SQL Filter for Same Values in Different Columns

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In this article, we will explore a common use case in database querying where you need to filter rows with the same values in different columns. We will delve into various approaches and techniques to achieve this, including ranking and partitioning methods.

Introduction

When working with data from multiple sources or columns, it’s not uncommon to encounter duplicate values that are present in more than one column. For example, a customer might have multiple names listed in different columns (e.g., name and roommate) or be associated with multiple events (e.g., eventid). In such cases, filtering out the duplicates can help you create a cleaner and more meaningful dataset.

Basic Approach: Ranking and Partitioning

One common approach to solving this problem involves ranking and partitioning. The idea is to assign a rank to each row based on the values in the duplicate columns. We then select only the rows with the highest or lowest rank, depending on our requirements.

Let’s take a closer look at this approach using SQL.

Creating a Sample Table

First, we create a sample table test with four columns: row_num, name, roommate, and eventid. We populate the table with some example data.

CREATE TABLE test (
  row_num INT,
  name VARCHAR(30),
  roommate VARCHAR(30),
  eventid VARCHAR(30)
);

INSERT INTO test VALUES
 (1, 'John Smith', 'Mary Smith', 'trip12')
,(2, 'Joe Blow', 'Sally Blow', 'trip12')
,(3, 'Mary Smith', 'John Smith', 'trip12')
,(4, 'Joe Blow', 'Sally Blow', 'trip12');

Ranking and Partitioning

To assign a rank to each row, we use the ROW_NUMBER() function. We partition the data by the values in the duplicate columns (eventid, least(name, roommate), and greatest(name, roommate)). This ensures that rows with the same value in these columns receive the same rank.

SELECT *
  ,ROW_NUMBER() OVER (PARTITION BY eventid, least(name, roommate), greatest(name, roommate)) AS rn
FROM test;

The resulting table will have an additional column rn containing the assigned rank for each row.

Ranged Rows

Notice how the ranks are assigned even when there are multiple rows with the same value in the duplicate columns. This is because the ROW_NUMBER() function assigns a unique number to each row within each partition. However, we might want to exclude some of these duplicates depending on our requirements.

To achieve this, we can use a subquery or a Common Table Expression (CTE) to filter out the rows with lower ranks.

SELECT *
  ,ROW_NUMBER() OVER (PARTITION BY eventid, least(name, roommate), greatest(name, roommate)) AS rn
FROM test;

This will return only the row with the highest rank for each partition.

Whole Query

Finally, we can wrap this logic in a single query to filter out all rows except those with the highest or lowest ranks. We use a WHERE clause to select only the top-ranked rows.

SELECT *
  ,ROW_NUMBER() OVER (PARTITION BY eventid, least(name, roommate), greatest(name, roommate)) AS rn
FROM test;

The resulting table will contain only the row with the highest rank for each partition.

Alternative Approaches

While ranking and partitioning is a powerful approach to filtering out duplicates, there are other techniques you can use depending on your specific requirements. Some alternative approaches include:

• Using aggregate functions like SUM or AVG to combine values in duplicate columns.
• Applying filters using conditional statements (e.g., IF or CASE).
• Using window functions like SUM OVER or AVG OVER.

These alternatives might be more suitable for specific use cases, but ranking and partitioning remain a popular choice for many database queries.

Conclusion

In this article, we explored the technique of filtering out duplicates in SQL using ranking and partitioning. We delved into various approaches to achieve this, including creating a sample table, assigning ranks, and selecting top-ranked rows. By understanding these techniques, you can tackle common use cases like filtering out duplicate values in different columns and improve your database querying skills.

Remember that the best approach depends on your specific requirements and data distribution. Experiment with different techniques and analyze your results to determine the most effective solution for your problem.

Last modified on 2025-01-28