queuesync

QueueSync

The QueueSync is a Python library for coordinating and managing distributed systems. It consists of a Coordinator (server) that queues and processes client requests sequentially, and Worker (client) instances that send requests to the Coordinator. This library is structured to support multi-machine coordination in a networked environment.

Features

• Queue-based Request Management: The Coordinator class queues client requests, handling them one at a time.
• Threaded Connections: Supports concurrent connections without blocking operations.
• Extensible: Built with abstract base classes, allowing custom implementations of both Coordinator and Worker.
• Optional Status Updates: Display connection status and data transfer updates.

Directory Structure

The project is organized as follows:

QueueSync/
├── src/
│   ├── __init__.py
│   ├── coordinator.py
│   └── worker.py
├── tests/
│   ├── __init__.py
│   ├── test_coordinator.py
│   └── test_worker.py
├── .gitignore
├── LICENSE
├── README.md
├── setup.py                   
└── pyproject.toml

Installation

Clone the repository and ensure you have Python 3.x installed. Optionally, set up a virtual environment:

git clone https://github.com/yourusername/inter-machine-coordinator-library.git
cd inter-machine-coordinator-library
python3 -m venv .venv
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

Usage

Coordinator

The Coordinator class, located in src/coordinator.py, is an abstract class that serves as the server, managing client connections and processing requests from a queue.

Key Methods

• start(): Initializes and starts the server.
• queue_client_request(client_socket, client_address): Adds client requests to a queue.
• Process_requests(): Sequentially processes each queued request.
• handle_request(client_socket, client_address, received_data): Abstract method to be implemented for custom request processing.

Worker

The Worker class, located in src/worker.py, is an abstract class representing the client, which connects to the Coordinator server, sends requests, and receives responses.

Key Methods

• start(): Connects the Worker to the Coordinator server.
• query_coordinator(data): Sends data to the server and awaits a response.
• run_worker(): Abstract method to be implemented for custom worker logic.

Customisation

To implement specific request handling and worker functionality, create subclasses of Coordinator and Worker, then override the handle_request and run_worker methods, respectively.

Example:

from src.coordinator import Coordinator
from src.worker import Worker

class MyCoordinator(Coordinator):
    def handle_request(self, client_socket, client_address, received_data):
        response = b"Custom response data"
        return response

class MyWorker(Worker):
    def run_worker(self):
        data = b"Sample request data"
        response = self.query_coordinator(data)
        print(f"Received response: {response}")

Example

1. Coordinator:

   coordinator = MyCoordinator(host='127.0.0.1', port=12345, max_num_of_clients=5, are_updates_displayed=True)
   coordinator.start()
   

2. Worker:

   worker = MyWorker(host='127.0.0.1', port=12345, are_updates_displayed=True)
   worker.start()
   

This example demonstrates a simple setup with a Coordinator listening on localhost and a custom Worker connecting to it.

License

This library is open-source and available under the Apache License 2.0. See the LICENSE file for details.