The real data type has a range that spans from approximately -3.40E+38 to -1.18E-38 for negative values, includes 0, and extends from 1.18E-38 to 3.40E+38 for positive values. This data type is used to store single-precision floating-point numbers.
Understanding the Real Data Type
In database systems like SQL Server, the real data type is an approximate-number data type, meaning that not all values within its range can be represented exactly. It stores floating-point numbers with a lower precision compared to the float data type.
Range and Characteristics of Real
The range of the real data type specifies the smallest and largest magnitudes it can store. The notation "E" signifies scientific notation, where "E+38" means multiplied by 10 to the power of 38, and "E-38" means multiplied by 10 to the power of -38 (a very small number close to zero).
Here's a breakdown of its key characteristics:
| Data Type | Range | Storage |
|---|---|---|
| real | -3.40E+38 to -1.18E-38, 0, and 1.18E-38 to 3.40E+38 | 4 Bytes |
This means real can handle very large and very small numbers. The "0" explicitly included in the range indicates that zero can also be stored. The gap between -1.18E-38 and 1.18E-38 (excluding zero) represents values that are too close to zero to be represented by the real data type; these would typically underflow to zero.
Practical Considerations and Usage
When deciding whether to use the real data type, consider the following practical aspects:
- Precision: As a single-precision floating-point number,
realoffers approximately 7 decimal digits of precision. This is suitable for many scientific or engineering calculations where a certain degree of approximation is acceptable. - Storage Efficiency: Occupying only 4 bytes of storage,
realis more memory-efficient thanfloat(which typically uses 8 bytes for double precision). This can be a factor in very large datasets where storage optimization is critical. - Approximate Nature: It's crucial to remember that
realstores approximate values. This means that direct equality comparisons (e.g.,WHERE column = 1.23) might not yield expected results due to tiny internal representation differences. For exact numerical storage, especially for financial data,decimalornumericdata types are preferred. - Use Cases:
realis often used for:- Measurements from sensors where high precision isn't achievable or necessary.
- Graphics and simulations where speed and smaller memory footprint are advantageous.
- Scientific computations where the inherent imprecision of floating-point arithmetic is understood and managed.
Choosing real depends on the specific requirements for range, precision, and storage efficiency of your application.
