Introduction to Kubernetes

These are custom notes that extend my notes from an Udemy course "Kubernetes for the Absolute Beginners - Hands-on". All course content rights belong to course creators.

Introduction

Kubernetes (k8s) is:

Platform for managing application containers (containerised applications, container-oriented applications) across multiple hosts (one or more host clusters)
Container orchestration technology - system for automating the operations and management of application containers in complex, multi-container workloads:

Container creation
Container deployment
Rolling deployment *)
Auto-scaling
Load balancing
Container health monitoring
Compute resource management
Volume management
Persistent storage
Networking
High availability by cluster federation

Open-source
Originally designed by Google, based upon their running of containers in production. Now maintained by the Cloud Native Computing Foundation
Supports hosting enhanced and complex applications on various kinds of architectures; it is designed to run anywhere:

on a bare metal
in our data center
on the public cloud - supported on any cloud platform
on the hybrid cloud

*) Rolling deployment:

A deployment strategy that slowly replaces previous versions of an application with new versions of an application by completely replacing the infrastructure on which the application is running;
It is renowned for its ability to update applications without downtime. Incrementally updating nodes or replicas ensures that the service remains available to users throughout the deployment process)
Rolling deployments use the concept of a window size—this is the number of servers that are updated at any given time. For example, if a Kubernetes cluster is running 10 instances of an application (10 pods), and you want to update two of them at a time, you can perform a rolling deployment with a window size of 2.

Containers: Docker overview

Let's say we need to deploy a stack of various technologies:

web server Node.js Express
MongoDB
Redis messaging system
Ansible as orchestration tool

Each of these components needs to be compatible with running host's hardware, OS and installed dependencies and libraries. But this is usually not the case. This is therefore named Matrix from hell.

Docker helps preventing these dependency issues. E.g. we can run each of these components in its own container, which contains libraries and dependencies that the component is compatible with. Docker runs on top of the OS (Win, Mac, Linux etc).

Containers are completely isolated environments. They have their own processes, network interfaces, mounts...just like virtual machines except they all share the same OS kernel (which is interfacing the hardware).

Docker adds an abstraction layer over LXC (LinuX Containers). Docker is like an extension of LXC. [LXC vs Docker: Why Docker is Better | UpGuard]

Ubuntu, Fedora, SUSE and CentOS share the same OS kernel (Linux) but have different software (GUI, drivers, compilers, file systems, ...) above it. This custom software differentiates OSes between each other.

Docker containers share the underlying OS kernel. For example, Docker on Ubuntu can run any flavour of Linux which runs on the same Linux kernel as Ubuntu. This is why we can't run Windows-based container on Docker running on Linux OS - they don't share the same kernel.

Hypervisor:
Abstracts away hardware for the virtual machines so they can run an operating system
Coordinates between the machine's physical hardware and virtual machines.
A container engine (e.g. Docker Engine):
Abstracts away an operating system so containers can run applications
Coordinates between the operating system and (Docker) containers
Docker containers are process-isolated and don't require a hardware hypervisor. This means Docker containers are much smaller and require far fewer resources than a VM.

What is a hypervisor? A beginner’s guide | Ubuntu

Unlike hypervisors, Docker is not meant to virtualize and run different operating systems and kernels on the same hardware.

The main purpose of Docker is to containerise applications, ship them and run them.

In case of Docker we have:

Containers (one or more) containing:

Application
Libraries & Dependencies

Docker
OS
Hardware

In case of virtual machine we have:

Virtual Machines (one or more) containing:

Application
Libraries & Dependencies
OS

Hypervisor
OS
Hardware

Docker uses less processing power, less disk space and has faster boot up time than VMs.

Docker containers share the same kernel while different VMs are completely isolated. We can run VM with Linux on the host with Windows.

Many companies release and ship their software products as Docker images, published on Docker Hub, public Docker registry.

We can run each application from the example above in its own container:

$ docker run nodejs
$ docker run mongodb
$ docker run redis
$ docker run ansible

Docker image is a template, used to create one or more containers.

Containers are running instances of images that are isolated and have their own environments and set of processes.

Dockerfile describes the image.

Container Orchestration

Applications run in their own containers.

What if one application depends on another e.g. web server, running in one container, depends on the DB running in another container?

What if the number of users increases and we need to scale out our application?

How to scale down when the load decreases?
How to build services across multiple machines without dealing with cumbersome network and storage settings?

How to manage and roll out our microservices by different service cycle?

We should have an underlying platform that takes care of these dependencies and scaling. This process of deploying and managing containers is called container orchestration.

Container orchestration technologies:

Docker Swarm

easy to set up
lacks advanced features

Kubernetes (Google)

most popular
difficult to set up
has lots of options to support deployments of complex architecture setups
supported on all main public cloud service providers like GCP, Azure, AWS

Mesos (Apache)

difficult to set up
has advanced features

Kubernetes advantages:

Used to deploy and manage hundreds or thousands of containers in a clustered environment
Kubernetes is designed with high availability (HA). We have multiple instances of our application running on different nodes so hardware failures on some nodes won't impact the availability. We are able to create multiple master nodes from preventing single point of failure.
Traffic is load balanced across multiple containers.
Scaling is done by scaling the number of containers running on a single host but also increasing the number of hosts (hardware scaling) if processing demands reach maximum thresholds on existing nodes.
The lifetime of containers might be short. They may be killed or stopped anytime when they exceed the limit of resource, how do we ensure our services always serve a certain number of containers? ReplicationController or ReplicaSet in Kubernetes will ensure a certain number of group of containers are up.
Kubernetes even supports liveness probe to help you define your application health.
For better resource management, we can also define the maximum capacity on Kubernetes nodes and the resource limit for each group of containers (a.k.a pod). Kubernetes scheduler will then select a node that fulfills the resource criteria to run the containers.
Kubernetes provides an optional horizontal pod auto-scaling feature. With this feature, we could scale a pod horizontally by resource or custom metrics.
Perfect match for microservices where it helps their CD (Continuous Delivery). We can create a Deployment to rollout, rollover, or roll back selected containers.
Containers are considered as ephemeral - they can quickly and/or often die. We can mount the volume into a container to preserve the data in a single host world. In the cluster world, a container might be scheduled to run on any host. Kubernetes Volumes and Persistent Volumes make the volume mounting work as permanent storage seamlessly.
This is all achieved with the set of declarative object configuration files.

