The Case for Concurrency in JavaScript

If you’ve been into software engineering and been coding long enough, I’m sure that you’ve seen the pattern. Every few years, as the technology is evolving at a rapid pace, something new and sensational comes up, like cloud, containers, serverless, and AI copilots. That’s the hype! But apart from that, there are silent heroes who shape how we build software. They’re structural and not hyped.

Concurrency is one of those superheroes. They don’t sustain on hype. You know that without concurrency, the Node.js server would collapse under a dozen connections, and your browser would freeze while a fetch call waits for data. Yeah! You heard it right. That’s the power that concurrency in the world of software development holds.

This isn’t another async/await tutorial. Instead, we will emphasise how concurrency is pivotal for JavaScript in both the browser and on the server. We’ll dive deeper into what concurrency is, how JavaScript implements it, what the tradeoffs are, and what the future does it holds.

Let’s first focus on clearing some confusion, because sometimes the understanding between concurrency and parallelism gets blurred even amongst the senior devs.

Concurrency in Programming: A Quick Recap

To make things quick and clear, Concurrency refers to the structuring of tasks so that multiple units of work make progress without blocking or hindering each other, whereas parallelism actually executes multiple tasks at the same time on different cores.

Let’s take a very simple analogy, in a restaurant, if you take one chef (concurrency), they’ll prepare dishes in stages. Let’s say, while the soup boils, they’ll chop the vegetables for the stir fry. On the other hand, two chefs will cook two different dishes at the same time, that’s parallelism for you.

How Concurrency Works Fundamentally

In many languages, concurrency is handled by the OS: threads forestall each other, the scheduler decides who runs, and developers debug race conditions.

JavaScript took a different path: cooperative concurrency. Tasks voluntarily yield control when they’re waiting, allowing the event loop to pick up other work.

This makes JavaScript efficient for I/O-bound workloads, but slightly difficult for CPU-heavy ones. A long calculation prioritises the thread, blocking everything else, from rendering the UI to handling new requests.

Let’s take a very simple example to make things crystal clear.

// Sequential: blocks everything until done
function task () {
  for (let i = 0; i < 1e9; i++) {} // simulate CPU-heavy work
  return "done";
}

console.log("Start");
console.log(heavyComputation());
console.log("End");

// Output: Start, completes the task, End (UI frozen until loop ends)

// Concurrent: schedule work without blocking
function taskAsync() {
  return new Promise((resolve) => {
    setTimeout(() => {
      for (let i = 0; i < 1e9; i++) {}
      resolve("done");
    }, 0);
  });
}

(async () => {
  console.log("Start");
  heavyComputationAsync().then(console.log);
  console.log("End");
})();

// Output: Start, End, done (no delays, no fuss)

Even though both versions of the above code achieve the same thing, the approach is different; the second one lets the event loop breathe, keeping the program responsive, whereas in the first case, the UI freezes for a while.

The Impact of Concurrency

The implications of concurrency in JavaScript are massive:

• Responsiveness: A UI stays smooth while data loads.

• Throughput: Node.js can handle thousands of connections because each one yields while waiting for I/O.

• Efficiency: Instead of haywiring threads, JS smoothly operates many tasks on one.

But every significant implication comes with downsides too; here are a few:

• Debugging order-of-execution bugs can be brutal.

• Misuse (like awaiting inside loops) silently kills performance, as it becomes a looping hell.

• Flooding with too much concurrency can overwhelm APIs or downstream services.

How JavaScript Thinks About Concurrency

At the heart of JavaScript is the event loop. What do we mean by it, though?

The event loop is a fundamental concept in JavaScript that enables non-blocking, asynchronous behaviour. The event loop is vital when it comes to building a responsive web application.

To make things simple, the flow of an event loop looks like this:

• Call stack: Executes functions.

• Task queue: Holds macrotasks (timers, I/O).

• Microtask queue: Holds promises and microtasks, always processed before the next macrotask.

