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

Running tests


Since we're responsible developers, we have written a full suite of unit tests for our application. For now, tests are run manually inside our Docker container. The Docker image used has py.test installed, as well as some coverage tools.

The only dependency to running tests is PostgreSQL. Docker again makes it very simple to run a PostgreSQL container and hook it up to our application container. Multiple strategies exist for this, from running Docker Compose to merely starting up a container with docker run and linking the containers manually. For simplicity, I use the latter option. See the targets in the repository Makefile for details.

To run tests, inside the container, we execute make tests. I have trimmed much of the output for brevity and clarity:

root@d8dd5cc4bb86:/code# make tests
py.test --cov=serverless/ --cov-report=html tests/
Connected to: postgresql://postgres:@cupping-rltest-postgres:5432/test_cupping_log
........
==== test session starts ====
platform linux -...