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

Running and debugging your AWS Lambda code locally

Sometimes you want to simulate an API Gateway payload with a local Lambda against a real instance of remote DynamoDB hosted in AWS. This allows you to debug and build up unit tests with real data. In addition, we will see how these can later be used in the integration test.

Batch-loading data into DynamoDB

We will first discuss how to batch-load data into DynamoDB from a comma-separated values (CSV) file called sample_data/dynamodb-sample-data.txt. Rather than insert an individual statement for each item, this is a much more efficient process, as the data file is decoupled from the Python code: