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Michael Paquier f5efc931c2 Doc: Improve description around ALTER TABLE ATTACH PARTITION 
This clarifies more how to use and how to take advantage of constraints
when attaching a new partition.

Author: Justin Pryzby
Reviewed-by: Amit Langote, Álvaro Herrera, Michael Paquier
Discussion: https://postgr.es/m/20191028001207.GB23808@telsasoft.com
Backpatch-through: 10
2019-11-05 10:18:05 +09:00

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<!-- doc/src/sgml/ddl.sgml -->
<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
schemas, and how privileges can be assigned to tables. Finally,
we will briefly look at other features that affect the data storage,
such as inheritance, table partitioning, views, functions, and
triggers.
</para>
<sect1 id="ddl-basics">
<title>Table Basics</title>
<indexterm zone="ddl-basics">
<primary>table</primary>
</indexterm>
<indexterm>
<primary>row</primary>
</indexterm>
<indexterm>
<primary>column</primary>
</indexterm>
<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 &mdash; 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 an unspecified 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>
<indexterm>
<primary>table</primary>
<secondary>creating</secondary>
</indexterm>
<para>
To create a table, you use the aptly named <xref
linkend="sql-createtable"> 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
usually 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 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.
</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>
<indexterm>
<primary>table</primary>
<secondary>removing</secondary>
</indexterm>
<para>
If you no longer need a table, you can remove it using the <xref
linkend="sql-droptable"> 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 any error
messages, so that the script works whether or not the table exists.
(If you like, you can use the <literal>DROP TABLE IF EXISTS</> variant
to avoid the error messages, but this is not standard SQL.)
</para>
<para>
If you need to modify a table that already exists, see <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 id="ddl-default">
<title>Default Values</title>
<indexterm zone="ddl-default">
<primary>default value</primary>
</indexterm>
<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, those
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 having to know what that value is.
(Details about data manipulation commands are in <xref linkend="dml">.)
</para>
<para>
<indexterm><primary>null value</primary><secondary>default value</secondary></indexterm>
If no default value is declared explicitly, the default value is the
null value. This usually makes sense because a null value can
be considered 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,
name text,
price numeric <emphasis>DEFAULT 9.99</emphasis>
);
</programlisting>
</para>
<para>
The default value can be an expression, which will be
evaluated whenever the default value is inserted
(<emphasis>not</emphasis> when the table is created). A common example
is for a <type>timestamp</type> column to have a default of <literal>CURRENT_TIMESTAMP</>,
so that it gets set to the time of row insertion. Another common
example is generating a <quote>serial number</> for each row.
In <productname>PostgreSQL</productname> this is typically done by
something like:
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>DEFAULT nextval('products_product_no_seq')</emphasis>,
...
);
</programlisting>
where the <literal>nextval()</> function supplies successive values
from a <firstterm>sequence object</> (see <xref
linkend="functions-sequence">). This arrangement is sufficiently common
that there's a special shorthand for it:
<programlisting>
CREATE TABLE products (
product_no <emphasis>SERIAL</emphasis>,
...
);
</programlisting>
The <literal>SERIAL</> shorthand is discussed further in <xref
linkend="datatype-serial">.
</para>
</sect1>
<sect1 id="ddl-constraints">
<title>Constraints</title>
<indexterm zone="ddl-constraints">
<primary>constraint</primary>
</indexterm>
<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
standard 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 be only 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 id="ddl-constraints-check-constraints">
<title>Check Constraints</title>
<indexterm>
<primary>check constraint</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>check</secondary>
</indexterm>
<para>
A check constraint is the most generic constraint type. It allows
you to specify that the value in a certain column must satisfy a
Boolean (truth-value) 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 &gt; 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>
<indexterm>
<primary>constraint</primary>
<secondary>name</secondary>
</indexterm>
<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 &gt; 0)
);
</programlisting>
So, to specify a named constraint, use the key word
<literal>CONSTRAINT</literal> followed by an identifier followed
by the constraint definition. (If you don't specify a constraint
name in this way, the system chooses a name for you.)
</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 &gt; 0),
discounted_price numeric CHECK (discounted_price &gt; 0),
<emphasis>CHECK (price &gt; discounted_price)</emphasis>
);
</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. Column definitions and these constraint
definitions can be listed in mixed order.
