MSSQL数字类型保留精度的最佳实践

Introduction

MSSQL is a widely-used relational database management system that offers different datatypes for storing and manipulating data. One of the critical aspects of data management is ensuring the precision of numeric data types. In this article, we'll explore the best practices for preserving the precision of numeric data types in MSSQL database.

1. Understanding Numeric Data Types

Numeric data types in MSSQL are divided into two broad categories: exact numeric data types and approximate numeric data types. The exact numeric data types include integer and decimal types, while the approximate numeric data types include float and real types.

1.1 Integer Data Types

Integer data types are used to store whole numbers that do not contain decimal points. There are different types of integer data types in MSSQL, such as tinyint, smallint, int, and bigint.

CREATE TABLE demo (

id int PRIMARY KEY,

age tinyint,

year smallint,

count int,

total bigint

);

1.2 Decimal Data Types

Decimal data types are used to store numbers with fractional parts. They are sometimes called fixed-point types because they store a fixed number of digits to the right and left of the decimal point. Decimal data types have a precision and a scale. The precision is the total number of digits that can be stored, and the scale is the number of digits to the right of the decimal point.

CREATE TABLE demo (

id int PRIMARY KEY,

price decimal(8,2)

);

1.3 Float and Real Data Types

Float and real data types are used to store approximate numeric values. Both data types are floating-point types, which means that they store values with a certain number of significant digits. However, these types are not guaranteed to preserve the precision of the stored values. The float data type has a precision of 53 bits, while the real data type has a precision of 24 bits.

CREATE TABLE demo (

id int PRIMARY KEY,

value float,

weight real

);

2. Preserving Numeric Data Types Precision

2.1 Avoid Implicit Conversions

Implicit conversions occur when MSSQL automatically converts one data type to another. Such conversions can lead to precision loss, especially if the target data type has a smaller range or a lower precision than the source data type. Therefore, it's essential to avoid implicit conversions when working with numeric data types.

To avoid implicit conversions, use explicit conversion functions such as CAST or CONVERT to convert data types explicitly.

SELECT CAST(price AS decimal(8,2)) AS new_price FROM demo;

2.2 Use Numeric Data Types Appropriately

Choosing the appropriate numeric data type for your data can help preserve its precision. For example, if you're storing currency values, use the decimal data type instead of the float or real data types. Similarly, if you're storing small integer values, use the tinyint or smallint data types instead of the int or bigint data types.

2.3 Specify Precision and Scale

When defining columns with decimal data types, it's essential to specify the precision and scale explicitly. If you use the default values for precision and scale, you might end up with unexpected results.

The precision and scale should be chosen based on the range of values you're storing and the level of precision required. For example, if you're storing monetary values, you might choose a precision of 10 and a scale of 2.

CREATE TABLE demo (

id int PRIMARY KEY,

price decimal(10,2)

);

2.4 Use Arithmetic Functions Carefully

MSSQL provides various arithmetic functions for performing mathematical operations on numeric data types. However, some functions can cause precision loss if used incorrectly. For example, the ROUND function can round off decimal values and cause precision loss. Similarly, the SUM function can cause overflow errors if the result exceeds the range of the target data type.

To avoid these issues, it's important to use arithmetic functions carefully and consider their impact on the precision and range of the data.

2.5 Store Data in Normalized Form

Storing data in normalized form can help preserve its precision and reduce redundancy. Normalization involves breaking down tables into smaller, more manageable tables and eliminating redundant data.

For example, instead of storing customer information in a single table, you might split the information into separate tables such as customers, orders, and order items. This approach can help ensure that each table contains only the necessary information and prevent duplication of data.

Conclusion

Precise management of numeric data types is essential for accurate data processing and analysis. By understanding the different types of numeric data available in MSSQL and following best practices for preserving precision, you can ensure that your data is reliable, consistent, and error-free.

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