Measuring and improving the reliability of technical systems has always been challenging. As an industry, we've developed several practices to try and address reliability concerns, such as incident response, observability, and Chaos Engineering. This led SREs and service owners to measure reliability in a handful of ways.
To many engineers, the idea that you can accurately and comprehensively track your application's user experience using just a few simple metrics might sound far-fetched. Believe it or not, there are four metrics that aim to do just that. They're called the four Golden Signals and should be a core part of your observability and reliability practices.
Reliability is a critical concern for software delivery teams. Every second of lackluster performance or service interruption comes with high costs. The consequences can extend beyond just monetary expenses and have a huge impact on a company’s reputation. In a survey conducted in 2022, participants reported that over 60% of digital infrastructure failures resulted in losses of $100,000 or more.
In 2011, a Netflix engineering team introduced the concept of chaos engineering with its release of Chaos Monkey. This was initially an in-house tool developed to orchestrate fault injection that Netflix eventually made open source. However, the reliance of Chaos Monkey on Spinnaker, another Netflix engineering innovation, establishes some limitations.
The past ten years marked a significant change in how software teams build and deploy applications. We moved away from bulky, slow, monolithic applications toward lightweight, scalable, distributed service-based applications. Meanwhile, tools like Docker, Kubernetes, and other container platforms helped accelerate this process. Despite this sudden growth, a fundamental question remains: what exactly is a service, and how does it fit into a microservice architecture?
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.
