Book Image

Becoming a Rockstar SRE

By : Jeremy Proffitt, Rod Anami
Book Image

Becoming a Rockstar SRE

By: Jeremy Proffitt, Rod Anami

Overview of this book

Site reliability engineering is all about continuous improvement, finding the balance between business and product demands while working within technological limitations to drive higher revenue. But quantifying and understanding reliability, handling resources, and meeting developer requirements can sometimes be overwhelming. With a focus on reliability from an infrastructure and coding perspective, Becoming a Rockstar SRE brings forth the site reliability engineer (SRE) persona using real-world examples. This book will acquaint you the role of an SRE, followed by the why and how of site reliability engineering. It walks you through the jobs of an SRE, from the automation of CI/CD pipelines and reducing toil to reliability best practices. You’ll learn what creates bad code and how to circumvent it with reliable design and patterns. The book also guides you through interacting and negotiating with businesses and vendors on various technical matters and exploring observability, outages, and why and how to craft an excellent runbook. Finally, you’ll learn how to elevate your site reliability engineering career, including certifications and interview tips and questions. By the end of this book, you’ll be able to identify and measure reliability, reduce downtime, troubleshoot outages, and enhance productivity to become a true rockstar SRE!

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Table of Contents (27 chapters)
Preface
Preface
Who is this book for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1 - Understanding the Basics of Who, What, and Why
Part 1 - Understanding the Basics of Who, What, and Why
Free Chapter
2
Chapter 1: SRE Job Role – Activities and Responsibilities
Chapter 1: SRE Job Role – Activities and Responsibilities
Making this journey personal
Understanding the mindset and hobbies of an SRE
DevOps engineers versus SRE versus others
Describing an SRE’s main responsibilities
An overview of the daily activities of an SRE
People that inspire
Summary
Further reading
3
Chapter 2: Fundamental Numbers – Reliability Statistics
Chapter 2: Fundamental Numbers – Reliability Statistics
SLA commitment – a conversation, not a number
Defining and leveraging SLOs and SLIs
Measuring the downtime with the MTTR
Understanding the customer and revenue impact
Summary
4
Chapter 3: Imperfect Habits – Duct Tape Architecture and Spaghetti Code
Chapter 3: Imperfect Habits – Duct Tape Architecture and Spaghetti Code
The business of software development – let’s start with the dollars
The A/B testing mindset – the art of change in customer interaction
Dedication to the craft of development – and why some are just here for a job
Reviewing the merge request – it’s about training, oversight, and reliability
Why businesses want us to outright ignore best practices
Mixing good and bad – tricks to wrapping bad code and making it resilient
Summary
5
Part 2 - Implementing Observability for Site Reliability Engineering
Part 2 - Implementing Observability for Site Reliability Engineering
6
Chapter 4: Essential Observability – Metrics, Events, Logs, and Traces (MELT)
Chapter 4: Essential Observability – Metrics, Events, Logs, and Traces (MELT)
Technical requirements
Accomplishing systems monitoring and telemetry
Understanding APM
Getting to know topology self-discovery, the blast radius, predictability, and correlation
Alerting – the art of doing it quietly
Mixing everything into observability
In practice – applying what you have learned
Summary
Further reading
7
Chapter 5: Resolution Path – Master Troubleshooting
Chapter 5: Resolution Path – Master Troubleshooting
Properly defining the problem – and what to ask and not ask
Breaking down and testing systems
Previous and common events – checking for the simple problems
Effective research both online and among peers
Breaking down source code efficiently
Logging plus code
In practice – applying what you’ve learned
Summary
8
Chapter 6: Operational Framework – Managing Infrastructure and Systems
Chapter 6: Operational Framework – Managing Infrastructure and Systems
Technical requirements
Approaching systems administration as a discipline
Understanding IT service management
Seeing systems administration as multiple layers and multiple towers
Automating systems provisioning and management
In practice – applying what you’ve learned
Summary
Further readings
9
Chapter 7: Data Consumed – Observability Data Science
Chapter 7: Data Consumed – Observability Data Science
Technical requirements
Making data-driven decisions
Solving problems through a scientific approach
Understanding the most common statistical methods
Using other mathematical models in observability
Visualizing histograms with Grafana
In practice – applying what you’ve learned
Summary
Further reading
10
Part 3 - Applying Architecture for Reliability
Part 3 - Applying Architecture for Reliability
11
Chapter 8: Reliable Architecture – Systems Strategy and Design
Chapter 8: Reliable Architecture – Systems Strategy and Design
Technical requirements
Designing for reliability
Splitting and balancing the workload
Failing over – almost as good
