Kubernetes Up Running, 3rd Edition (Burns, Brendan Beda, Joe Hightower etc.) (Z-Library)

# Kubernetes: Up & Running, 3rd Edition — Reading Guide ## 【One-Line Pitch】 The definitive practical guide to Kubernetes from four of its earliest contributors, teaching you how to build, deploy, and operate reliable, scalable cloud-native applications—whether you're new to distributed systems or already running production workloads. ## 【Book Arc】 - **Opening (~0%–10%)**: Introduces the core philosophy—velocity, immutability, declarative configuration, and self-healing systems—and explains why Kubernetes has become the standard API for cloud-native development, including how it enables efficient resource forecasting and team scaling through decoupling. - **Early (~10%–23%)**: Covers container fundamentals: building application images with Dockerfiles, optimizing image sizes, security considerations, multistage builds, and the distinction between system and application containers—the foundation for everything that follows. - **Early (~23%–32%)**: Explains the cloud-native ecosystem landscape, including the CNCF project maturity model (sandbox, incubating, graduated), and walks through deploying your first Kubernetes cluster using cloud services, minikube, or bare metal with kubeadm. - **Middle (~32%–48%)**: Dives into day-to-day Kubernetes operations: using kubectl to view and manipulate API objects, understanding RESTful resources, working with JSONPath queries, and managing contexts for multiple clusters and users. - **Middle (~48%–52%)**: Explores advanced topics including RBAC (roles, role bindings, ClusterRoles, groups), service meshes (mutual TLS, traffic shaping, introspection), and whether you actually need a service mesh for your use case. - **Late (~52%–65%)**: Covers storage integration: importing external services, services without selectors, running reliable singletons like MySQL, dynamic volume provisioning, and Kubernetes-native storage with StatefulSets. ## 【Key Takeaways】 - **Kubernetes decouples applications from infrastructure** (Early): By abstracting away individual machines, teams can share resources, forecast aggregate growth more accurately, and scale up or down automatically—reducing both cost and operational overhead. - **Application containers are the right granularity for orchestration** (Early): Unlike system containers that mimic VMs, running a single program per container provides the perfect composition unit for scalable distributed systems, a philosophy that underpins Pods. - **Dockerfiles enable reproducible image builds** (Early): Automating container creation with Dockerfiles ensures consistency across environments, and techniques like multistage builds help optimize image size and security. - **Start with managed Kubernetes services** (Middle): Cloud-based Kubernetes offerings are recommended for beginners—they're free in most clouds, eliminate cluster management complexity, and let you focus on learning Kubernetes itself before tackling bare-metal installation. - **kubectl is your window into the Kubernetes API** (Middle): Everything in Kubernetes is a RESTful resource at a unique HTTP path; mastering commands like `get`, `-o wide`, `-o json/yaml`, and JSONPath queries unlocks efficient debugging and automation. - **RBAC is essential for production security** (Middle): Understanding Roles, Role Bindings, ClusterRoles, and group-based bindings—plus testing with `can-i`—lets you manage authorization safely and keep RBAC configuration in source control. - **Service meshes solve real problems but aren't always necessary** (Middle): Mutual TLS, traffic shaping, and introspection are powerful capabilities, but evaluate whether your use case genuinely requires a mesh before adding its complexity. - **Storage integration requires careful design** (Late): From importing external services and running singletons like MySQL to dynamic volume provisioning and StatefulSets, Kubernetes offers multiple storage patterns—each with specific trade-offs around reliability and health checking. ## 【Reading Tips】 - **Skim Chapter 1 if you're experienced**: The opening philosophy is valuable context but may feel introductory if you've already worked with distributed systems—focus on the scaling and forecasting sections for practical insights. - **Deep-read the container chapters**: Chapter 2's Dockerfile patterns, image optimization, and security practices are foundational; get these right before moving to cluster deployment. - **Use the kubectl sections as a reference**: The commands and flags (get, -o wide, JSONPath, --no-headers) are best learned hands-on—keep this section bookmarked while you practice. - **Pay attention to the CNCF maturity model**: Understanding sandbox vs. incubating vs. graduated projects will save you from adopting immature tools in production. - **The storage chapter rewards careful reading**: External services, singletons, and StatefulSets each solve different problems—take time to understand which pattern fits your workload before implementing. ## 【Coverage Limits】 Excerpts cover roughly the first half of the book (through storage integration); later chapters on advanced topics like autoscaling, security hardening, and production operations are not included in this guide. Some technical details (e.g., specific kubectl output formats) are abbreviated in the source material. ##