Let’s try to understand it with a basic piece of code.

console.log("Start");

setTimeout(() => console.log("Macrotask"), 0);

Promise.resolve().then(() => console.log("Microtask"));

console.log("End");

// Output:
// Start
// End
// Microtask
// Macrotask

That’s why promises resolve before a zero-delay timeout. This ordering has real consequences when managing asynchronous tasks at scale.

JavaScript is single-threaded (it runs on one main thread), but concurrency comes into play through the event loop, async operations, and APIs like Promises, and async/await

Now that we have a basic understanding of concurrency in JavaScript, let’s take a look at the three major methods to have a more practical knowledge about how JavaScript thinks about concurrency.

Callbacks

console.log("Start");

setTimeout(() => {
  console.log("Async operation completed after 2 seconds");
}, 2000);

console.log("End");

The setTimeout callback is scheduled, allowing other code to run. Even though JS is single-threaded, this feels concurrent because long tasks don’t block execution, and the other tasks can go on too.

Promises

console.log("Fetching data...");

fetch("https://jsonplaceholder.typicode.com/posts/1")
  .then(response => response.json())
  .then(data => {
    console.log("Data received:", data);
  });

console.log("Request sent");

fetch runs asynchronously. The promise chain executes when the data arrives, without blocking the rest of the code.

Async/Await

async function fetchUser() {
  console.log("Fetching user...");

  const response = await fetch("https://jsonplaceholder.typicode.com/users/1");
  const user = await response.json();

  console.log("User data:", user);
}

fetchUser();
console.log("Function called, waiting for result...");

await pauses only inside the function while letting the event loop continue handling other tasks. It makes async code look synchronous.

Tradeoffs in the JavaScript Model

The concept of concurrency in JavaScript is sublime, but as mentioned before, there is always something that comes at a price.

Let’s talk about what the best part of concurrency in JavaScript is.

• Proper Resource Allocation: Concurrency allows a program to make better use of system resources by performing multiple operations at the same time, rather than waiting for one task to complete before starting another.

• Improved User Experience: Concurrency allows applications to remain responsive as it doesn’t block a program’s main execution path. For example, a user can fill up a form while downloading a file from the same site.

• Scalability: Concurrency scales the systems, making them capable of handling a high number of operations, therefore helping server environments where multiple client requests need to be handled simultaneously.

Now comes the tradeoff.

• Complexity in Management: Managing concurrent operations can be complex. Coordinating multiple tasks that run at the same time requires careful planning to ensure they don’t interfere with each other.

• Difficulty in Tracing and Debugging: Debugging concurrent programs is often more challenging than debugging sequential programs. This is because the issues may only arise under specific timing conditions, making them hard to reproduce and fix.

• Potential for Race Conditions and Deadlocks: Race Conditions occur when two or more operations need to read or write on shared data, and the final outcome depends on the order of execution. This can lead to unpredictable results. Deadlocks happen when two or more processes get stuck, each waiting for the other to release resources or complete tasks, resulting in a standstill.

The Future of Concurrency in JavaScript

JavaScript has always been single-threaded, built on its event loop. The simplicity behind it is what makes it magical, but it comes with its own cons. The future is about breaking through those limits with smarter tools.

We’ve already seen how Promises and async/await reshaped async programming. Next up?

• Built-in ways to handle race conditions, task prioritisation, and cancellation.

• Deeper support for reactive programming, making real-time data and event-driven apps easier to build.

Here’s the catch: JavaScript will stay single-threaded at its core, but it’s getting escape hatches when we need them, and concurrency is one significant practical implementation.

Concurrency in JavaScript isn’t about it being elegant; it’s about solving real-world problems. It hides enough complexity to stay approachable while giving us the tools to scale.

And as developers, our job is simple:

• Don’t block the thread.

• Don’t serialise what can run in parallel.

• Use new tools wisely, without losing sight of the simplicity that got us here.

As senior developers, our challenge isn’t just to use concurrency, it’s to use it wisely. Don’t block the thread. Don’t serialise what should run in parallel. And when the future arrives, embrace it without losing sight of the simplicity that got us here.