Implementing Site Reliability Engineering

Part 1 – Introduction

1. Introduction

2. The Production Environment at Google, From the Viewpoint of an SRE

3. Exercise: Mapping Your Production Environment

Part 2 – Principles

1. Embracing Risk

• Managing Risk
• Measuring Service Risk
• Risk Tolerance of Services
• Motivation for Error Budgets

2. Service-Level Objectives

• Service Level Terminology
• Indicators in Practice
• Objectives in Practice
• Agreements in Practice
• Exercise: Setting Service-Level Objectives

3. Eliminating Toil

• What Is Toil?
• Why Less Toil is Better
• What Qualifies as Engineering?
• Is Toil Always Bad?

4. Monitoring Distributed Systems

• Definitions
• Why Monitor?
• Setting Reasonable Expectations
• Symptoms Versus Causes
• Black Box Versus White Box
• The Four Golden Signals
• Worrying About Your Tail
• Choosing an Appropriate Resolution for Measurements
• As Simple as Possible, No Simpler
• Tying These Principles Together
• Monitoring for the Long Term

5. The Evolution of Automation at Google

• The Value of Automation
• The Value for Google SRE
• Use Cases for Automation
• Automate Yourself Out of a Job
• Soothing the Pain: Applying Automation to Cluster Turnups
• Borg: Birth of the Warehouse-Scale Computer
• Reliability is the Fundamental Feature

6. Release Engineering

• The Role of a Release Engineer
• Philosophy
• Continuous Build and Deployment
• Configuration Management

7. Simplicity

• System Stability Versus Agility
• The Virtue of Boring
• I Won't Give Up My Code!
• The "Negative Lines of Code" Metric
• Minimal APIs
• Modularity
• Release Simplicity

Part 3 – Practices

1. Practical Alerting

• Time-Series Monitoring Outside of Google
• Instrumentation of Applications
• Exporting Variables
• Collection of Exported Data
• Storage in the Time-Series Arena
• Rule Evaluation
• Alerting
• Sharding the Monitoring Topology
• Black-Box Monitoring
• Maintaining the Configuration

2. Being On-Call

• The Life of an On-Call Engineer
• Balanced On-Call
• Feeling Safe
• Avoiding Inappropriate Operational Load

3. Effective Troubleshooting

• Theory
• In Practice
• The Magic of Negative Results
• Making Troubleshooting Easier
• Exercise: Distributed System Troubleshooting

4. Emergency Response

• What to Do When Systems Break
• Test-Induced Emergency
• Challenge-Induced Emergency
• Process-Induced Emergency
• Don't Repeat the Past—Learn From It

5. Managing Incidents

• Unmanaged Incidents
• Managed Incidents
• When to Declare an Incident
• Elements of Incident Management Process

6. Postmortem Culture: Learning from Failure

• Google's Postmortem Philosophy
• Collaborate and Share Knowledge
• Introducing a Postmortem Culture
• Exercise: Blameless Postmortem

7. Tracking Outages

• Escalator
• Outalator

8. Testing for Reliability

• Types of Software Testing
• Creating a Test and Build Environment
• Testing at Scale

9. Software Engineering in SRE

• Why is Software Engineering Within SRE Important?
• Auxon Case Study
• Intent-Based Capacity Planning
• Fostering Software Engineering in SRE

10. Load Balancing at the Front End

• Load Balancing Using DNS
• Load Balancing at the Virtual IP Address

11. Load Balancing in the Datacenter

• Identifying Bad Tasks: Flow Control and Lame Ducks
• Limiting the Connections Pool with Subsetting
• Load-Balancing Policies

12. Handling Overload

• The Pitfalls of "Queries Per Second"
• Per-Customer Limits
• Client-Side Throttling
• Criticality
• Utilization Signals
• Handling Overload Errors
• Load from Connections

