Worker pool pattern in Node.js

Introduction

The worker pool pattern helps run many tasks at the same time without slowing down your main program. It uses a group of workers to share the work.

When you have many small tasks to do and want to finish them faster.

When your program needs to stay responsive while doing heavy work.

When you want to limit how many tasks run at once to avoid overload.

When you want to reuse workers instead of creating new ones each time.

When you want to handle tasks in parallel but control resource use.

Syntax

Node.js

const { Worker } = require('worker_threads');

class WorkerPool {
  constructor(numWorkers) {
    this.workers = [];
    this.freeWorkers = [];
    for (let i = 0; i < numWorkers; i++) {
      const worker = new Worker('./worker.js');
      this.workers.push(worker);
      this.freeWorkers.push(worker);
    }
  }

  runTask(taskData) {
    return new Promise((resolve, reject) => {
      if (this.freeWorkers.length === 0) {
        reject(new Error('No free workers'));
        return;
      }
      const worker = this.freeWorkers.pop();
      worker.once('message', (result) => {
        this.freeWorkers.push(worker);
        resolve(result);
      });
      worker.once('error', reject);
      worker.postMessage(taskData);
    });
  }

  close() {
    for (const worker of this.workers) {
      worker.terminate();
    }
  }
}

The Worker class comes from the worker_threads module in Node.js.

You create a pool by making several workers and keep track of which are free.

Examples

This creates a pool with 3 workers and runs a task sending number 10.

Node.js

const pool = new WorkerPool(3);
pool.runTask({ number: 10 }).then(console.log);

Runs another task and prints the result when done.

Node.js

pool.runTask({ number: 20 }).then(result => {
  console.log('Result:', result);
});

Sample Program

This example shows a worker file that squares a number sent to it. The main file creates a pool of 2 workers and runs 3 tasks. It prints the array of squared results.

Node.js

// worker.js
const { parentPort } = require('worker_threads');

parentPort.on('message', (task) => {
  // Simple task: square the number
  const result = task.number * task.number;
  parentPort.postMessage(result);
});

// main.js
const { Worker } = require('worker_threads');

class WorkerPool {
  constructor(numWorkers) {
    this.workers = [];
    this.freeWorkers = [];
    for (let i = 0; i < numWorkers; i++) {
      const worker = new Worker('./worker.js');
      this.workers.push(worker);
      this.freeWorkers.push(worker);
    }
  }

  runTask(taskData) {
    return new Promise((resolve, reject) => {
      if (this.freeWorkers.length === 0) {
        reject(new Error('No free workers'));
        return;
      }
      const worker = this.freeWorkers.pop();
      worker.once('message', (result) => {
        this.freeWorkers.push(worker);
        resolve(result);
      });
      worker.once('error', reject);
      worker.postMessage(taskData);
    });
  }

  close() {
    for (const worker of this.workers) {
      worker.terminate();
    }
  }
}

(async () => {
  const pool = new WorkerPool(2);
  const results = await Promise.all([
    pool.runTask({ number: 5 }),
    pool.runTask({ number: 10 }),
    pool.runTask({ number: 3 })
  ]).catch(console.error);
  console.log(results);
  pool.close();
})();

Important Notes

Workers run in separate threads, so they do not block the main program.

Always close workers when done to free resources.

If no workers are free, tasks can be queued or rejected depending on your design.

Summary

The worker pool pattern helps run many tasks at the same time efficiently.

It uses a fixed number of workers to share the work and keep your program fast.

You create workers, send tasks, get results, and then reuse workers for new tasks.

Practice

1. What is the main purpose of using the worker pool pattern in Node.js?

easy

A. To increase the memory usage by creating many workers

B. To avoid using any background threads or workers

C. To slow down the program by running tasks one after another

D. To run multiple tasks concurrently using a fixed number of workers

Solution

1. Step 1: Understand the worker pool pattern concept

   The worker pool pattern uses a limited number of workers to handle many tasks efficiently without creating too many threads.

2. Step 2: Identify the main goal in Node.js context

   It allows running tasks concurrently but limits the number of workers to keep resource use balanced and improve speed.

3. Final Answer:

   To run multiple tasks concurrently using a fixed number of workers -> Option D

4. Quick Check:

   Worker pool = fixed workers + concurrent tasks [OK]

Hint: Worker pool means fixed workers handle many tasks concurrently [OK]

Common Mistakes:

• Thinking worker pool creates unlimited workers
• Believing it slows down the program
• Confusing it with single-threaded execution

2. Which of the following is the correct way to create a worker pool using Node.js worker_threads module?

easy

A. const { Worker } = require('worker_threads'); const pool = new WorkerPool(4);

B. const pool = new WorkerPool(4); const { Worker } = require('worker_threads');

C. const { Worker } = require('worker_threads'); const pool = WorkerPool(4);

D. const pool = new Worker('worker.js', 4);

Solution

1. Step 1: Import Worker correctly from worker_threads

   The correct import is: const { Worker } = require('worker_threads');

2. Step 2: Create a worker pool instance properly

   Assuming a WorkerPool class exists, it should be instantiated with new WorkerPool(4); to create 4 workers.

