¶ Kubernetes
¶ Operating Systems
Anyone who has spent time building software in today’s world eventually reaches a moment when the complexity of running applications feels heavier than the applications themselves. Modern software rarely lives alone. It sits behind load balancers, speaks to databases, scales across replicas, logs events to some external system, receives configuration from somewhere else, and must keep running even when the machine it lives on falters. Once the simplicity of “run this program” gives way to the reality of distributed systems, a kind of vertigo sets in. It becomes clear that the old ways of deploying and managing applications simply don’t hold up anymore.
Kubernetes emerged from that tension. It wasn’t created as a luxury, or as a tool chasing trends, but as a response to real and growing needs. Organizations weren’t struggling to write software—they were struggling to run it. Containers had already offered a foundation by packaging applications and their dependencies in a predictable way, but containers alone didn’t solve the question of how to manage thousands of them, how to handle failures gracefully, how to update live systems without breaking them, and how to scale seamlessly when demand shifts. Kubernetes stepped in as a system that attempts to orchestrate all of this, quietly and reliably, in a world where reliability is anything but guaranteed.
This course of one hundred articles is meant to guide you through that world—slowly, clearly, and in a way that helps you form an intuition for why Kubernetes looks the way it does. Because Kubernetes can feel overwhelming at first glance. It carries a vocabulary all its own: pods, nodes, deployments, services, ingress, controllers, CRDs. It offers layers of abstraction, each with its own purpose. It encourages a way of working that is different from traditional operating systems yet deeply influenced by decades of system design. And underneath all its moving parts lies a simpler question: how do we build a stable, resilient platform for running applications at scale?
What makes Kubernetes interesting is that it’s not a traditional operating system—yet it behaves like one. In many ways, Kubernetes is an operating system for distributed clusters. It decides where workloads run. It monitors the health of everything. It handles restarts and relocations. It enforces boundaries and allocation rules. It provides a consistent API for interacting with the environment. Instead of managing processes within one machine, it manages workloads across many. Instead of assuming a single memory space or file system, it assumes a universe of loosely connected containers that must behave consistently across entirely different machines.
Once you begin to see Kubernetes not as a tangle of YAML and abstractions but as a kind of cluster-level operating system, its design becomes easier to understand. You begin to realize that the things Kubernetes does—scheduling, controlling, reconciling desired state with actual state—are the same problems operating systems have dealt with for decades, only scaled out to a level that spans racks, data centers, and clouds.
But Kubernetes is more than just a piece of software. It’s a philosophy about how systems should behave. It assumes that systems fail regularly. It assumes that machines are ephemeral. It assumes that workloads move. It assumes that declarative configuration is less error-prone than imperative commands. It assumes that automation is not a convenience but a necessity. And it assumes that a system should always try to correct itself when something drifts away from what was intended. These assumptions aren’t just technical design choices—they reflect a worldview shaped by years of experience running massive distributed systems.
The first time someone interacts with Kubernetes, it often feels like they’re learning a new language. They write a deployment manifest, apply it, and Kubernetes quietly begins carrying out instructions in the background: pulling images, creating pods, scheduling them on nodes, replacing them when they fail, scaling them when needed. It’s almost unsettling how much happens without any explicit commands. Kubernetes becomes the caretaker of your intent. You tell it what you want, and it continuously works to make the world match that description. That approach shifts the responsibility away from the developer micromanaging steps and toward the system maintaining a desired state.
As you immerse yourself in Kubernetes, you begin to appreciate the elegance of this “desired state” model. Instead of telling the system what to do, you tell it what you want. Kubernetes accepts that description, stores it, and delegates the job of aligning reality with your specifications to its controllers. This model isn’t merely convenient—it’s what makes Kubernetes resilient. When a node dies, Kubernetes doesn’t panic; it simply notices that the world no longer matches what it should be, and it works to restore balance. This self-healing nature is one of the major reasons Kubernetes has become so widespread.
Another revelation that comes with learning Kubernetes is how closely it mirrors ideas from classical distributed systems: consensus, reconciliation, eventual consistency, scheduling, lease management, leader election, and shared state. Kubernetes is built on top of these concepts, yet it attempts to hide much of their complexity behind simple APIs. It lets developers focus on applications, not on the machinery keeping those applications alive. It’s no surprise that once people grow comfortable with Kubernetes, their understanding of distributed systems tends to deepen as well.
Kubernetes also changes the way teams think about deployments. Traditional approaches often revolved around “pushing” changes: running commands that replaced processes, restarted servers, or updated settings directly on machines. Kubernetes flips this around. You update the manifest—the source of truth—and Kubernetes figures out the safe way to roll out the change. It might replace pods gradually, checking health along the way. It might pause if something looks wrong. It might automatically roll back. These workflows reflect hard-earned lessons from operating large systems: humans are fallible, surprises are inevitable, and automated safeguards often prevent disasters.
