Looking Around Reliability Corners During a Kubernetes Migration

Looking Around Reliability Corners During a Kubernetes Migration

Kubernetes has been a major benefit to the productivity of developers and the scalability of our applications. In this tutorial, we'll looking Around Reliability Corners During a Kubernetes Migration.

Kubernetes has been a major benefit to the productivity of developers and the scalability of our applications. Where containerizing applications gave us scalable architectures ideal for microservice applications, the number of containers quickly grew and orchestrating them got out of hand. Kubernetes was Google’s open source response to the combinatorial explosion of containers being deployed, operated and maintained. It simplified much of the operational tasks and abstracted away the orchestration detail for individual containers. As a result, adoption rates have skyrocketed to 45% of containerized environments.

However, as with all things in tech, the devil is in the details. Kubernetes is an extremely powerful tool, with enough tunability to be great for a vast majority of workloads, but also complex enough to be difficult to get right and to break in unpredictable ways. Where monoliths have one code base to step through, microservices add complex interrelations that are difficult to debug. Coupled with new, unique issues — such as dealing with latency, dependency failure, services scaling out and in rapidly, and pod failure — it’s a lot to test and fix. For companies new to Kubernetes, this can be daunting; and for companies with years of experience, the complexity only continues to grow.

At Gremlin, we’re working with companies early in their cloud native adoption, all the way to those on the bleeding edge — where vanilla configurations and core components no longer cut it. For those at the beginning of their journey, a method to build confidence that Kubernetes is deployed correctly, drastically speeds up the migration and development process.

Under Armour was able to migrate to Kubernetes four times faster when its engineers applied Chaos Engineering. Meanwhile, Workiva’s orchestration needs outstripped Kubernetes’ native capabilities for years, so the company hand-rolled their own. When Kubernetes caught up, they swapped its system for Kubernetes in order to reap the benefits of the Kubernetes roadmap, and then applied Chaos Engineering to confirm the new platform was as reliable as its previous orchestrator, before switching over.

kubernetes

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