3. Final Answer:

   const { Worker } = require('worker_threads'); const pool = new WorkerPool(4); -> Option A

4. Quick Check:

   Correct import + new instance = const { Worker } = require('worker_threads'); const pool = new WorkerPool(4); [OK]

Hint: Import Worker first, then create pool with new keyword [OK]

Common Mistakes:

• Calling WorkerPool without new keyword
• Wrong order of import and instantiation
• Using Worker constructor incorrectly

3. Given this simplified worker pool code snippet, what will be logged to the console?

const { Worker } = require('worker_threads');

class WorkerPool {
  constructor(size) {
    this.workers = Array(size).fill(null).map(() => new Worker('./worker.js'));
  }
  runTask(task) {
    return new Promise((resolve) => {
      const worker = this.workers.pop();
      worker.once('message', (result) => {
        this.workers.push(worker);
        resolve(result);
      });
      worker.postMessage(task);
    });
  }
}

const pool = new WorkerPool(2);
pool.runTask('task1').then(console.log);
pool.runTask('task2').then(console.log);

medium

A. Runs tasks sequentially, logs task1 then task2

B. Outputs results of task1 and task2 as they complete, order not guaranteed

C. Only task1 result is logged, task2 is ignored

D. Throws an error because workers array is empty after first pop

Solution

1. Step 1: Understand worker allocation and reuse

   The pool starts with 2 workers. Each runTask pops a worker, uses it, then pushes it back after message received.

2. Step 2: Analyze concurrency and output order

   Both tasks run concurrently on separate workers. Results log as tasks complete, order may vary.

3. Final Answer:

   Outputs results of task1 and task2 as they complete, order not guaranteed -> Option B

4. Quick Check:

   Workers reused, tasks run concurrently [OK]

Hint: Workers pop and push back, tasks run in parallel [OK]

Common Mistakes:

• Assuming workers array empties causing error
• Thinking tasks run one after another
• Believing only first task logs

4. Identify the bug in this worker pool code snippet and choose the correct fix:

class WorkerPool {
  constructor(size) {
    this.workers = [];
    for (let i = 0; i < size; i++) {
      this.workers.push(new Worker('./worker.js'));
    }
  }

  runTask(task) {
    if (this.workers.length === 0) {
      throw new Error('No workers available');
    }
    const worker = this.workers.pop();
    worker.once('message', (result) => {
      resolve(result);
      this.workers.push(worker);
    });
    worker.postMessage(task);
  }
}

medium

A. Replace pop() with shift() to get workers in order

B. Remove the check for empty workers array

C. Add a Promise wrapper around runTask and return it

D. Call worker.terminate() after task completes

Solution

1. Step 1: Check runTask return and resolve usage

   runTask uses resolve inside callback but does not return a Promise or define resolve, causing error.

2. Step 2: Fix by wrapping runTask in a Promise and returning it

   Wrap the logic inside return new Promise((resolve) => { ... }) so resolve is defined and caller can await result.

3. Final Answer:

   Add a Promise wrapper around runTask and return it -> Option C

4. Quick Check:

   runTask must return Promise with resolve [OK]

Hint: runTask uses resolve but lacks Promise wrapper [OK]

Common Mistakes:

• Ignoring missing Promise causes runtime error
• Removing worker availability check breaks logic
• Terminating worker too early stops reuse

5. You want to process 10 CPU-heavy tasks using a worker pool of size 3. Which approach best ensures all tasks run efficiently without overloading the system?

hard

A. Create 3 workers, queue tasks, assign next task when a worker finishes

B. Create 10 workers, one per task, and run all at once

C. Run all tasks in the main thread sequentially without workers

D. Create 3 workers but assign all tasks to the first worker only

Solution

1. Step 1: Understand resource limits and worker pool size

   Creating too many workers (10) can overload CPU and memory. Using 3 workers limits resource use.

2. Step 2: Use a task queue to assign tasks as workers become free

   Queue tasks and assign next task to a worker when it finishes ensures all tasks run efficiently without overload.

3. Final Answer:

   Create 3 workers, queue tasks, assign next task when a worker finishes -> Option A

4. Quick Check:

   Fixed workers + queued tasks = efficient processing [OK]

Hint: Use fixed workers and queue tasks for efficiency [OK]

Common Mistakes:

• Creating too many workers causing overload
• Running tasks sequentially wasting concurrency
• Assigning all tasks to one worker blocking others