debug-stack

Debug FastAPI 500 errors by triaging Docker container logs for tracebacks, verifying affected database records for stuck statuses, and inspecting Redis for rate_limit keys or stale TTL to produce a structured root-cause diagnosis.

Trace failing Celery worker tasks by inspecting Docker container logs for task exceptions, querying the database for impacted entities like campaigns or notifications, and checking Redis for Celery task state backlogs and queue issues.

Diagnose Celery beat scheduling problems by reviewing Docker logs for scheduler issues, verifying database records for expected entity updates, and inspecting Redis for stale cache TTL or task queue backlogs that indicate missed executions.

Troubleshoot notification delivery failures and subscriber syncs by cross-referencing Docker application logs with database records containing failure reasons, and checking Redis cache state for rate-limiting counters or synchronization blockages.

Yes, you can debug database anomalies and unexpected ticket statuses by querying relevant database entities for failure reasons, correlating findings with Docker container logs, and inspecting Redis cache freshness to isolate the root cause.

This production debugging approach requires targeted container log inspection, specific SQL queries for impacted entities, and Redis checks for rate limits and task state, meaning it relies on having accessible Docker, database, and Redis environments.