Node.js Backend Development Bootcamp · Aula

Construção de um Pool de Trabalhadores Reutilizável para Vazão

Projete um pool de trabalhadores baseado em fila de tarefas que recicle linhas de execução para maximizar a utilização da CPU sob carga.

Aula 4 de 413 etapas

Construção de um Pool de Trabalhadores Reutilizável para Vazão é uma aula grátis de Node.js Backend Development Bootcamp no CoddyKit. Esta é a aula 4 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de Node.js Backend Development Bootcamp, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de Node.js Backend Development Bootcamp inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

Why a Worker Pool?

Node.js runs your JavaScript on a single event-loop thread. That is great for I/O, but a CPU-bound task (hashing, image resizing, parsing, compression) blocks the loop and stalls every other request.

The worker_threads module lets you run JavaScript on separate OS threads. But spawning a brand-new Worker for every task is wasteful: thread startup costs tens of milliseconds and memory.

Goal: create a fixed set of long-lived workers once.
Recycle them across many tasks via a queue.
Maximize throughput by keeping every CPU core busy.

That recycled, queue-backed set of workers is a worker pool.

The Blocking Problem

Before building the pool, feel the pain. A synchronous CPU loop on the main thread freezes everything: timers, HTTP responses, even a simple setInterval heartbeat.

Run this and watch the heartbeat go silent while fib(42) burns the CPU.

function fib(n) {
  return n < 2 ? n : fib(n - 1) + fib(n - 2);
}

let ticks = 0;
const timer = setInterval(() => {
  console.log('heartbeat', ++ticks);
  if (ticks >= 3) clearInterval(timer);
}, 50);

console.log('start blocking work');
console.log('fib(38) =', fib(38)); // blocks the event loop
console.log('done blocking work');

A Single Worker Thread

The fix is to move CPU work off the main thread. A worker can be defined in the same file using isMainThread to branch behavior.

isMainThread is true in the parent, false inside the worker.
parentPort is the message channel back to the parent.
new Worker(__filename) re-runs this file on a new thread.

This is one worker for one task. The pool will generalize it.

const { Worker, isMainThread, parentPort } = require('node:worker_threads');

if (isMainThread) {
  const worker = new Worker(__filename);
  worker.on('message', (result) => {
    console.log('result =', result);
    worker.terminate();
  });
  worker.postMessage(40);
} else {
  parentPort.on('message', (n) => {
    const fib = (x) => (x < 2 ? x : fib(x - 1) + fib(x - 2));
    parentPort.postMessage(fib(n));
  });
}

Designing the Pool's Pieces

A reusable pool needs four moving parts that work together:

Workers array — a fixed number of long-lived threads, usually os.cpus().length.
Idle list — workers ready to accept a task right now.
Task queue — pending tasks waiting for a free worker.
Pending map — links each busy worker to the Promise it must resolve.

The core invariant: a queued task only runs when an idle worker exists; when a worker finishes, it pulls the next task or returns to the idle list.

The Worker Script (worker.js)

Keep the worker logic in its own file so the pool can spawn many copies of it. The worker listens for messages, computes, and posts a structured reply that distinguishes success from error.

Always wrap the work in try/catch so a thrown error becomes a message rather than a crashed thread.

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

function heavyTask(n) {
  const fib = (x) => (x < 2 ? x : fib(x - 1) + fib(x - 2));
  return fib(n);
}

parentPort.on('message', ({ id, payload }) => {
  try {
    const result = heavyTask(payload);
    parentPort.postMessage({ id, result });
  } catch (err) {
    parentPort.postMessage({ id, error: err.message });
  }
});

Pool Skeleton: Spawning Workers

The pool constructor spawns N workers up front and tracks which are idle. Each task carries a unique id so replies map back to the right Promise.

Note the _tagWorker helper attaches a per-worker message/error listener exactly once, not once per task.

const { Worker } = require('node:worker_threads');
const os = require('node:os');

class WorkerPool {
  constructor(workerPath, size = os.cpus().length) {
    this.workerPath = workerPath;
    this.idle = [];
    this.queue = [];
    this.pending = new Map(); // id -> { resolve, reject }
    this.nextId = 0;
    for (let i = 0; i < size; i++) this._spawn();
  }

  _spawn() {
    const worker = new Worker(this.workerPath);
    worker.on('message', (msg) => this._onResult(worker, msg));
    worker.on('error', (err) => this._onError(worker, err));
    this.idle.push(worker);
  }
}

Submitting Tasks and the Queue

run() returns a Promise and pushes a task onto the queue, then calls _dispatch(). Dispatch pairs a queued task with an idle worker; if none is free, the task simply waits.

If idle is empty, the task stays queued — no work is lost.
When a worker frees up, it drains the next queued task automatically.

This back-pressure is what keeps the pool stable under bursty load.

  run(payload) {
    return new Promise((resolve, reject) => {
      const id = this.nextId++;
      this.pending.set(id, { resolve, reject });
      this.queue.push({ id, payload });
      this._dispatch();
    });
  }

  _dispatch() {
    if (this.queue.length === 0 || this.idle.length === 0) return;
    const worker = this.idle.pop();
    const task = this.queue.shift();
    worker._currentId = task.id;
    worker.postMessage(task);
  }

Recycling: Handling Results

This is the heart of recycling. When a worker posts a result, the pool resolves the matching Promise, returns the worker to the idle list, and immediately tries to dispatch the next queued task.

The same worker handles task after task — no respawn — which is exactly what maximizes throughput.

