Introduction

As Python developers, we often focus on writing code that works. However, truly robust applications need to gracefully handle errors and provide meaningful insights when things go wrong. In this blog post, we'll explore advanced error handling techniques and logging practices that will take your Python skills to the next level.

Custom Exceptions: Tailoring Error Handling

While Python provides a rich set of built-in exceptions, sometimes you need more specific error types for your application. Creating custom exceptions allows you to handle errors more precisely and provide clearer information about what went wrong.

Here's how you can create a custom exception:

class InvalidUserInputError(Exception):
    def __init__(self, message, input_value):
        self.message = message
        self.input_value = input_value
        super().__init__(self.message)

def process_user_input(input_value):
    if not input_value.isalpha():
        raise InvalidUserInputError("Input must contain only letters", input_value)
    return input_value.upper()

try:
    result = process_user_input("hello123")
except InvalidUserInputError as e:
    print(f"Error: {e.message}. You entered: {e.input_value}")

By creating custom exceptions, you can provide more context-specific error handling and make your code easier to debug and maintain.

Context Managers: Streamlining Resource Management

Context managers are a powerful feature in Python that help you manage resources efficiently. They ensure that resources are properly acquired and released, even if errors occur. Let's create a custom context manager for handling file operations:

class FileHandler:
    def __init__(self, filename, mode):
        self.filename = filename
        self.mode = mode
        self.file = None

    def __enter__(self):
        self.file = open(self.filename, self.mode)
        return self.file

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.file:
            self.file.close()
        if exc_type is not None:
            print(f"An error occurred: {exc_val}")
        return True

# Using the context manager
with FileHandler("example.txt", "w") as f:
    f.write("Hello, World!")
    raise ValueError("Simulated error")

print("File operation completed")

This context manager ensures that the file is always closed, even if an error occurs during the file operation. It also provides a way to handle any exceptions that might be raised within the context.

Advanced Logging: Beyond print() Statements

Logging is crucial for understanding what's happening in your application, especially in production environments. Python's built-in logging module offers a flexible and powerful way to add logging to your applications.

Here's an example of setting up a comprehensive logging system:

import logging
from logging.handlers import RotatingFileHandler

def setup_logger():
    logger = logging.getLogger("MyApp")
    logger.setLevel(logging.DEBUG)

# Console handler
    console_handler = logging.StreamHandler()
    console_handler.setLevel(logging.INFO)
    console_format = logging.Formatter('%(name)s - %(levelname)s - %(message)s')
    console_handler.setFormatter(console_format)

# File handler
    file_handler = RotatingFileHandler("app.log", maxBytes=1024*1024, backupCount=5)
    file_handler.setLevel(logging.DEBUG)
    file_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    file_handler.setFormatter(file_format)

    logger.addHandler(console_handler)
    logger.addHandler(file_handler)

    return logger

# Using the logger
logger = setup_logger()

def divide_numbers(a, b):
    try:
        result = a / b
        logger.info(f"Division result: {result}")
        return result
    except ZeroDivisionError:
        logger.error("Attempted to divide by zero")
        raise

divide_numbers(10, 2)
divide_numbers(5, 0)

This setup creates a logger that writes to both the console and a rotating log file. It provides different levels of detail for each output, allowing you to have comprehensive logs for debugging while keeping the console output concise.

Putting It All Together

Let's combine these advanced techniques into a more complex example:

import logging
from contextlib import contextmanager

class DatabaseError(Exception):
    pass

@contextmanager
def database_connection(connection_string):
    logger = logging.getLogger("DatabaseManager")
    logger.info(f"Connecting to database: {connection_string}")
    try:

# Simulated database connection
        connection = {"connected": True}
        yield connection
    except Exception as e:
        logger.error(f"Database connection failed: {str(e)}")
        raise DatabaseError("Failed to connect to the database")
    finally:
        logger.info("Closing database connection")
        connection["connected"] = False

def perform_database_operation():
    try:
        with database_connection("mysql://example.com/mydb") as db:

# Simulated database operation
            if db["connected"]:
                logging.info("Performing database operation")

# Simulate an error
                raise ValueError("Unexpected data format")
    except DatabaseError as e:
        logging.error(f"Database operation failed: {str(e)}")
    except ValueError as e:
        logging.error(f"Data processing error: {str(e)}")

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Run the operation
perform_database_operation()

This example demonstrates how to use custom exceptions, context managers, and logging together to create a robust error handling system for a simulated database operation.

Conclusion

Advanced error handling and logging are essential skills for any Python expert. By implementing custom exceptions, leveraging context managers, and setting up comprehensive logging systems, you can create more reliable, maintainable, and debuggable Python applications. These techniques will not only make your code more robust but also significantly improve your ability to diagnose and fix issues when they arise.