Batch Processing Jobs

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

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