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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Implementing bulk insert operations with unique ID validation in Lightning requires careful governor limit management, Database.Stateful interface handling, and complex batch processing logic.
Here’s how to achieve enterprise-grade bulk processing with automatic unique ID validation without custom batch job development.
Execute enterprise-grade bulk processing with automatic validation using Coefficient
Coefficient provides optimized bulk processing architecture with configurable batch sizing and parallel processing. Handle large Salesforce datasets with automatic governor limit management and comprehensive unique ID validation.
How to make it work
Step 1. Import Excel data for bulk processing.
Upload your Excel file to Google Sheets to prepare for bulk operations. Google Sheets handles large datasets without browser memory limitations that affect custom Lightning components.
Step 2. Configure optimized batch processing.
Set up Coefficient export with configurable batch sizes (default 1000, maximum 10,000) based on your record complexity and org limits. The system automatically optimizes batch sizes for maximum throughput while respecting API limits.
Step 3. Enable pre-processing validation.
Use preview mode to validate unique_Id__c values against existing records before bulk operation execution. This identifies validation issues across your entire dataset before consuming API limits.
Step 4. Configure UPSERT with External ID.
Set up UPSERT action with unique_Id__c as External ID for automatic decision-making between insert and update operations. This eliminates the need for custom SOQL queries to check existing records before bulk processing.
Step 5. Enable parallel processing.
Configure multiple batches to process simultaneously for maximum throughput. Coefficient handles parallel batch execution while managing API limits and preventing conflicts between concurrent operations.
Step 6. Monitor with real-time tracking.
Track bulk operation progress with detailed batch-level reporting. Monitor processing speed, success rates, and API limit consumption in real-time without custom monitoring code.
Step 7. Handle automatic error recovery.
Failed batches automatically retry with exponential backoff logic. The system maintains detailed logs of all operations with timestamps and provides rollback capabilities by tracking record IDs.
Scale your bulk operations efficiently
This approach eliminates Database.Stateful complexity, custom batch job management, and bulk API integration challenges while providing superior performance monitoring and audit capabilities. Scale up your bulk processing today.
