tessl/pypi-mmh3
Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions.
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mmh3
mmh3 is a Python extension providing MurmurHash3 hash functions, a family of fast and robust non-cryptographic hash functions. It offers comprehensive hashing capabilities including 32-bit, 64-bit, and 128-bit hash functions with both signed and unsigned outputs, supporting architecture-specific optimizations for x86 and x64 platforms.
Package Information
- Package Name: mmh3
- Language: Python
- Installation:
pip install mmh3
Core Imports
import mmh3All functions and classes are available directly from the mmh3 module.
Basic Usage
import mmh3
# Basic 32-bit hashing
hash_value = mmh3.hash("foo") # -156908512
hash_with_seed = mmh3.hash("foo", seed=42) # -1322301282
unsigned_hash = mmh3.hash("foo", signed=False) # 4138058784
# 64-bit hashing (returns tuple of two 64-bit integers)
hash64_result = mmh3.hash64("foo") # (-2129773440516405919, 9128664383759220103)
# 128-bit hashing
hash128_result = mmh3.hash128("foo", seed=42) # 215966891540331383248189432718888555506
# Hash as bytes
hash_bytes = mmh3.hash_bytes("foo") # b'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~'
# Streaming hasher for large data
hasher = mmh3.mmh3_32(seed=42)
hasher.update(b"foo")
hasher.update(b"bar")
digest = hasher.digest() # bytes
sint_digest = hasher.sintdigest() # signed int
uint_digest = hasher.uintdigest() # unsigned intArchitecture
mmh3 provides two complementary interfaces:
- Simple Functions: Direct hash computation for immediate results
- Hasher Classes: Streaming interface for incremental hashing of large datasets
The library implements MurmurHash3 algorithms with architecture-specific optimizations:
- x64 optimization: For 64-bit architectures (default)
- x86 optimization: For 32-bit architectures
All hash functions support configurable seeds and signed/unsigned output options.
Capabilities
Simple Hash Functions
Direct hash computation functions for immediate results with various output formats and architecture optimizations.
def hash(key: StrHashable, seed: int = 0, signed: bool = True) -> int: ...
def hash_from_buffer(key: StrHashable, seed: int = 0, signed: bool = True) -> int: ...
def hash64(key: StrHashable, seed: int = 0, x64arch: bool = True, signed: bool = True) -> tuple[int, int]: ...
def hash128(key: StrHashable, seed: int = 0, x64arch: bool = True, signed: bool = False) -> int: ...
def hash_bytes(key: StrHashable, seed: int = 0, x64arch: bool = True) -> bytes: ...Streaming Hashers
hashlib-compatible hasher classes for incremental hashing of large datasets and streaming operations.
class Hasher:
def __init__(self, seed: int = 0) -> None: ...
def update(self, input: Hashable) -> None: ...
def digest(self) -> bytes: ...
def sintdigest(self) -> int: ...
def uintdigest(self) -> int: ...
def copy(self) -> Hasher: ...
@property
def digest_size(self) -> int: ...
@property
def block_size(self) -> int: ...
@property
def name(self) -> str: ...
class mmh3_32(Hasher): ...
class mmh3_x64_128(Hasher):
def stupledigest(self) -> tuple[int, int]: ...
def utupledigest(self) -> tuple[int, int]: ...
class mmh3_x86_128(Hasher):
def stupledigest(self) -> tuple[int, int]: ...
def utupledigest(self) -> tuple[int, int]: ...Types
from typing import Protocol, Union
class IntArrayLike(Protocol):
def __getitem__(self, index) -> int: ...
Hashable = Union[bytes, bytearray, memoryview, IntArrayLike]
StrHashable = Union[str, Hashable]Common Use Cases
- Data Mining & Machine Learning: Feature hashing and dimensionality reduction
- Bloom Filters: Fast set membership testing with probabilistic data structures
- MinHash Algorithms: Document similarity and near-duplicate detection
- Natural Language Processing: Text fingerprinting and similarity matching
- IoT Security Research: Shodan favicon hash calculations
- Distributed Systems: Consistent hashing and data partitioning
- Caching: Cache key generation with collision resistance