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Spis treści

Site Reliability Engineering. How Google Runs Production Systems eBook -- spis treści

• Foreword
• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
• I. Introduction
• 1. Introduction
  • The Sysadmin Approach to Service Management
  • Googles Approach to Service Management: Site Reliability Engineering
  • Tenets of SRE
    • Ensuring a Durable Focus on Engineering
    • Pursuing Maximum Change Velocity Without Violating a Services SLO
    • Monitoring
    • Emergency Response
    • Change Management
    • Demand Forecasting and Capacity Planning
    • Provisioning
    • Efficiency and Performance
  • The End of the Beginning
• 2. The Production Environment at Google, from the Viewpoint of an SRE
  • Hardware
  • System Software That Organizes the Hardware
    • Managing Machines
    • Storage
    • Networking
  • Other System Software
    • Lock Service
    • Monitoring and Alerting
  • Our Software Infrastructure
  • Our Development Environment
  • Shakespeare: A Sample Service
    • Life of a Request
    • Job and Data Organization
• II. Principles
• 3. Embracing Risk
  • Managing Risk
  • Measuring Service Risk
  • Risk Tolerance of Services
    • Identifying the Risk Tolerance of Consumer Services
      • Target level of availability
      • Types of failures
      • Cost
      • Other service metrics
    • Identifying the Risk Tolerance of Infrastructure Services
      • Target level of availability
      • Types of failures
      • Cost
      • Example: Frontend infrastructure
  • Motivation for Error Budgets
    • Forming Your Error Budget
    • Benefits
• 4. Service Level Objectives
  • Service Level Terminology
    • Indicators
    • Objectives
    • Agreements
  • Indicators in Practice
    • What Do You and Your Users Care About?
    • Collecting Indicators
    • Aggregation
    • Standardize Indicators
  • Objectives in Practice
    • Defining Objectives
    • Choosing Targets
    • Control Measures
    • SLOs Set Expectations
  • Agreements in Practice
• 5. Eliminating Toil
  • Toil Defined
  • Why Less Toil Is Better
  • What Qualifies as Engineering?
  • Is Toil Always Bad?
  • Conclusion
• 6. Monitoring Distributed Systems
  • Definitions
  • Why Monitor?
  • Setting Reasonable Expectations for Monitoring
  • Symptoms Versus Causes
  • Black-Box Versus White-Box
  • The Four Golden Signals
  • Worrying About Your Tail (or, Instrumentation and Performance)
  • Choosing an Appropriate Resolution for Measurements
  • As Simple as Possible, No Simpler
  • Tying These Principles Together
  • Monitoring for the Long Term
    • Bigtable SRE: A Tale of Over-Alerting
    • Gmail: Predictable, Scriptable Responses from Humans
    • The Long Run
  • Conclusion
• 7. The Evolution of Automation at Google
  • The Value of Automation
    • Consistency
    • A Platform
    • Faster Repairs
    • Faster Action
    • Time Saving
  • The Value for Google SRE
  • The Use Cases for Automation
    • Google SREs Use Cases for Automation
    • A Hierarchy of Automation Classes
  • Automate Yourself Out of a Job: Automate ALL the Things!
  • Soothing the Pain: Applying Automation to Cluster Turnups
    • Detecting Inconsistencies with Prodtest
    • Resolving Inconsistencies Idempotently
    • The Inclination to Specialize
    • Service-Oriented Cluster-Turnup
  • Borg: Birth of the Warehouse-Scale Computer
  • Reliability Is the Fundamental Feature
  • Recommendations
• 8. Release Engineering
  • The Role of a Release Engineer
  • Philosophy
    • Self-Service Model
    • High Velocity
    • Hermetic Builds
    • Enforcement of Policies and Procedures
  • Continuous Build and Deployment
    • Building
    • Branching
    • Testing
    • Packaging
    • Rapid
    • Deployment
  • Configuration Management
  • Conclusions
    • Its Not Just for Googlers
    • Start Release Engineering at the Beginning
• 9. Simplicity
  • System Stability Versus Agility
  • The Virtue of Boring
  • I Wont Give Up My Code!
