README

Doppar Bloom Filter - High-Performance Probabilistic Data Structure

Doppar Bloom Filter is a sophisticated, production-ready implementation of Bloom filters - space-efficient probabilistic data structures that test whether an element is a member of a set. Perfect for large-scale applications where memory efficiency and fast membership testing are critical. This library is an extended version of the denismitr/laravel-bloom package, adjusted for the Doppar framework.

Documentation

Read the documentation from doppar official site Doppar Bloom

Features

Storage Backends - Redis
Configurable Accuracy - Adjustable false positive rates and memory usage
Dual Hashing Algorithms - MD5 (consistent) and Murmur (high-performance)
Batch Operations - Efficient bulk additions and checks
Memory Efficient - Optimized bit manipulation and connection pooling
Fluent API - Clean, intuitive interface for everyday use

Perfect For

Duplicate Detection - Prevent duplicate user actions, URLs, or content
Cache Warming - Check if data exists before expensive database queries
Spam Filtering - Quickly check if email/URL is known spam
Recommendation Systems - Track viewed items without storing entire history
Database Optimization - Reduce unnecessary lookups

Performance Benchmarks

Operation	100K Items	1M Items	10M Items
Memory Usage	114 KB	1.14 MB	11.4 MB
Check Speed	0.1 ms	0.15 ms	0.2 ms
False Positive Rate	1%	1%	1%

Benchmarks based on default configuration with Redis backend

Contributing

Thank you for considering contributing to the Doppar framework! The contribution guide can be found in the Doppar documentation.

Code of Conduct

In order to ensure that the Doppar community is welcoming to all, please review and abide by the Code of Conduct.

Security Vulnerabilities

Please review our security policy on how to report security vulnerabilities.

License

The Doppar framework is open-sourced software licensed under the MIT license.

doppar / bloom

Maintainers

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