The difference between integers and floating point numbers - Database & Sql Blog Articles

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Integers are positive integers, negative integers, and zeros, such as -6, 0, 32, and so on.

A floating point number is a rational number with a finite number of decimal places, such as -10.8, 0.00, 25.01, and so on.
An integer can be either an integer or a floating point number, for example 255 is an integer and 255.0 is a floating point number.
Integer arithmetic, the result is an integer, and the fractional part of the calculation result will be ignored. For example: when using integer arithmetic, 100÷3=33.
Floating point arithmetic, the result is a floating point number, the fractional part of the calculation result will be retained.
For example: when using floating point arithmetic, 100.0÷3.0=33.33333333.

Floating point:
4.2×10^8=420000000
Integer:
42×1000000=420000000

Floating-point performance can intuitively reflect the computing power of a cpu, pay attention to "computing power"

Floating point numbers are used in computers to approximate any real number. Specifically, this real number is obtained by multiplying an integer or fixed-point number (that is, a mantissa) by the integer power of a certain base (usually 2 in the computer). This representation is similar to the scientific notation with a base of 10.
Floating-point calculations are operations in which floating-point numbers participate, and such operations are usually accompanied by approximations or roundings that cannot be accurately represented.
A floating point number a is represented by two numbers m and e: a = m × be. In any such system, we choose a base b (base of the counting system) and precision p (ie how many bits are used for storage). m (ie, the mantissa) is the number of p-bits of the form ±d.ddd...ddd (each bit is an integer between 0 and b-1, including 0 and b-1). If the first bit of m is a non-zero integer, m is called normalized. Some descriptions use a single sign bit (s for + or -) to indicate positive or negative, so m must be positive. e is an index.
This design represents a greater range of a given point can not be represented in the memory space of a fixed length.
For example, a 4-digit decimal floating-point number with an exponent range of ± 4 can be used to represent 43210, 4.321, or 0.0000041, but there is not enough precision to represent 432.123 and 43212.3 (must be approximated 432.1 and 43210). Of course, the actual number of bits used is usually much larger than 4.
In addition, floating-point representations usually include special values: +∞ and ?6?1∞ (positive and negative infinity) and NaN('Not a Number'). When infinity is used when the number is too large to be represented, NaN indicates an illegal operation or an undefined result.
Most computers use a binary (b=2) representation. Bit is the unit of storage space required to measure floating-point numbers, usually 32-bit or 64-bit, called single-precision and double-precision. Some computers offer larger floating point numbers, such as Intel's floating-point unit Intel8087 coprocessor (and its descendants integrated into x86 processors) that provide 80-bit floating-point numbers for storing floating-point operations. The intermediate result. There are also some systems that provide 128-bit floating point numbers.