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<!-- $Header: /cvsroot/pgsql/doc/src/sgml/ddl.sgml,v 1.1 2002/08/05 19:44:57 petere Exp $ -->
<chapter id="ddl">
<title>Data Definition</title>
<para>
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
namespaces, 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 &cite-programmer;.
</para>
<sect1 id="ddl-basics">
<title>Table Basics</title>
<para>
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 <xref
linkend="queries">. 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.
</para>
<para>
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.
</para>
<para>
<productname>PostgreSQL</productname> 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 <xref
linkend="datatype">. Some of the frequently used data types are
<type>integer</type> for whole numbers, <type>numeric</type> for
possibly fractional numbers, <type>text</type> for character
strings, <type>date</type> for dates, <type>time</type> for
time-of-day values, and <type>timestamp</type> for values
containing both date and time.
</para>
<para>
To create a table, you use the aptly named <literal>CREATE
TABLE</literal> 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:
<programlisting>
CREATE TABLE my_first_table (
first_column text,
second_column integer
);
</programlisting>
This creates a table named <literal>my_first_table</literal> with
two columns. The first column is named
<literal>first_column</literal> and has a data type of
<type>text</type>; the second column has the name
<literal>second_column</literal> and the type <type>integer</type>.
The table and column names follow the identifier syntax explained
in <xref linkend="sql-syntax-identifiers">. The type names are
also identifiers, but there are some exceptions. Note that the
column list is comma-separated and surrounded by parentheses.
</para>
<para>
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:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric
);
</programlisting>
(The <type>numeric</type> type can store fractional components, as
would be typical of monetary amounts.)
</para>
<tip>
<para>
When you create many interrelated tables it is wise to choose a
consistent naming patter 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.
</para>
</tip>
<para>
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.
</para>
<para>
If you don't need a table anymore, you can remove it using the
<literal>DROP TABLE</literal> command. For example:
<programlisting>
DROP TABLE my_first_table;
DROP TABLE products;
</programlisting>
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.
</para>
<para>
If you need to modify a table that already exists look into <xref
linkend="ddl-alter"> later in this chapter.
</para>
<para>
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 <xref linkend="dml"> and read the
rest of this chapter later.
</para>
</sect1>
<sect1>
<title>Default Values</title>
<para>
A column can be assigned a default value. When a new row is
created and no values are specified for some of the columns, the
columns will be filled with their respective default values. A
data manipulation command can also request explicitly that a column
be set to its default value, without knowing what this value is.
(Details about data manipulation commands are in the next chapter.)
</para>
<para>
If no default value is declared explicitly, the null value is the
default value. This usually makes sense because a null value can
be thought to represent unknown data.
</para>
<para>
In a table definition, default values are listed after the column
data type. For example:
<programlisting>
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric <emphasis>DEFAULT 9.99</emphasis>
);
</programlisting>
</para>
<para>
The default value may be a scalar expression, which well be
evaluated whenever the default value is inserted
(<emphasis>not</emphasis> when the table is created).
</para>
</sect1>
<sect1 id="ddl-constraints">
<title>Constraints</title>
<para>
Data types are a way to limit the kind of data that can be stored
in a table. For many applications, however, the constraint they
provide is too coarse. For example, a column containing a product
price should probably only accept positive values. But there is no
data type that accepts only positive numbers. Another issue is
that you might want to constrain column data with respect to other
columns or rows. For example, in a table containing product
information, there should only be one row for each product number.
</para>
<para>
To that end, SQL allows you to define constraints on columns and
tables. Constraints give you as much control over the data in your
tables as you wish. If a user attempts to store data in a column
that would violate a constraint, an error is raised. This applies
even if the value came from the default value definition.
