Profiling Node.js Applications

Imagine your application is running smoothly, but suddenly, you notice a high load with CPU usage spiking to 95% or even 100%. This is often an indication of CPU-bound tasks in your Node.js application.

CPU-bound tasks require substantial processing power and can't be easily shifted to other resources, such as I/O operations. These tasks include intensive calculations, image/video processing, cryptographic operations, and machine learning inference.

To find out the culprit code and fix the high CPU usage, you'll need to profile your application. This guide will explore some tools and techniques for profiling Node.js applications.

Let's get started!

Prerequisites

Before you dive in, ensure you have the following:

• The latest version of Node.js and npm installed on your machine.
• Familiarity with building basic applications using Node.js.

Step 1 — Downloading the demo project

To demonstrate how to profile an application, you'll work with a Node.js project that uses a Fastify server. This server includes an endpoint that allows users to register by submitting a password. The server hashes the password with a randomly generated salt for secure storage. The hashing process is CPU-bound, often resulting in high CPU usage.

Begin by cloning the following repository to your machine with the following command:

git clone https://github.com/betterstack-community/nodejs-profiler-demo.git

Next, navigate to the newly created directory:

cd nodejs-profiling-demo

Install dependencies, including Fastify (a web framework) and Autocannon (a load-testing tool), by running the following command:

npm install

After installing dependencies, launch the development server:

node index.js

You should see the following output:

server listening on 3000

Open another terminal to test the server:

curl -X POST -H "Content-Type: application/json" -d '{"password":"userPassword123"}' http://localhost:3000/register

Upon running the command, you will receive a response that looks similar to this but with different values:

{"salt":"c2f1e60a8d47afed730eb7f3d84b7e39","hashedPassword":"4f9f64c8c631418c46392faa46f4ea2f247e1216fbec302ca49af3add4d5e9428f7ae9e07c716038082e14aaf7d01eac62f0d2820eabf79e398426720c819e13"}

The password is hashed for secure storage instead of being stored directly in a database; the application merely logs it.

The hashing process can be observed in the index.js file, specifically within the /register endpoint:

fastify.post("/register", (request, reply) => {
  const { password } = request.body;

  if (!password) {
    return reply.status(400).send({ error: "Password is required" });
  }

  const salt = crypto.randomBytes(16).toString("hex");
  const hashedPassword = crypto
    .pbkdf2Sync(password, salt, 100000, 64, "sha512")
    .toString("hex");

  reply.send({ salt, hashedPassword });
});

However, the synchronous hashing operation with crypto.pbkdf2Sync blocks the event loop until the hashing process completes, which can lead to performance degradation and scalability issues. Consequently, incoming requests may experience delays or timeouts while waiting for the synchronous operation to finish.

As a result, incoming requests may experience delays or timeouts while awaiting the completion of the synchronous hashing operation.

Step 2 — Differentiating CPU-Bound vs I/O-Bound Tasks

Computer programs often have tasks classified into two major categories: I/O-bound tasks and CPU-bound tasks.

I/O-bound tasks involve operations typically managed by the operating system, such as file I/O, network requests, or database interactions. In Node.js, with its single-threaded nature, these tasks don't block the event loop because they're handled asynchronously. Node.js leverages an event-driven architecture and non-blocking I/O operations, allowing it to continue executing other code while waiting for I/O tasks to finish. This enables efficient management of multiple I/O operations simultaneously. Examples include fetching data from APIs, reading files from disk, or database queries.

On the other hand, CPU-bound tasks demand substantial processing power and require the CPU to be directly used. If they take too long to execute, these tasks can cause the event loop to block, leading to unresponsive applications. In Node.js, CPU-bound tasks pose a particular challenge due to the single-threaded event loop, which must wait for these tasks to complete before proceeding with other operations. Examples of CPU-bound tasks include image processing, video encoding, and complex calculations.

When troubleshooting high CPU usage and considering profiling CPU usage, it's often due to CPU-bound tasks.

Step 3 — Profiling with the built-in Node.js profiler

Node.js includes a built-in profiling tool that profiles a Node.js app as it runs. The tool uses the V8 profiler, which samples the application's call stack regularly. Each sample records the function at the top of the stack when the sample is taken. By analyzing these samples, you can identify where CPU usage spikes occur.

