Merging Records Based on Sequence Numbers Using SQL Solutions

Understanding the Problem and Requirements

The problem at hand is to merge records from a SEQUENCE_NUMBER table into one row based on their sequence number. The original data has multiple columns with varying values, and we need to transform it into a new format where each row has a specific set of columns.

We are given an example of how this can be achieved using SQL, but let’s break down the steps involved and explore them in more detail.

Background: Understanding Sequence Numbers

Sequence numbers are used to identify unique rows within a table. In the context of this problem, we assume that each row has a distinct sequence number assigned to it. This assumption is crucial for understanding how to merge records based on these sequence numbers.

Understanding the Desired Output Format

The desired output format consists of a single row with multiple columns. The column names are based on the original SEQUENCE_NUMBER table, but with some modifications. Specifically:

The first three columns (VALUE_1, VALUE_2, and VALUE_3) correspond to the original BUSINESS_VALUE column values.
The last two columns (TEMP_1 and TEMP_2) correspond to the original TEMP_V column values, but with an additional “Temp” prefix.
The remaining columns are null.

Exploring SQL Solutions

There are a few ways to achieve this using SQL. Let’s explore one of them:

Using CROSS APPLY

The example provided uses the CROSS APPLY operator to expand each row into multiple rows based on its sequence number. This is done using the values() function, which creates a table with the specified values.

Here’s a breakdown of how this works:

The Cross Apply operator applies a user-defined function (UDF) to each row in the input table.
In this case, the UDF uses the values() function to create a table with two columns: Item and Value.
The Item column represents the sequence number, while the Value column represents either the original BUSINESS_VALUE or TEMP_V value.

Using PIVOT

After expanding each row into multiple rows using CROSS APPLY, we can use the PIVOT operator to combine these rows into a single row with the desired column structure.

Here’s how it works:

The PIVOT operator combines rows from one or more tables based on a common field.
In this case, we’re pivoting on the sequence number ([SEQUENCE_NUMBER]) and grouping the values by each of the specified columns ([VALUE_1], [VALUE_2], etc.).
The MAX aggregation function is used to select the maximum value for each group.

Code Breakdown

Here’s the full code snippet from the original example:

Select *
From (
    Select A.[NUMBER]
          ,B.* 
     From  YourTable A
     Cross Apply (
                values (concat('Value_',[SEQUENCE_NUMBER]),[BUSINESS_VALUE])
                  ,(concat('Temp_',[SEQUENCE_NUMBER]),[TEMP_V])
             ) B(Item,Value)
) src
 Pivot (max(Value) for Item in ([VALUE_1],[VALUE_2],[VALUE_3],[VALUE_4],[TEMP_1],[TEMP_2],[TEMP_3],[TEMP_4]))pvt

This code is quite concise and straightforward. It uses CROSS APPLY to expand each row into multiple rows based on its sequence number, and then PIVOT to combine these rows into a single row with the desired column structure.

Alternative Solutions

While the example provided uses CROSS APPLY and PIVOT, there are other ways to achieve this result using SQL. Here are some alternatives:

Using UNPIVOT
Using Common Table Expressions (CTEs)
Using dynamic SQL

Let’s explore these alternatives in more detail.

Alternative 1: Using UNPIVOT

Instead of using CROSS APPLY and PIVOT, we can use the UNPIVOT operator to convert our rows into columns. Here’s an example:

Select *
From (
    Select A.[NUMBER]
          ,B.* 
     From  YourTable A
     Select B.[NUMBER] as Item, B.[BUSINESS_VALUE] as Value_1, B.[TEMP_V] as Temp_1
     From YourTable B
) src
Unpivot (Value for Item in ([VALUE_1],[VALUE_2],[VALUE_3],[VALUE_4],[TEMP_1],[TEMP_2],[TEMP_3],[TEMP_4]))

This code uses UNPIVOT to convert each row into a single row with the desired column structure.

Alternative 2: Using Common Table Expressions (CTEs)

Another alternative is to use CTEs to define a temporary view of our data. Here’s an example:

With ExpandedData As (
    Select A.[NUMBER]
          ,B.* 
     From  YourTable A
     Cross Apply (
                values (concat('Value_',[SEQUENCE_NUMBER]),[BUSINESS_VALUE])
                  ,(concat('Temp_',[SEQUENCE_NUMBER]),[TEMP_V])
             ) B(Item,Value)
)
Select *
From ExpandedData

This code uses a CTE to define a temporary view of our data after expanding it using CROSS APPLY.

Alternative 3: Using dynamic SQL

Finally, we can use dynamic SQL to achieve this result. This approach involves generating the necessary SQL queries dynamically based on the column structure.

Here’s an example:

DECLARE @sql nvarchar(max) = '
    Select *
    From YourTable
    PIVOT (
        max([BUSINESS_VALUE])
        for [SEQUENCE_NUMBER] in ([VALUE_1], [VALUE_2], [VALUE_3], [VALUE_4], [TEMP_1], [TEMP_2], [TEMP_3], [TEMP_4])
    ) AS pvt
';
Execute sp_executesql @sql;

This code uses dynamic SQL to generate the necessary query based on the column structure.

Conclusion

In this article, we explored how to merge records from a SEQUENCE_NUMBER table into one row based on their sequence number. We discussed different SQL solutions using CROSS APPLY, PIVOT, UNPIVOT, CTEs, and dynamic SQL.

We also broke down the code examples provided in the original question to understand how each solution works. Our goal is to provide a deep understanding of these technical concepts and help developers tackle similar challenges in their own projects.