Learn Kubernetes in a Month of Lunches

Learn Kubernetes in a Month of Lunches
contents
preface
acknowledgments
about this book
	Who should read this book
	How to use this book
		Your learning journey
		Try-it-now exercises
		Hands-on labs
		Additional resources
	About the code
	liveBook discussion forum
about the author
Week 1—Fast track to Kubernetes
	1 Before you begin
		1.1 Understanding Kubernetes
		1.2 Is this book for you?
		1.3 Creating your lab environment
			1.3.1 Download the book’s source code
			1.3.2 Install Docker Desktop
			1.3.3 Install Docker Community Edition and K3s
			1.3.4 Install the Kubernetes command-line tool
			1.3.5 Run a single-node Kubernetes cluster in Azure
			1.3.6 Run a single-node Kubernetes cluster in AWS
			1.3.7 Verify your cluster
		1.4 Being immediately effective
	2 Running containers in Kubernetes with Pods and Deployments
		2.1 How Kubernetes runs and manages containers
		2.2 Running Pods with controllers
		2.3 Defining Deployments in application manifests
		2.4 Working with applications in Pods
		2.5 Understanding Kubernetes resource management
		2.6 Lab
	3 Connecting Pods over the network with Services
		3.1 How Kubernetes routes network traffic
		3.2 Routing traffic between Pods
		3.3 Routing external traffic to Pods
		3.4 Routing traffic outside Kubernetes
		3.5 Understanding Kubernetes Service resolution
		3.6 Lab
	4 Configuring applications with ConfigMaps and Secrets
		4.1 How Kubernetes supplies configuration to apps
		4.2 Storing and using configuration files in ConfigMaps
		4.3 Surfacing configuration data from ConfigMaps
		4.4 Configuring sensitive data with Secrets
		4.5 Managing app configuration in Kubernetes
		4.6 Lab
	5 Storing data with volumes, mounts, and claims
		5.1 How Kubernetes builds the container filesystem
		5.2 Storing data on a node with volumes and mounts
		5.3 Storing clusterwide data with persistent volumes and claims
		5.4 Dynamic volume provisioning and storage classes
		5.5 Understanding storage choices in Kubernetes
		5.6 Lab
	6 Scaling applications across multiple Pods with controllers
		6.1 How Kubernetes runs apps at scale
		6.2 Scaling for load with Deployments and ReplicaSets
		6.3 Scaling for high availability with DaemonSets
		6.4 Understanding object ownership in Kubernetes
		6.5 Lab
Week 2—Kubernetes in the real world
	7 Extending applications with multicontainer Pods
		7.1 How containers communicate in a Pod
		7.2 Setting up applications with init containers
		7.3 Applying consistency with adapter containers
		7.4 Abstracting connections with ambassador containers
		7.5 Understanding the Pod environment
		7.6 Lab
	8 Running data-heavy apps with StatefulSets and Jobs
		8.1 How Kubernetes models stability with StatefulSets
		8.2 Bootstrapping Pods with init containers in StatefulSets
		8.3 Requesting storage with volume claim templates
		8.4 Running maintenance tasks with Jobs and CronJobs
		8.5 Choosing your platform for stateful apps
		8.6 Lab
	9 Managing app releases with rollouts and rollbacks
		9.1 How Kubernetes manages rollouts
		9.2 Updating Deployments with rollouts and rollbacks
		9.3 Configuring rolling updates for Deployments
		9.4 Rolling updates in DaemonSets and StatefulSets
		9.5 Understanding release strategies
		9.6 Lab
	10 Packaging and managing apps with Helm
		10.1 What Helm adds to Kubernetes
		10.2 Packaging your own apps with Helm
		10.3 Modeling dependencies in charts
		10.4 Upgrading and rolling back Helm releases
		10.5 Understanding where Helm fits in
		10.6 Lab
	11 App development— Developer workflows and CI/CD
		11.1 The Docker developer workflow
		11.2 The Kubernetes-as-a-Service developer workflow
		11.3 Isolating workloads with contexts and namespaces
		11.4 Continuous delivery in Kubernetes without Docker
		11.5 Evaluating developer workflows on Kubernetes
		11.6 Lab
Week 3—Preparing for production
	12 Empowering self-healing apps
		12.1 Routing traffic to healthy Pods using readiness probes
		12.2 Restarting unhealthy Pods with liveness probes
		12.3 Deploying upgrades safely with Helm
		12.4 Protecting apps and nodes with resource limits
		12.5 Understanding the limits of self-healing apps
		12.6 Lab
	13 Centralizing logs with Fluentd and Elasticsearch
		13.1 How Kubernetes stores log entries
