How to Use STUFF and XML/JSON in SQL for Aggregating Data

Learn to effectively aggregate multiple rows in SQL using the `STUFF` function along with XML or JSON techniques, even when experiencing compatibility issues with `STRING_AGG()`.
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How to Aggregate Multiple Rows in SQL with STUFF Function

In the realm of SQL querying, aggregating multiple rows into a single field can often present a challenging problem, especially when dealing with a dataset that requires grouping by an identifier. This guide will walk you through how to achieve that using the STUFF function in combination with XML or JSON data types, especially in cases where your SQL version does not support STRING_AGG() function.

Understanding the Problem

You may find yourself with a dataset resembling the following:

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

Your goal is to aggregate the rows so that they appear in a cleaner format, like this:

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

However, you discovered that the STRING_AGG() function is unavailable in your SQL version, which complicates the task. But fear not! There are alternative methods to achieve the desired output.

Solution Breakdown

1. Use of XML Approach

Instead of executing separate queries for each column, you can build an XML object that encapsulates the data and then parse it back out. Here’s how:

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

2. Using JSON Method

Alternatively, you can leverage JSON to obtain a similar result. This approach can sometimes be more readable and cleaner:

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

Conclusion

By using XML or JSON as methods of consolidating data, you can eliminate the complexity of using repetitive queries while grouping and aggregating similar rows in SQL. Both methods utilize the powerful STUFF function effectively, allowing you to combine and format the results into a clean output.

This approach enables you to overcome the limitations imposed by certain SQL functions and create a more readable and manageable dataset.

Feel free to experiment and apply the methods discussed in this post to your own SQL queries and enjoy the benefits of easier data manipulation!