Is your application fast, responsive, and a joy to use? Or does it sometimes leave users staring at a loading spinner, waiting for a task to complete? In modern web development, the difference often comes down to one crucial architectural choice: the use of background workers.
When a user triggers a long-running task—like processing a video, generating a report, or sending a batch of emails—handling it during the initial web request is a recipe for timeouts and a terrible user experience. The solution is to offload these heavy tasks to a separate process that runs in the background.
This frees up your main application to respond instantly, creating a snappy and professional feel. The challenge? Setting up and managing a robust background job system can be complex, involving message queues, server provisioning, and failure handling.
That's where worker.do comes in. We turn complex background processes into simple, manageable Services-as-Software, all accessible through a single API. Let's explore five critical background jobs your application probably needs and see how simple they are to implement.
The Problem: A user signs up for your service. They click "Submit," and your server creates their account, logs the event, and then attempts to send a welcome email via a third-party service. If the email provider's API is slow, the user is stuck waiting for several seconds before they're redirected to their dashboard.
The Background Worker Solution: Instead of making the user wait, your application should respond instantly after the account is created. The task of sending the email is handed off to a background worker. The user gets an immediate, snappy response, and the email arrives a few moments later.
With worker.do:
// Enqueue a job to send a welcome email
await dô.worker.enqueue({
task: 'send-welcome-email',
payload: { userId: 'usr_12345', template: 'welcome' }
});
The Problem: Your platform allows users to upload profile pictures or videos. A raw 4K video file might need to be transcoded into multiple formats (1080p, 720p, 480p), watermarked, and have thumbnails generated. Performing this work synchronously would cause the user's upload request to time out long before it's finished.
The Background Worker Solution: The user uploads the file, which is stored in a temporary location. Your application immediately returns a "Success! We're processing your video" message. A background job then picks up the file and performs all the necessary, time-consuming transformations.
With worker.do: This is a perfect example of a resource-intensive job that worker.do is built to handle.
// The main code example for worker.do
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;
}
The Problem: A business user wants to export a CSV of all sales from the last quarter. Querying the database, formatting the data, and generating a large file could take minutes. Forcing the user to keep their browser tab open and waiting is impractical and error-prone.
The Background Worker Solution: When the user requests the report, a job is queued. The user can continue using the application or even close their browser. Once the worker has finished generating the file, the application can send the user an email with a download link or show a notification in their dashboard.
With worker.do:
// Queue a report generation job
await dô.worker.enqueue({
task: 'generate-sales-report',
payload: { accountId: 'acc_67890', range: 'last-quarter' }
});
The Problem: Your application needs to stay in sync with an external service, like a CRM or a payment gateway. Calling these external APIs in real-time can an introduce unpredictable latency to your own API. Furthermore, if the external API is down, your application might fail.
The Background Worker Solution: Use a scheduled background job (a cron job) to periodically sync data from the third-party service. The worker can handle the complexity of rate limits and API failures. The platform's built-in retry logic ensures that transient errors from the external API don't cause permanent data inconsistencies. Your application then reads the fresh, synced data from your local database, making it incredibly fast and resilient.
With worker.do:
// Enqueue a job to sync data from an external API
// This could be triggered by a scheduler.
await dô.worker.enqueue({
task: 'sync-crm-contacts',
payload: { incremental: true }
});
The Problem: You've integrated a powerful Large Language Model (LLM) to power a new feature. A single AI inference can take anywhere from 2 to 30 seconds. A synchronous API call would lead to a frustratingly slow user interface.
The Background Worker Solution: The user's prompt is sent to your backend, which immediately enqueues a job for an AI worker. Your frontend can then poll for a result or wait for a websocket message. This "agentic workflow" allows you to build sophisticated AI features without freezing the user's screen.
With worker.do:
// Offload a slow AI inference to a background worker
await dô.worker.enqueue({
task: 'run-llm-inference',
payload: { prompt: 'Explain background workers in simple terms.' }
});
As you can see, transitioning from a slow, synchronous architecture to a fast, asynchronous one is a matter of identifying the right tasks and offloading them. With worker.do, you don't need to manage a single server, message queue, or scaling policy.
You simply define your tasks and enqueue jobs with a single API call. Our platform handles the rest:
Stop letting long-running tasks dictate your user experience. Visit worker.do today and turn your complex background processes into simple, manageable APIs.
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.