Universal Item Iteration: Use Cases

1. E-Commerce: Order Confirmation Emails

Scenario: Send personalized order confirmation emails to 500 customers daily at scheduled time.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "orders", "recipients": "@{input:item|customer|email}", "subject": "Order Confirmation: @{input:item|order_id}", "body": "Your order total: $@{input:item|total}", "breakOnFirstError": false, "retryFailedItems": 1 }

Sample Data

order_id	customer.email	total
ORD-2026-001	alice@example.com	$149.99
ORD-2026-002	bob@example.com	$299.50
ORD-2026-003	charlie@example.com	$75.00

Expected Timeline

500 orders, sequential processing @ 1 second each = ~8 minutes total

Key Properties:

breakOnFirstError: false → Continue even if one email fails
retryFailedItems: 1 → One retry for transient SMTP issues
No batching → Sequential emails maintain order

2. HR: Bulk Employee Record Update

Scenario: Update 1,000 inactive employee records in bulk, marking last review date.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "employees", "filterExpression": "@{input:item|status}=='inactive'", "batchSize": 100, "parallelExecution": false, "breakOnFirstError": false, "aggregateResults": true }

Execution Breakdown

Input: 1,000 employees total
After filter: 250 inactive employees
Batch size: 100
Batches: 3 batches

Batch 1: Items 1-100 (5s)
Batch 2: Items 101-200 (5s)
Batch 3: Items 201-250 (2s)
Total: ~12 seconds

Design Pattern:

filterExpression → Only process inactive employees (1000 → 250)
batchSize: 100 → Database efficiency
parallelExecution: false → Sequential batches respect DB connections
aggregateResults: true → Single array output for report

3. Finance: Parallel Credit Card Validation

Scenario: Validate 500 credit cards in parallel before charging for subscription renewals.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "card_tokens", "url": "https://payments.example.com/validate", "method": "POST", "body": "{ \"token\": \"@{input:item|token}\" }", "batchSize": 50, "parallelExecution": true, "timeoutPerIteration": 5000, "retryFailedItems": 2 }

Performance Gain

Sequential:
500 cards × 1s = 500 seconds (8+ minutes)

Parallel (50 concurrent):
500 ÷ 50 = 10 batches × 1s = 10 seconds

Expected Results

Out of 500: ~485 valid (97%), ~15 invalid (3%). Retries recover ~2 more. Final: 487 valid.

Rate Limiting:

Payment API allows ~100 concurrent → batchSize: 50 is safe
timeoutPerIteration: 5000 → 5-second limit per validation
retryFailedItems: 2 → Recover from transient network issues

4. CRM: Data Enrichment from External API

Scenario: Fetch company info from external service for 10,000 leads, rate-limited to 100 req/sec.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "leads", "url": "https://api.clearbit.com/v1/companies/find?domain=@{input:item|company_domain}", "batchSize": 10, "parallelExecution": true, "batchWaitTimeMs": 100, "maxAllowedCount": 1000, "filterExpression": "@{input:item|company_domain} != null" }

Rate Limit Handling

Batch	Items	Concurrency	Time	Rate
1	1-10	10 parallel	1s	10 req/s
			wait 100ms
2	11-20	10 parallel	1s	10 req/s
			wait 100ms
...	...	...	...	Effective: 100 req/s ✓

Expected Results

10,000 leads: 1,000 processed (maxAllowedCount), ~950 have domain data, ~950 enriched = 95% success

Key Strategy:

batchSize: 10, parallel: true → 10 concurrent requests
batchWaitTimeMs: 100 → 100ms wait between batches = 100 req/sec sustained
maxAllowedCount: 1000 → Safety limit (API costs money)
filterExpression → Skip leads without company domain

5. Reporting: Process Transaction Logs

Scenario: Transform and aggregate 100,000 transaction logs for daily report.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "transactions", "filterExpression": "@{input:item|status}=='completed'", "sortByExpression": "@{input:item|amount}", "reverseOrder": true, "batchSize": 1000, "aggregateResults": true }