13. Addressing Cascading Failures

• Causes of Cascading Failures and Designing to Avoid Them
• Preventing Server Overload
• Slow Startup and Cold Caching
• Triggering Conditions for Cascading Failures
• Testing for Cascading Failures
• Immediate Steps to Address Cascading Failures

14. Managing Critical State: Distributed Consensus for Reliability

• Motivating the Use of Consensus: Distributed Systems Coordination Failure
• How Distributed Consensus Works
• System Architecture Patterns for Distributed Consensus
• Distributed Consensus Performance
• Deploying Distributed Consensus-Based Systems

15. Distributed Periodic Scheduling with Cron

• Cron Jobs and Idempotency
• Cron at Large Scale
• Building Cron at Google

16. Data Processing Pipelines

• Origin of the Pipeline Design Pattern
• Initial Effect of Big Data on the Simple Pipeline Pattern
• Challenges with the Periodic Pipeline Pattern
• Trouble Caused by Uneven Work Distribution
• Drawbacks of Periodic Pipelines in Distributed Environments
• Introduction to Google Workflow
• Stages of Execution in Workflow
• Ensuring Business Continuity

17. Data Integrity: What You Read Is What You Wrote

• Data Integrity's Strict Requirements
• Google SRE Objectives in Maintaining Data Integrity and Availability
• How Google SRE Faces the Challenges of Data Integrity
• 1T Versus 1E: Not "Just" a Bigger Backup
• Knowing that Data Recovery Will Work
• Case Studies
• General Principles of SRE as Applied to Data Integrity

18. Reliable Product Launches at Scale

• Launch Coordination Engineering
• Setting Up a Launch Process
• Developing a Launch Checklist
• Selected Techniques for Reliable Launches
• Development of LCE
• Exercise: Develop a Production Readiness Review

Part 4 – Management

1. Accelerating SREs to On-Call and Beyond

• You've Hired Your Next SRE, Now What?
• Initial Learning Experiences: The Case for Structure Over Chaos
• Creating Stellar Reverse Engineers and Improvisational Thinkers
• Reverse Engineering a Production Service
• Five Practices for Aspiring On-Callers
• On-Call and Beyond: Rites of Passage and Practicing Continuing Education

2. Dealing with Interrupts

• Managing Operational Load
• Factors in Determining How Interrupts Are Handled
• Imperfect Machines

3. Embedding an SRE to Recover from Operational Overload

• Phase 1: Learn the Service and Get Context
• Phase 2: Sharing Context
• Phase 3: Driving Change

4. Communication and Collaboration in SRE

• Communications: Production Meetings
• Collaboration Within SRE
• Case Study: Viceroy
• Collaboration Outside SRE
• Case Study: Migrating DFP to F1

5. The Evolving SRE Engagement Model

• SRE Engagement: What, How, and Why
• The PRR Model
• The SRE Engagement Model
• Production Readiness Reviews: Simple PRR Model
• Evolving the Simple PRR Model: Early Engagement
• Evolving Services Development: Frameworks and SRE Platform

Part 5 – Conclusions

1. Lessons Learned From Other Industries

2. Conclusion

This site reliability engineering training course is perfect for anyone in the IT/SDLC field looking to implement SRE teams and practices in their organization.

Professionals who may benefit include:

• Software Engineers
• Systems Engineers
• Network Engineers
• Technical Program Managers
• Anyone in an IT Leadership role
• CIOs / CTOs
• Anyone involved with IT infrastructure
• IT Operations Staff

• Identify what SRE is and what it is not.
• Compares SRE to DevOps.
• Understand the difference between service-level indicators (SLI), service-level objectives (SLO), and service-level agreements (SLA).
• Develop the technical and professional skills an SRE needs.
• Determine what makes up a good SRE team.
• Practice common ceremonies like blameless postmortems and production readiness reviews.
• Gain an understanding of error budgets and how to calculate reliability costs.
• Embed SREs within development teams to increase operational stability.