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
Improving application reliability
Creating better error messages for users and developers
Implementing retry and circuit breaker patterns
Handling async/concurrent errors
Building fault-tolerant distributed systems

Core Concepts

1. Error Handling Philosophies

Exceptions vs Result Types:

Exceptions: Traditional try-catch, disrupts control flow
Result Types: Explicit success/failure, functional approach
Error Codes: C-style, requires discipline
Option/Maybe Types: For nullable values

When to Use Each:

Exceptions: Unexpected errors, exceptional conditions
Result Types: Expected errors, validation failures
Panics/Crashes: Unrecoverable errors, programming bugs

2. Error Categories

Recoverable Errors:

Network timeouts
Missing files
Invalid user input
API rate limits

Unrecoverable Errors:

Out of memory
Stack overflow
Programming bugs (null pointer, etc.)

Language-Specific Patterns

For detailed code examples in Python, TypeScript, Rust, and Go, see: 👉 examples/language-patterns.md

Universal Patterns

Pattern 1: Circuit Breaker

Prevent cascading failures in distributed systems.

from enum import Enum
from datetime import datetime, timedelta
from typing import Callable, TypeVar

T = TypeVar('T')

class CircuitState(Enum):
    CLOSED = "closed"       # Normal operation
    OPEN = "open"          # Failing, reject requests
    HALF_OPEN = "half_open"  # Testing if recovered

class CircuitBreaker:
    def __init__(
        self,
        failure_threshold: int = 5,
        timeout: timedelta = timedelta(seconds=60),
        success_threshold: int = 2
    ):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.success_threshold = success_threshold
        self.failure_count = 0
        self.success_count = 0
        self.state = CircuitState.CLOSED
        self.last_failure_time = None

    def call(self, func: Callable[[], T]) -> T:
        if self.state == CircuitState.OPEN:
            if datetime.now() - self.last_failure_time > self.timeout:
                self.state = CircuitState.HALF_OPEN
                self.success_count = 0
            else:
                raise Exception("Circuit breaker is OPEN")

        try:
            result = func()
            self.on_success()
            return result
        except Exception as e:
            self.on_failure()
            raise

    def on_success(self):
        self.failure_count = 0
        if self.state == CircuitState.HALF_OPEN:
            self.success_count += 1
            if self.success_count >= self.success_threshold:
                self.state = CircuitState.CLOSED
                self.success_count = 0

    def on_failure(self):
        self.failure_count += 1
        self.last_failure_time = datetime.now()
        if self.failure_count >= self.failure_threshold:
            self.state = CircuitState.OPEN

# Usage
circuit_breaker = CircuitBreaker()

def fetch_data():
    return circuit_breaker.call(lambda: external_api.get_data())

Pattern 2: Error Aggregation

Collect multiple errors instead of failing on first error.

class ErrorCollector {
  private errors: Error[] = [];

  add(error: Error): void {
    this.errors.push(error);
  }

  hasErrors(): boolean {
    return this.errors.length > 0;
  }

  getErrors(): Error[] {
    return [...this.errors];
  }

  throw(): never {
    if (this.errors.length === 1) {
      throw this.errors[0];
    }
    throw new AggregateError(
      this.errors,
      `${this.errors.length} errors occurred`,
    );
  }
}

// Usage: Validate multiple fields
function validateUser(data: any): User {
  const errors = new ErrorCollector();

  if (!data.email) {
    errors.add(new ValidationError("Email is required"));
  } else if (!isValidEmail(data.email)) {
    errors.add(new ValidationError("Email is invalid"));
  }

  if (!data.name || data.name.length < 2) {
    errors.add(new ValidationError("Name must be at least 2 characters"));
  }

  if (!data.age || data.age < 18) {
    errors.add(new ValidationError("Age must be 18 or older"));
  }

  if (errors.hasErrors()) {
    errors.throw();
  }

  return data as User;
}

Pattern 3: Graceful Degradation

Provide fallback functionality when errors occur.

from typing import Optional, Callable, TypeVar

T = TypeVar('T')

def with_fallback(
    primary: Callable[[], T],
    fallback: Callable[[], T],
    log_error: bool = True
) -> T:
    """Try primary function, fall back to fallback on error."""
    try:
        return primary()
    except Exception as e:
        if log_error:
            logger.error(f"Primary function failed: {e}")
        return fallback()

# Usage
def get_user_profile(user_id: str) -> UserProfile:
    return with_fallback(
        primary=lambda: fetch_from_cache(user_id),
        fallback=lambda: fetch_from_database(user_id)
    )

# Multiple fallbacks
def get_exchange_rate(currency: str) -> float:
    return (
        try_function(lambda: api_provider_1.get_rate(currency))
        or try_function(lambda: api_provider_2.get_rate(currency))
        or try_function(lambda: cache.get_rate(currency))
        or DEFAULT_RATE
    )

def try_function(func: Callable[[], Optional[T]]) -> Optional[T]:
    try:
        return func()
    except Exception:
        return None

Best Practices

Fail Fast: Validate input early, fail quickly
Preserve Context: Include stack traces, metadata, timestamps
Meaningful Messages: Explain what happened and how to fix it
Log Appropriately: Error = log, expected failure = don't spam logs
Handle at Right Level: Catch where you can meaningfully handle
Clean Up Resources: Use try-finally, context managers, defer
Don't Swallow Errors: Log or re-throw, don't silently ignore
Type-Safe Errors: Use typed errors when possible

# Good error handling example
def process_order(order_id: str) -> Order:
    """Process order with comprehensive error handling."""
    try:
        # Validate input
        if not order_id:
            raise ValidationError("Order ID is required")

        # Fetch order
        order = db.get_order(order_id)
        if not order:
            raise NotFoundError("Order", order_id)

        # Process payment
        try:
            payment_result = payment_service.charge(order.total)
        except PaymentServiceError as e:
            # Log and wrap external service error
            logger.error(f"Payment failed for order {order_id}: {e}")
            raise ExternalServiceError(
                f"Payment processing failed",
                service="payment_service",
                details={"order_id": order_id, "amount": order.total}
            ) from e

        # Update order
        order.status = "completed"
        order.payment_id = payment_result.id
        db.save(order)

        return order

    except ApplicationError:
        # Re-raise known application errors
        raise
    except Exception as e:
        # Log unexpected errors
        logger.exception(f"Unexpected error processing order {order_id}")
        raise ApplicationError(
            "Order processing failed",
            code="INTERNAL_ERROR"
        ) from e

Common Pitfalls

Catching Too Broadly: except Exception hides bugs
Empty Catch Blocks: Silently swallowing errors
Logging and Re-throwing: Creates duplicate log entries
Not Cleaning Up: Forgetting to close files, connections
Poor Error Messages: "Error occurred" is not helpful
Returning Error Codes: Use exceptions or Result types
Ignoring Async Errors: Unhandled promise rejections

Resources

references/exception-hierarchy-design.md: Designing error class hierarchies
references/error-recovery-strategies.md: Recovery patterns for different scenarios
references/async-error-handling.md: Handling errors in concurrent code
assets/error-handling-checklist.md: Review checklist for error handling
assets/error-message-guide.md: Writing helpful error messages
scripts/error-analyzer.py: Analyze error patterns in logs

implementing-error-handling