Kubernetes Architecture

Node (worker node, minion)

machine, physical or virtual, on which Kubernetes is installed
worker machine on which containers will be launched by Kubernetes; workers run containers
if node fails, our application will go down => we need to have more nodes

Cluster

Set of nodes grouped together
Even if one node fails, application is still accessible from other nodes
Having multiple nodes also helps sharing the load
Kubernetes cluster consists of two types of nodes, master nodes and worker nodes.

Master (master node)

responsible for managing the cluster
controls and schedules all activities in the cluster
stores the information about all members of the cluster
monitors nodes
when node fails, moves workload of the failed node to other worker nodes
Master is a node with Kubernetes installed on it and is configured as a master node
Master watches over the nodes in the cluster and is responsible for orchestration of containers on the worker nodes
Master nodes host the K8s control plane components. The master node will hold configuration and state data used to maintain the desired state.

When we install Kubernetes on the host, we install multiple components on it.

There are two types of nodes/servers: master and worker. And there is a set of components that make up Kubernetes. How are these components distributed across different types of servers? How does one server become a master and the other the slave?

master (controller) server (node):

API Server (kube-api-server)

this is what makes node a master
acts as the front end of Kubernetes
users, management devices, command line interfaces talk to it in order to interact with Kubernetes cluster

etcd service

All the information gathered are stored in a key value store based on the popular etcd framework
name is the abbreviation of Experimental Distributed Tracing Service (?)
key store
distributed reliable key-value store used by Kubernetes to store all data used to manage the cluster
when we have multiple nodes and multiple masters in the cluster, etcd stores all that information on all the nodes in the cluster in the distributed manner
responsible for implementing locks within the cluster to ensure there are no conflicts between the masters

controller

control manager
brain behind the orchestration
responsible for noticing and responding when nodes, containers or endpoints go down
make decisions to bring up new containers in such cases

scheduler

responsible for distributing work of containers across multiple nodes
it looks for newly created containers and assigns them to nodes

Master components
(credit: DevOps with Kubernetes by Hideto Saito, Hui-Chuan Chloe Lee and Cheng-Yang Wu)

(I/F = Interface)

This article describes well the control plane of the master node:

The Kubernetes API Server. In Kubernetes, all communications and… | by Rini Thomas | Medium

API Server and its clients
(image credit: Rini Thomas; source: https://medium.com/@rinithomas/the-kubernetes-api-server-430a39aec2d7)

All communications and operations between the control plane components and external clients, such as kubectl, are translated into RESTful API calls that are handled by the API server.

Effectively, the API server is a RESTful web application that processes RESTful API calls over HTTP to store and update API objects in the etcd datastore.

Control Plane on the master/controller node(s) consists of the API server, controller manager, and scheduler.

API server is the central management entity and the only component that talks directly with the distributed storage component etcd.

API server has the following core responsibilities:

To serve the Kubernetes API. This API is used :
cluster-internally by the:
master components
worker nodes
our Kubernetes-native apps
externally by clients such as kubectl
To proxy cluster components, such as the Kubernetes dashboard, or to stream logs, service ports, or serve kubectl exec sessions.

Serving the API means:
Reading state: getting single objects, listing them, and streaming changes
Manipulating state: creating, updating, and deleting objects.

kubectl command is translated into an HTTP API request in JSON format and is sent to the API server. Then, the API server returns a response to the client, along with any requested information.

API server is stateless (that is, its behavior will be consistent regardless of the state of the cluster) and is designed to scale horizontally. Usually, for the high availability of clusters, it is recommended to have at least three instances to handle the load and fault tolerance better.

API Server internal processes
(image credit: Rini Thomas; source: https://medium.com/@rinithomas/the-kubernetes-api-server-430a39aec2d7)

worker node (minion):

is where the containers are hosted e.g. Docker containers.
kubelet service (agent)

the agent that runs on each node in the cluster
interacts with a master to provide health information of the worker node and carry out actions requested by the master on the worker nodes
makes sure that containers are running as expected

Container Runtime

underlying software required for running containers on a system
Container Runtime can be be Docker, rkt or CRI-O
in our case it's Docker but there are other options as well

Node components
(credit: DevOps with Kubernetes by Hideto Saito, Hui-Chuan Chloe Lee and Cheng-Yang Wu)

Understanding what components constitute the master and worker nodes will help us install and configure the right components on different systems when we set up our infrastructure.

kubectl:

command line (CLI) tool for Kubernetes
command line utility known as the kube command line tool or kubectl or kube control
kubectl tool is used to:

interact with the Kubernetes cluster(s)
enables the interaction (to run commands against) the clusters in order to manage and inspect them
create pods, services and other components
deploy and manage applications on a Kubernetes cluster

kubectl run command is used to deploy an application on the cluster
example: kubectl run hello-minikube

inspect and manage cluster resources e.g. get cluster information

kubectl cluster-info command is used to view information about the cluster

get the status of other nodes in the cluster

kubectl get nodes command is used to list all the nodes part of the cluster

view logs
manage many other things

$ kubectl --help

kubectl controls the Kubernetes cluster manager.

Find more information at: https://kubernetes.io/docs/reference/kubectl/

Basic Commands (Beginner):

create Create a resource from a file or from stdin

expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes service

run Run a particular image on the cluster

set Set specific features on objects

Basic Commands (Intermediate):

explain Get documentation for a resource

get Display one or many resources

edit Edit a resource on the server

delete Delete resources by file names, stdin, resources and names, or by resources and label selector

Deploy Commands:

rollout Manage the rollout of a resource

scale Set a new size for a deployment, replica set, or replication controller

autoscale Auto-scale a deployment, replica set, stateful set, or replication controller

