Book Image

Flask Framework Cookbook - Second Edition

By : Shalabh Aggarwal

Book Image

Flask Framework Cookbook - Second Edition

By: Shalabh Aggarwal

Overview of this book

Flask, the lightweight Python web framework, is popular thanks to its powerful modular design that lets you build scalable web apps. With this recipe-based guide, you’ll explore modern solutions and best practices for Flask web development. Updated to the latest version of Flask and Python 3, this second edition of Flask Framework Cookbook moves away from some of the old and obsolete libraries and introduces new recipes on cutting-edge technologies. You’ll discover different ways of using Flask to create, deploy, and manage microservices. This Flask Python book starts by covering the different configurations that a Flask application can make use of, and then helps you work with templates and learn about the ORM and view layers. You’ll also be able to write an admin interface and get to grips with debugging and logging errors. Finally, you’ll learn a variety of deployment and post-deployment techniques for platforms such as Apache, Tornado, and Heroku. By the end of this book, you’ll have gained all the knowledge you need to confidently write Flask applications and scale them using standard industry practices.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Flask Configurations

Flask Configurations

Setting up our environment with virtualenv

Handling basic configurations

Configuring using class-based settings

Organizing static files

Being deployment-specific with instance folders

Composition of views and models

Creating a modular web app with blueprints

Making a Flask app installable using setuptools

Templating with Jinja2

Templating with Jinja2

Bootstrapping the recommended layout

Implementing block composition and layout inheritance

Creating a custom context processor

Creating a custom Jinja2 filter

Creating a custom macro for forms

Advanced date and time formatting

Data Modeling in Flask

Data Modeling in Flask

Creating an SQLAlchemy DB instance

Creating a basic product model

Creating a relational category model

Migrating databases using Alembic and Flask-Migrate

Indexing model data with Redis

Opting for the NoSQL way with MongoDB

Working with Views

Working with Views

Writing function-based views and URL routes

Writing class-based views

Implementing URL routing and product-based pagination

Rendering to templates

Dealing with XHR requests

Using decorators to handle requests beautifully

Creating custom 404 and 500 handlers

Flashing messages for better user feedback

Implementing SQL-based searching

Webforms with WTForms

Webforms with WTForms

Representing SQLAlchemy model data as a form

Validating fields on the server side

Creating a common forms set

Creating custom fields and validation

Creating a custom widget

Uploading files via forms

Protecting applications from cross-site request forgery (CSRF)

Authenticating in Flask

Authenticating in Flask

Creating a simple session-based authentication

Authenticating using the Flask-Login extension

Using Facebook for authentication

Using Google for authentication

Using Twitter for authentication

Authenticating with LDAP

RESTful API Building

RESTful API Building

Creating a class-based REST interface

Creating an extension-based REST interface

Creating a complete RESTful API

Admin Interface for Flask Apps

Admin Interface for Flask Apps

Creating a simple CRUD interface

Using the Flask-Admin extension

Registering models with Flask-Admin

Creating custom forms and actions

Using a WYSIWYG editor for textarea integration

Creating user roles

Internationalization and Localization

Internationalization and Localization

Adding a new language

Implementing lazy evaluation and the gettext/ngettext functions

Implementing the global language switching action

Debugging, Error Handling, and Testing

Debugging, Error Handling, and Testing

Setting up basic file logging

Sending emails on the occurrence of errors

Using Sentry to monitor exceptions

Debugging with pdb

Creating our first simple test

Writing more tests for views and logic

Nose library integration

Using mocking to avoid real API access

Determining test coverage

Using profiling to find bottlenecks

Deployment and Post-Deployment

Deployment and Post-Deployment

Deploying with Apache

Deploying with uWSGI and Nginx

Deploying with Gunicorn and Supervisor

Deploying with Tornado

Using S3 storage for file uploads

Deploying with Heroku

Deploying with AWS Elastic Beanstalk

Managing and monitoring application performance with New Relic

Microservices and Containers

Microservices and Containers

Containerization with Docker

Orchestrating containers with Kubernetes

Going serverless with Zappa on AWS Lambda

Other Tips and Tricks

Other Tips and Tricks

Implementing full-text search with Whoosh

Implementing full-text search with Elasticsearch

Working with signals

Using caching with your application

Implementing email support for Flask applications

Understanding asynchronous operations

Working with Celery

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

Indexing model data with Redis

There may be some features that we might want to implement, but do not want to have a persistent storage for them. So, we would like to have these stored in cache-like storage for some time and then vanish, for example, showing a list of the recently visited products to the visitors on the website. In this recipe, we will understand how to use Redis as an effective cache to store non-persistent data that can be accessed at a high speed.

Getting ready

We will do this with the help of Redis, which can be installed using the following command:

    $ pip3 install redis

Make sure that you run the Redis server for the connection to happen. To install and run a Redis server, refer to http://redis.io...