As you travel through the articles in this course, you’ll begin to see Kubernetes less as an intimidating creature and more as a carefully constructed machine with understandable principles. You’ll explore how Kubernetes organizes workloads into pods, why pods exist at all, how the scheduler chooses nodes, how controllers uphold the system’s continuity, and how Kubernetes abstracts networking in a way that makes containers feel like they live within a single logical network. You’ll see how storage integrates with otherwise ephemeral containers, how secrets and configuration are injected safely, and how scaling works both horizontally and vertically.
Just as importantly, you’ll develop an intuition for Kubernetes’ design patterns: reconciliation loops, resource specifications, declarative API objects, and the event-driven nature of the control plane. These patterns appear everywhere in Kubernetes, from built-in components to custom extensions. Once you understand the core ideas, the apparent complexity begins to simplify. Kubernetes becomes a place where everything is consistent, predictable, and rooted in the same small set of principles.
Kubernetes is often described as the “Linux of the cloud,” and while the metaphor isn’t perfect, it captures something essential. Kubernetes provides an environment in which applications run on top of abstracted resources. It offers processes that get scheduled, networks that connect everything, and volumes that store data. But just as Linux abstracts away hardware differences, Kubernetes abstracts away the differences between nodes, clusters, and even clouds. It frees applications from relying on any specific machine. This abstraction has become foundational to how modern applications are deployed and maintained.
One of the most rewarding parts of working with Kubernetes is realizing how much potential it unlocks for experimentation. Before Kubernetes, running complex distributed systems required an enormous amount of manual setup. With Kubernetes, you can define multi-service, multi-replica, highly available systems using just a few declarative files. You can spin them up, tear them down, iterate, test failure scenarios, simulate outages, and observe how the system responds. Kubernetes turns distributed system experimentation into something accessible rather than intimidating.
Over time, Kubernetes also changes how teams collaborate. Infrastructure is no longer scribbled on a whiteboard or described in scattered documents. It becomes code—reviewable, testable, shareable. Teams learn to treat deployments as part of the development process rather than a separate, mysterious stage. And because Kubernetes encourages good boundaries and well-defined responsibilities, the architecture of applications themselves often becomes clearer. Developers become more aware of how services interact, where bottlenecks exist, and how resource usage affects performance.
This course will walk you step by step into this world, not through memorization, but through understanding. You’ll learn how Kubernetes components communicate, how the API server stores and validates objects, how controllers work behind the scenes, and why certain patterns recur throughout the ecosystem. You’ll also gain a sense of how Kubernetes evolved from earlier systems, what problems it tries to solve, and what trade-offs it consciously accepts.
Kubernetes is not simple. It isn’t meant to be. Running distributed systems reliably is inherently challenging. But Kubernetes tries to bring order to that complexity, and the more deeply you explore it, the more its design begins to feel coherent, thoughtful, and often surprisingly intuitive. What once looked like an impossibly large system reveals itself as a set of building blocks, each with a purpose and each grounded in a small number of consistent ideas.
As you progress through all one hundred articles, the noise will fade. The complexity will become structure. The abstractions will make sense. And Kubernetes will shift from feeling like a foreign landscape to becoming a system you can reason about, navigate, and shape with confidence.
This course is an invitation to that understanding. Let’s begin.
¶ Kubernetes
¶ Beginner Level (Chapters 1-30)
1. Introduction to Kubernetes and Container Orchestration
2. Understanding Containers and Their Role in Modern OS
3. Overview of Operating Systems and Their Interaction with Kubernetes
4. Installing Kubernetes on Linux: Minikube and Kind
5. Setting Up a Kubernetes Cluster on Windows
6. Kubernetes Architecture: Master and Worker Nodes
7. Understanding Pods: The Smallest Deployable Units
8. Working with Containers: Docker and Kubernetes
9. Kubernetes Namespaces: Organizing Resources
10. Basic Kubectl Commands for Cluster Management
11. Exploring the Kubernetes API and Objects
12. Deploying Your First Application on Kubernetes
13. Understanding YAML Files for Kubernetes Deployments
