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Extended statistics (CREATE STATISTICS: dependencies, ndistinct, MCV)

Simple terms

Filter on city and postal_code together and the planner hands you a row estimate that is wildly too low, then picks a join to match. Here is why. By default it treats columns as unrelated: it takes the odds of each condition and multiplies them. For genuinely independent columns that is fine. For city and postcode, which move together, multiplying is nonsense, and a row count that is off by 100x sends the planner somewhere terrible. CREATE STATISTICS records how the columns actually relate so it stops guessing that way.

You might be asked

Two columns in a table are strongly correlated (think city and postal_code, or status and stage). A query filters on both with AND, the row estimate is off by 100x, and the planner picks a terrible join. Explain WHY the default planner gets this wrong, then walk me through the three kinds of extended statistics, which symptom each one fixes, what the planner actually does differently with each, and why even a 'perfect' dependency doesn't make the estimate exactly right.

TopicQuery planner row estimation / multi-column correlation / functional dependencies / multivariate ndistinct / multivariate MCV / independence assumption
PostgreSQL14, 15, 16, 17, 18
Tools usedEXPLAIN ANALYZE (estimated vs actual rows), CREATE STATISTICS (dependencies/ndistinct/mcv), pg_stats_ext, pg_mcv_list_items(), source navigation @REL_17_10, PG 17.10
Last reviewed2026-06-19

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