Unraveling MySQL’s Pivoting Puzzle: Selecting Highest to Lowest Order Value with Horizontal Pivot

When dealing with data that needs to be transformed from a vertical format to a horizontal one, often referred to as pivoting, it can be challenging. This is especially true when working with large datasets and complex transformations. In this article, we’ll delve into the world of MySQL’s pivot operation, exploring how to select the highest to lowest order value with a horizontal pivot.

Understanding the Problem

Let’s first examine the problem at hand. We have a table tbl_marks containing four columns representing marks from different quizzes: quiz_1_marks, quiz_2_marks, quiz_3_marks, and quiz_4_marks. The values in these columns are the marks obtained by students in each respective quiz.

The ultimate goal is to transform this data into a new table with four columns, where each column represents one of the original quiz marks. However, instead of simply selecting the highest mark from each quiz, we want to select the highest value for each position (in a horizontal pivot).

Introduction to MySQL’s Pivot Operation

MySQL provides several ways to perform pivoting operations. One such method is using conditional aggregation with the LATERAL keyword. This technique allows us to create a new table with columns that represent the values in the original table.

Cross Joining and Lateral Joins

The provided solution employs a cross join between the original table (tbl_marks) and a derived table created using row_number() over order by q.m desc. The lateral join keyword is essential for this approach, as it enables us to reference columns from the outer table in the inner query.

Here’s a breakdown of how the lateral join works:

• The outer query selects the maximum value for each position.
• The inner query assigns row numbers based on the descending order of the marks.
• The LATERAL keyword allows us to access the original marks (qm) using the assigned row number.

Code Breakdown

Let’s dissect the provided code snippet:

SELECT max1, max2, max3, max4
FROM tbl_marks
CROSS JOIN LATERAL (
  SELECT  
     max(case rn when 1 then qm end) max1,
     max(case rn when 2 then qm end) max2,
     max(case rn when 3 then qm end) max3,
     max(case rn when 4 then qm end) max4
  FROM (
     SELECT q.m qm, row_number() over(order by q.m desc) rn
     FROM (
        SELECT quiz_1_marks UNION ALL 
        SELECT quiz_2_marks UNION ALL 
        SELECT quiz_3_marks UNION ALL 
        SELECT quiz_4_marks
     ) q(m)
  ) t
) t;

This code can be explained in the following steps:

• Cross Joining: The outer query performs a cross join with the derived table, which includes all possible combinations of marks from tbl_marks.
• Lateral Join: Inside the inner query, we use row_number() to assign row numbers based on descending order. This allows us to reference the marks using the assigned row number.
• Conditional Aggregation: We then use conditional aggregation to select the maximum value for each position.

Breaking Down Conditional Aggregation

The key part of this code is the conditional aggregation:

max(case rn when 1 then qm end) max1,
max(case rn when 2 then qm end) max2,
max(case rn when 3 then qm end) max3,
max(case rn when 4 then qm end) max4

This code uses the CASE statement to determine which row number corresponds to each position. Here’s how it works:

• When rn = 1, we select the value corresponding to qm. This effectively selects the highest mark for the first position (max1).
• Similarly, when rn = 2, we select the value corresponding to qm to determine max2.
• The process repeats for rn = 3 and rn = 4.

Conclusion

In this article, we explored how MySQL’s pivot operation can be used to select the highest to lowest order value with a horizontal pivot. We delved into the world of conditional aggregation using LATERAL joins and cross joining, demonstrating the flexibility and power of MySQL’s pivoting capabilities.

By breaking down the provided code snippet and explaining each part in detail, we aimed to provide a comprehensive understanding of this complex technique. Whether you’re working with large datasets or need to transform your data into a new format, understanding how to use conditional aggregation with lateral joins can be a valuable skill in your MySQL journey.

Last modified on 2023-09-06