Book Image

Serverless Design Patterns and Best Practices

By : Brian Zambrano
Book Image

Serverless Design Patterns and Best Practices

By: Brian Zambrano

Overview of this book

Serverless applications handle many problems that developers face when running systems and servers. The serverless pay-per-invocation model can also result in drastic cost savings, contributing to its popularity. While it's simple to create a basic serverless application, it's critical to structure your software correctly to ensure it continues to succeed as it grows. Serverless Design Patterns and Best Practices presents patterns that can be adapted to run in a serverless environment. You will learn how to develop applications that are scalable, fault tolerant, and well-tested. The book begins with an introduction to the different design pattern categories available for serverless applications. You will learn thetrade-offs between GraphQL and REST and how they fare regarding overall application design in a serverless ecosystem. The book will also show you how to migrate an existing API to a serverless backend using AWS API Gateway. You will learn how to build event-driven applications using queuing and streaming systems, such as AWS Simple Queuing Service (SQS) and AWS Kinesis. Patterns for data-intensive serverless application are also explained, including the lambda architecture and MapReduce. This book will equip you with the knowledge and skills you need to develop scalable and resilient serverless applications confidently.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Index

Writing the logic layer


Adding this GraphQL endpoint will consist of the following:

  • Adding a new entry point to handle the new Lambda function
  • Passing the HTTP payload (which is a GraphQL query or mutation) to a function that will execute GraphQL code

Admittedly, GraphQL is new enough that libraries and the ecosystem are not entirely polished or rich with documentation, at least in my experience. Still, it's possible to make quick progress, and once the basics are solved, GraphQL by its nature enables a vast range of functionality.

Since the coffee cupping example application is implemented using Python, we will continue down that path and augment it with some additional libraries for GraphQL. At the time of writing, Graphene is the de facto library for working with GraphQL from Python. Along with the base library, there are several other libraries that make working with various data stores easier. Luckily for us, one of the add-on libraries is Graphene-SQLAlchemy, which will work with our own...