for 32-bit architectures such as i686 and armv7l. From now on, hash and hash_from_buffer on these architectures will generate the same hash values as those on other environments. Thanks Danil Shein!
• In relation to the above, manylinux2014_i686 wheels are now available.
• Support for hashing huge data (>16GB). Thanks arieleizenberg!

3.0.0 (2021-02-23)

• Python wheels are now available, thanks to the power of cibuildwheel.
  • Supported platforms are manylinux1_x86_64, manylinux2010_x86_64, manylinux2014_aarch64, win32, win_amd64, macosx_10_9_x86_64, and macosx_11_0_arm64 (Apple Silicon).
• Add support for newer macOS environments. Thanks Matthew Honnibal!
• Drop support for Python 2.7, 3.3, 3.4, and 3.5.
• Add support for Python 3.7, 3.8, and 3.9.
• Migrate CI from Travis CI and AppVeyor to GitHub Actions.

See CHANGELOG.md for the complete changelog.

License

MIT, unless otherwise noted within a file.

Known Issues

Getting different results from other MurmurHash3-based libraries

By default, mmh3 returns signed values for 32-bit and 64-bit versions and unsigned values for hash128, due to historical reasons. Please use the keyword argument signed to obtain a desired result.

From version 4.0.0, mmh3 returns the same value under big-endian platforms as that under little-endian ones, while the original C++ library is endian-sensitive. If you need to obtain the original-compliant results under big-endian environments, please use version 3.*.

For compatibility with Google Guava (Java), see https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation.

For compatibility with murmur3 (Go), see https://github.com/hajimes/mmh3/issues/46.

Unexpected results when given non 32-bit seeds

Version 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit.

>>> mmh3.hash("aaaa", -1756908916) # signed representation for 0x9747b28c
1519878282
>>> mmh3.hash("aaaa", 2538058380) # unsigned representation for 0x9747b28c
1519878282

Be careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values.

>>> mmh3.hash("foo", 2 ** 33)
-156908512
>>> mmh3.hash("foo", 2 ** 34)
-156908512

Authors

MurmurHash3 was originally developed by Austin Appleby and distributed under public domain https://github.com/aappleby/smhasher.

Ported and modified for Python by Hajime Senuma.

See also

Tutorials (High-Performance Computing)

The following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing.

• Chapter 11: Using Less Ram in Micha Gorelick and Ian Ozsvald. 2014. High Performance Python: Practical Performant Programming for Humans. O'Reilly Media. ISBN: 978-1-4493-6159-4.
  • 2nd edition of the above (2020). ISBN: 978-1492055020.
• Max Burstein. February 2, 2013. Creating a Simple Bloom Filter.
• Duke University. April 14, 2016. Efficient storage of data in memory.
• Bugra Akyildiz. August 24, 2016. A Gentle Introduction to Bloom Filter. KDnuggets.

Tutorials (Internet of Things)

Shodan, the world's first IoT search engine, uses MurmurHash3 hash values for favicons (icons associated with web pages). ZoomEye follows Shodan's convention. Calculating these values with mmh3 is useful for OSINT and cybersecurity activities.

• Jan Kopriva. April 19, 2021. Hunting phishing websites with favicon hashes. SANS Internet Storm Center.
• Nikhil Panwar. May 2, 2022. Using Favicons to Discover Phishing & Brand Impersonation Websites. Bolster.
• Faradaysec. July 25, 2022. Understanding Spring4Shell: How used is it?. Faraday Security.
• Debjeet. August 2, 2022. How To Find Assets Using Favicon Hashes. Payatu.

Similar libraries

• https://github.com/wc-duck/pymmh3: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani)
• https://github.com/escherba/python-cityhash: Python bindings for CityHash (Eugene Scherba)
• https://github.com/veelion/python-farmhash: Python bindigs for FarmHash (Veelion Chong)
• https://github.com/escherba/python-metrohash: Python bindings for MetroHash (Eugene Scherba)
• https://github.com/ifduyue/python-xxhash: Python bindings for xxHash (Yue Du)