Your application is a hit. Users are signing up, traffic is surging, and your database is humming. But with this success comes a new, insidious problem: your app is getting slower. User requests that were once instant now hang for seconds, sometimes timing out entirely. That spinning wheel of death has become your users' most frequent interaction.
This is a classic scaling bottleneck. The culprit? Long-running tasks—like processing a video upload, generating a PDF report, or calling a third-party API—that are handled synchronously within the web request. Every second your server spends on these tasks is a second it's not available to serve other users.
To handle millions of tasks a day and keep your application snappy, you need to shift your architecture from a synchronous to an asynchronous paradigm. This guide will show you how.
In a typical synchronous web application, the server follows a simple, linear path for every request:
This model is fine for quick operations. But what happens when the "work" involves sending thousands of welcome emails? The user's request hangs until the last email is sent. The web server process is completely tied up, unable to handle any other incoming requests. As traffic grows, requests pile up, and your entire application grinds to a halt.
This is the synchronous trap. You can't scale your application if its responsiveness is chained to its most time-consuming operations.
The solution is to decouple long-running tasks from the request-response cycle. Instead of doing the work immediately, your application can add the task to a job queue and immediately respond to the user, letting them know the task is underway.
A separate process, known as a background worker, constantly monitors this queue. When it sees a new job, it picks it up and executes it independently of your main application.
This simple architectural shift has a profound impact:
"I'll just set up Redis and write a worker script," many developers think. While possible, building and maintaining a robust background processing system is a massive undertaking fraught with hidden complexities:
Suddenly, you're not just building your product anymore; you're building and managing a complex, distributed infrastructure system.
This is where worker.do changes the game. We believe that developers should focus on building amazing features, not managing infrastructure. worker.do provides a robust, scalable background worker platform as a simple API.
We handle the infrastructure, scaling, retries, and monitoring, so you don't have to. You get all the benefits of an asynchronous architecture without any of the operational headaches.
Turning a complex background process into a manageable, scalable service is as simple as an API call.
import { Dô } from '@do/sdk';
// Initialize the .do client with your API key
const dô = new Dô(process.env.DO_API_KEY);
// Enqueue a new job to be processed by a worker
async function queueVideoProcessing(videoId: string) {
const job = await dô.worker.enqueue({
task: 'process-video',
payload: {
id: videoId,
format: '1080p',
watermark: true
}
});
console.log(`Job ${job.id} enqueued successfully!`);
return job.id;
}
With worker.do, you get:
Any long-running, resource-intensive, or deferrable task is a perfect candidate for a background worker. Here are a few examples:
Don't let synchronous tasks dictate your application's performance. By embracing asynchronous processing with worker.do, you can build a more resilient, scalable, and responsive application that's ready to handle millions of tasks a day.
Ready to stop managing infrastructure and start building features? Get started with worker.do today.
What is a background worker?
A background worker is a process that runs separately from your main application, handling tasks that would otherwise block the user interface or slow down request times. Common examples include sending emails, processing images, or generating reports.
How does worker.do handle scaling?
The .do platform automatically scales your workers based on the number of jobs in the queue. This means you get the processing power you need during peak times and save costs during lulls, all without managing any infrastructure.
What types of tasks are suitable for worker.do?
Any long-running or resource-intensive task is a great fit. This includes video encoding, data analysis, batch API calls, running AI model inferences, and handling scheduled jobs (cron).
How are job failures and retries managed?
.do provides built-in, configurable retry logic. If a job fails, the platform can automatically retry it with an exponential backoff strategy, ensuring transient errors are handled gracefully without manual intervention.