Scalable Sequence Generator

A professional Python tool for generating, managing, and streaming high-volume numerical sequences (like Fibonacci). Engineered for ultimate memory efficiency and seamless data persistence, this project demonstrates modern Pythonic patterns for large-scale data processing. It serves as a robust foundation for Big Data pipelines and analytics.

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Key Features

• Memory Efficient: Uses Python Generators (yield) to handle extremely large sequences without overloading RAM.
• Context Management: Implements custom Context Managers (__enter__, __exit__) for safe, automated file I/O operations and resource handling.
• Object-Oriented Design: Built with clean, modular classes following the DRY (Don't Repeat Yourself) principle.
• Robust Validation: Includes strict error handling to ensure data integrity during initialization and sequence calculation.
• Professional Testing: Comprehensive test suite built with pytest, utilizing advanced features like fixtures and parameterization.

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Tech Stack

• Language: Python 3.12
• Containerization: Docker & Docker Compose
• Testing: Pytest
• Storage: Flat-file (TXT) with planned SQL Integration

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Project Structure

├── main.py              # Core logic & SequenceGenerator class
├── tests/
│   └── tests.py         # Professional test suite (Logic & I/O)
├── Dockerfile           # Docker image configuration
├── docker-compose.yml   # Multi-container/service orchestration
├── requirements.txt     # Project dependencies
└── .flake8              # Linter configuration

Running Tests via Docker Compose

docker compose up test-runner

Running Tests via Docker:

1. Building an image

docker build -t scalable-sequence-generator .

2. Running container with tests

docker run -v .:/app scalable-sequence-generator pytest tests/tests.py

Local testing (without docker):

1. Install Testing Dependencies Ensure you have pytest installed in your environment:

pip install pytest

2. Use command

pytest tests/tests.py