Looping Collection Processor

Visual Flow

Rendering diagram…

When to Use This Pattern

Use a looping processor when:

• You have a small to medium collection (1–100 items) to process
• Items must be processed sequentially (order matters, or each depends on the previous result)
• The per-item processing is simple (a few steps, not heavy API calls)
• You don't need massive parallelism — simplicity beats speed for this use case

For larger collections or items that can be processed independently, see Fan-Out / Fan-In or Scheduled Batch Processor.

How It Works

items = get_collection()      // [item1, item2, item3, ...]
results = []
errors = []

FOR EACH item IN items:
    try:
        result = process(item)
        results.append(result)
    catch error:
        errors.append({ item, error })
        IF error.is_fatal:
            BREAK                 // Stop on critical error
        ELSE:
            CONTINUE              // Skip and move to next

generate_summary(results, errors)

Implementation Guide

Tips & Best Practices

• Process in batches. If calling an external API, batch API calls (e.g., send 10 records per request) instead of making one call per item. This is faster and gentler on the API.
• Preserve loop index. Track which item number you're on (index) in a variable. If the workflow fails, you know exactly which item caused the issue.
• Update progress. For long loops, update a status field periodically ("Processing item 35 of 47") so users can monitor progress.
• Test with edge cases. Empty collection (0 items), single item, and a collection with duplicate entries.