HPA in Kubernetes

HPA in Kubernetes is very important when it comes to application efficiency.

As modern applications grow in complexity and scale, ensuring they remain performant under varying loads is critical.

Kubernetes addresses this challenge with powerful autoscaling capabilities, allowing workloads to adapt dynamically to demand.

One of the core components in this autoscaling strategy is the Horizontal Pod Autoscaler (HPA).

What is Autoscaling in Kubernetes?

Autoscaling in Kubernetes refers to the automatic adjustment of the number of pods in a deployment, replica set, or stateful set based on observed metrics like CPU usage, memory consumption, or custom application metrics.

This helps ensure your applications maintain performance under high load while conserving resources during low usage periods.

Why Dynamic Scaling Matters

Dynamic scaling offers several advantages:

Cost efficiency: By running only as many pods as needed, you avoid overprovisioning resources.
Performance stability: Applications stay responsive even during traffic spikes.
Operational agility: Teams can focus on building features rather than manually adjusting infrastructure.

Introducing HPA in Kubernetes

The Horizontal Pod Autoscaler (HPA) is a native Kubernetes controller that automatically increases or decreases the number of pods in a deployment based on real-time metrics.

HPA is widely used to enable elastic workloads in cloud-native applications and is essential for maintaining scalability and resilience in production environments.

We’ll dive into how HPA works, how to configure it, and when to use it, along with advanced use cases and best practices.

If you’re new to Kubernetes deployments, check out our guide on Kubernetes Scale Deployment and Load Balancer for Kubernetes for foundational knowledge.

For additional background, Kubernetes’ official HPA documentation is a great place to get a sense of how the controller fits into the broader ecosystem.

What is HPA in Kubernetes?

Definition and Purpose

The Horizontal Pod Autoscaler (HPA) is a built-in Kubernetes controller that automatically adjusts the number of pod replicas in a deployment, replica set, or stateful set based on observed resource usage or custom metrics.

The main goal of HPA is to ensure applications are right-sized at all times—scaling out when demand increases and scaling in when it decreases.

This dynamic adjustment is crucial for applications that experience variable workloads, such as e-commerce sites during flash sales or APIs with unpredictable traffic patterns.

How HPA Works

At a high level, the HPA operates on a feedback loop.

Here’s how it works:

Metric Collection: HPA retrieves resource usage data (e.g., CPU or memory) from the metrics server or custom/external sources.
Evaluation: It compares the current metric values against the target thresholds defined in the HPA configuration.
Decision Making: Based on the comparison, the HPA determines whether to increase, decrease, or maintain the current number of replicas.
Scaling Action: If adjustment is needed, the HPA updates the target object (e.g., a Deployment) with the new replica count.

This cycle repeats every 15 seconds (by default), enabling near-real-time response to workload changes.

Key Components Involved

To function correctly, the HPA relies on several key components:

Metrics Server: A lightweight aggregator that collects metrics like CPU and memory usage from the Kubelet on each node. It’s a required component for HPA to work with built-in metrics.
Resource Requests and Limits: Kubernetes needs these to calculate usage percentages. If they’re not set in your container specs, HPA won’t function properly with resource-based metrics.
HPA Controller: A controller running in the Kubernetes control plane that continuously monitors metrics and adjusts the number of pods accordingly.

Here’s a basic YAML snippet showing what an HPA configuration might look like:

In this example, the HPA maintains CPU utilization around 60% by scaling the pod count between 2 and 10 replicas.

Prerequisites for HPA

Before you can start using the Horizontal Pod Autoscaler (HPA) in your Kubernetes cluster, you need to ensure that several prerequisites are in place.

Without these foundational components, HPA won’t function properly or at all.

1. Installing and Configuring the Kubernetes Metrics Server

The metrics-server is a vital component for HPA because it provides resource usage metrics (CPU and memory) to the Kubernetes control plane.

