{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"}],"enrichment":{"capability":"Implements a Bloom filter data structure in Rust with a Python API that mimics the built-in set type, enabling fast probabilistic membership testing for any hashable object.","skillfed_tags":["data-structures","rust-binding"],"use_cases":["Check membership in large datasets (IP blocklists, URL filters, spam detection) where exact storage is impractical","Deduplicate items in streaming data pipelines where occasional false positives are acceptable","Cache negative results: quickly rule out items not in a database before expensive lookups","Combine multiple filters using set operations (union, intersection) to aggregate or refine membership rules","Persist membership state across application restarts by serializing filters to disk or bytes"],"what_it_does":"rBloom is a Bloom filter library written in Rust and exposed to Python, designed to provide fast probabilistic membership testing with a set-like API. It takes two parameters\u2014expected item count and desired false positive rate\u2014and creates a compact bit array that can quickly answer \"is this item in the set?\" with a tunable error rate. Unlike exact set membership (which requires storing all items), a Bloom filter trades a small, configurable false positive rate for dramatic space savings and speed.\n\nThe library mimics Python's built-in set type where possible, supporting operations like add, contains, union, intersection, and subset comparisons. It works with any hashable Python object (strings, numbers, tuples, etc.) and includes methods to serialize filters to bytes or disk for persistence. The implementation is compiled against a stable ABI for portability across Python versions, and prebuilt wheels cover most common architectures.","worth_installing":"Yes, if you need fast probabilistic membership testing and can tolerate a small, tunable false positive rate. The library is actively maintained, has no dependencies, and offers significantly better performance than pure-Python alternatives. The main caveat is the unclear license\u2014verify it meets your use case before committing to production. Install friction is moderate but manageable for common platforms."},"id":"rbloom","links":{"html":"https://skillfed.io/packages/rbloom","md":"https://skillfed.io/packages/rbloom.md","pypi":"https://pypi.org/project/rbloom/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-09","license_spdx":null,"license_treatment":"unclear","name":"rbloom","python_support":"supports_current","summary":"Highly optimized Bloom filter that mimics the Python set API, written in Rust"},"popularity":{"monthly_downloads":386080,"position":7053,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.4"}