  • The Negative Lines of Code Metric
  • Minimal APIs
  • Modularity
  • Release Simplicity
  • A Simple Conclusion
• III. Practices
• 10. Practical Alerting from Time-Series Data
  • The Rise of Borgmon
  • Instrumentation of Applications
  • Collection of Exported Data
  • Storage in the Time-Series Arena
    • Labels and Vectors
  • Rule Evaluation
  • Alerting
  • Sharding the Monitoring Topology
  • Black-Box Monitoring
  • Maintaining the Configuration
  • Ten Years On
• 11. Being On-Call
  • Introduction
  • Life of an On-Call Engineer
  • Balanced On-Call
    • Balance in Quantity
    • Balance in Quality
    • Compensation
  • Feeling Safe
  • Avoiding Inappropriate Operational Load
    • Operational Overload
    • A Treacherous Enemy: Operational Underload
  • Conclusions
• 12. Effective Troubleshooting
  • Theory
  • In Practice
    • Problem Report
    • Triage
    • Examine
    • Diagnose
      • Simplify and reduce
      • Ask what, where, and why
      • What touched it last
      • Specific diagnoses
    • Test and Treat
  • Negative Results Are Magic
    • Cure
  • Case Study
  • Making Troubleshooting Easier
  • Conclusion
• 13. Emergency Response
  • What to Do When Systems Break
  • Test-Induced Emergency
    • Details
    • Response
    • Findings
      • What went well
      • What we learned
  • Change-Induced Emergency
    • Details
    • Response
    • Findings
      • What went well
      • What we learned
  • Process-Induced Emergency
    • Details
    • Response
    • Findings
      • What went well
      • What we learned
  • All Problems Have Solutions
  • Learn from the Past. Dont Repeat It.
    • Keep a History of Outages
    • Ask the Big, Even Improbable, Questions: What If?
    • Encourage Proactive Testing
  • Conclusion
• 14. Managing Incidents
  • Unmanaged Incidents
  • The Anatomy of an Unmanaged Incident
    • Sharp Focus on the Technical Problem
    • Poor Communication
    • Freelancing
  • Elements of Incident Management Process
    • Recursive Separation of Responsibilities
    • A Recognized Command Post
    • Live Incident State Document
    • Clear, Live Handoff
  • A Managed Incident
  • When to Declare an Incident
  • In Summary
• 15. Postmortem Culture: Learning from Failure
  • Googles Postmortem Philosophy
  • Collaborate and Share Knowledge
  • Introducing a Postmortem Culture
  • Conclusion and Ongoing Improvements
• 16. Tracking Outages
  • Escalator
  • Outalator
    • Aggregation
    • Tagging
    • Analysis
      • Reporting and communication
    • Unexpected Benefits
• 17. Testing for Reliability
  • Types of Software Testing
    • Traditional Tests
      • Unit tests
      • Integration tests
      • System tests
    • Production Tests
      • Configuration test
      • Stress test
      • Canary test
  • Creating a Test and Build Environment
  • Testing at Scale
    • Testing Scalable Tools
    • Testing Disaster
    • The Need for Speed
    • Pushing to Production
    • Expect Testing Fail
    • Integration
    • Production Probes
  • Conclusion
• 18. Software Engineering in SRE
  • Why Is Software Engineering Within SRE Important?