</para>
<para>
We say that the first two constraints are column constraints, whereas the
third one is a table constraint because it is written separately
from any one column definition. Column constraints can also be
written as table constraints, while the reverse is not necessarily
possible, since a column constraint is supposed to refer to only the
column it is attached to. (<productname>PostgreSQL</productname> doesn't
enforce that rule, but you should follow it if you want your table
definitions to work with other database systems.) The above example could
also be written as:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric,
CHECK (price &gt; 0),
discounted_price numeric,
CHECK (discounted_price &gt; 0),
CHECK (price &gt; discounted_price)
);
</programlisting>
or even:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric CHECK (price &gt; 0),
discounted_price numeric,
CHECK (discounted_price &gt; 0 AND price &gt; discounted_price)
);
</programlisting>
It's a matter of taste.
</para>
<para>
Names can be assigned to table constraints in the same way as
column constraints:
<programlisting>
CREATE TABLE products (
product_no integer,
name text,
price numeric,
CHECK (price &gt; 0),
discounted_price numeric,
CHECK (discounted_price &gt; 0),
<emphasis>CONSTRAINT valid_discount</> CHECK (price &gt; discounted_price)
);
</programlisting>
</para>
<indexterm>
<primary>null value</primary>
<secondary sortas="check constraints">with check constraints</secondary>
</indexterm>
<para>
It should be noted that a check constraint is satisfied if the
check expression evaluates to true or the null value. Since most
expressions will evaluate to the null value if any operand is null,
they will not prevent null values in the constrained columns. To
ensure that a column does not contain null values, the not-null
constraint described in the next section can be used.
</para>
</sect2>
<sect2>
<title>Not-Null Constraints</title>
<indexterm>
<primary>not-null constraint</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>NOT NULL</secondary>
</indexterm>
<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 functionally 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 this
way.
</para>
<para>
Of course, a column can have more than one constraint. Just write
the constraints one after another:
<programlisting>
CREATE TABLE products (
product_no integer NOT NULL,
name text NOT NULL,
price numeric NOT NULL CHECK (price &gt; 0)
);
</programlisting>
The order doesn't matter. It does not necessarily determine 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 selects the default behavior that the column might be null.
The <literal>NULL</literal> constraint is not present in the SQL
standard and should not be used in portable applications. (It was
only added to <productname>PostgreSQL</productname> to be
compatible with some 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 id="ddl-constraints-unique-constraints">
<title>Unique Constraints</title>
<indexterm>
<primary>unique constraint</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>unique</secondary>
</indexterm>
<para>
Unique constraints ensure that the data contained in a column, or a
group of columns, is unique among 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>
To define a unique constraint for a group of columns, write it as a
table constraint with the column names separated by commas:
<programlisting>
CREATE TABLE example (
a integer,
b integer,
c integer,
<emphasis>UNIQUE (a, c)</emphasis>
);
</programlisting>
This specifies that the combination of values in the indicated columns
is unique across the whole table, though any one of the columns
need not be (and ordinarily isn't) unique.
</para>
<para>
You can assign your own name for a unique constraint, in the usual way:
<programlisting>
CREATE TABLE products (
product_no integer <emphasis>CONSTRAINT must_be_different</emphasis> UNIQUE,
name text,
price numeric
);
</programlisting>
</para>
<para>
Adding a unique constraint will automatically create a unique B-tree
index on the column or group of columns listed in the constraint.
A uniqueness restriction covering only some rows cannot be written as
a unique constraint, but it is possible to enforce such a restriction by
creating a unique <link linkend="indexes-partial">partial index</link>.
</para>
<indexterm>
<primary>null value</primary>
<secondary sortas="unique constraints">with unique constraints</secondary>
</indexterm>
<para>
In general, a unique constraint is violated if there is more than
one row in the table where the values of all of the
columns included in the constraint are equal.
However, two null values are never considered equal in this
comparison. That means even in the presence of a
unique constraint it is possible to store duplicate
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 might not follow this rule. So be
careful when developing applications that are intended to be
portable.
</para>
</sect2>
<sect2 id="ddl-constraints-primary-keys">
<title>Primary Keys</title>
<indexterm>
<primary>primary key</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>primary key</secondary>
</indexterm>
<para>
A primary key constraint indicates that a column, or group of columns,
can be used as a unique identifier for rows in the table. This
requires that the values be both unique and not null. 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 span 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>
Adding a primary key will automatically create a unique B-tree index
on the column or group of columns listed in the primary key, and will
force the column(s) to be marked <literal>NOT NULL</>.
