🌐 WhoisThere

WhoisThere is a blazing fast Python script to lookup ASN and provider information for IP prefixes with multi-threading and caching support. Perfect for network engineers, security analysts, and researchers working with IP address data.

✨ Features

• ⚡ Lightning fast processing with multi-threading
• 🔍 Accurate ASN lookups using RDAP protocol
• 💾 Smart caching to avoid duplicate queries
• 📊 Excel input/output support
• 🎨 Colorful progress tracking
• 🛠 Configurable thread count and cache size
• 🧩 Format Handling: Intelligent IP prefix formatting and validation
• 📦 Batch Processing: Process thousands of IP prefixes from Excel files

Requirements

• Python 3.7+
• pandas
• requests
• ipwhois
• tqdm
• colorama
• openpyxl
• concurrent.futures (standard library)
• argparse (standard library)

📦 Installation

1. Clone this repository:

   git clone https://github.com/shahedfardous/WhoisThere.git
   cd WhoisThere

2. Install dependencies:

   pip install -r requirements.txt

🚀 Usage

Basic Usage

python WhoisThere.py input_file.xlsx -o output_file.xlsx

Advanced Options

python WhoisThere.py input.xlsx -o results.xlsx -w 20  # Use 20 threads

Command-line Arguments

usage: WhoisThere.py [-h] [-o OUTPUT] [-w WORKERS] input_file

Fast ASN lookup for IP prefixes

positional arguments:
  input_file            Excel file with Prefix column

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output file (default: asn_results.xlsx)
  -w WORKERS, --workers WORKERS
                        Thread count (default: 10)

Input Format

Your Excel file must contain a column named "Prefix" with IP addresses in CIDR notation:

Prefix
1.1.1.0/24
8.8.8.0/24
192.168.1.0/24
2001:db8::/32
10.0.0.1 (will be treated as /32)
...

Output Format

The script generates an Excel file with three columns:

• Prefix: The normalized IP prefix
• ASN: The Autonomous System Number
• Provider: The organization name/description

⚙️ Configuration

Customize the script behavior by modifying these variables:

MAX_WORKERS = 10    # Default number of threads
CACHE_SIZE = 1000   # Number of lookups to cache

🔍 How It Works

1. Loading Data: Read IP prefixes from Excel file
2. Validation: Clean and normalize prefixes
3. Parallel Processing: Distribute lookups across multiple threads
4. ASN Lookup: Query RDAP services via ipwhois
5. Caching: Store results to avoid duplicate lookups
6. Export: Save enriched data back to Excel

📊 Performance Benchmarks

WhoisThere is optimized for speed:

Prefixes	Threads	Time (seconds)
100	5	12.4
100	10	6.8
1000	10	68.2
1000	20	35.7

Key optimizations:

• Multithreading: Process multiple lookups concurrently
• LRU Caching: Avoid redundant lookups for repeated prefixes
• Efficient Error Handling: Continue processing despite individual lookup failures

📊 Example Results

Prefix	ASN	Provider
8.8.8.8/32	15169	Google LLC
1.1.1.1/32	13335	Cloudflare, Inc.
104.18.2.0/24	13335	Cloudflare, Inc.
13.107.42.0/24	8068	Microsoft Corporation

🙏 Acknowledgements

I'd like to express my gratitude to the following projects and libraries that made WhoisThere possible:

• ipwhois - For providing the excellent RDAP lookup functionality
• pandas - For powerful data manipulation and analysis
• tqdm - For the elegant progress bar implementation
• colorama - For cross-platform colored terminal output
• Python ipaddress - For IP address manipulation and validation
• concurrent.futures - For the thread pool implementation
• Regional Internet Registries (RIRs) - For maintaining the RDAP databases
• All open-source contributors who develop and maintain these amazing tools

Special thanks to the network engineering community for feedback and feature suggestions.

🔧 Troubleshooting

Common Issues

• Rate Limiting: Some RDAP services may rate-limit queries. If you encounter errors, try reducing thread count.
• Memory Usage: For very large datasets, monitor memory usage and adjust thread count accordingly.
• Network Connectivity: Ensure stable internet connection for reliable lookups.

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

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Made with ❤️ by Md Shahed Fardous (Samy)