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ASP FAQ Tutorials :: Databases :: Other Articles :: What datatype should I use for numeric columns?

What datatype should I use for numeric columns?

Some people just accept whatever datatype was assigned by the Access upsizing wizard, or a DTS import; others just use MONEY if they're storing prices or salaries, or INT if they know they don't need decimal places. 
There should be more careful thought taken when considering datatypes for efficient and scalable database design. First, let's review the different datatypes, numeric capacities, and storage requirements. 
Data typeCapacityStorage (bytes)
BIT0, 1, or NULL<=1 *
TINYINT0 -> 255 
0 -> 28-1
SMALLINT-32,768 -> 32,767 
-(215) -> (215)-1
INT-2,147,483,648 -> 2,147,483,647 
-(231) -> (231)-1
BIGINT-9,223,372,036,854,775,808 -> 9,223,372,036,854,775,807 
-(263)/10000 -> ((263)-1)/10000
SMALLMONEY-214,748.3648 -> 214,748.3647 
-(231)/10000 -> ((231)-1)/10000
MONEY-922,337,203,685,477.5808 -> 922,337,203,685,477.5807 
-(263)/10000 -> ((263)-1)/10000
REAL-3.40E + 38 -> 3.40E + 384
FLOAT-1.79E + 308 -> 1.79E + 3084 or 8
NUMERIC-1038 -> 1038-15-17
DECIMAL-1038 -> 1038-15-17
* Up to 8 BIT columns can share 1 byte. 
Now, let's examine some known limitations with a few of these datatypes. 
The BIT datatype doesn't have enough selectivity to make an index useful (though people seem to always want to apply an index to a BIT column). Keep in mind that BIT is not equivalent to BOOLEAN; BIT allows three values (0, 1, or unknown), while a true BOOLEAN type requires exactly two values (TRUE or FALSE). There is no BOOLEAN datatype in SQL Server, and this can cause some confusion. 
An important problem with these datatypes is the potential loss of precision when using multiplication or division. For example, compare these results: 
SELECT [money] = $0.46 / $345.70, 
    [decimal] = 0.46 / 345.70
The results are hardly shocking, and in small calculations like the tax on a pair of shoes, are not likely to be an issue. But for larger transactions, this behavior could certainly mean a tangible difference in the outcome. 
The problem with the REAL and FLOAT datatypes is that the store a close, but not exact, representation of the value. And while they follow the IEEE 754 specification for approximate data types, this can still cause some highly unexpected results (see Article #2477 for an example). 

So, what now? 
Now that we've touched on the capabilities and a few of the problems with each of the numeric data types, how do you know which to use in your situation? 
Whole numbers 
For whole numbers, use the smallest datatype that will hold your maximum potential value. This will make indexes on the column perform their best, and will keep your table size down. It is easy to later a column to make it wider, if you later realize you need to allow larger values; it can be much more difficult making a column more narrow when you need to recover space. 
INT is probably a better option for salary than MONEY, since decimal points are usually not needed. SMALLINT would be suitable if you work at a non-profit with very low salary caps. :-) 
Decimal numbers 
REAL and FLOAT should be avoided when possible. Since approximate numbers can lead to imprecise results, opt for NUMERIC or DECIMAL. Each are functionally equivalent; I tend to use DECIMAL, if only because it better describes the nature of the data. 
For a column that stores an hourly wage, a DECIMAL with a precision of 5 and scale of 2 (see footnotes for definitions) is probably a better choice than MONEY or SMALLMONEY. Both of the latter types have far too much precision and scale to be useful for a value that is almost always going to be less than $40.00. 
I don't know of any true advantages to using the MONEY / SMALLMONEY datatypes; while there are specific STR and CONVERT expressions that can format money in desirable ways, I usually leave this formatting to the client (see Article #2188). Because of the problems with multiplication and division of money values (e.g. calculating a discount), it is usually safer to use a DECIMAL data type. This also allows you to keep the datatype as small as it needs to be. For example, an online store that sells bee and honey equipment does not need to support the upper bound of the SMALLINT datatype (over $214,000!), never mind the upper bound of the MONEY datatype. 

What are precision and scale? 
NUMERIC and DECIMAL datatypes are defined as: 
DECIMAL(precision, scale) 
NUMERIC(precision, scale)
Precision is the number of decimal places that the column can take; scale is the number of decimal places allowed to the right of the decimal point. So, if you need to store percentages (e.g. 48.97%) you would use DECIMAL(5,2) — assuming you needed to cover the 100.00% case. If you wanted more accuracy, you could use DECIMAL(8,5), which would allow a value like 48.96523%. 
There are known problems with the ISNUMERIC() function in T-SQL. Keep in mind that, just because a value returns ISNUMERIC(value)=1, that does not mean that the value can be converted seamlessly to any of the above data types. See Article #2390 for more information.

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Created: 12/7/2003 | Last Updated: 1/24/2004 | broken links | helpful | not helpful | statistics
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