Hey n8n community! I’m used to working with Make and I’m moving some scenarios from n8n for testing purposes and I can’t seem to understand the difference between Make’s Iterate module and n8n’s Loop Over Items and Split Out.
In particular, I had problems when using the Airtable’s Search operation to fetch records from my database.
In Make they would be fetched as single ‘bundles’ of data, but with n8n - when trying the execute the workflow the same way - only the first ‘bundle’ is getting processed.
In order to process all the records, I found and tested two nodes: Loop Over Items and Split Out. In this case, they produced a similar result - they allowed me to process all data fetched.
However, I have troubles understanding types of situations where I should choose one node over another. The documentation is not clear enough to me in this regard.
Could you share your tips on deciding when to use Loop Over Items and when Split Out?
Information on your n8n setup
n8n version: 1.37.3
Database (default: SQLite): SQLite
n8n EXECUTIONS_PROCESS setting (default: own, main): own
Running n8n via (Docker, npm, n8n cloud, desktop app): docker
Thanks for posting here and welcome to the community!
So, the Loop Over Items Node basically performs a process on each item and is best when you want to process items in a straightforward manner, keeping them within the same workflow context. It’s like dealing with each item one by one but under a single roof. Good for simple operations like adding a field or minor calculations. For example: if you fetch a list of records from Airtable and want to add a timestamp to each record or perform a simple calculation.
The Split Out node essentially converts an array into items, so they can be used individually. You can use this when your tasks are more complex or need to be isolated from each other—think multiple, independent API calls or varied processing for each item. It’s like giving each item its own stage to perform separately. For example: If each record from Airtable requires a series of API calls, complex data transformations, or very individual conditional processing per item.
