How to handle large JSON in n8n?

The simplest reliable way to handle large JSON in n8n is to avoid loading the entire JSON into a single node’s memory. Instead, you stream it, chunk it, paginate it, or store it outside n8n (like S3, a database, or a temporary file) and only pass around references or small slices of the data. n8n can process large data, but it cannot hold huge JSON blobs (tens or hundreds of MB) inside one execution because the whole execution state sits in RAM.

 

Why this matters

 

n8n keeps each node’s output as JSON in memory during the workflow execution. If you inject a massive JSON object (for example, a 50 MB API response or a 200k-row array), you can hit:

• Memory limits (container or server crashes)
• Execution slowdowns
• Timeouts on Webhook triggers or HTTP Request nodes
• UI freezes because the Editor tries to render huge JSON

So the trick is: don’t let the workflow carry huge data directly. Keep each step light.

 

Common production strategies

 

Below are the practical, battle-tested ways teams handle large JSON in real n8n deployments.

• Use pagination whenever the API supports it. Instead of fetching a 100k-record dataset in one go, fetch 1,000 at a time, process them, and iterate. This reduces memory usage dramatically.
• Chunk large arrays using the Split In Batches node. This node lets you process only part of the data at a time. You decide the batch size (for example, 500 items). After a batch is processed, the node moves to the next batch, without keeping all processed items in RAM.
• Offload the raw JSON to storage. Instead of keeping megabytes of JSON inside the workflow, store the file in:
  • S3 / MinIO
  • Google Cloud Storage
  • A database
  • A temporary file on a filesystem (File node)
  Then your workflow only passes a URL, file ID, or pointer — not the huge JSON itself.
• Disable "Include Binary Data" when not needed. Binary objects increase memory size dramatically. Keep them out of the main execution when possible.
• Avoid huge Function nodes. If your JavaScript block parses a 100 MB JSON, n8n must load the whole thing. Instead, process smaller slices, or let a backend service handle heavy transformations.
• If using Webhook triggers, return early. Don’t let a webhook request wait while you process massive payloads. Immediately respond 200 OK, hand off the large-data processing to a separate workflow using a Webhook call, MQ, or a Wait trigger.

 

Concrete patterns that actually work

 

This is an example of handling a large JSON array using Split In Batches:

 

// Function node before Split In Batches
// Suppose you already fetched a large list from an API.
// Store it in items[0].json.bigList
return items[0].json.bigList.map(entry => { return { json: entry }; });

Then connect it to Split In Batches:

 

// Inside Split In Batches, you configure batch size = 500
// No code here, Split In Batches handles iteration automatically.

Then each downstream node only receives 500 items at a time, not the entire dataset.

 

When to NOT use n8n for large JSON

 

n8n is an orchestration tool. It’s not a data-processing engine. If you're dealing with:

• 100 MB+ JSON files
• High-frequency events with large payloads
• Heavy transformations (joins, large aggregations, ML processing)

You're better off processing data in a microservice, Lambda, Cloud Function, or a database job — then let n8n orchestrate, trigger, and react to results.

 

Summary you can apply immediately

 

To handle large JSON safely in n8n, reduce the amount of data flowing through the workflow at any moment. Use pagination or Split In Batches, offload large blobs to storage, and only process chunks. n8n works beautifully with slices — but will struggle with giant single payloads.