Imagine a perfect world where software releases ship bug-free. Developers write perfect code the first time, all tests pass without issues, operations teams effortlessly deploy builds to production, and customers never experience defects. Everyone's happy, and the Engineering team can focus exclusively on building and delivering features. Of course, we don't live in a perfect world.
For many businesses, prioritizing reliability is an ongoing challenge. Building reliable systems and services is critical for growing revenue and customer trust, but other initiatives—like building new products and features—often take precedence since they provide a clearer and more immediate return. That's not to say reliability doesn't have clear value, but proving this value to business leaders can be tricky.
Transport Layer Security (TLS), and its preceding protocol, Secure Sockets Layer (SSL), are essential to the modern Internet. Encrypting network communications using TLS protects users and organizations from publicly exposing in-transit data to third parties. This is especially important for the web, where TLS secures HTTP traffic (HTTPS) between backend servers and customers’ browsers.
How can you tell if your systems are reliable when under load? A common answer is to open your observability dashboards, wait for a high-traffic event (like Black Friday), and cross your fingers. While this approach is certainly effective, it's far from ideal. Without proactive reliability and load testing, we have no idea if a system will hold up to real-world usage patterns, which could mean a production outage at the worst possible time.
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.
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.
