The unbearable lightness of one more index (boringsql.com)

In a recent analysis, a developer scrutinized the impact of AI-generated database schemas, noting a marked improvement in index creation since the advent of coding agents. Over a test period, 30 model-generated schemas were evaluated, revealing that while many indexes demonstrated solid construction, they often led to significant performance penalties due to over-indexing. For instance, one model produced 16 indexes on a `tickets` table, causing a 1.8x increase in Write-Ahead Logging (WAL) and nearly doubling update times compared to a hand-crafted schema with only 7 indexes. The significance of this finding lies in the balance between read and write operations in databases. AI models built indexes with an eye towards optimizing read queries but overlooked the ensuing write traffic implications, particularly on "hot" tables. This over-indexing results in increased disk I/O, prolonged VACUUM times, and inflated storage costs, as WAL data is generated for every index update. As the utilization of AI tools in database management grows, understanding their limitations is crucial; developers must still actively assess the fitness of generated schemas for their specific workload patterns to avoid potential performance bottlenecks.