When Should I Use UNION Instead of OR for Better SQL Performance?

SQL performance optimization: comparison of UNION and OR

A recent article on SQL optimization suggested replacing SQL statements using OR with UNION. However, a quick EXPLAIN analysis shows that UNION seems to do twice as much execution work as OR. This article explores the circumstances under which UNION can improve database performance more than OR.

Equivalence of OR and IN

When comparing values ​​within a column, OR can be rewritten as IN. For example, the following query:

select username from users where company = 'bbc' or company = 'itv';

is equivalent to:

select username from users where company IN ('bbc', 'itv');

In this case, MySQL can effectively use the index on the company column to locate matching rows. No UNION is required here.

OR dilemma involving different columns

The situation becomes more complicated when the OR condition involves different columns. Consider the following query:

select username from users where company = 'bbc' or city = 'London';

If both company and city columns have indexes, MySQL may have difficulty choosing which index to use. UNION offers a solution in this case:

select username from users where company = 'bbc'
union
select username from users where city = 'London';

This allows each subquery to be searched using its own index and the results combined via UNION.

UNION’s deduplication overhead

Although UNION eliminates duplicate rows by sorting the result set, there is a performance overhead in this process. However, in most cases this overhead is minimal due to the smaller result set after the index search.

Detailed analysis of specific situations

Ultimately, which one is better between UNION and OR depends on the data and specific search criteria. In order to determine the best solution, it is recommended to analyze both methods using MySQL Query Analyzer and compare their performance. By understanding the respective advantages and disadvantages of UNION and OR, database administrators can optimize SQL queries to increase efficiency.

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