Feature Showcase
Complete feature overview and capabilities matrix.
Core Iteration Features
- Built-in Batching: Group items into batches for efficient processing
- Parallel Execution: Process multiple items concurrently
- Sequential Processing: Process items one at a time (default)
- Rate Limiting: Control speed with batch wait times
- Item Filtering: Filter items before iteration starts
- Item Sorting: Sort items by any expression before iteration
- Staggered Startup: Delay iteration start for load distribution
Error Handling Features
- Break on First Error: Stop processing immediately on failure
- Continue on Error: Skip failed items and continue
- Automatic Retries: Retry failed items up to N times
- Exponential Backoff: Automatic delay between retries
- Per-Item Timeout: Prevent hanging operations (configurable)
- Error Reporting: Control which error is returned to caller
- Empty Array Handling: Skip silently or error on empty input
Result Management Features
- Independent Results: Each item's result flows downstream separately
- Aggregated Results: Collect all results into single array
- Last Result Only: Return only the final iteration's result
- Partial Success: Collect both successful and failed results
- Result Filtering: Apply filter expressions to reduce dataset
Performance Features
- Unlimited Scalability: Process any number of items (memory dependent)
- Memory Efficient: Process in batches to manage memory usage
- I/O Parallelization: 10-100× speedup for network/I/O operations
- CPU Optimization: Sequential processing for CPU-bound tasks
- Timeout Protection: Prevent resource exhaustion from hanging operations
- Batch Size Control: Fine-tune batching for your workload
- Execution Limits: Set min/max item counts for safety
Variable Access Features
- Current Item: Access @{input:item} and nested properties
- Iteration Index: Know which item you're processing (@{input:current_iteration_index})
- Static Input: Access top-level input properties in all iterations
- Nested Paths: Access deeply nested properties with pipe notation
- Variable Scope: Predictable scope (node-level, not workflow-level)
Supported Node Types
| Node Type | Purpose | Parallel | Typical Use |
|---|---|---|---|
| SMTP/EmailSmtp | Send emails | ✓ | Bulk email campaigns |
| HTTP Request | API calls | ✓ | Parallel API requests |
| DataMapping | Transform data | ✓ | Transform batches |
| Database | Query/Update | ✗ | Bulk inserts/updates |
| Custom | Any logic | ✓ | Custom processing |
Universal vs. Loop Node
Universal Item Iteration
| Feature |
|---|
| ✓ Single-node ops |
| ✗ Multi-node workflows |
| ✓ Fast setup (2 min) |
| ✓ Advanced batching |
| ✓ Parallel execution |
| ✗ Conditional branches |
| ✗ Full scope isolation |
| ✓ Simpler configs |
Loop Node
| Feature |
|---|
| ✓ Single-node ops |
| ✓ Multi-node workflows |
| ✗ Slower setup (10 min) |
| ✓ Basic batching |
| ✓ Parallel execution |
| ✓ Conditional branches |
| ✓ Full scope isolation |
| ✗ More complex |
Rule of thumb: Use Universal Iteration for single-node bulk operations. Use Loop Node for multi-step workflows per item.
Performance Characteristics
| Strategy | Configuration | Speedup (vs. Sequential) | Best For |
|---|---|---|---|
| Sequential | batchSize: null | 1× (baseline) | CPU-bound work, order matters |
| Batch Sequential | batchSize: 100, parallel: false | ~1× (same as seq.) | Database batching |
| Batch Parallel | batchSize: 10, parallel: true | 10× (I/O bound) | API calls, HTTP requests |
| Large Parallel | batchSize: 50, parallel: true | 50× (I/O bound) | Email, webhooks, downloads |
Key Advantages
Simple configuration, one property per feature, minimal setup time.
Built-in batching and parallelization for 10-100× speedup.
Automatic retries, error handling, and timeout protection.
Filter, sort, rate limit, and customize every aspect.
Limitations
Can't have multiple steps per item (use Loop Node instead).
All items follow same path (no if/then logic per item).
Scope is node-level, not iteration-level (variables don't isolate per item).
Only works with nodes that have SupportsItemIteration enabled.
Common Use Cases
Bulk Email
Send thousands of personalized emails to customers
Parallel APIs
Fetch data for multiple items concurrently
Batch Database
Insert/update many records efficiently
Data Enrichment
Enrich datasets from external sources
Webhooks
Send webhooks with reliability and rate limiting
Image Processing
Resize/transform images in parallel
Payments
Charge customers with automatic retries
Data Migration
Move data between systems with batching
Real-World Performance Examples
| Scenario | Items | Sequential Time | With Universal Iteration | Speedup |
|---|---|---|---|---|
| Bulk Email | 10,000 | ~3 hours | ~6 minutes | 30× |
| API Calls | 1,000 | ~16 minutes | ~20 seconds | 50× |
| DB Inserts | 100,000 | ~40 minutes (seq.) | ~8 minutes (batch 500) | 5× |
| Image Resize | 1,000 | ~17 minutes | ~2 minutes (8-worker) | 8× |