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Container OrchestrationAdvanced Difficulty11 min read

Production Kubernetes (K8s) Cluster Architecture

Kubernetes orchestrates containerized workloads with declarative state reconciliation, self-healing pod scheduling, horizontal auto-scaling, and automated rollouts.

Estimated Traffic50,000 QPS across 200 microservices
5-Year Data FootprintPetabyte-scale distributed block & object volumes
Target Latency< 15 milliseconds
Availability Target99.99% (4 Nines)
Need custom numbers for your interview?Calculate QPS & capacity in System Design Cheat Sheet →

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Blueprint:Kubernetes Cluster
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1. Problem & Challenge

Managing hundreds of containerized microservices manually causes configuration drift, cascading downtime during server crashes, and painful rollouts. The platform needs declarative desired-state management, automated health probes, and dynamic load balancing.

2. Core Building Blocks & Responsibilities

👉 Desliza la tabla para ver roles y responsabilidades
ComponentRolePlain-English Explanation
Kube-API Server (Control Plane)Central State REST GatewayExposes the Kubernetes REST API, processes declarative manifests, and acts as the only component talking directly to etcd.
etcd Distributed Consensus StoreCluster Truth & State StoreConsistent and highly-available key-value store implementing Raft consensus to back all cluster data.
Kube-Scheduler & Controller ManagerWorkload Placement & ReconciliationAssigns unassigned pods to nodes based on resource limits/taints and reconciles current state against desired state.
Ingress Controller (NGINX / Envoy)Traffic Entrypoint & RoutingDirects external HTTP/HTTPS requests to internal cluster Services and Pod IPs.
Worker Nodes & KubeletContainer Lifecycle & Health MonitoringAgent on each node ensuring that containers described in PodSpecs are running and healthy.
CoreDNS & Kube-ProxyService Discovery & Packet RoutingProvides cluster-internal DNS names (service.namespace) and manages iptables/IPVS packet routing rules.

3. Step-by-Step Request Flow

1

Manifest Submission & Auth

Engineer or CI/CD pipeline applies deployment YAML via kubectl to Kube-API Server.

2

Consensus Commit to etcd

API Server validates schema, runs admission controllers, and commits desired state into etcd quorum.

3

Intelligent Pod Scheduling

Kube-Scheduler filters worker nodes by CPU/RAM capacity, affinity rules, and taints, binding pods to target nodes.

4

Kubelet Execution

Kubelet daemon on target worker node commands containerd runtime to pull images and initialize pods.

5

Live Service Ingress

Ingress controller detects new ready endpoints via readiness probes and routes production traffic to the new pods.

4. Architectural Trade-offs

Decision:

Managed K8s (EKS / GKE) vs Self-Hosted Control Plane

Chosen: Managed Cloud Control Plane

Rationale: Self-hosting etcd quorum requires complex cross-AZ quorum recovery and continuous backup snapshots. Managed services provide 99.95% control plane SLAs with zero operator maintenance.

Decision:

Horizontal Pod Autoscaler (HPA) vs Overprovisioning

Chosen: Metrics-driven HPA with Cluster Autoscaler

Rationale: Static overprovisioning wastes 60%+ cloud spend. HPA scales pods up on CPU/custom queue metrics, triggering Cluster Autoscaler to add EC2 nodes dynamically.

Interview Tip

Explain the Reconciliation Loop: Kubernetes never executes imperative commands. It runs continuous loops: `Observe Current State` -> `Compare with Desired State in etcd` -> `Execute Difference Action`. If a node crashes, the Node Lifecycle Controller notices missing heartbeats and automatically reschedules pods.

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