</para>
<para>
A table can have at most one primary key. (There can be any number
of unique and not-null constraints, which are functionally almost the
same thing, but only one can be identified as the primary key.)
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>
<para>
Primary keys are 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. There are also various ways in which the database system
makes use of a primary key if one has been declared; for example,
the primary key defines the default target column(s) for foreign keys
referencing its table.
</para>
</sect2>
<sect2 id="ddl-constraints-fk">
<title>Foreign Keys</title>
<indexterm>
<primary>foreign key</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>foreign key</secondary>
</indexterm>
<indexterm>
<primary>referential integrity</primary>
</indexterm>
<para>
A foreign key constraint specifies that the values in a column (or
a group of columns) must match the values appearing in some row
of another table.
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 non-NULL
<structfield>product_no</structfield> 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 <emphasis>REFERENCES products</emphasis>,
quantity integer
);
</programlisting>
because in absence of a column list the primary key of the
referenced table is used as the referenced column(s).
</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 the constrained columns need to
match the number and type of the referenced columns.
</para>
<para>
You can assign your own name for a foreign key constraint,
in the usual way.
</para>
<para>
A table can have 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>
Notice that the primary key overlaps with the foreign keys in
the last table.
</para>
<indexterm>
<primary>CASCADE</primary>
<secondary>foreign key action</secondary>
</indexterm>
<indexterm>
<primary>RESTRICT</primary>
<secondary>foreign key action</secondary>
</indexterm>
<para>
We know that the foreign keys disallow creation of orders that
do not relate to any products. But what if a product is removed
after an order is created that references it? SQL allows you to
handle 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> prevents deletion of a
referenced row. <literal>NO ACTION</literal> means that if any
referencing rows still exist when the constraint is checked, an error
is raised; this is the default behavior if you do not specify anything.
(The essential difference between these two choices is that
<literal>NO ACTION</literal> allows the check to be deferred until
later in the transaction, whereas <literal>RESTRICT</literal> does not.)
<literal>CASCADE</> specifies that when a referenced row is deleted,
row(s) referencing it should be automatically deleted as well.
There are two other options:
<literal>SET NULL</literal> and <literal>SET DEFAULT</literal>.
These cause the referencing column(s) in the referencing row(s)
to be set to nulls or their default
values, respectively, when the referenced row is deleted.
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 constraint, the
operation will fail.
</para>
<para>
Analogous to <literal>ON DELETE</literal> there is also
<literal>ON UPDATE</literal> which is invoked when a referenced
column is changed (updated). The possible actions are the same.
In this case, <literal>CASCADE</> means that the updated values of the
referenced column(s) should be copied into the referencing row(s).
</para>
<para>
Normally, a referencing row need not satisfy the foreign key constraint
if any of its referencing columns are null. If <literal>MATCH FULL</>
is added to the foreign key declaration, a referencing row escapes
satisfying the constraint only if all its referencing columns are null
(so a mix of null and non-null values is guaranteed to fail a
<literal>MATCH FULL</> constraint). If you don't want referencing rows
to be able to avoid satisfying the foreign key constraint, declare the
referencing column(s) as <literal>NOT NULL</>.
</para>
<para>
A foreign key must reference columns that either are a primary key or
form a unique constraint. This means that the referenced columns always
have an index (the one underlying the primary key or unique constraint);
so checks on whether a referencing row has a match will be efficient.
Since a <command>DELETE</command> of a row from the referenced table
or an <command>UPDATE</command> of a referenced column will require
a scan of the referencing table for rows matching the old value, it
is often a good idea to index the referencing columns too. Because this
is not always needed, and there are many choices available on how
to index, declaration of a foreign key constraint does not
automatically create an index on the referencing columns.
</para>
<para>
More information about updating and deleting data is in <xref
linkend="dml">. Also see the description of foreign key constraint
syntax in the reference documentation for
<xref linkend="sql-createtable">.
</para>
</sect2>
<sect2 id="ddl-constraints-exclusion">
<title>Exclusion Constraints</title>
<indexterm>
<primary>exclusion constraint</primary>
</indexterm>
<indexterm>
<primary>constraint</primary>
<secondary>exclusion</secondary>
</indexterm>
<para>
Exclusion constraints ensure that if any two rows are compared on
the specified columns or expressions using the specified operators,
at least one of these operator comparisons will return false or null.