  • Auxon Case Study: Project Background and Problem Space
    • Traditional Capacity Planning
      • Brittle by nature
      • Laborious and imprecise
    • Our Solution: Intent-Based Capacity Planning
  • Intent-Based Capacity Planning
    • Precursors to Intent
      • Dependencies
      • Performance metrics
      • Prioritization
    • Introduction to Auxon
    • Requirements and Implementation: Successes and Lessons Learned
      • Approximation
    • Raising Awareness and Driving Adoption
      • Set expectations
      • Identify appropriate customers
      • Customer service
      • Designing at the right level
    • Team Dynamics
  • Fostering Software Engineering in SRE
    • Successfully Building a Software Engineering Culture in SRE: Staffing and Development Time
    • Getting There
  • Conclusions
• 19. Load Balancing at the Frontend
  • Power Isnt the Answer
  • Load Balancing Using DNS
  • Load Balancing at the Virtual IP Address
• 20. Load Balancing in the Datacenter
  • The Ideal Case
  • Identifying Bad Tasks: Flow Control and Lame Ducks
    • A Simple Approach to Unhealthy Tasks: Flow Control
    • A Robust Approach to Unhealthy Tasks: Lame Duck State
  • Limiting the Connections Pool with Subsetting
    • Picking the Right Subset
    • A Subset Selection Algorithm: Random Subsetting
    • A Subset Selection Algorithm: Deterministic Subsetting
  • Load Balancing Policies
    • Simple Round Robin
      • Small subsetting
      • Varying query costs
      • Machine diversity
      • Unpredictable performance factors
    • Least-Loaded Round Robin
    • Weighted Round Robin
• 21. Handling Overload
  • The Pitfalls of Queries per Second
  • Per-Customer Limits
  • Client-Side Throttling
  • Criticality
  • Utilization Signals
  • Handling Overload Errors
    • Deciding to Retry
  • Load from Connections
  • Conclusions
• 22. Addressing Cascading Failures
  • Causes of Cascading Failures and Designing to Avoid Them
    • Server Overload
    • Resource Exhaustion
      • CPU
      • Memory
      • Threads
      • File descriptors
      • Dependencies among resources
    • Service Unavailability
  • Preventing Server Overload
    • Queue Management
    • Load Shedding and Graceful Degradation
    • Retries
    • Latency and Deadlines
      • Picking a deadline
      • Missing deadlines
      • Deadline propagation
      • Bimodal latency
  • Slow Startup and Cold Caching
    • Always Go Downward in the Stack
  • Triggering Conditions for Cascading Failures
    • Process Death
    • Process Updates
    • New Rollouts
    • Organic Growth
    • Planned Changes, Drains, or Turndowns
      • Request profile changes
      • Resource limits
  • Testing for Cascading Failures
    • Test Until Failure and Beyond
    • Test Popular Clients
    • Test Noncritical Backends
  • Immediate Steps to Address Cascading Failures
    • Increase Resources
    • Stop Health Check Failures/Deaths
    • Restart Servers
    • Drop Traffic
    • Enter Degraded Modes
    • Eliminate Batch Load
    • Eliminate Bad Traffic
  • Closing Remarks
• 23. Managing Critical State: Distributed Consensus for Reliability
  • Motivating the Use of Consensus: Distributed Systems Coordination Failure
    • Case Study 1: The Split-Brain Problem
    • Case Study 2: Failover Requires Human Intervention
    • Case Study 3: Faulty Group-Membership Algorithms
  • How Distributed Consensus Works
    • Paxos Overview: An Example Protocol
  • System Architecture Patterns for Distributed Consensus
    • Reliable Replicated State Machines
    • Reliable Replicated Datastores and Configuration Stores
    • Highly Available Processing Using Leader Election
    • Distributed Coordination and Locking Services
    • Reliable Distributed Queuing and Messaging
  • Distributed Consensus Performance
    • Multi-Paxos: Detailed Message Flow
    • Scaling Read-Heavy Workloads
    • Quorum Leases
    • Distributed Consensus Performance and Network Latency
    • Reasoning About Performance: Fast Paxos
    • Stable Leaders
    • Batching
    • Disk Access
  • Deploying Distributed Consensus-Based Systems
    • Number of Replicas
    • Location of Replicas
