The Horizontal Pod Autoscaler (HPA) can trigger scaling based on several types of metrics:
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CPU Utilization: The most common metric, where HPA scales pods based on the average CPU usage across the pods in a deployment.
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Memory Utilization: Similar to CPU, HPA can scale based on the average memory usage of the pods.
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Custom Metrics: You can define your own metrics, such as application-specific performance indicators (e.g., request latency, queue length) using the Kubernetes Metrics API.
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External Metrics: HPA can also scale based on metrics from external sources, such as cloud provider metrics or third-party monitoring tools.
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Pod Metrics: HPA can scale based on the number of pods in a specific deployment or stateful set.
These metrics allow HPA to make informed decisions about scaling up or down based on the actual resource demands of your applications.
