kubernetes-devops

This repository provides an end-to-end microservice deployment pipeline for Kubernetes, incorporating robust DevOps principles and best practices. The pipeline is designed to streamline the deployment process, from development to production, while ensuring scalability, reliability, and maintainability.

Components

This repository is organized into the following components:

• backend: Contains the backend microservices responsible for handling core business logic and data processing.
• common: Shared modules, utilities, and configurations used across microservices for consistent functionality.
• consumer: Microservice consuming processed data and presenting it to end-users through APIs or interfaces.
• data: Microservice managing data storage, retrieval, and communication with external data sources.
• fluentd: Logging and monitoring module that collects, processes, and forwards log data for analysis.
• frontend: Frontend microservices responsible for delivering an intuitive user interface and experience.
• kubernetes: Kubernetes configurations and deployment manifests for orchestrating containerized applications.
• mlflow: Integration of MLflow for managing the end-to-end machine learning lifecycle.
• pipeline: Definition and orchestration of the deployment pipeline, ensuring seamless integration and testing.
• recommender: Microservice implementing recommendation algorithms based on user behavior and preferences.
• scripts: Helper scripts and automation tools for various development and deployment tasks.
• secrets: Placeholder for sensitive information (e.g., API keys, passwords). Handle with utmost care.
• skaffold: Tooling for automating Kubernetes resource deployment and application reloading.
• .dvcignore, .flake8, .gitignore: Version control and linting configurations for code quality and organization.
• .pre-commit-config.yaml: Configuration for pre-commit hooks to enforce code standards before commits.
• Makefile: Collection of common commands and tasks to simplify development and deployment workflows.
• pyproject.toml, setup.py: Python project configurations and metadata for packaging and distribution.

Pipeline Overview

Deployment pipeline follows these high-level stages:

1. 👩‍💻 Code Development: Developers write and test code changes in their respective microservices.
2. 🔄 Continuous Integration: Pre-commit hooks and automated tests ensure code quality and consistency.
3. 🐳 Containerization: Applications are containerized using Docker for consistent deployment.
4. ☸️ Kubernetes Deployment: Kubernetes configurations define how services are deployed and scaled.
5. 🚀 Continuous Deployment: Automated pipelines deploy code changes to staging and production environments.
6. 📊 Monitoring and Logging: Fluentd gathers logs for monitoring, debugging, and analysis.
7. 🤖 MLflow Integration: MLflow manages machine learning experiments, models, and deployments.
8. 🌐 User Interface: Frontend microservices provide intuitive interfaces for user interaction.

Configuration

The pipeline can be customized and configured through various files and settings, including:

• Kubernetes configuration files: Modify these to adjust resource allocation, scaling, and networking.
• .env files: Store environment-specific variables, database connections, and secret keys.
• .yaml and .json files: Configure application settings, MLflow tracking server, and logging options.