It would be incorrect to say that floating-point numbers should never be used as an SQL data type for arithmetic. I will stick to double-precision floating-point data types for SQL Server that are suitable for my requirements.

The double-precision floating-point data type is ideal for modeling weather systems or displaying trajectories but not for the type of calculations the average organization may use in the database. The biggest difference is in the accuracy when creating the database. You need to analyze the data types and fields to ensure no errors and insert the data values for maximum accuracy. If there is a large deviation, the data will not be processed during the calculation. If you detect incorrect use of the data type with double precision, you can switch to a suitable decimal or number type.

What are the differences between numeric, float, and decimal data types, and should they be used in which situations?

- Approximate numeric data types do not store the exact values specified for many numbers; they store an extremely close approximation of the value
- Avoid using float or real columns in WHERE clause search conditions, especially the = and <> operators

For example, suppose the data that the report has received is summarized at the end of the month or end of the year. In that case, the decimal data for calculation becomes integer data and is added to the summary table.

In SQL Server, the data type float _ n corresponds to the ISO standard with a value from n = 1 to 53. The floating-point data is approximated not by the data type’s value but by the range of what can be represented. Both float- and float-related numeric SQL types consist of a significant numeric value and an exponent, a signed integer that indicates the size of the numeric value.

And float-related numeric SQL data types are precise positive integers that define the number of significant digits and exponents of a base number. This type of data representation is called floating-point representation. A float is an approximate number, meaning that not all values can be displayed in the data type range because it is a rounded value.

You can’t blame people for using a data type called Money to store the money supply. In SQL Server, decimal, number, Money, and SmallMoney data types have a decimal place to store values. Precision means the total number of digits after the decimal point.

From a mathematical point of view, there is a natural tendency to use floats. People who use float spend their lives rounding up values and solving problems that shouldn’t exist. As I mentioned earlier, there are places where it makes sense to hover above the real, but these are for scientific calculations, not business calculations.

SmallMoney (2144783647, 4 bytes) We can use this data type for Money- or currency values. The double type can be used as a data type with real values for dealing with Money.

Type Description Memory bits Integer 0 1 null TinyInt allows integers 0 to 255 1 bytes TinyInt allows integers 32767 2 bytes Int allows integers 2147483647 4 bytes BigInt allows integers 9223372036854775807 8 bytes Decimal P is a precisely scaled number. The parameter p specifies the maximum total number of digits stored to the left or right of the decimal point. The data type low and upper range storage observations Real 340E 38 4 Bytes We can use float924 as an ISO synonym for real.

In MariaDB, the number of seconds has elapsed since the beginning of the 1970s (01-01) with a decimal accuracy of 6 digits (0 is the default). The same range of precision is the SQL Server type range (bytes) MariaDB type range size (bytes) Precision notes Date 0001 01-01-99.99 12: 31: 3 They cover the same range: Date 0.001-03-01 9.99912: 31 8: 0: 3 Round DateTime 0.01 0.1-02.9999 12: 31 8 0: 6 In MariaDB the value is near impossible to specify (see below). We can insert a value that requires fewer bits than that assigned to the null-bit pad on the left.

A binary string is a sequence of octets, not a character set, and the associated sorting is described by the binary data type descriptor. Decimal (p) is the exact numerical precision (p scale (n)) of a decimal number that is any number with a decimal point. A Boolean data type consists of different truth values (true, false, and boolean), and it supports unknown truth values, zeroes, and forbidden (not zero) constraints.

This syntax was deprecated in MySQL 8.0.17.7 and will be removed in future versions of MySQL: float (p) A floating-point number. MySQL uses the p-value to specify whether to use a float or a double due to the data type.

Creating data types in PostgreSQL is done with the create-type command. For example, the following commonly used data types are organized into categories with a brief description of the value range and memory size. The native data type is the text data type, the numeric data type, and the date/time Boolean data type.

To understand what floating-point SQL is and what numerical data types are, you need to study computer science a little. Floating-point arithmetic was developed when saving memory was a priority and was used as a versatile method for calculating large numbers. The SQL Prompt Code Analysis Rule (BP023) warns you when using Floating over Real data types. It introduces significant inaccuracies into the type of calculations that many companies do with their SQL Server data.

The difference between a float and a p is that a real float is binary (not decimal) and has an accuracy equal to or greater than the defined value.

The reason for this difference is that the SQL standard specifies a default from 0 to D. Still, the implementation is free to choose a default M. This means that an operation of this type will result in a result different from the result it would produce for MariaDB type if you use enough decimal places. It is important to remember that numerical SQL data types sacrifice precision ranges to approximate the names.