High Performance SRE : Automation, error budgeting, RPAs, SLOs, and SLAs with site reliability engineering

Table of contents :
Cover
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgement
Preface
Table of Contents
1. Introduction to Site Reliability Engineer
Introduction
Structure
Objectives
Historical context and origin of the SRE role
Type of DevOps teams in different companies
Roles and responsibilities of SRE
Bridging the gap between development and operations
Maintaining system and service reliability
Importance of SRE in the modern tech ecosystem
Skills and knowledge for SRE
Necessary technical skills
Soft skill requirements
Culture of SREs and DevOps
Understanding DevOps
SRE’s role in promoting the DevOps culture
Effect on the process of making and delivering software
Importance of SRE in the digital age
Effect of service downtime on businesses
SRE’s role in reducing and preventing downtime
Prospects and developments for SREs in the future
Career path and professional development
Starting point and prerequisites for becoming an SRE
Continuous learning and upskilling
Career progression for SREs
Evolving SRE role
Conclusion
Multiple choice questions
Answers
2. DevOps to Site Reliability Engineering
Introduction
Structure
Objectives
DevOps to site reliability engineering
Need for site reliability engineering
Site reliability engineering team structure
Site reliability engineering discipline
Unspoken commitments
Site reliability engineering engagement model
Site reliability engineering implements DevOps
Site reliability engineering strategy adoption
Site reliability engineering challenges
Site reliability engineering best practices
Site reliability engineering best practices tools
Conclusion
Multiple choice questions
Answers
3. Monitoring
Introduction
Structure
Objectives
Need for monitoring
Pillars of monitoring
Latency
Errors
Saturation
Threshold monitoring
Monitoring and observability
Application monitoring
Monitoring best practices
Examples of monitoring and observability tools
Conclusion
Multiple choice questions
Answers
4. Incident Management and Risk Mitigation
Introduction
Structure
Objectives
Purpose of incident management
More about software risks
Incident prioritization
Incident severity level
Use of severity level
Difference between severity and priority
Defining incident severity levels
Incident response planning
Risks to consider
Analyzing the risks
Production incident lifecycle
Cost of reliability
Response plan
Best practices to reduce production incidents
Risk and mitigation
Best practices for risk mitigation
Conclusion
Multiple choice questions
Answers
5. Error Budgets
Introduction
Structure
Objectives
Purpose of error budgets
Defining error budgets
Error budget equation
Prioritizing development over end-user experience
Relation of error budgets with SLI and SLO
Benefits to setting the proper error budgets
Outage policies
Action items if the error budget is exceeded
Best practices to get the correct error budgets
Conclusion
Multiple choice questions
Answers
6. SLI/SLO/SLA
Introduction
Structure
Objectives
Introduction to service level management
Overview of service level management
Key components of SLM: SLI, SLO, and SLA
Benefits of implementing an SLM program
Understanding service level indicators
Purpose of SLIs
Types of SLIs and their use cases
Key features of selecting appropriate SLI
Importance of SLIs
Setting service level objectives
Purpose of SLOs
Setting up appropriate SLOs
Creating service level agreements
Purpose of SLAs
Components of SLA
Negotiations of SLA
Implementing and managing the SLM program
Steps for implementing the SLM program
Best practices for managing SLIs, SLOs, and SLAs
Common challenges in setting up correct SLA
Role of technology in automating SLM
Case studies and real-world examples
Netflix
Adobe
LinkedIn
Conclusion
Multiple choice questions
Answers
7. Capacity Planning
Introduction
Structure
Objectives
Importance of capacity planning
Principles of capacity management
Understanding resource requirements
Identifying key resources
Analyzing historical usage data
Forecasting future usage patterns
Capacity analysis
Capacity analysis to determine workload resources
Trade-offs between performance, availability, and cost
Scaling strategies
Choosing the right scaling strategy
Considerations for auto-scaling and load balancing
Monitoring and alerting
Setting up monitoring tools
Defining alerting thresholds for key metrics
Strategies for proactive capacity planning
Capacity planning in the cloud
Understanding cloud resource allocation
Leveraging cloud provider tools
Capacity planning for disaster recovery
Disaster recovery capacity needs
Developing disaster recovery capacity plans