Scaling up and out – horizontal versus vertical
In practice – applying what you’ve learned
Summary
Further reading
12
Chapter 9: Valued Automation – Toil Discovery and Elimination
Chapter 9: Valued Automation – Toil Discovery and Elimination
Technical requirements
Eliminating toil
Treating automation as a software problem
Automating the (in)famous CI/CD pipeline
In practice – applying what you’ve learned
Summary
Further reading
13
Chapter 10: Exposing Pipelines – GitOps and Testing Essentials
Chapter 10: Exposing Pipelines – GitOps and Testing Essentials
A basic pipeline – building automation to deploy infrastructure as code architecture and code
Automating compliance and security in pipelines
Automated linting for code quality and standards
Validating functionality during deployment with automated testing
The reduction of developer toil through automated processes
In practice – applying what you’ve learned
Summary
14
Chapter 11: Worker Bees – Orchestrations of Serverless, Containers, and Kubernetes
Chapter 11: Worker Bees – Orchestrations of Serverless, Containers, and Kubernetes
Technical requirements
The multiple definitions of serverless
Containers and why we love them
Kubernetes and other ways to orchestrate containers
Deployment techniques and workers
Automation and rolling back failed deployments
In practice – applying what you’ve learned
Summary
15
Chapter 12: Final Exam – Tests and Capacity Planning
Chapter 12: Final Exam – Tests and Capacity Planning
Technical requirements
Understanding types of testing
Adopting TDD
Using test automation frameworks
Staying ahead with capacity planning
In practice – applying what you’ve learned
Summary
Further reading
16
Part 4 - Mastering the Outage Moments
Part 4 - Mastering the Outage Moments
17
Chapter 13: First Thing – Runbooks and Low Noise Outage Notifications
Chapter 13: First Thing – Runbooks and Low Noise Outage Notifications
Technical requirements
What makes a good runbook – the basics
Beyond the runbook – code and comments
What’s in a good dashboard?
The basics of priority levels
In practice – applying what you’ve learned
Summary
18
Chapter 14: Rapid Response – Outage Management Techniques
Chapter 14: Rapid Response – Outage Management Techniques
Where to meet – an effective strategy for communicating good information
Leveraging the people involved in the response
The opportunity to respond at the right time
Messaging customers and leadership
In practice – applying what you’ve learned
Summary
19
Chapter 15: Postmortem Candor – Long-Term Resolution
Chapter 15: Postmortem Candor – Long-Term Resolution
The content of the postmortem in executive summary style
Decisions are not blame
The cost of more reliability as a business decision
Training and skill sets – they matter
Creating future action plans
In-practice – an example of a postmortem
Custom Hat Company postmortem
Impact
Timeline
Technical details and response
Resolution
Future actions
Summary
20
Part 5 - Looking into Future Trends and Preparing for SRE Interviews
Part 5 - Looking into Future Trends and Preparing for SRE Interviews
21
Chapter 16: Chaos Injector – Advanced Systems Stability
Chapter 16: Chaos Injector – Advanced Systems Stability
Technical requirements
Comprehending the wheel-of-misfortune game
Understanding chaos engineering for reliability
In practice – employing the wheel-of-misfortune game
In practice – injecting chaos into systems
Summary
Further reading
22
Chapter 17: Interview Advice – Hiring and Being Hired
Chapter 17: Interview Advice – Hiring and Being Hired
What we’re looking for in a candidate
Common interview questions and answers
Researching the company
Are you over-or under-certified?
Tips for landing the job with a great salary
Summary
23
Index
Index
Why subscribe?
24
Other Books You May Enjoy
Other Books You May Enjoy
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Appendix A – The Site Reliability Engineer Manifesto
Appendix A – The Site Reliability Engineer Manifesto
The manifesto
How to adopt it
How to contribute to it
Appendix B – The 12-Factor App Questionnaire
Appendix B – The 12-Factor App Questionnaire
The questionnaire
How to adopt this questionnaire
How to contribute to this questionnaire

Measuring the downtime with the MTTR

The MTTR is the average amount of time an issue takes to be resolved. It is generated from the average of that time span. The MTTR is often thought of as response time – how effectively a fire department can get to your fire and put it out. It is also the amount of time we are often impacted by each outage, so when the MTTR goes up, we often see a decrease in revenue and customer satisfaction. The MTTR has multiple smaller elements inside of it, each contributing to the overall outage time. Let’s step through a typical outage and quickly examine each of these elements:

  • Detection time: The time between the outage start and when someone noticed it. This often starts with the root cause and measures up until the first person or automated notification says that something is wrong.
  • Notification time: The time it takes between detection and when engineering assets first respond. This could be the time it takes for someone to...
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