When an API starts responding slowly, the initial indicators are often response time metrics, also known as latency, which measure the total duration from when a request is sent to when a response is received. Monitoring tools collect these at various points, such as the API Gateway or load balancer, for individual API endpoints, and for the internal processing within the API service. A sudden increase in average or percentile latency for a specific endpoint highlights that endpoint or its direct dependencies as the potential cause. Simultaneously, throughput metrics, which quantify the number of requests processed per unit of time, help differentiate if the slowdown is due to an overwhelming increase in load or if the system is simply performing slower under normal conditions. An accompanying rise in error rates can also signal underlying issues that contribute to perceived slowness, such as failed requests leading to client-side retries, increasing overall load.
To pinpoint the exact part of the API system causing the slowdown, an expert then analyzes resource utilization metrics for the servers or containers hosting the API services. These include CPU utilization, which is the percentage of processor time being actively used; high CPU usage often indicates intensive comput....
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