production-workflow-explanation.md

Production Workflow and Best Practices

Architecture Overview

      graph TD
         A[GitHub Private Repo] -->|Trigger| B[GitHub Actions CI/CD]
         B -->|Build & Test| C[Container Registry]
         B -->|Deploy| D[GKE Cluster]
         D -->|Read/Write| E[Cloud SQL]
         D -->|Log| F[Cloud Logging]
         D -->|Monitor| G[Cloud Monitoring]
         H[Cloud IAM] -->|Manage Access| D
         H -->|Manage Access| E
         I[Secret Manager] -->|Provide Secrets| D
         J[Cloud NAT] -->|Outbound Traffic| D
         K[Load Balancer] -->|Inbound Traffic| D
         L[Terraform] -->|Manage| D
         L -->|Manage| E
         L -->|Manage| J
         L -->|Manage| K

Workflow Explanation

Our production workflow follows a GitOps approach, leveraging Infrastructure as Code (IaC) and Continuous Integration/Continuous Deployment (CI/CD) principles. Here's a step-by-step breakdown of the workflow:

1. Code Changes:

   • Developers make changes to the application code or infrastructure definitions in the private GitHub repository.
   • Changes are proposed via pull requests for review.

2. CI/CD Pipeline Trigger:

   • GitHub Actions CI/CD pipeline is triggered on pull requests and pushes to the main branch.

3. Infrastructure Provisioning:

   • Terraform job in the CI/CD pipeline manages infrastructure:
     • Initializes Terraform
     • Plans infrastructure changes
     • Applies changes (only on merge to main)

4. Build and Test:

   • Application code is built and unit tests are run.
   • Container images are built and pushed to Container Registry.

5. Deployment:

   • Updated container images are deployed to the GKE cluster.
   • Kubernetes manifests are applied to update deployments.

6. Monitoring and Logging:

   • Deployed applications send logs to Cloud Logging.
   • Cloud Monitoring tracks system and application metrics.

Best Practices Implemented

1. Version Control and Code Review

• All code (application and infrastructure) is version-controlled in GitHub.
• Pull request reviews ensure code quality and security.

2. Infrastructure as Code (IaC)

• Terraform is used to define and manage GCP resources.
• Infrastructure changes go through the same review process as application code.

3. CI/CD Automation

• GitHub Actions automates testing, building, and deployment processes.
• Reduces manual errors and ensures consistent deployments.

4. Containerization

• Applications are containerized for consistency across environments.
• Container images are versioned and stored in Container Registry.

5. Orchestration

• Google Kubernetes Engine (GKE) is used for container orchestration.
• Ensures high availability and scalability of applications.

6. Secret Management

• Sensitive information is stored in Secret Manager.
• Secrets are securely injected into applications at runtime.

7. Network Security

• Cloud NAT is used for secure outbound traffic from private GKE nodes.
• Load Balancer manages inbound traffic, improving security and performance.

8. Database Management

• Cloud SQL provides a managed database solution.
• Regular backups and update management are handled by GCP.

9. Monitoring and Logging

• Comprehensive monitoring with Cloud Monitoring.
• Centralized logging with Cloud Logging for easier troubleshooting and auditing.

10. Access Control

• Cloud IAM manages access to GCP resources.
• Principle of least privilege is enforced.

11. Scalability

• GKE and Cloud SQL provide built-in scalability options.
• Infrastructure can be easily scaled using Terraform.

12. Disaster Recovery

• Multi-region deployments can be implemented for critical services.
• Regular backups and documented recovery procedures ensure business continuity.

Conclusion

This workflow embraces DevOps principles, ensuring a secure, scalable, and maintainable production environment. By leveraging GCP services and following these best practices, we create a robust system that can reliably deliver and scale our applications.