Cloud Native Engineering: Kubernetes — Building and Operating Container Platforms at Scale

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Cloud Native Engineering: Kubernetes — Building and Operating Container Platforms at Scale

Author: Elliot Grayson

Description: Containers solved packaging, but created an operational crisis. The same technology that made deployment easy made managing hundreds of containers nearly impossible without a new kind of system. Scaling beyond a handful of containers demands orchestration, and Kubernetes emerged as the answer rooted in Google's decade-long experience running global services. This book does not walk through kubectl commands. It reveals the architecture and operational principles behind production-grade Kubernetes platforms, so you can design, operate, and evolve clusters that scale with your organization. Cloud Native Engineering: Kubernetes — Building and Operating Container Platforms at Scale is a comprehensive engineering guide that bridges the gap between isolated containers and self-service internal platforms. It traces the journey from the limits of manual management to the control plane mechanics that make Kubernetes tick, then tackles the hardest real-world challenges: stateful workloads, networking, security, and multi-tenancy. The book culminates in a synthesis of ecosystem tools like Helm, Operators, and GitOps into a coherent platform engineering strategy. • Understand why orchestration is inevitable and how Borg/Omega shaped Kubernetes design. • Deconstruct the control plane: API server, etcd, controllers, and scheduling logic. • Master pod lifecycle, health checks, and failure recovery for reliable workloads. • Deploy software at scale with rollouts, rollbacks, and advanced release strategies. • Navigate Kubernetes networking: service discovery, DNS, ingress, and API gateways. • Tackle persistent storage and run databases on Kubernetes with confidence. • Build observability with metrics, logs, and traces, then secure the cluster with RBAC, network policies, and supply chain security. • Scale operations across teams using multi-tenancy, resource governance, and platform standards. • Package complexity with Helm, automate operations with Operators, and adopt GitOps for declarative delivery. • Design a production internal developer platform (IDP) and look ahead to service meshes, WASM, and the future of cloud native. This book is for platform engineers, software engineers, DevOps practitioners, and technical architects who already know basic container concepts and want to move beyond surface-level Kubernetes usage. It assumes experience with Linux systems, Docker, and foundational networking, but it does not waste time on introductory tutorials. Every chapter pushes toward production-grade understanding — from the scheduler's filter-and-score algorithm to the trade-offs of running Postgres on Kubernetes versus using a managed database service. The architecture-first approach keeps theory grounded in operational reality. Each chapter opens with a concrete problem — a cascading failure, a scaling bottleneck, a security incident — then explains the Kubernetes mechanisms that solve it. Trade-offs are discussed openly, and real-world case studies from engineering blogs and CNCF documentation illustrate how patterns succeed or fail in practice. The result is a mental model of Kubernetes not as a black-box deployment tool, but as a programmable distributed operating system that shifts operational burden from humans to automated control loops. If you are building or maintaining Kubernetes clusters in production and want to move from reactive troubleshooting to proactive platform design, this book gives you the architectural depth and operational wisdom to do it. It transforms how you think about infrastructure — from application developer to platform engineer, treating the cluster as a product that enables developer self-service and organizational scaling. Read it sequentially through the first two parts, then use the remaining chapters as architectural references for the challenges your team faces today. Cloud native engineering is not just about running containers; it is about building systems that evolve with your business. This guide shows you how.

AI summary: Cloud Native Engineering: Kubernetes offers an architecture-first exploration of Kubernetes, covering the control plane, scheduling, networking, stateful workloads, observability, security, and GitOps. It is designed for engineers with container experience who want to build and operate production-grade clusters and internal developer platforms. The book emphasizes real-world trade-offs and patterns from companies like Google and Netflix.

Uygun okuyucu

Platform engineers, DevOps engineers, software engineers, and technical architects with intermediate Kubernetes experience

Okur profili

A platform engineer or architect who understands containers and wants to design, build, and operate scalable Kubernetes platforms in production, moving beyond basic usage to production-grade practices.

Arama amacı

Readers search for a comprehensive, architecture-focused Kubernetes guide to solve real-world operational challenges like scaling, networking, stateful workloads, and multi-tenancy.

Özgün açı

Unlike command-focused tutorials, this book takes an architecture-first perspective, deconstructing Kubernetes as a distributed operating system and synthesizing patterns like GitOps and Operators into a coherent platform engineering strategy.

