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Mastering Flask Web and API Development

Mastering Flask Web and API Development

By : Sherwin John C. Tragura
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Mastering Flask Web and API Development

Mastering Flask Web and API Development

5 (2)
By: Sherwin John C. Tragura

Overview of this book

Flask is a popular Python framework known for its lightweight and modular design. Mastering Flask Web and API Development will take you on an exhaustive tour of the Flask environment and teach you how to build a production-ready application. You’ll start by installing Flask and grasping fundamental concepts, such as MVC and ORM database access. Next, you’ll master structuring applications for scalability through Flask blueprints. As you progress, you’ll explore both SQL and NoSQL databases while creating REST APIs and implementing JWT authentication, and improve your skills in role-based access security, utilizing LDAP, OAuth, OpenID, and databases. The new project structure, managed by context managers, as well as ASGI support, has revolutionized Flask, and you’ll get to grips with these crucial upgrades. You'll also explore out-of-the-box integrations with technologies, such as RabbitMQ, Celery, NoSQL databases, PostgreSQL, and various external modules. The concluding chapters discuss enterprise-related challenges where Flask proves its mettle as a core solution. By the end of this book, you’ll be well-versed with Flask, seeing it not only as a lightweight web and API framework, but also as a potent problem-solving tool in your daily work, addressing integration and enterprise issues alongside Django and FastAPI.
Table of Contents (18 chapters)
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1
Part 1:Learning the Flask 3.x Framework
6
Part 2:Building Advanced Flask 3.x Applications
12
Part 3:Testing, Deploying, and Building Enterprise-Grade Applications

Using asynchronous background tasks for resource-intensive computations

There are implementations of many approximation algorithms and P-complete problems that can create memory-related issues, thread problems, or even memory leaks. To avoid imminent problems when handling solutions for NP-hard problems with indefinite data sets, implement the solutions using asynchronous background tasks.

But first, install the celery client using the pip command:

pip install celery

Also, install the Redis database server for its broker. Place celery_config.py, which contains celery_init_app(), in the project directory and call the method in the main.py module.

After the setup and installations, create a service package in the Blueprint module folder. ch06-project has the following Celery task in the hpi_formula.py service module found in the internal Blueprint module:

@shared_task(ignore_result=False)
def compute_hpi_laspeyre(df_json):
    async def compute_hpi_task...

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