    • Capacity and Load Balancing
      • Quorum composition
  • Monitoring Distributed Consensus Systems
  • Conclusion
• 24. Distributed Periodic Scheduling with Cron
  • Cron
    • Introduction
    • Reliability Perspective
  • Cron Jobs and Idempotency
  • Cron at Large Scale
    • Extended Infrastructure
    • Extended Requirements
  • Building Cron at Google
    • Tracking the State of Cron Jobs
    • The Use of Paxos
    • The Roles of the Leader and the Follower
      • The leader
      • The follower
      • Resolving partial failures
    • Storing the State
    • Running Large Cron
  • Summary
• 25. 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
    • Monitoring Problems in Periodic Pipelines
    • Thundering Herd Problems
    • Moiré Load Pattern
  • Introduction to Google Workflow
    • Workflow as Model-View-Controller Pattern
  • Stages of Execution in Workflow
    • Workflow Correctness Guarantees
  • Ensuring Business Continuity
  • Summary and Concluding Remarks
• 26. Data Integrity: What You Read Is What You Wrote
  • Data Integritys Strict Requirements
    • Choosing a Strategy for Superior Data Integrity
    • Backups Versus Archives
    • Requirements of the Cloud Environment in Perspective
  • Google SRE Objectives in Maintaining Data Integrity and Availability
    • Data Integrity Is the Means; Data Availability Is the Goal
    • Delivering a Recovery System, Rather Than a Backup System
    • Types of Failures That Lead to Data Loss
    • Challenges of Maintaining Data Integrity Deep and Wide
      • Scaling issues: Fulls, incrementals, and the competing forces of backups and restores
      • Retention
  • How Google SRE Faces the Challenges of Data Integrity
    • The 24 Combinations of Data Integrity Failure Modes
    • First Layer: Soft Deletion
    • Second Layer: Backups and Their Related Recovery Methods
    • Overarching Layer: Replication
    • 1T Versus 1E: Not Just a Bigger Backup
    • Third Layer: Early Detection
      • Challenges faced by cloud developers
      • Out-of-band data validation
    • Knowing That Data Recovery Will Work
  • Case Studies
    • GmailFebruary, 2011: Restore from GTape
      • Sunday, February 27, 2011, late in the evening
    • Google MusicMarch 2012: Runaway Deletion Detection
      • Tuesday, March 6th, 2012, mid-afternoon
      • Discovering the problem
      • Assessing the damage
      • Resolving the issue
        • Parallel bug identification and recovery efforts
        • First wave of recovery
        • Second wave of recovery
      • Addressing the root cause
  • General Principles of SRE as Applied to Data Integrity
    • Beginners Mind
    • Trust but Verify
    • Hope Is Not a Strategy
    • Defense in Depth
  • Conclusion
• 27. Reliable Product Launches at Scale
  • Launch Coordination Engineering
    • The Role of the Launch Coordination Engineer
  • Setting Up a Launch Process
    • The Launch Checklist
    • Driving Convergence and Simplification
    • Launching the Unexpected
  • Developing a Launch Checklist
    • Architecture and Dependencies
      • Example checklist questions
      • Example action items
    • Integration
      • Example action items
    • Capacity Planning
      • Example checklist questions
    • Failure Modes
      • Example checklist questions
      • Example action items
    • Client Behavior
      • Example checklist question
      • Example action items
    • Processes and Automation
      • Example checklist question
      • Example action items
    • Development Process
      • Example action items
    • External Dependencies
      • Example checklist questions
    • Rollout Planning
      • Example action items
  • Selected Techniques for Reliable Launches
    • Gradual and Staged Rollouts
    • Feature Flag Frameworks
    • Dealing with Abusive Client Behavior
    • Overload Behavior and Load Tests
  • Development of LCE
    • Evolution of the LCE Checklist
    • Problems LCE Didnt Solve
      • Scalability changes
      • Growing operational load
      • Infrastructure churn
  • Conclusion
• IV. Management
• 28. Accelerating SREs to On-Call and Beyond
  • Youve Hired Your Next SRE(s), Now What?
