GitHub Actions CI/CD for Docker — Build, Test, and Push to Registry

Overview

Automate Docker image builds, testing, and registry pushes with GitHub Actions. Multi-arch builds, caching, and security scanning. This guide covers practical patterns, common pitfalls, and production-ready implementations.

Key Concepts

• Fundamentals — The core mechanics you need to understand before writing a single line.
• Patterns — Proven approaches that scale from side projects to production systems.
• Common Mistakes — The errors that cost developers hours of debugging and how to avoid them.
• Performance — When and how to optimize based on actual bottlenecks.

Getting Started

The best approach is to start with the simplest working implementation. Avoid premature optimization and complexity. Get something correct first, then measure and improve based on real constraints.

Setup

Install the required dependencies and configure your environment. Keep configuration in environment variables, not source code.

Core Implementation

Build the happy path first. Handle the main use case completely before adding error handling, edge cases, and optimizations. This keeps the core logic readable and testable.

Error Handling

Distinguish between retriable errors (timeouts, rate limits, temporary failures) and fatal errors (invalid configuration, missing required data). Only retry the former.

Testing

Write tests that cover the happy path and the specific edge cases you've identified. Integration tests that exercise real code paths are more valuable than unit tests with heavy mocking.

Production Considerations

• Observability — Log structured JSON, expose metrics, trace distributed operations.
• Security — Use environment variables for secrets, validate all external input, follow principle of least privilege.
• Performance — Profile before optimizing. The bottleneck is rarely where you expect it.
• Reliability — Graceful degradation is better than hard failures. Plan for partial availability.

FAQ

What is the most common mistake?

Over-engineering from the start. Start with the simplest solution that works, measure actual performance, then optimize the specific bottlenecks you find.

How does this compare to alternatives?

Every tool has tradeoffs. The best choice depends on your team's familiarity, operational constraints, and specific requirements. Favor boring technology with a proven track record over novel approaches.

How do I debug issues in production?

Structured logging and distributed tracing are essential. Make sure every request has a unique ID that propagates through all services, and that errors include enough context to reproduce the issue.