HPA dynamically adjusts the number of Pod replicas by monitoring load indicators to achieve automatic scaling. Its core indicators include CPU usage, memory usage and custom metrics such as request delay. HPA pulls the indicator data every 15 seconds and calculates the required number of replicas based on the ratio of the current indicator total value to the target value. For example, when the average CPU usage exceeds the set target, the system will automatically increase the number of replicas. To avoid frequent scaling, HPA considers load trends rather than instantaneous fluctuations. Pay attention to when configuring HPA: 1. Ensure that resource request value is set; 2. Avoid setting too low target value; 3. Be cautious when using it with VPA; 4. Pay attention to metric delay issues to match rapidly changing load requirements. The entire process is automatically completed by the controller, and developers only need to configure the parameters reasonably.

HPA (Horizontal Pod Autoscaler) is one of the core mechanisms for achieving automatic scaling in Kubernetes. It mainly dynamically adjusts the number of pod replicas by monitoring load metrics. Simply put, when the application's load increases, HPA will automatically increase the number of pods to share the pressure; when the load drops, reduce the number of pods to save resources.

What indicators are monitored?

HPA makes decisions based on CPU usage by default, but can also be configured to use memory, custom metrics (such as request delay or queue length), etc. These metrics are collected by Metrics Server or other monitoring components and provided to the HPA controller.

• CPU Usage : The most common and easy-to-understand metric, such as setting a Deployment to each Pod, the average CPU usage rate reaches 50% and start expanding.
• Memory usage : Although not as commonly used as CPUs, it is also very critical in some scenarios, such as when handling big data tasks.
• Custom metrics : such as requests per second (RPS), response time, etc., which are suitable for business-specific needs.

HPA pulls the current metric data every once in a while (default 15 seconds), and then calculates whether the number of replicas needs to be adjusted based on the target value.

How to calculate the number of replicas?

The logic of HPA's replica count is not complicated, it will compare the ratio of the current total value of the indicator to the expected value. For example:

If you have a Deployment that has 3 replicas set, each replica has a target CPU utilization of 50%, and the current average CPU of all Pods is 75%, then HPA calculates that it needs to increase to 4 or 5 replicas to reduce the average utilization.

This process is not linear, and Kubernetes will consider the "jitter" problem to avoid frequent expansion and reduction in capacity (also called "oscillation") in a short period of time. For example, the load only soars in an instant, and HPA will not react immediately, but will wait for several cycles to confirm the trend.

Notes on configuring HPA

In actual use, there are several points that are easy to ignore but very critical to pay attention to:

• Make sure that resource limits are set (resources.requests.cpu/memory)
  HPA relies on the requests value to determine the load ratio, and may not work properly if not set.

• Don't blindly set too low target value
  For example, if the CPU target is set to 20%, the system may expand frequently, which will affect performance and stability.

• Use with VPA (Vertical Pod Autoscaler)? Be careful
  HPA adjusts the number of replicas, VPA adjusts the resource size of a single Pod, and the two may conflict when running at the same time.

• Pay attention to indicator delay problem
  If your service load changes rapidly, it is recommended to cooperate with a more real-time monitoring solution, otherwise HPA may not keep up with the pace.

Let's summarize

The core logic of HPA is: Monitoring metrics → Comparing targets → Calculating replicas → Updating Deployment . The entire process is automatically completed by the controller, and developers only need to reasonably configure the target value and monitoring source. Although the mechanism seems simple, it still needs to be tuned according to business characteristics in actual use, such as setting appropriate thresholds to avoid resource waste or performance bottlenecks.

Basically that's all, not complicated but with many details.

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