Observability Engineering Achieving Production Excellence 1st Edition by Charity Majors, Liz Fong Jones, George Miranda ISBN 9781492076445 1492076449

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ISBN 10: 1492076449
ISBN 13: 9781492076445
Author: Charity Majors, Liz Fong Jones, George Miranda

Observability Engineering Achieving Production Excellence 1st Edition Table of contents:

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

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

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 Can’t 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

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 Honeycomb’s 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

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. Observability’s 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

Deliver High-Quality Code

Manage Complexity and Technical Debt

Release on a Predictable Cadence

Understand User Behavior

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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Tags: Charity Majors, Liz Fong Jones, George Miranda, Observability Engineering, Production Excellence