SQL Query: Finding Common Columns with Different Values in Other Columns

Introduction

As a data enthusiast, working with large datasets can be challenging. Understanding how to extract specific information from these datasets is crucial for making informed decisions. In this article, we will delve into a common problem where you need to find rows that have common values in one column but different values in another column.

We’ll use the provided Stack Overflow post as our starting point and explore the concept using real-world examples.

Problem Statement

Suppose we have a table with columns A, B, and C. We want to find rows where column C has both values of 4 and 5 for a particular value of column A. Additionally, for these specific values of A, we need to filter rows based on the difference in values between columns B.

Understanding the Query

Let’s break down the given SQL query:

select a, b, c
from (
    select tbl.*, 
       count(*) over(partition by a) cnt,
       row_number() over (partition by a order by b) brn,
       row_number() over (partition by a order by c) crn
    from tbl
    where c in (4, 5)
) t
where cnt = 2 and brn = crn;

This query consists of three main parts:

1. Subquery: This subquery selects all columns (*) from the table tbl along with two aggregate functions:

   • count(*) over(partition by a) counts the number of occurrences for each value of column A.
   • row_number() assigns unique row numbers to rows within each partition (grouped by column A). The first row is assigned brn = 1, and subsequent rows are assigned consecutive integers. Similarly, crn assigns row numbers based on the values in column C.

2. Outer Query: This outer query filters the results of the subquery to include only those rows where:

   • The count of occurrences for a given value of A (cnt) equals 2.
   • The row number assigned by brn equals the row number assigned by crn.

The purpose of both these functions is to ensure that we’re looking at unique combinations of values in columns A and C.

Using Subqueries with Joining Tables

Now, let’s explore how to use subqueries when joining tables. The second example demonstrates this using a Common Table Expression (CTE):

with params(val, pos) as (
    select 4,2 union all
    select 5,1
)
select a, b, c
from (
    select tbl.*, 
       count(*) over(partition by a) cnt,
       row_number() over (partition by a order by b) brn,
       p.pos
    from tbl 
    join params p on tbl.c = p.val
) t
where cnt = (select count(*) from params) and brn = pos;

Here, we define a temporary view params that contains the values to be matched against. We then use this CTE to filter the results based on these conditions.

Real-World Example

To illustrate how to apply this concept in real-world scenarios, consider the following example:

Suppose you have a table called orders, which includes columns for order ID (ord_id), customer ID (cust_id), and product type (prod_type). You want to identify orders where there is both a sale (indicated by prod_type == 'sale') and a purchase (indicated by prod_type == 'purchase') for the same customer.

Using SQL, this would involve finding rows with:

• A count of occurrences equal to 2 (cnt).
• Row numbers ordered first by cust_id (brn) and then by ord_id (crn).

Here’s an example query that demonstrates how to achieve this:

select ord_id, cust_id, prod_type
from (
    select *, 
       count(*) over(partition by cust_id) cnt,
       row_number() over (partition by cust_id order by ord_id) brn,
       row_number() over (partition by cust_id order by prod_type) crn
    from orders
    where prod_type in ('sale', 'purchase')
) t
where cnt = 2 and brn = crn;

This query identifies unique combinations of customer ID and product type, providing insights into sales and purchases for specific customers.

Conclusion

Working with complex datasets can be overwhelming. By understanding how to extract specific information from these datasets using SQL queries like the ones discussed here, you’ll be better equipped to make informed decisions based on your data.

Remember that breaking down problems into smaller subqueries or CTEs can simplify the process of finding common columns with different values in other columns.

Feel free to ask any questions or share examples you’d like us to explore further!

Last modified on 2023-07-17