How do you scale applications in Kubernetes?

Scaling applications in Kubernetes means adjusting the number of running containers or their allocated resources to match demand. Kubernetes automates this process through built-in controllers that monitor your applications and make scaling decisions. You can scale manually using simple commands or automatically based on metrics like CPU usage. This approach helps you maintain performance during traffic spikes whilst controlling infrastructure costs.

What does scaling mean in Kubernetes?

Scaling in Kubernetes refers to adjusting your application's capacity by changing the number of pod replicas or modifying resource allocations. Unlike traditional infrastructure where you scale entire servers, Kubernetes lets you scale individual containerised applications independently. This granular approach gives you precise control over resource usage.

When you scale an application in Kubernetes, the control plane coordinates with worker nodes to start or stop pod instances. The scheduler finds suitable nodes with available resources, whilst the kubelet on each node manages the actual container lifecycle. This orchestration happens automatically once you specify your desired state.

Kubernetes makes scaling easier because it abstracts away infrastructure complexity. You tell Kubernetes how many replicas you want, and it handles the distribution, networking, and health monitoring. If a pod fails, Kubernetes automatically replaces it to maintain your desired count. This self-healing capability ensures your applications remain available even during scaling operations.

How do you manually scale a deployment in Kubernetes?

Manual scaling in Kubernetes involves using the kubectl scale command to change the number of pod replicas in a deployment. You specify the deployment name and desired replica count, and Kubernetes adjusts the running pods accordingly. This method works well for planned capacity changes or testing scenarios.

The basic command looks like this:

kubectl scale deployment your-deployment-name --replicas=5

This command tells Kubernetes to maintain exactly five running pods for your deployment. Behind the scenes, the deployment controller compares the current state with your desired state and creates or terminates pods to match your specification.

You can also scale by editing the deployment YAML file directly. Change the replicas field in the spec section, then apply the updated configuration with kubectl apply -f deployment.yaml. This approach maintains your configuration files as the source of truth for your infrastructure.

Manual scaling makes sense when you know exactly when capacity changes are needed, such as before scheduled events or maintenance windows. It gives you direct control but requires you to monitor application performance and make scaling decisions yourself.

What is horizontal pod autoscaling and how does it work?

Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pod replicas based on observed metrics like CPU or memory usage. It continuously monitors your application's resource consumption and scales pods up or down to maintain target utilisation levels. This automation removes the need for manual intervention during traffic fluctuations.

HPA requires the metrics server to function properly. The metrics server collects resource usage data from kubelets and exposes it through the Kubernetes API. HPA queries this API every 15 seconds by default, calculates whether scaling is needed, and adjusts replica counts accordingly.

The scaling decision follows a straightforward formula. HPA compares current metric values against your target thresholds. If average CPU usage exceeds your target, HPA increases replicas. If usage drops significantly below the target, it decreases replicas. The system includes stabilisation windows to prevent rapid scaling fluctuations.

Automatic scaling provides several advantages over manual approaches. Your applications respond to demand changes without human oversight, maintaining performance during unexpected traffic spikes. You also avoid over-provisioning during quiet periods, which helps control infrastructure costs. The system scales both up and down, adapting continuously to actual workload patterns.

What's the difference between horizontal and vertical scaling in Kubernetes?

Horizontal scaling adds or removes pod replicas to handle load changes, whilst vertical scaling adjusts CPU and memory resources allocated to existing pods. Each approach serves different needs and comes with distinct trade-offs.

Horizontal scaling works better for stateless applications that can run multiple identical copies. Web servers, API services, and microservices typically benefit from this approach. You distribute load across multiple pods, which also improves fault tolerance since traffic continues flowing if individual pods fail.

Vertical scaling suits applications that cannot easily run multiple instances, such as databases or legacy systems with single-instance limitations. The Vertical Pod Autoscaler (VPA) monitors resource usage and recommends or automatically applies resource adjustments. However, changing pod resources often requires restarting the pod, which can cause brief service interruptions.