To profile your application, pass the --prof flag to the node command like this:

node --prof index.js

Next, you should put the application under heavy load for a more meaningful CPU profiling. You can achieve this using a load-testing tool like Autocannon.

Open a second terminal and load test the application for 11 seconds with the following command:

npx autocannon --renderStatusCodes  -d 11 -m POST -H "Content-Type: application/json" -b '{"password":"userPassword123"}' http://localhost:3000/register

When the load testing finishes, it will display output that looks like this:

Running 11s test @ http://localhost:3000/register
10 connections


┌─────────┬────────┬────────┬─────────┬─────────┬────────────┬───────────┬─────────┐
│ Stat    │ 2.5%   │ 50%    │ 97.5%   │ 99%     │ Avg        │ Stdev     │ Max     │
├─────────┼────────┼────────┼─────────┼─────────┼────────────┼───────────┼─────────┤
│ Latency │ 217 ms │ 991 ms │ 3683 ms │ 5830 ms │ 1068.69 ms │ 767.62 ms │ 5830 ms │
└─────────┴────────┴────────┴─────────┴─────────┴────────────┴───────────┴─────────┘
┌───────────┬────────┬────────┬─────────┬─────────┬─────────┬───────┬────────┐
│ Stat      │ 1%     │ 2.5%   │ 50%     │ 97.5%   │ Avg     │ Stdev │ Min    │
├───────────┼────────┼────────┼─────────┼─────────┼─────────┼───────┼────────┤
│ Req/Sec   │ 8      │ 8      │ 9       │ 10      │ 8.91    │ 0.67  │ 8      │
├───────────┼────────┼────────┼─────────┼─────────┼─────────┼───────┼────────┤
│ Bytes/Sec │ 2.9 kB │ 2.9 kB │ 3.27 kB │ 3.63 kB │ 3.23 kB │ 242 B │ 2.9 kB │
└───────────┴────────┴────────┴─────────┴─────────┴─────────┴───────┴────────┘
┌──────┬───────┐
│ Code │ Count │
├──────┼───────┤
│ 200  │ 98    │
└──────┴───────┘

Req/Bytes counts sampled once per second.
# of samples: 11

108 requests in 11.07s, 35.6 kB read

After completing the load test, stop the server in the first terminal with CTRL + C. The profiler output will be written to a file in the current directory. The file's name will be isolate-0x<number>-v8.log, where 0x<number>-v8 represents a hexadecimal string.

List the directory contents with the following command:

ls -l

You will see the file in the output:

total 7264
-rw-rw-r--  1 stanley stanley     623 Jun  4 11:35 index.js
-rw-rw-r--  1 stanley stanley 7399577 Jun  4 11:39 isolate-0x650d000-56183-v8.log
...

The isolate-0x<number>-v8.log file holds raw data that's not easily readable. To make it human-readable, run the following command with the --prof-process flag:

node --prof-process isolate-0x<number>-v8.log > profile.txt

Open profile.txt with your preferred text editor:

code profile.txt

Navigate to the [Summary] section within the file:

 [Summary]:
   ticks  total  nonlib   name
     23    0.2%  100.0%  JavaScript
      0    0.0%    0.0%  C++
     50    0.4%  217.4%  GC
  11674   99.8%          Shared libraries

Node.js executes your JavaScript file and runs some C++ code and other libraries. The summary output indicates that most ticks occurred in JavaScript, suggesting that your attention should be directed to the JavaScript code.

Proceed to the [JavaScript] section of the file to identify which functions are consuming the most CPU time:

 [JavaScript]:
   ticks  total  nonlib   name
      2    0.0%    8.7%  JS: ^processTimers node:internal/timers:499:25
      2    0.0%    8.7%  JS: +wrappedFn node:internal/errors:535:21
      2    0.0%    8.7%  JS: *normalizeString node:path:66:25
      1    0.0%    4.3%  RegExp: ^((?:@[^/\\%]+\/)?[^./\\%][^/\\%]*)(\/.*)?$
      1    0.0%    4.3%  JS: ^toRealPath node:internal/modules/helpers:49:20
      1    0.0%    4.3%  JS: ^sendTrailer /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/reply.js:765:22
      1    0.0%    4.3%  JS: ^pbkdf2Sync node:internal/crypto/pbkdf2:61:20
    ...
      1    0.0%    4.3%  JS: +<anonymous> node:internal/validators:458:42

This section reveals which functions are taking the most CPU time, enabling you to target specific areas for optimization. Notably, ticks from the crypto module suggest that cryptographic operations might be significant contributors to CPU usage. This insight can guide you in optimizing specific parts of your code.

