Observability Engineering

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• Foreword
• Preface
  • Who This Book Is For
  • Why We Wrote This Book
  • What You Will Learn
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. The Path to Observability
• 1. What Is Observability?
  • The Mathematical Definition of Observability
  • Applying Observability to Software Systems
  • Mischaracterizations About Observability for Software
  • Why Observability Matters Now
    • Is This Really the Best Way?
    • Why Are Metrics and Monitoring Not Enough?
  • Debugging with Metrics Versus Observability
    • The Role of Cardinality
    • The Role of Dimensionality
  • Debugging with Observability
  • Observability Is for Modern Systems
  • Conclusion
• 2. How Debugging Practices Differ Between Observability and Monitoring
  • How Monitoring Data Is Used for Debugging
    • Troubleshooting Behaviors When Using Dashboards
    • The Limitations of Troubleshooting by Intuition
      • Example 1: Insufficient correlation
      • Example 2: Not drilling down
      • Example 3: Tool-hopping
    • Traditional Monitoring Is Fundamentally Reactive
  • How Observability Enables Better Debugging
  • Conclusion
• 3. Lessons from Scaling Without Observability
  • An Introduction to Parse
  • Scaling at Parse
  • The Evolution Toward Modern Systems
  • The Evolution Toward Modern Practices
  • Shifting Practices at Parse
  • Conclusion
• 4. How Observability Relates to DevOps, SRE, and Cloud Native
  • Cloud Native, DevOps, and SRE in a Nutshell
  • Observability: Debugging Then Versus Now
  • Observability Empowers DevOps and SRE Practices
  • Conclusion
• II. Fundamentals of Observability
• 5. Structured Events Are the Building Blocks of Observability
  • Debugging with Structured Events
  • The Limitations of Metrics as a Building Block
  • The Limitations of Traditional Logs as a Building Block
    • Unstructured Logs
    • Structured Logs
  • Properties of Events That Are Useful in Debugging
  • Conclusion
• 6. Stitching Events into Traces
  • Distributed Tracing and Why It Matters Now
  • The Components of Tracing
  • Instrumenting a Trace the Hard Way
  • Adding Custom Fields into Trace Spans
  • Stitching Events into Traces
  • Conclusion
• 7. Instrumentation with OpenTelemetry
  • A Brief Introduction to Instrumentation
  • Open Instrumentation Standards
  • Instrumentation Using Code-Based Examples
    • Start with Automatic Instrumentation
    • Add Custom Instrumentation
      • Starting and finishing trace spans
      • Adding wide fields to an event
      • Recording process-wide metrics
    • Send Instrumentation Data to a Backend System
  • Conclusion
• 8. Analyzing Events to Achieve Observability
  • Debugging from Known Conditions
  • Debugging from First Principles
    • Using the Core Analysis Loop
    • Automating the Brute-Force Portion of the Core Analysis Loop
  • This Misleading Promise of AIOps
  • Conclusion
• 9. How Observability and Monitoring Come Together
  • Where Monitoring Fits
  • Where Observability Fits
  • System Versus Software Considerations
  • Assessing Your Organizational Needs
    • Exceptions: Infrastructure Monitoring That Cant Be Ignored
    • Real-World Examples
  • Conclusion
• III. Observability for Teams
• 10. Applying Observability Practices in Your Team
  • Join a Community Group
  • Start with the Biggest Pain Points
  • Buy Instead of Build
  • Flesh Out Your Instrumentation Iteratively
  • Look for Opportunities to Leverage Existing Efforts
  • Prepare for the Hardest Last Push
  • Conclusion
• 11. Observability-Driven Development
  • Test-Driven Development
  • Observability in the Development Cycle
  • Determining Where to Debug
  • Debugging in the Time of Microservices
  • How Instrumentation Drives Observability
  • Shifting Observability Left
  • Using Observability to Speed Up Software Delivery
  • Conclusion
• 12. Using Service-Level Objectives for Reliability
  • Traditional Monitoring Approaches Create Dangerous Alert Fatigue
  • Threshold Alerting Is for Known-Unknowns Only
  • User Experience Is a North Star
  • What Is a Service-Level Objective?
