node cookbook
N8N Split In Batches Node Cookbook
Use Split In Batches, now documented as Loop Over Items, when a workflow needs to process many items in controlled batches instead of all at once.
Independent third-party notes. n8n is a trademark of its owner and is referenced only for compatibility and troubleshooting context.
Quick Answer
Use Split In Batches, now documented as Loop Over Items, when a workflow needs to process many items in controlled batches instead of all at once.
Key Facts
- Node role
- Loop through items in smaller batches.
- Best fit
- Rate-limited APIs, large lists, and workflows that should avoid processing every item at once.
- Common benefit
- Lower memory pressure and more controlled external API calls.
- Common companion nodes
- HTTP Request, Code, database nodes, IF, and Wait.
Recommended Steps
- Place the loop node after the node that outputs the item list.
- Choose a batch size appropriate for the external API or workflow load.
- Put the processing nodes inside the loop path.
- Make sure the loop continues until all items are processed.
- Test with a small list before running a full production batch.
Verification
- All expected items are processed exactly once.
- API rate limit errors decrease or disappear.
- Workflow memory usage is lower than processing the full list at once.
Warnings
- Batch processing can still create duplicate side effects if retries are not idempotent.
- A very large batch size may not reduce memory or API pressure enough.
Common Mistakes
- Setting batch size too high and still hitting memory or API limits.
- Forgetting idempotency before retrying a partially completed batch.
- Not testing the final partial batch.
- Building a loop that never reaches completion.
Examples
Input: 1,000 leads
Batch size: 25
Inside loop: HTTP Request -> Set -> database update
After loop: Slack summaryFAQ
Does batching make a workflow faster?
Not always. It usually makes the workflow more controlled and reliable, especially with large data or rate-limited APIs.
What batch size should I use?
Start small enough to avoid API and memory pressure, then increase after measuring execution time and failure rate.