Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Serverless Design Patterns and Best Practices
  • Toc
  • feedback
Serverless Design Patterns and Best Practices

Serverless Design Patterns and Best Practices

By : Zambrano
5 (1)
close
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)
close

Introduction to MapReduce


MapReduce as a pattern and programming model has been around for many years, arising from parallel computing research and industry implementations. Most famously, MapReduce hit the mainstream with Google's 2004 paper entitled MapReduce—Simplified Data Processing on Large Clusters (https://research.google.com/archive/mapreduce.html). Much of the benefit of Google's initial MapReduce implementation was:

  • Automatic parallelization and distribution
  • Fault-tolerance
  • I/O scheduling
  • Status and monitoring

If you take a step back and look at that list, it should look familiar. FaaS systems such as AWS Lambda give us most of these benefits. While status and monitoring aren't inherently baked into FaaS platforms, there are ways to ensure our functions are executing successfully. On that same topic, MapReduce systems were initially, and still are, very often, managed at the OS level, meaning operators are in charge of taking care of crashed or otherwise unhealthy nodes.

Note

The preceding...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete