Calculating Column Sums and Differences Between Rows in a Grouped Table

In this article, we’ll delve into the world of SQL query optimization and explore how to calculate column sums and differences between rows in a grouped table.

Understanding the Problem Statement

The problem statement presents two tables: table1 and table2. The goal is to calculate the difference between rows based on group by SELL_ID in table1, which will produce the desired output in table2.

Here’s an excerpt from table1:

+---------+---------+----------+----------+------------------+---------+
| seq_ID  | REQ_ID  | CALL_ID  | SELL_ID  |     REGION       |  COUNT  |
+---------+---------+----------+----------+------------------+---------+
|    1    |    123  | C001     | S1       | AGL              |  510563 |
|    2    |    123  | C001     | S1       | USL              |  122967 |
|    3    |    123  | C001     | S1       | VALIC            |  614106 |
|    4    |    123  | C001     | S2       | Inforce          |1247636 |
|    5    |    123  | C001     | S2       | NB               |       0 |
|    6    |    123  | C001     | S3       | Seriatim Summary |1247636 |
+---------+---------+----------+----------+------------------+---------+

And here’s the desired output in table2:

+---------+---------+----------+----------+-------+
| seq_ID  | REQ_ID  | CALL_ID  | Summary  | COUNT |
+---------+---------+----------+----------+-------+
|    1    |    123  | C001     | S1_vs_S2 |     0 |
|    2    |    123  | C001     | S2_vs_S3 |     0 |
|    3    |    123  | C001     | S3_vs_s1 |     0 |
+---------+---------+----------+----------+-------+

The Initial Query

The initial query provided by the user is as follows:

INSERT INTO table2 (SEQ_ID, REQ_ID,call_id,summary,count) 
SELECT min(seq_id) seq_id
     , req_id
     , call_id
     , S1_vs_S2
     ,((SELECT sum(c2) FROM TABLE_STG_CTRL WHERE source='S1')-
        SELECT sum(c2) FROM TABLE_STG_CTRL WHERE source='S2'))
FROM table1
GROUP BY req_ID, Ctrl_ID, c1, source 
ORDER BY SEQ_ID ;

Issues with the Initial Query

There are several issues with this query:

• The Ctrl_ID and source columns are not present in table1, which will cause a syntax error.
• The S1_vs_S2 column is calculated using subqueries, which can be slow for large tables.
• The query uses the ORDER BY SEQ_ID clause, but this does not guarantee any specific order of rows.

Optimized Query

The optimized query to solve this problem is as follows:

SELECT req_id, call_id, sell_id,
       lead(sell_id) over (partition by req_id, call_id order by seq_id) as next_sell_id,
       (cnt -
        lead(cnt) over (partition by req_id, call_id order by seq_id)
       ) as diff
from (select req_id, call_id, sell_id, sum(count) as cnt, min(seq_id) as seq_id
      from t
      group by req_id, call_id, sell_id
     ) t

How the Optimized Query Works

This query uses a combination of window functions and subqueries to achieve the desired output.

1. The subquery calculates the sum of count values for each group of rows with the same req_id, call_id, and sell_id.
2. The outer query selects these sums as cnt columns.
3. The lead window function is used to calculate the difference between consecutive rows for each group.

How Window Functions Work

Window functions in SQL allow you to perform calculations across a set of rows that are related to the current row, such as aggregating values or calculating differences between rows.

In this case, the lead window function calculates the next value in the sequence (next_sell_id) and the difference between consecutive rows (diff).

Advantages of the Optimized Query

This query has several advantages over the initial query:

• It is more efficient because it avoids using subqueries to calculate column sums.
• It is more accurate because it ensures that the results are ordered correctly.

Conclusion

Calculating column sums and differences between rows in a grouped table can be a challenging task. However, by understanding how window functions work and applying them correctly, you can achieve efficient and accurate results.

Last modified on 2025-05-05