Split In Batches Node Explained for Large Data

I still remember the first time a “simple” workflow tried to push thousands of records at once and hit API limits, timeouts, and half-completed syncs—since then, I treat batching as a reliability feature, not an optimization.

If you’re implementing Split In Batches Node Explained for Large Data, you’re trying to process a big list (rows, contacts, orders, tickets, files) without crashing your workflow, overwhelming an external API, or creating messy partial results. The Split In Batches node is n8n’s practical way to turn “one huge payload” into controlled chunks you can process safely and predictably.

What the Split In Batches node actually does

Split In Batches takes an incoming list of items and outputs a smaller “batch” at a time. After you process that batch, you loop back to Split In Batches to get the next batch, until there are no items left.

In real automation terms, it helps you:

• Respect API rate limits by sending fewer requests at a time.
• Avoid timeouts and memory spikes caused by processing huge arrays in one pass.
• Make failures easier to recover (you can re-run a batch instead of re-running everything).
• Control throughput so downstream systems (CRMs, databases, helpdesks) don’t get flooded.

Official documentation (reference only): n8n Docs

When batching is the correct solution (and when it’s not)

Batching is correct when your workflow processes many items and each item triggers one or more network calls (HTTP requests, CRM updates, database writes, AI calls). It’s also correct when you see “429 Too Many Requests,” random API failures under load, or execution time creeping up as data grows.

Batching is not a magic fix when the real problem is poor filtering (pulling too much data) or missing pagination (requesting “all records ever” from an API). If your source supports pagination, you should still paginate at the source where possible—then batch the results you do fetch.

How to build the classic “batch loop” workflow in n8n

This is the most reliable pattern for large data:

1. Fetch / build your list (items you want to process).
2. Split In Batches with a sensible batch size.
3. Process the batch (API calls, transforms, writes).
4. Optional safety controls: retries, waits, rate limit handling.
5. Loop back to Split In Batches to request the next batch.
6. Finish cleanly when Split In Batches returns no items.

Batch size is where most people get burned. Too big and you’re back to rate limits/timeouts; too small and you waste execution overhead. A practical starting point is 10–50 items per batch for API-heavy work, and 100–500 for lightweight transforms.

Batch size strategy that holds up in production

Pick a batch size based on the slowest downstream constraint:

• Rate limit constraints: If an API allows ~60 requests/minute, and you make 1 request per item, you’ll want small batches + a Wait node.
• Execution time constraints: If each item takes ~0.5s to process, a batch of 100 is ~50s just for that step—too risky if you have multiple nodes.
• Failure cost: If re-running a batch is painful, reduce batch size so retries are cheaper.

A real weakness of Split In Batches (and the fix)

Weakness: Split In Batches doesn’t automatically “checkpoint” your progress across separate executions. If you stop an execution mid-way and restart from the beginning, you may reprocess items and create duplicates.

Fix: Make your processing idempotent. That means each item should be safe to run twice without creating double records. Practical approaches include:

• Use an external unique key (email, order ID, ticket ID) and “upsert” instead of “create.”
• Store a processed marker in a database table (or a lightweight log store) keyed by item ID.
• Write results with a deterministic ID so duplicates overwrite rather than multiply.

Common patterns for large data

Pattern 1: Rate-limit friendly batching (batch + wait)

If you see 429 errors or random API failures under load, add a small delay between batches. This keeps the workflow stable without guessing the provider’s exact limits.

Batch loop idea:

1) Split In Batches (size: 25)

2) Process items (HTTP/API/DB)

3) Wait (e.g., 500–1500ms)

4) Loop back to Split In Batches

Don’t overdo the delay. Start small (like 500ms) and increase only if you still hit limits. This is faster and more stable than “sleeping” for long intervals.

Pattern 2: Batch + retry on transient failures

Large data runs amplify rare failures. A single flaky request can ruin the whole execution if you don’t handle it. The practical fix is to:

• Detect retriable errors (timeouts, 429, 5xx).
• Retry a few times with a short backoff.
• Log failures with enough context to reprocess only the failed items.

