liveBook · Manning

What you will learn in Cloud Observability in Action

by Michael Hausenblas

Chapter by chapter

Chapter 1: End-to-end observability

1.1 What is observability? · 1.2 Observability use cases · 1.3 Roles and goals · 1.4 Example microservices app · 1.5 Challenges and how observability helps · 1.5.1 Return on investment · 1.5.2 Signal correlation · 1.5.3 Portability · 1.6 Summary

What you learn in chapter 1 →

Chapter 2: Signal types

2.1 Reference example · 2.2 Assessing instrumentation costs · 2.3 Logs · 2.3.1 Instrumentation · 2.3.2 Telemetry · 2.3.3 Costs and benefits · 2.3.4 Observability with logs · 2.4 Metrics · 2.4.1 Instrumentation · 2.4.2 Telemetry · 2.4.3 Costs and benefits · 2.4.4 Observability with metrics · 2.5 Traces · 2.5.1 Instrumentation · 2.5.2 Telemetry · 2.5.3 Costs and benefits · 2.5.4 Observability with traces · 2.6 Selecting signals · 2.7 Summary

What you learn in chapter 2 →

Chapter 3: Sources

3.1 Selecting sources · 3.2 Compute-related sources · 3.2.1 Basics · 3.2.2 Containers · 3.2.3 Kubernetes · 3.2.4 Serverless compute · 3.3 Storage-related sources · 3.3.1 Relational databases and NoSQL data stores · 3.3.2 File systems and object stores · 3.4 Network-related sources · 3.4.1 Network interfaces · 3.4.2 Higher-level network sources · 3.5 Your code · 3.5.1 Instrumentation · 3.5.2 Proxy sources · 3.6 Summary

What you learn in chapter 3 →

Chapter 4: Agents and instrumentation

4.1 Log routers · 4.1.1 Fluentd and Fluent Bit · 4.1.2 Other log routers · 4.2 Metrics collection · 4.2.1 Prometheus · 4.2.2 Other metrics agents · 4.3 OpenTelemetry · 4.3.1 Instrumentation · 4.3.2 Collector · 4.4 Other agents · 4.5 Selecting an agent · 4.5.1 Security for and of the agent · 4.5.2 Agent performance and resource usage · 4.5.3 Agent nonfunctional requirements · 4.6 Summary

What you learn in chapter 4 →

Chapter 5: Backend destinations

5.1 Backend destination terminology · 5.2 Backend destinations for logs · 5.2.1 Cloud providers · 5.2.2 Open source log backends · 5.2.3 Commercial offerings for log backends · 5.3 Backend destinations for metrics · 5.3.1 Cloud providers · 5.3.2 Open source metrics backends · 5.3.3 Commercial offerings for metrics backends · 5.4 Backend destinations for traces · 5.4.1 Cloud providers · 5.4.2 Open source traces backends · 5.4.3 Commercial offerings for trace backends · 5.5 Columnar data stores · 5.6 Selecting backend destinations · 5.6.1 Costs · 5.6.2 Open standards · 5.6.3 Back pressure · 5.6.4 Cardinality and queries · 5.7 Summary

What you learn in chapter 5 →

Chapter 6: Frontend destinations

6.1 Frontends · 6.1.1 Grafana · 6.1.2 Kibana and OpenSearch Dashboards · 6.1.3 Other open source frontends · 6.1.4 Cloud providers and commercial frontends · 6.2 All-in-ones · 6.2.1 CNCF Jaeger · 6.2.2 CNCF Pixie · 6.2.3 Zipkin · 6.2.4 Apache SkyWalking · 6.2.5 SigNoz · 6.2.6 Uptrace · 6.2.7 Commercial offerings · 6.3 Selecting frontends and all-in-ones · 6.4 Summary

What you learn in chapter 6 →

Chapter 7: Cloud operations

7.1 Incident management · 7.1.1 Health and performance monitoring · 7.1.2 Handling the incident · 7.1.3 Learning from the incident after the fact · 7.2 Alerting · 7.2.1 Prometheus alerting · 7.2.2 Using Grafana for alerting · 7.2.3 Cloud providers · 7.3 Usage tracking · 7.3.1 Users · 7.3.2 Costs · 7.4 Summary

What you learn in chapter 7 →

Chapter 8: Distributed tracing

8.1 Intro and terminology · 8.1.1 Motivational example · 8.1.2 Terminology · 8.1.3 Use cases · 8.2 Using distributed tracing in a microservices app · 8.2.1 Example app overview · 8.2.2 Implementing the example app · 8.2.3 The “happy path” · 8.2.4 Exploring a failure in the example app · 8.3 Practical considerations · 8.3.1 Sampling · 8.3.2 Observability tax · 8.3.3 Traces vs. metrics vs. logs · 8.4 Summary

What you learn in chapter 8 →

Chapter 9: Developer observability

9.1 Continuous profiling · 9.1.1 The humble beginnings · 9.1.2 Common technologies · 9.1.3 Open source CP tooling · 9.1.4 Commercial continuous profiling offerings · 9.1.5 Using continuous profiling to assess continuous profiling · 9.2 Developer productivity · 9.2.1 Challenges · 9.2.2 Tooling · 9.3 Tooling considerations · 9.3.1 Symbolization · 9.3.2 Storing profiles · 9.3.3 Querying profiles · 9.3.4 Correlation · 9.3.5 Standards · 9.3.6 Using tooling in production · 9.4 Summary

What you learn in chapter 9 →

Chapter 10: Service level objectives

10.1 The fundamentals of SLOs · 10.1.1 Types of services · 10.1.2 Service level indicator · 10.1.3 Service level objective · 10.1.4 Service level agreement · 10.2 Implementing SLOs · 10.2.1 High-level example · 10.2.2 Using Prometheus to implement SLOs · 10.2.3 Commercial SLO offerings · 10.3 Considerations · 10.4 Summary

What you learn in chapter 10 →

Chapter 11: Signal correlation

11.1 Correlation fundamentals · 11.1.1 Correlation with OpenTelemetry · 11.1.2 Correlating traces · 11.1.3 Correlating metrics · 11.1.4 Correlating logs · 11.1.5 Correlating profiles · 11.2 Using Prometheus, Jaeger, and Grafana for correlation · 11.2.1 Metrics–traces correlation example setup · 11.2.2 Using metrics–traces correlation · 11.3 Signal correlation support in commercial offerings · 11.4 Considerations · 11.4.1 Early days · 11.4.2 Signals · 11.4.3 User experience · 11.5 Conclusion · 11.6 Summary

What you learn in chapter 11 →

Concepts explained in this book

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