We’re experiencing critical timeout issues with inventory batch synchronization after migrating to cloud infrastructure. Our batch jobs are configured to sync inventory across 12 warehouses with approximately 85,000 SKUs, but they consistently fail after 20-25 minutes with timeout exceptions.
The error we’re seeing:
Batch job 'InventSync_Daily' terminated
Exception: System.TimeoutException
at InventBatchSync.processRecords(line 156)
Execution time exceeded: 1500 seconds
We’ve tried adjusting the batch timeout configuration and implementing basic data chunking, but the parallel processing setup doesn’t seem to leverage cloud resources effectively. The query performance also appears suboptimal when processing large inventory datasets. This is blocking our daily inventory reconciliation and causing significant delays in warehouse operations. Any guidance on optimizing batch framework configuration for cloud deployment would be greatly appreciated.
One more thing to check - are you using the OData batch processing APIs or the traditional batch framework? For cloud deployments, the OData approach can sometimes provide better performance for data synchronization scenarios. It handles throttling more gracefully and provides built-in retry mechanisms. Just something to consider if you continue experiencing issues after implementing the other suggestions.
Your data chunking strategy needs refinement. Instead of processing all 85K records in one batch task, implement a chunking mechanism that breaks the workload into manageable units. I typically recommend chunks of 5,000-10,000 records for inventory sync operations in cloud deployments. This allows the batch framework to distribute work more effectively across available resources and prevents individual tasks from exceeding timeout thresholds. You’ll also want to implement proper error handling so that if one chunk fails, it doesn’t cascade to the entire job.
Thanks for the suggestions. We’ve reviewed the batch group configuration and it’s currently set to use a single thread. Should we be increasing the maximum batch threads for cloud deployment? Also, regarding the chunking strategy, how do we ensure data consistency when processing inventory updates in smaller batches? We’re concerned about partial updates causing discrepancies in inventory counts.
The query optimization aspect is crucial here. With 85K SKUs across multiple warehouses, you’re likely hitting database throttling limits. Have you implemented proper indexing on your inventory dimension tables? Also, check if your synchronization logic is using set-based operations or row-by-row processing. Row-by-row processing in cloud environments can trigger throttling much faster than on-premises due to the way Azure SQL handles connection pooling and resource governance.