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Implementing Cloud Design Patterns for AWS

You're reading from   Implementing Cloud Design Patterns for AWS Solutions and design ideas for solving system design problems

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789136203
Length 274 pages
Edition 2nd Edition
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Authors (4):
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Clive Harber Clive Harber
Author Profile Icon Clive Harber
Clive Harber
Sean Keery Sean Keery
Author Profile Icon Sean Keery
Sean Keery
Rick Farmer Rick Farmer
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Rick Farmer
Marcus Young Marcus Young
Author Profile Icon Marcus Young
Marcus Young
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics FREE CHAPTER
2. Introduction to Amazon Web Services 3. Core Services - Building Blocks for Your Product 4. Availability Patterns - Understanding Your Needs 5. Security - Ensuring the Integrity of Your Systems 6. Section 2: DevOps Patterns
7. Continuous Deployment - Introducing New Features with Minimal Risk 8. Ephemeral Environments - Sandboxes for Experiments 9. Operation and Maintenance - Keeping Things Running at Peak Performance 10. Application Virtualization - Using Cloud Native Patterns for Your Workloads 11. Antipatterns - Avoiding Counterproductive Solutions 12. Section 3: Persistence Patterns
13. Databases - Identifying Which Type Fits Your Needs 14. Data Processing - Handling Your Data Transformation 15. Observability - Understanding How Your Products Are Behaving 16. Anti-Patterns - Bypassing Inferior Options 17. Other Books You May Enjoy

Testing your environment

As we move workloads to the cloud, we have the benefit of nearly unlimited capacity. In Chapter 3, Availability Patterns - Understanding Your Needs, we looked into how we could create an auto-scaling group. The resource we created used Spot Instances, a low-cost ephemeral compute capacity, to increase our number of instances: 

scaling_adjustment = 4
adjustment_type = "ChangeInCapacity"

In the Persistence Patterns section of the book, we will dive into how to use our AWS metrics to choose what to measure. For now, we need to understand that whatever we choose as our default will at some point be suboptimal. Odds are that it will be on initial creation. We must constantly be validating our assumptions about what we've built.

Load testing with real data is one of the best ways to substantiate we are moving in the right direction...

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