🌸 Blossom

A high-performance Bloom filter library written in Go, featuring multiple concurrency-safe implementations optimized for different workload patterns. Blossom also includes a REST API built with Gin for creating named Bloom filters, adding items, checking membership, and retrieving filter statistics.

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✨ Features

• 🚀 High-performance Bloom filter implementation
• 🔒 Thread-safe concurrent operations
• ⚡ Multiple implementations:
  • RWMutex-based Bloom Filter
  • Atomic Bloom Filter
  • Sharded Bloom Filter
• 🌐 REST API built with Gin
• 🧩 Create and manage multiple named Bloom filters
• ➕ Add elements to filters
• 🔍 Check element membership
• 📊 Retrieve filter statistics
• 🔑 Murmur3 hashing for efficient indexing
• 📦 Modular and extensible architecture

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🏗️ Project Structure

blossom/
├── bloom/          # Core Bloom filter implementations
├── bloom_api/      # REST API handlers and routes
├── gloom/          # Shared/internal components
├── main.go         # Application entry point
├── go.mod
└── go.sum

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🛠️ Tech Stack

Category	Technologies
Language	Go (Golang)
Web Framework	Gin
Hashing	Murmur3
Concurrency	RWMutex, sync/atomic, Sharding
API	RESTful API
Version Control	Git, GitHub

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🚀 Getting Started

Prerequisites

• Go 1.20+
• Git

Clone the Repository

git clone https://github.com/akshitajundiya/blossom.git
cd blossom

Install Dependencies

go mod tidy

Run the Application

go run main.go

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📡 API Overview

Create a Bloom Filter

POST /filters

Add an Item

POST /filters/{name}/add

Check Membership

GET /filters/{name}/contains

Get Filter Statistics

GET /filters/{name}/stats

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📈 Why Bloom Filters?

Bloom filters are space-efficient probabilistic data structures used to quickly determine whether an element may exist in a set.

Advantages

• Extremely memory efficient
• Fast insert and lookup operations
• Constant-time performance (O(k))
• Ideal for caching, databases, networking, and distributed systems

Limitations

• May produce false positives
• No false negatives
• Standard Bloom filters do not support deletion

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🎯 Use Cases

• Database query optimization
• Distributed caching
• Web crawlers
• API request filtering
• Duplicate detection
• Network packet filtering
• Recommendation systems

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch

git checkout -b feature/my-feature

3. Commit your changes

git commit -m "Add my feature"

4. Push to the branch

git push origin feature/my-feature

5. Open a Pull Request

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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⭐ Support

If you find this project useful, consider giving it a ⭐ on GitHub!

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Built with ❤️ using Go.