In modern web applications, certain operations are too slow or resource-intensive to run synchronously within an HTTP request. This is where task queues come to the rescue. They allow you to offload time-consuming work to background processes while keeping your web application responsive and scalable. This guide provides a brief yet comprehensive introduction to three essential Python task queue technologies: RQ Worker, and Celery. The app refers to a Flask Application or a Python automation script as well Prerequisite: This tutorial is part of the Docker Series. 📚 View the Complete Docker Series ⬅ Previous Part Preliminary: In this tutorial, I will walk you through how to build a simple one (RQ_worker) and a medium one (Celery) using Docker Compose. The file and folder for both containers are as follows: 1) Simple_redis 2) Medium_redis What is Redis? Redis (which stands for RE mote DI ctionary S erver) is an open-source, in-memory data struc...
In a previous tutorial , sending an email directly from a request can slow down the user experience. Tasks such as generating PDF attachments, connecting to an SMTP server, and delivering emails may take several seconds to complete. During this time, the user is left waiting for the operation to finish. A better approach is to move email delivery to a background worker. In this tutorial, we will use Redis as a job queue, RQ (Redis Queue) as the background worker, and PostgreSQL to maintain a permanent audit trail of all outgoing emails Prerequisite: This tutorial is part of the Automated Sales Invoice Series. 📚 View the Complete Automated Sales Invoice Series ⬅ Previous Part Preliminary: In this tutorial, I will walk you through how to queue jobs in Redis and update the details and status in storage. The file and folder are shown as follows: All the files remain intact as in the previous tutorial , except now I will add 3 files: poller.py, tasks.py, and worker.py, the app folde...