Our team is designing a batch processing system for recipe management in Agile 9.3.4. We’re handling bulk recipe updates, ingredient modifications, and formula adjustments through the REST API. The challenge is determining optimal batch size for performance while maintaining data consistency.
We’ve tested batch sizes from 10 to 500 recipes per API call. Smaller batches are safer but slower, larger batches risk partial failures and rollback complexity. We’re particularly concerned about transactional integrity when processing fails midway through a batch.
What batch size optimization strategies have you found effective? How do you handle error recovery when a batch partially succeeds? Looking for real-world experiences with recipe API batch operations.
Monitor your batch processing metrics closely. We track success rate, average processing time, and failure patterns. This data revealed that batches over 75 recipes had exponentially higher failure rates due to API timeout limits. We also discovered that processing recipes with complex ingredient hierarchies required smaller batches (20-30) compared to simple recipes (100+). Implement dynamic batch sizing based on recipe complexity scores.
After implementing batch processing systems across multiple Agile deployments, here’s what works consistently:
Batch Size Optimization:
The optimal batch size isn’t fixed - it depends on recipe complexity, network conditions, and server load. Start with 50 recipes as a baseline. Implement dynamic sizing that adjusts based on:
- Average recipe processing time (if >200ms per recipe, reduce batch size)
- API response times (increase batch size if consistently under 5 seconds)
- Failure rates (reduce if failures exceed 2% of batches)
Use a sliding window algorithm that monitors the last 100 batches and adjusts size within a 25-150 recipe range. This adapts to changing conditions automatically.
Transactional Integrity:
Implement a multi-level transaction strategy:
- API-level transactions for individual recipe updates (atomic operations)
- Application-level transaction coordination across the batch
- Compensation transactions for rollback scenarios
Use database savepoints every 20-30 recipes. If a failure occurs, roll back to the last savepoint rather than the entire batch. This minimizes wasted processing while maintaining consistency.
Implement optimistic locking with version checking. Before updating a recipe, verify its version hasn’t changed since batch preparation. This prevents lost update problems in concurrent processing scenarios.
Error Recovery Strategies:
Design a comprehensive recovery framework:
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Immediate Retry: Transient failures (network timeouts, temporary locks) get three immediate retries with exponential backoff (1s, 2s, 4s).
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Batch Segmentation: If a batch fails, split it into smaller segments (divide by 2) and reprocess. Continue splitting until you isolate the problematic recipe(s).
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Dead Letter Queue: Failed recipes after segmentation go to a DLQ with detailed error context. Process these separately with human review if needed.
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Compensation Logic: For partially completed batches, maintain a completion registry. Track which recipes succeeded and only reprocess failures. Use idempotency keys (recipe ID + timestamp + operation type) to prevent duplicate processing.
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Circuit Breaker Pattern: If failure rate exceeds 10% over 5 minutes, pause batch processing and alert operations. This prevents cascading failures from overwhelming the system.
Practical implementation tips:
- Log every recipe operation with batch ID, recipe ID, timestamp, and result
- Implement health checks that verify API connectivity before starting batches
- Use async processing with callbacks for large batches to avoid timeout issues
- Maintain processing metrics dashboard showing throughput, error rates, and batch sizes
We process 50,000+ recipes daily using this approach with 99.8% success rate and automatic recovery for most failures.
We settled on batches of 50 recipes after extensive testing. This provides a good balance between throughput and error isolation. For transactional integrity, we implemented a two-phase commit pattern - validate all recipes first, then commit as a unit. If validation fails on any recipe, the entire batch is rejected before any data changes. We also maintain a processing log table that tracks batch status, so recovery is straightforward.
Error recovery is critical for batch operations. We use a three-tier approach: immediate retry for transient failures, quarantine for data validation errors, and manual review for structural issues. Each recipe in a failed batch gets tagged with failure reason and retry count. Our batch processor maintains a dead letter queue for recipes that fail after three attempts. This prevents problematic recipes from blocking the entire pipeline while ensuring nothing gets lost.