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Split List divides a large list into smaller, manageable batches. Perfect for processing large datasets in chunks to avoid timeouts, API limits, or overwhelming systems.

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When to Use It

  • Process large campaign lists in smaller batches
  • Break down hundreds of URLs into manageable groups
  • Split client lists to avoid API rate limits
  • Create controlled processing for large datasets

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Inputs

FieldTypeRequiredDescription
List to SplitListYesThe large list you want to break into batches
Batch SizeNumberYesNumber of items per batch (1-100)

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Outputs

OutputDescription
BatchesList of smaller lists, each containing the specified number of items

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Credit Cost

Free to use - no credits required.

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How It Works

Takes your large list and creates multiple smaller lists based on your batch size: Example: 
Input List: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Batch Size: 3
Output Batches: [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]]
The last batch may contain fewer items if the total doesn’t divide evenly.

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Real-World Examples

Large Campaign Processing: 
Google Ads Get Report (500 campaigns) → Split List (batch size: 50) → Loop Over List
"Process 500 campaigns in 10 batches of 50 each"
URL Batch Processing: 
Extract URLs from Sitemap → Split List (batch size: 25) → Loop Over List → Web Scrape
"Scrape 200 URLs in batches of 25 to avoid timeouts"
Email Campaign Batches: 
Sheets Read Data (client emails) → Split List (batch size: 10) → Loop Over List → Send Email
"Send personalized emails in batches of 10 to manage sending limits"
API Rate Limit Management: 
Generate List (1000 keywords) → Split List (batch size: 100) → Loop Over List → Add delay between batches
"Process 1000 keywords while respecting API limits"

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Batch Processing Strategy

With Loop Over List:
  1. Split your large list into batches
  2. Connect Split List to Loop Over List
  3. Inside the loop, process each batch as a complete unit
  4. Each iteration handles one batch (multiple items)
Example Workflow: 
Large URL List (200 items)
↓
Split List (batch size: 20)
↓
Loop Over List (10 iterations)
↓
Each loop processes 20 URLs together

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Tips

Choosing Batch Size:
  • Start with smaller batches (10-25) for testing
  • Increase based on system performance and API limits
  • Consider processing time and memory usage
API Limits:
  • Check rate limits for your data sources
  • Batch size should stay well under hourly/daily limits
  • Add delays between batches if needed
Error Handling:
  • If one batch fails, other batches can still process
  • Smaller batches make debugging easier
  • Consider retry logic for failed batches
Performance Balance:
  • Larger batches = fewer loop iterations but more data per iteration
  • Smaller batches = more control but more overhead
  • Test to find the optimal size for your use case
Memory Management:
  • Large batches use more memory
  • Important for data-heavy operations like image processing
  • Monitor workflow performance with different batch sizes

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FAQ

The last batch will contain the remaining items. For example, 10 items with batch size 3 creates batches of [3, 3, 3, 1]. This is normal and expected behavior.
Connect Split List to Loop Over List. Each loop iteration will process one complete batch (not individual items). Inside the loop, you can work with the entire batch at once.
Start with 10-25 items and test performance. Increase for faster processing, decrease if you hit memory limits or API restrictions. The optimal size depends on your specific workflow complexity.
Yes, but if your list is smaller than the batch size, you’ll get one batch containing all items. For example, 5 items with batch size 10 creates one batch of 5 items.
Loop Over List processes items one by one. Split List + Loop Over List processes items in groups. Use splitting when you need to handle multiple items together in each iteration.
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