İçerik türü

technical architecture and operations guide

Kısa özet

• This book provides an architecture-first approach to Kubernetes, focusing on the control plane and reconciliation loops.
• It covers production challenges such as running stateful databases on Kubernetes and securing multi-tenant clusters.
• Readers learn to design internal developer platforms using Helm, Operators, and GitOps.
• The target audience includes platform engineers, DevOps practitioners, and technical architects with intermediate Kubernetes knowledge.

Key topics: Kubernetes architecture, control plane, scheduling, pod lifecycle, service discovery, ingress, persistent storage, StatefulSets, observability, RBAC, multi-tenancy, Helm

Entities: Kubernetes, Docker, etcd, API server, kube-proxy, CNI, Prometheus, RBAC, Helm, ArgoCD, StatefulSet, service mesh

Karşılanan ihtiyaçlar

• Scaling container management from dozens to thousands of containers
• Scheduling and resource allocation across clusters
• Managing persistent storage and stateful applications
• Ensuring security with RBAC and network policies
• Implementing declarative deployments and rollbacks

Şunlar için oku

• Platform engineers building internal developer platforms
• DevOps engineers managing Kubernetes clusters in production
• Software engineers transitioning to infrastructure roles
• Technical architects designing cloud native architectures
• Site reliability engineers responsible for cluster reliability

Şu durumda uygun olmayabilir

• Complete beginners without basic container knowledge
• Developers seeking a kubectl cookbook or tutorial
• Readers looking for vendor-specific cloud managed Kubernetes guides

İçindekiler

1. Introduction (introduction)
2. Why Orchestration Changed Everything (part)
3. The Limits of Containers Alone (chapter)
4. The Container Explosion (section)
5. Operational Complexity (section)
6. The Need for Automation (section)
7. The Orchestration Problem (section)
8. From Borg to Kubernetes (chapter)
9. Google's Internal Journey (section)
10. Borg (section)
11. Omega (section)
12. The Birth of Kubernetes (section)
13. The Rise of Cloud Native Platforms (section)
14. Thinking Like a Platform Engineer (chapter)
15. Applications vs Platforms (section)
16. Infrastructure as a Product (section)
17. Self-Service Systems (section)
18. The Platform Mindset (section)
19. The Kubernetes Control Plane (part)
20. The Brain of the Cluster (chapter)
21. The API Server (section)
22. Etcd (section)
23. Controllers (section)
24. Schedulers (section)
25. Control Loops (section)
26. How Workloads Find a Home (chapter)
27. Scheduling Decisions (section)
28. Resource Management (section)
29. Affinity and Anti-Affinity (section)
30. Taints and Tolerations (section)
31. Cluster Capacity (section)
32. The Life of a Pod (chapter)
33. Pod Creation (section)
34. Pod Lifecycle (section)
35. Health Checks (section)
36. Restarts (section)
37. Failure Recovery (section)
38. Building Application Platforms (part)
39. Deploying Software at Scale (chapter)
40. Deployments (section)
41. ReplicaSets (section)
42. Rollouts (section)
43. Rollbacks (section)
44. Release Strategies (section)
45. Connecting Services Across the Cluster (chapter)
46. Service Discovery (section)
47. Internal Networking (section)
48. DNS (section)
49. Service Communication (section)
50. Traffic Management (section)
51. Entering the Cluster (chapter)
52. Ingress (section)
53. API Gateways (section)
54. TLS Management (section)
55. Public Access (section)
56. Edge Architecture (section)
57. State in a Stateless World (part)
58. The Storage Challenge (chapter)
59. Why State Is Hard (section)
60. Persistent Volumes (section)
61. Storage Classes (section)
62. Data Durability (section)
63. Running Databases on Kubernetes (chapter)
64. Stateful Workloads (section)
65. Backup Strategies (section)
66. Recovery Planning (section)
67. Operational Trade-offs (section)
68. Data Reliability at Scale (chapter)
69. Replication (section)
70. High Availability (section)
71. Disaster Recovery (section)
72. Storage Architectures (section)
73. Operating Production Clusters (part)
74. Observing the Invisible System (chapter)
75. Metrics (section)
76. Logs (section)
77. Traces (section)
78. Monitoring Foundations (section)
79. Securing the Cluster (chapter)
80. Authentication (section)

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