Why Database Indexes Become a Performance Problem

Lucas and Luna explore a counterintuitive database performance issue: indexes that actually slow queries down. They examine how B-tree index depth grows with data volume, turning fast lookups into multi-level tree traversals. The discussion uses a concrete example of a 10-million-row table on PostgreSQL and shows how a poorly chosen composite index on an e-commerce orders table caused query times to jump from 2 milliseconds to over 200 milliseconds after a schema migration. They explain index fragmentation, the impact of high-cardinality columns, and why write-heavy workloads suffer from index maintenance overhead. Practical tuning advice includes using pg_stat_user_indexes to spot unused indexes and considering partial indexes for skewed data distributions. The episode closes with a reminder that indexes are not set-and-forget—they need periodic review. #DatabaseIndexes #PostgreSQL #BTree #QueryPerformance #IndexTuning #DatabaseOptimization #CompositeIndex #IndexFragmentation #Cardinality #PartialIndex #pg_stat_user_indexes #WriteHeavyWorkloads #Technology #DatabaseTech #FexingoBusiness #BusinessPodcast #SQL #DataStorage Keep every episode free: buymeacoffee.com/fexingo