Flask Api Scaling: ParAllel Processing With Rq & Supervisor

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Flask Api Scaling: Parallel Processing With Rq & Supervisor
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 594.50 MB | Duration: 1h 15m

Level Up Your Flask Microservices: Advanced Hands-On Pattern for Job Queue Management to Scale and Speed Up Workflows

What you'll learn

Build Scalable Flask APIs: Set up and structure a microservice-based Flask API capable of handling high traffic and concurrent requests

Implement Background Task Processing with RQ: Use Redis Queue (RQ) to manage and execute background tasks in parallel

Deploy Flask Applications with Docker: Learn how to containerize your Flask applications using Docker for consistent deployment across different environments

Monitor and Manage Workers with Supervisor and RQ Dashboard: Gain hands-on experience in worker management

Requirements

A foundational understanding of Flask and experience with Python programming are required, as this course is geared toward more advanced concepts in API scaling and parallel processing

Description

Unlock the Power of Scalable Flask APIs with Parallel ProcessingAre you ready to scale your Flask applications and boost your backend performance? "Flask API Scaling: Parallel Processing with RQ & Supervisor" is a comprehensive course crafted to help you create responsive, high-performance Flask Microservice APIs. Why Enroll in This Course?Comprehensive Flask Microservice Setup: Learn how to build a modular and scalable Flask API, setting up a solid foundation for creating reliable microservices.Efficient Task Handling with Redis Queue (RQ): Discover how to manage background processes seamlessly. By integrating Redis Queue (RQ), you'll enable parallel task execution that ensures smooth API performance, even under heavy traffic.Streamlined Deployment with Docker: Master the deployment of your Flask applications using Docker. Containerize your microservices for consistent, environment-independent operation and simplified scaling.Inter-Process Communication: Implement a Pub/Sub (publish/subscribe) mechanism, allowing multiple processes to communicate efficiently, making your application more modular and robust.Advanced Worker Management: Learn to control and monitor your background tasks with Redis CLI and track real-time updates with RQ Dashboard for smooth workflow management and effective scaling.What You'll LearnSetting Up Flask Microservices: Develop a microservice skeleton and main API endpoints.Task Management with RQ & Supervisor: Configure Redis Queue and manage processes with Supervisor.Dockerized Deployment: Containerize your Flask app for easy deployment and scaling.Inter-Process Communication: Implement custom workers and a Pub/Sub mechanism.Worker Control & Monitoring: Utilize Redis CLI for worker management and track tasks with RQ Dashboard.Who Should Take This Course?Python Developers who are familiar with Flask and are ready to take their API skills to the next level by implementing parallel processing, enhancing scalability, and optimizing performance under heavy loads.Enroll Now and Scale Your Flask API Performance Today!Take this opportunity to become proficient in scalable API design and unlock the full potential of Flask, RQ, and Supervisor. Enroll now and start building high-performance, scalable APIs!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Microservice Setup

Lecture 2 Flask Skeleton

Lecture 3 Main Endpoint

Lecture 4 Short Intermission

Lecture 5 RQ and Supervisor

Section 3: Docker

Lecture 6 Dockerized Microservice Application

Section 4: Inter-Process Communication

Lecture 7 Custom Workers with Pub/Sub Mechanism

Section 5: Worker Management

Lecture 8 Worker Management with Redis CLI

Lecture 9 RQ Dashboard

Section 6: Production Server

Lecture 10 Setting Up uWSGI in Docker

Section 7: Outro

Lecture 11 Congratulations!

Lecture 12 Bonus Lecture - Courses Links

Intermediate to advanced Python developers who are familiar with Flask

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