Building Scalable Systems with Python and Docker

When I started building ANIA, I knew I wanted a system that could:

• Aggregate news from 30+ sources
• Run reliably in production
• Scale horizontally if needed
• Be easy to maintain and debug

This post covers the architectural decisions, key challenges, and how Docker + APScheduler became the backbone of the system.

Why Docker?

Docker solved three problems for us:

1. Dependency hell — No more "works on my machine"
2. Auto-restart — systemd was fragile; Docker handles it gracefully
3. Persistence — Named volumes for SQLite databases

Lessons Learned

The biggest win was moving from nohup + manual background processes to Docker containers with restart policies. This alone prevented dozens of "agent went down at 2am" incidents.

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