Percentile

When dealing with performance monitoring and optimization, percentiles are a crucial tool for understanding the distribution of values within a dataset. For example, when analyzing the response times of a web service, the 90th percentile (usually noted P90) represents the value below which 90% of the response times fall. This is particularly useful for identifying outliers and understanding the overall user experience.

Suppose we have an array of response times from a web server:

const responseTimes = [100, 150, 200, 250, 300, 350, 400, 450, 500, 550];

To calculate the 90th percentile of these response times, we can use the following code:

function percentile(arr, p) {
  arr.sort((a, b) => a - b);
  const index = Math.ceil(p * arr.length / 100) - 1;
  return arr[index];
}

const p90 = percentile(responseTimes, 90);
console.log(`The 95th percentile of response times is ${p95} milliseconds.`);

With large datasets, it is recommended to measure worse case scenarios using P90 or other percentiles, as relying on the maximum latency can be misleading due to outliers.