The syntax is:
<programlisting>
CREATE TABLE circles (
c circle,
EXCLUDE USING gist (c WITH &amp;&amp;)
);
</programlisting>
</para>
<para>
See also <link linkend="SQL-CREATETABLE-EXCLUDE"><command>CREATE
TABLE ... CONSTRAINT ... EXCLUDE</></link> for details.
</para>
<para>
Adding an exclusion constraint will automatically create an index
of the type specified in the constraint declaration.
</para>
</sect2>
</sect1>
<sect1 id="ddl-system-columns">
<title>System Columns</title>
<para>
Every table has several <firstterm>system columns</> that are
implicitly defined by the system. Therefore, these names cannot be
used as names of user-defined columns. (Note that these
restrictions are separate from whether the name is a key word or
not; quoting a name will not allow you to escape these
restrictions.) You do not really need to be concerned about these
columns; just know they exist.
</para>
<indexterm>
<primary>column</primary>
<secondary>system column</secondary>
</indexterm>
<variablelist>
<varlistentry>
<term><structfield>oid</></term>
<listitem>
<para>
<indexterm>
<primary>OID</primary>
<secondary>column</secondary>
</indexterm>
The object identifier (object ID) of a row. This column is only
present if the table was created using <literal>WITH
OIDS</literal>, or if the <xref linkend="guc-default-with-oids">
configuration variable was set at the time. This column is of type
<type>oid</type> (same name as the column); see <xref
linkend="datatype-oid"> for more information about the type.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>tableoid</></term>
<listitem>
<indexterm>
<primary>tableoid</primary>
</indexterm>
<para>
The OID of the table containing this row. This column is
particularly handy for queries that select from inheritance
hierarchies (see <xref linkend="ddl-inherit">), since without it,
it's difficult to tell which individual table a row came from. The
<structfield>tableoid</structfield> can be joined against the
<structfield>oid</structfield> column of
<structname>pg_class</structname> to obtain the table name.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>xmin</></term>
<listitem>
<indexterm>
<primary>xmin</primary>
</indexterm>
<para>
The identity (transaction ID) of the inserting transaction for
this row version. (A row version is an individual state of a
row; each update of a row creates a new row version for the same
logical row.)
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>cmin</></term>
<listitem>
<indexterm>
<primary>cmin</primary>
</indexterm>
<para>
The command identifier (starting at zero) within the inserting
transaction.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>xmax</></term>
<listitem>
<indexterm>
<primary>xmax</primary>
</indexterm>
<para>
The identity (transaction ID) of the deleting transaction, or
zero for an undeleted row version. It is possible for this column to
be nonzero in a visible row version. That usually indicates that the
deleting transaction hasn't committed yet, or that an attempted
deletion was rolled back.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>cmax</></term>
<listitem>
<indexterm>
<primary>cmax</primary>
</indexterm>
<para>
The command identifier within the deleting transaction, or zero.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term><structfield>ctid</></term>
<listitem>
<indexterm>
<primary>ctid</primary>
</indexterm>
<para>
The physical location of the row version within its table. Note that
although the <structfield>ctid</structfield> can be used to
locate the row version very quickly, a row's
<structfield>ctid</structfield> will change if it is
updated or moved by <command>VACUUM FULL</>. Therefore
<structfield>ctid</structfield> is useless as a long-term row
identifier. The OID, or even better a user-defined serial
number, should be used to identify logical rows.
</para>
</listitem>
</varlistentry>
</variablelist>
<para>
OIDs are 32-bit quantities and are assigned from a single
cluster-wide counter. In a large or long-lived database, it is
possible for the counter to wrap around. Hence, it is bad
practice to assume that OIDs are unique, unless you take steps to
ensure that this is the case. If you need to identify the rows in
a table, using a sequence generator is strongly recommended.
However, OIDs can be used as well, provided that a few additional
precautions are taken:
<itemizedlist>
<listitem>
<para>
A unique constraint should be created on the OID column of each
table for which the OID will be used to identify rows. When such
a unique constraint (or unique index) exists, the system takes
care not to generate an OID matching an already-existing row.