Cluster Management Commands:

certificate Modify certificate resources

cluster-info Display cluster information

top Display resource (CPU/memory) usage

cordon Mark node as unschedulable

uncordon Mark node as schedulable

drain Drain node in preparation for maintenance

taint Update the taints on one or more nodes

Troubleshooting and Debugging Commands:

describe Show details of a specific resource or group of resources

logs Print the logs for a container in a pod

attach Attach to a running container

exec Execute a command in a container

port-forward Forward one or more local ports to a pod

proxy Run a proxy to the Kubernetes API server

cp Copy files and directories to and from containers

auth Inspect authorization

debug Create debugging sessions for troubleshooting workloads and nodes

events List events

Advanced Commands:

diff Diff the live version against a would-be applied version

apply Apply a configuration to a resource by file name or stdin

patch Update fields of a resource

replace Replace a resource by file name or stdin

wait Experimental: Wait for a specific condition on one or many resources

kustomize Build a kustomization target from a directory or URL

Settings Commands:

label Update the labels on a resource

annotate Update the annotations on a resource

completion Output shell completion code for the specified shell (bash, zsh, fish, or powershell)

Subcommands provided by plugins:

Other Commands:

api-resources Print the supported API resources on the server

api-versions Print the supported API versions on the server, in the form of "group/version"

config Modify kubeconfig files

plugin Provides utilities for interacting with plugins

version Print the client and server version information

Usage:

kubectl [flags] [options]

Use "kubectl <command> --help" for more information about a given command.

Use "kubectl options" for a list of global command-line options (applies to all commands).

Quiz:

What is a worker machine in Kubernetes known as?

A Node in Kubernetes can only be a physical machine and can never be a virtual machine.

Multiple nodes together form what?

Which of the following processes runs on Kubernetes Master Node?

Which of the following is a distributed reliable key-value store used by kubernetes to store all data used to manage the cluster?

Which of the following services is responsible for distributing work or containers across multiple nodes.

Which of the following is the underlying framework that is responsible for running application in containers like Docker?

Which is the command line utility used to manage a kubernetes cluster?

Pods

Let's assume that the following have been set up already:

The application is already developed and built into Docker images and it is available on a Docker repository like Docker Hub so Kubernetes can pull it down
Kubernetes cluster has already been set up and is working

this could be a single node setup or a multi node setup
all the services need to be in a running state

With Kubernetes, our ultimate aim is to deploy our application in the form of containers on a set of machines that are configured as worker nodes in a cluster
Kubernetes does not deploy containers directly on the worker nodes. The containers are encapsulated into a Kubernetes object known as pods.
A pod is:

a single instance of an application
the smallest object that you can create in Kubernetes
the most basic and the smallest unit in Kubernetes

The simplest case: a single node Kubernetes cluster with a single instance of the application running in a single Docker container encapsulated in a pod
What if the number of users accessing your application increase and you need to scale your application?

We need to add additional instances of your web application to share the load.

Where would we spin up additional instances?

We don't bring up new container instance within the same pod.
We create new pod altogether with a new instance of the same application.
We now have two instances of our web application running on two separate pods on the same Kubernetes system or node.

What if the user base further increases and your current node has no sufficient capacity?

We can always deploy additional pods on a new node in the cluster
We will have a new node added to the cluster to expand the cluster's physical capacity

Pods usually have a 1 to 1 relationship with containers running our application.

To scale up, we create new pods
To scale down we delete existing pods
We do NOT add additional containers to an existing pod to scale our application.

We can also achieve load balancing between the containers.

Multi-container pods

Pods usually have a 1 to 1 relationship with the containers, but we are NOT restricted to having a single container in a single pod. A single pod can have multiple containers except for the fact that they're usually not multiple containers of the same kind.

To scale our application, we would need to create additional pods.

Sometimes we might have a scenario where we have a helper container that might be doing some kind of supporting task for our web application, such as processing a user entered data, processing a file uploaded by the user etc and we want these helper containers to live alongside our application container. In that case, we can have both of these containers part of the same pod so that:

when a new application container is created, the helper is also created
when it dies, the helper also dies since they are part of the same pod
The two containers can also communicate with each other directly by referring to each other as local host since they share the same network space
They can easily share the same storage space as well

Why do we need Kubernetes?

Let's for a moment keep Kubernetes out of our discussion and talk about simple Docker containers. Let's assume we were developing a process or a script to deploy our application on a Docker host. Then we would first simply deploy our application using a simple docker run command, and the application runs fine and our users are able to access it:

docker run python-app

When the load increases, we deploy more instances of our application by running the docker run commands many more times:

docker run python-app --name app1
docker run python-app --name app2
docker run python-app --name app3
docker run python-app --name app4

Sometime in the future our application is further developed, undergoes architectural changes and grows and gets complex. We now have a new helper container that helps our web application by processing or fetching data from elsewhere (NOTE: --link is a legacy option for docker run; it is recommend using user-defined networks to facilitate communication between two containers instead of using --link; see Legacy container links | Docker Docs):

docker run helper --link app1
docker run helper --link app2
docker run helper --link app3
docker run helper --link app4

These helper containers maintain a 1 to 1 relationship with our application container and thus needs to communicate with the application containers directly and access data from those containers. For this, we need to (manually):

maintain a map of what app and helper containers are connected to each other
establish network connectivity between these containers ourselves using links and custom networks
create shareable volumes and share it among the containers. We would need to maintain a map of that as well.
monitor the state of the application container

When it dies, manually kill the helper container as well as it's no longer required.
When a new container is deployed, we would need to deploy the new helper container as well with pods.

Kubernetes does all of this for us automatically. We just need to define what containers a pod consists of and the containers in a pod by default will have access to the same storage, the same network namespace and same fate as in they will be created together and destroyed together.
Even if our application didn't happen to be so complex and we could live with a single container, Kubernetes still requires you to create pods, but this is good in the long run as your application is now equipped for architectural changes and scale in the future.
However, multi-containers pods are a rare use case. Single containers per pod is the most common use case.

My observation: Kubernetes pods seem to be doing a job very similar to docker-compose. What are the similarities and what are the differences between two?

Docker Compose vs Kubernetes - Differences Explained

When to Use Docker Compose vs. Kubernetes - Earthly Blog

Kubernetes Pods deployment as an alternative to docker compose when you don't need HA? : r/kubernetes

How to deploy/create pods?

kubectl run command

e.g. kubectl run nginx
deploys a Docker container by creating a pod named nginx

it first creates a pod automatically
then deploys an instance of the Nginx Docker image

we need to specify the application image name using the image parameter:
kubectl run nginx --image nginx
The application image, in this case, the nginx image is downloaded from the Docker Hub Repository. Docker Hub is a public repository where latest Docker images of various applications are stored.
We can configure Kubernetes to pull the image from the public Docker hub or a private repository within the organization.
in the current state, we haven't made the web server accessible to external users but we can access it internally from the node

$ kubectl run --help

Create and run a particular image in a pod.

