Batch Processing in n8n: Process 1000+ Records
Scale your n8n workflows to handle massive datasets efficiently. Loop nodes, rate limiting, points optimization, and performance best practices for AppHighway APIs.
TL;DR
- Use Loop nodes to process large datasets in batches
- Add Wait nodes between batches to respect rate limits
- Monitor points consumption with IF nodes to avoid running out
- Optimize batch size: 10-50 records per batch depending on API
- Use SplitInBatches node for automatic chunking
- Save progress incrementally to avoid data loss on failures
The Batch Processing Challenge
Processing 1000+ records in n8n requires careful planning. Rate limits, points consumption, and memory management become critical. This guide shows you how to build scalable batch workflows.
Batch Processing Strategies
1. SplitInBatches Node (Recommended)
Automatically chunks data into batches
Batch Size: 10-50 records per batch
✓ Built-in progress tracking and error recovery
2. Loop Node with Counter
Manual loop control for complex logic
When you need custom batch logic or conditional processing
3. Scheduled Batches
Process batches over time with Schedule Trigger
Spread 10,000 records over 24 hours to minimize costs
Next Steps
Master large-scale automation
Download Batch Templates
Get pre-configured batch processing workflows for common use cases.
Combine with Error Handling
Learn how to add robust error handling to batch workflows.
Scale with Confidence
Batch processing in n8n with AppHighway APIs lets you automate massive data operations. With proper batching, rate limiting, and points optimization, you can process thousands of records reliably.
Ready to scale? Download our batch processing templates and process your first 1000 records today.