Book Image

Python Microservices Development

Book Image

Python Microservices Development

Overview of this book

We often deploy our web applications into the cloud, and our code needs to interact with many third-party services. An efficient way to build applications to do this is through microservices architecture. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other. This book will teach you how to overcome these issues and craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: you’ll build everything using Python 3 and its amazing tooling ecosystem. You will understand the principles of TDD and apply them. You will use Flask, Tox, and other tools to build your services using best practices. You will learn how to secure connections between services, and how to script Nginx using Lua to build web application firewall features such as rate limiting. You will also familiarize yourself with Docker’s role in microservices, and use Docker containers, CoreOS, and Amazon Web Services to deploy your services. This book will take you on a journey, ending with the creation of a complete Python application based on microservices. By the end of the book, you will be well versed with the fundamentals of building, designing, testing, and deploying your Python microservices.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Introduction

Coroutines


To make asynchronous programming more straightforward, the await and async keywords were introduced in Python 3.5, along with the coroutine type. The await call is almost equivalent to yield from, as its goal is to let you call a coroutine from another coroutine.

The difference is that you can't use the await call to call a generator (yet).

The async keyword marks a function, a for or a with loop, as being a native coroutine, and if you try to use that function, you will not retrieve a generator but a coroutine object.

The native coroutine type that was added in Python is like a fully symmetric generator, but all the back and forth is delegated to an event loop, which is in charge of coordinating the execution.

In the example that follows, the asyncio library is used to run main(), which, in turn, calls several coroutines in parallel:

    import asyncio 
 
    async def compute(): 
        for i in range(5): 
            print('compute %d' % i) 
            await asyncio.sleep(.1)...