Examples:

# Start a nginx pod

kubectl run nginx --image=nginx

# Start a hazelcast pod and let the container expose port 5701

kubectl run hazelcast --image=hazelcast/hazelcast --port=5701

# Start a hazelcast pod and set environment variables "DNS_DOMAIN=cluster" and

"POD_NAMESPACE=default" in the container

kubectl run hazelcast --image=hazelcast/hazelcast --env="DNS_DOMAIN=cluster"

--env="POD_NAMESPACE=default"

# Start a hazelcast pod and set labels "app=hazelcast" and "env=prod" in the container

kubectl run hazelcast --image=hazelcast/hazelcast --labels="app=hazelcast,env=prod"

# Dry run; print the corresponding API objects without creating them

kubectl run nginx --image=nginx --dry-run=client

# Start a nginx pod, but overload the spec with a partial set of values parsed from JSON

kubectl run nginx --image=nginx --overrides='{ "apiVersion": "v1", "spec": { ... } }'

# Start a busybox pod and keep it in the foreground, don't restart it if it exits

kubectl run -i -t busybox --image=busybox --restart=Never

# Start the nginx pod using the default command, but use custom arguments (arg1 .. argN) for that

command

kubectl run nginx --image=nginx -- <arg1> <arg2> ... <argN>

# Start the nginx pod using a different command and custom arguments

kubectl run nginx --image=nginx --command -- <cmd> <arg1> ... <argN>

Options:

--allow-missing-template-keys=true:

If true, ignore any errors in templates when a field or map key is missing in the

template. Only applies to golang and jsonpath output formats.

--annotations=[]:

Annotations to apply to the pod.

--attach=false:

If true, wait for the Pod to start running, and then attach to the Pod as if 'kubectl

attach ...' were called. Default false, unless '-i/--stdin' is set, in which case the

default is true. With '--restart=Never' the exit code of the container process is

returned.

--command=false:

If true and extra arguments are present, use them as the 'command' field in the container,

rather than the 'args' field which is the default.

--dry-run='none':

Must be "none", "server", or "client". If client strategy, only print the object that

would be sent, without sending it. If server strategy, submit server-side request without

persisting the resource.

--env=[]:

Environment variables to set in the container.

--expose=false:

If true, create a ClusterIP service associated with the pod. Requires `--port`.

--field-manager='kubectl-run':

Name of the manager used to track field ownership.

--image='':

The image for the container to run.

--image-pull-policy='':

The image pull policy for the container. If left empty, this value will not be specified

by the client and defaulted by the server.

-l, --labels='':

Comma separated labels to apply to the pod. Will override previous values.

--leave-stdin-open=false:

If the pod is started in interactive mode or with stdin, leave stdin open after the first

attach completes. By default, stdin will be closed after the first attach completes.

-o, --output='':

Output format. One of: (json, yaml, name, go-template, go-template-file, template,

templatefile, jsonpath, jsonpath-as-json, jsonpath-file).

--override-type='merge':

The method used to override the generated object: json, merge, or strategic.

--overrides='':

An inline JSON override for the generated object. If this is non-empty, it is used to

override the generated object. Requires that the object supply a valid apiVersion field.

--pod-running-timeout=1m0s:

The length of time (like 5s, 2m, or 3h, higher than zero) to wait until at least one pod

is running

--port='':

The port that this container exposes.

--privileged=false:

If true, run the container in privileged mode.

-q, --quiet=false:

If true, suppress prompt messages.

--restart='Always':

The restart policy for this Pod. Legal values [Always, OnFailure, Never].

--rm=false:

If true, delete the pod after it exits. Only valid when attaching to the container, e.g.

with '--attach' or with '-i/--stdin'.

--save-config=false:

If true, the configuration of current object will be saved in its annotation. Otherwise,

the annotation will be unchanged. This flag is useful when you want to perform kubectl

apply on this object in the future.

--show-managed-fields=false:

If true, keep the managedFields when printing objects in JSON or YAML format.

-i, --stdin=false:

Keep stdin open on the container in the pod, even if nothing is attached.

--template='':

Template string or path to template file to use when -o=go-template, -o=go-template-file.

The template format is golang templates

[http://golang.org/pkg/text/template/#pkg-overview].

-t, --tty=false:

Allocate a TTY for the container in the pod.

Usage:

kubectl run NAME --image=image [--env="key=value"] [--port=port] [--dry-run=server|client]

[--overrides=inline-json] [--command] -- [COMMAND] [args...] [options]

Use "kubectl options" for a list of global command-line options (applies to all commands).

How do we see the list of pods available?

kubectl get pods command:

lists all pods in our cluster
also shows their current state e.g. pod can be in ContainerCreating state and soon changes to a Running state when it is actually running

$ kubectl get --help

Display one or many resources.

Prints a table of the most important information about the specified resources. You can filter the

list using a label selector and the --selector flag. If the desired resource type is namespaced you

will only see results in your current namespace unless you pass --all-namespaces.

By specifying the output as 'template' and providing a Go template as the value of the --template

flag, you can filter the attributes of the fetched resources.

Use "kubectl api-resources" for a complete list of supported resources.

Examples:

# List all pods in ps output format

kubectl get pods

# List all pods in ps output format with more information (such as node name)

kubectl get pods -o wide

# List a single replication controller with specified NAME in ps output format

kubectl get replicationcontroller web

# List deployments in JSON output format, in the "v1" version of the "apps" API group

kubectl get deployments.v1.apps -o json

# List a single pod in JSON output format

kubectl get -o json pod web-pod-13je7

# List a pod identified by type and name specified in "pod.yaml" in JSON output format

kubectl get -f pod.yaml -o json

# List resources from a directory with kustomization.yaml - e.g. dir/kustomization.yaml

kubectl get -k dir/

# Return only the phase value of the specified pod

kubectl get -o template pod/web-pod-13je7 --template={{.status.phase}}

# List resource information in custom columns

kubectl get pod test-pod -o

custom-columns=CONTAINER:.spec.containers[0].name,IMAGE:.spec.containers[0].image

# List all replication controllers and services together in ps output format

kubectl get rc,services

# List one or more resources by their type and names

kubectl get rc/web service/frontend pods/web-pod-13je7

# List the 'status' subresource for a single pod

kubectl get pod web-pod-13je7 --subresource status

Options:

-A, --all-namespaces=false:

If present, list the requested object(s) across all namespaces. Namespace in current

context is ignored even if specified with --namespace.