Processing Steps

Input: 100,000 transactions
Filter: Only completed (95,000)
Sort: By amount descending
Process: 1,000 per batch

Batches: 95
Time: ~95 seconds
Output: Single array
Use: Dashboard, reports

Optimization:

Filter + Sort pre-processing → Reduce dataset before iteration
Large batchSize: 1000 → Process many at once (memory efficient)
Reverse order: true → Highest amounts processed first
aggregateResults: true → Single report-ready array

6. Marketing: Segment Customers and Send Campaigns

Scenario: Filter by engagement score, segment into tiers, send targeted campaign emails.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "customers", "filterExpression": "@{input:item|engagement_score} >= 50", "sortByExpression": "@{input:item|engagement_score}", "reverseOrder": true, "recipients": "@{input:item|email}", "subject": "@{input:item|engagement_score} > 80 ? 'VIP Offer' : 'Exclusive Deal'", "batchSize": 100, "parallelExecution": true }

Data Filtering Example

Segment	Score Range	Count	Message
VIP	80-100	500	VIP Exclusive Offer
Gold	60-79	2,500	Gold Member Deal
Silver	50-59	1,000	Member Exclusive
Bronze	<50	6,000	Not sent (filtered out)

Expected Timeline

4,000 emails (500 + 2,500 + 1,000), 100 parallel batches × 1s = ~40 seconds

Segmentation Strategy:

filterExpression → 4,000 of 10,000 customers (40%) qualify
sortByExpression → Process highest engagement first
reverseOrder → VIP customers processed first
parallelExecution → Speed up email sending

7. Support: Process Support Tickets in Priority Order

Scenario: Process 500 support tickets, highest priority first, assign to agents.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "tickets", "sortByExpression": "@{input:item|priority}", "reverseOrder": true, "body": "Assign ticket @{input:item|id} (priority @{input:item|priority}) to next available agent" }

Processing Order Example

Input order: [P2, P5, P1, P3, P4, P1, P2]

After sort (descending): [P5, P4, P3, P2, P2, P1, P1]

Processing: Highest priority (P5) → ... → Lowest priority (P1)

Why Sort Tickets?

Critical issues (P5) resolved first → Improves customer satisfaction
Fair assignment → High-priority get top agents
SLA compliance → Urgent tickets don't wait in queue

8. Data Migration: Move Data Between Systems

Scenario: Migrate 50,000 customer records from old CRM to new system with batching.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "old_crm_records", "batchSize": 500, "parallelExecution": false, "batchWaitTimeMs": 500, "breakOnFirstError": true, "retryFailedItems": 3, "maxAllowedCount": 50000 }

Migration Timeline

50,000 records, 500 per batch = 100 batches, 5 seconds per batch + 500ms wait = ~550 seconds (~9 minutes)

Safety Measures

Reliability:

Batch size: 500
Sequential (no parallel)
Retries: 3

Control:

breakOnFirstError: true
batchWaitTimeMs: 500
maxAllowedCount: 50000

Migration Strategy:

Conservative batching → Database connection management
Fail fast → Stop if data corruption detected
Retries → Handle transient network/database issues
Waits → Let target system keep up

9. Analytics: Event Processing with Aggregation

Scenario: Process 100,000 user events, aggregate by event type for dashboard.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "events", "batchSize": 5000, "parallelExecution": true, "aggregateResults": true, "returnLastResultOnly": false }

Event Distribution Example

Event Type	Count	Percentage
page_view	60,000	60%
click	25,000	25%
form_submit	10,000	10%
scroll	5,000	5%

Output

Aggregated array: 100,000 processed events ready for dashboard ingestion

Why Parallelization?

