An Introduction to the Node.js Performance API

Ever faced times when your application lags, impacting user experience with slow load times? Pinpointing the exact performance bottlenecks within your application's code can often feel like searching for a needle in a haystack.

Thankfully, Node.js provides a powerful toolset for this challenge: the Performance Measurement API, accessible through the perf_hooks module. This suite of tools not only captures and stores performance metrics automatically but also offers methods for actively measuring and enhancing your application's performance.

Through this tutorial, you will learn to leverage these APIs for tracking various key metrics in your application.

Prerequisites

To follow through with this tutorial, you need basic knowledge of Node.js and a recent version installed on your machine.

Inside the Node.js Performance API

Node.js has adopted a subset of the Web Performance API in its perf_hooks module to streamline the process of tracking server performance. Its core elements are divided into four main categories:

1. High-resolution Time
2. Performance Timeline
3. User Timing
4. Resource Timing

Let's begin by briefly exploring each component, starting with the High-resolution time:

1. High-resolution Time

Prior to the introduction of the Performance API in Node.js, developers often used Date.now() to track execution times of an operation. This method, however, is susceptible to system clock changes, which could affect the accuracy of the measurements:

const startTime = Date.now();
doTask(); // Example task
console.log(`Task duration: ${Date.now() - startTime} milliseconds`);

To overcome this, Node.js now provides a High-resolution Time API that produces a monotonic timestamp that is unaffected by clock adjustments. The monotonic clock always progresses forward and cannot be adjusted backward:

import { performance } from "node:perf_hooks";

console.log(performance.now()); // produces a high resolution timestamp

The output from this snippet represents the milliseconds elapsed since the start of the Node.js process till the console.log() statement:

68.89512500003912

To differentiate further between Date.now() and performance.now(), here's a brief comparison:

Note that all the perf_hooks APIs use the high-resolution time for performance measurements so keep that in mind as we move forward with other examples.

2. Performance Timeline

The Node.js performance timeline is designed to track performance metrics across the entire lifespan of a program. These metrics are represented as subclasses of the PerformanceEntry object, which is characterized by the following key attributes:

• name: The identifier for the performance metric.
• duration: How long (in milliseconds) the metric took to complete.
• startTime: The high-resolution timestamp (in milliseconds) marking when the metric began.
• type: The category of the performance metric.

Performance measurements can be automatically entered in the timeline through the Node.js process, APIs like fetch, or custom instrumentation. All entries in the timeline are derived from the PerformanceEntry class. Here's an example:

Performance {
  nodeTiming: PerformanceNodeTiming {
    name: 'node',
    entryType: 'node',
    startTime: 0,
    duration: 75.03541599959135,
    nodeStart: 20.790207999758422,
    v8Start: 35.35679099988192,
    bootstrapComplete: 60.899207999929786,
    environment: 47.57658299989998,
    loopStart: 67.81720799999312,
    loopExit: -1,
    idleTime: 0
  },
  timeOrigin: 1711344543959.874
}

This entry showcases the four core attributes of a PerformanceEntry instance —name, duration, startTime, and type—complemented by extra properties. You can view the Performance Timeline at any point in your program using performance.getEntries().

3. User Timing

The User Timing API allows custom metrics about your application to be added to the Node.js Performance Timeline. There are two types of user timings:

1. PerformanceMark

PerformanceMark entries can be recorded at any point in your application using the performance.mark() method:

   PerformanceMark {
     name: 'mark_fetch_start',
     entryType: 'mark',
     startTime: 21.876291000284255,
     duration: 0,
     detail: null
   }

2. PerformanceMeasure

PerformanceMeasure allows you to measure the difference between two PerformanceMark entries in the timeline, creating a PerformanceMeasure object that looks like this:

   PerformanceMeasure {
     name: 'measureTask',
     entryType: 'measure',
     startTime: 100,
     duration: 567.891
   }

4. Resource Timing

Resource Timing is the part of the Performance API that lets you retrieve network timing data related to fetching resources. It provides detailed information about various stages involved in fetching a resource, such as the time it takes for DNS lookup, establishing a connection, and the duration of the request.

