How Can I Accurately Calculate SUMs from Multiple Tables in SQL?

Querying Multiple Tables for SUM Calculations

In the context of relational databases, it is common to require data aggregation across multiple tables. This is especially useful for summarizing values or calculating grand totals. However, it is important to understand the principles of data summarization to ensure accurate results.

One such scenario occurs when querying two tables, AP and INV, which share a common column named [PROJECT]. The objective is to return a resultset containing PROJECT as the primary key and two summary columns: SUM_AP and SUM_INV, which represent the sum of the Value column for each project in AP and INV tables, respectively.

A common approach to address this problem is to employ an INNER JOIN operation, as seen in the following code:

SELECT AP.[PROJECT],
SUM(AP.Value) AS SUM_AP, 
SUM(INV.Value) AS SUM_INV
FROM AP INNER JOIN INV ON (AP.[PROJECT] =INV.[PROJECT])
WHERE AP.[PROJECT] = 'XXXXX'
GROUP BY AP.[PROJECT]

However, this query returns incorrect results because the grouping operation (GROUP BY) is applied before the summation (SUM). This results in duplicate values being included in the sum, leading to inflated totals.

To resolve this issue, a more accurate approach is to use sub-selects to perform the aggregation separately for each table:

SELECT 
    AP1.[PROJECT],
    (SELECT SUM(AP2.Value) FROM AP AS AP2 WHERE AP2.PROJECT = AP1.PROJECT) AS SUM_AP,
    (SELECT SUM(INV2.Value) FROM INV AS INV2 WHERE INV2.PROJECT = AP1.PROJECT) AS SUM_INV
FROM AP AS AP1 
    INNER JOIN INV AS INV1 
        ON (AP1.[PROJECT] =INV1.[PROJECT])
WHERE AP1.[PROJECT] = 'XXXXX'
GROUP BY AP1.[PROJECT]

This revised query employs sub-selects within the SUM functions to calculate the values of SUM_AP and SUM_INV for each project. The sub-selects independently aggregate the Value column for each table based on the matching PROJECT value, eliminating the issue of duplicate values.

By utilizing this approach, you can effectively query multiple tables and perform accurate aggregation operations to obtain the desired summary results without any incorrect computations.

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