</para>
<sect2>
<title>Check Constraints</title>
<para>
A check constraint is the most generic constraint type. It allows
you to specify that the value in a certain column must satisfy an
arbitrary expression. For instance, to require positive product
prices, you could use:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric <emphasis>CHECK (price > 0)</emphasis>
);
</programlisting>
</para>
<para>
As you see, the constraint definition comes after the data type,
just like default value definitions. Default values and
constraints can be listed in any order. A check constraint
consists of the key word <literal>CHECK</literal> followed by an
expression in parentheses. The check constraint expression should
involve the column thus constrained, otherwise the constraint
would not make too much sense.
</para>
<para>
You can also give the constraint a separate name. This clarifies
error messages and allows you to refer to the constraint when you
need to change it. The syntax is:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric <emphasis>CONSTRAINT positive_price</emphasis> CHECK (price > 0)
);
</programlisting>
To specify a named constraint, use the key word
<literal>CONSTRAINT</literal> followed by an identifier followed
by the constraint definition.
</para>
<para>
A check constraint can also refer to several columns. Say you
store a regular price and a discounted price, and you want to
ensure that the discounted price is lower than the regular price.
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price > 0),
discounted_price numeric CHECK (discounted_price > 0),
CHECK (price > discounted_price)
);
</programlisting>
</para>
<para>
The first two constraints should look familiar. The third one
uses a new syntax. It is not attached to a particular column,
instead it appears as a separate item in the comma-separated
column list. In general, column definitions and constraint
definitions can be listed in mixed order.
</para>
<para>
We say that the first two are column constraints, whereas the
third one is a table constraint because it is written separately
from the column definitions. Column constraints can also be
written as table constraints, while the reverse is not necessarily
possible. The above example could also be written as
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric,
CHECK (price > 0),
discounted_price numeric,
CHECK (discounted_price > 0),
CHECK (price > discounted_price)
);
</programlisting>
or even
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price > 0),
discounted_price numeric,
CHECK (discounted_price > 0 AND price > discounted_price)
);
</programlisting>
It's a matter of taste.
</para>
<para>
It should be noted that a check constraint is satisfied if the
check expression evaluates to true or the null value. To ensure
that a column does not contain null values, the not-null
constraint described in the next section should be used.
</para>
</sect2>
<sect2>
<title>Not-Null Constraints</title>
<para>
A not-null constraint simply specifies that a column must not
assume the null value. A syntax example:
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>NOT NULL</emphasis>,
name text <emphasis>NOT NULL</emphasis>,
price numeric
);
</programlisting>
</para>
<para>
A not-null constraint is always written as a column constraint. A
not-null constraint is equivalent to creating a check constraint
<literal>CHECK (<replaceable>column_name</replaceable> IS NOT
NULL)</literal>, but in <productname>PostgreSQL</productname>
creating an explicit not-null constraint is more efficient. The
drawback is that you cannot give explicit names to not-null
constraints created that way.
</para>
<para>
Of course, a column can have more than one constraint. Just write
the constraints after one another:
<programlisting>
CREATE TABLE products (
product_no integer NOT NULL,
name text NOT NULL,
price numeric NOT NULL CHECK (price > 0)
);
</programlisting>
The order doesn't matter. It does not necessarily affect in which
order the constraints are checked.
</para>
<para>
The <literal>NOT NULL</literal> constraint has an inverse: the
<literal>NULL</literal> constraint. This does not mean that the
column must be null, which would surely be useless. Instead, this
simply defines the default behavior that the column may be null.
The <literal>NULL</literal> constraint is not defined in the SQL
standard and should not be used in portable applications. (It was
only added to <productname>PostgreSQL</productname> to be
compatible with other database systems.) Some users, however,
like it because it makes it easy to toggle the constraint in a
script file. For example, you could start with
<programlisting>
CREATE TABLE products (
product_no integer NULL,
name text NULL,
price numeric NULL
);
</programlisting>
and then insert the <literal>NOT</literal> key word where desired.
</para>
<tip>
<para>
In most database designs the majority of columns should be marked
not null.
