Horizontal Pod Autoscaler in Kubernetes - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Horizontal Pod Autoscaler (HPA) in Kubernetes?

HPA automatically adjusts the number of pods in a deployment or replica set based on observed CPU utilization or other select metrics to keep applications running smoothly.

beginner

Which metric is most commonly used by HPA to scale pods?

CPU utilization is the most common metric used by HPA to decide when to add or remove pods.

intermediate

What command creates a Horizontal Pod Autoscaler for a deployment named 'webapp' targeting 50% CPU usage?

kubectl autoscale deployment webapp --cpu-percent=50 --min=1 --max=5

beginner

What happens if the CPU usage goes above the target set in HPA?

HPA increases the number of pods to distribute the load and reduce CPU usage per pod.

intermediate

Can Horizontal Pod Autoscaler scale based on custom metrics other than CPU?

Yes, HPA can scale pods based on custom metrics like memory usage, request rate, or any metric exposed via the Kubernetes metrics API.

What does Horizontal Pod Autoscaler adjust in Kubernetes?

A Number of pods

B Pod CPU speed

C Node count

D Container image version

HPA changes the number of pods to handle load, not CPU speed or nodes.

Which command creates an HPA for deployment 'app' with max 10 pods and target 60% CPU?

A kubectl autoscale deployment app --cpu-percent=60 --min=1 --max=10

B kubectl scale deployment app --cpu=60 --max=10

C kubectl set hpa app --cpu=60 --max=10

D kubectl create hpa app --cpu=60 --max=10

The correct syntax uses 'kubectl autoscale deployment' with --cpu-percent, --min, and --max flags.

If CPU usage is below the target, what does HPA do?

A Increases pods

B Decreases pods

C Restarts pods

D Does nothing

HPA reduces the number of pods to save resources when CPU usage is low.

Which Kubernetes API is required for HPA to get metrics?

A Authentication API

B Storage API

C Networking API

D Metrics API

HPA relies on the Metrics API to fetch CPU and custom metrics.

Can HPA scale pods based on memory usage?

A Only if pods use less than 50% memory

B No, only CPU is supported

C Yes, with custom metrics configured

D Only on nodes with special labels

HPA supports custom metrics like memory if properly configured.

Explain how Horizontal Pod Autoscaler helps maintain application performance in Kubernetes.

Think about how adding or removing pods affects workload handling.

Describe the steps to create and verify a Horizontal Pod Autoscaler for a deployment.

Focus on commands and what you check after creation.

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