Horizontal Pod Autoscaler in Kubernetes - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

Output of HPA status command

What is the output of the following command after deploying an HPA targeting a deployment named webapp with min replicas 2 and max replicas 5, currently scaling to 3 replicas based on CPU usage?

Kubernetes

kubectl get hpa webapp

A

NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
webapp  Deployment/webapp   60%/50%   2         5         3          10m

B

NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
webapp  Deployment/webapp   70%/50%   2         5         4          10m

C

NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
webapp  Deployment/webapp   60%/60%   2         5         2          10m

D

NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
webapp  Deployment/webapp   50%/60%   2         5         3          10m

💡 Hint

Look carefully at the TARGETS column showing current CPU usage versus target CPU utilization.

The kubectl get hpa command shows the current CPU usage as the first number in TARGETS and the target CPU utilization as the second number. The REPLICAS column shows the current number of pods running.

Option A correctly shows 60% current usage against 50% target, with 3 replicas running.

🧠 Conceptual

intermediate

Understanding HPA scaling behavior

If a Horizontal Pod Autoscaler is configured with minReplicas=1, maxReplicas=4, and target CPU utilization of 50%, what happens when the CPU usage stays at 30% for a long time?

A The HPA will keep the pods at the current number, ignoring CPU usage below target.

B The HPA will scale down the pods to 1, the minimum number of replicas.

C The HPA will scale up the pods to 4, the maximum number of replicas.

D The HPA will delete all pods because CPU usage is low.

💡 Hint

Think about what the minimum replicas setting means for scaling down.

The HPA will reduce the number of pods when CPU usage is below the target, but it will never go below the minimum replicas configured.

Since CPU usage is 30%, below the 50% target, the HPA will scale down to the minimum of 1 pod.

❓ Configuration

advanced

Correct HPA YAML for CPU autoscaling

Which YAML snippet correctly defines a Horizontal Pod Autoscaler for a deployment named api-server with min 3 replicas, max 10 replicas, targeting 75% CPU utilization?

A

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: api-server
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-server
  minReplicas: 3
  maxReplicas: 10
  targetMemoryUtilizationPercentage: 75

B

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: api-server
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-server
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 75

C

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-server
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-server
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 75

D

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-server
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-server
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 75

💡 Hint

The autoscaling/v2 API uses a metrics array with resource type and target details.

Option D uses the autoscaling/v2 API with the correct metrics field specifying CPU utilization target.

Option D uses autoscaling/v1 which supports only CPU target but with a different field name; it is valid but less flexible.

Options C and D incorrectly target memory instead of CPU.

❓ Troubleshoot

advanced

Troubleshooting HPA not scaling up

You created an HPA for a deployment, but the pods never scale beyond 1 even when CPU usage is high. Which is the most likely cause?

A The maxReplicas is set to 1, limiting scaling to a single pod.

B The HPA minReplicas is set to 1, so it cannot scale beyond that.

C The deployment's resource requests for CPU are not set, so HPA cannot calculate usage properly.

D The deployment has too many replicas already running.

💡 Hint

Check the maxReplicas setting to see if it limits scaling.

If maxReplicas is set to 1, the HPA cannot scale beyond one pod regardless of CPU usage.

Resource requests missing can cause inaccurate metrics but usually do not prevent scaling entirely.

✅ Best Practice

expert

Best practice for HPA metrics configuration

Which approach is best practice when configuring Horizontal Pod Autoscaler metrics for a web application that experiences variable CPU and memory usage?

A Avoid using metrics and set fixed replica counts to prevent unpredictable scaling.

B Configure only memory usage metric because CPU usage fluctuates too much to be reliable.

C Configure multiple metrics including CPU utilization and memory usage with appropriate targets to allow balanced scaling.

D Configure only CPU utilization metric because memory usage is less important for scaling decisions.

💡 Hint

Consider how multiple metrics can improve scaling decisions.

Using multiple metrics like CPU and memory allows the HPA to scale pods based on the most relevant resource pressure, improving responsiveness and stability.

Relying on a single metric may cause suboptimal scaling.

Practice

1. What is the main purpose of a Horizontal Pod Autoscaler (HPA) in Kubernetes?

easy

A. Manually increase pod count when needed

B. Automatically adjust the number of pods based on resource usage

C. Scale the number of nodes in the cluster

D. Backup pod data automatically

Solution

1. Step 1: Understand HPA functionality

   HPA monitors resource usage like CPU or memory and adjusts pod count automatically.

2. Step 2: Differentiate from other scaling types

   HPA scales pods, not nodes or manual actions, and does not handle backups.