A more reliable method for confirming high CPU usage involves examining the call stack. Navigate to the "Bottom up" section of the profiling output:

Bottom up (heavy) profile]:
  Note: percentage shows a share of a particular caller in the total
  amount of its parent calls.
  Callers occupying less than 1.0% are not shown.

   ticks parent  name
  10566   90.3%  /home/stanley/.nvm/versions/node/v22.2.0/bin/node
   9272   87.8%    JS: ^pbkdf2Sync node:internal/crypto/pbkdf2:61:20
   9272  100.0%      JS: ^<anonymous> file:///home/stanley/nodejs-profiling-demo/index.js:6:27
   9272  100.0%        JS: ^preHandlerCallback /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:126:29
   9272  100.0%          JS: ^validationCompleted /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:103:30
   9272  100.0%            JS: ^preValidationCallback /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:83:32
    829    7.8%    JS: ~pbkdf2Sync node:internal/crypto/pbkdf2:61:20
    829  100.0%      JS: ~<anonymous> file:///home/stanley/nodejs-profiling-demo/index.js:6:27
    829  100.0%        JS: ~preHandlerCallback /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:126:29
    829  100.0%          JS: ~validationCompleted /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:103:30
    829  100.0%            JS: ~preValidationCallback /home/stanley/nodejs-profiling-demo/node_modules/fastify/lib/handleRequest.js:83:32

This section further confirms that the pbkdf2Sync function is consuming most of the CPU time.

Step 4 — Profiling with Chrome DevTools

Another approach is using Chrome DevTools, which can collect performance data and generate a report on which functions use the most CPU time. This allows you to easily identify performance bottlenecks.

To initiate this process, start your server with the --inspect flag:

node --inspect index.js

Once the server is up and running, launch Chrome or any Chromium-based browser and enter chrome://inspect in the address bar. Locate the inspect link corresponding to your Node.js script and click on it:

This action opens the DevTools window. Next, switch to the "Performance" tab and click the record button indicated in the screenshot below:

Now, return to your terminal and rerun the load test:

npx autocannon --renderStatusCodes -d 11 -m POST -H "Content-Type: application/json" -b '{"password":"userPassword123"}' http://localhost:3000/register

After completing the load testing, return to the DevTools window and click Stop to conclude the profiling process:

Upon stopping, a comprehensive performance profile is presented. Initially, it appears as a series of boxes:

This is known as a frame chart. It has a horizontal axis that signifies time, and a vertical axis that represents the call stack.

You can zoom in on the horizontal axis to closely examine the call stack. The easiest way is to click on any point in the horizontal axis, then drag while holding the click to select a small portion:

This allows you to focus on specific time frames and better understand the sequence and duration of function calls.

Upon zooming, the call stack will be displayed in detail. The events at the top are the ones that cause the events at the bottom. Here, DevTools shows the execution path of a Node.js function. The process begins with internal Node.js mechanisms (processTicksAndRejections, endReadableNT, emit) and progresses through custom parsing (defaultJsonParser, onEnd). It includes various request handling steps preValidationCallback, validationCompleted, preHandlerCallback, handler. The sequence culminates with an anonymous function and the critical pbkdf2Sync call.

To see the callback from the bottom up, switch to the "Bottom-Up" tab to see the aggregated time spent on the actions:

Here, you will see that 99% of the time is spent on run. When you expand run, you will see that the pbkdf2Sync function is there, which is a big hint that it consumes most of the CPU time.

With that, you can now clear the contents:

That takes care of profiling with Chrome DevTools. Next, you will use the Node Inspector API.