    • Reliable Alerting with SLOs
    • Changing Culture Toward SLO-Based Alerts: A Case Study
  • Conclusion
• 13. Acting on and Debugging SLO-Based Alerts
  • Alerting Before Your Error Budget Is Empty
  • Framing Time as a Sliding Window
  • Forecasting to Create a Predictive Burn Alert
    • The Lookahead Window
      • Extrapolating the future from current burn rate
      • Short-term burn alerts
      • Context-aware burn alerts
    • The Baseline Window
    • Acting on SLO Burn Alerts
  • Using Observability Data for SLOs Versus Time-Series Data
  • Conclusion
• 14. Observability and the Software Supply Chain
  • Why Slack Needed Observability
  • Instrumentation: Shared Client Libraries and Dimensions
  • Case Studies: Operationalizing the Supply Chain
    • Understanding Context Through Tooling
    • Embedding Actionable Alerting
    • Understanding What Changed
  • Conclusion
• IV. Observability at Scale
• 15. Build Versus Buy and Return on Investment
  • How to Analyze the ROI of Observability
  • The Real Costs of Building Your Own
    • The Hidden Costs of Using Free Software
    • The Benefits of Building Your Own
    • The Risks of Building Your Own
  • The Real Costs of Buying Software
    • The Hidden Financial Costs of Commercial Software
    • The Hidden Nonfinancial Costs of Commercial Software
    • The Benefits of Buying Commercial Software
    • The Risks of Buying Commercial Software
  • Buy Versus Build Is Not a Binary Choice
  • Conclusion
• 16. Efficient Data Storage
  • The Functional Requirements for Observability
    • Time-Series Databases Are Inadequate for Observability
    • Other Possible Data Stores
    • Data Storage Strategies
  • Case Study: The Implementation of Honeycombs Retriever
    • Partitioning Data by Time
    • Storing Data by Column Within Segments
    • Performing Query Workloads
    • Querying for Traces
    • Querying Data in Real Time
    • Making It Affordable with Tiering
    • Making It Fast with Parallelism
    • Dealing with High Cardinality
    • Scaling and Durability Strategies
    • Notes on Building Your Own Efficient Data Store
  • Conclusion
• 17. Cheap and Accurate Enough: Sampling
  • Sampling to Refine Your Data Collection
  • Using Different Approaches to Sampling
    • Constant-Probability Sampling
    • Sampling on Recent Traffic Volume
    • Sampling Based on Event Content (Keys)
    • Combining per Key and Historical Methods
    • Choosing Dynamic Sampling Options
    • When to Make a Sampling Decision for Traces
  • Translating Sampling Strategies into Code
    • The Base Case
    • Fixed-Rate Sampling
    • Recording the Sample Rate
    • Consistent Sampling
    • Target Rate Sampling
    • Having More Than One Static Sample Rate
    • Sampling by Key and Target Rate
    • Sampling with Dynamic Rates on Arbitrarily Many Keys
    • Putting It All Together: Head and Tail per Key Target Rate Sampling
  • Conclusion
• 18. Telemetry Management with Pipelines
  • Attributes of Telemetry Pipelines
    • Routing
    • Security and Compliance
    • Workload Isolation
    • Data Buffering
    • Capacity Management
      • Rate limiting
      • Sampling
      • Queuing
    • Data Filtering and Augmentation
    • Data Transformation
    • Ensuring Data Quality and Consistency
  • Managing a Telemetry Pipeline: Anatomy
  • Challenges When Managing a Telemetry Pipeline
    • Performance
    • Correctness
    • Availability
    • Reliability
    • Isolation
    • Data Freshness
  • Use Case: Telemetry Management at Slack
    • Metrics Aggregation
    • Logs and Trace Events
  • Open Source Alternatives
  • Managing a Telemetry Pipeline: Build Versus Buy
  • Conclusion
• V. Spreading Observability Culture
• 19. The Business Case for Observability
  • The Reactive Approach to Introducing Change
  • The Return on Investment of Observability
  • The Proactive Approach to Introducing Change
  • Introducing Observability as a Practice
  • Using the Appropriate Tools
    • Instrumentation
    • Data Storage and Analytics
    • Rolling Out Tools to Your Teams
  • Knowing When You Have Enough Observability
  • Conclusion
• 20. Observabilitys Stakeholders and Allies
  • Recognizing Nonengineering Observability Needs
  • Creating Observability Allies in Practice
    • Customer Support Teams
    • Customer Success and Product Teams
    • Sales and Executive Teams
  • Using Observability Versus Business Intelligence Tools
    • Query Execution Time
    • Accuracy
    • Recency
    • Structure
    • Time Windows
    • Ephemerality
  • Using Observability and BI Tools Together in Practice
  • Conclusion
• 21. An Observability Maturity Model
  • A Note About Maturity Models
  • Why Observability Needs a Maturity Model
  • About the Observability Maturity Model
  • Capabilities Referenced in the OMM
    • Respond to System Failure with Resilience
      • If your team is doing well
      • If your team is doing poorly
      • How observability is related
    • Deliver High-Quality Code
      • If your team is doing well
      • If your team is doing poorly
      • How observability is related
    • Manage Complexity and Technical Debt
      • If your team is doing well
      • If your team is doing poorly
      • How observability is related
    • Release on a Predictable Cadence
      • If your team is doing well
      • If your team is doing poorly
      • How observability is related
    • Understand User Behavior
      • If your team is doing well
      • If your team is doing poorly
      • How observability is related
  • Using the OMM for Your Organization
  • Conclusion
• 22. Where to Go from Here
  • Observability, Then Versus Now
  • Additional Resources
  • Predictions for Where Observability Is Going
• Index

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