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🧠 DEEP DIVE USE CASE
How Kubernetes Operators Work Internally
“What exactly is an Operator, and how is it different from a Controller?”
It’s a common question that comes up once you start automating Kubernetes workloads beyond the basics. Both aim to maintain desired state, but they operate at different scopes.
Controllers manage built in Kubernetes resources like Pods and Deployments.
Operators manage custom, domain specific applications using Custom Resource Definitions and control loops.
Before diving into, let’s first understand what forms the foundation of an Operator.
How Kubernetes Controllers Work
Kubernetes controllers form the automation layer that keeps the cluster in its desired state. They constantly watch the system, detect drifts, and act to correct them. To understand how this works, we start with the core concept of Desired State and Current State, and then look at how the controller architecture enforces it.
Desired State vs Current State
When you create a Kubernetes object such as a Deployment or Service, you define its desired state, for example “3 replicas of Nginx should be running.”
Kubernetes continuously compares this desired state with the current state, which represents what is actually running in the cluster. If there is a difference, the controller acts to reconcile it.
If one Pod is down, it creates a new one.
If extra Pods exist, it removes them.
If configurations differ, it updates them to match the definition.
This continuous reconciliation makes Kubernetes self healing and declarative.
Controller Architecture
A controller’s logic runs in the control plane and interacts closely with the API Server.
Here is how the workflow happens in practice:
User submits a manifest defining the desired state such as replicas, configurations, or resource limits.
API Server stores the object and makes it available to all cluster components.
Shared Informer watches for changes and keeps an updated local cache.
Controller processes the change and adds it to a WorkQueue for sequential handling.
Reconciliation loop runs to compare desired and current state and perform corrective actions through the API Server.
This loop operates continuously, ensuring the cluster remains stable even when workloads fail, nodes go offline, or configurations drift.
Test how your controller reconciles drift. Missed events or slow loops can silently break automation.
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