Manage Multicluster Kubernetes with Operators

Manage Multicluster Kubernetes with Operators

At Kubermatic, we have chosen to do multicluster management with Kubernetes Operators. This blog post will cover why you need multicluster management, how Kubermatic Kubernetes Platform leverages Kubernetes Operators to automate cluster life cycle management across multiple clusters, clouds, and regions and how you can get started with it today.

With Kubermatic Kubernetes Platform, we extend the Operators paradigm beyond applications to manage the clusters themselves.

At Kubermatic, we have been helping our customers deliver Kubernetes clusters and other cloud native solutions since before they were buzzwords. We helped customers build clusters using Ansible, Terraform, and a variety of other non cloud native tools… and we helped them rebuild the clusters when we ran into the limits of these tools.

In these early days, two things quickly became clear to us: 1) Kubernetes is not a single large cluster solution, but rather requires a larger number of smaller clusters 2) Kubernetes multicluster management needs cloud native tools built for a declarative, API driven world. Since then, these ideas have largely been validated by a variety of organizations around the world including the Cloud Native Computing Foundation, Twitter, USA Today, Zalando, and Alibaba. Knowing that every company running Kubernetes at scale would need to effectively administer multicluster management, we created the open source Kubermatic Kubernetes Platform. This blog post will cover why you need multicluster management, how Kubermatic Kubernetes Platform leverages Kubernetes Operators to automate cluster life cycle management across multiple clusters, clouds, and regions and how you can get started with it today.

Why You Need Multicluster Management

Kubernetes lacks hard multitenancy capabilities that give users, organizations, or operators the ability to allow untrusted tenants to share infrastructure resources or separate different pieces of software. This presents both a security and operational problem. When operators seek to separate workloads by type (sensitive vs nonsensitive data processing) or even just production vs non-production there is no way to do this on the cluster level; creating a security nightmare. On the operational side, trying to deploy too many applications into the same cluster can result in version conflicts, configuration conflicts, and problems with software lifecycle management. Finally, without proper isolation there is an increased risk of cascading failures.

Without hard multitenancy within a cluster, separate clusters must be used to provide adequate separation for workloads with different security requirements. Having multiple clusters to deploy applications into also allows operators to deploy similar applications together while segregating those with different life cycles from each other. Applications deployed into the same cluster can be upgraded together to reduce the operational load while applications that require different versions, configurations, and dependencies can run in separate clusters and be upgraded on their own.

If running multiple clusters is the only solution to meeting these workload and infrastructure requirements, the operational burden of this model must also be considered. Running a multitude of clusters is a massive operational challenge if done manually. For this reason, any operator considering running Kubernetes at scale should carefully evaluate their multicluster management strategy. At Kubermatic, we have chosen to do multicluster management with Kubernetes Operators.

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