Replacing Duplicate Columns in a SELECT Query: A Deep Dive into Subqueries and Window Functions for Efficient Data Processing

Replacing Duplicate Columns in a SELECT Query: A Deep Dive into Subqueries and Window Functions

As a database developer, you’ve likely encountered situations where duplicate records or columns need to be replaced with a specific value. In this article, we’ll delve into the world of subqueries and window functions to explore how to achieve this goal using SQL.

Problem Statement

The problem at hand involves a database design for an auto repair shop. Each car model has a specific cost for a specific service. The query aims to invoice a customer their bill, taking into account the usage of multiple spare parts for each service. When only one part is used, the query works correctly. However, when multiple parts are used, the cost of the service is charged multiple times along with the cost of the part.

To resolve this issue, we need to write a query that replaces duplicate services (with different parts) with a cost of $0.00 in the final bill.

Initial Query Analysis

The initial query provided uses several joins to gather data from various tables:

work_order: contains information about work orders
client: contains client-related data
vehicle: contains vehicle-related data
model: contains model-related data
completed_work_order: contains completed work order data
service: contains service-related data
model_services: contains model-service relationships
used_parts: contains used part data
part: contains part-related data

The query joins these tables using various conditions and returns the following columns:

Service name (s.name)
Cost of service (cost)
Part used (p.name)
Quantity (quantity)
Price sold (price_sold)
Total bill (Total Bill)

However, this initial query has a flaw: it duplicates services with different parts and charges the customer for the service cost multiple times.

Subquery Solution

To address this issue, we can use a subquery to calculate the total amount for each service. The subquery is divided into two cases:

If the current service name is not the same as the previous one (or null), it calculates the total amount by adding the current service cost and the sum of quantities times part costs for all parts used.
Otherwise, it sets the total amount to 0.

Here’s the subquery solution in SQL:

SELECT t1.*, 
sum(total_amount) over (order by service_name, part_name) final_running_total
FROM 
(select t.*, 
case when service_name &lt;&gt; ifnull((lead(service_name) over (order by null)), 'null') 
then amount  + sum(qty * part_cost) over (partition by service_name 
order by part_name)
else 
0 end as total_amount
from test t)
t1

This subquery uses a combination of window functions, aggregations, and conditional logic to calculate the total amount for each service.

Window Function Explanation

The sum(total_amount) over (order by service_name, part_name) expression is a window function that calculates the cumulative sum of total_amount values across all rows in the result set. The over clause specifies the order and partitioning criteria:

order by service_name: orders the results by service name
partition by service_name: partitions the results by service name

The window function returns the running total for each service, which helps to eliminate duplicate services with different parts.

Bonus: Adding a Custom Row for the Total Bill

To add a custom row at the end that displays the sum of all “Total Bill” values, we can use another window function:

SELECT t1.*, 
       sum(t2.total_amount) over () as total_bill_sum
FROM 
(select t.*, 
case when service_name &lt;&gt; ifnull((lead(service_name) over (order by null)), 'null') 
then amount  + sum(qty * part_cost) over (partition by service_name 
order by part_name)
else 
0 end as total_amount
from test t)
t1
left join 
(select sum(t2.total_amount) over () as total_bill_sum from 
(select t.*, 
case when service_name &lt;&gt; ifnull((lead(service_name) over (order by null)), 'null') 
then amount  + sum(qty * part_cost) over (partition by service_name 
order by part_name)
else 
0 end as total_amount
from test t)) t2 on true

This solution joins the original result set with a derived table that calculates the sum of all “Total Bill” values using another window function. The left join ensures that all rows from the original result set are included in the final output.

Conclusion

In this article, we explored how to replace duplicate columns in a SELECT query by using subqueries and window functions. We analyzed an initial query with a flaw and developed a solution using a subquery to calculate the total amount for each service. Additionally, we discussed adding a custom row for the total bill sum. By applying these techniques, you can write more efficient and effective SQL queries to solve complex data processing tasks.