When it comes to building reliable and scalable software, few organizations have as much authority and expertise as Google. Their Site Reliability Engineering Handbook, first published in 2016, details their practices to maintain reliability as Google scaled. But when you have over a million servers running thousands of services across more than twenty data centers, how do you monitor them in a consistent, logical, and relevant way?

Legendary race car driver Carroll Smith once said, "until we have established reliability, there is no sense at all in wasting time trying to make the thing go faster." Even though he was referring to cars, the same goes for technology: no amount of code optimization or new features can replace stable systems. Unfortunately, much like race cars, it's hard to know that a system is unreliable until it blows a tire, the brakes stop working, or the steering wheel comes off the column.

Reliability and chaos might seem like opposite ideas. But, as Netflix learned in 2010, introducing a bit of chaos—and carefully measuring the results of that chaos—can be a great recipe for reliability. Although most software is created in a tightly controlled environment and carefully tested before release, the production environment is harsher and much less controlled.

More and more teams are moving away from monolithic applications and towards microservice-based architectures. As part of this transition, development teams are taking more direct ownership over their applications, including their deployment and operation in production. A major challenge these teams face isn't in getting their code into production (we have containers to thank for that), but in making sure their services are reliable.