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How to Search for Date and Time Values Using Microsoft SQL Server 2000

Before you can effectively query date/time (or temporal) data, you have to know something about how date/time values are stored. SQL Server supports two date/time data types: datetime and smalldatetime. This article explains in detail the differences between the two, talks about the performance effect of database design on querying, and teaches how to search by date and by time.

uppose you’re writing a query to find all the invoices that were written on January 6, 2003. You know from the control totals that 122 invoices were written that day. But when you run this query:

SELECT * FROM Invoices
WHERE InvoiceDate = '2003-01-06'

The result set is empty. What’s going on?

How Dates and Times Are Stored in SQL Server
Before you can effectively query date/time (or temporal) data, you have to know something about how date/time values are stored. SQL Server supports two date/time data types: datetime and smalldatetime. The difference between the two is the amount of storage used. Datetime uses 8 bytes of storage, while smalldatetime uses only 4 bytes. For this reason, datetime can represent date/time values within a wider range and with more precision than smalldatetime. These differences are summarized in the table below.






Jan 1, 1753

Dec 31, 9999
(0.003 seconds until midnight)

To the nearest
3.33 milliseconds


Jan 1, 1900

Jun 6, 2079
(1 minute until midnight)

To the nearest

Both datetime and smalldatetime represent the date and time as a value that’s equal to the number of days in relationship to a base date. In SQL Server, that base date is midnight on January 1, 1900. As you can see in the table, the smalldatetime type can only represent dates from this base date on. In contrast, the datetime type can also represent dates that are before January 1, 1900. To do that, it stores those values as negative numbers.

To visualize how date/time values are stored, you can think of them as consisting of two parts. The integer portion represents the number of whole days since January 1, 1900. The fractional portion represents the fraction of a day that’s passed since midnight. For example, the date/time value representing noon on January 4, 1900 is stored as 3.5. In this case, 3 represents three full days since the base date and 0.5 represents one half of a day between midnight and noon. To see this, submit the following query:

SELECT CAST(CAST('1900-01-04 12:00' AS datetime) AS float)

Note: The CAST function explicitly changes the data type of a value as specified. So in this statement, the inner CAST changes the string literal '1900-01-04 12:00' to a value of data type datetime. Then, the outer CAST changes that datetime value to a value of data type float. The final result is a floating-point representation of the datetime value that represents noon on January 4, 1900.

So far, so good. But the problems that crop up in querying date/time data are caused by confusion over two fundamental facts that aren’t so obvious. First, date/time data types are approximate numerics, not exact numerics. Second, date/time data types can’t store a date without a time or a time without a date.

Date/Time Values Are Approximate Numerics
Datetime and smalldatetime are like the floating-point data types, float and real, in that they’re approximate numerics. That means the value retrieved from SQL Server may be different from the value that was originally stored. For example, if you store the expression 10/3.0 in a column of data type float, you’ll retrieve a value 3.3333330000000001. Although this is a reasonable representation of ten thirds, it’s not exact since it’s rounded past the 6th digit. In fact, if you add three such values together, you get 9.9999990000000007, not 10. Of course, most programmers understand this as a rounding error. And it’s a persistent problem for all digital computers, not just those running SQL Server. Still, you need to be aware of it as you code search conditions.

In contrast, when working with exact numeric data, the value retrieved from SQL Server is exactly the value that was originally stored. For example, if you store 10/3.0 in a column of data type int, it’s stored as 3 and retrieved as 3. In this case, SQL Server implicitly casts the result of the expression as a real value, 3.333333. Then, SQL Server implicitly casts 3.333333 as an integer because it’s being stored in a column of type int. Although this is still a rounding error, it occurs before the value is stored, not as a result of the physical limitations of computer storage. In other words, the error was introduced by using the wrong data type, not by the inherent limitation of the data type itself. Since the system always returns the same value as was stored, the data type is exact.

Now, to see how this affects date/time values, consider the date and time value for 8:00AM on January 4, 1900. As you saw above, noon on this day is stored as 3.5, or halfway through the fourth day. In contrast, 8:00AM is one third of the way through the day, so its representation will be approximate. To see this for yourself, submit the following query:

SELECT CAST(CAST('1900-01-04 08:00' AS datetime) AS float)

You’ll get the following result:


But if you submit this query:

SELECT CAST(3.3333333 AS datetime), CAST(3.3333334 AS datetime)

you’ll get the following results:

1900-01-04 07:59:59.997             1900-01-04 08:00:00.003

As you can see, these three values are all quite close. In fact, they’re close enough to be considered 8:00AM for most applications. However, in a search condition based on a single value, such as:

WHERE (DTValue = '1900-01-04 08:00')

You’d only match those rows where the stored value exactly matches 3.3333333333333335. You’ll see how to get around this later in this article.

Dates Without Times and Times Without Dates
SQL Server doesn’t provide data types for storing just the date or just the time. So if you store a date/time value without an explicit time, the fractional portion of the value is set to zero. This represents midnight as 00:00:00. Similarly, if you store a date/time value without an explicit date, the integer portion of the value is set to zero. This represents the date January 1, 1900. To see this, submit the following query:

SELECT CAST('1900-01-04' AS datetime), CAST('10:00' AS datetime)

which returns the following result:

1900-01-04 00:00:00.000              1900-01-01 10:00:00.000

Whether you can ignore the date or the time component when you query a date/time column depends on how the column has been designed and used.

The Effect of Database Design on Querying
Database designers don’t always use date/time columns appropriately. At the time the database is designed, each date/time column should be identified as to whether it will store both dates and times, dates only, or times only. The designer, by using defaults, constraints, and triggers, can enforce these rules to prevent the accidental storage of data that are either unnecessary or not applicable.

For example, a column in an accounts payable system for the date an invoice is received is unlikely to need the time. In that case, the designer should plan to use the column solely for dates and never store the time component. A trigger could be assigned to prevent the non-integer portion of the date value from being stored when updating or inserting.

Generally, however, the programmer is forced to work with an existing database. In this case, you should examine the way in which the date/time values are being used before you assume the designer did his or her job correctly.

The simplest way to do that is to submit a query using a search condition similar to the following, where DT is the date/time column in question:

WHERE CAST(FLOOR(CAST(DT AS float))AS datetime) = 0 OR
      DT - CAST(FLOOR(CAST(DT AS float))AS datetime) = 0

Note: The FLOOR function returns the largest integer that is less than or equal to the specified value. In this expression, FLOOR is applied to the floating-point representation of the DT column. This simply strips off the fractional portion of the number.

The first expression returns the date (integer) portion of the value, while the second returns the time portion. If this query returns no rows, it’s likely that the column has been used consistently to store both dates and times, since the date is never 0 and the time is never 0.

Keep in mind, of course, that if the above query returns rows, it doesn’t necessarily imply that the column has been used inconsistently. If the time happens to be exactly midnight or the date happens to be January 1, 1900, then it’ll show up in the result set. In that case, you can test for columns with time-only or date-only data by using either of these two queries:

WHERE TOnly <> Tonly - (CAST(FLOOR(CAST(TOnly AS float))AS datetime))

WHERE DOnly <> CAST(FLOOR(CAST(DOnly AS float))AS datetime)

Here, TOnly and DOnly are date/time columns that you expect contain only times or dates, respectively. If the query returns rows, then those rows don’t contain the type of data you expected.

Determining what kind of data are stored in the date/time columns of each table is important for intelligent querying. If the columns are used consistently, then your job is easier. However, even if the columns are used inconsistently, you’ll at least know which query pitfalls to watch out for as you code your queries.

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