--allow-missing-template-keys=true:

If true, ignore any errors in templates when a field or map key is missing in the

template. Only applies to golang and jsonpath output formats.

--chunk-size=500:

Return large lists in chunks rather than all at once. Pass 0 to disable. This flag is beta

and may change in the future.

--field-selector='':

Selector (field query) to filter on, supports '=', '==', and '!='.(e.g. --field-selector

key1=value1,key2=value2). The server only supports a limited number of field queries per

type.

-f, --filename=[]:

Filename, directory, or URL to files identifying the resource to get from a server.

--ignore-not-found=false:

If the requested object does not exist the command will return exit code 0.

-k, --kustomize='':

Process the kustomization directory. This flag can't be used together with -f or -R.

-L, --label-columns=[]:

Accepts a comma separated list of labels that are going to be presented as columns. Names

are case-sensitive. You can also use multiple flag options like -L label1 -L label2...

--no-headers=false:

When using the default or custom-column output format, don't print headers (default print

headers).

-o, --output='':

Output format. One of: (json, yaml, name, go-template, go-template-file, template,

templatefile, jsonpath, jsonpath-as-json, jsonpath-file, custom-columns,

custom-columns-file, wide). See custom columns

[https://kubernetes.io/docs/reference/kubectl/#custom-columns], golang template

[http://golang.org/pkg/text/template/#pkg-overview] and jsonpath template

[https://kubernetes.io/docs/reference/kubectl/jsonpath/].

--output-watch-events=false:

Output watch event objects when --watch or --watch-only is used. Existing objects are

output as initial ADDED events.

--raw='':

Raw URI to request from the server. Uses the transport specified by the kubeconfig file.

-R, --recursive=false:

Process the directory used in -f, --filename recursively. Useful when you want to manage

related manifests organized within the same directory.

-l, --selector='':

Selector (label query) to filter on, supports '=', '==', and '!='.(e.g. -l

key1=value1,key2=value2). Matching objects must satisfy all of the specified label

constraints.

--server-print=true:

If true, have the server return the appropriate table output. Supports extension APIs and

CRDs.

--show-kind=false:

If present, list the resource type for the requested object(s).

--show-labels=false:

When printing, show all labels as the last column (default hide labels column)

--show-managed-fields=false:

If true, keep the managedFields when printing objects in JSON or YAML format.

--sort-by='':

If non-empty, sort list types using this field specification. The field specification is

expressed as a JSONPath expression (e.g. '{.metadata.name}'). The field in the API

resource specified by this JSONPath expression must be an integer or a string.

--subresource='':

If specified, gets the subresource of the requested object. Must be one of [status scale].

This flag is beta and may change in the future.

--template='':

Template string or path to template file to use when -o=go-template, -o=go-template-file.

The template format is golang templates

[http://golang.org/pkg/text/template/#pkg-overview].

-w, --watch=false:

After listing/getting the requested object, watch for changes.

--watch-only=false:

Watch for changes to the requested object(s), without listing/getting first.

Usage:

kubectl get

(TYPE[.VERSION][.GROUP] [NAME | -l label] | TYPE[.VERSION][.GROUP]/NAME ...) [flags] [options]

Use "kubectl options" for a list of global command-line options (applies to all commands).

minikube

A Kubernetes cluster can be deployed on either physical or virtual machines. To get started with Kubernetes development, you can use Minikube. Minikube is a lightweight Kubernetes implementation that creates a VM on your local machine and deploys a simple cluster containing only one node. Minikube is available for Linux, macOS, and Windows systems. The Minikube CLI provides basic bootstrapping operations for working with your cluster, including start, stop, status, and delete. [Using Minikube to Create a Cluster | Kubernetes]

Our goal is to install / set up a a basic Kubernetes cluster on our local machine using the minikube utility
minikube creates one Node cluster, where the master and worker processes are on the same machine
minikube creates a non-production sandbox environment that we can use to test out Kubernetes
minikube is used for quick tests within the single cluster, in a single host on the local machine before launching the full blown Kubernetes multi-node cluster.

Before installing minikube:

1) We ~~must~~ can install the kubectl utility locally

kubectl command line tool is what we will use to manage our Kubernetes resources and our cluster after it is set up using minikube
Installing the kubectl utility before installing minikube will allow minikube to configure the kubectl utility to work with the cluster when it provisions it
kubectl utility can work with multiple clusters, local or remote, at the same time
if kubectl is not installed locally, minikube already includes kubectl which can be used like this: minikube kubectl -- <kubectl commands>
if kubectl is already installed, minikube will automatically take care of configuring it when it starts, when it provisions a Kubernetes cluster
To install kubectl (on Linux), follow instructions from here: Install and Set Up kubectl on Linux | Kubernetes

kubectl needs to be configured so it knows to which cluster it needs to talk to (to which master node). $KUBECONFIG environment variable contains the path to the directory that contains minikube configuration and which is usually ~/.kube/.

Upon fresh kubectl installation, before it's configured, ~/.kube/ is not created and KUBECONFIG is not specified.

Default kubectl config file is ~/.kube/config. It initially does not exist and before we use kubectl to talk to Kubernetes cluster we need to create it or it can be created by Minikube on its startup (be careful as Minikube might overwrite existing kubeconfig file!)

To learn more about kubeconfig file: What is a kubeconfig file? | Enable Sysadmin

2) We need to make sure that virtualization is enabled.

On Linux, it is enabled if the following command returns non-empty output:

$ grep -E --color 'vmx|svm' /proc/cpuinfo

If it's not enabled, there should be an option in BIOS to enable virtualization.

3) We need to install a virtual machine manager (hypervisor) or container manager/engine such as: Docker, QEMU, Hyperkit, Hyper-V, KVM, Parallels, Podman, VirtualBox, or VMware Fusion/Workstation.

