Efficiently Looping Through a Delimited String in TSQL to Update Database Records

Learn how to properly loop through a delimited string in TSQL to update records in SQL Server. This guide provides detailed steps and efficient methods to accomplish this.
---
This video is based on the question https://stackoverflow.com/q/76921019/ asked by the user 'Surender Raja' ( https://stackoverflow.com/u/3240790/ ) and on the answer https://stackoverflow.com/a/76921321/ provided by the user 'Charlieface' ( https://stackoverflow.com/u/14868997/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Looping in TSQL and update statement not works

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Looping Through a Delimited String in TSQL: A Step-by-Step Guide to Updating Database Records

In many practical scenarios, you may find yourself needing to update a SQL Server database based on values derived from a delimited string. For instance, you might have a table (dbo.omega) that stores object names and their associated versions. When you have a string like "employee:0,dept:1", updating this table efficiently can be challenging, especially when considering loops in TSQL. Let's explore how to address this common problem effectively.

Understanding the Problem

Given a comma-delimited string input (like @ source = "employee:0,dept:1"), the goal is to update a table column based on the individual name-value pairs in the string. Specifically, we want to achieve the following updates:

Set latest_version to 0 where obj_name is 'employee'

Set latest_version to 1 where obj_name is 'dept'

However, directly using loops like while or for in TSQL can be inefficient and complicated. Therefore, understanding how to manipulate this data without traditional looping constructs is key.

Solution

Step 1: Using STRING_SPLIT to Separate Values

Instead of looping through the string manually, you can use the STRING_SPLIT function, introduced in SQL Server 2016, to break the string into manageable parts. Below is an example of how to use it:

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

Breakdown of the Query:

STRING_SPLIT(@ source, ','): This function separates the string at each comma, allowing us to work with each part individually.

CROSS APPLY: This is used to apply the CHARINDEX and SUBSTRING functions, separating the names from their corresponding values.

JOIN: Finally, we join the results with the original table to perform the updates efficiently.

Step 2: Using Table Valued Parameters (Optional)

For improved performance, especially in scenarios where you expect to update large data sets, you might want to consider using Table Valued Parameters or a table variable. Here’s how you can do that:

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

Why Use Table Valued Parameters?

Efficiency: It allows batch processing instead of individual updates, which reduces the overhead associated with multiple table writes.

Clarity: This method makes it easier to define and manage multiple key-value pairs without cumbersome string manipulations.

Conclusion

In conclusion, updating your SQL Server records based on a delimited string can be simplified significantly by leveraging built-in functions like STRING_SPLIT, along with more structured approaches like Table Valued Parameters. By using these techniques, you can enhance both the performance and maintainability of your SQL code, transforming a complex problem into a straightforward solution.

If you find yourself frequently working with similar updates in your database, consider adopting these practices for cleaner and more efficient data management.