Book Image

Building Serverless Microservices in Python

By : Richard Takashi Freeman
Book Image

Building Serverless Microservices in Python

By: Richard Takashi Freeman

Overview of this book

Over the last few years, there has been a massive shift from monolithic architecture to microservices, thanks to their small and independent deployments that allow increased flexibility and agile delivery. Traditionally, virtual machines and containers were the principal mediums for deploying microservices, but they involved a lot of operational effort, configuration, and maintenance. More recently, serverless computing has gained popularity due to its built-in autoscaling abilities, reduced operational costs, and increased productivity. Building Serverless Microservices in Python begins by introducing you to serverless microservice structures. You will then learn how to create your first serverless data API and test your microservice. Moving on, you'll delve into data management and work with serverless patterns. Finally, the book introduces you to the importance of securing microservices. By the end of the book, you will have gained the skills you need to combine microservices with serverless computing, making their deployment much easier thanks to the cloud provider managing the servers and capacity planning.
Table of Contents (13 chapters)
Title Page

Creating a Lambda to query DynamoDB

Now that we have the security and user-visits table set up with some data, and know how to write code to query that DynamoDB table, we will write the Lambda Python code.

Creating the Lambda function

Now we have the IAM role with two IAM policies attached, create the Lambda function itself. Here, we are creating a function from scratch, as we want to walk through the full details to deepen your understanding of what is involved in creating a serverless stack. The following diagram shows data API architecture involving CloudWatch, DynamoDB, IAM, and Lambda:

Perform the following steps:

  1. Sign in to the AWS Management Console and open the AWS Lambda console at