Python Logging Best Practices — ELI5
Imagine an airplane without a flight recorder. If something goes wrong, nobody can reconstruct what happened. Logs are your software flight recorder.
Good logs answer three questions fast:
- What happened?
- When did it happen?
- Which user/request was affected?
Many beginners log too little (“error occurred”) or too much (every tiny step with no context). Both are painful. You want clear, useful messages at important moments.
A great log line includes context, such as order ID, request ID, and action. Then when a bug appears, you can follow the story across services.
Use levels wisely:
DEBUGfor deep troubleshootingINFOfor normal key eventsWARNINGfor unusual but recoverable statesERRORfor failures that need attention
And never log secrets like passwords or raw tokens.
Logs are not only for incidents. They also help teams understand behavior changes after deploys and catch slowdowns early.
The one thing to remember: logs are evidence—write them so future-you can solve problems quickly.
See Also
- Python Alerting Patterns Alerting is a smoke detector for your code — it wakes you up when something is burning, not when someone is cooking.
- Python Correlation Ids Correlation IDs are name tags for requests — they let you follow one visitor's journey through a crowded theme park of services.
- Python Grafana Dashboards Python Grafana turns boring numbers from your Python app into colorful, real-time dashboards — like a car's dashboard but for your code.
- Python Log Aggregation Elk ELK collects scattered log files from all your services into one searchable place — like gathering every sticky note in the office into a single filing cabinet.
- Python Logging Handlers Think of logging handlers as mailboxes that decide where your app's messages end up — screen, file, or faraway server.