🔎 Mini-Google: A Distributed Search Engine

A scalable, high-performance search engine built from scratch with web crawling, inverted indexing, ranking algorithms, and a modern web interface. Designed for learning, performance, and extensibility.

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🌟 Features

Core Search Engine

• 🕷️ Intelligent Web Crawler – BFS/DFS crawling with politeness controls
• 📚 Inverted Index – Efficient term-to-document mapping with TF-IDF scoring
• 🎯 Advanced Ranking – Multi-signal relevance scoring with cosine similarity
• ⚡ Real-time Search – Sub-100ms query response times
• 🔧 Query Processing – Spell correction, stemming, and query expansion

User Experience

• 💬 Auto-complete – Real-time search suggestions with fuzzy matching
• 🎨 Modern Web UI – Responsive design with clean search interface
• 📊 Search Analytics – Query statistics and performance metrics
• 🔍 Advanced Search – Boolean queries, phrase search, field-specific search

Production Features

• 🚀 Scalable Architecture – Microservices with horizontal scaling
• 💾 Multi-tier Caching – Redis + in-memory caching for performance
• 🛡️ Security – Rate limiting, input sanitization, XSS protection
• 📈 Monitoring – Logging, metrics, and performance tracking

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🚀 Quick Start

Prerequisites

• Python 3.9+
• Redis (for caching)
• MongoDB (optional, for document storage)

Installation

git clone https://github.com/yourusername/mini-google.git
cd mini-google

# macOS
brew install redis mongodb-community

# Ubuntu/Debian
sudo apt-get install redis-server mongodb

# Install dependencies
pip install -r requirements.txt
Run Setup
bash
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chmod +x setup.sh
./setup.sh
Start the Pipeline
bash
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# Option 1: Full automated pipeline
python scripts/full_pipeline.py

# Option 2: Manual steps
python scripts/crawl.py        # Crawl web pages
python scripts/build_index.py  # Build search index
python web/app.py              # Start web server
Access the search engine → http://localhost:5000

📖 Usage Examples
Web Interface
Visit http://localhost:5000 and search away!

Command Line
bash
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python scripts/search_demo.py
API Usage
bash
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# Search API
curl "http://localhost:5000/api/search?q=python+programming"

# Auto-complete API
curl "http://localhost:5000/api/suggestions?q=mach"

# Statistics API
curl "http://localhost:5000/api/stats"
Programmatic Usage
python
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from search.search_api import SearchAPI

search = SearchAPI()
results = search.search("machine learning", max_results=10)
print(results)

suggestions = search.get_suggestions("pytho")
print("Suggestions:", suggestions)
🏗️ Architecture
text
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┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Web Crawler   │ -> │   Document Store │ -> │     Indexer     │
│   (Async BFS)   │    │  (File/MongoDB)  │    │ (Inverted Index)│
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  Web Interface  │ <- │  Search Engine   │ <- │  Ranking Engine │
│    (Flask)      │    │      (API)       │    │   (TF-IDF)      │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │
                       ┌──────────────────┐
                       │  Cache Manager   │
                       │    (Redis)       │
                       └──────────────────┘
📁 Project Structure
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mini-google/
├── 🕷️ crawler/             # Web crawling components
│   ├── web_crawler.py
│   ├── url_manager.py
│   └── content_processor.py
├── 📚 indexer/             # Indexing & ranking
│   ├── inverted_index.py
│   ├── tfidf_calculator.py
│   └── spark_indexer.py
├── 🔍 search/              # Search engine core
│   ├── query_processor.py
│   ├── ranking_engine.py
│   ├── suggestion_engine.py
│   └── search_api.py
├── 💾 storage/             # Data persistence
├── 🌐 web/                 # Web interface (Flask, templates, static)
├── 🧪 tests/               # Test suite
├── 📜 scripts/             # Utility scripts
└── ⚙️ config/              # Configuration files
⚡ Performance Metrics
Metric	Performance
Query Response Time	< 100ms (95th percentile)
Crawling Speed	10–50 pages/sec
Indexing Rate	1,000–5,000 docs/sec
Concurrent Users	50+ simultaneous queries
Memory Usage	100–500MB (10K–100K docs)
Cache Hit Rate	85–95%

🛠️ Technologies Used
Backend
Python 3.9+

Flask (REST APIs)

AsyncIO / aiohttp

NLTK, scikit-learn

Redis (caching), MongoDB (storage)

Frontend
HTML5, CSS3

JavaScript ES6+

Bootstrap

Infrastructure
Docker, Kubernetes

Apache Spark (optional)

Elasticsearch (optional backend)

🧪 Testing
bash
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# Run all tests
python -m pytest tests/ -v

# Component-specific
python tests/test_crawler.py
python tests/test_indexer.py
python tests/test_search.py

# Performance benchmarks
python scripts/benchmark.py
📊 Monitoring & Analytics
Built-in analytics: query frequency, response times, cache hit rates

Integrations: Prometheus, Grafana, ELK Stack, custom alerts

📈 Roadmap
Version 2.0 (Planned):

ML-based Ranking (learning-to-rank)

Real-time Indexing

Personalized Search

Image & Voice Search

Version 1.1 (In Progress):

Faceted Search

Geographic Search

Advanced Analytics (A/B testing)

API Rate Limiting

🏆 Benchmarks
Feature	Mini-Google	Elasticsearch	Solr	Google CSE
Setup Time	< 5 mins	~30 mins	~45 mins	Instant
Customization	Full	High	High	Limited
Learning Value	Maximum	Medium	Medium	Minimal
Production Ready	Yes	Yes	Yes	Yes
Cost	Free	Free/Paid	Free	Paid

🤝 Contributing
We welcome contributions!

Fork the repo

Create a feature branch

Submit a PR

bash
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git clone https://github.com/yourusername/mini-google.git
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements-dev.txt
pre-commit install
python -m pytest