Step 5 — Profiling with the Node Inspector

Another approach is to use the Node Inspector API, which allows you to interact with the V8 inspector programmatically. You can use the Inspector API to profile your application by following these steps.

First, create an inspector.js file with the following contents in the root directory:

import * as inspector from "node:inspector/promises";
import fs from "node:fs/promises"; // Use promises for cleaner async/await usage

const session = new inspector.Session();

async function enableProfiling() {
  try {
    await session.connect();
    await session.post("Profiler.enable");
  } catch (error) {
    console.error("Error enabling profiling:", error);
  }
}

async function startCpuProfiling() {
  try {
    await enableProfiling();
    await session.post("Profiler.start");
  } catch (error) {
    console.error("Error starting CPU profiling:", error);
  }
}

async function stopCpuProfiling() {
  try {
    const { profile } = await session.post("Profiler.stop");
    await fs.writeFile("./profile.cpuprofile", JSON.stringify(profile));
  } catch (error) {
    console.error("Error stopping CPU profiling:", error);
  } finally {
    await session.disconnect();
  }
}

process.on("SIGUSR1", startCpuProfiling);
process.on("SIGUSR2", stopCpuProfiling);

In this code, you import necessary modules and create a inspector.Session instance. The enableProfiling function connects to the session and enables the Profiler.

The startCpuProfiling function calls enableProfiling and starts CPU profiling. In contrast, the stopCpuProfiling function stops profiling, saves the profile data to a file, and disconnects the session, handling any errors that occur.

The script listens for SIGUSR1 and SIGUSR2 signals. Receiving SIGUSR1 starts profiling, and SIGUSR2 stops profiling and saves the results, allowing control over profiling through Unix signals.

To ensure that this code runs when you start the development server, import the module in index.js:

import Fastify from "fastify";
import crypto from "node:crypto";
import "./inspector.js";

With that, run the development server without any flags and put the process in the background using &:

node index.js &

It will show the process ID; on my system, it is 15565:

[1] 15565

Now trigger CPU profiling by sending the SIGUSR1 signal:

kill -SIGUSR1 <your_process_id>

Start the load testing again:

npx autocannon --renderStatusCodes  -d 11 -m POST -H "Content-Type: application/json" -b '{"password":"userPassword123"}' http://localhost:3000/register

When the load testing finishes, send the SIGUSR2 signal to stop profiling:

kill -SIGUSR2 <your_process_id>

Stopping profiling creates a profile.cpuprofile file in the root directory, which contains the profiling data that has been collected. Fortunately, this file can be read by Chrome.

To quickly analyze it in the DevTools, return to the "Performance" tab, then click the Load profile button:

After Chrome analyzes the file, it generates a frame chart like the one you saw in the previous section.

With that, you can terminate the Node process:

kill -9 <your_process_id>

Step 6 — Profiling with the perf Tool

We have looked at some ways to profile Node.js applications, but now we'll use perf, a powerful Linux tool that can profile Node.js applications and those written in other languages. It has many features, including recording CPU samples, context switches, and detailed kernel information.

First, check if perf is installed:

perf --version

perf version 6.8.1

Now, run your Node.js application with the --perf-basic-prof flag:

node --perf-basic-prof index.js &

This tells the compiler to include filenames when translating the code to machine code. Without this, perf will show only memory addresses instead of function names during profiling.

Next, note the process ID and run the perf command:

sudo perf record -F 99 -p <your_process_id> -g

You can proceed with load testing in another terminal:

npx autocannon --renderStatusCodes  -d 11 -m POST -H "Content-Type: application/json" -b '{"password":"userPassword123"}' http://localhost:3000/register

After the load testing finishes, send a SIGINT (Ctrl-C) to stop the perf process. The output will look like this:

