Cost optimization in Kubernetes - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set a resource limit for CPU in a pod specification.

Kubernetes

resources:
  limits:
    cpu: [1]

Drag options to blanks, or click blank then click option'

A500m

B2CPU

C1000Mi

D2GB

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory units like '2GB' for CPU limits

Using '2CPU' which is not a valid unit

The CPU limit in Kubernetes is specified in millicores, so 500m means half a CPU core.

2fill in blank

medium

Complete the command to view the current resource usage of pods in a namespace.

Kubernetes

kubectl top pods -n [1]

Drag options to blanks, or click blank then click option'

Asystem

Bdefault

Call

Dkube-system

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'all' which is not a valid namespace

Using 'kube-system' which is for system pods

The default namespace is the common namespace where pods run if none is specified.

3fill in blank

hard

Fix the error in the YAML snippet to request 1 CPU core for a container.

Kubernetes

resources:
  requests:
    cpu: [1]

Drag options to blanks, or click blank then click option'

A1Mi

B1GB

C1000m

Done

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory units like '1GB' or '1Mi'

Using words like 'one' instead of numeric units

CPU requests use millicores, so 1000m means 1 full CPU core.

4fill in blank

hard

Fill both blanks to create a Horizontal Pod Autoscaler that scales between 2 and 5 replicas based on CPU usage.

Kubernetes

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  minReplicas: [1]
  maxReplicas: [2]
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

Drag options to blanks, or click blank then click option'

A2

B10

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting maxReplicas lower than minReplicas

Using very high maxReplicas causing cost spikes

The minimum replicas is 2 and maximum replicas is 5 to control scaling limits.

5fill in blank

hard

Fill all three blanks to create a resource quota limiting pods and CPU usage in a namespace.

Kubernetes

apiVersion: v1
kind: ResourceQuota
metadata:
  name: cpu-pod-quota
spec:
  hard:
    pods: [1]
    requests.cpu: [2]
    limits.cpu: [3]

Drag options to blanks, or click blank then click option'

A10

B4

C8

D1000m

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing CPU units with pod counts

Using invalid CPU units like '4' without units

This quota limits pods to 10, CPU requests to 1000m (1 CPU), and CPU limits to 8 cores.

Practice

1. What is the main purpose of setting resource requests and limits on Kubernetes pods for cost optimization?

easy

A. To disable autoscaling features in the cluster

B. To control how much CPU and memory a pod can use, preventing waste

C. To increase the number of pods running simultaneously

D. To allow pods to use unlimited resources

Solution

1. Step 1: Understand resource requests and limits

   Requests define minimum resources a pod needs; limits set maximum usage.

2. Step 2: Link resource control to cost optimization

   By setting these, Kubernetes schedules pods efficiently and avoids resource waste.

3. Final Answer:

   To control how much CPU and memory a pod can use, preventing waste -> Option B

4. Quick Check:

   Resource limits prevent waste = C [OK]

Hint: Requests and limits control pod resource use to save costs [OK]

Common Mistakes:

• Thinking limits increase pod count
• Confusing requests with autoscaling
• Assuming unlimited resources save money

2. Which of the following is the correct YAML snippet to set a CPU request of 500m and a memory limit of 256Mi for a container in Kubernetes?

easy

A. resources:\n requests:\n cpu: '500m'\n limits:\n memory: '256Mi'

B. resources:\n limits:\n cpu: '500m'\n requests:\n memory: '256Mi'

C. resources:\n requests:\n cpu: 500\n memory: 256

D. resources:\n requests:\n cpu: '0.5'\n limits:\n memory: '256MB'

Solution

1. Step 1: Check correct YAML structure for resources

   Requests and limits must be under resources, with proper indentation and units.

2. Step 2: Validate units and order

   CPU request '500m' means 0.5 CPU; memory limit '256Mi' is correct unit. resources:\n requests:\n cpu: '500m'\n limits:\n memory: '256Mi' matches this.

3. Final Answer:

   resources:\n requests:\n cpu: '500m'\n limits:\n memory: '256Mi' -> Option A

4. Quick Check:

   Correct YAML with proper units = B [OK]

Hint: Requests before limits, use 'm' for CPU and 'Mi' for memory [OK]

Common Mistakes:

• Swapping requests and limits
• Using wrong units like 'MB' instead of 'Mi'
• Omitting quotes around values

3. Given this Horizontal Pod Autoscaler (HPA) YAML snippet:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  minReplicas: 2
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

What happens when CPU usage exceeds 50%?

medium

A. Pods restart automatically

B. The number of pods decreases to 2 to save cost

C. The number of pods increases up to 5 to handle load

D. CPU limits are increased automatically

Solution

1. Step 1: Understand HPA behavior with CPU utilization

   HPA increases pod count when average CPU usage exceeds target utilization (50%).

2. Step 2: Check min and max replicas

   Pods scale between 2 and 5 replicas based on load; exceeding 50% triggers scaling up.

3. Final Answer:

   The number of pods increases up to 5 to handle load -> Option C

4. Quick Check:

   CPU > 50% triggers scale up = A [OK]

Hint: HPA scales pods up when CPU usage exceeds target [OK]

Common Mistakes:

• Thinking pods scale down on high CPU
• Assuming pods restart on high CPU
• Believing CPU limits auto-increase

4. You notice your Kubernetes cluster is overspending because pods are not scaling down after load decreases. Which is the most likely cause?

medium

A. CPU requests are set higher than limits

B. Resource limits are set too low

C. Pods have no readinessProbe configured

D. The Horizontal Pod Autoscaler has a high minReplicas value

Solution

1. Step 1: Analyze autoscaling parameters

   A high minReplicas prevents scaling below that number, causing overspending.

2. Step 2: Evaluate other options

   Low limits or readiness probes don't directly prevent scaling down; CPU requests > limits is invalid.

3. Final Answer:

   The Horizontal Pod Autoscaler has a high minReplicas value -> Option D

4. Quick Check:

   High minReplicas blocks scale down = A [OK]

Hint: Check minReplicas to allow scaling down [OK]

Common Mistakes:

• Confusing limits with requests
• Ignoring minReplicas effect
• Assuming readinessProbe affects scaling

5. You want to optimize costs by automatically scaling your Kubernetes cluster nodes based on pod resource usage. Which combination of tools and settings should you use?

hard

A. Cluster Autoscaler with properly set pod resource requests and limits

B. Manual node scaling with no pod resource limits

C. Disable Horizontal Pod Autoscaler and increase node count permanently

D. Set pod resource limits to zero and rely on node autoscaling

Solution

1. Step 1: Understand cluster autoscaling

   Cluster Autoscaler adjusts node count based on pod scheduling needs and resource requests.

2. Step 2: Importance of pod resource requests and limits

   Proper requests and limits let the autoscaler know actual resource needs to scale nodes efficiently.

3. Step 3: Evaluate other options

   Manual scaling wastes resources; disabling HPA or zero limits causes inefficiency or errors.

4. Final Answer:

   Cluster Autoscaler with properly set pod resource requests and limits -> Option A

5. Quick Check:

   Autoscaler + resource requests = cost savings [OK]

Hint: Use Cluster Autoscaler plus pod requests/limits for best cost control [OK]

Common Mistakes:

• Relying on manual scaling only
• Disabling autoscaling features
• Setting resource limits to zero