{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"}],"enrichment":{"capability":"Implements Bloom filters in Python with serialization/deserialization compatible with Java's Guava library, enabling cross-language Bloom filter interchange.","skillfed_tags":["bloom-filter","java-interop","probabilistic-data-structure"],"use_cases":["Exchanging Bloom filter state between Python microservices and Java backend systems without data loss or format conversion.","Caching membership tests in distributed systems where false positives are acceptable but false negatives are not.","Deduplication pipelines that need to share filter snapshots across language boundaries for consistency.","Testing Java Guava Bloom filter implementations by round-tripping serialized data through Python.","Building probabilistic data structures for URL or item deduplication in web crawlers or ETL pipelines."],"what_it_does":"bloomfilter-py is a Bloom filter library designed to bridge Python and Java ecosystems. It implements the probabilistic data structure for fast membership testing (answering 'is X in this set?' with no false negatives but possible false positives) and focuses specifically on serializing and deserializing Bloom filters in a format compatible with Java's Guava library. This solves the problem of exchanging Bloom filter state between Python and Java applications without reimplementing the entire structure.\n\nThe package provides multiple serialization formats\u2014raw bytes, hex strings, and base64-encoded data\u2014making it flexible for storage and transmission. You create a filter by specifying expected insertions and error rate, add items with `put()`, test membership with the `in` operator, and serialize/deserialize using `dumps()`, `loads()`, and their format-specific variants. It depends on bitarray for efficient bit manipulation and mmh3 for hashing.","worth_installing":"Yes, if you need Bloom filter functionality with Java Guava compatibility. The low install friction, permissive MIT license, and stable API make it straightforward to adopt. However, dormant maintenance (last activity 2024-08-10, no recent commits) means you should verify compatibility with your target Python version and be prepared to fork or switch if future dependency updates break compatibility. No known vulnerabilities."},"id":"bloomfilter-py","links":{"html":"https://skillfed.io/packages/bloomfilter-py","md":"https://skillfed.io/packages/bloomfilter-py.md","pypi":"https://pypi.org/project/bloomfilter-py/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-08-10","license_spdx":null,"license_treatment":"permissive","name":"bloomfilter-py","python_support":"supports_current","summary":"Yet another bloomfilter implementation in Python"},"popularity":{"monthly_downloads":249786,"position":8646,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}