Chaos Engineering? What if there’s a better way?

Let’s face it: no matter how much you try, errors happen. Most of the time, this happens unintentionally, but given the topic of this blog, it can also happen on purpose.

What is Chaos Engineering?

Chaos engineering is a practice of testing where devs deliberately introduce failures and faulty scenarios in the application code to increase confidence in its ability to resist turbulence during production. In other words, deliberately break your system to identify its weaknesses. By doing so, you may fix problems before they unexpectedly break and harm your users and the company. You learn more about system resilience as you do more chaos experiments (tests). This helps in minimising downtime, and lowers SLA breaches & boosts revenue results. But what if there was a better way to ensure zero bugs without all the chaos?

Principles of Chaos Engineering

Before we answer that question, let’s look at the principles of chaos engineering:

• Create a plan This entails making broad assumptions about how a system will react when unstable elements and circumstances are introduced relative to the surrounding environment. Additionally, this is the point at which you choose the metrics that will be measured throughout the chaos experiment, such as error rates, latency, throughput, etc.

• Forecast the effects Think about what may happen if these fictitious occurrences occurred in actual circumstances. What will happen to your entire system, for instance, if your server unexpectedly dies or there is a huge rise in traffic? It’s important to identify variables and anticipate effects beforehand.

• Initiate the experiment Your chaos experiment should ideally be carried out in a real-world production setting. However, safeguards must be put in place to avoid the worst-case scenario. In case the experiment doesn't go as planned, you want to make sure you still have some control over the surroundings. This is sometimes referred to as “explosion radius control.” In addition to being more sustainable, these experiments can be automated for greater analysis. A full-fledged test environment is another technique that is occasionally employed, however, this might not accurately represent what occurs in the real world.

• Measure the results How do the outcomes measure up to the original theory? Was the experiment too limited, or does it need to be scaled up to more accurately discover errors and flaws based on the metrics that were specified in the hypothesis? Was the blast zone too small? Perhaps it should be scaled to cause the flaws that would show up in a real-world situation. This experiment can also turn up new issues that need to be looked at.