  _onResult(worker, msg) {
    const { id, result, error } = msg;
    const job = this.pending.get(id);
    this.pending.delete(id);
    worker._currentId = null;
    this.idle.push(worker);   // recycle the worker
    this._dispatch();          // pull the next queued task
    if (!job) return;
    if (error) job.reject(new Error(error));
    else job.resolve(result);
  }

Recycling on Failure

A worker can crash (uncaught exception, OOM). If you only handle message, a dead worker silently shrinks your pool and its in-flight Promise hangs forever.

On error, reject the in-flight task and respawn a replacement so the pool keeps its size. This self-healing behavior is essential for long-running services.

  _onError(worker, err) {
    const id = worker._currentId;
    if (id != null && this.pending.has(id)) {
      this.pending.get(id).reject(err);
      this.pending.delete(id);
    }
    // remove the dead worker, keep pool size constant
    this.idle = this.idle.filter((w) => w !== worker);
    worker.terminate();
    this._spawn();
    this._dispatch();
  }

  async destroy() {
    await Promise.all(this.idle.map((w) => w.terminate()));
  }

A Complete, Runnable Pool

Putting it together in a single file using isMainThread branching so it runs standalone. The pool fans 8 tasks across the available cores and resolves each via a Promise.

Notice every task resolves even though there are fewer workers than tasks — the queue handles the overflow.

const { Worker, isMainThread, parentPort } = require('node:worker_threads');
const os = require('node:os');

if (!isMainThread) {
  const fib = (x) => (x < 2 ? x : fib(x - 1) + fib(x - 2));
  parentPort.on('message', ({ id, payload }) => {
    parentPort.postMessage({ id, result: fib(payload) });
  });
} else {
  class Pool {
    constructor(size) {
      this.idle = []; this.queue = []; this.pending = new Map(); this.id = 0;
      for (let i = 0; i < size; i++) this._spawn();
    }
    _spawn() {
      const w = new Worker(__filename);
      w.on('message', ({ id, result }) => {
        this.pending.get(id).resolve(result);
        this.pending.delete(id);
        this.idle.push(w); this._dispatch();
      });
      this.idle.push(w);
    }
    _dispatch() {
      if (!this.queue.length || !this.idle.length) return;
      const w = this.idle.pop(); const t = this.queue.shift();
      w.postMessage(t);
    }
    run(payload) {
      return new Promise((resolve) => {
        const id = this.id++;
        this.pending.set(id, { resolve });
        this.queue.push({ id, payload }); this._dispatch();
      });
    }
    destroy() { this.idle.forEach((w) => w.terminate()); }
  }

  const pool = new Pool(Math.min(4, os.cpus().length));
  const jobs = [30, 31, 32, 33, 30, 31, 32, 33];
  Promise.all(jobs.map((n) => pool.run(n))).then((results) => {
    console.log('results:', results);
    pool.destroy();
  });
}

Sizing and Tuning for Throughput

Pool size is a real decision, not a guess:

CPU-bound work: size = number of physical cores (os.cpus().length). More threads than cores just adds context-switch overhead.
Mixed work: a few extra workers can hide occasional I/O waits, but measure first.
Transfer cost: large payloads serialize via structured clone; for big buffers use postMessage(buf, [buf]) to transfer ownership and avoid copying.

Always benchmark with realistic load. Throughput, not thread count, is the metric that matters.

const buf = new Uint8Array(1024 * 1024).fill(7);
// Transfer the buffer instead of copying it (zero-copy handoff)
worker.postMessage({ id, payload: buf }, [buf.buffer]);
// After transfer, buf is detached in the sender: buf.byteLength === 0

Quick Check

Test your understanding of the pool's recycling design.

Responda na aula interativa

Recap

You designed a reusable worker pool that turns CPU-bound work into parallel throughput:

Why: CPU-bound tasks block Node's single event loop; worker_threads moves them to OS threads.
Pieces: a fixed workers array, an idle list, a task queue, and a pending map keyed by task id.
Recycling: finished workers return to the idle list and pull the next queued task — no per-task respawn.
Resilience: handle the error event to reject the in-flight task and respawn a replacement so pool size stays constant.
Tuning: size to physical cores for CPU work, and transfer large buffers instead of copying them.

The result is a self-healing, back-pressured pool that keeps every core busy under load.

Grátis para começar

Aprenda JavaScript com um tutor de IA — grátis

Escreva e execute código real no seu navegador, obtenha ajuda instantânea de um tutor de IA 24/7 e continue de onde parou na web ou no app.

Cursos: 22
Aulas: 92

Começar a aprender grátis Ver o curso completo

Perguntas Frequentes

A aula “Construção de um Pool de Trabalhadores Reutilizável para Vazão” é grátis?

Sim — o texto completo de “Construção de um Pool de Trabalhadores Reutilizável para Vazão” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de Node.js Backend Development Bootcamp, atualize para CoddyKit PRO. O curso de Node.js Backend Development Bootcamp inclui 4 aulas no total.

O que vou aprender em “Construção de um Pool de Trabalhadores Reutilizável para Vazão”?

Projete um pool de trabalhadores baseado em fila de tarefas que recicle linhas de execução para maximizar a utilização da CPU sob carga. Você pratica Node.js Backend Development Bootcamp com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar Node.js Backend Development Bootcamp?

Nenhuma experiência prévia é necessária. Node.js Backend Development Bootcamp no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 4 de 4.

Quanto tempo leva a aula “Construção de um Pool de Trabalhadores Reutilizável para Vazão”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de Node.js Backend Development Bootcamp?

Sim. Cada aula de Node.js Backend Development Bootcamp inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

Por que o Loop de Eventos Para com Trabalho Limitado pela CPU
Criação de Linhas de Trabalho e Envio de Mensagens
Compartilhamento de Memória com SharedArrayBuffer e Atomics
Construção de um Pool de Trabalhadores Reutilizável para Vazão

← Voltar para Node.js Backend Development Bootcamp