Disaster recovery plans and capacity
Conclusion
Multiple choice questions
Answers
8. On-call and First-response
Introduction
Structure
Objectives
Understanding on-call
Types of on-call rotations
Key responsibilities of on-call engineers
First response processes
Common steps in first response processes
Best practices for first response
Preparing for on-call and first-response
Importance of proactive preparation
Key tools and resources for on-call engineers
Strategies for reducing stress and avoiding burnout
Communicating during incidents
Importance of effective communication
Best practices for communicating with stakeholders
Tools for effective incident communication
Incident review and post-mortems
Incidents and post-mortems
Common post-mortem processes and best practices
Preventing incidents with post-mortems
Case studies
Google
Amazon
Atlassian
Netflix
Conclusion
Multiple choice questions
Answers
9. RCA and Post-mortem
Introduction
Structure
Objectives
Root cause analysis
Understanding the RCA process
Problem identification
Data collection
Root cause identification
Implementing solutions
Reviewing the efficiency of the solutions
Various methods of RCA
The five whys
Fishbone/Ishikawa diagrams
Fault tree analysis
Role of RCA in problem-solving and actions
Post-mortem
How to conduct a post-mortem
Gathering data and information
Analyzing the incident
Identifying actions for improvement
Implementing changes
Role of a blameless post-mortem
Role of post-mortem in learning and improvement
Real-world examples of effective post-mortems
Challenges and pitfalls in conducting post-mortems
Relationship between RCA and post-mortem
RCA feeds into the post-mortem process
RCA and post-mortem: Synergies and differences
Optimizing incident management
Future trends
Applying AI and ML to RCA and post mortem
Post-mortem best practices
Conclusion
Multiple choice questions
Answers
10. Chaos Engineering
Introduction
Structure
Objectives
Principles of chaos engineering
Building a hypothesis
Introducing real-world events
Observing the system
Verifying the hypothesis
Incremental complexity
Role of chaos engineering in SRE
Key concepts in chaos engineering
Blast radius
Failure injection
Steady-state
Observability and monitoring
Chaos experiments
Game days
Preparing for chaos engineering
Setting objectives and metrics
Building an observability infrastructure
Establishing a strong incident response strategy
Implementing chaos testing
Tools and technologies for chaos engineering
Chaos toolkit
Gremlin
Chaos Monkey
Case studies on chaos engineering
Netflix
Amazon
Google
Future of chaos engineering
Conclusion
Multiple choice questions
Answers
11. Artificial Intelligence for Site Reliability Engineering
Introduction
Structure
Objectives
Role of AI in transforming SRE processes
Automated testing and quality assurance
Role of AI in test case generation and automation
Role of AI in testing
Intelligent debugging
AI techniques for code analysis and issue identification
Real-time insights and suggestions for issue resolution
Impact of intelligent debugging on system stability
Predictive maintenance
AI for maintenance and upgrades
Predicting potential failures and resource depletion
Predictive maintenance and resource optimization
Code generation and augmentation
Code snippets and faster development
AI-assisted code review for improved code quality
Enhanced development and coding practices
Performance optimization
Monitoring and analysis
Bottleneck detection and root cause analysis
Automated performance tuning
Predictive and adaptive scaling
User experience optimization
Anomaly detection and security
AI for anomaly detection
Leveraging AI to prevent security threats
Enhancing system security and maintaining data
Continuous integration and deployment
Automation of CI/CD processes using AI
AI-driven code analysis and release management
Software delivery and development
Natural language processing for SRE
Role of NLP in processing requirements
Tools for requirement analysis
Sentiment analysis and user feedback
Future trends and challenges
Potential challenges and ethical considerations
Future of AI in SRE
Conclusion
Multiple choice questions
Answers
12. Case Studies
Introduction
Structure
Objectives
Google
Background and difficulties
Google’s software reliability engineering model
Netflix
Background and difficulties
Netflix’s software reliability engineering methodology
Core ideas of Netflix’s SRE strategy
Spotify
Background and difficulties
Spotify’s software reliability engineering approach
LinkedIn
Background and difficulties
Journey of LinkedIn’s software reliability engineering
Amazon
Background and challenges
SRE at Amazon
Conclusion
Index