Lindsey  Koepp

Lindsey Koepp

1610595064

Asynchronous Tasks with Flask and Celery

If a long-running process is part of your application’s workflow, rather blocking the response, you should handle it in the background, outside the normal request/response flow.

Perhaps your web application requires users to submit a thumbnail (which will probably need to be re-sized) and confirm their email when they register. If your application processed the image and sent a confirmation email directly in the request handler, then the end user would have to wait unnecessarily for them both to finish processing before the page loads or updates. Instead, you’ll want to pass these processes off to a task queue and let a separate worker process deal with it, so you can immediately send a response back to the client. The end user can then do other things on the client-side while the processing takes place. Your application is also free to respond to requests from other users and clients.

To achieve this, we’ll walk you through the process of setting up and configuring Celery and Redis for handling long-running processes in a Flask app. We’ll also use Docker and Docker Compose to tie everything together. Finally, we’ll look at how to test the Celery tasks with unit and integration tests.

Objectives

By the end of this tutorial, you will be able to:

  1. Integrate Celery into a Flask app and create tasks.
  2. Containerize Flask, Celery, and Redis with Docker.
  3. Run processes in the background with a separate worker process.
  4. Save Celery logs to a file.
  5. Set up Flower to monitor and administer Celery jobs and workers.
  6. Test a Celery task with both unit and integration tests.

Background Tasks

Again, to improve user experience, long-running processes should be run outside the normal HTTP request/response flow, in a background process.

Examples:

  1. Sending confirmation emails
  2. Scraping and crawling
  3. Analyzing data
  4. Processing images
  5. Generating reports

As you’re building out an app, try to distinguish tasks that should run during the request/response lifecycle, like CRUD operations, from those that should run in the background.




#flask #python #redis #celery #docker

What is GEEK

Buddha Community

Asynchronous Tasks with Flask and Celery

Celery for Task Management with Flask and SQS

Assumed background knowledge

This article assumes the reader has familiarity with Python, Flask, Celery, and AWS SQS.

Introduction

The fundamental thing to grasp when building a Flask app that utilizes Celery for asynchronous task management is that there are really three parts to consider, outside of the queue and result backends. These are (1) the Flask instance, which is your web or micro-service frontend, (2) the Celery instance, which feeds tasks to the queue, and (3) the Celery worker, which pulls tasks off the queue and completes the work. The Flask and Celery instances are deployed together and work in tandem at the interface of the application. The Celery worker is deployed separately and works effectively independent from the instances.

At first glance setting up an application for using these three components appears very simple. However, the complication arises when attempting to implement the Flask instance and Celery instance using the Flask application factory pattern, because the approach causes a circular import issue.

The objective of this article of to:

  1. clarify how to initialize these three parts of the Flask+Celery service
  2. explain how to containerize the pieces of these services for deployment to a production environment
  3. point out a some notes on best practices

#flask #task-management #python #celery #sqs

Lindsey  Koepp

Lindsey Koepp

1610595064

Asynchronous Tasks with Flask and Celery

If a long-running process is part of your application’s workflow, rather blocking the response, you should handle it in the background, outside the normal request/response flow.

Perhaps your web application requires users to submit a thumbnail (which will probably need to be re-sized) and confirm their email when they register. If your application processed the image and sent a confirmation email directly in the request handler, then the end user would have to wait unnecessarily for them both to finish processing before the page loads or updates. Instead, you’ll want to pass these processes off to a task queue and let a separate worker process deal with it, so you can immediately send a response back to the client. The end user can then do other things on the client-side while the processing takes place. Your application is also free to respond to requests from other users and clients.

To achieve this, we’ll walk you through the process of setting up and configuring Celery and Redis for handling long-running processes in a Flask app. We’ll also use Docker and Docker Compose to tie everything together. Finally, we’ll look at how to test the Celery tasks with unit and integration tests.

Objectives

By the end of this tutorial, you will be able to:

  1. Integrate Celery into a Flask app and create tasks.
  2. Containerize Flask, Celery, and Redis with Docker.
  3. Run processes in the background with a separate worker process.
  4. Save Celery logs to a file.
  5. Set up Flower to monitor and administer Celery jobs and workers.
  6. Test a Celery task with both unit and integration tests.

Background Tasks

Again, to improve user experience, long-running processes should be run outside the normal HTTP request/response flow, in a background process.

Examples:

  1. Sending confirmation emails
  2. Scraping and crawling
  3. Analyzing data
  4. Processing images
  5. Generating reports

As you’re building out an app, try to distinguish tasks that should run during the request/response lifecycle, like CRUD operations, from those that should run in the background.




#flask #python #redis #celery #docker

Steve  Kunde

Steve Kunde

1592723950

Download a Flask template ready to plug in your business logic

Let’s assume after lots of hard work you have your machine learning model running the way it should. This model could be one which responds to a user’s request to classify a tweet sentiment or identify objects in an image or recommend a product or some other algorithm unique to your needs. You would now like to quickly deploy this model. The article below is an explanation of the template that I have created to get you up and running quickly.

#flask-framework #flask-python-appengine #flask-sqlalchemy #marshmallow #flask #programming

Web development with python and flask: part 3

In this part of the series, we will be taking a look at the HTTP protocol, request/response objects, their application in flask, properties, and their related methods. We will take steps to import it from the flask module, use its properties, and look at some of its related usages

Web applications implement one of the internet data and message exchange architectures that is based on HTTP protocol. The HTTP protocol is just one of the many application layers of TCP/IP. The TCP/IP(Transmission Control Protocol/Internet Protocol) is used as a standard for transmitting data over networks. In simple terms, HTTP has rules, properties, and methods that implement the transmission of messages in form of hyperlinks over the communication structures enforced by the TCP/IP.

.You must know that the internet is based on connected physical computational devices over either copper wires, fiber optical cables, wireless, and other media to form data transmission and retrieval systems across the globe. Trust me, that is a whole career field in itself and we are not interested in its elaborate ramblings in this post.

#flask #web developemnt #flask #flask requests #webdevelopment

Jon  Gislason

Jon Gislason

1632383589

Asynchronous Tasks with Flask, Celery, Redis 2021

Learn About Asynchronous Tasks with Flask, Celery, Redis 2021

By the end of this tutorial, you will be able to:

  1. Integrate Celery into a Flask app and create tasks.
  2. Containerize Flask, Celery, and Redis with Docker.
  3. Run processes in the background with a separate worker process.
  4. Save Celery logs to a file.
  5. Set up Flower to monitor and administer Celery jobs and workers.
  6. Test a Celery task with both unit and integration tests.

#flask #Python