Problem solving with PlanetScale Insights

This post details how to use PlanetScale Insights to diagnose and resolve database performance issues, specifically focusing on query latency in PlanetScale Postgres. It explains latency percentiles (p50, p95, p99, p99.9, Max), how to interpret the query list and its various metrics (like rows read vs. returned), and advanced search syntaxes. It also covers using other graphs (Queries, Rows read, Rows written) for context. The post introduces the 'Summarize query' LLM feature and discusses actionable steps like adding indexes (including GIN trigram indexes for wildcard searches) and rethinking schema/application design. Finally, it introduces the MCP server and agentic workflows for automated slow query detection and resolution, providing a sample prompt and configuration details.