Scaling is one of the core features of Kubernetes — and Horizontal Pod Autoscaler (HPA) makes it effortless by automatically adjusting the number of Pods based on resource usage or custom metrics.
In this article, we’ll understand how HPA works in Google Kubernetes Engine (GKE), what the Metrics Server does, and how to visualize the autoscaling flow with an easy-to-understand diagram.
⚙️ What is Kubernetes Horizontal Pod Autoscaling?
The Horizontal Pod Autoscaler (HPA) automatically increases or decreases the number of Pods in your Kubernetes workloads based on observed metrics such as CPU and memory usage.
🔄 It can scale your applications using:
- CPU and Memory Utilization (default metrics)
- Custom Metrics from within your Kubernetes cluster
- External Metrics (like Cloud Pub/Sub messages, HTTP requests, or load balancer metrics)
- Managed Service for Prometheus (for advanced custom metrics)
🧩 HPA Works With These Workload Types:
- ReplicaSet
- ReplicationController
- Deployment
- StatefulSet
💡 It ensures your app can:
- Scale out automatically to handle high demand
- Scale in when demand drops — freeing cluster resources and saving cost
🧠 How Horizontal Pod Autoscaler Works — Explained with the Diagram
Below is a simple visual representation of how HPA functions inside a GKE Cluster:
Let’s break it down 👇
🔹 Step 1: Query for Metrics
Inside your GKE cluster, the Metrics Server (running in the kube-system namespace) continuously collects metrics like CPU and memory usage from all Pods through Kubelets.
These metrics are then made available through the Metrics API, which HPA uses to make scaling decisions.
🕒 This process runs in a control loop — every 15 seconds.
🔹 Step 2: Calculate the Required Replicas
The Horizontal Pod Autoscaler (HPA) controller compares the actual resource usage of your Pods (from the Metrics API) against the target utilization you defined.
For example:
If you set CPU target to 50%, and your Pods are using 90%, HPA will calculate how many additional Pods are needed to balance the load.
🔹 Step 3: Scale the Deployment (MyApp1)
After calculating the new desired number of Pods, HPA automatically scales the corresponding Deployment, ReplicaSet, or StatefulSet.
This means:
- If demand increases → more Pods are created.
- If demand decreases → Pods are terminated automatically.
✅ The scaling happens smoothly without any downtime.
🧮 Example: Imperative Command
You can create an autoscaler using a simple kubectl command:
kubectl autoscale deployment my-app --min=4 --max=6 --cpu-percent=50
Explanation:
- my-app: Target Deployment name
- --min=4: Minimum of 4 Pods
- --max=6: Maximum of 6 Pods
- --cpu-percent=50: Target CPU utilization threshold
When the average CPU utilization of the Pods exceeds 50%, HPA increases the number of Pods (up to 6). When it’s below 50%, it scales down (not below 4).
📊 Kubernetes Metrics Server
The Metrics Server is the key component that enables autoscaling in Kubernetes.
🔸 What it Does:
- Collects resource metrics (CPU, memory) from Kubelets
- Exposes them via Metrics API
- Provides data for commands like:
kubectl top nodes
kubectl top pods
🔸 Key Points:
- Collects metrics every 15 seconds
- Lightweight — uses about 1 millicore CPU and 2 MB memory per node
- Optimized for autoscaling, not for monitoring dashboards
- Do not use it as a monitoring solution (use Prometheus or Cloud Monitoring for that)
- Also supports Vertical Pod Autoscaler recommendations
💡 Benefits of HPA
✅ Automatic Scaling — Kubernetes adjusts Pod count dynamically
✅ Cost Efficiency — Uses only the resources you need
✅ Performance Stability — Keeps workloads responsive during spikes
✅ Less Manual Work — No need to scale deployments manually
🧠 Quick Recap
| Component | Purpose |
|---|---|
| Metrics Server | Collects Pod resource usage |
| HPA Controller | Calculates replicas every 15 seconds |
| Deployment / StatefulSet | Scaled automatically by HPA |
| kubectl top | Shows live metrics for debugging |
| Target Metrics | CPU %, Memory %, or custom metrics |
🏁 Final Thoughts
The Horizontal Pod Autoscaler (HPA) in GKE makes your applications elastic — scaling up when demand increases and scaling down to save cost when demand drops.
It’s one of the most powerful automation tools in Kubernetes, ensuring your workloads stay efficient, responsive, and cost-optimized without manual effort.
✨ Example Use Cases
- Autoscaling web applications during traffic spikes
- Scaling data-processing jobs when queue length increases
- Optimizing microservices automatically based on load
🌟 Thanks for reading! If this post added value, a like ❤️, follow, or share would encourage me to keep creating more content.
— Latchu | Senior DevOps & Cloud Engineer
☁️ AWS | GCP | ☸️ Kubernetes | 🔐 Security | ⚡ Automation
📌 Sharing hands-on guides, best practices & real-world cloud solutions
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