Each resource timing is represented by a PerformanceResourceTiming entry that looks like this:

  PerformanceResourceTiming {
    name: 'https://example.com/resource',
    entryType: 'resource',
    startTime: 50.125,
    duration: 2000.75,
    initiatorType: 'fetch',
    ....
    secureConnectionStart: 85,
    requestStart: 100,
    responseStart: 110,
    responseEnd: 2050.875,
    transferSize: 500,
    encodedBodySize: 200,
    decodedBodySize: 800
  }

With some of the basic terms now understood, let's examine the specific perf_hooks APIs and methods more closely so that you're able to carry out performance measurements in Node.js effectively.

Exploring the Node.js Performance APIs

To demonstrate the various APIs I'll be introducing in this section, we'll use this simple function that returns a promise that resolves after a random amount of time up to a specified maximum delay:

function completeAfterRandomTime(maxDelay) {
  const delay = Math.random() * maxDelay;

  return new Promise((resolve) => {
    setTimeout(() => {
      resolve(`Completed after ${delay.toFixed(2)} milliseconds`);
    }, delay);
  });
}

export { completeAfterRandomTime }

This helps simulate some work being done asynchronously, such as fetching data from a remote server so that you can apply various Performance API methods to track the performance of each invocation.

Below is a list of the API methods and interfaces we will cover:

1. PerformanceNodeTiming
2. Performance.now()
3. Performance.mark()
4. Performance.measure()
5. Performance.getEntries()
6. PerformanceObserver
7. Performance.clearMarks()
8. Performance.clearMeasures()
9. PerformanceResourceTiming
10. setResourceTimingBufferSize()

Let's begin!

1. PerformanceNodeTiming

In any Node.js application, the PerformanceNodeTiming object is the initial entry logged in the Performance Timeline. It offers insights into the app's startup process and helps you identify early-stage performance issues by detailing key phases such as the initialization of V8, bootstrapping, and periods of idleness.

To inspect the PerformanceNodeTiming data, access the performance object from the perf_hooks module as demonstrated below:

import { performance } from "node:perf_hooks";

console.log(performance);

Executing this will yield an output akin to:

Performance {
  nodeTiming: PerformanceNodeTiming {
    name: 'node',
    entryType: 'node',
    startTime: 0,
    duration: 24.63241699989885,
    nodeStart: 1.9587079999037087,
    v8Start: 4.290624999906868,
    bootstrapComplete: 13.435583000071347,
    environment: 8.796583000104874,
    loopStart: 18.111583000048995,
    loopExit: -1,
    idleTime: 0
  },
  timeOrigin: 1711429082121.026
}

This nodeTiming object captures critical timings of the application's boot sequence, outlined as follows:

• name: Indicates the name, node in this case.
• entryType: Indicates the entry type set to node for Node.js processes.
• startTime: Marks the beginning of measurement with a high-resolution timestamp, with 0 denoting the beginning of the process execution.
• duration: The time taken from the startTime till the console.log() statement.
• nodeStart, v8Start, bootstrapComplete, environment, loopStart, and loopExit: These properties record various milestones in milliseconds, from process start through V8 initialization to the event loop's start and potential exit. loopExit is -1 because the event loop is still active at the time of recording.
• idleTime: Indicates how long the event loop was idle, starting at 0 if the event loop hasn't commenced.

Now that you are familiar with the PerformanceNodeTiming entry, we will look at Performance.now() next.

2. Performance.now()

The Performance.now() method enables precise measurement of task durations by generating high-resolution timestamps. You can use it directly in your code by calling performance.now() but also note that all timestamps in PerformanceEntry objects are also generated internally through the now() method.

