Let me share a game – changing debugging technique that saved our team countless hours..

Ever wondered why your code works perfectly in test but crawls in production?.

The Problem
A customer creation routine was fast with 50 records but ground to a halt with 5,000+ customers. Classic performance degradation that only shows up at scale.
Root Cause Discovery
Used the AL Profiler and found the culprit: repeated queries inside loops checking if customers already exist. Each check hit the database, creating an O(n²) nightmare.
The Fix
* SetLoadFields() for targeted data retrieval – Only load the fields you actually need. Instead of pulling entire customer records, we loaded just “No.” and other needed fields – cut memory usage by 70%.
* Smart Filter Ordering – Put your most selective filters first (SetRange before SetFilter). The database engine will thank you with faster index hits.
* Explicit Blank Validation – Never assume fields have values. Add explicit checks for blank/empty values to prevent silent data corruption.
* Transaction Management Strategic use of Commit() after batch operations ensures data consistency and releases locks faster.
Tips for Scale
Use Get() instead of FindFirst() when you have a primary key
Order SetRange() calls by selectivity (most restrictive first)
Add error handling that helps debug – include record identifiers in error messages
Test with production-scale data, not just sample sets
The Bottom Line Performance isn’t about complex algorithms – it is about respecting database fundamentals and avoiding unnecessary work.