Demystifying SQL: DISTINCT vs. GROUP BY for Multiple Columns

Here's how it works:

Scenario: Imagine a table named customers with columns name and city:

| name      | city        |
|-----------|--------------|
| foo   | New York    |
| Jane Smith | Seattle     |
| foo   | New York    | (duplicate)
| Mike Jones | Los Angeles |
| Jane Smith | Seattle     | (duplicate)

Problem: You want to retrieve a list of unique combinations of name and city.

Solution 1: Using DISTINCT with both columns:

SELECT DISTINCT name, city
FROM customers;

This query will only return unique combinations of name and city:

| name      | city        |
|-----------|--------------|
| foo   | New York    |
| Jane Smith | Seattle     |
| Mike Jones | Los Angeles |

Explanation: The DISTINCT keyword ensures that only rows where both name and city are unique are included in the result set.

Important Note: This approach only guarantees uniqueness for the specified columns (name and city in this case). Other columns in the table might still have duplicates even if the chosen combination is unique.

Solution 2: Using GROUP BY for additional columns:

While DISTINCT effectively removes duplicate combinations based on specified columns, what if you want to retrieve other columns along with the distinct combinations? This is where GROUP BY comes in.

SELECT name, city, COUNT(*) AS count
FROM customers
GROUP BY name, city;

This query uses GROUP BY to group rows with the same name and city combinations. Then, it uses the COUNT(*) function to count the number of rows in each group.

Here's the result:

| name      | city        | count |
|-----------|--------------|-------|
| foo   | New York    | 2     |
| Jane Smith | Seattle     | 2     |
| Mike Jones | Los Angeles | 1     |

Explanation: GROUP BY ensures only unique combinations of name and city are considered. The COUNT(*) function then provides the number of occurrences for each unique combination.

Related Issues and Solutions:

1. Incomplete Uniqueness: While DISTINCT can handle multiple columns, it won't guarantee overall uniqueness if other columns have duplicates within the same combination. For complete uniqueness, consider using a unique identifier column or creating a unique constraint.
2. Performance Impact: Using DISTINCT or GROUP BY can slightly impact performance, especially on large datasets. Analyze your needs and consider alternatives if performance is critical.