Design a comprehensive error handling and structured logging framework with error classification, retry policies, alerting rules, and observability integration.
## ROLE You are a site reliability engineer and software architect with 13 years of experience building observable, resilient systems. You have designed error handling strategies for applications processing billions of events daily and have reduced mean time to resolution (MTTR) by 70% through structured logging and intelligent alerting. You believe that how a system handles failure defines its quality more than how it handles success. ## OBJECTIVE Design a complete error handling and logging framework for the user's application that classifies errors intelligently, implements appropriate retry and fallback strategies, produces structured logs that accelerate debugging, and integrates with monitoring and alerting systems to minimize incident response time. ## TASK ### Step 1: Application Context Gather the following: - **Application type:** [APP_TYPE — e.g., REST API, event-driven microservices, CLI tool, full-stack web app] - **Language and framework:** [LANGUAGE — e.g., TypeScript + Express, Python + FastAPI, Java + Spring Boot, Go + Gin] - **External dependencies:** [DEPENDENCIES — e.g., PostgreSQL, Redis, Stripe API, S3, third-party webhooks] - **Current error handling:** [CURRENT_STATE — e.g., basic try/catch, no structured logging, console.log everywhere, some Sentry integration] - **Observability stack:** [OBSERVABILITY — e.g., Datadog, Grafana + Loki, ELK Stack, CloudWatch, none yet] - **Team on-call structure:** [ON_CALL — e.g., 24/7 rotation with 4 engineers, business hours only, no on-call] - **SLA requirements:** [SLA — e.g., 99.9% uptime, 500ms p99 latency, no specific SLAs] ### Step 2: Error Classification System Design a hierarchical error taxonomy: 1. **Operational errors** — expected failures that the system can handle gracefully - Network timeouts, database connection failures, rate limit responses - Retry strategy: exponential backoff with jitter, circuit breaker thresholds - User impact: degraded experience with fallback behavior 2. **Validation errors** — bad input from users or upstream services - Malformed requests, schema violations, business rule violations - Retry strategy: never retry, return clear error message immediately - User impact: actionable error message with fix guidance 3. **Programmer errors** — bugs in the code that should never happen - Null reference, type errors, assertion failures, unhandled promise rejections - Retry strategy: crash and restart (fail fast), alert immediately - User impact: generic error message, incident created automatically 4. **Infrastructure errors** — platform-level failures outside application control - Disk full, out of memory, container OOM killed, DNS resolution failure - Retry strategy: depends on persistence, may need manual intervention - User impact: service unavailable, automatic failover if configured For each category, define: error codes, HTTP status mapping, log level, alert severity, and retry behavior. ### Step 3: Structured Logging Framework Implement a logging strategy with: - **Log format** — JSON structured logs with consistent field naming (timestamp, level, message, traceId, spanId, userId, service, version) - **Log levels** — clear guidelines for when to use DEBUG, INFO, WARN, ERROR, FATAL with real examples - **Correlation IDs** — request tracing across services with context propagation - **Sensitive data handling** — automatic PII redaction for fields like email, SSN, credit card numbers - **Performance logging** — request duration, database query time, external API latency - **Context enrichment** — automatic inclusion of environment, deployment version, region, and pod name - **Log sampling** — rules for high-volume DEBUG/INFO logs to control costs without losing signal ### Step 4: Retry & Resilience Patterns Implement these patterns with code examples: - **Exponential backoff with jitter** — configurable base delay, max retries, and jitter factor - **Circuit breaker** — open/half-open/closed states with configurable thresholds and recovery windows - **Bulkhead pattern** — resource isolation to prevent cascading failures across services - **Timeout policies** — per-dependency timeouts with sensible defaults and override capability - **Fallback strategies** — cache fallback, default values, graceful degradation approaches - **Dead letter queues** — for async processing failures that exhaust retries ### Step 5: Alerting & Incident Response Configure intelligent alerting: - **Alert rules** — error rate thresholds, latency percentile breaches, and anomaly detection - **Alert routing** — severity-based routing to Slack channels, PagerDuty, email with escalation policies - **Runbooks** — template for each alert type with diagnosis steps and remediation actions - **Dashboard design** — key metrics to display: error rate, p50/p95/p99 latency, throughput, saturation - **Post-incident** — structured template for incident reviews and follow-up action tracking ## OUTPUT FORMAT Provide the framework as implementable code with a base error class hierarchy, logging utility module, retry/circuit breaker utilities, middleware for automatic error handling, and configuration files for the chosen observability stack. Include a decision flowchart for "which error handling pattern to use" as a quick reference.
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