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