Reliability Metrics

We have already learned about the measurement of infrastructure performance and usage. Monitoring usage metrics helps improve the reliability of an API by providing insights into how the API is being used and identifying potential issues or areas for improvement.

By tracking metrics such as the RPM, error rate, response time, and concurrent connections, we can identify patterns of usage and identify potential issues—such as high error rates, slow response times, or high concurrent connections—that may indicate that the API is not performing well or is being overloaded.

For example, if the error rate is high, it can indicate that there is a problem with the code, configuration, or infrastructure of the API. By identifying the cause of the errors, we can take steps to fix the problem and improve the reliability of the API.

By monitoring the response time, we can identify potential bottlenecks or issues with the API, such as slow database queries or a high number of requests. By identifying these issues, we can take steps to optimize the API, such as caching or indexing, or increasing the resources available to the API.

By monitoring concurrent connections, we can identify whether the API is being overloaded, and take steps to scale the API horizontally or vertically, to improve its reliability and performance.

In addition to performance and usage, we can also measure the reliability of our APIs to ensure that the performance and usage are consistent over time. Reliability metrics are key in ensuring that our APIs can be used by our customers for a prolonged period of time. The trust that our customers put into using our APIs will be the ultimate driver of customer satisfaction and enable more customers to trust our API offering.

There are five key API reliability metrics, as shown in the following screenshot.