Chaos Mesh Introducing Chaos in Kubernetes
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The document discusses chaos engineering, a method to find system weaknesses before they become major outages by simulating failures. It introduces Chaos Mesh, an open-source platform tailored for chaos experiments in Kubernetes, highlighting its features and various types of chaos experiments, such as pod and network failures. Key principles of chaos engineering include defining normal operation, hypothesizing impacts, introducing realistic faults, and observing system behavior.
Chaos Mesh Introducing Chaos in Kubernetes
- 1. Chaos Mesh: Introducing Chaos in Kubernetes Shubham Chaubey Software Consultant DevOps Competency
- 2. Lack of etiquette and manners is a huge turn off. KnolX Etiquettes Punctuality Join the session 5 minutes prior to the session start time. We start on time and conclude on time! Feedback Make sure to submit a constructive feedback for all sessions as it is very helpful for the presenter. Silent Mode Keep your mobile devices in silent mode, feel free to move out of session in case you need to attend an urgent call. Avoid Disturbance Avoid unwanted chit chat during the session.
- 3. 1. What is Chaos Engineering? 2. Principles of Chaos Engineering 3. Introduction to Chaos Mesh 4. Key Features of Chaos Mesh 5. How Chaos Mesh Works 6. Types of Chaos Experiments 7. Demo
- 5. What is Chaos Engineering? Chaos Engineering is a disciplined approach to identifying failures before they become outages. In Chaos engineering, we simulate real-world failures to understand system behavior and how the system reacts to it. Proactively identifies weaknesses and improves reliability. Chaos Engineering lets you compare what you think will happen to what actually happens in your systems. You literally “break things on purpose” to learn how to build more resilient systems.
- 6. Principles of Chaos Engineering Define Steady State: Determine what normal operation looks like for your system (e.g., response times, error rates). Hypothesize Impact: Predict how introducing certain failures will impact the steady state (e.g., will the system still respond quickly?). Introduce Realistic Faults: Inject controlled failures that mimic real-world issues (e.g., server crashes, network latency). Observe and Learn: Monitor the system to see if it behaves as expected and identify areas for improvement.
- 8. Introduction to Chaos Mesh Chaos Mesh is an open-source cloud-native Chaos Engineering platform. It is designed to orchestrate chaos experiments specifically in Kubernetes environment. It offers various types of fault simulation and has an enormous capability to orchestrate fault scenarios. Helps in identifying system vulnerabilities and improving resilience.
- 9. Key Features of Chaos Mesh Supports various types of faults including: - NetworkChaos - HTTPChaos - PodChaos - StressChaos - And more... Chaos Mesh provides the Chaos Dashboard component for visualized operations, which greatly simplifies Chaos experiments. Allows you to run a single experiment or create a series of experiments termed as a workflow Integration with other cloud-native tools.
- 10. Types of Chaos Experiments PodChaos: simulates Pod failures, such as Pod node restart, Pod's persistent unavailablility, and certain container failures in a specific Pod. NetworkChaos: simulates network failures, such as network latency, packet loss, packet disorder, and network partitions. DNSChaos: simulates DNS failures, such as the parsing failure of DNS domain name and the wrong IP address returned. HTTPChaos: simulates HTTP communication failures, such as HTTP communication latency. StressChaos: simulates CPU race or memory race. IOChaos: simulates the I/O failure of an application file, such as I/O delays, read and write failures. TimeChaos: simulates the time jump exception. KernelChaos: simulates kernel failures, such as an exception of the application memory allocation. AWSChaos: simulates AWS platform failures, such as the AWS node restart. GCPChaos: simulates GCP platform failures, such as the GCP node restart. JVMChaos: simulates JVM application failures, such as the function call delay.