In the ever-evolving universe of DevOps, a practice exists that may seem counterintuitive at first glance but is essential for ensuring the stability of digital systems – Chaos Engineering.

Though it may seem alarming initially, Chaos Engineering is a proactive approach involving active experimentation with production systems to test their capacity to withstand unexpected disruptions.

Facing the Eye of the Storm

Initially, Chaos Engineering originated during the streaming revolution, pioneered by the giant, Netflix.

In 2008, when Netflix formally launched its streaming service, the company experienced a major database corruption problem in 2011.

From then on, this experience marked Netflix’s decision to migrate to AWS (Amazon Web Services) cloud, a process that took eight years to complete.

However, in 2015, AWS suffered an outage, leaving Netflix offline for hours. This outage raised questions about the reliability and robustness of cloud computing, as many expected benefits, like scalability and uptime, were not realized as anticipated.

As a result, Netflix decided it needed a way to test and prepare for these unexpected problems, leading to the birth of Chaos Engineering.

What is Chaos Engineering?

In essence, Chaos Engineering is the practice of orchestrating conscious experiments that reveal weaknesses in software systems.

For example, imagine a laboratory where you are purposefully introducing chaotic variables to test and improve the system’s resilience.

This practice is not simply about causing failures, but rather identifying weak points before they become problems in a real production environment.

In this way, the goal is always to improve the system’s resilience, not to cause unnecessary damage.

Principles and Steps of Chaos Engineering

Chaos Engineering does not seek to create chaos for the sake of it. It follows a set of well-defined principles and steps, carefully creating chaos experiments with the goal of learning to mitigate risk in large systems and distributed networks.

There are several steps to creating a general guideline for chaos experiments:

1. Create a hypothesis: Make general assumptions about how the system will respond when chaos factors are introduced. Decide which metrics, such as error rates, latency, throughput, will be measured during the experiment.
2. Identify variables and anticipate effects: Consider what might happen when hypothetical events occur in real life. For example, what will be the effect if a server fails unexpectedly or if there is a significant increase in traffic?
3. Initiate the experiment: Conduct the chaos experiment in a live production environment, with safeguards to prevent greater damage. Ensure that you still have control over the environment if the experiment gets out of hand. This is also known as “blast radius control.”
4. Measure the impact: Compare the results with the initial hypothesis. Based on the metrics defined in the hypothesis, was the experiment too limited or does it need to be scaled to better identify errors and failures?

Therefore, it’s an essential practice to ensure systems are prepared for any eventuality.

This approach helps detect hidden vulnerabilities and improve the system’s recovery ability in failure situations.

Common Practices of Chaos Engineering

Chaos Engineering practices in DevOps can range from simple experiments of shutting down instances to complex experiments involving the total disruption of a data center region.

Consequently, there are various methods to implement Chaos Engineering. Netflix, for example, developed the “Simian Army,” a suite of tools that includes Chaos Monkey.

As the name suggests, the army is made up of monkeys – each one being a different tool.

Therefore, each monkey focuses on a specific objective, such as disabling part or all of the system or removing resources that are in use or do not meet certain rules.

This toolkit is open-sourced on GitHub for those interested in the details. Unfortunately, this project is no longer actively maintained, and several solutions have been provided by independent projects, but it is worth first considering the concepts and applications of Chaos Engineering.

Other common practices include latency injection, where the response time of services is deliberately slowed, and fault injection, where failures are intentionally introduced into the system.

Such experiments provide valuable insight into how a system reacts under stress.

Mastering Chaos

Chaos Engineering is a powerful practice that can significantly strengthen the resilience of software systems.

Thus, by proactively introducing chaos into a controlled system, developers can discover and fix faults before they become problems in a production environment.

DevOps, with its focus on automation and continuous integration, is the ideal setting for implementing Chaos Engineering.

Briteris can help your company have more resilient systems, capable of handling the variety and unpredictability of the digital world.

Contact us to discover how this methodology can be crucial to your business.