14. Managing Pod Lifecycles: Create, Update, and Delete
15. Introduction to Kubernetes Networking: Pod-to-Pod Communication
16. Configuring Kubernetes on Different Operating Systems
17. Using Kubernetes with Linux Containers (LXC)
18. Introduction to Kubernetes Storage: Volumes and Persistent Storage
19. Managing Environment Variables in Kubernetes
20. Debugging Pods and Containers in Kubernetes
21. Introduction to Kubernetes Services: ClusterIP, NodePort, and LoadBalancer
22. Exploring Kubernetes DNS and Service Discovery
23. Securing Kubernetes: Role-Based Access Control (RBAC)
24. Managing Kubernetes Resources: Requests and Limits
25. Introduction to Kubernetes ConfigMaps and Secrets
26. Monitoring Kubernetes Clusters with Basic Tools
27. Logging in Kubernetes: Viewing and Managing Logs
28. Introduction to Kubernetes Ingress: Routing External Traffic
29. Scaling Applications with Kubernetes ReplicaSets
30. Understanding Kubernetes Scheduler and Its Role in OS Resource Allocation
¶ Intermediate Level (Chapters 31-60)
31. Deep Dive into Kubernetes Networking: CNI Plugins
32. Configuring Kubernetes with Systemd on Linux
33. Managing Kubernetes Nodes: Adding and Removing Nodes
34. Advanced Pod Scheduling: Node Affinity and Taints
35. Using Kubernetes with Windows Containers
36. Managing Stateful Applications with StatefulSets
37. Configuring Persistent Volumes (PVs) and Persistent Volume Claims (PVCs)
38. Implementing Storage Classes in Kubernetes
39. Kubernetes Security: Pod Security Policies (PSPs)
40. Securing Kubernetes with Network Policies
41. Managing Multi-Tenancy in Kubernetes Clusters
42. Using Helm for Kubernetes Application Deployment
43. Introduction to Kubernetes Operators
44. Automating Kubernetes Deployments with CI/CD Pipelines
45. Monitoring Kubernetes with Prometheus and Grafana
46. Logging with Fluentd, Elasticsearch, and Kibana (EFK Stack)
47. Troubleshooting Kubernetes Clusters: Common Issues and Solutions
48. Managing Resource Quotas and Limits in Kubernetes
49. Configuring High-Availability (HA) Kubernetes Clusters
50. Understanding Kubernetes API Extensions: CRDs
51. Implementing Custom Controllers in Kubernetes
52. Using Kubernetes with Cloud Providers: AWS, GCP, and Azure
53. Managing Kubernetes Clusters with Kubeadm
54. Configuring Kubernetes with Ansible and Terraform
55. Exploring Kubernetes Service Mesh: Istio and Linkerd
56. Managing Kubernetes Clusters on Bare Metal
57. Integrating Kubernetes with Linux Security Modules (LSMs)
58. Using Kubernetes with SELinux and AppArmor
59. Configuring Kubernetes with Cgroups and Namespaces
60. Managing Kubernetes Clusters with Rancher and K3s
¶ Advanced Level (Chapters 61-90)
61. Deep Dive into Kubernetes Networking: IPVS and eBPF
62. Implementing Multi-Cluster Kubernetes with Federation
63. Managing Kubernetes Clusters with GitOps: ArgoCD and Flux
64. Advanced Kubernetes Scheduling: Custom Schedulers
65. Optimizing Kubernetes for High-Performance Workloads
66. Managing Kubernetes Clusters with OpenShift
67. Implementing Zero-Trust Security in Kubernetes
68. Using Kubernetes with GPU and Hardware Accelerators
69. Managing Kubernetes Clusters with Edge Computing
70. Configuring Kubernetes with Custom CNI Plugins
71. Implementing Service Mesh Policies with Istio
72. Managing Kubernetes Clusters with Virtual Kubelet
73. Exploring Kubernetes and Serverless: Knative
74. Implementing Chaos Engineering in Kubernetes
75. Managing Kubernetes Clusters with Policy Engines: OPA and Gatekeeper
76. Configuring Kubernetes with Custom Admission Controllers
77. Implementing Multi-Tenancy with Virtual Clusters
78. Managing Kubernetes Clusters with Cluster API
79. Exploring Kubernetes and WebAssembly (Wasm)
80. Implementing Kubernetes with Confidential Computing
81. Managing Kubernetes Clusters with AI/ML Workloads
82. Configuring Kubernetes with Custom Resource Definitions (CRDs)
83. Implementing Kubernetes with Distributed Storage: Ceph and Rook
84. Managing Kubernetes Clusters with HPC Workloads
85. Exploring Kubernetes and Blockchain Integration
86. Implementing Kubernetes with Custom Metrics and HPA
87. Managing Kubernetes Clusters with Custom Controllers
88. Configuring Kubernetes with Custom API Servers
89. Implementing Kubernetes with Custom Schedulers
90. Managing Kubernetes Clusters with Custom Networking Stacks
¶ Expert Level (Chapters 91-100)
91. Designing Custom Kubernetes Distributions
92. Implementing Kubernetes with Custom Runtime Classes
93. Managing Kubernetes Clusters with Custom Security Policies
94. Exploring Kubernetes and Quantum Computing
95. Implementing Kubernetes with Custom Hardware Integration
96. Managing Kubernetes Clusters with Custom Observability Stacks
97. Configuring Kubernetes with Custom Authentication Mechanisms
98. Implementing Kubernetes with Custom Load Balancing Algorithms
99. Managing Kubernetes Clusters with Custom Orchestration Layers
100. Future Trends: Kubernetes and Operating Systems Integration