The statements used for the three new conversion functions, which is very similar to the previous ones, are shown below: If an error occurs, it will return NULL value. If the conversion succeeds, it will return the expression value after conversion. The functions try to convert the provided expression to the requested data type. The three newly introduced conversion functions are: TRY_CAST, TRY_CONVERT and TRY_PARSE. SQL Server 2012 introduced new conversion functions that have the same functionality as the previous three converting functions, in addition to data type conversion exception handling mechanism to handle the errors generated by invalid conversions. If you include the previous conversion functions within your script to perform SQL Server data type conversion operations that may fail due to data issue, the query will throw an error and fail, rolling back the parent transaction that it runs inside. The TIME statistics shown below tell you not to take the risk of using the PARSE function to convert into DATETIME data type: What we need to concentrate on also here is the execution time of the PARSE function which is 12 times the time consumed by the CAST function, and the CPU time of it is about 120 times the CPU time consumed by the CAST function. The syntax that is used for the CAST function is shown below:Įxtra overhead will be noticed from the execution time increase and the CPU time increase when converting to DATETIME data type using the three conversion functions, keeping the previous rank of the CAST function as the fastest and the PARSE as the slowest. Being supported by ANSI SQL Standard, it is a preferable choice to convert between the different data types, especially when this conversion query will be executed on different DBMS such as Oracle or MySQL. You need to provide the CAST function with the expression that will be converted and the target data type that the provided value will be converted to. The cast function is an ANSI SQL-92 compliant function that is used to convert an expression from one SQL Server data type to another. Before going through the performance comparison between the three conversion functions, we will briefly describe these functions. The main concern in this article is to compare the performance of these conversion methods and see which method is the best to use. To perform the explicit data conversion, SQL Server provides us with three main conversion functions CAST, CONVERT and PARSE. But you may face other cases, in which you need to manually, aka explicitly, perform a suitable data type conversion, in order to avoid the combination error. They may be automatically, aka implicitly, converted into the suitable data type, making the generated combination better, without raising any errors. When two expressions with different SQL Server data types are combined together by an operator, they may not always play well together. Other Data Types, such as CURSOR, XML and Spatial Geography Types. Unicode Character Strings, such as NCHAR and NVARCHAR.īinary Strings, such as BINARY and VARBINARY. SQL Server data types can be categorized into seven main categories:Įxact Numeric types, such as INT, BIGINT and numeric.Īpproximate Numeric types, such as FLOAT and REAL.ĭate and Time types, such as DATE, TIME and DATETIME.Ĭharacter Strings, such as CHAR and VARCHAR. You can also define your own customized user defined data type using T-SQL script. SQL Server provides us with a big library of system data types that define all types of data that can be used with SQL Server, from which we can choose the SQL Server data type that is suitable for the data we will store in that object. This attribute is called the SQL Server Data Type. When you define SQL Server database tables, local variables, expressions or parameters, you should specify what kind of data will be stored in those objects, such as text data, numbers, money or dates.
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