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What a Kubernetes cluster actually contains
The official architecture is straightforward: “A Kubernetes cluster consists of a control plane plus a set of worker machines, called nodes, that run containerized applications.” The control plane makes decisions and records the desired state; nodes provide the compute capacity where application Pods run. See the Kubernetes Cluster Architecture documentation.
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Those are logical responsibilities, not necessarily two permanently separate kinds of hardware. A small test cluster can run control-plane and node services on one machine. A production cluster commonly spreads control-plane services across several machines or failure zones, while workload nodes are added separately. The installation or managed-service model determines where each component runs.
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kube-apiserver: the cluster’s front door
The kube-apiserver exposes the Kubernetes API. Tools, controllers, kubelets and administrators communicate through it rather than changing cluster state directly. It authenticates and authorizes requests, validates objects and provides the watch mechanisms used by other components. The API server is designed to scale horizontally, so several instances can sit behind a load balancer in a highly available control plane.
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etcd: the backing store for cluster data
etcd is a consistent, highly available key-value store used for Kubernetes’ cluster data: object definitions, configuration and observed state. It is not a general application-data store and does not hold container images. Because losing etcd can mean losing the control plane’s authoritative state, production operations require an etcd backup and recovery plan. etcd can be colocated with control-plane services or operated externally, depending on the chosen architecture.
kube-scheduler: choosing a node for an unbound Pod
The scheduler watches for newly created Pods that do not yet have a node assignment. It evaluates available capacity and constraints, then records a suitable node choice. Decisions can include CPU and memory requests, hardware or software requirements, policy restrictions, node and inter-pod affinity or anti-affinity, locality, interference and deadlines. The scheduler selects a destination; it does not start the container itself.
kube-controller-manager: reconciliation loops
The controller manager runs controller processes. A controller continually compares the desired state declared through the API with the cluster’s observed state and takes corrective action. For example, a workload controller can create replacement Pods when replicas are missing. Controllers react to changes; they are not a second scheduler.
cloud-controller-manager: optional cloud integration
In clusters that need cloud-provider integration, the cloud-controller-manager connects Kubernetes to infrastructure functions such as provider-specific node, route or load-balancer operations. It is optional, not a universal requirement. Bare-metal and other environments can run without it. Component descriptions are summarized in the Kubernetes Components reference.
What runs on each node
“Each node is managed by the control plane and contains the services necessary to run Pods.” A node can be a physical server or a virtual machine, as described in the Nodes documentation.
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Kubelet
The kubelet is the node agent. It receives Pod specifications from configured sources and makes sure the specified containers are running and healthy. It reports status back to the API server and works with the container runtime. The kubelet does not take responsibility for arbitrary containers started outside Kubernetes.
Container runtime
The runtime downloads images, creates and starts containers, and manages their lifecycle on behalf of the kubelet. Kubernetes communicates with runtimes through the Container Runtime Interface (CRI). Common CRI-compatible choices include containerd and CRI-O; the important requirement is a functioning CRI implementation, not a particular brand.
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kube-proxy traditionally maintains node-level network rules that implement part of Kubernetes Service behavior, forwarding connections to the appropriate backend Pods. It is optional when the selected network plugin supplies equivalent Service-forwarding behavior. Therefore, a diagram that places kube-proxy on every node describes one common setup, not a mandatory rule.
Network plugin (CNI)
A Container Network Interface (CNI) plugin allocates Pod IP addresses and enables Pod-to-Pod communication across nodes. Networking is a cluster layer that must be installed and configured, but it is not a control-plane decision-making component. The exact plugin and its features depend on the distribution and requirements.
How a machine becomes a Kubernetes node
There is no permanent hardware identity called “worker.” A machine becomes eligible to host workloads when node services are installed, it is represented by a Node object in the API, and the control plane considers it healthy and suitable for scheduling.
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- Prepare the machine. Install a supported operating system and Kubernetes node components, including a kubelet and CRI-compatible container runtime. Configure the required networking and credentials according to the chosen distribution.
- Create the Node representation. Kubernetes supports two approaches: the kubelet can self-register with the API server, or an administrator can create a Node object manually. Self-registration is common in automated joins; manual objects are useful when an operator wants explicit control.
- Establish health reporting. The kubelet reports node status and sends Lease-based heartbeats. The control plane uses these signals to distinguish an available node from one that has stopped responding.
- Advertise capacity. The node reports capacity and allocatable resources. The scheduler uses those values, together with Pod requests and placement constraints, when deciding whether a Pod can run there.
- Allow scheduling. Once the node is registered and healthy, the scheduler can bind eligible, unbound Pods to it. Labels, taints, affinity rules and policy can still restrict which workloads it accepts.
Registration alone does not guarantee that every workload will run on the machine. A node may be cordoned, tainted, out of capacity or incompatible with a Pod’s requirements. Conversely, a node can be healthy while hosting no Pods.