Why it’s important: HPA relies on these metrics to determine whether to scale your workloads.
Installation: You can deploy it easily using the following command:
bash
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
Verification: After installation, run the following to ensure it’s working:
bash
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes"
Common issue: If you’re on a secure cluster (like on AWS EKS), you might need to tweak the deployment by adding the --kubelet-insecure-tls flag to the container args.

2. Defining CPU/Memory Requests and Limits

Kubernetes uses resource requests and limits to calculate usage percentages for autoscaling.

If these are missing from your pod specs, HPA won’t know how to scale based on CPU or memory.

Here’s an example container configuration:

💡 Without requests, HPA won’t have a baseline for calculating CPU/memory utilization.

Make sure each container in your deployment specifies these values—especially for workloads you plan to scale.

3. Version Compatibility and API Usage

HPA has evolved over time, and newer Kubernetes versions use updated APIs:

autoscaling/v2 (recommended): Supports multiple metrics, scaling policies, and custom metrics.
autoscaling/v2beta2 (older, deprecated in recent versions)
autoscaling/v1: Supports only CPU-based autoscaling.

To check your Kubernetes version:

Ensure your manifests use the appropriate API version based on your cluster. Most production environments today should use autoscaling/v2.

Creating and Configuring HPA

Once the prerequisites are in place—like the Metrics Server and resource requests—the next step is to create and configure the Horizontal Pod Autoscaler (HPA) for your workload.

Kubernetes provides multiple ways to define an HPA, including declarative YAML manifests and imperative commands via kubectl.

Basic HPA Manifest Example

A simple example of an HPA manifest that scales based on CPU utilization:

Explanation:

scaleTargetRef: The Deployment (or StatefulSet) that the HPA controls.
minReplicas and maxReplicas: The scaling boundaries.
metrics: Defines the type of metric and the utilization threshold (e.g., 60% CPU usage).

Apply it using:

Using `kubectl autoscale`

Kubernetes also offers a quick CLI method to create a basic HPA:

This creates an HPA that targets 60% average CPU utilization, with a minimum of 2 and maximum of 10 replicas.

📝 Note: This CLI method only supports CPU-based scaling. For more complex configurations (like memory or custom metrics), use a YAML manifest.

Scaling on CPU Utilization

CPU is the most common metric for autoscaling. The HPA controller constantly monitors CPU usage and compares it against the target average.

If usage exceeds the threshold, the number of replicas increases; if usage drops, HPA scales it down.

You can view CPU metrics with:

Configuring Min/Max Replicas and Target Utilization

Choosing the right minReplicas and maxReplicas is essential:

minReplicas ensures baseline availability.
maxReplicas prevents over-provisioning and unnecessary resource use.
target utilization should be tuned based on application performance under load.

For production workloads, monitor trends and adjust these values regularly.

In the next section, we’ll explore scaling with custom metrics—ideal for use cases where CPU/memory alone isn’t enough to drive autoscaling behavior.

Advanced HPA with Custom Metrics

While CPU and memory usage are common triggers for autoscaling, many real-world applications require more granular control.

Kubernetes supports custom metrics, enabling scaling based on HTTP requests, queue length, latency, or even business-specific KPIs.

Overview of the Custom Metrics API

To support scaling beyond CPU and memory, Kubernetes exposes the Custom Metrics API.

This allows the Horizontal Pod Autoscaler (HPA) to query arbitrary metrics provided by third-party monitoring systems.

With this API, you can configure the HPA to scale based on:

HTTP request rate
Queue depth
Database connection count
Application-specific metrics like number of active sessions

However, using custom metrics requires additional setup since Kubernetes does not natively provide these metrics.

Using Prometheus Adapter for Custom Metrics

A popular way to expose custom metrics is by integrating Prometheus with the Prometheus Adapter, which implements the custom metrics API server.

Key steps:

Install Prometheus and Prometheus Adapter in your cluster (can be done via Helm):
bash
helm install prometheus prometheus-community/kube-prometheus-stack helm install prometheus-adapter prometheus-community/prometheus-adapter
Expose custom metrics in your application, typically via an HTTP endpoint like /metrics.
Configure Prometheus Adapter to map Prometheus queries to custom metric names readable by Kubernetes.
Create an HPA manifest that uses a custom metric, such as:

This configuration tells the HPA to maintain an average of 100 HTTP requests per second per pod.