Many production environments combine both approaches. You might horizontally scale your application tier whilst vertically scaling your database pods. This hybrid strategy optimises resource usage across different workload types.

How do you set up autoscaling for your Kubernetes applications?

Setting up autoscaling requires installing the metrics server, defining resource requests and limits, and creating an HPA resource. Start by ensuring your cluster has the metrics server deployed, as HPA depends on it for resource usage data.

Your deployment must specify resource requests in the pod template. These requests tell Kubernetes how much CPU and memory each pod needs. HPA calculates utilisation as a percentage of these requests, so accurate values are important for effective scaling:

• Define CPU and memory requests that reflect typical usage
• Set limits to prevent pods from consuming excessive resources
• Test your application under load to validate request values

Create an HPA resource that references your deployment and specifies scaling parameters. You define minimum and maximum replica counts, target metrics, and thresholds. A basic HPA configuration might target 70% CPU utilisation with 2-10 replicas.

The command to create an HPA looks like this:

kubectl autoscale deployment your-deployment-name --cpu-percent=70 --min=2 --max=10

Common setup issues include missing resource requests, metrics server not installed, or unrealistic scaling thresholds. If HPA shows "unknown" for metrics, check that your metrics server is running properly. If scaling happens too aggressively, adjust your target thresholds or increase the stabilisation window.

What factors should you consider when scaling Kubernetes applications?

Resource limits and cluster capacity directly affect your scaling capabilities. You need sufficient node resources to accommodate additional pods during scale-up events. Monitor cluster capacity and plan for headroom that allows scaling without exhausting resources. If your cluster lacks capacity, scaling requests will fail or pods will remain pending.

Application architecture significantly impacts scaling behaviour. Stateless applications scale smoothly because new pods start independently without coordination. Stateful applications require careful consideration of data persistence, pod identity, and startup dependencies. You might need StatefulSets instead of Deployments for applications that maintain state.

Cost implications deserve attention when configuring autoscaling. Aggressive scaling policies might spin up many pods during brief spikes, increasing infrastructure costs unnecessarily. Set sensible minimum and maximum replica counts that balance performance needs with budget constraints. Consider scale-down stabilisation windows to prevent rapid scaling cycles.

Performance monitoring helps you tune scaling parameters effectively. Track metrics like response times, error rates, and resource utilisation alongside replica counts. This data reveals whether your scaling thresholds align with actual application behaviour. You might discover that memory pressure triggers performance issues before CPU thresholds are reached.

Best practices for scaling parameters include:

• Start with conservative thresholds and adjust based on observed behaviour
• Set realistic minimum replicas to handle baseline load
• Configure maximum replicas that your cluster can support
• Use multiple metrics for scaling decisions when appropriate
• Implement proper health checks so scaling works with healthy pods only

Avoid over-provisioning by monitoring actual resource usage patterns over time. Many applications show predictable daily or weekly patterns. You can optimise costs by scheduling different minimum replica counts for peak and off-peak periods using tools like CronJobs to adjust HPA settings.

Conclusion

Scaling Kubernetes applications effectively requires understanding both manual and automatic approaches. You can start with manual scaling for predictable workloads, then implement horizontal pod autoscaling as your needs grow. Remember to define appropriate resource requests, monitor your applications, and adjust scaling parameters based on real usage patterns.

The choice between horizontal and vertical scaling depends on your application architecture and requirements. Most modern cloud-native applications benefit from horizontal scaling, which Kubernetes handles elegantly through its built-in controllers. Proper configuration ensures your applications maintain performance whilst controlling infrastructure costs.

At Falconcloud, we provide the infrastructure foundation you need for running Kubernetes workloads effectively. Our cloud platform gives you the flexibility to scale your containerised applications across multiple global data centres, with predictable per-minute billing that aligns with your actual usage patterns.