We can use e.g. VirtualBox virtualization solution but minikube can also run without a hypervisor, directly on the host using Docker.

The Docker driver allows you to install Kubernetes into an existing Docker install. On Linux, this does not require virtualization to be enabled. [docker | minikube]

VirtualBox is minikube’s original driver. It may not provide the fastest start-up time, but it is the most stable driver available for users of Microsoft Windows Home. [virtualbox | minikube]

VirtualBox installation: Linux_Downloads – Oracle VM VirtualBox

When we provision a cluster using minikube, it will automatically create a virtual machine as required.

Minikube installation: minikube start | minikube

Upon minikube installation, we have a new directory that minikube uses for state/configuration: .minikube. By default, it is created in home directory (~/) and it contains several sub-directories, all of which are initially empty:

$ ls -la ~/.minikube/

total 36

drwxr-xr-x .

drwxr-xr-x ..

drwxr-xr-x addons

drwxr-xr-x cache

drwxr-xr-x certs

drwxr-xr-x config

drwxr-xr-x files

drwxr-xr-x logs

drwxr-xr-x machines

MINIKUBE_HOME environment variable contains the path to the .minikube directory but if it's not specified, it defaults to ~/.minikube. For more details, see: Configuration | minikube.

Let's explore minikube CLI:

$ minikube --help

minikube provisions and manages local Kubernetes clusters optimized for development workflows.

Basic Commands:

start Starts a local Kubernetes cluster

status Gets the status of a local Kubernetes cluster

stop Stops a running local Kubernetes cluster

delete Deletes a local Kubernetes cluster

dashboard Access the Kubernetes dashboard running within

the minikube cluster

pause pause Kubernetes

unpause unpause Kubernetes

Images Commands:

docker-env Provides instructions to point your terminal's docker-cli to the Docker Engine inside minikube.

(Useful for building docker images directly inside minikube)

podman-env Configure environment to use minikube's Podman service

cache Manage cache for images

image Manage images

Configuration and Management Commands:

addons Enable or disable a minikube addon

config Modify persistent configuration values

profile Get or list the current profiles (clusters)

update-context Update kubeconfig in case of an IP or port change

Networking and Connectivity Commands:

service Returns a URL to connect to a service

tunnel Connect to LoadBalancer services

Advanced Commands:

mount Mounts the specified directory into minikube

ssh Log into the minikube environment (for debugging)

kubectl Run a kubectl binary matching the cluster version

node Add, remove, or list additional nodes

cp Copy the specified file into minikube

Troubleshooting Commands:

ssh-key Retrieve the ssh identity key path of the specified node

ssh-host Retrieve the ssh host key of the specified node

ip Retrieves the IP address of the specified node

logs Returns logs to debug a local Kubernetes cluster

update-check Print current and latest version number

version Print the version of minikube

options Show a list of global command-line options (applies to all commands).

Other Commands:

completion Generate command completion for a shell

license Outputs the licenses of dependencies to a directory

Use "minikube <command> --help" for more information about a given command.

To test minikube installation we can provision a Kubernetes cluster:

$ minikube start

😄 minikube v1.33.0 on Ubuntu 22.04

🆕 Kubernetes 1.30.0 is now available. If you would like to upgrade, specify: --kubernetes-version=v1.30.0

✨ Using the virtualbox driver based on existing profile

💿 Downloading VM boot image ...

> minikube-v1.33.0-amd64.iso....: 65 B / 65 B [---------] 100.00% ? p/s 0s

> minikube-v1.33.0-amd64.iso: 314.16 MiB / 314.16 MiB 100.00% 3.72 MiB p/

👍 Starting "minikube" primary control-plane node in "minikube" cluster

🔄 Restarting existing virtualbox VM for "minikube" ...

❗ Image was not built for the current minikube version. To resolve this you can delete and recreate your minikube cluster using the latest images. Expected minikube version: v1.32.0 -> Actual minikube version: v1.33.0

🐳 Preparing Kubernetes v1.28.3 on Docker 24.0.7 ...| Bad local forwarding specification '0:localhost:8443'

🔗 Configuring bridge CNI (Container Networking Interface) ...

🔎 Verifying Kubernetes components...

▪ Using image gcr.io/k8s-minikube/storage-provisioner:v5

🌟 Enabled addons: storage-provisioner, default-storageclass

❗ /usr/local/bin/kubectl is version 1.30.0, which may have incompatibilities with Kubernetes 1.28.3.

▪ Want kubectl v1.28.3? Try 'minikube kubectl -- get pods -A'

🏄 Done! kubectl is now configured to use "minikube" cluster and "default" namespace by default

We can also explicitly specify the driver (virtualization tool) to be used e.g. VirtualBox:

$ minikube start --driver=virtualbox

minikube downloaded the minikube ISO image for mini cube. This image is then used to provision a VM on VirtualBox. It downloaded Kubernetes version 1.28.3 and any other required binaries.

We can open VirtualBox UI we can see that a virtual machine by the name minikube has been created and it is in a running state:

Note the last line in the minikube start output. Minikube set up ~/.kube/config to point to minikube cluster. kubectl utility is now configured to use the Kubernetes cluster provisioned using minikube.

So, before we run kubectl for the first time and we want to use Minikube cluster, we first need to run minikube start.

To check kubectl configuration:

$ kubectl config view

apiVersion: v1

clusters:

- cluster:

certificate-authority: /home/bojan/.minikube/ca.crt

extensions:

- extension:

last-update: Sun, 12 May 2024 22:31:20 BST

provider: minikube.sigs.k8s.io

version: v1.33.0

server: https://192.168.59.100:8443

contexts:

- context:

cluster: minikube

extensions:

- extension:

last-update: Sun, 12 May 2024 22:31:20 BST

provider: minikube.sigs.k8s.io

version: v1.33.0

namespace: default

user: minikube

current-context: minikube

kind: Config

preferences: {}

users:

- name: minikube

user:

client-certificate: /home/bojan/.minikube/profiles/minikube/client.crt

client-key: /home/bojan/.minikube/profiles/minikube/client.key

If we run some kubectl command but we didn't start minikube we'll get the error:

$ kubectl get deployment

E0513 12:37:29.927893 8136 memcache.go:265] couldn't get current server API group list: Get "https://192.168.59.100:8443/api?timeout=32s": dial tcp 192.168.59.100:8443: i/o timeout

192.168.59.100 is the IP address of the apiserver node (a singe node acting both as master and worker) in case of Minikube) and 8443 is the default listening port on it.

