Article Directory
- 1. Data type conversion scenario
- 2. Data type conversion and examples
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- 1、cast(value AS type)
- 2、to_date(text)
- 3、to_date(text,format)
- 4、to_char(datetime, format)
- 5、to_char(string)
- 6. CASE expression
- 3. Summary
1. Data type conversion scenario
Data type conversion is very common in practical applications. As an enterprise-level distributed relational database, GaussDB cannot avoid data type conversion in actual business scenarios. The following are some application scenarios for data type conversion:
Data cleaning and conversion: In data analysis and processing, it is often necessary to clean and convert data, such as converting text data to digital format, converting date format to text format, etc.
Data formatting: When outputting data, the data needs to be formatted into an appropriate format, such as formatting numbers into currency format, percentage format, etc.
Data calculation: When performing data calculation, it is necessary to convert the data type, such as converting integer type data to floating point type data, in order to perform accurate calculation or processing.
Data storage: When storing data in the database, it is necessary to convert different types of data into the data types supported by the database in order to store and query the data correctly.
Data transmission: In the process of data transmission, different types of data need to be converted into the same data type in order to transmit the data correctly.
In short, data type conversion has a wide range of applications in the fields of data processing, data analysis, data storage, and data transmission.
2. Data type conversion and examples
In the SQL language, every piece of data is associated with a datatype that determines its behavior and usage. GaussDB provides an extensible data type system that is more general and flexible than other SQL implementations. Thus, most type conversions in GaussDB are governed by generic rules.
The database allows some data types to perform implicit type conversion (assignment, parameters of function calls, etc.), and some data types do not allow implicit data type conversion. You can try to use the type conversion function provided by GaussDB.
1、cast(value AS type)
Description: CAST performs data type conversion. Values can be explicitly converted to the specified type if necessary.
1) Integer to floating point
SELECT CAST(1 AS FLOAT8); – Convert the integer 1 to a floating point number
2) SELECT
CAST(3.14 AS INT4); – convert the floating point number 3.14 to integer 3
3) Convert Boolean to integer
Use the CAST function to convert Boolean data into integer data, where TRUE is converted to 1, and FALSE is converted to 0, for example:
SELECT CAST(TRUE AS INT4),CAST(FALSE AS INT4); – Convert Boolean data TRUE is converted to integer 1; Boolean data FALSE is converted to integer 0
2、to_date(text)
Description: Converts a value of type text to a timestamp in the specified format.
Format 1: Dates without separators, such as 20230314, need to include the complete year, month, and day.
Format 2: Date with delimiter, such as 2023-03-14, the delimiter can be any single non-numeric character.
SELECT TO_DATE('20230314'),TO_DATE('2023-03-14');
3、to_date(text,format)
Description: Convert a value of type string to a date in the specified format.
SELECT TO_DATE('14 MAR 2023', 'DD MON YYYY'),TO_DATE('20230314','YYYYMMDD');
4、to_char(datetime, format)
Description: Date and time type to character type.
SELECT TO_CHAR(NOW(), 'YYYY-MM-DD HH24:MI:SS'); – Convert the current date and time data to character data in the format of 'YYYY-MM-DD HH24:MI:SS'
5、to_char(string)
Description: Convert CHAR, VARCHAR, VARCHAR2, and CLOB types to VARCHAR types.
SELECT TO_CHAR(1110)
6. CASE expression
Convert Boolean to character, use CASE expression to convert Boolean data to character data, for example:
1) SELECT CASE WHEN TRUE THEN 'TRUE' ELSE 'FALSE' END; – convert Boolean data TRUE to character data' TRUE'
2) SELECT CASE WHEN FALSE THEN 'TRUE' ELSE 'FALSE' END; – Convert Boolean data FALSE to character data 'FALSE'
3. Summary
Datatype conversion is the process of converting one datatype to another. In , we often need to convert data types to meet the needs of the code. In many programming languages, data type conversion can be divided into two types: implicit conversion and explicit conversion. Implicit conversion means that when operations such as assignment, operation, or comparison are performed in the code, the programming language will automatically convert the data type to ensure the correctness and legality of the operation.
GaussDB supports a variety of data type conversions, the following are commonly used data type conversion methods in GaussDB:
Implicit conversion: GaussDB supports implicit conversion, that is, in expressions, if operands of different data types participate in operations, GaussDB will automatically convert One of the data types is converted to another data type to meet the operation requirements. For example, if an integer value is operated on with a floating point value, GaussDB will convert the integer to a floating point number before performing the operation.
Explicit conversion: GaussDB supports explicit conversion using the CAST function. The CAST function converts a value of one data type to a value of another data type. For example, use the CAST function to convert a string type to an integer type.
-Number conversion: GaussDB supports converting numeric types to other numeric types, such as converting integers to decimals, converting decimals to integers, etc.
- String conversion: GaussDB supports converting string types to other data types, such as converting strings to integers, converting strings to date types, etc.
- Date conversion: GaussDB supports converting date types to other date types, such as converting date to time type, converting time type to date type, etc.
- Boolean conversion: GaussDB supports converting Boolean data to other data types, such as converting Boolean to integer, converting Boolean to string, etc.
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It should be noted that when performing data type conversion, problems such as data precision, data overflow, and data distortion should be considered, and errors caused by incompatible data types should also be avoided. Of course, data type conversion will also affect query efficiency and performance, which needs to be optimized and adjusted according to actual business needs and data volume.
This is the end of the above examples. For more type conversions, please refer to the official website documentation. You are welcome to test and communicate.