Event processing is CPU-bound → Can parallelize within limits
batchSize: 5000 → Large batches for efficiency
aggregateResults: true → Single output for downstream processing

10. Image Processing: Resize Images in Parallel

Scenario: Resize 1,000 images in parallel using 8 concurrent workers.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "images", "batchSize": 8, "parallelExecution": true, "timeoutPerIteration": 10000 }

Performance Comparison

Strategy	Workers	Time per Image	Total Time
Sequential	1	1 second	1,000 seconds (16+ min)
Parallel (8)	8	1 second	~125 seconds (2 min)
Speedup	8× faster

CPU-Bound Optimization:

batchSize: 8 → Matches typical CPU core count (8-core system)
parallelExecution: true → Use all cores
timeoutPerIteration: 10000 → Prevent stuck resizes

11. Payment Processing: Charge Customers with Error Handling

Scenario: Charge 100 customers monthly; retry failures, report results.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "customers", "url": "https://payments.example.com/charge", "method": "POST", "breakOnFirstError": false, "retryFailedItems": 3, "timeoutPerIteration": 30000, "aggregateResults": true }

Expected Results

Status	Initial Attempt	After Retries	Final
Success	94 (94%)	+4 from retry	98 (98%)
Failed	6 (6%)	-4 recovered	2 (2%)
Success Rate: 98%

Retry Outcomes

Failures (6 total):

Insufficient funds: 2
Card expired: 1
Network timeout: 3

After Retries (3 each):

Network timeout → Success: 3
Insufficient funds → Still fail: 2
Card expired → Still fail: 1

Payment Strategy:

breakOnFirstError: false → Process all customers even if some fail
retryFailedItems: 3 → Recover from transient network issues (not permanent failures)
timeoutPerIteration: 30000 → 30-second limit for payment processing
aggregateResults: true → Single report of all results

12. Webhook Delivery: Send Webhooks to External Services

Scenario: Deliver webhooks to 10,000 external services with rate limiting.

Configuration

{ "itemsIterationEnabled": true, "itemsIterationPropertyName": "webhook_subscriptions", "url": "@{input:item|webhook_url}", "method": "POST", "body": "{ \"event\": \"order.created\", \"orderId\": \"@{input:item|order_id}\" }", "batchSize": 5, "parallelExecution": true, "batchWaitTimeMs": 2000, "retryFailedItems": 5, "timeoutPerIteration": 10000 }

Delivery Timeline

10,000 webhooks, 5 concurrent, 2-second wait between batches:

10,000 ÷ 5 = 2,000 batches × 2 seconds = ~4,000 seconds (~67 minutes)

Reliability

Attempt	Success Rate	Cumulative Success
Initial (1st attempt)	95%	95%
Retry 1	2%	97%
Retry 2	1%	98%
Retry 3-5	1%	99%
Final Success Rate: 99%

Webhook Delivery Strategy:

batchSize: 5, parallel: true → Moderate concurrency
batchWaitTimeMs: 2000 → Backoff to handle slow subscribers
retryFailedItems: 5 → Aggressive retry for delivery reliability
timeoutPerIteration: 10000 → 10-second per-webhook limit

← Back to Overview | Best Practices →

Universal Item Iteration: Real-World Use Cases

1. E-Commerce: Order Confirmation Emails

Configuration

Sample Data

Expected Timeline

2. HR: Bulk Employee Record Update

Configuration

Execution Breakdown

3. Finance: Parallel Credit Card Validation

Configuration

Performance Gain

Expected Results

4. CRM: Data Enrichment from External API

Configuration

Rate Limit Handling

Expected Results

5. Reporting: Process Transaction Logs

Configuration

Processing Steps

6. Marketing: Segment Customers and Send Campaigns

Configuration

Data Filtering Example

Expected Timeline

7. Support: Process Support Tickets in Priority Order

Configuration

Processing Order Example

8. Data Migration: Move Data Between Systems

Configuration

Migration Timeline

Safety Measures

9. Analytics: Event Processing with Aggregation

Configuration

Event Distribution Example

Output

10. Image Processing: Resize Images in Parallel

Configuration

Performance Comparison

11. Payment Processing: Charge Customers with Error Handling

Configuration

Expected Results

Retry Outcomes

12. Webhook Delivery: Send Webhooks to External Services

Configuration

Delivery Timeline

Reliability