Scaling in Kubernetes is a dynamic process driven by real-time metrics. Kubernetes continuously monitors resource consumption and application performance to make intelligent decisions about adding or removing pods or nodes. Understanding the metrics used for scaling is essential for optimizing application performance and resource utilization.
Here are the key Kubernetes scaling metrics every DevOps engineer or developer should know:
1. CPU Utilization
What it is:
Measures the percentage of CPU used by a pod or container relative to its requested resources.
Why it matters:
- One of the most common metrics used by the Horizontal Pod Autoscaler (HPA).
- Helps maintain CPU efficiency by scaling out when load increases.
Example usage:
yaml
targetCPUUtilizationPercentage: 70
2. Memory Utilization
What it is:
Represents the amount of memory used compared to the pod’s memory request.
Why it matters:
- Prevents memory exhaustion and out-of-memory (OOM) kills.
- Ensures memory-bound applications scale appropriately.
Note:
HPA supports memory-based scaling with custom metrics adapters or external metrics servers.
3. Custom Metrics
What they are:
Application-specific or business-related metrics such as:
- Request latency
- Queue depth
- Error rates
- Concurrent users
Why they matter:
- Allow fine-tuned scaling based on actual business logic or user-defined KPIs.
- Require integration with monitoring tools like Prometheus Adapter, Datadog, or CloudWatch.
4. Request Rate
What it is:
Measures the number of incoming HTTP requests or API calls handled over time.
Why it matters:
- High request rates signal demand spikes, prompting Kubernetes to add more pods.
- Especially useful for web servers, REST APIs, and microservices.
5. Network Throughput
What it is:
Amount of data sent and received by a pod or container over the network.
Why it matters:
- Applications like streaming services or data-heavy APIs can benefit from scaling based on bandwidth usage.
- Prevents network bottlenecks that impact latency and performance.
6. Pod Startup Time
What it is:
The time it takes for a pod to initialize and become "Ready".
Why it matters:
- Long startup times can delay application readiness during traffic spikes.
- Metrics related to readiness probes can be used to anticipate load surges and pre-scale pods.
7. Queue Length
What it is:
Tracks the number of unprocessed tasks or requests in a queue.
Why it matters:
- Useful for event-driven or asynchronous systems like message queues and job processors.
- Scaling based on queue depth ensures timely task execution and prevents latency spikes.
How to Access These Metrics
- Prometheus + Custom Metrics Adapter: Popular stack for collecting and exposing custom metrics to the HPA.
- Kubernetes Metrics Server: Provides CPU and memory utilization for HPA.
- Application APM tools: Tools like New Relic, Datadog, and Dynatrace can expose additional metrics to Kubernetes.
- Cloud-native dashboards: GKE, EKS, and AKS offer native integrations for metric-based autoscaling.
Conclusion
Effective scaling in Kubernetes depends on choosing the right metrics for your workload. While CPU and memory are foundational, leveraging custom application metrics, request rates, and queue depth can help you design intelligent, responsive autoscaling strategies tailored to real-world usage.
Whether you’re managing web apps, batch jobs, or event-driven systems, mastering these scaling metrics empowers you to build Kubernetes environments that are both performant and cost-efficient.