Why Are My LEFT JOINs Sometimes Faster Than INNER JOINs in SQL Server?

SQL Server Join Performance: Debunking the LEFT JOIN Myth

A common misconception exists regarding SQL Server join performance: that LEFT JOIN operations are inherently faster than INNER JOIN operations. This is generally inaccurate. LEFT JOINs introduce extra processing overhead because they must perform all the work of an INNER JOIN and then add rows with NULL values for unmatched entries in the right table. The larger result set also contributes to increased execution time.

Why Your LEFT JOIN Might Have Been Faster

If you observed a faster LEFT JOIN query, the reason likely stems from factors unrelated to the join type itself:

• Insufficient Indexing: The absence of appropriate indexes on candidate and foreign keys is a major performance bottleneck, especially when joining numerous tables (e.g., nine tables without sufficient indexes).
• Small Table Sizes: When the smaller tables involved in the join contain very few rows, the overhead of the LEFT JOIN might be negligible compared to the time spent on other query operations.

When LEFT JOINs Might Show an Advantage

The only scenario where a LEFT JOIN might outperform an INNER JOIN is under very specific conditions:

• Extremely Small Tables: The tables involved have an exceptionally small number of rows.
• Lack of Indexes: The query lacks effective indexes, making the additional LEFT JOIN overhead less significant than the index-related performance issues.

Illustrative Example

Consider these tables:

CREATE TABLE #Test1 (ID int PRIMARY KEY, Name varchar(50) NOT NULL);
CREATE TABLE #Test2 (ID int PRIMARY KEY, Name varchar(50) NOT NULL);
INSERT INTO #Test1 VALUES (1, 'One'), (2, 'Two'), (3, 'Three');
INSERT INTO #Test2 VALUES (1, 'One'), (2, 'Two'), (3, 'Three');

An INNER JOIN query:

SELECT * FROM #Test1 t1 INNER JOIN #Test2 t2 ON t2.Name = t1.Name;

A LEFT JOIN query:

SELECT * FROM #Test1 t1 LEFT JOIN #Test2 t2 ON t2.Name = t1.Name;

In this minimal example, with few rows and no indexes, the LEFT JOIN might appear faster. However, if the join condition used the ID column (the primary key), the INNER JOIN would be considerably faster due to efficient index utilization. This highlights the importance of proper indexing in optimizing join performance regardless of join type.

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