How to Use All the Cores on Multi-Core Machines in node.js

Node.js is designed to be single-threaded, but you can take advantage of multi-core machines by utilizing the cluster module or by creating separate Node.js processes manually.

Using the cluster Module

The cluster module allows you to create child processes (workers) that share the same port, enabling load balancing across CPU cores. Here's a basic example:

const cluster = require('cluster');
const os = require('os');

if (cluster.isMaster) {
  // Fork workers
  for (let i = 0; i < os.cpus().length; i++) {
    cluster.fork();
  }

  cluster.on('exit', (worker, code, signal) => {
    console.log(`Worker ${worker.process.pid} died`);
  });
} else {
  // Your application logic here
  const express = require('express');
  const app = express();

  app.get('/', (req, res) => {
    res.send('Hello from worker ' + cluster.worker.id);
  });

  app.listen(3000, () => {
    console.log(`Worker ${cluster.worker.id} running`);
  });
}

In this example, the master process forks child processes, each running a worker. Each worker is a separate instance of your application, and they can share the load.

Using Multiple Processes Manually

Another approach is to create multiple Node.js processes manually and distribute the workload. This can be done using a process manager like pm2 or by using tools like child_process to spawn multiple Node.js processes. Here's a simplified example:

const { fork } = require('child_process');
const os = require('os');

// Fork processes
for (let i = 0; i < os.cpus().length; i++) {
  const worker = fork('worker.js');
  console.log(`Forked worker ${worker.pid}`);
}

// worker.js
const express = require('express');
const app = express();

app.get('/', (req, res) => {
  res.send('Hello from worker ' + process.pid);
});

app.listen(3000, () => {
  console.log(`Worker ${process.pid} running`);
});

This example manually forks multiple Node.js processes, each running the same worker.js application.

Choose the method that best fits your application and deployment environment. Note that effective load balancing may require additional considerations, and the choice between these methods depends on your specific use case.

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