Loops, Splits &
Batch Processing

1The SplitInBatches Node

When you need to process a large list of items — think 1,000 contacts, 500 orders, or 10,000 spreadsheet rows — sending them all at once is dangerous. The SplitInBatches node divides your data into fixed-size chunks and loops through each batch automatically, giving you fine control over throughput.

2How N8N Naturally Iterates

Here is something powerful about N8N that beginners often miss: every node automatically processes all items in sequence. If a previous node outputs 10 items, the next node runs 10 times — once per item. No loop node required for basic use cases.

This means for most workflows, you do not need any loop node at all. N8N's execution engine handles iteration automatically. You only need SplitInBatches when you want to control the chunk size for rate limiting, add delays between batches, or manage server memory on very large datasets.

3Rate Limiting — Why Batching Matters

Beyond rate limits, batching also protects your N8N server's memory. Processing 50,000 spreadsheet rows all at once can crash a low-memory instance. Batching keeps memory usage flat and predictable across long-running workflows.

4N8N Loop Expressions in Code

When using the Code node inside a SplitInBatches loop, you can access batch context information using N8N's built-in node variables. Here is how to work with batch data programmatically and add metadata to processed items:

// Get current batch info from SplitInBatches context
const batchIndex = $node["SplitInBatches"].context.currentRunIndex;
const totalItems = items.length;

// Log progress for debugging in execution log
console.log(`Processing batch #${batchIndex + 1} with ${totalItems} items`);

// Process each item — spread keeps all original fields
return items.map(item => ({
  json: {
    ...item.json,              // keep all original fields
    processed: true,           // add processed flag
    batchNumber: batchIndex,   // track which batch
    processedAt: new Date().toISOString()
  }
}));

5Loop Approaches Compared

N8N gives you three distinct ways to loop over data. Choosing the right approach depends on your data volume, API rate limits, and whether you need in-memory transformations or external API calls.

6Batch Processing by the Numbers