Description

Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods in a Kubernetes deployment, replica set, or stateful set based on observed CPU utilization or other custom metrics.

Features

• Automatic Scaling: Scales the number of pods up or down based on CPU utilization or custom metrics.
• Custom Metrics: Supports custom metrics from various sources like Prometheus or custom APIs.
• Integration: Works seamlessly with Kubernetes deployments, replica sets, and stateful sets.
• Configurable Thresholds: Allows setting thresholds and target utilization for scaling decisions.
• Continuous Monitoring: Continuously monitors resource usage to ensure optimal pod scaling.

Limitations

• Metric Dependency: Scaling decisions are dependent on the accuracy and availability of metrics.
• Latency: There can be a delay between metric collection and scaling action.
• Resource Overhead: Increased number of pods can lead to higher resource consumption and costs.
• Limited to Pods: Only scales the number of pods, not the resources within each pod.

Use Cases

• E-commerce Platforms: Automatically scaling the number of pods to handle varying traffic during sales events or peak shopping times.
• Real-time Data Processing: Scaling pods in response to changes in data processing workloads to maintain performance.
• API Services: Ensuring API services can handle fluctuating request loads by dynamically adjusting the number of pods.