Batch Processing Jobs
Table of Contents
Overview
Implement scalable batch processing systems for handling large-scale data processing, scheduled tasks, and async operations efficiently.
When to Use
- Processing large datasets
- Scheduled report generation
- Email/notification campaigns
- Data imports and exports
- Image/video processing
- ETL pipelines
- Cleanup and maintenance tasks
- Long-running computations
- Bulk data updates
Quick Start
Minimal working example:
import Queue from "bull";
import { v4 as uuidv4 } from "uuid";
interface JobData {
id: string;
type: string;
payload: any;
userId?: string;
metadata?: Record<string, any>;
}
interface JobResult {
success: boolean;
data?: any;
error?: string;
processedAt: number;
duration: number;
}
class BatchProcessor {
private queue: Queue.Queue<JobData>;
private resultQueue: Queue.Queue<JobResult>;
constructor(redisUrl: string) {
// Main processing queue
// ... (see reference guides for full implementation)Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Bull Queue (Node.js) | Bull Queue (Node.js) |
| Celery-Style Worker (Python) | Celery-Style Worker (Python) |
| Cron Job Scheduler | Cron Job Scheduler |
Best Practices
✅ DO
- Implement idempotency for all jobs
- Use job queues for distributed processing
- Monitor job success/failure rates
- Implement retry logic with exponential backoff
- Set appropriate timeouts
- Log job execution details
- Use dead letter queues for failed jobs
- Implement job priority levels
- Batch similar operations together
- Use connection pooling
- Implement graceful shutdown
- Monitor queue depth and processing time
❌ DON'T
- Process jobs synchronously in request handlers
- Ignore failed jobs
- Set unlimited retries
- Skip monitoring and alerting
- Process jobs without timeouts
- Store large payloads in queue
- Forget to clean up completed jobs