Optimized Search System for Product Catalog - MegaStore

MegaStore, a retail giant with an expansive e-commerce operation, offers millions of products across categories like electronics, fashion, home decor, and food. With such a large catalog, the company faces a major challenge: helping customers find relevant products quickly and effectively.

This project implements an optimized search and recommendation system in Rust that leverages graph-based algorithms to improve product discovery. It enhances search precision, handles user typos via input correction, and provides related product recommendations through breadth-first traversal (BFS) of a graph.

Modules & Components

• main.rs: CLI interaction and runtime execution
• functions.rs: Core algorithms including graph search and fuzzy matching
• lib.rs: Exports functionality for modular use and testing
• products.json: Product catalog and similarity edges in JSON format

Algorithms and Data Structures

• Graph Structure: Products are nodes; edges represent related items (e.g., similar brand or category)
• Breadth-First Search (BFS): Used for traversing the product graph to find and recommend related products
• Hash Maps (via HashMap): Provide fast access to product data for search and recommendation
• Levenshtein Distance: Used to suggest corrections for misspelled inputs

Performance and Scalability Considerations

• Efficiency: The BFS algorithm ensures that recommendations are generated in linear time relative to the number of products visited.
• Scalability: Use of hash maps enables constant-time lookups, suitable for scaling with millions of products.
• Fault-Tolerance: Input correction via fuzzy matching improves user experience and mitigates data entry errors.
• Test Coverage: Unit and integration tests ensure consistent performance under edge cases and larger datasets.

Project Structure

├── Cargo.toml # Project dependencies and metadata
├── products.json # JSON file containing product and recommendation data
├── src/
│ ├── functions.rs # Core functions for search and recommendations
│ ├── lib.rs # Library module
│ ├── main.rs # Entry point of the application
│ ├── tests.rs # Unit tests for core functions
├── tests/
│ ├── integration_test.rs # Integration tests for the application
├── target/ # Build artifacts (ignored by Git)
└── README.md # Project documentation

Getting Started

Prerequisites

• Rust (version 1.70 or later)

Installation

1. Clone the repository:

   git clone https://github.com/RobisonTorres/search_graph.git
   cd search_graph

2. Build the project:

   cargo build

If the build fails, temporarily disable your antivirus software and try again, as it may interfere with the build process.

3. Run the tests:

   cargo test

   Or

   cargo test --test integration_test

Usage

Run the application:

cargo run

Enter a product name, brand, or category when prompted. The application will display the product details and recommendations.

Example

Input:

Smartphone

Output:

-----------------------------------
Name: Smartphone
Price: $699.99
Brand: Samsung

See also: 
- Mouse
- Headphones
- Laptop
- Monitor
- Bluetooth Speaker
- Smartwatch

Dependencies

• serde: For JSON serialization and deserialization.
• serde_json: For working with JSON data.
• strsim: For calculating Levenshtein distance.
• Inflector: For string case conversions.

License

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