What’s Different in the Second Edition

First, we have a new coauthor. We are delighted to welcome Austin Parker, with deep subject matter expertise in AI and OpenTelemetry.

We’re also excited to incorporate a broader range of voices for this edition of the book, featuring a stellar lineup of guest writers. The opportunity to spotlight some of the people we look up to and enjoy working with has been one of the greatest pleasures of putting this book together.

In order of presentation in the book:

• Jeremy Morrell wrote one of the most comprehensive guides to using wide events and creating custom attributes that we’ve ever seen. He has contributed greatly to an update of Chapter 5, “Structured Events Are the Building Blocks of Observability”, and has entirely contributed Chapter 6, “Making Structured Events Arbitrarily Wide”.
• Boris Tane explored agentic AI use cases and examined the principles necessary to generate and retain context, which continues to be key for success as we shift toward more automation. We think you’ll enjoy the principles outlined in his contributed content, Chapter 10, “The Role of AI Agents for Observability”.
• Mat Vine provided an in-depth case study on SLOs adoption for an updated look at its challenges and solutions. This replaces the Honeycomb-focused use case previously used in Chapter 11, “Using Service Level Objectives for Reliability”.
• The engineering team at ClickHouse contributed an in-depth look at how their open source datastore is tuned to meet the needs of observability workloads in Chapter 14, “Efficient Data Storage with ClickHouse”. This new chapter provides an alternative implementation to the Honeycomb storage engine, detailed in Chapter 13, “Efficient Data Storage with Retriever”.
• Mike Kelly contributed an in-depth look at telemetry management by examining challenges and solutions through the lens of the open source Bindplane project in Chapter 16, “Telemetry Management with Pipelines”.
• Frank Chen, guest contributor in the first edition, returned to the second edition with a look at ontologies and how to think about your entire instrumentation chain in Chapter 17, “Ontologies as a Shared Language for Humans and AI”.

• Hugo Santos contributed greatly to an update of Frank Chen’s first-edition take on instrumenting tests and continuous delivery pipelines in a new Chapter 18, “Observability for CI/CD Pipelines”.
• Hanson Ho and Matt Klein presented a look at the complex and challenging workflows necessary for implementing high-observability workflows in the mobile device domain in Chapter 19, “Observability for Mobile and Frontend”.
• Phillip Carter contributed greatly when applying the principles in this book to the world of running generative AI applications in production in Chapter 21, “Observability for Large Language Models”.
• Kesha Mykhailov contributed a first-person narrative of the observability journey at Fin (formerly Intercom) in Chapter 22, “Fin’s Case Study in Modern Engineering”. This chapter demonstrates how many of the concepts detailed in the book often come together in real-world scenarios.
• Darragh Curran contributed an open letter to chief technology officers (CTOs) detailing how his organization used observability to tackle the problem of accelerating organizational learning speed in an AI era in Chapter 23, “Organizational Learning Speed Is Now Your Biggest Constraint: An Open Letter to CTOs”.
• Rick Clark contributed a deep dive into driving change without formal authority in Chapter 28, “The Organizational Shift”. This chapter covers how to recruit champions, drive consensus, and shepherd disruptive transformational change through complex sociotechnical systems.
• Hazel Weakly wrote a wonderful foreword for us on the shifting horizons of software development, and contributed greatly to Chapter 31, “Instrumentation for Observability Teams”, especially for teams operating in highly secure environments.