This chapter covers how one creates the database structures that will hold one's data. In a
relational database, the raw data is stored in tables, so the majority of this chapter is
devoted to explaining how tables are created and modified and what features are available to
control what data is stored in the tables. Subsequently, we discuss how tables can be
organized into schemas, and how privileges can be assigned to tables. Finally, we will briefly
look at other features that affect the data storage, such as views, functions, and triggers.
Detailed information on these topics is found in the PostgreSQL 7.3
Programmer's Guide.
A table in a relational database is much like a table on paper: It consists of rows and
columns. The number and order of the columns is fixed, and each column has a name. The
number of rows is variable -- it reflects how much data is stored at a given moment. SQL
does not make any guarantees about the order of the rows in a table. When a table is read,
the rows will appear in random order, unless sorting is explicitly requested. This is
covered in Chapter 4.
Furthermore, SQL does not assign unique identifiers to rows, so it is possible to have
several completely identical rows in a table. This is a consequence of the mathematical
model that underlies SQL but is usually not desirable. Later in this chapter we will see how
to deal with this issue.
Each column has a data type. The data type constrains the set of possible values that can
be assigned to a column and assigns semantics to the data stored in the column so that it
can be used for computations. For instance, a column declared to be of a numerical type will
not accept arbitrary text strings, and the data stored in such a column can be used for
mathematical computations. By contrast, a column declared to be of a character string type
will accept almost any kind of data but it does not lend itself to mathematical
calculations, although other operations such as string concatenation are available.
PostgreSQL includes a sizable set of built-in data types
that fit many applications. Users can also define their own data types. Most built-in data
types have obvious names and semantics, so we defer a detailed explanation to Chapter 5. Some of the
frequently used data types are integer for whole numbers, numeric for possibly fractional numbers, text for
character strings, date for dates, time for
time-of-day values, and timestamp for values containing both date and
time.
To create a table, you use the aptly named CREATE TABLE command.
In this command you specify at least a name for the new table, the names of the columns and
the data type of each column. For example:
CREATE TABLE my_first_table (
first_column text,
second_column integer
);
This creates a table named my_first_table with two columns. The
first column is named first_column and has a data type of text; the second column has the name second_column
and the type integer. The table and column names follow the identifier
syntax explained in Section
1.1.1. The type names are usually also identifiers, but there are some exceptions. Note
that the column list is comma-separated and surrounded by parentheses.
Of course, the previous example was heavily contrived. Normally, you would give names to
your tables and columns that convey what kind of data they store. So let's look at a more
realistic example:
CREATE TABLE products (
product_no integer,
name text,
price numeric
);
(The numeric type can store fractional components, as would be
typical of monetary amounts.)
Tip: When you create many interrelated tables it is wise to choose a
consistent naming pattern for the tables and columns. For instance, there is a choice of
using singular or plural nouns for table names, both of which are favored by some
theorist or other.
There is a limit on how many columns a table can contain. Depending on the column types,
it is between 250 and 1600. However, defining a table with anywhere near this many columns
is highly unusual and often a questionable design.
If you don't need a table anymore, you can remove it using the DROP
TABLE command. For example:
DROP TABLE my_first_table;
DROP TABLE products;
Attempting to drop a table that does not exist is an error. Nevertheless, it is common in
SQL script files to unconditionally try to drop each table before creating it, ignoring the
error messages.
How to choose and buy a barcode scanner.
Shopping guide for breast enhancement products.
Buying guide for colon cleanser and using it
correctly.
If you need to modify a table that already exists look into Section 2.6 later in
this chapter.
With the tools discussed so far you can create fully functional tables. The remainder of
this chapter is concerned with adding features to the table definition to ensure data
integrity, security, or convenience. If you are eager to fill your tables with data now you
can skip ahead to Chapter 3
and read the rest of this chapter later.
