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Hands-On Python for DevOps

You're reading from   Hands-On Python for DevOps Leverage Python's native libraries to streamline your workflow and save time with automation

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835081167
Length 220 pages
Edition 1st Edition
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Author (1):
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Ankur Roy Ankur Roy
Author Profile Icon Ankur Roy
Ankur Roy
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to DevOps and role of Python in DevOps
2. Chapter 1: Introducing DevOps Principles FREE CHAPTER 3. Chapter 2: Talking about Python 4. Chapter 3: The Simplest Ways to Start Using DevOps in Python Immediately 5. Chapter 4: Provisioning Resources 6. Part 2: Sample Implementations of Python in DevOps
7. Chapter 5: Manipulating Resources 8. Chapter 6: Security and DevSecOps with Python 9. Chapter 7: Automating Tasks 10. Chapter 8: Understanding Event-Driven Architecture 11. Chapter 9: Using Python for CI/CD Pipelines 12. Part 3: Let’s Go Further, Let’s Build Bigger
13. Chapter 10: Common DevOps Use Cases in Some of the Biggest Companies in the World 14. Chapter 11: MLOps and DataOps 15. Chapter 12: How Python Integrates with IaC Concepts 16. Chapter 13: The Tools to Take Your DevOps to the Next Level 17. Index 18. Other Books You May Enjoy

Performing rollback

I have said this many times during the course of this book: making mistakes is okay. That is because most mistakes are reversible. Some are not, but those are actually quite easy to recognize if you have your wits about you. In DevOps, this stands true as well. You can reverse your mistakes. Often, the question becomes how quickly, quietly, and effectively you can do this. That is exactly what rollbacks do. They aid in the identification and correction of problems.

Rollbacks can be performed manually, or they can be automated. Having a manual rollback at times is too slow and requires people to actually be aware of an incident or an error, something that can take a long time if the team is off on a weekend, for example. In such cases – and in a lot of others – an automatic rollback based on a metric is necessary. And as we all know, Python is good at that stuff.

There are many ways to roll back on a deployment: Blue/Green (50% on the old instance...

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