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Working with Columns

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In this chapter, learn many techniques that will serve you well throughout your career as a SQL programmer. Extend your knowledge of the SELECT statement by working with columns in the database tables; learn how to manipulate data using operators, functions, constants, and the CASE statement; explore how to use the CONVERT and CAST functions to change the data type of a value from one type to another, and to format dates differently; and finally, discover how to use the CASE expression to evaluate the values in selected columns and return a different value based on those contents.
This chapter was selected from Sams Teach Yourself Transact-SQL in 21 Days, Second Edition.

Day 3: Working with Columns

Yesterday, you took your first step in understanding the unique language of database access. Learning how to filter and sort the data returned by a SELECT statement provides you with the skills to get data from your database. Of course, this has only scratched the surface of what you can do with a SELECT statement.

In today's lesson, you will extend your knowledge of the SELECT statement by working with the columns in the database tables. We will cover a lot of information today, so prepare yourself. As you read this, you might ask yourself, "What will this do for me?" The answer to that question might not become clear until later in this book. Much of this lesson will help you throughout your career as a SQL programmer. The topics that will be discussed today are

  • Data types

  • Column manipulation

  • The CONVERT and CAST functions

  • Arithmetic and string operators

  • String functions

  • Date functions

  • Numeric functions

  • System functions

  • The CASE statement

Column Characteristics

When a column is added to a database table, you set several parameters that define the characteristics of that column. These include the name of the column, its data type, its length, and a default value. In this section, you will learn all about data types, what they are and how you should use them. In addition, you will see how empty or null columns are treated by the SQL processor.

Data Types

In a database, every column, variable, expression, and parameter has a related data type associated with it. A data type is an attribute that specifies the type of data (integer, string, date, and so on) that object can contain. All the data that a specific column holds must be of the same data type. A data type also determines how the data for a particular column is accessed, indexed, and physically stored on the server.

Tables 3.1 and 3.2 describe each of the available data types in Microsoft SQL Server 2000 and provide an example of what each might be used for. If the data you are working with is of different lengths, such as names, addresses, and other text, you should use variable-length data types. Fixed-length data types are best used for data, such as phone numbers, Social Security numbers, and ZIP Codes.

Table 3.1  Data Types in SQL Server 2000

Long Name




Variable character



Variable-length character fields are best for most strings.




Fixed-length character fields are best for most strings.

National variable characters



Variable-length Unicode data with a maximum length of 4,000 characters.



Jan 1, 200012:15:00.000 pm

Datetime fields are used for precise storage of dates and times. Datetimes can range from Jan 1, 1753 to Dec 31, 9999. Values outside this range must be stored as character.

Small datetime


Jan 2, 200012:15pm

Small datetimes are half the size of datetimes. They use increments of one minute and represent dates from Jan 1, 1900 to Jun 6, 2079.

Precise decimal

decimal(4,2) or numeric(4,2)


Decimal/numeric data types store fractional numerics precisely. The first parameter specifies how many digits are allowed in the field. The second parameter specifies how many digits may come after the decimal. In this example, I could represent numbers from –99.99 to 99.99.

Big floating point



Floating-point numbers are not guaranteed to be stored precisely. SQL Server rounds up numbers that binary math can't handle. Floats take a parameter specifying the total number of digits.

Little float



Half the size of a float; the same rules apply.




Integers are four bytes wide and store numbers between plus or minus two billion.

Small integer



Small integers are half the size of integers, ranging from –32,768 through 32,767.

Tiny integer



Tiny integers are half again the size of small integers, a single byte, and may not be negative. Values run from 0 to 255. Perfect for an age column.




Bits are the smallest data type available today. They are one bit in size, one-eighth of a byte. Bits may not be null and can have a value of 0 or 1. This is the actual language of all computers.




Fixed-length binary data with a maximum length of 8,000 bytes.




Money types range from +/- 922 trillion. Money types store four digits to the right of the decimal and are stored as fixed-point integers.

Small money



Small money can handle about +/ $214,000, with four digits to the right of the decimal. Half the size of Money.



"We the people..."

Text fields can be up to 2GB in size. Text fields are treated at Binary Large Objects-(BLOBs) and are subject to a great many limitations They cannot be used in an ORDER BY, indexed, or grouped, and handling the inside an application program takes some extra work. (BLOBs will be discussed on Day 21, "Handling BLOBs in T-SQL.")