</para>
</tip>
</sect2>
<sect2>
<title>Unique Constraints</title>
<para>
Unique constraints ensure that the data contained in a column or a
group of columns is unique with respect to all the rows in the
table. The syntax is
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>UNIQUE</emphasis>,
name text,
price numeric
);
</programlisting>
when written as a column constraint, and
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric,
<emphasis>UNIQUE (product_no)</emphasis>
);
</programlisting>
when written as a table constraint.
</para>
<para>
If a unique constraint refers to a group of columns, the columns
are listed separated by commas:
<programlisting>
CREATE TABLE example (
a integer,
b integer,
c integer,
<emphasis>UNIQUE (a, c)</emphasis>
);
</programlisting>
</para>
<para>
It is also possible to assign names to unique constraints:
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>CONSTRAINT must_be_different</emphasis> UNIQUE,
name text,
price numeric
);
</programlisting>
</para>
<para>
In general, a unique constraint is violated when there are (at
least) two rows in the table where the values of each of the
corresponding columns that are part of the constraint are equal.
However, null values are not considered equal in this
consideration. That means, in the presence of a multicolumn
unique constraint it is possible to store an unlimited number of
rows that contain a null value in at least one of the constrained
columns. This behavior conforms to the SQL standard, but we have
heard that other SQL databases may not follow this rule. So be
careful when developing applications that are intended to be
portable.
</para>
</sect2>
<sect2>
<title>Primary Keys</title>
<para>
Technically, a primary key constraint is simply a combination of a
unique constraint and a not-null constraint. So, the following
two table definitions accept the same data:
<programlisting>
CREATE TABLE products (
product_no integer UNIQUE NOT NULL,
name text,
price numeric
);
</programlisting>
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>PRIMARY KEY</emphasis>,
name text,
price numeric
);
</programlisting>
</para>
<para>
Primary keys can also constrain more than one column; the syntax
is similar to unique constraints:
<programlisting>
CREATE TABLE example (
a integer,
b integer,
c integer,
<emphasis>PRIMARY KEY (a, c)</emphasis>
);
</programlisting>
</para>
<para>
A primary key indicates that a column or group of columns can be
used as a unique identifier for rows in the table. (This is a
direct consequence of the definition of a primary key. Note that
a unique constraint does not, in fact, provide a unique identifier
because it does not exclude null values.) This is useful both for
documentation purposes and for client applications. For example,
a GUI application that allows modifying row values probably needs
to know the primary key of a table to be able to identify rows
uniquely.
</para>
<para>
A table can have at most one primary key (while it can have many
unique and not-null constraints). Relational database theory
dictates that every table must have a primary key. This rule is
not enforced by <productname>PostgreSQL</productname>, but it is
usually best to follow it.
</para>
</sect2>
<sect2 id="ddl-constraints-fk">
<title>Foreign Keys</title>
<para>
A foreign key constraint specifies that the values in a column (or
a group of columns) must match the values in some other column.
We say this maintains the <firstterm>referential
integrity</firstterm> between two related tables.
</para>
<para>
Say you have the product table that we have used several times already:
<programlisting>
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
</programlisting>
Let's also assume you have a table storing orders of those
products. We want to ensure that the orders table only contains
orders of products that actually exist. So we define a foreign
key constraint in the orders table that references the products
table:
<programlisting>
CREATE TABLE orders (
order_id integer PRIMARY KEY,
product_no integer <emphasis>REFERENCES products (product_no)</emphasis>,
quantity integer
);
</programlisting>
Now it is impossible to create orders with
<literal>product_no</literal> entries that do not appear in the
products table.
</para>
<para>
We say that in this situation the orders table is the
<firstterm>referencing</firstterm> table and the products table is
the <firstterm>referenced</firstterm> table. Similarly, there are
referencing and referenced columns.
</para>
<para>
You can also shorten the above command to
<programlisting>
CREATE TABLE orders (
order_id integer PRIMARY KEY,
product_no integer REFERENCES products,
quantity integer
);
</programlisting>
because in absence of a column list the primary key of the
referenced table is used as referenced column.
