Embracing Chaos to Improve System Resilience: Chaos Engineering

Imagine it's the early 2000s, and you're a developer with a bold idea. You want to test your software not in a safe, controlled environment but right where the action is: the production environment. This is where real users interact with your system. Back then, suggesting something like this might have gotten you some strange looks from your bosses. But now, testing in the real world is not just okay; it's often recommended.

Why the big change? A few reasons stand out. Systems today are more complex than ever, pushing us to innovate faster and ensure our services are both reliable and strong. The rise of cloud technology, microservices, and distributed systems has changed the game. We've had to adapt our methods and mindsets accordingly.

Our goal now is to make systems that can handle anything—be it a slowdown or a full-blown outage. Enter Chaos Engineering.

In this issue, we dive into what chaos engineering is all about. We'll break down its key principles, how it's practiced, and examples from the real world. You'll learn how causing a bit of controlled chaos can actually help find and fix weaknesses before they become major problems.

Prepare to see how embracing chaos can lead to stronger, more reliable systems. Let's get started!

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What is Chaos Engineering?

So, what exactly is chaos engineering? It's a way to deal with unexpected issues in software development and keep systems up and running. Some folks might think that a server running an app will continue without a hitch forever. Others believe that problems are just part of the deal and that downtime is inevitable.

Chaos engineering strikes a balance between these views. It recognizes that things can go wrong but asserts that we can take steps to prevent these issues from impacting our systems and the performance of our apps.

This approach involves experimenting on our live, production systems to identify weak spots and areas that aren't as reliable as they should be. It's about measuring how much we trust our system's resilience and working to boost that confidence

However, it's important to understand that being 100% sure nothing will go wrong is unrealistic. Through chaos engineering, we intentionally introduce unexpected events to uncover vulnerabilities. These events can vary widely, such as taking down a server randomly, disrupting a data center, or tampering with load balancers and application replicas.

In short, chaos engineering is about designing experiments that rigorously test our systems' robustness. 

Defining Chaos Engineering

There are many ways to describe chaos engineering, but here's a definition that captures its essence well, sourced from https://principlesofchaos.org/.

“Chaos Engineering is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production.”

This definition highlights the core objective of chaos engineering: to ensure our systems can handle the unpredictable nature of real-world operations.

Performance Engineering vs. Chaos Engineering

When we talk about ensuring our systems run smoothly, two concepts often come up: performance engineering and chaos engineering. Let's discuss what sets these two apart and how they might overlap.

Many developers are already familiar with performance engineering, which is in the same family as DevOps. It involves using a combination of tools, processes, and technologies to monitor our system's performance and make continuous improvements. This includes conducting various types of testing, such as load, stress, and endurance tests, all aimed at boosting the performance of our applications.

On the flip side, chaos engineering is about intentionally breaking things. Yes, this includes stress testing, but it's more about observing how systems respond under unexpected stress. Stress testing could be seen as a form of chaos experiment. So, one way to look at it is to consider performance engineering as a subset of chaos engineering or the other way around, depending on how you apply these practices.

Another way to view these two is as distinct disciplines within an organization. One team might focus solely on conducting chaos experiments and learning from the failures, while another might immerse itself in performance engineering tasks like testing and monitoring. Depending on the structure of the organization, the skill sets of the team, and various other factors, we might have separate teams for each discipline or one team that tackles both.

Chaos Engineering in Practice

Let's consider an example to better understand chaos engineering. Imagine we have a system with a load balancer that directs requests to web servers. These servers then connect to a payment service, which, in turn, interacts with a third-party API and a cache service, all located in Availability Zone A. If the payment service fails to communicate with the third-party API or the cache, requests need to be rerouted to Availability Zone B to maintain high availability.