📢 Open to New Features & Improvements for Fault Tolerance

[Kluzko]

I’m open to new features and ideas to improve the current code and make it more fault-tolerant. If you have suggestions for handling errors, optimizing performance, or improving stability, please share them here!

What You Can Contribute

✅ Improve error handling (e.g., better handling of network failures)
✅ Optimize caching & reduce unnecessary API calls
✅ Enhance logging & debugging features
✅ Propose new architecture improvements
✅ Any other ideas to make the library more robust!

How to Suggest a Feature

Describe your idea clearly – What problem does it solve?
Explain how it improves the current code – Why is it better?
Provide examples or code snippets if possible.

I welcome all contributions! Feel free to discuss your ideas below. 🚀

[pallasite99]

Here are some key improvements for dukascopy-fx:

1. Error Handling: Implement retry with exponential backoff, set timeouts, and improve error messages.
2. Optimize Caching: Add cache validation and configurable policies to reduce redundant API calls.
3. Enhanced Logging: Use structured logging, log levels, and trace identifiers for better debugging.
4. Architecture Improvements: Introduce async processing, modular design, and dependency injection.
5. Additional Enhancements: Strengthen input validation, expand test coverage, and integrate CI for stability.

1️⃣ Implement Retry with Exponential Backoff for Network Requests

🚨 Problem

Network failures (e.g., API rate limiting, connectivity issues) can cause immediate request failures, requiring manual retries.

✅ Solution

Implement an automatic retry mechanism with exponential backoff to enhance resilience.

🔧 Code Example (Python)

import time
import requests

def fetch_with_retry(url, max_retries=5, backoff_factor=2):
    retries = 0
    while retries < max_retries:
        try:
            response = requests.get(url, timeout=5)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            retries += 1
            wait_time = backoff_factor ** retries
            print(f"Request failed ({e}), retrying in {wait_time} seconds...")
            time.sleep(wait_time)
    
    raise Exception("Max retries reached. Request failed.")

# Example usage
data = fetch_with_retry("https://api.example.com/data")

🔥 Why It’s Better

✔ Prevents unnecessary failures by retrying failed requests automatically.
✔ Uses exponential delay to avoid overloading the API.
✔ Fully configurable retry count and backoff timing.

---

2️⃣ Optimize Caching & Reduce Unnecessary API Calls

🚨 Problem

Redundant API calls lead to performance issues and excessive server requests.

✅ Solution

Use in-memory caching to store previous responses and reduce duplicate API calls.

🔧 Code Example (Python using functools.lru_cache)

from functools import lru_cache
import requests

@lru_cache(maxsize=100)  # Cache up to 100 unique responses
def fetch_data(url):
    response = requests.get(url, timeout=5)
    response.raise_for_status()
    return response.json()

# Example usage
data = fetch_data("https://api.example.com/data")

🔥 Why It’s Better

✔ Faster responses by avoiding redundant requests.
✔ Reduces API rate limits and unnecessary load.
✔ Configurable cache size for memory efficiency.

---

3️⃣ Enhance Logging & Debugging

🚨 Problem

Logs are hard to read, inconsistent, and lack traceability.

✅ Solution

Implement structured logging with log levels for better debugging and monitoring.

🔧 Code Example (Python using logging)

import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

def fetch_data(url):
    logging.info(f"Fetching data from {url}")
    try:
        response = requests.get(url, timeout=5)
        response.raise_for_status()
        logging.info("Data fetched successfully")
        return response.json()
    except requests.exceptions.RequestException as e:
        logging.error(f"Request failed: {e}")
        raise

# Example usage
data = fetch_data("https://api.example.com/data")

🔥 Why It’s Better

✔ Provides timestamped logs for better tracking.
✔ Supports different log levels (INFO, WARNING, ERROR).
✔ Helps with debugging failures quickly.

---

4️⃣ Architecture Improvements

🚨 Problem

The current structure lacks modularity and scalability.

✅ Solution

Refactor into a modular design with dependency injection to improve maintainability.

🔧 Code Example (Python)

class APIClient:
    def __init__(self, base_url):
        self.base_url = base_url

    def fetch_data(self, endpoint):
        response = requests.get(f"{self.base_url}/{endpoint}", timeout=5)
        response.raise_for_status()
        return response.json()

# Inject dependency
client = APIClient("https://api.example.com")
data = client.fetch_data("data")

🔥 Why It’s Better

✔ Decouples dependencies for easier unit testing.
✔ Improves code reusability and modularity.
✔ Facilitates future enhancements with minimal changes.

---

5️⃣ Additional Enhancements

🚀 Strengthen Input Validation

• Validate all external inputs to prevent crashes and security risks.
• Example: Use pydantic for type-checked data validation.

from pydantic import BaseModel

class APIResponse(BaseModel):
    id: int
    value: str

response = APIResponse.parse_obj({"id": 123, "value": "example"})

---

🚀 Expand Test Coverage

• Write unit and integration tests to cover edge cases.
• Example: Use pytest for automated testing.

import pytest
from my_module import fetch_data

def test_fetch_data():
    result = fetch_data("https://api.example.com/data")
    assert "expected_key" in result

pytest.main()

---

🚀 Set Up Continuous Integration (CI)

• Use GitHub Actions to automate testing and code quality checks.
• Example .github/workflows/test.yml:

name: Run Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.10"
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run tests
        run: pytest

---

🎯 Summary

Feature	Benefit
🔄 Retry with Exponential Backoff	Prevents request failures & avoids API overload
🔥 Optimized Caching	Reduces redundant API calls & improves performance
📜 Structured Logging	Easier debugging & monitoring
🏗️ Modular Architecture	Improves maintainability & scalability
🔍 Input Validation & Testing	Prevents crashes & enhances stability
🚀 Continuous Integration	Ensures code quality & automates testing

---

These suggestions will boost reliability, performance, and maintainability in my opinion.

[pallasite99]

Suggested Architecture Improvements

Below is a diagram illustrating proposed improvements to the DukascopyFxService architecture:

+------------------+
|  Client Request  |
+------------------+
        |
        v
+------------------+
|    API Call     |
+------------------+
        |
        v
+---------------------------+
|   DukascopyFxService      |
+---------------------------+
        |
        v
+--------------------+
|  Validation Layer  |
+--------------------+
        |
        v
+------------------+
|  Cache Layer    |
+------------------+
    |       |
    |       v
    |   +---------------------+
    |   |  Return Cached Data |
    |   +---------------------+
    |       |
    v       v
+------------------+
|  Data Fetcher   |
+------------------+
        |
        v
+-------------------+
| Parsing Layer    |
+-------------------+
        |
        v
+----------------------+
|  Data Processing   |
+----------------------+
    |       |
    |       v
    |   +----------------+
    |   | Store in Cache |
    |   +----------------+
    v       v
+------------------+
|  Client Response |
+------------------+