As we’ve discussed in part one and part two of this series, Service Level Objectives (SLOs) are essential performance indicators for organizations that want a real understanding of how their systems are performing. However, these indicators are driven by vast amounts of raw data and information. That being said, how do we make sense of it all and quantify our SLOs? Let’s take a look.

Deriving meaningful insights from third-party logs has always been a difficult yet necessary task. Most analysis occurs after-the-fact, when something has gone wrong. Very few tools allow real-time monitoring of logs, so SREs have become accustomed to backfilling log data into various analysis tools. Postmortem log analysis is the de facto standard, yet should it be? Why shouldn’t you be able to monitor your server logs in real-time?

Statistical analysis is a critical – but often complicated – component in determining your ideal Service Level Objectives (SLOs). So, a “deep-dive” on the subject requires much more detail than can be explored in a blog post. However, we aim to provide enough information here to give you a basic understanding of the math behind a smart SLO – and why it’s so important that you get it right.