Observability Engineering

Chapter 8. Analyzing Events to Achieve Observability

In the first two chapters of this part, you learned about telemetry fundamentals that are necessary to create a data set that can be properly debugged with an observability tool. While having the right data is a fundamental requirement, observability is measured by what you can learn about your systems from that data. This chapter explores debugging techniques applied to observability data and what separates them from traditional techniques used to debug production systems.

We’ll start by closely examining common techniques for debugging issues with traditional monitoring and application performance monitoring tools. As highlighted in previous chapters, traditional approaches presume a fair amount of familiarity with previously known failure modes. In this chapter, that approach is unpacked a bit more so that it can then be contrasted with debugging approaches that don’t require the same degree of system familiarity to identify issues.

Then, we’ll look at how observability-based debugging techniques can be automated and consider the roles that both humans and computers play in creating effective debugging workflows. When combining those factors, you’ll understand how observability tools help you analyze telemetry data to identify issues that are impossible to detect with traditional tools.

This style of hypothesis-driven debugging—in which you form hypotheses and then explore the data to confirm or deny them—is not only more ...