Solution here is to run minikube start.

On one of the subsequent runs of minikube start I got the following output:

$ minikube start

😄 minikube v1.33.0 on Ubuntu 22.04

🆕 Kubernetes 1.30.0 is now available. If you would like to upgrade, specify: --kubernetes-version=v1.30.0

✨ Using the virtualbox driver based on existing profile

👍 Starting "minikube" primary control-plane node in "minikube" cluster

🔄 Restarting existing virtualbox VM for "minikube" ...

🐳 Preparing Kubernetes v1.28.3 on Docker 24.0.7 ...- Bad local forwarding specification '0:localhost:8443'

🔗 Configuring bridge CNI (Container Networking Interface) ...

▪ Using image docker.io/kubernetesui/dashboard:v2.7.0

▪ Using image docker.io/kubernetesui/metrics-scraper:v1.0.8

▪ Using image gcr.io/k8s-minikube/storage-provisioner:v5

╭───────────────────────────────────────────────────────────────────────────────────────────────────╮

│ │

│ You have selected "virtualbox" driver, but there are better options ! │

│ For better performance and support consider using a different driver: │

│ - kvm2 │

│ - qemu2 │

│ │

│ To turn off this warning run: │

│ │

│ $ minikube config set WantVirtualBoxDriverWarning false │

│ │

│ To learn more about on minikube drivers checkout https://minikube.sigs.k8s.io/docs/drivers/ │

│ To see benchmarks checkout https://minikube.sigs.k8s.io/docs/benchmarks/cpuusage/ │

│ │

╰───────────────────────────────────────────────────────────────────────────────────────────────────╯

🔎 Verifying Kubernetes components...

💡 Some dashboard features require the metrics-server addon. To enable all features please run:

minikube addons enable metrics-server

🌟 Enabled addons: storage-provisioner, default-storageclass, dashboard

❗ /usr/local/bin/kubectl is version 1.30.0, which may have incompatibilities with Kubernetes 1.28.3.

▪ Want kubectl v1.28.3? Try 'minikube kubectl -- get pods -A'

🏄 Done! kubectl is now configured to use "minikube" cluster and "default" namespace by default

If we already have kubeconfig file set for some non-Minikube cluster, we can make Minikube create its own kubeconfig at some different location or with different name by changing the value of KUBECONFIG env variable:

$ KUBECONFIG=/home/user/.kube/minikube-config minikube start

To ensure that everything has been set up correctly we'll run:

$ minikube status

minikube

type: Control Plane

host: Running

kubelet: Running

apiserver: Running

kubeconfig: Configured

Our cluster is now set up. We will deploy some applications on the cluster in order to make sure it's working as expected.

To check if kubectl commands are working and also check how many nodes are in the cluster we can run:

$ kubectl get node

NAME STATUS ROLES AGE VERSION

minikube Ready control-plane 10d v1.28.3

We can see that it is a single node cluster with node running Kubernetes v1.28.3.

ROLES can also have a value master.

$ kubectl get node

NAME STATUS ROLES AGE VERSION

controlplane Ready control-plane,master 19m v1.29.0+k3s1

Some more examples:

$ kubectl get nodes

NAME STATUS ROLES AGE VERSION

controlplane Ready control-plane,master 30m v1.29.0+k3s1

If we specify output format via -o (--output) argument as wide, we'll get a bit more info about the nodes:

$ kubectl get nodes -o wide

NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME

controlplane Ready control-plane,master 30m v1.29.0+k3s1 192.10.75.9 <none> Alpine Linux v3.16 5.4.0-1106-gcp containerd://1.7.11-k3s2

To get the more details about the cluster:

$ kubectl cluster-info

Kubernetes control plane is running at https://127.0.0.1:6443

CoreDNS is running at https://127.0.0.1:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy

Metrics-server is running at https://127.0.0.1:6443/api/v1/namespaces/kube-system/services/https:metrics-server:https/proxy

To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.

To get more detailed information about cluster (and here we filter out info about node(s)):

$ kubectl cluster-info dump | grep node

"node-role.kubernetes.io/control-plane": "true",

"node-role.kubernetes.io/master": "true",

"node.kubernetes.io/instance-type": "k3s"

"alpha.kubernetes.io/provided-node-ip": "192.10.75.9",

"k3s.io/node-args": "[\"server\",\"--advertise-address\",\"192.10.75.9\",\"--tls-san\",\"controlplane\",\"--flannel-iface\",\"eth0\"]",

"k3s.io/node-config-hash": "QSE3CODHPKSU74VVY6TG2XX6OZXX5OMS37YI54QW2G4MHIETIL2Q====",

"k3s.io/node-env": "{\"K3S_DATA_DIR\":\"/var/lib/rancher/k3s/data/e5efd5aeb0cb8e4c1d802582bd7085968576e89d8f34ca22a450b4f4ae4d4c15\"}",

"node.alpha.kubernetes.io/ttl": "0",

"wrangler.cattle.io/node"

"nodeInfo": {

"nodePort": 30812

"nodePort": 31811

...

To get more information about the node of interest:

$ kubectl describe node controlplane

Name: controlplane

Roles: control-plane,master

Labels: beta.kubernetes.io/arch=amd64

beta.kubernetes.io/instance-type=k3s

beta.kubernetes.io/os=linux

kubernetes.io/arch=amd64

kubernetes.io/hostname=controlplane

kubernetes.io/os=linux

node-role.kubernetes.io/control-plane=true

node-role.kubernetes.io/master=true

node.kubernetes.io/instance-type=k3s

Annotations: alpha.kubernetes.io/provided-node-ip: 192.10.75.9

flannel.alpha.coreos.com/backend-data: {"VNI":1,"VtepMAC":"02:0e:eb:5a:ab:97"}

flannel.alpha.coreos.com/backend-type: vxlan

flannel.alpha.coreos.com/kube-subnet-manager: true

flannel.alpha.coreos.com/public-ip: 192.10.75.9

k3s.io/hostname: controlplane

k3s.io/internal-ip: 192.10.75.9

k3s.io/node-args: ["server","--advertise-address","192.10.75.9","--tls-san","controlplane","--flannel-iface","eth0"]

k3s.io/node-config-hash: QSE3CODHPKSU74VVY6TG2XX6OZXX5OMS37YI54QW2G4MHIETIL2Q====

k3s.io/node-env: {"K3S_DATA_DIR":"/var/lib/rancher/k3s/data/e5efd5aeb0cb8e4c1d802582bd7085968576e89d8f34ca22a450b4f4ae4d4c15"}

node.alpha.kubernetes.io/ttl: 0

volumes.kubernetes.io/controller-managed-attach-detach: true

CreationTimestamp: Mon, 06 May 2024 06:19:39 +0000

Taints: <none>

Unschedulable: false

Lease:

