Getting started with Argo and Kubectl

Overview

Teaching: 10 min
Exercises: 0 min

Questions

• How to use Kubectl commands?

• What is kubectl?

• What is Argo workflows?

Objectives

• Lear what the kubectl command can do

• Appreciate the necessity for the Argo workflows tool (or similar)

• Lear how to set up different services/resources to get the most of your cluster

The kubectl command

Kubernetes provides a kubectl for communicating with a Kubernetes cluster’s control plane, using the Kubernetes API. Use the following syntax to run kubectl commands from your terminal window:

kubectl [command] [TYPE] [NAME] [flags]

where command, TYPE, NAME, and flags are:

command: Specifies the operation that you want to perform on one or more resources, for example create, get, describe, delete.
TYPE: Specifies the resource type.
NAME: Specifies the name of the resource.
flags:Specifies optional flags.

For installation instructions, see Installing kubectl; for a quick guide:

• See the cheat sheet.
• The kubectl command is the main tool for interacting with your K8s cluster. You will use it to do essentially anything in the cluster. Here is the official cheatsheet, which is very useful but already very long.

Let’s run a few examples.

• Get the status of the nodes in your cluster:

kubectl get nodes  

• Get the cluster info:

kubectl cluster-info  # Display addresses of the master and services

Let’s list some kubernetes components:

• Check pods

kubectl get pod

• Check the services

kubectl get services

We don’t have much going on. Let’s create some components.

• Inspect the create operation

kubectl create -h

Note there is no pod on the list, so in K8s you don’t create pods but deployments. These will create pods, which will run under the hood.

• Let’s create an application, it does not matter which. Let’s go for nginx:

kubectl create deployment mynginx-depl --image=nginx

The nginx image will be pulled down from the Docker Hub. This is the most minimalist way of creating a deployment.

• Check the deployments

kubectl get deployment

• Check pods

kubectl get pod

Argo

Argo is a collection of open source tools that let us to extend the functions in Kubernetes. We can find some benefits from use argo.

• Cloud agnostic service
• Argo can execute on absolutely in all clusters in kubernetes.
• We can review the state of resources constantly.
• There is a large capacity of executing jobs at the same time and from different nodes.
• It’s possible correct debug errors. We are going to explain 3 of the tools most important for working with argo.

Argo as a workflow engine

• Is used to execute complex job orchestration, including serial and parallel execution where each stage is executed like a container.
• It is the most popular workflow execution engine for kubernetes.
• You can run thousands of workflows a day, each with thousands of concurrent tasks.
• Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments.

While jobs can also be run manually, a workflow engine makes defining and submitting jobs easier. In this tutorial, we use argo. Install it into your working environment with the following commands (all commands to be entered into the cloud shell):

While jobs can also be run manually, a workflow engine makes defining and submitting jobs easier. In this tutorial, we use argo quick start page to install it.

Namespaces

Namespaces are a kind of reservations in your K8s cluster. Let’s create one for the Argo workflow we will user

kubectl create ns <NAMESPACE>

Kubernetes namespaces

The above commands as well as most of the following use a flag -n argo, which defines the namespace in which the resources are queried or created. Namespaces separate resources in the cluster, effectively giving you multiple virtual clusters within a cluster.

You can change the default namespace to argo as follows:

kubectl config set-context --current --namespace=argo

Key Points

• kubectl is the ruler of GKE

• Argo is a very useful tool for running workflows and parallel jobs

• To be able to write, read and extract data, a few services/resources need to be set up on the GCP