bloom-filter2
Pure Python Bloom Filter module
Decision gist · record as of 2026-08-14
Yes, if you need a simple, dependency-free Bloom filter and can tolerate the lack of active maintenance. The implementation is marked stable and has no known vulnerabilities. However, verify compatibility with your Python version and consider whether an actively maintained alternative is available for production systems where you need ongoing support or bug fixes.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- However, the package is abandoned—last release was 2021-05-05 and the repository shows no commits since 2021-07-06.
- It is marked Production/Stable but receives no active maintenance.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions—suitable for most projects without licensing concerns.
last release 2021-05-05 (1927 days) · last repo commit 2021-07-06 · 44 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 431,376 downloads/mo, #6,717 on PyPI
Alternatives
Verify before relying
pip install bloom-filter2
from bloom_filter2 import BloomFilter
bloom = BloomFilter(max_elements=10000, error_rate=0.1)
bloom.add("test-key")
assert "test-key" in bloom- Whether the package works reliably on modern Python versions beyond the last release date (2021-05-05)
- Performance characteristics and memory footprint compared to active alternatives
- Whether mmap and disk-seek backends are production-ready or have known limitations
What it is and what it does
bloom-filter2 provides a Bloom filter—a space-efficient probabilistic data structure that answers "have I seen this element?" with a tunable false positive rate. You specify the maximum number of elements and acceptable error rate, and the module calculates the required bit array size and hash functions automatically. It supports three storage backends: in-memory arrays, memory-mapped files, and disk-seek operations, letting you trade speed for storage footprint.
The package is a pure Python implementation with no external dependencies, making it portable across CPython 3.x, PyPy, and Jython. It is useful when you need fast approximate membership testing on large datasets where occasional false positives are acceptable but false negatives are not—typical in caching, duplicate detection, and network routing.
Use it for
- Deduplicate incoming data streams without storing the full dataset in memory
- Cache-layer membership testing to avoid expensive lookups for items you've never seen
- Network packet filtering or URL blacklist checking with bounded memory
- Approximate set operations in data pipelines where false positives are tolerable
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a simple, dependency-free Bloom filter and can tolerate the lack of active maintenance.
The implementation is marked stable and has no known vulnerabilities. However, verify compatibility with your Python version and consider whether an actively maintained alternative is available for production systems where you need ongoing support or bug fixes.
Install
bloom-filter2 on PyPI
Before you install
Low install friction with no runtime dependencies. However, the package is abandoned—last release was 2021-05-05 and the repository shows no commits since 2021-07-06. It is marked Production/Stable but receives no active maintenance.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions—suitable for most projects without licensing concerns.
Quickstart
pip install bloom-filter2
from bloom_filter2 import BloomFilter
bloom = BloomFilter(max_elements=10000, error_rate=0.1)
bloom.add("test-key")
assert "test-key" in bloomVerify before relying
- Whether the package works reliably on modern Python versions beyond the last release date (2021-05-05)
- Performance characteristics and memory footprint compared to active alternatives
- Whether mmap and disk-seek backends are production-ready or have known limitations
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 1,927 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 431,376 / month, #6,717 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersProgramming Language :: Python :: 3 |
Evidence: bloom_filter2-2.0.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “low storage set datastructure”
- bloom-filter2A pure Python implementation of a Bloom filter—a probabilistic data…
- red-black-tree-modProvides Python implementations of red-black trees with optional…
- aiomcacheAsyncio-based memcached client providing non-blocking get, set,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also eth-bloom · preshed · pybloom-live · pyprobables · bloomfilter-py · rbloom · mmhash3 · datasketch · floret · mmh3