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Resolving the Error converting datatype nvarchar to numeric in SQL Server

Learn how to fix the common error `Error converting datatype nvarchar to numeric` in SQL Server when working with tables that contain a mix of text and numeric data. --- This video is based on the question https://stackoverflow.com/q/71749051/ asked by the user 'Sixthsense' ( https://stackoverflow.com/u/4025652/ ) and on the answer https://stackoverflow.com/a/71749637/ provided by the user 'Tom Page' ( https://stackoverflow.com/u/3338171/ ) 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: Error converting datatype nvarchar to numeric 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. --- Troubleshooting SQL Server: Error Converting Datatype nvarchar to Numeric When working with SQL Server, you may encounter a frustrating error message: Error converting datatype nvarchar to numeric. This error often arises when you attempt to perform arithmetic operations on nvarchar fields that contain non-numeric values. In this guide, we will explore this problem and present a solution to help you efficiently manage your data conversions. Understanding the Problem The issue typically occurs in scenarios where columns are defined as nvarchar but contain numeric values, alongside possible non-numeric entries such as text. For instance, consider a table with two columns (Amount1 and Amount2), where some entries are valid numeric values (like 100 or 200), while others contain letters or may be blank (like A). Sample Table Data Amount1Amount2Expected Result1002004.17%A500500B201001.67%When trying to select results across the entire table for a specific calculation, you may receive the error message mentioned above, which stops the query from executing properly. The SQL Statements Causing the Error Successful query for a single row: [[See Video to Reveal this Text or Code Snippet]] Failing query for all rows: [[See Video to Reveal this Text or Code Snippet]] In the second case, the presence of non-numeric characters in either Amount1 or Amount2 results in the conversion error. The Solution: Using TRY_CAST or TRY_CONVERT To effectively handle this error, we can employ the TRY_CAST function (or alternatively, TRY_CONVERT). These functions allow conversions that return NULL for rows where the conversion fails, preventing the entire query from failing. Implementation Steps Replacing CAST with TRY_CAST: Instead of using: [[See Video to Reveal this Text or Code Snippet]] Use: [[See Video to Reveal this Text or Code Snippet]] When the conversion cannot be performed, it will return NULL instead of throwing an error. Using Conditional Logic: Begin your SQL statement with a CASE structure that checks if the values are valid for conversion: [[See Video to Reveal this Text or Code Snippet]] Final SQL Query Putting it all together, your final query should look similar to this: [[See Video to Reveal this Text or Code Snippet]] Conclusion In conclusion, the Error converting datatype nvarchar to numeric in SQL Server can be resolved with a smarter approach to type conversion. By using TRY_CAST and incorporating checks into your SQL statements, you can manage non-numeric entries without experiencing query failures. This method helps maintain data integrity and ensures that you can work with mixed data types effectively. Next time you encounter this error, remember that a few small changes in your SQL queries can lead to seamless calculations and risk-free data handling.