Error Handling Patterns

Build resilient applications with robust error handling strategies that gracefully handle failures and provide excellent debugging experiences.

When to Use This Skill

• Implementing error handling in new features
• Designing error-resilient APIs
• Debugging production issues
• Implementing retry/circuit breaker patterns
• Handling async/concurrent errors

Workflow

1. Categorize: Is it recoverable (network timeout) or unrecoverable (null pointer, corrupt memory)?
2. Define Strategy: Exception hierarchy vs Result types?
3. Implement: Use try/catch contexts, retry logic, or circuit breakers.
4. Recover: Graceful degradation, fallback values, cleanup.

Instructions

1. Core Philosophies

• Exceptions: For unexpected conditions. Disrupts flow. (Python, Java behavior)
• Result Types: For expected failures. Functional, type-safe. (Rust, FP behavior)
• Panic/Crash: Only for impossible states/dev bugs.

2. Language-Specific Patterns

Python:

• Use custom exception hierarchies (ApplicationError -> ValidationError).
• Example: raise NotFoundError("User not found", code="404").
• Use contextmanager for cleanup (transactions, streams).

TypeScript/JavaScript:

• Async/Await: ALWAYS use try/catch.
• Define custom class AppError extends Error.
• Functional: Consider a Result<T, E> type wrapper for operations.

Rust:

• Use Result<T, E> for recoverable errors.
• Use ? operator to propagate.
• Use Option<T> for nullable values.

3. Universal Resilience Patterns

Circuit Breaker: Stop calling a failing service to prevent cascading failure. Open -> Half-Open -> Closed states.

Error Aggregation: Validate all fields and return all errors at once, rather than failing on the first one.

Graceful Degradation: Primary fails? -> Try Secondary -> Try Cache -> Return Default.

Exponential Backoff: Retry… wait 1s… retry… wait 2s… retry… wait 4s… stop.

Resources

• Exception Hierarchy Design
• Error Recovery Strategies