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How Kubernetes Really Works – Explained Like a System Blueprint

  • 20 hours ago
  • 3 min read

You’ve probably deployed a pod. Maybe scaled a service. But have you ever truly mapped out what’s happening under the hood of your Kubernetes cluster?


At Ananta Cloud, we believe that to operate, secure, and scale cloud-native systems effectively, it's not enough to just use Kubernetes—you need to understand it like a system engineer. Not just in theory, but in architecture.


So, let’s break Kubernetes down. Component by component. Like a blueprint.

Control Plane – The Brain of the Cluster

The control plane doesn't run your application workloads. Instead, it orchestrates them. It tells the worker nodes what to run, where to run it, and how to keep it running.


Here’s what makes up the control plane:

🔹API Server – The Front Door to the Cluster

This is the beating heart of Kubernetes. Every kubectl command, every deployment manifest, and every automation pipeline interacts with the API server.

  • Acts as the single point of truth.

  • Validates and processes all cluster operations.

  • Exposes RESTful endpoints for clients and internal components alike.


Think of it as the gatekeeper and command router for everything that happens in Kubernetes.

🔹etcd – The Memory Store

Every bit of cluster state lives here: nodes, pods, secrets, config maps, deployments—all of it.

  • A consistent, distributed key-value store.

  • Backed by the Raft consensus algorithm for high availability.

  • It’s where Kubernetes turns intention into state.


Want to know what your cluster "remembers"? Look in etcd.

🔹Controller Manager – The Enforcer

You tell Kubernetes what should exist. The controller manager watches what does exist and takes action to reconcile the difference.

  • Runs various controllers: Node, Replication, Job, etc.

  • Ensures your cluster is always moving toward the desired state.

  • Example: If a pod crashes, the controller spins up a new one automatically.


It’s not reactive. It’s proactive. Constantly working to make things "right."

🔹Scheduler – The Matchmaker

Where should your pod run? The scheduler decides.

  • Evaluates pod specs against cluster resources.

  • Factors in things like CPU, memory, affinity/anti-affinity, taints/tolerations.

  • Assigns pods to the optimal node.


No scheduling? No workload. This is Kubernetes' decision engine.

Worker Nodes – The Muscles of the Cluster

If the control plane is the brain, the worker nodes are the biceps. This is where the actual workloads run—your containers, your apps, your services.


Each worker node runs several core components:

🔹 Kubelet – The Node Supervisor

Each node has a kubelet, and it acts like a personal trainer for the pods running there.

  • Takes orders from the API server.

  • Ensures the containers in the pods are running and healthy.

  • Interfaces with the container runtime to manage lifecycle.


If your pod isn't behaving, chances are the kubelet is either confused—or doing its job perfectly.

🔹Kube-proxy – The Network Traffic Cop

Routing, forwarding, and network rules—kube-proxy handles them all.

  • Manages virtual IPs and cluster-level DNS.

  • Implements NAT rules and service discovery.

  • Think of it as DNS + firewall + switchboard, rolled into one.


This is how services reach each other across the cluster, even if pods shift around constantly.

🔹Container Runtime – The Engine Underneath

Kubernetes doesn't run containers directly—it delegates to a runtime.

  • Examples: Docker, containerd, CRI-O.

  • Pulls images, starts containers, manages isolation.

  • Communicates with the kubelet via the Container Runtime Interface (CRI).


Your containers don’t care about Kubernetes. But Kubernetes definitely cares about your containers.

Pods – Where Code Meets Runtime

A pod is the smallest deployable unit in Kubernetes. It encapsulates one or more containers that:

  • Share the same network namespace (IP address, port space).

  • Share volumes and storage.

  • Share the same lifecycle—if the pod dies, all containers die with it.


Even a multi-container pod (sidecars, init containers, etc.) still acts as a single unit of scheduling.

Want to run code in Kubernetes? You start with a pod.

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The Real Truth: Kubernetes = Linux at Scale

At its core, Kubernetes is just Linux distributed at scale:

  • Namespaces → process isolation

  • cgroups → resource control

  • iptables → service routing

  • systemd-like reconciliation → controller pattern


Kubernetes isn’t “magic.” It’s a clever orchestration of system primitives that you already know—just abstracted, automated, and wrapped in an API.

Why This Matters

Understanding this architecture isn't just academic.

It’s how you:

  • Debug production issues faster.

  • Harden security with proper isolation.

  • Optimize performance across nodes.

  • Build platforms your team can trust.

So… Do You Really Know Your Cluster?

At Ananta Cloud, we help teams not just use Kubernetes—but master it. From secure platform architecture to GitOps pipelines to multi-cloud scalability, we bring clarity and control to your cloud-native journey.


If your cluster feels like a black box—it’s time to open the lid.


Let’s build better systems. Blueprint first.





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