To measure the duration of a task, invoke the method at the start and end of the operation you're measuring, then calculate the elapsed time by subtracting these two values. Here's how it's applied:

import { performance } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const startTime = performance.now();
await completeAfterRandomTime(2000);
const endTime = performance.now();
console.log(`Task completed in ${endTime - startTime} milliseconds.`);

When executed, it outputs something like:

Task completed in 1581.4101660000001 milliseconds

While this method of time keeping suits many scenarios, the perf_hooks module offers even more versatile performance measurement tools, which we will explore next.

3. Performance.mark()

The performance.mark() method is used to place named markers in the Performance Timeline. These markers facilitate the precise measurement of durations between function calls and allow for their subsequent analysis directly from the timeline. Here's its signature:

performance.mark(name[, options])

Here, name denotes the marker's identifier, and the optional options argument provides additional context for the marker.

Consider the example below:

import { performance } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const markStart = performance.mark('mark_function_start');
await completeAfterRandomTime(2000);
const markEnd = performance.mark('mark_function_end');

console.log(markStart);
console.log(markEnd);

After you run the file, you will see an output that looks similar to this:

PerformanceMark {
  name: 'mark_function_start',
  entryType: 'mark',
  startTime: 29.752355,
  duration: 0,
  detail: null
}
PerformanceMark {
  name: 'mark_function_end',
  entryType: 'mark',
  startTime: 1839.77119,
  duration: 0,
  detail: null
}

Markers generated via performance.mark() include a startTime indicating when they were placed in the Performance Timeline, which can later be used for calculating the elapsed time for a task.

Additionally, you can enrich markers with context using the options parameter:

import { performance } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const options = {
  detail: {
    functionName: 'completeAfterRandomTime',
  },
};

const markStart = performance.mark('mark_function_start', options);
await completeAfterRandomTime(2000);
const markEnd = performance.mark('mark_function_end', options);

console.log(markStart);
console.log(markEnd);

This yields markers enriched with further details:

PerformanceMark {
  name: 'mark_function_start',
  entryType: 'mark',
  startTime: 30.379528,
  duration: 0,
  detail: { functionName: 'completeAfterRandomTime' }
}
PerformanceMark {
  name: 'mark_function_end',
  entryType: 'mark',
  startTime: 906.525729,
  duration: 0,
  detail: { functionName: 'completeAfterRandomTime' }
}

Next, we will delve into measuring the intervals between these markers.

4. Performance.measure()

Performance.measure() is designed to generate a PerformanceMeasure object within the performance timeline that measures the period between two predefined PerformanceMark entries. Here's its signature:

performance.measure(name[, startMarkOrOptions[, endMark]])

Passing just the name parameter measures the time from the program's start to the method invocation:

import { performance } from "node:perf_hooks";
import { completeAfterRandomTime } from './random.js';

completeAfterRandomTime2000);
console.log(performance.measure("programDuration"));

This results in a PerformanceMeasure entry, capturing the duration since the program's start:

PerformanceMeasure {
  name: 'programDuration',
  entryType: 'measure',
  startTime: 0,
  duration: 2014.819769
}

For measuring the interval between two markers, include their names as the second and third arguments, respectively:

import { performance } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const markStart = performance.mark('mark_function_start');
await completeAfterRandomTime(2000);
const markEnd = performance.mark('mark_function_end');

console.log(
  performance.measure(
    'measure_func_perf',
    'mark_function_start',
    'mark_function_end'
  )
);

The program then outputs a PerformanceMeasure reflecting the span between these markers:

PerformanceMeasure {
  name: 'measure_func_perf',
  entryType: 'measure',
  startTime: 29.751993,
  duration: 809.9894330000001
}

Here, startTime indicates when the measurement commenced relative to the program's start (mark_function_start's startTime), and duration reveals the elapsed time between the markers. This feature offers a structured way to pinpoint and assess performance metrics for specific code segments.

5. Performance.getEntries()

Now that you've learned about the mark() and measure() functions that add to the Performance Timeline, it's time to see how we can retrieve these recorded entries. The performance.getEntries() method does just that, pulling PerformanceEntry objects from the timeline for review.

