Performance optimization improves execution speed, resource efficiency, or throughput. In practice, it means finding costly database operations or implementation choices, then requesting changes that scale better and satisfy explicit performance requirements. section 10.3 section 11.2
From slow lookups to scalable data structures
When an AI assistant used arrays for content-filtering lookups, performance concerns prompted a better question: which JavaScript structure remains efficient as data grows? The suggested change was to use Maps. State requirements such as performance, error handling, and scalability explicitly, then review the result for edge cases. section 11.2
SELECT TOP 10
DB_NAME(ps.database_id) AS DatabaseName,
OBJECT_NAME(ps.[object_id], ps.database_id) AS StoredProcedure,
ps.execution_count AS ExecutionCount,
ps.total_elapsed_time / ps.execution_count AS AvgElapsedTime,
ps.total_worker_time / ps.execution_count AS AvgCPU,
ps.total_logical_reads / ps.execution_count AS AvgReads
FROM sys.dm_exec_procedure_stats AS ps
ORDER BY ps.total_elapsed_time / ps.execution_count DESC;
Use this SQL Server query to rank the ten stored procedures with the highest average execution time, CPU use, and logical reads. section 10.3
