Scalable Human Blog

What is Chaos Engineering and where did it come from?

Chaos Engineering is a proactive approach to improving the resilience and reliability of software systems. In software development, it involves intentionally introducing failures, errors, or unexpected events into a system to test its ability to withstand and recover from these disruptions. The main goal of Chaos Engineering is to uncover and address potential weaknesses or vulnerabilities in a system before they become problems in production.

The concept of Chaos Engineering was popularised by Netflix, which developed a tool called Chaos Monkey to test the resilience of its infrastructure. Since then, many other companies have adopted similar practices to ensure their systems are robust and can gracefully handle failures.

Where can Chaos Engineering be applied in Kubernetes?

Chaos Engineering can be applied to various areas of a Kubernetes environment to test and improve the resilience of the system. Here are some key areas where you can apply Chaos Engineering in Kubernetes:

1. Node failures: Simulate node crashes, terminations, or network partitioning to test how your application and the Kubernetes control plane handle node-level failures.
2. Pod failures: Kill or restart pods to evaluate how your application handles pod failures, and to ensure that Kubernetes reschedules the affected workloads as expected.
3. Network issues: Inject network latency, packet loss, or DNS failures to test your application’s tolerance to network-related problems and the ability of Kubernetes services to discover and communicate with each other.
4. Resource constraints: Introduce CPU or memory constraints to simulate resource exhaustion and observe how your application and Kubernetes handle resource contention or autoscaling.
5. Persistent storage: Simulate failures or disruptions in persistent storage systems, such as volumes or storage classes, to test data durability and recovery mechanisms.
6. Control plane components: Induce failures or disruptions in key Kubernetes control plane components, like the API server, etcd, or the kube-controller-manager, to evaluate the resilience and failover capabilities of the control plane.
7. Third-party integrations: Inject failures in external services, such as databases, message brokers, or monitoring systems, to test how your application and Kubernetes handle dependencies on external systems.
8. Security: Test the impact of compromised secrets, misconfigured RBAC policies, or unauthorised access on your application and Kubernetes environment, helping you identify potential security vulnerabilities.
9. Configuration changes: Introduce configuration changes, either in your application or Kubernetes components, to test the system’s ability to adapt to and recover from configuration errors.
10. Upgrades and rollouts: Simulate failures during version upgrades or rolling deployments to test the system’s ability to handle and recover from issues during such processes.

By targeting these areas with Chaos Engineering experiments, you can identify potential weaknesses in your Kubernetes environment and make improvements to enhance the resilience and reliability of your systems.

Example process

Let’s assume you have a Kubernetes cluster running several nodes, each hosting multiple pods running your application. Your application is designed to handle failures by distributing load across the available nodes and pods. To test its resilience, you can simulate a node or pod failure using Chaos Engineering.

Here’s an outline of a simple Chaos Engineering experiment:

1. Define the steady state: Determine the normal behaviour of your application in terms of performance, error rates, and other key metrics. You can use monitoring tools like Prometheus and Grafana to collect and visualise this data.
2. Hypothesize: Formulate a hypothesis about how your application should behave when a node or a pod fails. For example, you might expect that the remaining nodes will distribute the load evenly and the overall performance will degrade gracefully.
3. Introduce chaos: There are various tools available for injecting chaos into Kubernetes, such as Litmus, PowerfulSeal, and Chaos Mesh. Using one of these tools, you can simulate a node or pod failure by either deleting a node, draining it, or killing a specific pod.

For example, using the kubectl command-line tool, you can delete a pod with the following command:

kubectl delete pod <pod_name> -n <namespace>

4. Observe and measure: Monitor your application’s behavior during and after the experiment. Keep an eye on metrics like request latency, error rates, and the distribution of the load across the remaining nodes and pods.
5. Analyze and learn: Evaluate the results of the experiment and compare the observed behavior with your hypothesis. If the application didn’t handle the failure as expected, investigate the cause and make improvements to the system.
6. Iterate: Repeat the process, simulating different types of failures and continually refining your application’s resilience and robustness.

Remember that this is just one example of a Chaos Engineering simulation in Kubernetes. There are many other failure scenarios you can explore, such as network latency, service unavailability, or resource constraints. The key is to identify potential weak points in your system and proactively test their resilience.

📚 Further Reading & Related Topics

If you’re exploring chaos engineering in Kubernetes, these related articles will provide deeper insights:

• Troubleshooting Common Issues in Kubernetes Deployments – Learn how to diagnose and resolve issues in Kubernetes environments, an essential step before introducing chaos engineering experiments.

• Managing Stateful Applications with Kubernetes StatefulSets – Understand how to manage stateful workloads in Kubernetes and how chaos testing impacts persistent applications.