SELECT DISTINCT vs. GROUP BY: Which is Faster for Unique Value Retrieval in MySQL?

MySQL: SELECT DISTINCT vs. GROUP BY for Unique Values

When retrieving unique values from a MySQL table, SELECT DISTINCT and GROUP BY might seem interchangeable, but their performance can differ significantly. This article compares their efficiency.

Scenario: Unique Profession Retrieval

Consider a query to retrieve unique professions from the users table for employees at 'XYZ':

<code class="language-sql">SELECT u.profession FROM users u
WHERE u.employer = 'XYZ'
ORDER BY u.profession</code>

To ensure only unique professions are returned, we can use either SELECT DISTINCT or GROUP BY:

Option 1: SELECT DISTINCT

<code class="language-sql">SELECT DISTINCT u.profession FROM users u
WHERE u.employer = 'XYZ'
ORDER BY u.profession</code>

Option 2: GROUP BY

<code class="language-sql">SELECT u.profession FROM users u
WHERE u.employer = 'XYZ'
GROUP BY u.profession
ORDER BY u.profession</code>

Performance Considerations

While both queries produce the same output, SELECT DISTINCT generally offers better performance. MySQL optimizes SELECT DISTINCT using techniques like bitmaps or hash tables, efficiently filtering duplicates in a single pass. GROUP BY, conversely, involves grouping rows and additional aggregation overhead, potentially slowing down query execution.

Exceptions: When GROUP BY Might Be Faster

Although SELECT DISTINCT usually prevails, GROUP BY can outperform it in specific situations. If the ORDER BY clause includes columns beyond the profession column, GROUP BY might offer superior optimization.

Recommendation

For retrieving unique values in MySQL, SELECT DISTINCT is typically the more efficient choice. However, benchmarking both queries with your specific data is crucial to determine the optimal approach for your application.

The above is the detailed content of SELECT DISTINCT vs. GROUP BY: Which is Faster for Unique Value Retrieval in MySQL?. For more information, please follow other related articles on the PHP Chinese website!