Book Image

Building Serverless Applications with Python

Book Image

Building Serverless Applications with Python

Overview of this book

Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python. The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production. By the end of the book, you will be equipped with the knowledge required to build scalable and cost-efficient Python applications with a serverless framework.
Table of Contents (11 chapters)

Lambda functions

Lambda functions are the core operating parts of a serverless architecture. They contain the code which is supposed to be executed. These functions are executed whenever the trigger attached to it has been set off. We have already learned about some of the most popular Lambda triggers in the previous section.

Whenever a Lambda function is triggered, it creates a container with the respective settings set by the user. We'll learn more about the container in our next section.

The spinning up of containers takes a bit of time, which may result in a latency whenever a fresh invocation of a Lambda function is done, as it takes time to set up the environment and bootstrap the settings mentioned by the user in the Advanced settings tab. So, to overcome this latency, AWS thaws a container for some time for reuse in case of another Lambda invocation within the thawing...