How a machine becomes a control-plane host
A control-plane role is established by the deployment method that installs and configures the API server, etcd, scheduler and controller processes on a machine. Kubernetes does not conduct a general-purpose “master election” during ordinary cluster creation. Managed Kubernetes services operate this machinery for you; self-managed distributions provide their own bootstrap procedure.
Example: kubeadm initialization
kubeadm init illustrates the process, but it is one workflow rather than a universal rule:
- Initialize the first control-plane machine. kubeadm validates prerequisites and creates the certificates and configuration needed for the initial control plane.
- Write static-Pod manifests. It places manifests for the kube-apiserver, kube-controller-manager and kube-scheduler in the kubelet’s static-Pod manifest directory. When an external etcd is not configured, kubeadm also creates a manifest for a local etcd static Pod.
- Let the local kubelet supervise them. The kubelet watches that directory and starts the declared static Pods. These Pods are managed directly by that kubelet rather than created by the scheduler.
- Make the API available. Once the API server and its dependencies are running, the cluster can accept objects and report the new machine as a control-plane node.
- Configure joining credentials. kubeadm sets up bootstrap-token and TLS-bootstrap mechanisms used by additional nodes to authenticate and obtain their configuration.
- Join additional machines. A worker join installs or enables the node services, authenticates to the API server and registers a new Node object. A further control-plane join uses the distribution’s control-plane join procedure to place control-plane services on another machine.
Static Pods are a kubelet feature: the kubelet watches local manifest files and keeps those Pods running even though they are not scheduled in the normal way. The behavior is documented at Static Pods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common layouts: one machine, separated roles and high availability
| Layout | Where components run | Useful for | Main trade-off |
|---|---|---|---|
| Single-node cluster | Control-plane services and node services share one machine. | Learning, development and small experiments. | A machine failure removes both management and workload capacity. |
| Separated control plane and workers | One or more control-plane machines run management services; other machines run application Pods. | Clear resource boundaries and growing workloads. | More machines, networking and operational work. |
| Highly available control plane | Control-plane services are distributed across multiple machines, often across failure zones; API servers can be load-balanced and etcd is arranged for availability. | Production systems that must tolerate a machine or zone outage. | Quorum, certificates, upgrades, backups and failure recovery become more complex. |
| Mixed-role machines | A machine runs control-plane services and may also be allowed to schedule application Pods. | Resource-constrained environments or deliberate small deployments. | Workloads compete with management services unless scheduling protections and capacity planning are applied. |
Setup guidance distinguishes self-managed installations from managed Kubernetes services and highlights availability, security, maintenance, operator expertise and workload requirements as architecture decisions. Consult Kubernetes Getting Started and the Production environment guidance for the model that fits your operation.
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What determines whether a node can run a particular Pod?
- Resources: requested CPU and memory must fit the node’s allocatable capacity, along with actual headroom for system processes.
- Hardware and operating system: a Pod may require a GPU, a specific CPU feature, Windows, a particular kernel capability or another node property.
- Labels and affinity: required or preferred placement rules can attract Pods to particular nodes or keep them apart.
- Taints and tolerations: a taint can repel ordinary workloads; only Pods with a matching toleration can be scheduled there.
- Health and readiness: missing heartbeats, failed conditions or administrative cordoning can prevent new assignments.
- Locality and policy: storage location, topology, interference limits, deadlines and organizational policies can influence the scheduler’s choice.
Failure modes that reveal the role system
The machine is visible but receives no Pods
Check whether it is cordoned or tainted, whether its allocatable resources satisfy the Pod’s requests, and whether labels or affinity rules exclude it. A registered Node object is only the starting point for scheduling.
The node changes to NotReady
Investigate kubelet health, runtime errors, network connectivity to the API server and Lease or status updates. A stopped heartbeat tells the control plane that the machine may be unavailable; it does not automatically repair the underlying host.
The control plane is running but workloads cannot start
Verify that a CRI runtime is active, the kubelet can communicate with it, and a compatible CNI plugin is installed. A healthy API server and scheduler cannot launch containers on a node whose runtime or networking is missing.
A single control-plane machine fails
In a single-node or single-control-plane layout, management availability and often workload availability are concentrated in that machine. Multi-machine control planes reduce this single point of failure, but only when etcd, API access, certificates, load balancing and recovery procedures are designed consistently.
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A reliable mental model
Think of the process as a chain:
machine + kubelet/runtime/networking → Node registration → heartbeats and capacity → scheduler placement → kubelet and runtime start Pods.
For control-plane setup, add a separate chain:
chosen distribution’s bootstrap tool → control-plane manifests and credentials → kubelet-supervised control-plane services → API server and etcd state → additional nodes join.
That model explains why “master” and “worker” are convenient shorthand rather than identities assigned by Kubernetes itself. The control plane is a set of management services, a node is a machine running the services needed for Pods, and one physical or virtual machine can perform both roles when the deployment intentionally configures it that way.
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