Scaling Based on Business Metrics

With custom metrics, you’re not limited to infrastructure metrics—you can scale based on application-level signals like:

Number of active users
Number of open orders
API error rate
Custom SLAs

This enables a smarter autoscaling strategy tailored to your actual workload patterns.

Up next: We’ll walk through use cases for HPA.

Use Cases for HPA

Horizontal Pod Autoscaling (HPA) is a powerful tool in Kubernetes that enables your applications to scale dynamically based on real-time demand.

By monitoring resource usage or custom metrics, HPA ensures optimal performance and cost-efficiency.

Below are some common and practical use cases where HPA shines.

Autoscaling Web Applications Based on Traffic

One of the most common use cases is scaling stateless web applications based on CPU or HTTP request load.

For instance, during high traffic periods (e.g., a product launch or marketing campaign), HPA can automatically increase the number of pods to handle the additional requests and scale down afterward to save resources.

Example:

A frontend application is set to scale between 2 and 10 replicas.
HPA monitors CPU usage and increases pods when usage exceeds 70%.
As traffic subsides, the pod count returns to the baseline.

This helps maintain low latency and high availability without manual intervention.

Scaling Workers Based on Queue Length or Job Load

For background jobs and asynchronous processing, custom metrics (like queue length) are often better indicators of load than CPU or memory.

Example:

A message processor consumes jobs from a RabbitMQ or Kafka queue.
HPA, configured with a Prometheus custom metric, monitors queue depth.
When queue length exceeds a threshold, the number of worker pods scales up to reduce backlog.

This is especially useful for batch processing, email sending, video encoding, or data pipeline workloads.

Real-World Examples of Dynamic Scaling in Production

E-commerce Platforms

Retail websites often experience seasonal surges in traffic.

Using HPA, they can automatically scale web servers and APIs during events like Black Friday without overprovisioning year-round.

SaaS Applications

SaaS products with usage-based pricing models can scale backend services up and down based on customer activity, improving performance and reducing operational costs.

CI/CD Systems

Build and test agents in CI/CD pipelines can be autoscaled using HPA based on job queue metrics, optimizing compute usage and job throughput.

Coming up next: We’ll dive into monitoring and best practices for running HPA at scale in your Kubernetes clusters.

Monitoring and Troubleshooting HPA

Once you’ve configured Horizontal Pod Autoscaling (HPA) in your Kubernetes cluster, monitoring its behavior and diagnosing issues becomes critical to ensure it works as expected.

This section covers essential techniques and tools for observing and troubleshooting HPA.

Inspecting HPA Objects with `kubectl describe hpa`

The primary way to inspect the state of an HPA is via the kubectl describe command:

This command provides valuable information, such as:

Current and target metrics (CPU%, memory, or custom metrics)
Current replica count
Scale events and timestamps
Conditions (e.g., able to scale, metrics available)

It’s a great first step in verifying if HPA is scaling correctly and when it last triggered a scale-up or scale-down event.

Common Issues and How to Resolve Them

❌ Missing or Unavailable Metrics

Problem: HPA remains idle, even under load.

Cause: The Metrics Server may not be installed or running correctly.

Solution:

Ensure Metrics Server is deployed:
bash
kubectl get deployment metrics-server -n kube-system
Check logs for errors:
bash
kubectl logs -n kube-system deployment/metrics-server

⚠️ Under-scaling or Over-scaling

Problem: Your application isn’t scaling appropriately, even when metrics are available.

Causes:

Resource requests/limits are not properly set on pods.
The target utilization is misconfigured (too high or too low).
Custom metrics are noisy or misrepresent real usage.

Solutions:

Double-check resource requests/limits.
Tune your HPA configuration for more accurate responsiveness.
Use Prometheus or another metric backend to validate metric behavior.