		13.2 Collecting logs from nodes with Fluentd
		13.3 Shipping logs to Elasticsearch
		13.4 Parsing and filtering log entries
		13.5 Understanding logging options in Kubernetes
		13.6 Lab
	14 Monitoring applications and Kubernetes with Prometheus
		14.1 How Prometheus monitors Kubernetes workloads
		14.2 Monitoring apps built with Prometheus client libraries
		14.3 Monitoring third-party apps with metrics exporters
		14.4 Monitoring containers and Kubernetes objects
		14.5 Understanding the investment you make in monitoring
		14.6 Lab
	15 Managing incoming traffic with Ingress
		15.1 How Kubernetes routes traffic with Ingress
		15.2 Routing HTTP traffic with Ingress rules
		15.3 Comparing ingress controllers
		15.4 Using Ingress to secure your apps with HTTPS
		15.5 Understanding Ingress and ingress controllers
		15.6 Lab
	16 Securing applications with policies, contexts, and admission control
		16.1 Securing communication with network policies
		16.2 Restricting container capabilities with security contexts
		16.3 Blocking and modifying workloads with webhooks
		16.4 Controlling admission with Open Policy Agent
		16.5 Understanding security in depth in Kubernetes
		16.6 Lab
Week 4—Pure and applied Kubernetes
	17 Securing resources with role-based access control
		17.1 How Kubernetes secures access to resources
		17.2 Securing resource access within the cluster
		17.3 Binding roles to groups of users and service accounts
		17.4 Discovering and auditing permissions with plugins
		17.5 Planning your RBAC strategy
		17.6 Lab
	18 Deploying Kubernetes: Multinode and multiarchitecture clusters
		18.1 What’s inside a Kubernetes cluster?
		18.2 Initializing the control plane
		18.3 Adding nodes and running Linux workloads
		18.4 Adding Windows nodes and running hybrid workloads
		18.5 Understanding Kubernetes at scale
		18.6 Lab
	19 Controlling workload placement and automatic scaling
		19.1 How Kubernetes schedules workloads
		19.2 Directing Pod placement with affinity and antiaffinity
		19.3 Controlling capacity with automatic scaling
		19.4 Protecting resources with preemption and priorities
		19.5 Understanding the controls for managing workloads
		19.6 Lab
	20 Extending Kubernetes with custom resources and Operators
		20.1 How to extend Kubernetes with custom resources
		20.2 Triggering workflows with custom controllers
		20.3 Using Operators to manage third-party components
		20.4 Building Operators for your own applications
		20.5 Understanding when to extend Kubernetes
		20.6 Lab
	21 Running serverless functions in Kubernetes
		21.1 How serverless platforms work in Kubernetes
		21.2 Triggering functions from HTTP requests
		21.3 Triggering functions from events and schedules
		21.4 Abstracting serverless functions with Serverless
		21.5 Understanding where serverless functions fit
		21.6 Lab
	22 Never the end
		22.1 Further reading by chapter
		22.2 Choosing a Kubernetes platform
		22.3 Understanding how Kubernetes is built
		22.4 Joining the community
Appendix A—Packaging applications from source code into Docker Images
	A.1 Who needs a build server when you have a Dockerfile?
	A.2 App walkthrough: Java source code
	A.3 App walkthrough: Node.js source code
	A.4 App walkthrough: Go source code
	A.5 Understanding multi-stage Dockerfiles
	A.6 Lab
Appendix B—Adding observability with containerized monitoring
	B.1 The monitoring stack for containerized applications
	B.2 Exposing metrics from your application
	B.3 Running a Prometheus container to collect metrics
	B.4 Running a Grafana container to visualize metrics
	B.5 Understanding the levels of observability
	B.6 Lab
Appendix C—Application configuration management in containers
	C.1 A multi-tiered approach to app configuration
	C.2 Packaging config for every environment
	C.3 Loading configuration from the runtime
	C.4 Configuring legacy apps in the same way as new apps
	C.5 Understanding why a flexible configuration model pays off
	C.6 Lab
Appendix D—Writing and managing application logs with Docker
	D.1 Welcome to stderr and stdout!
	D.2 Relaying logs from other sinks to stdout
	D.3 Collecting and forwarding container logs
	D.4 Managing your log output and collection
	D.5 Understanding the container logging model
	D.6 Lab
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