</para>
<para>
A foreign key can also constrain and reference a group of columns.
As usual, it then needs to be written in table constraint form.
Here is a contrived syntax example:
<programlisting>
CREATE TABLE t1 (
a integer PRIMARY KEY,
b integer,
c integer,
<emphasis>FOREIGN KEY (b, c) REFERENCES other_table (c1, c2)</emphasis>
);
</programlisting>
Of course, the number and type of constrained columns needs to
match the number and type of referenced columns.
</para>
<para>
A table can contain more than one foreign key constraint. This is
used to implement many-to-many relationships between tables. Say
you have tables about products and orders, but now you want to
allow one order to contain possibly many products (which the
structure above did not allow). You could use this table structure:
<programlisting>
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
CREATE TABLE orders (
order_id integer PRIMARY KEY,
shipping_address text,
...
);
CREATE TABLE order_items (
product_no integer REFERENCES products,
order_id integer REFERENCES orders,
quantity integer,
PRIMARY KEY (product_no, order_id)
);
</programlisting>
Note also that the primary key overlaps with the foreign keys in
the last table.
</para>
<para>
We know that the foreign keys disallow creation of orders that
don't relate to any products. But what if a product is removed
after an order is created that references it? SQL allows you to
specify that as well. Intuitively, we have a few options:
<itemizedlist spacing="compact">
<listitem><para>Disallow deleting a referenced product</para></listitem>
<listitem><para>Delete the orders as well</para></listitem>
<listitem><para>Something else?</para></listitem>
</itemizedlist>
</para>
<para>
To illustrate this, let's implement the following policy on the
many-to-many relationship example above: When someone wants to
remove a product that is still referenced by an order (via
<literal>order_items</literal>), we disallow it. If someone
removes an order, the order items are removed as well.
<programlisting>
CREATE TABLE products (
product_no integer PRIMARY KEY,
name text,
price numeric
);
CREATE TABLE orders (
order_id integer PRIMARY KEY,
shipping_address text,
...
);
CREATE TABLE order_items (
product_no integer REFERENCES products <emphasis>ON DELETE RESTRICT</emphasis>,
order_id integer REFERENCES orders <emphasis>ON DELETE CASCADE</emphasis>,
quantity integer,
PRIMARY KEY (product_no, order_id)
);
</programlisting>
</para>
<para>
Restricting and cascading deletes are the two most common options.
<literal>RESTRICT</literal> can also be written as <literal>NO
ACTON</literal> and it's also the default if you don't specify
anything. There are two other options for what should happen with
the foreign key columns when a primary key is deleted:
<literal>SET NULL</literal> and <literal>SET DEFAULT</literal>.
Note that these do not excuse you from observing any constraints.
For example, if an action specifies <literal>SET DEFAULT</literal>
but the default value would not satisfy the foreign key, the
deletion of the primary key wil fail.
</para>
<para>
Analogous to <literal>ON DELETE</literal> there is also
<literal>ON UPDATE</literal> which is invoked when a primary key
is changed (updated). The possible actions are the same.
</para>
<para>
More information about updating and deleting data is in <xref
linkend="dml">.
</para>
<para>
Finally, we should mention that a foreign key must reference
columns that are either a primary key or form a unique constraint.
If the foreign key references a unique constraint, there are some
additional possibilities regarding how null values are matched.
These are explained in the <literal>CREATE TABLE</literal> entry
in &cite-reference;.
</para>
</sect2>
</sect1>
<sect1 id="ddl-inherit">
<title>Inheritance</title>
<comment>This section needs to be rethought. Some of the
information should go into the following chapters.</comment>
<para>
Let's create two tables. The capitals table contains
state capitals which are also cities. Naturally, the
capitals table should inherit from cities.
