We’ve all been there. A user clicks "Generate Report," and the entire application seems to hold its breath. A spinning loader becomes the user's new best friend. Behind the scenes, your web server is maxing out its CPU, desperately trying to churn through a massive dataset, process a video upload, or stitch together data from a dozen different APIs.
This is the classic symptom of a synchronous bottleneck. When we ask our primary application to handle heavy, resource-intensive tasks directly within the request-response cycle, we're setting ourselves up for slow performance, frustrated users, and a brittle architecture.
But what if there was a better way? It's time to rethink your architecture. By offloading complex, long-running tasks from your primary application to a dedicated, scalable processing service, you can build faster, more resilient, and more efficient systems.
Keeping heavy lifting inside your main backend service might seem simpler at first, but it creates a cascade of problems:
The solution is to decouple the task initiation from its execution. This "fire-and-forget" model fundamentally changes your application's responsibility. Instead of doing the work itself, your API's only job is to validate the request and hand it off to a specialized service.
This simple architectural shift unlocks immediate benefits:
This is precisely the problem we built processing.services.do to solve. We provide an agentic platform designed to transform complex data pipelines and business workflows into simple, scalable API calls.
Instead of building and maintaining your own distributed queueing and worker system, you can offload any task with a single API call. Here’s how simple it is to run a data enrichment workflow:
import { Do } from "@do-sdk/core";
const processing = new Do("processing.services.do", {
apiKey: "your-api-key",
});
// Define and run a data enrichment workflow
const job = await processing.run({
workflow: "enrich-user-profile",
payload: {
userId: "usr_12345",
sources: ["clearbit", "linkedin", "internal_db"],
},
config: {
priority: "high",
onComplete: "https://myservice.com/webhook/job-done",
},
});
console.log(`Job started with ID: ${job.id}`);
Notice what's happening here. The processing.run() call returns a job.id almost instantly. The actual work happens asynchronously on our massively parallel infrastructure. When the job is finished, our platform sends a notification to the onComplete webhook URL you provided. Your application remains fast and responsive, and your complex task is handled reliably in the background.
processing.services.do is more than just a job queue; it's a full-fledged orchestration engine. You define your business logic as containerized agents. Our platform acts as the orchestrator, invoking your agents with the provided payload and managing the execution state, scalability, and error handling for you.
This agentic workflows model allows you to tackle sophisticated use cases with ease:
Your backend doesn't have to be a monolith that does everything. By embracing an asynchronous model, you can build applications that are faster, more reliable, and scale more intelligently. Offloading heavy tasks is no longer a complex infrastructure project—it's a simple API call.
Stop letting resource-intensive tasks block your event loop and dictate your architecture. Let your API be fast and focused, and let a dedicated service handle the heavy lifting.
Ready to transform your backend? Discover scalable data and workflow processing with processing.services.do.
What kind of processing can I perform with processing.services.do?
You can run virtually any custom logic. Common use cases include data transformation (ETL), batch processing, image/video rendering, financial calculations, and orchestrating sequences of microservice calls. If you can code it, we can process it.
How do I define my processing logic?
You define your business logic as containerized agents. processing.services.do acts as the orchestrator, invoking your agents with the provided payload and managing the execution state, scalability, and error handling for you.
Is the processing service scalable?
Yes. Our platform is engineered for high-throughput, parallel processing. It automatically scales compute resources based on your workload, ensuring your jobs are completed efficiently, whether you're running one task or a million.
Can I run long-running tasks?
Absolutely. The platform is designed for both synchronous (quick) and asynchronous (long-running) jobs. For long jobs, you can provide a webhook URL to be notified upon completion, allowing you to build robust, event-driven systems.