G.O.D Framework

Introduction

The error_handler.py script is an essential part of the G.O.D Framework aimed at managing, logging, and tracking application errors and exceptions. It ensures robustness and reliability by centralizing exception handling, facilitating consistent logging, and providing meaningful insights into system failures.

Purpose

This module was designed to:

• Provide uniform exception handling across all scripts in the G.O.D Framework.
• Log errors in a structured and readable manner for debugging and auditing purposes.
• Enable seamless integration with monitoring tools and alert systems.
• Reduce duplicate error-handling code across modules.

Key Features

• Custom Exceptions: Introduces application-specific exceptions to maintain code clarity.
• Centralized Logging: Ensures all errors are logged consistently using structured log formats.
• Error Categorization: Classifies errors based on severity or module for better monitoring.
• Integration with Monitoring Systems: Hooks into external monitoring tools (e.g., Sentry, Prometheus).
• Retry Support: Built-in mechanisms for retrying failed operations upon transient errors.

Logic and Implementation

The error_handler.py module uses a combination of Python's built-in exception handling and logging packages to capture and manage errors. Below is a simplified implementation example:

import logging
import traceback


class ErrorHandler:
    """
    A centralized module for handling and logging errors within the G.O.D Framework.
    """

    def __init__(self, log_file="error_log.log"):
        # Configure the logger
        self.logger = logging.getLogger("ErrorHandler")
        self.logger.setLevel(logging.ERROR)
        handler = logging.FileHandler(log_file)
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        handler.setFormatter(formatter)
        self.logger.addHandler(handler)

    def log_exception(self, error, context=""):
        """
        Logs an exception with its stack trace and context.

        Args:
            error (Exception): The exception to be logged.
            context (str): Custom context or message for the error.
        """
        self.logger.error(f"Exception occurred: {context}\n{traceback.format_exc()}")

    def retry_operation(self, function, retries=3, context=""):
        """
        Retries a function in case of failure.

        Args:
            function (Callable): Function to be executed.
            retries (int): Number of retry attempts.
            context (str): Explanation or context about the operation.

        Returns:
            Any: Result of the function, if successful.
        """
        for attempt in range(retries):
            try:
                self.logger.info(f"Attempt {attempt + 1}: {context}")
                return function()
            except Exception as error:
                self.logger.warning(f"Attempt {attempt + 1} failed: {error}")
                self.log_exception(error, context)
        self.logger.error(f"All {retries} attempts failed for {context}.")
        raise Exception(f"Failed after {retries} retries.")

# Example Usage
if __name__ == "__main__":
    def sample_function():
        # This function simulates a failing operation
        raise ValueError("Sample error")

    handler = ErrorHandler("framework_errors.log")
    try:
        handler.retry_operation(sample_function, 3, context="Executing sample function")
    except Exception as e:
        print(f"Operation failed: {e}")
        

Dependencies

• logging: For structured and centralized logging of errors.
• traceback: To capture and display stack trace information.

Integration with the G.O.D Framework

The error_handler.py module integrates across the G.O.D Framework to ensure uniform error management. It works effectively with:

• ai_error_tracker.py: Sends critical errors to the AI-based error tracking system.
• ci_cd_pipeline.py: Ensures all CI/CD pipeline errors are logged consistently.
• data_fetcher.py: Recovers from transient errors encountered during API calls.

Future Enhancements

• Add support for asynchronous error handling.
• Integrate with external logging platforms like Sentry and Logstash.
• Expand functionality for categorizing and prioritizing exceptions.
• Integrate real-time notifications for critical errors using Slack or email systems.
• Include advanced analytics for identifying patterns in logged errors.