SQL Server TRY_PARSE() Function: A Powerful Tool for Data Conversion
Data conversion is a crucial aspect of working with databases, and SQL Server provides a variety of functions to facilitate it. One of the most interesting and powerful of these functions is TRY_PARSE().
TRY_PARSE() is a conversion function that attempts to convert an input expression to the specified data type, using the specified culture. In this article, we will explore the syntax and parameters of TRY_PARSE(), as well as provide some examples of how it can be used to convert strings to date/time and number, and how it can be used with a CASE expression.
Syntax of TRY_PARSE() Function
The syntax of the TRY_PARSE() function is straightforward and easy to understand. It takes three parameters: an expression that you want to convert, the data type to which you want to convert it, and the culture that the input string is being converted from.
The general syntax for TRY_PARSE() is as follows:
TRY_PARSE(expression AS data_type [ USING culture ])
Parameters of TRY_PARSE() Function
The TRY_PARSE() function takes three parameters: expression, data_type, and culture. 1.
Expression: This is the expression that you want to convert. It can be a string, a number, a date/time value, or any other type of value that can be converted to the specified data type.
2. Data_type: The data type to which you want to convert the expression.
The data types that are supported by TRY_PARSE() include date/time, numeric, and string data types. 3.
Culture: The culture that the input string is being converted from. The culture parameter is optional, and if you don’t specify it, SQL Server will use the default culture of the server.
Examples of Using SQL Server TRY_PARSE() Function
Here are some examples that demonstrate the use of TRY_PARSE() function in SQL Server. Example 1: Converting a string to date/time
One of the most common use cases for TRY_PARSE() is to convert a string to date/time.
Here’s an example of how you can use TRY_PARSE() to convert a string to date/time. SELECT TRY_PARSE(‘2022-01-02 10:20:30’ AS datetime)
The output of this query will be: 2022-01-02 10:20:30.000
Example 2: Converting a string to number
Another common use case for TRY_PARSE() is to convert a string to a number.
Here’s an example of how you can use TRY_PARSE() to convert a string to a number. SELECT TRY_PARSE(‘123.45’ AS numeric(10,2))
The output of this query will be: 123.45
Example 3: Using TRY_PARSE() function with CASE expression
TRY_PARSE() is often used in conjunction with a CASE expression to handle cases where the conversion might fail.
Here’s an example of how you can use TRY_PARSE() with a CASE expression to handle cast succeeded/failed cases. SELECT column1,
CASE WHEN TRY_PARSE(column2 as int) IS NULL THEN ‘N/A’
ELSE TRY_PARSE(column2 AS int) END as column2_int
FROM mytable
In this example, if the conversion of column2 to int fails, the CASE expression returns ‘N/A’.
Final Thoughts
TRY_PARSE() is a powerful function that can be used to convert data between different data types. Its ability to convert strings to date/time and numbers makes it a valuable addition to any database professional’s toolkit.
We hope this article has provided you with a brief overview of the syntax and parameters of TRY_PARSE(), as well as some practical examples of how it can be used in SQL Server. SQL Server TRY_PARSE() Function: A Powerful Tool for Data Conversion
Data conversion is a crucial aspect of working with databases.
It is, therefore, crucial to have conversion functions that facilitate this process. SQL Server offers various conversion functions, among which is TRY_PARSE().
TRY_PARSE() is a helpful function that attempts to convert an input expression to the specified data type, using the specified culture. In this article, we have explored the syntax and parameters of TRY_PARSE(), as well as provided some examples of how it can be used to convert strings to date/time and numbers, and how it can be used with a CASE expression.
Syntax of TRY_PARSE() Function
The TRY_PARSE() function has a simple syntax that takes three parameters an expression to convert, the desired data type, and the culture used for conversion (optional). Using the TRY_PARSE() function enables data scientists and database administrators to convert data between data types easily.
For example, when a user wants to convert a string to a date/time format, they can use the TRY_PARSE() function as follows:
TRY_PARSE(‘20220102’ AS datetime)
The output will be ‘2022-01-02 00:00:00.000.’
Another example is to convert numeric data types. Here is a code snippet on converting string to a numeric data type using the TRY_PARSE() function:
SELECT TRY_PARSE(‘1234’ AS int)
The output will be 1234.
Parameters of TRY_PARSE() Function
The TRY_PARSE() function has three parameters expression, data_type, and culture, as shown below. 1.
Expression: This is the expression that needs conversion. It could be a number, string, date/time value, or any data type that can be converted.
2. Data type: This refers to the data type to which the expression should be converted.
The TRY_PARSE() function can convert string, numeric, and date/time data types. 3.
Culture: The culture parameter is optional. This parameter is used to specify the user’s preferred culture for conversion.
Hence, the result obtained from executing the TRY_PARSE() function would be in the user’s preferred culture.
Examples of Using SQL Server TRY_PARSE() Function
There are various ways to use the TRY_PARSE() function in SQL Server. The following examples demonstrate how to use it for string to date/time conversion, string to number conversion, and using TRY_PARSE() with a CASE expression.
Example 1: Converting a string to date/time
One of the most common use cases of TRY_PARSE() is to convert a string to date/time format. Here’s an example of how to use the TRY_PARSE() function to convert a string to date/time format:
SELECT TRY_PARSE(‘2022-01-02 10:20:30’ AS datetime)
The output of the above code will be ‘2022-01-02 10:20:30.000.’
Example 2: Converting a string to a number
Another typical use case for the TRY_PARSE() function is to convert a string to a numeric data type.
Here is an example of how to use the TRY_PARSE() function to convert a string to a numeric data type:
SELECT TRY_PARSE(‘123.45’ AS numeric(10,2))
The output for this query is 123.45. Example 3: Using TRY_PARSE() function with CASE expression
The TRY_PARSE() function combines well with the CASE statement to handle situations where the conversion fails.
Here’s an example of how to use the TRY_PARSE() function with the CASE statement to manage the cast succeeded/failed cases:
SELECT column1,
CASE WHEN TRY_PARSE(column2 as int) IS NULL THEN ‘N/A’
ELSE TRY_PARSE(column2 AS int) END as column2_int
FROM mytable;
The query above checks whether a conversion of column2 to an integer is successful. Then, if the conversion of column2 to an integer fails, the CASE function displays ‘N/A’ for column2_int.
Final Thoughts
TRY_PARSE() is a powerful function that allows for the conversion of data between various data types in SQL Server. It is a helpful tool for flexible data conversion and a valuable addition to a database professional’s toolkit.
Here, we have provided an overview of the TRY_PARSE() function’s syntax and parameters, as well as some examples of how to use this function in SQL Server. Therefore, this function enables data analysts and database administrators to convert data with ease and accuracy, increasing productivity and efficiency in the workplace.
In conclusion, the SQL Server TRY_PARSE() function is a powerful tool for flexible data conversion between various data types. Its three parameters enable easy and accurate data conversion, including string to date/time and numeric data types.
The function also works well with the CASE statement to handle cast succeeded/failed cases. The article has provided a clear overview of the function’s syntax and parameters, accompanied by practical examples of its application.
As a valuable addition to any database professional’s toolkit, maximizing the use of the SQL Server TRY_PARSE() function enhances productivity and efficiency in the workplace, and ensures accurate and reliable data conversion.