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

Results


After running this system for a few days, I was able to produce many S3 files as expected, as well as keeping track of each trade in DynamoDB. As I mentioned earlier in this chapter, implementing some view layer wasn't feasible for this example system. However, querying DynamoDB is quite easy, provided primary keys and sort keys are set up correctly. Any view layer that needed to get historical data could easily grab files from S3. 

The following screenshot shows listings of S3 files for a given hour. Each file has the format shown earlier with individual prices, along with the pre-computed average for each currency:

Looking one level up in the S3 hierarchy, we can see the hour files that contain the average prices: