G.O.D Framework

Documentation: data_fetcher.py

A centralized module for fetching and managing data from diverse sources.

Introduction

The data_fetcher.py script is the backbone of data acquisition within the G.O.D Framework. It automates the retrieval, processing, and preparation of data from various sources such as APIs, databases, files, and external systems. It ensures consistency and scalability in data ingestion.

Purpose

This module serves the following purposes:

Key Features

Logic and Implementation

The data_fetcher.py module abstracts the complexities of fetching data. It supports both synchronous and asynchronous data retrieval. Below is a simplified example of its implementation: 


import requests
import logging
import time
from functools import lru_cache

class DataFetcher:
    """
    Handles data retrieval from various sources like APIs, databases, and files.
    """

    def __init__(self, base_url=None, headers=None):
        self.base_url = base_url
        self.headers = headers or {"Content-Type": "application/json"}
        self.logger = logging.getLogger("DataFetcher")

    def fetch_from_api(self, endpoint, params=None, max_retries=3):
        """
        Fetches data from a REST API with retry logic.

        Args:
            endpoint (str): API endpoint to fetch data from.
            params (dict): Optional query parameters for the API call.
            max_retries (int): Number of retry attempts for failed requests.

        Returns:
            dict: Parsed response data if the request is successful.
        """
        url = f"{self.base_url}{endpoint}"
        retries = 0
        while retries < max_retries:
            try:
                self.logger.info(f"Fetching data from API: {url}")
                response = requests.get(url, headers=self.headers, params=params, timeout=10)
                response.raise_for_status()
                return response.json()
            except requests.exceptions.RequestException as e:
                self.logger.warning(f"Failed to fetch data ({retries + 1}/{max_retries}): {e}")
                retries += 1
                time.sleep(2 ** retries)  # Exponential backoff
        self.logger.error(f"Could not fetch data after {max_retries} attempts")
        raise Exception("Data fetch failed")

    @lru_cache(maxsize=512)
    def fetch_with_caching(self, endpoint, params=None):
        """
        Fetches data while caching the results to optimize performance.

        Args:
            endpoint (str): API endpoint to fetch data from.
            params (dict): Optional query parameters for the API call.

        Returns:
            dict: Cached or newly fetched response data.
        """
        return self.fetch_from_api(endpoint, params)

# Example usage
if __name__ == "__main__":
    fetcher = DataFetcher(base_url="https://api.example.com")
    data = fetcher.fetch_from_api("/data", {"query": "example"})
    print("Fetched Data:", data)

This implementation demonstrates fetching data via REST APIs with built-in retries, caching, and error handling, making it suitable for high-resilience applications.

Dependencies

Integration with the G.O.D Framework

The data_fetcher.py module integrates seamlessly with the following components:

Future Enhancements