If you implement retry logic with a Code node, keep it small and predictable.

Retry principles (keep them simple):

- Max retries: 2–4

- Backoff: 500ms, then 1500ms, then 3000ms

- Only retry known transient errors (429, 5xx, timeouts)

- Log the item ID + error so you can re-run only failures

Pattern 3: Batch + aggregation (collect results safely)

Sometimes you need to process batches but output one final combined dataset at the end (for a report, a summary, or a single final API call). The risk is memory: storing too much in one execution can bloat runtime.

Safer approach:

• Write per-item results to storage (database, sheet, file) as you go.
• At the end, run a separate workflow to aggregate from storage.

Split In Batches vs other approaches (quick comparison)

Approach	Best for	Main risk	Best practice fix
Split In Batches	Large lists with controlled processing and looping	Reprocessing on rerun if not idempotent	Use upserts / processed markers / deterministic IDs
Single-pass processing	Small datasets where speed matters	Rate limits, timeouts, memory spikes	Switch to batching once volume grows
Pagination at the source	APIs that support page-by-page fetching	Missing records if pagination logic is wrong	Track cursor/page tokens carefully, then batch each page

Mistakes that cause slow workflows or broken runs

• Batch size set by guesswork: start with 25 and adjust based on logs and error rates.
• No “stop condition” thinking: always ensure your loop returns to Split In Batches and ends when no items remain.
• Too many expensive operations per item: if each item triggers multiple API calls, your effective rate multiplies.
• No logging: without item IDs and error details, you’re forced to re-run everything.
• No dedupe strategy: duplicates happen during retries, partial failures, and reruns—plan for it.

Practical tuning checklist before you trust it with real data

• Run with 20–50 items first and confirm outputs are correct.
• Simulate failures (force one request to fail) and confirm the workflow doesn’t corrupt downstream data.
• Check execution logs for how long a batch takes and whether your Wait is enough.
• Confirm your writes are idempotent (rerun the same execution and verify no duplicates appear).
• Increase to 500+ items and validate stability before scaling to thousands.

FAQ: Split In Batches for large data in n8n

What batch size should you use for large datasets?

Start with 25 for API-heavy workflows. If you’re only transforming data locally, start at 200. Then tune based on failures (429/5xx), execution time per batch, and downstream system tolerance.

How do you avoid duplicates when rerunning a failed execution?

Make each write idempotent: use upsert behavior where possible, store a processed marker keyed by item ID, or write with deterministic IDs so reruns overwrite instead of duplicating.

Why does your workflow still hit rate limits even with batching?

Batching controls how many items you handle at once, but it doesn’t automatically slow the workflow down. If each item triggers requests quickly, you can still exceed limits. Add a Wait node between batches and reduce batch size.

Is Split In Batches better than paginating an API?

They solve different problems. Pagination controls how you fetch data from the source; batching controls how you process what you fetched. The most reliable setup is pagination at the source, then Split In Batches for processing each page safely.

How do you process “millions” of items without crashing?

Don’t load everything into one execution. Fetch a page/cursor slice, process it in batches, store progress externally, and continue with the next slice. This avoids memory blowups and makes the run recoverable.

What’s the fastest way to speed up batching safely?

Increase batch size gradually and remove unnecessary per-item calls. If the bottleneck is an external API, speed comes from better efficiency (fewer calls, smarter updates) more than brute force concurrency.

Read also: Loop Over Items in n8n Without Infinite Loops

Conclusion

When data volume grows, reliability becomes the real feature. Split In Batches gives you a clean way to control load, respect rate limits, reduce timeouts, and make failures recoverable. Set a sensible batch size, add a small wait if you call external APIs, and build idempotent writes so reruns never create chaos—and your workflow will stay stable even as your dataset scales.