Introduction to Complex SQL Queries: Selecting Multiple Columns Count One Column and Grouping by One Column in One Table

As a technical blogger, I’ve encountered numerous questions on Stack Overflow that challenge my understanding of SQL and its capabilities. In this article, we’ll delve into a particularly complex query that requires us to select multiple columns, count one column, and group by another column in a single table.

Understanding the Requirements

The problem at hand involves a table named [delivery] with columns [ID], [Employee], [Post], [c_name], [Deli_Date], and [note]. We’re required to select all columns, count the occurrences of each Employee, and then group by Employee.

Initial Query Attempt

The initial query provided attempts to achieve this using a simple SELECT statement with an aggregate function:

select Employee, count(Employee) as TIMES from delivery
group by Employee

This query produces a result that only shows the Employee column and the count of occurrences for each employee. However, we need to include other columns in our final result.

Solution Using Window Functions

To overcome this limitation, we can utilize window functions, which allow us to perform calculations across rows without having to use subqueries or joins.

The solution uses a combination of row_number() and count(*) with partitioning by the Employee column. This enables us to assign a unique row number to each occurrence for each employee, effectively “resetting” the count for each new employee.

Here’s the modified query:

select d.Employee, 
       d.cnt, 
       d.Post, 
       d.c_name, 
       d.Deli_Date, 
       d.Note 
from ( 
    select d.*,
         count(*) over (partition by employee) as cnt,
         row_number() over (partition by employee order by deli_date desc) as seqnum
     from delivery d
) d
where seqnum = 1;

This query works as follows:

• The subquery selects all columns (d.*) from the delivery table.
• It calculates the count of occurrences for each employee using count(*) over (partition by employee) and assigns a unique row number to each occurrence within each group using row_number() over (partition by employee order by deli_date desc).
• The outer query selects only rows where the row number is 1 (seqnum = 1), which effectively gives us one row per employee with the correct count.

Result Set

The result set produced by this query includes all columns from the original table, along with the count of occurrences for each Employee. Here’s an example:

| Employee| cnt | Post      |c_name  |Deli_Date   |Note |
|---------|-----|-----------|---------------------|-----|
|  DAVID  |   4   |MANAGER    | a      |11/11/2018  |note |
|  JAMES  |   1   |DELIVERYMAN| b      |05/05/2015  |note |
|  OLIVER |   1   |DRIVER     | b      |02/02/2014  |note |
|  SAM    |   2   |ACOUNTS    | c      |10/10/2016  |note |

This result set meets our requirements by showing all columns, along with the count of occurrences for each Employee.

Conclusion

In conclusion, solving this complex query required us to think creatively about how to utilize window functions to achieve the desired outcome. By partitioning by the Employee column and using a combination of row_number() and count(*), we were able to produce a result set that meets our requirements.

This article has demonstrated the power of SQL window functions in solving complex queries, which is an essential skill for any database administrator or developer working with relational databases.

Last modified on 2024-06-07