Why Do Multiple SQL LEFT JOINs Produce Incorrect Visit Counts?

Understanding Inaccurate Visit Counts with Multiple SQL LEFT JOINs

This article investigates a common SQL query issue: obtaining incorrect visit counts when using multiple LEFT JOIN operations. The goal is to retrieve user data, including account balance, grocery store visits, and fish market visits. A naive query often produces inflated results (e.g., "1", "12", "12").

The problem stems from the left-to-right execution of LEFT JOINs. Each subsequent join multiplies the row count if there are multiple matching entries in the joined tables. For instance, joining three grocery tables to one user results in three rows. A subsequent join with four fish market tables then expands this to twelve rows, leading to inaccurate visit counts.

The Solution: Pre-aggregation with Subqueries

The solution involves pre-aggregating the visit counts for each table before joining them to the users table. This prevents the row multiplication issue. The corrected query uses subqueries to achieve this:

SELECT u.id, u.account_balance, g.grocery_visits, f.fishmarket_visits
FROM users u
LEFT JOIN (
   SELECT user_id, COUNT(*) AS grocery_visits
   FROM grocery
   GROUP BY user_id
   ) g ON g.user_id = u.id
LEFT JOIN (
   SELECT user_id, COUNT(*) AS fishmarket_visits
   FROM fishmarket
   GROUP BY user_id
   ) f ON f.user_id = u.id
ORDER BY u.id;

Handling Missing Values with COALESCE

To gracefully handle cases where a user has no visits to a particular location, the COALESCE function can replace NULL values with 0 (or any other desired value):

SELECT u.id, u.account_balance, COALESCE(g.grocery_visits, 0) AS grocery_visits, COALESCE(f.fishmarket_visits, 0) AS fishmarket_visits
FROM users u
LEFT JOIN (
   SELECT user_id, COUNT(*) AS grocery_visits
   FROM grocery
   GROUP BY user_id
   ) g ON g.user_id = u.id
LEFT JOIN (
   SELECT user_id, COUNT(*) AS fishmarket_visits
   FROM fishmarket
   GROUP BY user_id
   ) f ON f.user_id = u.id
ORDER BY u.id;

This improved query accurately reflects the number of grocery and fish market visits for each user. By pre-aggregating the data, we avoid the pitfalls of cascading LEFT JOINs and ensure accurate results.

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