Horizontal Pod Autoscaler in Kubernetes - Step-by-Step Execution

Process Flow - Horizontal Pod Autoscaler

Monitor Metrics

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Compare Current Load with Target

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Decide to Scale Up or Down

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Increase

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Change Pod Count

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Wait & Repeat

The Horizontal Pod Autoscaler watches metrics, compares them to targets, then adjusts pod count up or down accordingly, repeating continuously.

Execution Sample

Kubernetes

kubectl autoscale deployment myapp --min=2 --max=5 --cpu-percent=50

This command creates an autoscaler for 'myapp' deployment to keep CPU usage near 50%, scaling pods between 2 and 5.

Process Table

Step	Current CPU %	Target CPU %	Pods Before	Decision	Pods After
1	30	50	2	CPU below target, no scale change	2
2	55	50	2	CPU above target, scale up	3
3	70	50	3	CPU well above target, scale up	4
4	45	50	4	CPU below target, scale down	3
5	20	50	3	CPU well below target, scale down	2
6	50	50	2	CPU at target, no change	2

💡 Autoscaler continuously monitors; this trace shows decisions over 6 checks.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6
Current CPU %	N/A	30	55	70	45	20	50
Pods Count	2	2	3	4	3	2	2

Key Moments - 3 Insights

Why doesn't the pod count change when CPU is below target but not very low (Step 1)?

At Step 1, CPU is 30%, below the 50% target but not low enough to trigger scaling down, so pods remain at 2 as shown in execution_table row 1.

Why does the pod count increase by only one pod at each scale-up step?

The autoscaler adjusts pods gradually to avoid sudden changes; at Steps 2 and 3, it increases pods by one each time, as seen in execution_table rows 2 and 3.

Can the pod count go below the minimum set (2 pods)?

No, the autoscaler respects the minimum pods limit; even when CPU is very low at Step 5, pods do not go below 2, as shown in execution_table row 5.

Visual Quiz - 3 Questions

Look at the execution table, what is the pod count after Step 3?

A 4

B 3

C 2

D 5

💡 Hint

Check the 'Pods After' column in row for Step 3.

At which step does the autoscaler decide to scale down the pods for the first time?

A Step 2

B Step 4

C Step 1

D Step 5

💡 Hint

Look for the first 'scale down' decision in the 'Decision' column.

If the CPU target was changed to 60%, how would the decision at Step 2 change?

A Still scale up

B Scale down

C No scale change

D Scale up by 2 pods

💡 Hint

Compare current CPU (55%) with new target (60%) in Step 2 row.

Concept Snapshot

Horizontal Pod Autoscaler (HPA):
- Monitors pod metrics (e.g., CPU usage).
- Compares current metric to target.
- Scales pod count up/down within min/max limits.
- Runs continuously to keep app responsive.
- Command example: kubectl autoscale deployment NAME --min=X --max=Y --cpu-percent=Z

Full Transcript

The Horizontal Pod Autoscaler watches the CPU usage of pods in a deployment. It compares the current CPU percentage to a target value. If the CPU is higher than the target, it increases the number of pods by one to handle more load. If the CPU is lower than the target, it decreases the number of pods by one to save resources, but never below the minimum set. This process repeats continuously to keep the application running efficiently. The example command sets up autoscaling for a deployment named 'myapp' with a minimum of 2 pods, maximum of 5 pods, and a target CPU usage of 50%. The execution table shows how the pod count changes step-by-step based on CPU usage readings.