(Of course, this is only possible if the table contains fewer
than 2<superscript>32</> (4 billion) rows, and in practice the
table size had better be much less than that, or performance
might suffer.)
</para>
</listitem>
<listitem>
<para>
OIDs should never be assumed to be unique across tables; use
the combination of <structfield>tableoid</> and row OID if you
need a database-wide identifier.
</para>
</listitem>
<listitem>
<para>
Of course, the tables in question must be created <literal>WITH
OIDS</literal>. As of <productname>PostgreSQL</productname> 8.1,
<literal>WITHOUT OIDS</> is the default.
</para>
</listitem>
</itemizedlist>
</para>
<para>
Transaction identifiers are also 32-bit quantities. In a
long-lived database it is possible for transaction IDs to wrap
around. This is not a fatal problem given appropriate maintenance
procedures; see <xref linkend="maintenance"> for details. It is
unwise, however, to depend on the uniqueness of transaction IDs
over the long term (more than one billion transactions).
</para>
<para>
Command identifiers are also 32-bit quantities. This creates a hard limit
of 2<superscript>32</> (4 billion) <acronym>SQL</acronym> commands
within a single transaction. In practice this limit is not a
problem &mdash; note that the limit is on the number of
<acronym>SQL</acronym> commands, not the number of rows processed.
Also, only commands that actually modify the database contents will
consume a command identifier.
</para>
</sect1>
<sect1 id="ddl-alter">
<title>Modifying Tables</title>
<indexterm zone="ddl-alter">
<primary>table</primary>
<secondary>modifying</secondary>
</indexterm>
<para>
When you create a table and you realize that you made a mistake, or
the requirements of the application change, 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 to existing
tables. Note that this is conceptually distinct from altering
the data contained in the table: here we are interested in altering
the definition, or structure, of the table.
</para>
<para>
You can:
<itemizedlist spacing="compact">
<listitem>
<para>Add columns</para>
</listitem>
<listitem>
<para>Remove columns</para>
</listitem>
<listitem>
<para>Add constraints</para>
</listitem>
<listitem>
<para>Remove constraints</para>
</listitem>
<listitem>
<para>Change default values</para>
</listitem>
<listitem>
<para>Change column data types</para>
</listitem>
<listitem>
<para>Rename columns</para>
</listitem>
<listitem>
<para>Rename tables</para>
</listitem>
</itemizedlist>
All these actions are performed using the
<xref linkend="sql-altertable">
command, whose reference page contains details beyond those given
here.
</para>
<sect2 id="ddl-alter-adding-a-column">
<title>Adding a Column</title>
<indexterm>
<primary>column</primary>
<secondary>adding</secondary>
</indexterm>
<para>
To add a column, use a command like:
<programlisting>
ALTER TABLE products ADD COLUMN description text;
</programlisting>
The new column is initially filled with whatever default
value is given (null if you don't specify a <literal>DEFAULT</> clause).
</para>
<para>
You can also define constraints on the column at the same time,
using the usual syntax:
<programlisting>
ALTER TABLE products ADD COLUMN description text CHECK (description &lt;&gt; '');
</programlisting>
In fact all the options that can be applied to a column description
in <command>CREATE TABLE</> can be used here. Keep in mind however
that the default value must satisfy the given constraints, or the
<literal>ADD</> will fail. Alternatively, you can add
constraints later (see below) after you've filled in the new column
correctly.
</para>
<tip>
<para>
Adding a column with a default requires updating each row of the
table (to store the new column value). However, if no default is
specified, <productname>PostgreSQL</productname> is able to avoid
the physical update. So if you intend to fill the column with
mostly nondefault values, it's best to add the column with no default,
insert the correct values using <command>UPDATE</>, and then add any
desired default as described below.
</para>
</tip>
</sect2>
<sect2 id="ddl-alter-removing-a-column">
<title>Removing a Column</title>
<indexterm>
<primary>column</primary>
<secondary>removing</secondary>
</indexterm>
<para>
To remove a column, use a command like:
<programlisting>
ALTER TABLE products DROP COLUMN description;
</programlisting>
Whatever data was in the column disappears. Table constraints involving
the column are dropped, too. However, if the column is referenced by a
foreign key constraint of another table,
<productname>PostgreSQL</productname> will not silently drop that
constraint. You can authorize dropping everything that depends on
the column by adding <literal>CASCADE</>:
<programlisting>
ALTER TABLE products DROP COLUMN description CASCADE;
</programlisting>
See <xref linkend="ddl-depend"> for a description of the general
mechanism behind this.
