How Database Indexes Work and Why Your Queries Fail at Scale

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Queries that run smoothly in development often break down in production when table sizes grow from thousands to millions of rows, typically due to full table scans. Database indexes, most commonly implemented as B-trees, allow the query engine to locate matching rows in roughly 20 comparisons instead of scanning millions, cutting query time from seconds to milliseconds. However, indexes are not free — each one added to a table increases the cost of write operations like INSERT, UPDATE, and DELETE. A key concept called selectivity determines whether the query planner will even choose to use an index, based on the fraction of rows a filter is expected to return. Developers can use EXPLAIN plans to inspect query execution and catch performance bottlenecks before slow queries reach production.

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