How can I measure the execution time of a npm/yarn task/script?

When using npm or yarn for task management, measuring the execution time of tasks or scripts is a valuable metric that helps optimize script performance. Below, I will guide you through step-by-step methods to measure the execution time of npm or yarn scripts.

For npm:

1. Using the time command

• On Unix-like systems (such as Linux and macOS), you can use the built-in time command to measure command execution time. For example, if you have an npm script named build, run it in the command line:

bash
 time npm run build

• This outputs the execution time of the build script, including user time, system time, and wall-clock time.

1. Modifying scripts in package.json

• Add time measurement functionality to the scripts section in package.json. For instance, modify a script to:

json
 "scripts": {
   "build": "time webpack --config webpack.config.js"
 }

• This ensures that every npm run build execution measures and displays the webpack execution time.

For yarn:

1. Using the time command

• Similar to npm, use the time command to measure any yarn script's execution time. The command format is:

bash
 time yarn run build

• This outputs the execution time, including various time metrics.

1. Using the --verbose flag

• Yarn provides a --verbose flag that outputs detailed information, including time statistics, during script execution. Run the command as follows:

bash
 yarn run build --verbose

• This displays detailed execution logs and provides the execution time at the end.

Real-World Application Example:

• In my previous project, we needed to optimize CI/CD pipeline build times. I added the time command to key scripts in package.json and analyzed task durations using CI tool logs. We discovered that certain dependency installations were slow, so we switched to a faster npm mirror source, significantly improving overall build efficiency.
• By implementing this approach, we can measure each script's execution time and make data-driven optimization decisions, ultimately enhancing development and deployment efficiency.