NetEase Fuxi AI Lab is China’s first professional game AI research institution. Researchers use our Kubernetes-based Danlu platform for algorithm development, training and tuning, and online publishing. Thanks to the integration with Kubernetes, our platform is much more efficient. However, due to Kubernetes- and microservices-related issues, we are constantly testing and improving our platform to make it more stable.
“Necessity is the mother of invention”; similarly, Netflix is not only a platform for online media streaming. Netflix gave birth to Chaos engineering because of their necessity.
Chaos Mesh is proud to be in Hacktoberfest 2020!
Hosted by DigitalOcean, Intel and DEV, Hacktoberfest is an open source celebration open to everyone in our global community. This month-long (Oct 1 - Oct 31) event encourages everyone to help drive the growth of open source and make positive contributions to an ever-growing community, whether you’re an experienced developer or open-source newbie learning to code. As long as you submit 4 PRs before Oct 31, you are eligible to claim a limit edition T-shirt (70000 in total on a first-come-first-served basis)!
Today, we are proud to announce the general availability of Chaos Mesh 1.0, following its entry into CNCF as a sandbox project in July, 2020.
Chaos Mesh is a cloud-native chaos testing platform that orchestrates chaos in Kubernetes environments. While it’s well received in the community with its rich fault injection types and easy-to-use dashboard, it was difficult to use Chaos Mesh with end-to-end testing or the continuous integration (CI) process. As a result, problems introduced during system development could not be discovered before the release.
In this article, I will share how we use chaos-mesh-action, a GitHub action to integrate Chaos Mesh into the CI process.
Chaos Mesh is an open-source chaos engineering platform for Kubernetes. Although it provides rich capabilities to simulate abnormal system conditions, it still only solves a fraction of the Chaos Engineering puzzle. Besides fault injection, a full chaos engineering application consists of hypothesizing around defined steady states, running experiments in production, validating the system via test cases, and automating the testing.
This article describes how we use TiPocket, an automated testing framework to build a full Chaos Engineering testing loop for TiDB, our distributed database.
Chaos Mesh, an easy-to-use, open-source, cloud-native chaos engineering platform for Kubernetes (K8s), has a new feature, TimeChaos, which simulates the clock skew phenomenon. Usually, when we modify clocks in a container, we want a minimized blast radius, and we don't want the change to affect the other containers on the node. In reality, however, implementing this can be harder than you think. How does Chaos Mesh solve this problem?
Chaos Engineering is a way to test a production software system's robustness by simulating unusual or disruptive conditions. For many people, however, the transition from learning Chaos Engineering to practicing it on their own systems is daunting. It sounds like one of those big ideas that require a fully-equipped team to plan ahead. Well, it doesn't have to be. To get started with chaos experimenting, you may be just one suitable platform away.
In the world of distributed computing, faults can happen to your clusters unpredictably any time, anywhere. Traditionally we have unit tests and integration tests that guarantee a system is production ready, but these cover just the tip of the iceberg as clusters scale, complexities amount, and data volumes increase by PB levels. To better identify system vulnerabilities and improve resilience, Netflix invented Chaos Monkey and injects various types of faults into the infrastructure and business systems. This is how Chaos Engineering was originated.