Why Deploying workloads on EKS in AWS? - Purpose & Use Cases

The Big Idea

What if you could deploy your app everywhere with just one command, no sweat?

The Scenario

Imagine you have a website or app that needs to run on many computers at once to handle lots of visitors. You try to set up each computer by hand, installing software and copying files one by one.

The Problem

This manual way is slow and tiring. If you make a small mistake on one computer, your app might break. Also, when you want to add more computers or fix problems, it takes a lot of time and effort.

The Solution

Using EKS (Elastic Kubernetes Service) lets you tell the system what your app needs, and it automatically runs your app on many computers. It handles starting, stopping, and fixing your app without you doing it all manually.

Before vs After

✗ Before

ssh server1
install app
ssh server2
install app
...

✓ After

kubectl apply -f app-deployment.yaml

What It Enables

You can easily run and manage your app at large scale without worrying about each computer individually.

Real Life Example

A company launches a new online store and uses EKS to quickly deploy their app so it can handle thousands of shoppers at once without downtime.

Key Takeaways

Manual setup is slow and error-prone.

EKS automates running apps on many computers.

This makes scaling and managing apps simple and reliable.

Practice

1. What is the main purpose of Amazon EKS when deploying workloads?

easy

A. To run containerized applications using Kubernetes without managing servers

B. To store large amounts of data in the cloud

C. To create virtual machines for running applications

D. To manage user access and permissions

Solution

1. Step 1: Understand EKS functionality

   Amazon EKS is a managed Kubernetes service that lets you run containers without managing the underlying servers.

2. Step 2: Compare options

   Storing data relates to services like S3, creating VMs to EC2, and managing permissions to IAM--none of which is EKS's core purpose.

3. Final Answer:

   To run containerized applications using Kubernetes without managing servers -> Option A

4. Quick Check:

   EKS = Managed Kubernetes for containers [OK]

Hint: EKS = Kubernetes service for containers, not storage or VMs [OK]

Common Mistakes:

• Confusing EKS with storage services like S3
• Thinking EKS manages virtual machines directly
• Mixing EKS with IAM user management

2. Which of the following is the correct command to deploy an application using a YAML file named app-deployment.yaml on EKS?

easy

A. kubectl run app-deployment.yaml

B. eksctl create app app-deployment.yaml

C. aws eks deploy app-deployment.yaml

D. kubectl apply -f app-deployment.yaml

Solution

1. Step 1: Identify the kubectl command for applying YAML

   The correct command to deploy resources defined in a YAML file is kubectl apply -f filename.

2. Step 2: Evaluate other options

   kubectl run app-deployment.yaml is for running individual pods, not applying YAML manifests. eksctl create app app-deployment.yaml misuses eksctl, which is primarily for EKS cluster creation. aws eks deploy app-deployment.yaml is not a valid AWS CLI command.

3. Final Answer:

   kubectl apply -f app-deployment.yaml -> Option D

4. Quick Check:

   Deploy YAML with kubectl apply -f [OK]

Hint: Use kubectl apply -f for YAML deployment files [OK]

Common Mistakes:

• Using eksctl for direct YAML deployment
• Confusing kubectl run with apply
• Trying AWS CLI commands that don't exist

3. Given this YAML snippet for a Deployment on EKS:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp-container
        image: nginx:latest

What will be the number of running pods after applying this deployment?

medium

A. 1

B. 3

C. 0

D. Depends on the node capacity

Solution

1. Step 1: Read the replicas field in the Deployment spec

   The YAML specifies replicas: 3, which means Kubernetes will create 3 pods for this deployment.

2. Step 2: Understand pod creation behavior

   Unless there is a scheduling issue, Kubernetes will run 3 pods as requested.

3. Final Answer:

   3 -> Option B

4. Quick Check:

   replicas = number of pods running [OK]

Hint: replicas field sets pod count in Deployment spec [OK]

Common Mistakes:

• Assuming only 1 pod runs by default
• Thinking pods depend on node capacity without error
• Confusing selector labels with pod count

4. You tried to deploy a workload on EKS using kubectl apply -f deployment.yaml, but got an error: error: unable to recognize "deployment.yaml": no matches for kind "Deploy". What is the likely cause?

medium

A. The cluster is not running

B. kubectl is not installed

C. The YAML file has a typo in the kind field; it should be 'Deployment' not 'Deploy'

D. The image name in the YAML is incorrect

Solution

1. Step 1: Analyze the error message

   The error says no matches for kind "Deploy", which means Kubernetes does not recognize this resource kind.

2. Step 2: Check the YAML kind field

   The correct kind for workload deployment is "Deployment", not "Deploy". This typo causes the error.

3. Final Answer:

   The YAML file has a typo in the kind field; it should be 'Deployment' not 'Deploy' -> Option C

4. Quick Check:

   Kind field must be exact Kubernetes resource name [OK]

Hint: Check resource kind spelling in YAML for errors [OK]

Common Mistakes:

• Ignoring error details about resource kind
• Assuming cluster or kubectl issues without checking YAML
• Blaming image name for syntax errors

5. You want to deploy a stateless web app on EKS that automatically scales between 2 and 5 pods based on CPU usage. Which combination of Kubernetes resources should you define in your YAML files?

hard

A. A Deployment with replicas set to 2 and a Horizontal Pod Autoscaler targeting CPU usage with max replicas 5

B. A StatefulSet with replicas 5 and a LoadBalancer service

C. A DaemonSet to run pods on every node and a Cluster Autoscaler

D. A Deployment with replicas fixed at 5 and a NodePort service

Solution

1. Step 1: Identify resource for managing pod replicas with scaling

   A Deployment manages stateless pods and can be combined with a Horizontal Pod Autoscaler (HPA) to scale pods based on CPU.

2. Step 2: Understand autoscaling setup

   Set Deployment replicas to minimum 2, and HPA to scale up to max 5 pods based on CPU usage.

3. Step 3: Evaluate other options

   StatefulSet is for stateful apps, DaemonSet runs pods on all nodes (not scaling pods), fixed replicas do not autoscale.

4. Final Answer:

   A Deployment with replicas set to 2 and a Horizontal Pod Autoscaler targeting CPU usage with max replicas 5 -> Option A

5. Quick Check:

   Deployment + HPA = autoscaling pods [OK]

Hint: Use Deployment + HPA for CPU-based pod autoscaling [OK]

Common Mistakes:

• Using StatefulSet for stateless apps
• Confusing DaemonSet with autoscaling pods
• Setting fixed replicas without autoscaler