...
[ perf record: Woken up 1 times to write data ]
[ perf record: Captured and wrote 0.240 MB perf.data (1156 samples) ]

perf will create a file in the /tmp folder, often named like /tmp/perf-<process_id>.map, containing traces of the functions called. To aggregate the results, run:

sudo perf script > perfs.out

This also creates a perf.data file with binary data. You can open the perf.out file in your text editor to locate the call stack

node   56676 444594.095023:   10101010 task-clock:ppp:
        7db1716add39 cfree+0x19 (/usr/lib/x86_64-linux-gnu/libc.so.6)
             2189d76 evp_md_ctx_clear_digest+0x36 (/home/stanley/.nvm/versions/node/v22.2.0/bin/node)
             218a4e3 EVP_MD_CTX_copy_ex+0x73 (/home/stanley/.nvm/versions/node/v22.2.0/bin/node)
             21c7d30 HMAC_CTX_copy+0x90 (/home/stanley/.nvm/versions/node/v22.2.0/bin/node)
             22af0df kdf_pbkdf2_derive+0x3ef (/home/stanley/.nvm/versions/node/v22.2.0/bin/node)
             21b2274 PKCS5_PBKDF2_HMAC+0x234 (/home/stanley/.nvm/versions/node/v22.2.0/bin/node)
          ....
             374723 v8::internal::(anonymous namespace)::Invoke(v8::internal::Isolate*, v8::internal

The call stack information from perfs.out shows the execution sequence for a Node.js process, starting with memory management (cfree from libc) and moving through several cryptographic functions (evp_md_ctx_clear_digest, EVP_MD_CTX_copy_ex, HMAC_CTX_copy, kdf_pbkdf2_derive, PKCS5_PBKDF2_HMAC). The stack continues with internal Node.js and V8 engine operations, indicating extensive use of CPU resources for cryptographic processing and function invocations within the Node.js runtime environment.

Reading this file can be overwhelming and not very informative due to the large amount of data. A better way to understand this is to visualize the data.

To visualize this data, you can use the FlameGraph tool. First, move back to the home directory:

cd ~

Then, clone the FlameGraph repository:

git clone https://github.com/brendangregg/FlameGraph

Move into the directory:

cd FlameGraph/

Next, copy the perf.data file into the current directory:

cp ../nodejs-profiling-demo/perf.data .

Now, create the flame graph with the following command:

sudo perf script | ./stackcollapse-perf.pl |./flamegraph.pl > perf-flamegraph.svg

The command creates a perf-flamegraph.svg file, which is a flame graph in SVG format.

Next, open the perf-flamegraph.svg file in the browser of your choice to view the flame graph:

To read the frames, first, ignore the colours, as they are mainly for presentation purposes and can be random.

Each box corresponds to a function, showing the call stack depth on the y-axis, with the top box representing the function currently using the CPU and parent functions below. The x-axis shows the function's CPU time. The width of each box indicates the total CPU time the function used, either because it was slow or called frequently. The wider the box, the more prominent it is, so focus on the widest boxes first. The x-axis groups similar functions together without indicating time progression.

Analyzing the flame graph, you can see that pbkdf2Sync uses significant CPU time. This is evident from the wide boxes representing pbkdf2Sync and related cryptographic functions, indicating high CPU usage.

Another exciting feature is that you can click on the boxes to see more details or even perform a search.

Using the perf command and visualizing the data with FlameGraph allows you to analyze CPU usage in your applications effectively.

Step 7 — Understanding continuous profiling

So far, you have manually profiled the application to identify performance issues. However, this can be demanding, especially with a microservices architecture where multiple services run on different machines.

To keep up-to-date with CPU utilization continuously, you can use continuous profiling. This is a dynamic method that profiles applications constantly, making it easier to monitor CPU usage and identify the culprits behind high memory or CPU consumption.

To implement continuous profiling, you can use tools like:

• Pyroscope: a continuous profiling platform that helps monitor and analyze CPU utilization in real-time.
• Parca: an open-source continuous profiling project that collects, stores, and makes profiles queryable using its custom query language.
• Google Cloud Profiler: a continuous profiling tool integrated with Google Cloud, which helps to visualize and optimize performance by collecting CPU and memory profiles.
• Conprof: a continuous profiling tool for the Prometheus ecosystem, making it easy to integrate with existing Prometheus setups.

Using these tools, you can effectively monitor and optimize CPU utilization in your applications.

Final thoughts

In this article, you explored various techniques and tools for profiling Node.js applications to identify high CPU usage sources. Profiling is crucial for performance optimization, but further enhancing your application involves learning about memory leak detection and prevention, which you can learn from this guide.

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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