What is O11y? - Metrics

O11y Serie

Welcome to the second part of my series on observability engineering! The first part can be found here. I hope this series proves helpful to you, and I’m excited to continue working on the upcoming posts.

Introduction

I won’t delve into a full explanation or analogy of observability engineering again (for a refresher, please refer to the first post in the series); however, let’s quickly recap. Observability engineering involves measuring the internal states of a system or application by examining its outputs. It’s straightforward – no secret sauce, and no hocus pocus! In this series, we’ll cover essential topics within observability engineering that should benefit both newcomers and seasoned engineers alike.

As always, my strong opinions are based on extensive experience, but I hold them lightly. I take great pride in my ability to change my views when presented with new information or different experiences.

Pillar Two - Metrics

What are metrics?

First, what are metrics? Metrics are simply the numeric representation of data measured over intervals of time. Using my pilot analogy from the first post, a commercial pilot needs real-time metrics to make informed decisions throughout a flight. This data is crucial for the pilot’s situational awareness. If it were in log format, the pilot wouldn’t have time to sift through detailed logs to get the necessary information, which could be disastrous!

For the aeroplane manufacturer, having historical numeric data points is essential for hypothesis-based investigations. For instance, if a new engine version is introduced, manufacturers might hypothesise that these engines run significantly hotter than previous models. By analysing historical data points, they can mark when the new engines were installed and validate or refute their hypothesis. Metrics might not explain why the engines run hotter, but they provide a clear picture of when and what.