<programlisting>
CREATE TABLE cities (
name text,
population float,
altitude int -- (in ft)
);
CREATE TABLE capitals (
state char(2)
) INHERITS (cities);
</programlisting>
In this case, a row of capitals <firstterm>inherits</firstterm> all
attributes (name, population, and altitude) from its
parent, cities. The type of the attribute name is
<type>text</type>, a native <productname>PostgreSQL</productname> type for variable length
ASCII strings. The type of the attribute population is
<type>float</type>, a native <productname>PostgreSQL</productname> type for double precision
floating-point numbers. State capitals have an extra
attribute, state, that shows their state. In <productname>PostgreSQL</productname>,
a table can inherit from zero or more other tables,
and a query can reference either all rows of a
table or all rows of a table plus all of its
descendants.
<note>
<para>
The inheritance hierarchy is actually a directed acyclic graph.
</para>
</note>
</para>
<para>
For example, the following query finds the names of all cities,
including state capitals, that are located at an altitude
over 500ft:
<programlisting>
SELECT name, altitude
FROM cities
WHERE altitude &gt; 500;
</programlisting>
which returns:
<programlisting>
name | altitude
-----------+----------
Las Vegas | 2174
Mariposa | 1953
Madison | 845
</programlisting>
</para>
<para>
On the other hand, the following query finds
all the cities that are not state capitals and
are situated at an altitude over 500ft:
<programlisting>
SELECT name, altitude
FROM ONLY cities
WHERE altitude &gt; 500;
name | altitude
-----------+----------
Las Vegas | 2174
Mariposa | 1953
</programlisting>
</para>
<para>
Here the <quote>ONLY</quote> before cities indicates that the query should
be run over only cities and not tables below cities in the
inheritance hierarchy. Many of the commands that we
have already discussed -- <command>SELECT</command>,
<command>UPDATE</command> and <command>DELETE</command> --
support this <quote>ONLY</quote> notation.
</para>
<para>
In some cases you may wish to know which table a particular tuple
originated from. There is a system column called
<structfield>TABLEOID</structfield> in each table which can tell you the
originating table:
<programlisting>
SELECT c.tableoid, c.name, c.altitude
FROM cities c
WHERE c.altitude &gt; 500;
</programlisting>
which returns:
<programlisting>
tableoid | name | altitude
----------+-----------+----------
139793 | Las Vegas | 2174
139793 | Mariposa | 1953
139798 | Madison | 845
</programlisting>
(If you try to reproduce this example, you will probably get different
numeric OIDs.) By doing a join with pg_class you can see the actual table
names:
<programlisting>
SELECT p.relname, c.name, c.altitude
FROM cities c, pg_class p
WHERE c.altitude &gt; 500 and c.tableoid = p.oid;
</programlisting>
which returns:
<programlisting>
relname | name | altitude
----------+-----------+----------
cities | Las Vegas | 2174
cities | Mariposa | 1953
capitals | Madison | 845
</programlisting>
</para>
<note>
<title>Deprecated</title>
<para>
In previous versions of <productname>PostgreSQL</productname>, the
default was not to get access to child tables. This was found to
be error prone and is also in violation of SQL99. Under the old
syntax, to get the sub-tables you append <literal>*</literal> to the table name.
For example
<programlisting>
SELECT * from cities*;
</programlisting>
You can still explicitly specify scanning child tables by appending
<literal>*</literal>, as well as explicitly specify not scanning child tables by
writing <quote>ONLY</quote>. But beginning in version 7.1, the default
behavior for an undecorated table name is to scan its child tables
too, whereas before the default was not to do so. To get the old
default behavior, set the configuration option
<literal>SQL_Inheritance</literal> to off, e.g.,
<programlisting>
SET SQL_Inheritance TO OFF;
</programlisting>
or add a line in your <filename>postgresql.conf</filename> file.
</para>
</note>
<para>
A limitation of the inheritance feature is that indexes (including
unique constraints) and foreign key constraints only apply to single
tables, not to their inheritance children. Thus, in the above example,
specifying that another table's column <literal>REFERENCES cities(name)</>
would allow the other table to contain city names but not capital names.
