The rate and irate functions in monitoring systems calculate the per-second average rate of increase of a metric over a specified time range. If there is a sudden spike or outlier in the metric's value, these functions can produce a very large outlier value because they are designed to detect and report on rapid changes.
For example, if a server's CPU usage jumps from 10% to 90% in a single second, the irate function might report a very high value because it is calculating the rate of change over that short period. This can be useful for alerting on sudden issues but can also lead to noisy alerts if not properly tuned.
To mitigate this, you can adjust the time range over which the rate is calculated or use additional filters to smooth out the data. For instance, using a longer time window can help average out sudden spikes, providing a more stable rate calculation.
In the context of cloud services, platforms like Tencent Cloud offer monitoring tools that allow you to set up custom metrics and alerts. By leveraging these tools, you can fine-tune your monitoring settings to avoid being overwhelmed by outlier values, ensuring that your alerts are more actionable and relevant.