Concurrency vs. Parallelism in JavaScript: Understanding the Difference

Introduction

If you’ve worked with JavaScript for a while, you’ve probably heard terms like concurrency and parallelism tossed around. Maybe you’ve wondered, “Are they the same thing?” or “Why does JavaScript handle concurrency so well if it’s single-threaded?” Understanding these concepts is essential for writing efficient and performant code, especially in modern web development.

In this article, we’ll demystify concurrency and parallelism, explore how they relate to JavaScript’s event loop and multithreading, and provide beginner-friendly examples to solidify your understanding. By the end, you’ll know how to handle tasks concurrently in JavaScript and understand the difference between concurrency and parallelism.

What Are Concurrency and Parallelism?

Concurrency

Concurrency refers to the ability to handle multiple tasks at the same time. However, these tasks don’t necessarily run simultaneously. Instead, they progress in small chunks, sharing resources like a single CPU core.

Think of a single waiter managing multiple tables at a restaurant. The waiter can attend to multiple customers by switching between them, but only handles one request at a time.

Parallelism

Parallelism, on the other hand, is the ability to execute multiple tasks simultaneously. This requires multiple resources, such as multiple CPU cores. Tasks don’t just progress side by side — they are truly executed at the same time.

Now think of multiple waiters, each attending to their own tables simultaneously.

JavaScript: A Single-Threaded Language

JavaScript is single-threaded, which means it can only execute one task at a time in its main thread. However, JavaScript achieves concurrency through its event loop and asynchronous programming.

The Event Loop

JavaScript uses an event loop to handle tasks asynchronously. Here’s a simplified explanation:

1. Call Stack: The place where JavaScript keeps track of what function it’s currently executing.
2. Web APIs: Browser-provided features (like “ setTimeout ” or “ fetch ”) that handle asynchronous tasks.
3. Task Queue: A queue where asynchronous tasks wait to be executed after the current stack is cleared.

Example: Asynchronous Task with setTimeout

console.log("Start");

setTimeout(() => {
  console.log("This happens later");
}, 1000);

console.log("End");

Output:

Start
End
This happens later

• The “ setTimeout ” function delegates the task to the Web APIs, which handles the delay and adds the callback to the task queue after 1 second.
• The event loop ensures the callback executes only after the main stack is cleared.

Concurrency in JavaScript

JavaScript achieves concurrency by breaking tasks into smaller chunks and executing them in an interleaved manner. This makes it seem like multiple tasks are happening simultaneously, even though they’re actually being managed by the event loop.

Example: Concurrent Tasks Using Promise.all

Let’s fetch data from multiple APIs concurrently:

const fetchData = (url) => {
  return new Promise((resolve) => {
    setTimeout(() => {
      resolve(`Data from ${url}`);
    }, Math.random() * 2000); // Simulate variable network latency
  });
};

const fetchAll = async () => {
  const urls = ["api1.com", "api2.com", "api3.com"];
  console.log("Fetching data...");
  
  const results = await Promise.all(urls.map(fetchData));
  console.log(results);
};

fetchAll();

Output (varies):

Fetching data...
["Data from api1.com", "Data from api2.com", "Data from api3.com"]

Here’s what happens:

• All fetch operations are initiated concurrently.
• JavaScript manages these operations asynchronously through the event loop.
• The “ promise.all ” waits for all tasks to complete before proceeding.

This demonstrates concurrency — tasks are interleaved and managed efficiently, but they aren’t running in parallel because JavaScript’s main thread is single-threaded.

Parallelism in JavaScript

Parallelism requires multiple threads or cores. While JavaScript itself is single-threaded, modern environments like Node.js and browsers allow for parallelism through:

1. Web Workers: Browser-based parallelism.
2. Worker Threads: Node.js implementation for parallelism.

Example: Parallelism with Web Workers

Web Workers allow JavaScript to run code in a separate thread, enabling true parallel execution.

Setting Up a Web Worker

1. Create a file “ worker.js ” :

// worker.js
onmessage = function (event) {
  console.log("Worker received:", event.data);
  const result = event.data * 2; // Simulate a CPU-intensive task
  postMessage(result);
};

1. Create a main script:

// main.js
const worker = new Worker("worker.js");

worker.onmessage = function (event) {
  console.log("Result from worker:", event.data);
};

worker.postMessage(10); // Send data to the worker

Output:

Worker received: 10
Result from worker: 20

The Web Worker runs independently of the main thread, allowing parallelism for computationally heavy tasks.

Concurrency vs. Parallelism: Side-by-Side Comparison

Definition

• Concurrency: Refers to tasks being managed at the same time but not necessarily running simultaneously. Tasks are interleaved, sharing a single thread or resource.
• Parallelism: Involves tasks being executed simultaneously on separate resources, such as multiple CPU cores.

How It Works in JavaScript

• Concurrency: Achieved through asynchronous programming using “ async/await ”, Promises, and the event loop.
• Parallelism: Achieved using technologies like Web Workers in the browser or Worker Threads in Node.js.

Analogy

• Concurrency: Think of a single waiter managing multiple tables at a restaurant. They switch between tasks but only handle one request at a time.
• Parallelism: Imagine multiple waiters, each attending to a different table simultaneously.

Best Use Cases

• Concurrency: Ideal for I/O-bound tasks such as network requests or reading files.
• Parallelism: Best suited for CPU-bound tasks like image processing, data computations, or other intensive operations.

This format breaks down the comparison into digestible subheadings, making it easier to understand and more readable for a wider audience.

Practical Use Cases

Concurrency: Managing Multiple API Calls

When your application needs to fetch data from multiple sources concurrently, JavaScript’s async/await and “ promise.all ” are perfect.

Parallelism: Handling Computationally Heavy Tasks

For tasks like image processing or complex calculations, Web Workers or Worker Threads can offload the work to separate threads, keeping the main thread responsive.

Best Practices for Working with Concurrency and Parallelism

1. Avoid Blocking the Main Thread: Use Web Workers or offload tasks to the backend for CPU-intensive operations.
2. Use Promises for I/O Tasks: Promises and “ async/await ” make handling concurrency easier and more readable.
3. Test for Bottlenecks: Identify performance bottlenecks using tools like Chrome DevTools or Node.js Performance Hooks.
4. Leverage Libraries: For advanced use cases, consider libraries like “ workpool ” for managing worker threads in Node.js.

Conclusion

Understanding the difference between concurrency and parallelism is essential for writing efficient JavaScript applications. Concurrency allows JavaScript to handle multiple tasks through its event loop, while parallelism takes advantage of additional threads for true simultaneous execution.

By mastering these concepts and leveraging tools like Promises, Web Workers, and Worker Threads, you can build highly performant and scalable applications. Whether you’re optimizing API calls or handling CPU-heavy tasks, knowing when to use concurrency and parallelism will help you deliver faster, more responsive apps.