Understanding SQL Server Queries: Summing Up Data from Multiple Tables

Описание: Discover how to effectively sum data from multiple tables in SQL Server with a simple and efficient query structure.
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This video is based on the question https://stackoverflow.com/q/63887170/ asked by the user 'MaxCB' ( https://stackoverflow.com/u/13381914/ ) and on the answer https://stackoverflow.com/a/63887366/ provided by the user 'Gordon Linoff' ( https://stackoverflow.com/u/1144035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding SQL Server Queries: Summing Up Data from Multiple Tables

When working with data in SQL Server, it’s common to face challenges while trying to aggregate information from multiple tables. In this guide, we’ll tackle a specific example: pulling information from Purchase Orders and Sales Orders and summing items from both tables accurately.

Let’s break down the problem and solution step by step.

The Problem

You might find yourself needing to track the quantity of items ordered and sold from separate tables; for instance, you may want to know:

How many items have been ordered (from Purchase Orders).

How many items have pending sales (from Sales Orders).

In this scenario, you have two tables:

Purchase Orders Lines

Sales Orders Lines

Example Scenario

Imagine you are placing an order for 10 tables. You need to determine:

How many tables have already been ordered previously (let's say 20).

How many tables are currently in sales orders (maybe 25).

The Initial Query

The following SQL query was initially attempted:

[[See Video to Reveal this Text or Code Snippet]]

However, the results were unexpected; while the sum from Sales Orders was correct, the sum from Purchase Orders was disproportionate—50 times higher than anticipated.

Understanding the Issue

Why Do Joins Affect Aggregation?

The issue arises from how SQL handles joins. When you join two tables, if there are multiple matches on either side, the rows are combined. This can lead to inflated sums. In your query, when joining Purchase Orders with Sales Orders, the multiplication of rows can generate inaccurate aggregate results.

The Solution: Using Correlated Subqueries

The recommended approach to resolve this issue is to use correlated subqueries. This method ensures you retrieve the sums without unintended row multiplication from joins.

Here’s a more streamlined version of the query:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the New Query

Correlated Subqueries: Each subquery sums the CSopen values from the respective tables based solely on the matching ItemID.

(SELECT SUM(sl.CSopen) FROM SalesOrdersLines sl WHERE sl.ItemID = s.ItemID) gets the total from Sales Orders.

(SELECT SUM(pl.CSopen) FROM PurchaseOrdersLines pl WHERE pl.ItemID = s.ItemID) retrieves the total from Purchase Orders.

No Join Required: Since we're not joining the tables directly, there's no risk of row multiplication affecting your sums.

Simplified structure: The query becomes much easier to read, understand, and maintain.

Conclusion

In conclusion, when faced with summing values from multiple tables in SQL Server, be cautious of how joins could inflate your results. Implementing correlated subqueries offers a robust solution without the complications resulting from joins. This not only simplifies your SQL queries but also ensures data accuracy.

If you have further questions or specific cases you'd like to explore, feel free to reach out!