Database Indexing

A database index is conceptually similar to a book's index: instead of scanning every page to find a topic, you look up the topic in the index and jump directly to the relevant pages. Common index types include B-tree indexes for range queries, hash indexes for exact lookups, GIN indexes for full-text search, and specialized indexes for geometric, JSON, or array data.

For AI product teams, proper indexing is fundamental because AI features often query large datasets under tight latency constraints. A recommendation service that looks up user interaction history, a search feature that filters candidates by multiple attributes, or an analytics query that aggregates events across time ranges all depend on well-designed indexes. Growth teams should ensure that the queries powering experiment analysis, cohort segmentation, and funnel metrics have appropriate indexes, since slow analytics queries delay experiment conclusions and decision-making. Vector indexes, specifically designed for similarity search in high-dimensional spaces, are increasingly important for AI applications that use embeddings for semantic search, recommendation, and content matching.