Consider the following example:

import { performance } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const markStart = performance.mark('mark_function_start');
await completeAfterRandomTime(2000);
const markEnd = performance.mark('mark_function_end');

performance.measure(
  'measure_func_perf',
  'mark_function_start',
  'mark_function_end'
);

console.log(performance.getEntries());

You will see an array of three entries representing the markers and their measurement:

[
  PerformanceMark {
    name: 'mark_function_start',
    entryType: 'mark',
    startTime: 29.210924,
    duration: 0,
    detail: null
  },
  PerformanceMeasure {
    name: 'measure_func_perf',
    entryType: 'measure',
    startTime: 29.210924,
    duration: 1289.521015
  },
  PerformanceMark {
    name: 'mark_function_end',
    entryType: 'mark',
    startTime: 1318.731939,
    duration: 0,
    detail: null
  }
]

If your interest is in a specific entry, such as mark_function_start, the performance.getEntriesByName() method allows for targeted retrieval:

console.log(performance.getEntriesByName('mark_function_start'));

Resulting in:

[
  PerformanceMark {
    name: 'mark_function_start',
    entryType: 'mark',
    startTime: 29.66662500053644,
    duration: 0,
    detail: null
  }
]

Another useful method is getEntriesByType(), which returns only the entries of a certain type from the performance timeline such as marks or measures:

console.log(performance.getEntriesByType("mark"));

6. PerformanceObserver

The PerformanceObserver interface enhances how you work with the Node.js performance timeline by offering a way to monitor performance metrics as they're logged. This method is preferable to static retrieval functions like getEntries() for two main reasons:

1. It provides real-time monitoring of the performance timeline, capturing entries as they happen, which is more efficient than querying the entire timeline repeatedly.

2. It allows for targeted observation, enabling you to specify exactly which types of entries to track, thereby optimizing resource use and avoiding the accumulation of unneeded data in memory.

Here's a basic guide to utilizing a PerformanceObserver:

// Create a PerformanceObserver instance
const observer = new PerformanceObserver((list, observer) => {
  for (const entry of list.getEntries()) {
    if (entry.name === '<your_entry_name>') {
      // Perform actions based on the measurement
    }
  }
});

// Specify the entry types you want to observe
observer.observe({ entryTypes: ['<entry_type>'] });

In this setup, you determine the types of performance entries you wish to observe, such as mark or measure, and then process them within a callback function as they are recorded.

Here is a more practical example that uses an observer:

import { performance, PerformanceObserver } from 'node:perf_hooks';
import { completeAfterRandomTime } from './random.js';

const observer = new PerformanceObserver((list, observer) => {
  for (const entry of list.getEntries()) {
    if (entry.name === 'measure_func_perf') {
      console.log(`Function execution time: ${entry.duration} milliseconds`);
      observer.disconnect();
    }
  }
});

observer.observe({ entryTypes: ['measure'] });

const markStart = performance.mark('mark_function_start');
await completeAfterRandomTime(2000);
const markEnd = performance.mark('mark_function_end');
performance.measure(
  'measure_func_perf',
  'mark_function_start',
  'mark_function_end'
);

This practical implementation sets the observer to watch for measure entries, logging the duration of a specified function once detected:

[output
Function execution time: 7.202374999877065 milliseconds

You are not limited to observing performance entries of one type; you can specify multiple entry types as needed:

observer.observe({ entryTypes: ['mark', 'measure'] });

To see the list of entry types supported by the observer, you can use:

import { PerformanceObserver } from "node:perf_hooks";

console.log(PerformanceObserver.supportedEntryTypes);

[
  'dns',      'function',
  'gc',       'http',
  'http2',    'mark',
  'measure',  'net',
  'resource'
]

Now that you are familiar with methods that help you inspect and add to the performance timeline, the next step is to understand how to clear entries.

