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Serverless Design Patterns and Best Practices

Serverless Design Patterns and Best Practices

By : Zambrano
5 (1)
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Serverless Design Patterns and Best Practices

Serverless Design Patterns and Best Practices

5 (1)
By: 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 (12 chapters)
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Understanding the limitations of serverless MapReduce


MapReduce on a serverless platform can work very well. However, there are limitations that you need to keep in mind. First and foremost, memory, storage, and time limits will ultimately determine whether this pattern is possible for your dataset. Additionally, systems such as Hadoop are frameworks that one may use for any analysis. When implementing MapReduce in a serverless context, you will likely be implementing a system that will solve a particular problem.

I find that a serverless MapReduce implementation is viable when your final dataset is relatively small (a few hundred megabytes) such that your reducer can process all of the data without going over the memory limits for your FaaS provider. I will talk through some of the details behind that sentiment in the following.

Memory limits

In the reducer phase, all of the data produced from the mappers must, at some point, be read and stored in memory. In our example application, the reducer...

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