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batch-processing-jobslisted

Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.
planifest/planifest-framework · ★ 0 · AI & Automation · score 70
Install: claude install-skill planifest/planifest-framework
# Batch Processing Jobs ## Table of Contents - [Overview](#overview) - [When to Use](#when-to-use) - [Quick Start](#quick-start) - [Reference Guides](#reference-guides) - [Best Practices](#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: ```typescript 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)](references/bull-queue-nodejs.md) | Bull Queue (Node.js) | | [Celery-Style Worker (Python)](references/celery-style-worker-python.md) | Celery-Style Worker (Py