Unpivoting Table Diffs in SQL

Table diffs can be an essential feature for comparing data between two tables. In this article, we’ll explore how to output table diffs cleanly using SQL.

Understanding Table Diffs

A table diff is a representation of the differences between two tables. It typically includes information such as the column name, old value, and updated value. The goal of this article is to provide a method for generating table diffs in SQL.

The Problem with Existing Solutions

Currently, there are several ways to achieve table diffs using SQL. However, most of these solutions rely on C# or other programming languages to handle the logic. In this article, we’ll focus on finding a purely SQL solution that can generate table diffs without relying on additional programming languages.

Unpivoting in SQL

One way to approach table diffs is by unpivoting the tables. Unpivoting involves transforming a set of rows into a set of columns. In the context of table diffs, unpivoting allows us to transform the differences between two tables into a more convenient format.

To achieve this, we can use a combination of cross applies and joins in SQL Server.

Cross Apply

The cross apply operator is used to create a virtual table that contains the results of an expression. In our case, we’ll use cross apply to generate multiple columns for each row in the tables.

with co as (
  select v.*
  from clients_old co cross apply
       (values (co.client_id, co.firstname, 'firstname'),
               (co.client_id, co.lastname, 'lastname'),
               (co.client_id, co.age, 'age')
       ) v(client_id, val, col)
),
cu as (
  select v.*
  from clients_updated cu cross apply
       (values (cu.client_id, cu.firstname, 'firstname'),
               (cu.client_id, cu.lastname, 'lastname'),
               (cu.client_id, cu.age, 'age')
       ) v(client_id, val, col)
)

In the above code, we’re using cross apply to generate multiple columns for each row in both tables. The values expression is used to create a virtual table that contains the column values.

Joining Tables

After unpivoting the tables, we need to join them together based on the client ID and column name.

select co.client_id, co.col, co.val as old_value, cu.val as updated_value
from co join
     cu
     on co.client_id = cu.client_id and co.col = cu.col and
        co.val < cu.val;

In this code, we’re joining the two tables based on the client ID and column name. The co.val and cu.val expressions are used to compare the old and updated values.

Handling NULL Values

One potential issue with the above approach is handling NULL values. If either the old or updated value is NULL, the comparison may not work as expected.

To handle this situation, we can modify the join condition to include a check for NULL values.

select co.client_id, co.col, co.val as old_value, cu.val as updated_value
from co join
     cu
     on co.client_id = cu.client_id and co.col = cu.col and
        (co.val is null or cu.val is not null) and
        (cu.val is null or co.val <> cu.val);

In this modified code, we’re adding a check for NULL values in the join condition. If either value is NULL, the comparison will be false.

Conclusion

Unpivoting table diffs in SQL can provide a more efficient and elegant solution to comparing data between two tables. By using cross apply and joins, we can transform the differences into a convenient format that includes column names, old values, and updated values.

While this approach may require some additional logic for handling NULL values, it provides a solid foundation for generating table diffs in SQL.

Example Use Cases

Here’s an example use case that demonstrates how to use the above code:

-- Create sample tables
CREATE TABLE Clients_Old (
    ClientID INT,
    FirstName VARCHAR(255),
    LastName VARCHAR(255),
    Age INT
);

INSERT INTO Clients_Old (ClientID, FirstName, LastName, Age)
VALUES (1, 'John', 'Doe', 20), (2, 'Jane', 'Doe', 20);

CREATE TABLE Clients_Updated (
    ClientID INT,
    FirstName VARCHAR(255),
    LastName VARCHAR(255),
    Age INT
);

INSERT INTO Clients_Updated (ClientID, FirstName, LastName, Age)
VALUES (1, 'John', 'Smith', 99), (2, 'Jane', 'Doe', 99);

-- Unpivot table diffs using SQL
WITH co AS (
    SELECT v.*
    FROM Clients_Old co
    CROSS APPLY (
        VALUES (co.ClientID, co.FirstName, 'firstname'),
               (co.ClientID, co.LastName, 'lastname'),
               (co.ClientID, co.Age, 'age')
    ) v(client_id, val, col)
),
cu AS (
    SELECT v.*
    FROM Clients_Updated cu
    CROSS APPLY (
        VALUES (cu.ClientID, cu.FirstName, 'firstname'),
               (cu.ClientID, cu.LastName, 'lastname'),
               (cu.ClientID, cu.Age, 'age')
    ) v(client_id, val, col)
)
SELECT co.client_id, co.col, co.val AS old_value, cu.val AS updated_value
FROM co JOIN cu
ON co.client_id = cu.client_id AND co.col = cu.col AND co.val < cu.val;

This code creates two sample tables and then unpivots the table diffs using the above SQL logic. The resulting output is a clean and concise representation of the differences between the two tables.

Future Work

While this approach provides a solid foundation for generating table diffs in SQL, there are several potential areas for future work:

• Improving performance: As the size of the tables increases, performance may become an issue. Optimizing the join conditions and indexing strategies could help improve performance.
• Handling non-string data types: The current implementation only works with string data types. Integrating support for other data types, such as integers or dates, would make the solution more versatile.
• Extending to multiple tables: Currently, the code only handles differences between two tables. Expanding it to handle multiple tables and complex join conditions could create a powerful toolset for data analysis.

By exploring these areas, we can refine the solution and create a more comprehensive and efficient tool for generating table diffs in SQL.

Last modified on 2024-10-30