Batch Operations via SyteLine REST API for Bulk Data Processing
High-volume integrations with SyteLine often require processing thousands of records in a single synchronization cycle. Sending individual REST API calls for each record is impractical at scale due to network latency, authentication overhead, and IDO Runtime thread consumption. SyteLine supports batch operations that bundle multiple IDO commands into a single HTTP request, dramatically improving throughput for data migrations, nightly syncs, and bulk imports from external systems.
Constructing Batch API Requests
SyteLine batch operations use the UpdateCollection endpoint with a JSON payload containing an array of IDO commands. Each command specifies its operation type (Insert, Update, or Delete) along with the property-value pairs. The ION API Gateway supports the OData $batch endpoint, which wraps multiple individual requests into a single multipart HTTP request. For direct IDO REST access, the /IDORequestService/ido/update/{IDOName} endpoint accepts a commands array that the IDO Runtime processes within a single database transaction.
- OData $batch: POST /api/$batch with Content-Type: multipart/mixed; boundary=batch_boundary containing individual requests
- Direct batch update: POST /IDORequestService/ido/update/SLItems with {"commands": [{"action": "insert", "properties": {...}}, ...]}
- Group related operations by IDO name to minimize context switching in the IDO Runtime
- Set batch size between 50-200 commands per request to balance throughput against transaction timeout limits
- Include all required properties for inserts; include only changed properties plus RowPointer for updates
Performance Tuning for Bulk Operations
Batch performance depends on several factors: batch size, IDO complexity, extension class overhead, and SQL Server capacity. Start with a batch size of 100 commands and measure throughput. If the IDO has lightweight extension logic, you can increase to 200-500 commands per batch. If extension classes perform cross-IDO lookups or external API calls, reduce batch size to 25-50 to avoid transaction timeouts. Monitor SQL Server wait statistics during batch runs to identify bottlenecks. For large data migrations exceeding 100,000 records, implement a producer-consumer pattern with parallel batch submission.
- Measure baseline throughput: time a batch of 100 inserts and calculate records per second
- Scale batch size based on IDO complexity: simple IDOs tolerate 200-500, complex IDOs perform better at 25-50
- Implement parallel batch submission with 2-4 concurrent threads for linear throughput scaling
- Set the IDO Runtime command timeout higher for batch operations: default 30s may need 120s for large batches
- Disable non-essential extension class logic during bulk loads using a batch mode flag on the IDO
Error Handling and Partial Failure Recovery
Batch operations introduce complexity in error handling because a single failed command can roll back the entire batch transaction. Implement a strategy for handling partial failures: either use all-or-nothing transactional batches for data integrity, or switch to individual command processing for the failed items. Log each command's result status and maintain a dead-letter queue for records that repeatedly fail processing. After a batch failure, parse the error response to identify the specific command index and error details, then retry only the failed subset.
- Parse batch response: each command returns an individual status code and error message in the response array
- Implement dead-letter queue: persist failed commands with error details for manual review and retry
- Use idempotency keys on insert operations to safely retry batches without creating duplicate records
- Log batch statistics: total commands, successful count, failed count, duration, and records per second
- Schedule bulk operations during off-peak hours to minimize impact on interactive SyteLine users
Netray AI agents optimize your SyteLine batch integration pipelines, auto-tuning batch sizes, managing error recovery, and maximizing throughput. Accelerate your bulk data operations today.
Related Resources
SyteLine REST API CRUD Operations Guide
Perform CRUD operations against SyteLine via REST API. Step-by-step examples for creating, reading, updating, and deleting records through IDO REST endpoints.
Infor SyteLineSyteLine REST API Rate Limiting Guide
Manage SyteLine REST API rate limits and throttling. Configure request quotas, implement backoff strategies, and optimize throughput for CloudSuite Industrial APIs.
Infor SyteLineSyteLine REST API Error Codes Reference
Complete SyteLine REST API error code reference. Diagnose HTTP status codes, IDO error responses, and ION API Gateway errors in CloudSuite Industrial integrations.