دانلود کتاب Learn Kubernetes in a Month of Lunches

فهرست مطالب :

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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