Tools for Visualizing HPA Behavior

To understand how your HPA behaves over time, especially in dynamic workloads, visualization helps.

Here are some tools to monitor and analyze HPA:

📈 Prometheus + Grafana

Prometheus collects CPU, memory, and custom metrics.
Grafana provides dashboards for visualizing HPA metrics over time, including:
- Replica count
- CPU/memory usage
- Target utilization vs. actual
- Scaling events

🔍 Kubernetes Dashboard

For a GUI-based overview, the Kubernetes Dashboard allows you to view HPA objects and associated workloads.

It’s less detailed than Grafana but helpful for quick checks.

By actively monitoring your HPA configuration and behavior, and addressing common pitfalls, you can ensure your applications remain performant and efficient even during changing workloads.

Next up: Let’s look at how HPA compares to other autoscaling strategies like VPA and KEDA.

Best Practices for Using HPA in Kubernetes

To get the most value out of Horizontal Pod Autoscaling (HPA), it’s essential to go beyond basic setup and follow proven best practices.

This helps ensure smooth scaling behavior, resource efficiency, and system stability.

✅ Set Proper Resource Requests and Limits

HPA relies on resource requests (not limits) to calculate utilization.

If you don’t define CPU or memory requests on your pods, HPA won’t have a baseline to compare against, and autoscaling may not work.

Best practice:

Always define resources.requests.cpu and/or resources.requests.memory in your deployment specs.
Set realistic values based on actual application needs, ideally informed by performance testing or prior observability data.

Example:

⚖️ Avoid Flapping and Instability

Flapping occurs when your HPA frequently scales pods up and down in rapid succession, causing resource churn and instability.

Tips to reduce flapping:

Set sensible min/max replica counts.
Avoid extremely aggressive target utilization values (e.g., don’t set CPU target at 20%).
Use stabilization windows (available in Kubernetes v1.18+) to delay scale-downs:
yaml
behavior: scaleDown: stabilizationWindowSeconds: 300

🤝 Combine HPA with Cluster Autoscaler

While HPA adjusts pod counts based on application-level metrics, it doesn’t account for whether the underlying infrastructure can support new pods.

That’s where the Cluster Autoscaler comes in.

Best practice:

Use HPA for workload scaling.
Use Cluster Autoscaler to scale your node pool(s) automatically based on resource demand.
Ensure that your cloud provider setup supports autoscaling (e.g., AWS Auto Scaling Groups, GCP node pools).

This combination allows your apps to scale up smoothly even when existing nodes are fully utilized.

By following these best practices, you’ll ensure that your HPA configuration is both stable and effective, delivering consistent performance while minimizing unnecessary resource consumption.

Up next: Let’s wrap up with a conclusion and final recommendations for implementing HPA effectively in your Kubernetes environment.

Conclusion

Horizontal Pod Autoscaler (HPA) is a foundational feature in Kubernetes that enables your applications to scale automatically based on real-time demand.

Whether you’re handling sudden spikes in traffic or optimizing resource usage for cost savings, HPA ensures your workloads stay resilient and responsive.

🔁 Recap of HPA Benefits

Dynamic scaling based on CPU, memory, or custom metrics
Improved resource efficiency, avoiding over-provisioning
Automatic responsiveness to changing workloads
Integration with tools like Prometheus and Cluster Autoscaler for advanced use cases

🎯 When and Why to Use HPA

Use HPA when:

Your workloads experience variable traffic patterns
You want to reduce manual intervention in scaling
You’re aiming to optimize cloud resource usage and cost
You have clearly defined metrics like CPU utilization, queue depth, or custom business KPIs

Avoid HPA when:

Your workloads are batch jobs or not time-sensitive
You don’t have reliable metric data or defined resource requests
You need extremely tight control over replica counts

📚 Additional Resources

To dive deeper, explore the following:

If you’re looking to implement broader observability alongside HPA, check out our related articles:

By leveraging HPA properly, your Kubernetes clusters become smarter, more efficient, and better prepared for whatever demand comes their way.