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:
typescriptimport 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