  • Initial Learning Experiences: The Case for Structure Over Chaos
    • Learning Paths That Are Cumulative and Orderly
    • Targeted Project Work, Not Menial Work
  • Creating Stellar Reverse Engineers and Improvisational Thinkers
    • Reverse Engineers: Figuring Out How Things Work
    • Statistical and Comparative Thinkers: Stewards of the Scientific Method Under Pressure
    • Improv Artists: When the Unexpected Happens
    • Tying This Together: Reverse Engineering a Production Service
  • Five Practices for Aspiring On-Callers
    • A Hunger for Failure: Reading and Sharing Postmortems
    • Disaster Role Playing
    • Break Real Things, Fix Real Things
    • Documentation as Apprenticeship
    • Shadow On-Call Early and Often
  • On-Call and Beyond: Rites of Passage, and Practicing Continuing Education
  • Closing Thoughts
• 29. Dealing with Interrupts
  • Managing Operational Load
  • Factors in Determining How Interrupts Are Handled
  • Imperfect Machines
    • Cognitive Flow State
      • Cognitive flow state: Creative and engaged
      • Cognitive flow state: Angry Birds
    • Do One Thing Well
      • Distractibility
      • Polarizing time
    • Seriously, Tell Me What to Do
      • General suggestions
      • On-call
      • Tickets
      • Ongoing responsibilities
      • Be on interrupts, or dont be
    • Reducing Interrupts
      • Actually analyze tickets
      • Respect yourself, as well as your customers
• 30. Embedding an SRE to Recover from Operational Overload
  • Phase 1: Learn the Service and Get Context
    • Identify the Largest Sources of Stress
    • Identify Kindling
  • Phase 2: Sharing Context
    • Write a Good Postmortem for the Team
    • Sort Fires According to Type
  • Phase 3: Driving Change
    • Start with the Basics
    • Get Help Clearing Kindling
    • Explain Your Reasoning
    • Ask Leading Questions
  • Conclusion
• 31. Communication and Collaboration in SRE
  • Communications: Production Meetings
    • Agenda
    • Attendance
  • Collaboration within SRE
    • Team Composition
    • Techniques for Working Effectively
  • Case Study of Collaboration in SRE: Viceroy
    • The Coming of the Viceroy
    • Challenges
    • Recommendations
  • Collaboration Outside SRE
  • Case Study: Migrating DFP to F1
  • Conclusion
• 32. The Evolving SRE Engagement Model
  • SRE Engagement: What, How, and Why
  • The PRR Model
  • The SRE Engagement Model
    • Alternative Support
      • Documentation
      • Consultation
  • Production Readiness Reviews: Simple PRR Model
    • Engagement
    • Analysis
    • Improvements and Refactoring
    • Training
    • Onboarding
    • Continuous Improvement
  • Evolving the Simple PRR Model: Early Engagement
    • Candidates for Early Engagement
    • Benefits of the Early Engagement Model
      • Design phase
      • Build and implementation
      • Launch
      • Post-launch
      • Disengaging from a service
  • Evolving Services Development: Frameworks and SRE Platform
    • Lessons Learned
    • External Factors Affecting SRE
    • Toward a Structural Solution: Frameworks
    • New Service and Management Benefits
      • Significantly lower operational overhead
      • Universal support by design
      • Faster, lower overhead engagements
      • A new engagement model based on shared responsibility
  • Conclusion
• V. Conclusions
• 33. Lessons Learned from Other Industries
  • Meet Our Industry Veterans
  • Preparedness and Disaster Testing
    • Relentless Organizational Focus on Safety
    • Attention to Detail
    • Swing Capacity
    • Simulations and Live Drills
    • Training and Certification
    • Focus on Detailed Requirements Gathering and Design
    • Defense in Depth and Breadth
  • Postmortem Culture
  • Automating Away Repetitive Work and Operational Overhead
  • Structured and Rational Decision Making
  • Conclusions
• 34. Conclusion
• A. Availability Table
• B. A Collection of Best Practices for Production Services
  • Fail Sanely
  • Progressive Rollouts
  • Define SLOs Like a User
  • Error Budgets
  • Monitoring
  • Postmortems
  • Capacity Planning
  • Overloads and Failure
  • SRE Teams
• C. Example Incident State Document
• D. Example Postmortem
  • Lessons Learned
    • What went well
    • What went wrong
    • Where we got lucky
  • Timeline
  • Supporting information:
• E. Launch Coordination Checklist
• F. Example Production Meeting Minutes
• Bibliography
• Index