pybloom-live
Bloom filter: A Probabilistic data structure
Decision gist · record as of 2026-08-14
Yes, if you need efficient set membership testing and can tolerate the source-only install friction. The package is stable and permissively licensed, but maintenance is aging (no releases since October 2022). Install only if your use case genuinely benefits from Bloom filter semantics; for simple set membership in memory-unconstrained applications, a plain Python set is simpler and faster.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Source-only distribution requires a C compiler and build tools at install time; no pre-built wheels are available.
- High install friction: the package distributes as a source tarball with no pre-built wheels, requiring compilation at install time.
- Maintenance status is aging—the latest release dates to October 2022, over three years old, though the repository remains active with recent commits.
License · maintenance · safety
MIT License (permissive) — MIT License (permissive): you may use, modify, and distribute this package freely in both open-source and commercial projects, provided you include the license notice.
last release 2022-10-15 (1399 days) · last repo commit 2025-11-13 · 167 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 128,111 downloads/mo, #11,719 on PyPI
Alternatives
Verify before relying
pip install pybloom-live
import pybloom_live
# Create a fixed-capacity filter
f = pybloom_live.BloomFilter(capacity=1000, error_rate=0.001)
f.add("apple")
print("apple" in f) # True
print("grape" in f) # False- Whether the package works with current Python versions (requires_python is unspecified in metadata)
- Performance characteristics relative to alternative Bloom filter libraries
- Whether xxHash is bundled or requires a system dependency
What it is and what it does
pybloom_live provides two Bloom filter implementations for Python: a fixed-capacity BloomFilter for known dataset sizes, and a ScalableBloomFilter that automatically expands as elements are added. Bloom filters are probabilistic data structures that answer set membership queries with certainty for negative results (element definitely not in set) and probabilistic results for positive results (element might be in set, with a tunable false positive rate). The package uses xxHash for fast non-cryptographic hashing and supports set operations (union, intersection), serialization to disk, and both small and large growth modes for scalable filters.
The library is designed for scenarios where memory efficiency and speed matter more than perfect accuracy—web crawlers tracking visited URLs, caching systems doing pre-filters before expensive lookups, database query optimization, and distributed system reconciliation. It has no runtime dependencies and works with Python 3.6 and later.
Use it for
- Web crawlers: Track visited URLs to avoid re-crawling the same pages repeatedly
- Cache pre-filtering: Quick membership checks before expensive database or network lookups
- Database optimization: Pre-filter query results to avoid unnecessary disk reads
- Spell checkers: Fast dictionary lookups for word validation
- Distributed systems: Efficient set reconciliation and membership queries across nodes
- Network packet filtering: Fast classification of packets in routers and firewalls
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need efficient set membership testing and can tolerate the source-only install friction.
The package is stable and permissively licensed, but maintenance is aging (no releases since October 2022). Install only if your use case genuinely benefits from Bloom filter semantics; for simple set membership in memory-unconstrained applications, a plain Python set is simpler and faster.
Install
pybloom-live on PyPI
Before you install
High install friction: the package distributes as a source tarball with no pre-built wheels, requiring compilation at install time. Maintenance status is aging—the latest release dates to October 2022, over three years old, though the repository remains active with recent commits.
Source-only distribution requires a C compiler and build tools at install time; no pre-built wheels are available.
License in practice
MIT License (permissive): you may use, modify, and distribute this package freely in both open-source and commercial projects, provided you include the license notice.
Quickstart
pip install pybloom-live
import pybloom_live
# Create a fixed-capacity filter
f = pybloom_live.BloomFilter(capacity=1000, error_rate=0.001)
f.add("apple")
print("apple" in f) # True
print("grape" in f) # FalseVerify before relying
- Whether the package works with current Python versions (requires_python is unspecified in metadata)
- Performance characteristics relative to alternative Bloom filter libraries
- Whether xxHash is bundled or requires a system dependency
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Aging 1,399 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 128,111 / month, #11,719 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Database :: Database Engines/ServersTopic :: Software Development :: Libraries :: Python ModulesTopic :: Utilities |
Evidence: pybloom_live-4.0.0.tar.gz
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See also bloom-filter2 · rbloom · bloomfilter-py · preshed · eth-bloom · pyprobables · murmurhash · floret · multiset · py-multihash