G.O.D Framework

Introduction

The retry_mechanism.py module provides a reusable mechanism to handle transient errors by retrying failed operations. It's designed to ensure reliability and fault-tolerance in the G.O.D Framework, especially in scenarios involving unpredictable external dependencies (e.g., network requests, database queries).

The module uses configurable retry policies, including the number of retries, delay intervals, and exponential backoff.

Purpose

The key objectives of retry_mechanism.py are:

• To abstract and centralize the retry logic for various operations in the framework.
• To improve system resilience by gracefully handling transient failures.
• To allow developers to define custom retry policies with minimal effort.
• To minimize downtime and disruptions resulting from external service failures.

Key Features

• Configurable Retry Policies: Specify max retries, delays, backoff strategies, and timeouts.
• Exponential Backoff: Gradually increase retry intervals to avoid overloading external systems.
• Customizable Exception Handling: Define specific exceptions to trigger retries.
• Logging Integration: Tracks retry attempts and exceptions for debugging and monitoring.
• Decorator-Based Implementation: Simplifies integration with existing functions.

Logic and Implementation

The script is implemented using Python's decorators for ease of integration with other functions. Below is the reference implementation:

import time
import logging
from functools import wraps


def retry(
    max_retries=3,
    delay=2,
    backoff=2,
    exceptions=(Exception,),
    logger=None
):
    """
    A decorator to retry a function in case of an exception.

    Args:
        max_retries (int): The maximum number of retry attempts.
        delay (int): The initial delay between retries (in seconds).
        backoff (int): The multiplier for exponential backoff.
        exceptions (tuple): A tuple of exception classes to retry.
        logger (logging.Logger): The logger for tracking retries (optional).

    Returns:
        function: The wrapped function with retry logic.
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            _retries = 0
            _delay = delay
            while _retries < max_retries:
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    _retries += 1
                    if logger:
                        logger.warning(
                            f"Retry {_retries}/{max_retries} for {func.__name__} failed with error: {str(e)}. Retrying in {_delay} seconds..."
                        )
                    time.sleep(_delay)
                    _delay *= backoff
            if logger:
                logger.error(f"Function {func.__name__} failed after {max_retries} retries.")
            raise
        return wrapper
    return decorator


# Example Usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger("RetryMechanism")

    @retry(max_retries=5, delay=1, backoff=2, exceptions=(ValueError,), logger=logger)
    def unstable_function():
        import random
        if random.choice([True, False]):
            raise ValueError("Simulated transient error!")
        return "Success!"

    try:
        result = unstable_function()
        logger.info(f"Function succeeded with result: {result}")
    except Exception as e:
        logger.error(f"Function failed with error: {str(e)}")
        

This implementation applies a flexible retry policy to transient failures, logging each attempt and tracking success/failure rates.

Dependencies

• time: Used for handling delays between retry attempts.
• logging: For tracking retries and errors, providing detailed reports.
• functools: Wraps functions to apply the retry decorator seamlessly.

Integration with the G.O.D Framework

The retry_mechanism.py module can be applied across many components within the framework to ensure resilient operations. Examples include:

• ai_crawling_data_retrieval.py: For retrying failed external API or web scraping requests.
• ai_pipeline_optimizer.py: To handle retries during automated optimization tasks.
• error_handler.py: As a fallback mechanism to reattempt failed tasks.
• data_fetcher.py: For retrying database queries or network-based data retrieval.

Future Enhancements

• Implement retries with a maximum timeout period to avoid indefinite loops.
• Add support for asynchronous functions using asyncio.
• Integrate monitoring hooks to visualize retry behavior via the G.O.D Dashboard.
• Provide pre-built retry configurations for common use cases (e.g., network failures, database errors).