Modern applications are a web of interdependent services. As applications grow in size and complexity, and as more engineering teams adopt service-based architectures like microservices, this web becomes deeper and denser. Eventually, keeping track of the interdependencies between services becomes a complex and time-consuming task in and of itself. In addition, if any of these dependencies fails, it can have cascading impacts on the rest of your services and on the application as a whole.

Part of a successful reliability program is being able to monitor and review your progress toward improving reliability. Being able to run tests on services is a big part of it, but how can you tell you're making progress if you can only see your latest test results? There should be a way to track improvements or regressions in your reliability testing practice across your organization in a way that's easy to digest. That's where the Reliability Dashboard comes in.

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.

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.

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.

In order to make reliability improvements tangible, there needs to be a way to quantify and track the reliability of systems and services in a meaningful way. This "reliability score" should indicate at a glance how likely a service is to withstand real-world causes of failure without having to wait for an incident to happen first. Gremlin's upcoming feature allows you to do just that.

Chaos Engineering, where engineers intentionally inject failure to test the reliability of their systems, is becoming a regular practice for companies who value uptime and availability. As cloud-based systems have grown more complex, Chaos Engineering has become a critical part of the software testing and release process to uncover surprise dependencies, fix problems before they become 3am outages, and bake reliability into every feature.