FLOAT DataType in MySQL

• In MySQL Workbench, the FLOAT data type is used to store approximate numeric values with single-precision floating-point representation. It is suitable for storing decimal numbers with a moderate range of values and fractional parts. The FLOAT data type occupies 4 bytes (32 bits) of storage.

Let's explore the details of the FLOAT data type:

• FLOAT:
• Size: 4 bytes (32 bits)
• Range: The FLOAT data type can store values with approximately 7 decimal digits of precision.
• Usage: Use the FLOAT data type when you need to store decimal numbers with moderate precision and a relatively wide range of values.

Example of creating a table with a FLOAT column:

    CREATE TABLE example_table (

        id INT,

        temperature FLOAT

    );

• In the example above, the "temperature" column is a FLOAT column that can store decimal numbers representing temperatures, such as 23.5 or -10.75.

• The FLOAT data type uses a binary floating-point representation, which means that not all decimal numbers can be precisely represented. Some decimal values might be subject to rounding or approximation, leading to a potential loss of precision. If high precision is essential for your application, you may consider using the DOUBLE data type instead, which offers double-precision and greater accuracy.

When using the FLOAT data type, consider the following points:

• Floating-point numbers are approximate, so you might encounter some rounding errors in calculations.

• The FLOAT data type is suitable for applications where high precision is not critical, and you want to optimize storage space compared to DOUBLE.

• If you need higher precision and a wider range of values, consider using the DOUBLE data type instead.

• Remember that the choice of data type should be based on the specific requirements of your application. Selecting the appropriate data type is crucial for ensuring data accuracy and efficient storage in your MySQL database. Using a data type that is too large may lead to unnecessary storage consumption, while using a data type that is too small can result in data truncation or loss of precision.