Understanding the Problem and Query

The problem at hand is to take a table structure of a CouponInvoicePrescription and split its columns into two equal number of rows. The original table has four columns: Name, MobileNumber, CouponNumber, and IsDispatched. We need to rearrange these columns in such a way that each column, except for the last one (IsDispatched), is mirrored across a certain number of rows.

What Does This Mean?

To illustrate this concept, let’s take the example given. In the original table, we have two rows with Name and two distinct values for CouponNumber. We want to split these columns into two equal parts and mirror them across rows. The resulting query should produce a new table with four rows:

The key here is to understand how we can manipulate rows based on a certain condition and then mirror that manipulation across the columns.

Common Table Expressions (CTEs)

One of the most powerful SQL concepts is the Common Table Expression (CTE). A CTE allows you to define a temporary result set that can be referenced within a single SELECT, INSERT, UPDATE, or DELETE statement. Think of it as a temporary view that we can use to simplify our queries.

In this problem, we want to create an input table with the same data but divided into two parts based on certain conditions. We will achieve this using a CTE called input.

Defining the CTE

The CTE input is defined as follows:

WITH
  input(ord, name, mobno, couponno, isdispatched, status) AS (
    SELECT 0, 'amar', 8888888888, 'CPever901', FALSE, 1
UNION ALL 
    SELECT 1, 'amar', 8888888888, 'CP00005', FALSE, 1
UNION ALL 
    SELECT 2, 'pt3', 7777777777, 'cp9090', FALSE, 1
    ...
)

Here, we are selecting specific values from the original table and combining them into a new temporary table. This temporary table is called input, and it will be used to manipulate rows based on certain conditions.

Window Functions

Window functions in SQL allow us to perform calculations across rows that are related by some key column(s). In our problem, we want to split the columns into two equal parts and mirror them across rows. We can use window functions like ROW_NUMBER() or RANK() to achieve this.

However, there’s a more efficient approach using CTEs with aggregation.

How Window Functions Can Be Used

If we were working with data that needed to be sorted in some way, we might use a window function like ROW_NUMBER(). But since our main goal is to rearrange rows and columns, not sort them, this isn’t directly applicable here. However, it does highlight how SQL can manipulate tables at the row level.

Using CTEs with Aggregation

Now that we have defined the temporary table input, let’s see how we can split its columns into two equal parts using a CTE and window functions.

We will create another CTE called output to represent our desired result. We’ll use aggregation functions like MAX() or CASE with aggregation to achieve this.

Defining the output CTE

WITH 
  input(ord, name, mobno, couponno, isdispatched, status) AS (
    SELECT 0, 'amar', 8888888888, 'CPever901', FALSE, 1
UNION ALL 
    SELECT 1, 'amar', 8888888888, 'CP00005', FALSE, 1
UNION ALL 
    SELECT 2, 'pt3', 7777777777, 'cp9090', FALSE, 1
    ...
),
output(ord, name, couponno1, couponno2, isdispatched, status) AS (
  SELECT ord / 2,
         name
        , MAX(CASE WHEN ord % 2 = 1 THEN couponno END) AS couponno1
        , MAX(CASE WHEN ord % 2 = 0 THEN couponno END) AS couponno2
        , isdispatched, status
    FROM input
    GROUP BY ord / 2, name, isdispatched, status
)

How the output CTE Works

Here’s what’s happening:

• We select ord / 2, because we want to split the columns into two equal parts. This automatically determines which row will be mirrored by which value of the next column.
• We use a CASE expression within the MAX() function to select either the first or second half of the values for each column, depending on whether ord % 2 = 1 or 0.
• Finally, we group the rows by ord / 2, name, isdispatched, and status. This ensures that each row in our desired result appears once.

Combining CTEs with a Final SELECT Statement

Now that we have defined both the temporary table input and the final table output, let’s see how to use them together to produce our desired result.

SELECT * FROM output;

This query selects all columns from the output CTE, which contains our rearranged data. It should look like this:

This shows that our SQL query has successfully rearranged the columns into two equal parts, as requested.

Handling Edge Cases

What about handling edge cases where there’s no second half of a column? In this case, we simply choose not to include values for couponno2.

For example:

WITH input(ord, name, mobno, couponno, isdispatched, status) AS (
    SELECT 0, 'amar', 8888888888, 'CPever901', FALSE, 1
UNION ALL 
    SELECT 1, 'amar', 8888888888, 'CP00005', FALSE, 1
UNION ALL 
    SELECT 2, 'pt3', 7777777777, 'cp9090', FALSE, NULL
)

And the output CTE would become:

WITH input(ord, name, mobno, couponno, isdispatched, status) AS (
    SELECT 0, 'amar', 8888888888, 'CPever901', FALSE, 1
UNION ALL 
    SELECT 1, 'amar', 8888888888, 'CP00005', FALSE, 1
UNION ALL 
    SELECT 2, 'pt3', 7777777777, 'cp9090', FALSE, NULL
),
output(ord, name, couponno1, isdispatched, status) AS (
  SELECT ord / 2,
         name
        , MAX(CASE WHEN ord % 2 = 1 THEN couponno END) AS couponno1
        , isdispatched, status
    FROM input
    GROUP BY ord / 2, name, isdispatched, status
)

This way, we can still split the columns into two equal parts but handle edge cases where there’s no second half.

Conclusion

In this article, we’ve explored how to split a table structure in SQL into two equal number of rows by rearranging its columns. We used Common Table Expressions (CTEs) and aggregation functions like CASE with aggregation to achieve this goal.

We defined an intermediate CTE called input, which represented our original data but divided into two parts based on certain conditions. We then created another CTE called output, where we applied these rules to rearrange the columns.

This approach is particularly useful for simplifying complex SQL queries, especially when dealing with large datasets.

Note:

Last modified on 2024-04-16