Distinct SQL Based on Condition with More Than Two Columns

Introduction

When working with data that has duplicate values in multiple columns, finding distinct records based on specific conditions can be challenging. In this article, we’ll explore a solution using subqueries and joins to achieve this goal.

Problem Statement

We have a table structure like this:

We want to write a SQL query that returns the distinct records based on the following conditions:

• If there’s only one row with Column2 = 'A' and Column1, then return all rows.
• If there are multiple rows with Column2 = 'A', then pick any of these rows.
• If there’s no row with Column2 = 'A', then look for rows with Column2 = 'B'.
• For each distinct record, also print the value of Column3.

Solution

The provided answer uses a subquery to first find the minimum value of Column2 for each group of Column1. This is done by using a common table expression (CTE) or a derived table.

SELECT   tab.Column1,
         tab.Column2,
         MIN(tab.Column3)
FROM     (SELECT Column1,
                 MIN(Column2) as min_column2
          FROM   tab
          GROUP BY Column1
         ) t
JOIN     tab
  ON     tab.Column1 = t.Column1
 AND     tab.Column2 = t.min_column2
GROUP BY tab.Column1,
         tab.Column2;

Let’s break down how this works:

1. The subquery finds the minimum value of Column2 for each group of Column1. This is done using the MIN() function and grouping by Column1.
2. The results from the subquery are assigned to a temporary table or CTE, which we’ll call t.
3. The main query joins this t table with the original table tab on both Column1 and min_column2. This ensures that we’re only considering rows where Column2 has its minimum value for each group of Column1.
4. Finally, we use the MIN() function again to find the minimum value of Column3 for each distinct record.

Explanation

The key insight here is that by using a subquery to find the minimum value of Column2, we’re effectively creating a “mask” that filters out rows with higher values of Column2. This allows us to focus on the most recent or least recently used (LRU) row for each group of Column1.

In the case where there’s only one row with Column2 = 'A', this LRU approach is beneficial because it ensures that we consider all rows. In other cases, where there are multiple rows with Column2 = 'A', this approach picks any of these rows, which may not be the most recent or least recently used.

Benefits and Drawbacks

Benefits:

• This solution is plain ANSI-SQL, meaning it’s compatible with a wide range of databases.
• It doesn’t depend on the specific database version or configuration.
• The use of subqueries and joins makes the code relatively easy to understand and maintain.

Drawbacks:

• Performance-wise, this approach can be slower than using window functions (like ROW_NUMBER() or RANK()) because it requires joining with a temporary table.
• If the table is very large, this approach may consume more memory.

Alternative Solutions

If you’re working with a database that supports window functions, you can use these to achieve a similar result:

SELECT Column1,
       Column2,
       Column3,
       RANK() OVER (PARTITION BY Column1 ORDER BY CASE WHEN Column2 = 'A' THEN 0 ELSE 1 END) AS Rank_A
FROM tab;

This query uses the RANK() window function to assign a rank to each row based on whether its Column2 value is 'A'. You can then use this rank to filter out rows with higher ranks.

Conclusion

Finding distinct records based on multiple conditions and columns can be challenging. By using subqueries and joins, we can achieve a solution that’s both effective and efficient. While there are alternative solutions available, the original approach remains a reliable choice for many use cases.

Last modified on 2025-02-25