This deficiency will probably be fixed in some future release.
</para>
</sect1>
<sect1 id="ddl-alter">
<title>Modifying Tables</title>
<para>
When you create a table and you realize that you made a mistake,
then you can drop the table and create it again. But this is not a
convenient option if the table is already filled with data, or if
the table is referenced by other database objects (for instance a
foreign key constraint). Therefore
<productname>PostgreSQL</productname> provides a family of commands
to make modifications on existing tables.
</para>
<para>
You can
<itemizedlist spacing="compact">
<listitem>
<para>Add columns,</para>
</listitem>
<listitem>
<para>Add constraints,</para>
</listitem>
<listitem>
<para>Remove constraints,</para>
</listitem>
<listitem>
<para>Change default values,</para>
</listitem>
<listitem>
<para>Rename a column,</para>
</listitem>
<listitem>
<para>Rename the table.</para>
</listitem>
</itemizedlist>
In the current implementation you cannot
<itemizedlist spacing="compact">
<listitem>
<para>Remove a column,</para>
</listitem>
<listitem>
<para>Change the data type of a column.</para>
</listitem>
</itemizedlist>
These may be possible in a future release.
</para>
<comment>
OK, now explain how to do this. There's currently so much activity
on <literal>ALTER TABLE</literal> that I'm holding off a bit.
</comment>
</sect1>
<sect1 id="ddl-schemas">
<title>Schemas</title>
<comment>to be filled in</comment>
</sect1>
<sect1 id="ddl-others">
<title>Other Database Objects</title>
<para>
Tables are the central objects in a relational database structure,
because they hold your data. But they are not the only objects
that exist in a database. Many other kinds of objects can be
created to make the use and management of the data more efficient
or convenient. They are not discussed in this chapter, but we give
you a list here so that you are aware of what is possible.
</para>
<itemizedlist>
<listitem>
<para>
Views
</para>
</listitem>
<listitem>
<para>
Functions, operators, data types, domains
</para>
</listitem>
<listitem>
<para>
Triggers and rewrite rules
</para>
</listitem>
</itemizedlist>
</sect1>
<sect1 id="ddl-depend">
<title>Dependency Tracking</title>
<para>
When you create complex database structures involving many tables
with foreign key constraints, views, triggers, functions, etc. you
will implicitly create a net of dependencies between the objects.
For instance, a table with a foreign key constraint depends on the
table it references.
</para>
<para>
To ensure the integrity of the entire database structure,
<productname>PostgreSQL</productname> makes sure that you cannot
drop objects that other objects still depend on. For example,
attempting to drop the products table we had considered in <xref
linkend="ddl-constraints-fk">, with the orders table depending on
it, would result in an error message such as this:
<screen>
<userinput>DROP TABLE products;</userinput>
NOTICE: constraint $1 on table orders depends on table products
ERROR: Cannot drop table products because other objects depend on it
Use DROP ... CASCADE to drop the dependent objects too
</screen>
The error message contains a useful hint: If you don't want to
bother deleting all the dependent objects individually, you can run
<screen>
DROP TABLE products CASCADE;
</screen>
and all the dependent objects will be removed. Actually, this
doesn't remove the orders table, it only removes the foreign key
constraint.
</para>
<para>
All drop commands in <productname>PostgreSQL</productname> support
specifying <literal>CASCADE</literal>. Of course, the nature of
the possible dependencies varies with the type of the object. You
can also write <literal>RESTRICT</literal> instead of
<literal>CASCADE</literal> to get the default behavior which is to
restrict drops of objects that other objects depend on.
</para>
<note>
<para>
According to the SQL standard, specifying either
<literal>RESTRICT</literal> or <literal>CASCADE</literal> is
required. No database system actually implements it that way, but
the defaults might be different.
</para>
</note>
</sect1>
</chapter>
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