Error Handling Best Practices

Error handling in software development means preparing for unexpected situations and managing them effectively, not just reacting when something breaks.

1. Be Specific About Errors
   • Why: Catching all errors generically can hide the root cause, making it harder to debug.
   • How: Create specific error types that clearly describe the problem.
     Example: Instead of catching all errors, create a custom error for user data validation issues:

     class DataValidationError(Exception)

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2. Handle Errors in One Place
   • Why: Scattered error handling can cause inconsistent behavior and make debugging harder.
   • How: Use tools to centralize error responses for uniformity.
     Example: In a web app, use a single function to handle all errors:

     						@app.errorhandler(Exception)
     						def handle_exception(e):
     							return {"error": "Something went wrong"}, 500
     						

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3. Log Information Wisely
   • Why: Logs are crucial for diagnosing problems, but logging sensitive data can lead to security risks.
   • How: Log only necessary details and avoid exposing private information.
     Example:

     						logging.error(f"Failed to process request. Reason: {str(e)}")
     						

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4. Check Inputs Early
   • Why: Catching bad data upfront prevents it from causing errors later in the program.
   • How: Validate data as soon as it’s received.
     Example using a library like Pydantic:

     						from pydantic import BaseModel, ValidationError
     							
     							class UserData(BaseModel):
     								username: str
     								email: str
     
     							try:
     								user = UserData(username="John", email="[email protected]"
     							except ValidationError as e:
     								print(e.json())
     						

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5. Show Friendly Error Messages
   • Why: Users don’t need to see complex error details. Clear, simple messages improve their experience.
   • How: Hide technical details and provide meaningful feedback.
     Example:

     						return {"error": "We encountered an issue. Please try again later."}, 500
     					

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6. Retry When Possible
   • Why: Some errors, like temporary network issues, resolve with another attempt.
   • How: Use retry logic with gradual delays to avoid overwhelming the system.
     Example using the Tenacity library:

     							from tenacity import retry, wait_exponential
     
     								@retry(wait=wait_exponential(multiplier=1, max=10))
     					            def fetch_data():
     									# Fetch logic here
     						

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7. Monitor Problems in Real-Time
   • Why: Quick error notifications allow faster fixes and less downtime.
   • How: Use tools like Sentry to track errors as they occur.
     Example:

     							import sentry_sdk
     								
     								sentry_sdk.init(dsn="YOUR_SENTRY_DSN")
     						

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8. Prevent Overload with Circuit Breakers
   • Why: Too many retries can overwhelm a system, making things worse.
   • How: Stop retries after a certain number of failures.
     Example using PyBreaker:

     							from pybreaker import CircuitBreaker
     
     								breaker = CircuitBreaker(fail_max=5, reset_timeout=60)
     
     								@breaker
     								def unreliable_service():
     									# Logic here
     						

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9. Test for Errors
   • Why: Many bugs happen at the edges, so testing these cases prevents surprises.
   • How: Write tests that simulate errors and validate your handling logic.
     Example:

     						import pytest
     
     							def test_zero_division():
     								with pytest.raises(ZeroDivisionError):
     									result = 1 / 0
     					

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10. Keep Core Functions Running
    • Why: If one feature fails, the rest of your app should still work.
    • How: Use feature flags to isolate non-critical functions.
      Example:

      							if feature_enabled("new_feature"):
      								# Execute new logic
      							else:
      								# Use fallback
      						

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11. Track Error Trends
    • Why: Understanding patterns in errors helps prioritize fixes and improve reliability.
    • How: Use analytics tools to collect and visualize error data.
      Example tools: Datadog, ELK Stack (Elasticsearch, Logstash, Kibana).

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Final Thoughts

Good error handling means thinking ahead, designing systems to anticipate issues, and minimizing their impact. These practices ensure that your software remains reliable, secure, and user-friendly - even when things go wrong.