3. Final Answer:

   Automatically adjust the number of pods based on resource usage -> Option B

4. Quick Check:

   HPA = automatic pod scaling [OK]

Hint: HPA changes pod count automatically by resource use [OK]

Common Mistakes:

• Confusing HPA with manual scaling
• Thinking HPA scales nodes instead of pods
• Assuming HPA handles backups

2. Which of the following is the correct command to create a Horizontal Pod Autoscaler for a deployment named webapp targeting 50% CPU usage with minimum 2 and maximum 5 pods?

easy

A. kubectl autoscale deployment webapp --cpu=50 --min=2 --max=5

B. kubectl create hpa webapp --cpu=50 --min-pods=2 --max-pods=5

C. kubectl autoscale deployment webapp --cpu-percent=50 --min=2 --max=5

D. kubectl autoscale deployment webapp --cpu-percent=50 --min-pods=2 --max-pods=5

Solution

1. Step 1: Recall correct kubectl autoscale syntax

   The correct command is kubectl autoscale deployment [name] --cpu-percent=[value] --min=[minPods] --max=[maxPods].

2. Step 2: Match options with correct syntax

   kubectl autoscale deployment webapp --cpu-percent=50 --min=2 --max=5 matches the correct syntax exactly with proper flags and values.

3. Final Answer:

   kubectl autoscale deployment webapp --cpu-percent=50 --min=2 --max=5 -> Option C

4. Quick Check:

   Correct autoscale syntax = kubectl autoscale deployment webapp --cpu-percent=50 --min=2 --max=5 [OK]

Hint: Use kubectl autoscale with --cpu-percent, --min, --max [OK]

Common Mistakes:

• Using wrong flags like --min-pods or --cpu
• Misspelling 'autoscale' or 'deployment'
• Omitting required flags

3. Given this HPA YAML snippet:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 4
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 60

What happens if the CPU usage stays at 80% for a while?

medium

A. The number of pods will increase up to 4 to reduce CPU load

B. The number of pods will decrease to 1

C. The number of pods will stay the same regardless of CPU

D. The deployment will be deleted automatically

Solution

1. Step 1: Understand HPA scaling behavior

   HPA increases pods when CPU usage exceeds target averageUtilization (60%). Here CPU is 80%, which is higher.

2. Step 2: Check min and max replicas limits

   Pods can scale between 1 and 4. Since CPU is high, pods will increase up to max 4 to reduce load.

3. Final Answer:

   The number of pods will increase up to 4 to reduce CPU load -> Option A

4. Quick Check:

   CPU > target => pods increase [OK]

Hint: CPU above target means pods scale up within min-max [OK]

Common Mistakes:

• Thinking pods scale down when CPU is high
• Ignoring min/max replica limits
• Assuming deployment deletion on high CPU

4. You applied this HPA YAML but it does not scale pods as expected:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: test-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: test-app
  minReplicas: 2
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 70

What is the most likely reason for no scaling?

medium

A. The pods do not expose memory metrics, so HPA has no data

B. minReplicas is set too low

C. maxReplicas is less than minReplicas

D. The deployment name is incorrect

Solution

1. Step 1: Check metric type and data availability

   HPA needs metrics from pods. If memory metrics are not exposed or collected, HPA cannot scale.

2. Step 2: Verify other spec values

   minReplicas and maxReplicas are valid (2 and 5). Deployment name looks correct. So likely missing metrics cause no scaling.

3. Final Answer:

   The pods do not expose memory metrics, so HPA has no data -> Option A

4. Quick Check:

   Missing metrics = no scaling [OK]

Hint: HPA needs metrics; no metrics means no scaling [OK]

Common Mistakes:

• Assuming minReplicas too low stops scaling
• Confusing minReplicas and maxReplicas values
• Ignoring metric availability

5. You want to create an HPA that scales a deployment named api-server based on both CPU usage (target 70%) and custom metric requests_per_second (target 100). The minimum pods should be 3 and maximum 10. Which YAML snippet correctly defines this HPA?

hard

A. apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: api-server-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: api-server
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Resource
resource:
name: requests_per_second
target:
type: AverageValue
averageValue: "100"

B. apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: api-server-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: api-server
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: AverageValue
averageValue: "70"
- type: Pods
pods:
metric:
name: requests_per_second
target:
type: AverageValue
averageValue: "100"

C. apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
name: api-server-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: api-server
minReplicas: 3
maxReplicas: 10
targetCPUUtilizationPercentage: 70

D. apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: api-server-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: api-server
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Pods
pods:
metric:
name: requests_per_second
target:
type: AverageValue
averageValue: "100"

Solution

1. Step 1: Use autoscaling/v2 API for multiple metrics

   autoscaling/v2 supports multiple metrics including resource and custom metrics.

2. Step 2: Check metric types and targets

   CPU uses type Utilization with averageUtilization; custom metric uses type Pods with metric name and AverageValue target.

3. Step 3: Verify minReplicas and maxReplicas

   Minimum 3 and maximum 10 pods are correctly set in spec.

4. Final Answer:

   YAML with cpu resource metric (Utilization) and pods metric for requests_per_second -> Option D

5. Quick Check:

   Multiple metrics with correct types = apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: api-server-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: api-server minReplicas: 3 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - type: Pods pods: metric: name: requests_per_second target: type: AverageValue averageValue: "100" [OK]

Hint: Use autoscaling/v2 with resource and pods metrics for multiple targets [OK]

Common Mistakes:

• Using autoscaling/v1 which does not support multiple metrics
• Wrong metric types or target fields
• Mixing resource and custom metric syntax incorrectly