From One to a Million: Scaling Batch Processing Jobs Effortlessly

Batch processing is a classic developer tale. What starts as a simple script to process a handful of records—enriching user profiles, resizing images, or running financial models—works beautifully. But as your application grows, that handful becomes a thousand, then a hundred thousand, then a million. Suddenly, that "simple script" is a source of constant headaches, timeouts, and frantic infrastructure management.

The challenges are predictable: How do you parallelize the work? How do you handle failures and retries gracefully? How do you provision enough compute power without breaking the bank? And how do you do all of this without building a whole new distributed systems team?

The truth is, you shouldn't have to. The business logic—the what—is your value. The complex, brittle infrastructure required to execute it at scale—the how—is undifferentiated heavy lifting. It's time for a better way.

The Scaling Cliff: Why Batch Jobs Break

When you move from small-scale processing to high-throughput workloads, you hit a scaling cliff. The manual approach that worked for 100 jobs collapses under the weight of 1,000,000. Here’s why:

• Infrastructure Nightmare: You're suddenly in the business of managing server fleets, configuring auto-scaling groups, wrestling with container orchestrators like Kubernetes, and trying to optimize costs with spot instances. Your focus shifts from your product to your infrastructure.
• Concurrency Chaos: Running jobs one by one is too slow. Running them all at once can overwhelm your databases and downstream services. Building a robust queueing system with proper concurrency controls, backpressure, and rate-limiting is a massive engineering project in itself.
• Error Handling & Retries: In a distributed system, failures are a guarantee, not an exception. A network blip, a temporary API outage, or a malformed record can bring your entire batch to a halt. Implementing idempotent retries with exponential backoff is critical but complex.
• Observability Black Hole: When a job fails among a million others, how do you find it? Aggregating logs, tracking metrics, and tracing execution across countless ephemeral workers is a significant challenge. Without it, you're flying blind.

A Smarter Approach: Intelligent Processing, On-Demand

Instead of building a complex processing platform from scratch, what if you could just call an API?

This is the core idea behind processing.services.do. We believe you should focus on defining your business logic as code. Our platform acts as the intelligent orchestrator, handling the scalability, reliability, and state management for you. We call this model agentic workflows. You create self-contained, containerized "agents" that encapsulate a piece of business logic. Then, you use a simple API to tell us to run that agent with your data.

Our platform manages the rest:

• Instant Scaling: We automatically scale compute resources to match your workload, whether you submit one job or a million.
• Built-in Resilience: We manage state, handle retries, and provide the plumbing for robust error handling.
• Asynchronous by Design: Long-running tasks are a first-class citizen. Fire off a job and get notified via webhook upon completion.

Putting it into Practice

Let's see just how simple it is to offload a complex task. Imagine you have a workflow named enrich-user-profile that takes a userId and enriches it with data from multiple sources.

With processing.services.do, you don't need to write the orchestration code. You just run the job:

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}`);

Let's break that down:

1. workflow: 'enrich-user-profile': You're invoking your pre-defined logic, not just a script.
2. payload: {...}: This is the unique data for this specific task.
3. config: { onComplete: '...' }: This is where the magic happens for asynchronous work. You're telling the platform, "Run this job, which might take a while, and ping my service at this webhook URL when you're done." This enables powerful, event-driven architectures without the complexity.

From One Job to One Million

Here's the best part. The code to run one million jobs looks almost identical to the code for running one. You simply loop through your dataset and tell the platform to run a job for each item.

// Pseudocode for running a massive batch job
for (const user of allUsers) {
  // No need to manage queues, concurrency, or workers. Just fire and forget.
  await processing.run({
    workflow: 'enrich-user-profile',
    payload: { userId: user.id, sources: ['...'] },
    config: { onComplete: '...' }
  });
}

You submit the jobs, and processing.services.do takes care of the rest. The platform queues the work, spins up the required number of workers in parallel, manages failures, and ensures every job is executed efficiently. You've just scaled from one to a million without changing a single line of infrastructure code.

Frequently Asked Questions

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.

Stop Building Infrastructure, Start Delivering Value

Your team's time is best spent building features that delight your customers, not wrestling with YAML files and auto-scaling policies. By abstracting away the complexity of distributed systems, processing.services.do lets you focus on your unique business logic.

Ready to stop wrestling with infrastructure and start scaling effortlessly? Explore processing.services.do today and transform your biggest data challenges into simple API calls.