Image data can be used to store any type of binary data, including images (gif, jpg, and so on), executables, or anything else you can store on your disk drive. Images are also BLOBs, subject to the same limitations.

Table 3.2  New Data Types in SQL Server 2000

Long Name




Big integer



A large integer that can hold a number +/- 2 raised to the 63rd power. Twice as large as an integer.




A data type that stores values of other supported data types, except text, ntext, and sql_variant. You can use this to hold data from any other data type without having to know the data type in advance.




This is a special data type that can be used to store a result set for later processing. It is primarily used for temporary storage of a set of rows.

Data Type Precedence

When two expressions of different data types are combined using one or more operators or functions, the data type precedence rules specify which data type is converted to the other. The data type with the lower precedence is converted to the data type with the higher precedence. If the conversion is not a supported implicit conversion, an error is returned. If both expressions have the same data type, the resulting object has the same data type. The order of precedence for the data types are shown in Table 3.3.

Table 3.3  Data Type Order of Precedence

Precedence Number

Data Type















































An example of the precedence can be seen when you add two unlike numbers together as shown here:

Select quantity * price as Sale_Total from "Order Details"

The quantity column is an integer data type, whereas the price column is a money data type. When this calculation is performed, the result would be a money data type.

Note - Don't worry about how to use the multiplication operator, or what the as Sale_Total means. We will cover these issues later in this lesson.

Using Null Data

When a column is empty, it is treated differently than when a column is blank or zero. These might sound the same to you, but to the computer, they are very different. A blank is an actual character that takes a position in the column. Of course, zero is its own explanation. A null means that the column actually contains nothing. To see how a null is displayed, try executing the following SQL statement in the Query Analyzer:

use pubs
select title_id, advance
from titles
order by title_id


title_id advance
-------- ---------------------
BU1032   5000.0000
BU1111   5000.0000
BU2075   10125.0000
BU7832   5000.0000
MC2222   .0000
MC3021   15000.0000
MC3026   NULL
PC1035   7000.0000
PC8888   8000.0000
PC9999   NULL
TC4203   4000.0000
TC7777   8000.0000

(18 row(s) affected)

You should see that null values are displayed using the string NULL. However, most standard comparisons will not recognize the null value. The next example shows you what happens when I add a where clause to the SELECT statement. I am looking for all title IDs where the advance amount is less than $5,000. You might think that 'null' or 'empty' are identical, but they're not. Although you will be covering functions later in this lesson, I want to cover one in this section.

The isnull() function permits a way to include null values in aggregate calculations. The function requires two arguments and its syntax is shown here:

Isnull(<expression>, <value>)

The first argument is the expression on which the calculation will be performed; usually this is a column from the database. The second argument is the value that will replace the null value for display or calculation purposes. If the expression contains a null, the second parameter is returned by this function. If the expression is not null, the value in the expression is returned.

The following SQL query shows the effect of the isnull() function on the titles table in the pubs database.

use pubs
select title_id, price, isnull(price, $45)
from titles
order by price

The output of this query shows which prices were null:

title_id price
-------- --------------------- ---------------------
MC3026   NULL                  45.0000
PC9999   NULL                  45.0000
MC3021   2.9900                 2.9900
BU2075   2.9900                 2.9900
PS2106   7.0000                 7.0000
PS3333  19.9900                19.9900
PC8888  20.0000                20.0000
TC3218  20.9500                20.9500
PS1372  21.5900                21.5900
PC1035  22.9500                22.9500

(18 row(s) affected)

As you can see in the output, there are two titles in the table that have null in their price column. When the isnull() function evaluates those columns, it returns $45 for the third column in the result set. For the other columns, it simply returns the value of the column.

You can use the isnull() function inside an aggregate function. If you want to know the average price of a book, you would ask for the avg(price) on the titles table. However, the two books that have null for a price would be excluded from the calculation. Suppose that you know that those books will be priced at $29 each. You could use the isnull() function to include those books in the calculation. The example shows you the query without the isnull() function and then with the isnull() function.

select avg(price)
from titles



(1 row(s) affected)

Warning: Null value is eliminated by an aggregate or other SET operation.

As you can see, the server displays a warning message informing you that there were null values found and they were not used.

select avg(isnull(price, $29))
from titles



(1 row(s) affected)

In the second version of the query, you can see that the average returned is different because the two books with nulls were included in the calculation. The isnull() function doesn't change the value of the row in the table; it only assumes a value for the purposes of a single query.

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