HolderIdentity: controlplane

AcquireTime: <unset>

RenewTime: Mon, 06 May 2024 06:45:50 +0000

Conditions:

Type Status LastHeartbeatTime LastTransitionTime Reason Message

---- ------ ----------------- ------------------ ------ -------

MemoryPressure False Mon, 06 May 2024 06:45:11 +0000 Mon, 06 May 2024 06:19:39 +0000 KubeletHasSufficientMemory kubelet has sufficient memory available

DiskPressure False Mon, 06 May 2024 06:45:11 +0000 Mon, 06 May 2024 06:19:39 +0000 KubeletHasNoDiskPressure kubelet has no disk pressure

PIDPressure False Mon, 06 May 2024 06:45:11 +0000 Mon, 06 May 2024 06:19:39 +0000 KubeletHasSufficientPID kubelet has sufficient PID available

Ready True Mon, 06 May 2024 06:45:11 +0000 Mon, 06 May 2024 06:19:39 +0000 KubeletReady kubelet is posting ready status

Addresses:

InternalIP: 192.10.75.9

Hostname: controlplane

Capacity:

cpu: 36

ephemeral-storage: 1016057248Ki

hugepages-1Gi: 0

hugepages-2Mi: 0

memory: 214587056Ki

pods: 110

Allocatable:

cpu: 36

ephemeral-storage: 988420490080

hugepages-1Gi: 0

hugepages-2Mi: 0

memory: 214587056Ki

pods: 110

System Info:

Machine ID:

System UUID: 82ad48cc-cf0c-c0bf-7b55-2c21495826a1

Boot ID: 6914c0b2-ef4d-4ff1-9180-75737388e9af

Kernel Version: 5.4.0-1106-gcp

OS Image: Alpine Linux v3.16

Operating System: linux

Architecture: amd64

Container Runtime Version: containerd://1.7.11-k3s2

Kubelet Version: v1.29.0+k3s1

Kube-Proxy Version: v1.29.0+k3s1

PodCIDR: 10.42.0.0/24

PodCIDRs: 10.42.0.0/24

ProviderID: k3s://controlplane

Non-terminated Pods: (5 in total)

Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits Age

--------- ---- ------------ ---------- --------------- ------------- ---

kube-system coredns-6799fbcd5-hl6zl 100m (0%) 0 (0%) 70Mi (0%) 170Mi (0%) 26m

kube-system local-path-provisioner-84db5d44d9-qgvm2 0 (0%) 0 (0%) 0 (0%) 0 (0%) 26m

kube-system svclb-traefik-f39a148c-87mtw 0 (0%) 0 (0%) 0 (0%) 0 (0%) 25m

kube-system metrics-server-67c658944b-5dsjt 100m (0%) 0 (0%) 70Mi (0%) 0 (0%) 26m

kube-system traefik-f4564c4f4-4qvgx 0 (0%) 0 (0%) 0 (0%) 0 (0%) 25m

Allocated resources:

(Total limits may be over 100 percent, i.e., overcommitted.)

Resource Requests Limits

-------- -------- ------

cpu 200m (0%) 0 (0%)

memory 140Mi (0%) 170Mi (0%)

ephemeral-storage 0 (0%) 0 (0%)

hugepages-1Gi 0 (0%) 0 (0%)

hugepages-2Mi 0 (0%) 0 (0%)

Events:

Type Reason Age From Message

---- ------ ---- ---- -------

Normal Starting 26m kube-proxy

Normal NodePasswordValidationComplete 26m k3s-supervisor Deferred node password secret validation complete

Normal Synced 26m cloud-node-controller Node synced successfully

Normal Starting 26m kubelet Starting kubelet.

Warning InvalidDiskCapacity 26m kubelet invalid capacity 0 on image filesystem

Normal NodeHasSufficientMemory 26m (x2 over 26m) kubelet Node controlplane status is now: NodeHasSufficientMemory

Normal NodeHasNoDiskPressure 26m (x2 over 26m) kubelet Node controlplane status is now: NodeHasNoDiskPressure

Normal NodeHasSufficientPID 26m (x2 over 26m) kubelet Node controlplane status is now: NodeHasSufficientPID

Normal NodeAllocatableEnforced 26m kubelet Updated Node Allocatable limit across pods

Normal NodeReady 26m kubelet Node controlplane status is now: NodeReady

Normal RegisteredNode 26m node-controller Node controlplane event: Registered Node controlplane in Controller

To create a deployment using this cluster:

$ kubectl create deployment hello-minikube --image=kicbase/echo-server:1.0

deployment.apps/hello-minikube created

To check it:

$ kubectl get deployments

NAME READY UP-TO-DATE AVAILABLE AGE

hello-minikube 1/1 1 1 68s

To expose this deployment as a service on port 8080:

$ kubectl expose deployment hello-minikube --type=NodePort --port=8080

service/hello-minikube exposed

To get info on the service:

$ kubectl get services hello-minikube

NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE

hello-minikube NodePort 10.104.92.181 <none> 8080:30235/TCP 4m5s

To get the URL of the exposed service:

$ minikube service hello-minikube --url

http://192.168.59.100:30235

If we copy this URL and paste it into a browser:

Cleanup:

$ kubectl delete services hello-minikube

service "hello-minikube" deleted

$ kubectl delete deployment hello-minikube

deployment.apps "hello-minikube" deleted

$ kubectl get pods

No resources found in default namespace.

To continue your Kubernetes learning journey, read the next article in these serie: Managing Pods In A Minikube Cluster | My Public Notepad

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