Week 2: Task 6 (Error handling protocols)
Error handling protocols are the agreed rules and workflows an organization or system follows to detect errors, classify them, respond safely, and learn from them so they don’t recur. What error handling protocols are Error handling protocols formally define what counts as an error, how it is detected (monitoring, validations, exceptions), who is notified, and how the system and people should react. They apply both at the technical level (e.g., API returning correct status codes) and the organizational level (e.g., incident playbooks, escalation paths). Core principles - Fail safely: When something goes wrong, protect data integrity and safety first, even if that means degrading or temporarily disabling functionality. - Be explicit: Errors should be clearly signaled (codes, messages, logs), not silently ignored or hidden. - Be predictable: The same type of error should trigger the same type of response, so behavior is consistent and testable. Typical protocol steps Most robust error handling protocols include: 1. Detection: Input validation, exception handling, health checks, and monitoring alerts to spot errors early. 2. Classification: Categorizing errors (e.g., client vs server, transient vs permanent, security vs functional) to choose the right response. 3. Immediate response: Returning safe responses, rolling back transactions, putting components into a safe state, or activating a fallback. 4. Notification and escalation: Alerting on-call engineers or responsible teams when thresholds or critical conditions are met. 5. Recovery: Retries, circuit breakers, failover, restoring from backups, or guiding users to correct the problem. 6. Recording and learning: Logging, post-incident reviews, and updating documentation or code to prevent recurrence. Technical best practices - Clear error contracts: Define standard error formats and codes for APIs and services (e.g., HTTP status + structured body with code, message, and correlation ID). - Graceful degradation: Provide reduced functionality instead of total failure (e.g., cached data if a live service is down). - Context-rich logging: Log enough context (what was attempted, identifiers, environment) to debug without logging sensitive data in plain text. - Isolation and containment: Use timeouts, bulkheads, and circuit breakers so one failing component does not cascade across the system.