API Monitoring and Logging: Tracking and Troubleshooting in Real Time
Shows how to add structured observability to a Flask API. Covers basic logging with Python's built-in logging module, exception capture with error-level log entries, and switching to JSON-formatted log output for easier parsing by tools like Datadog or Elastic Stack. Then moves into real-time monitoring using Prometheus and Grafana, including Docker setup, a request-count metric, a /metrics endpoint, and a YAML alert rule that fires when error rates spike above a threshold.