7. Performance.clearMarks()

In long-running applications, you may need to reset the performance timeline so that it doesn't take too much memory. One of the methods you can use is performance.clearMarks() function, which clears all PerformanceMark entries from the timeline:

performance.clearMarks();

You can also target and remove a specific mark by its name:

performance.clearMarks("mark_function_start");

8. Performance.clearMeasures()

Similarly, clearing PerformanceMeasure entries from the timeline is made easy with:

performance.clearMeasures();

Similar to clearMarks(), you can also provide a name to delete a specific entry from the timeline.

9. PerformanceResourceTiming

Earlier in this article, we discussed the Resource Timing API which is used for recording network-based metrics for requests made within the application. If you're using the fetch API, you can inspect the PerformanceResourceTiming objects created after each invocation like this:

import { performance } from 'node:perf_hooks';

const url = 'https://dummyjson.com/products/1';
const response = await fetch(url);
await response.json();
console.log(performance.getEntriesByType('resource'));

Executing this program will yield:

[
  PerformanceResourceTiming {
    name: 'https://dummyjson.com/products/1',
    entryType: 'resource',
    startTime: 133.451,
    duration: 1240.7135,
    initiatorType: 'fetch',
    nextHopProtocol: undefined,
    workerStart: 0,
    redirectStart: 0,
    redirectEnd: 0,
    fetchStart: 133.451,
    domainLookupStart: undefined,
    domainLookupEnd: undefined,
    connectStart: undefined,
    connectEnd: undefined,
    secureConnectionStart: undefined,
    requestStart: 0,
    responseStart: 0,
    responseEnd: 1374.1645,
    transferSize: 300,
    encodedBodySize: 0,
    decodedBodySize: 0
  }
]

The PerformanceResourceTiming object contains several key attributes:

• name: Identifies the resource's URL.
• entryType: Specifies the type of entry, which is resource for these objects.
• startTime: Records the precise moment the resource fetching commenced, represented in high-resolution milliseconds.
• duration: Reflects the total fetch time for the resource, calculated as the interval between responseEnd and startTime.
• initiatorType: Describes the method by which the resource fetch was initiated, such as "fetch".
• fetchStart: The exact timestamp marking the start of the resource fetch.
• responseEnd: The timestamp indicating the conclusion of the resource's response.
• transferSize: The total size of the resource received, in bytes, including the response headers and the payload.

Note that this entry is only recorded after await response.json() is utilized.

Adjusting resource timing buffer sizes

The PerformanceResourceTiming object is automatically recorded for each fetch request and stored in a buffer limited to 250 entries by default. You may need to expand this buffer to accommodate the tracking of more network resources.

To modify the buffer size, use the setResourceTimingBufferSize() method as shown below:

performance.setResourceTimingBufferSize(600);

This adjustment allows for the inclusion of up to 600 PerformanceResourceTiming objects within the performance timeline.

Additionally, you can clear all PerformanceResourceTiming entries from the timeline with:

performance.clearResourceTimings();

Node.js also emits a resourcetimingbufferfull event when the buffer hits its limit. You can listen for this event to expand or clear the buffer as needed:

performance.on("resourcetimingbufferfull", () => {
  if (performance.getEntriesByType("resource").length < 900) {
    performance.setResourceTimingBufferSize(900); // Expanding the buffer if under a certain threshold
  } else {
    performance.clearResourceTimings(); // Clearing the buffer if the threshold is exceeded
  }
});

Final thoughts

In this article, we explored the Performance Measurement APIs available in Node.js, and you learned how to use them to record various performance metrics.

Stay tuned for our upcoming article, which will delve into practical applications of these API techniques in real-world scenarios.

Thanks for reading!

Article by

Stanley Ulili

Stanley Ulili is a technical educator at Better Stack based in Malawi. He specializes in backend development and has freelanced for platforms like DigitalOcean, LogRocket, and AppSignal. Stanley is passionate about making complex topics accessible to developers.

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