</para>
</sect2>
<sect2 id="ddl-alter-adding-a-constraint">
<title>Adding a Constraint</title>
<indexterm>
<primary>constraint</primary>
<secondary>adding</secondary>
</indexterm>
<para>
To add a constraint, the table constraint syntax is used. For example:
<programlisting>
ALTER TABLE products ADD CHECK (name &lt;&gt; '');
ALTER TABLE products ADD CONSTRAINT some_name UNIQUE (product_no);
ALTER TABLE products ADD FOREIGN KEY (product_group_id) REFERENCES product_groups;
</programlisting>
To add a not-null constraint, which cannot be written as a table
constraint, use this syntax:
<programlisting>
ALTER TABLE products ALTER COLUMN product_no SET NOT NULL;
</programlisting>
</para>
<para>
The constraint will be checked immediately, so the table data must
satisfy the constraint before it can be added.
</para>
</sect2>
<sect2 id="ddl-alter-removing-a-constraint">
<title>Removing a Constraint</title>
<indexterm>
<primary>constraint</primary>
<secondary>removing</secondary>
</indexterm>
<para>
To remove a constraint you need to know its name. If you gave it
a name then that's easy. Otherwise the system assigned a
generated name, which you need to find out. The
<application>psql</application> command <literal>\d
<replaceable>tablename</replaceable></literal> can be helpful
here; other interfaces might also provide a way to inspect table
details. Then the command is:
<programlisting>
ALTER TABLE products DROP CONSTRAINT some_name;
</programlisting>
(If you are dealing with a generated constraint name like <literal>$2</>,
don't forget that you'll need to double-quote it to make it a valid
identifier.)
</para>
<para>
As with dropping a column, you need to add <literal>CASCADE</> if you
want to drop a constraint that something else depends on. An example
is that a foreign key constraint depends on a unique or primary key
constraint on the referenced column(s).
</para>
<para>
This works the same for all constraint types except not-null
constraints. To drop a not null constraint use:
<programlisting>
ALTER TABLE products ALTER COLUMN product_no DROP NOT NULL;
</programlisting>
(Recall that not-null constraints do not have names.)
</para>
</sect2>
<sect2>
<title>Changing a Column's Default Value</title>
<indexterm>
<primary>default value</primary>
<secondary>changing</secondary>
</indexterm>
<para>
To set a new default for a column, use a command like:
<programlisting>
ALTER TABLE products ALTER COLUMN price SET DEFAULT 7.77;
</programlisting>
Note that this doesn't affect any existing rows in the table, it
just changes the default for future <command>INSERT</> commands.
</para>
<para>
To remove any default value, use:
<programlisting>
ALTER TABLE products ALTER COLUMN price DROP DEFAULT;
</programlisting>
This is effectively the same as setting the default to null.
As a consequence, it is not an error
to drop a default where one hadn't been defined, because the
default is implicitly the null value.
</para>
</sect2>
<sect2>
<title>Changing a Column's Data Type</title>
<indexterm>
<primary>column data type</primary>
<secondary>changing</secondary>
</indexterm>
<para>
To convert a column to a different data type, use a command like:
<programlisting>
ALTER TABLE products ALTER COLUMN price TYPE numeric(10,2);
</programlisting>
This will succeed only if each existing entry in the column can be
converted to the new type by an implicit cast. If a more complex
conversion is needed, you can add a <literal>USING</> clause that
specifies how to compute the new values from the old.
</para>
<para>
<productname>PostgreSQL</> will attempt to convert the column's
default value (if any) to the new type, as well as any constraints
that involve the column. But these conversions might fail, or might
produce surprising results. It's often best to drop any constraints
on the column before altering its type, and then add back suitably
modified constraints afterwards.
</para>
</sect2>
<sect2>
<title>Renaming a Column</title>
<indexterm>
<primary>column</primary>
<secondary>renaming</secondary>
</indexterm>
<para>
To rename a column:
<programlisting>
ALTER TABLE products RENAME COLUMN product_no TO product_number;
</programlisting>
</para>
</sect2>
<sect2>
<title>Renaming a Table</title>
<indexterm>
<primary>table</primary>
<secondary>renaming</secondary>
</indexterm>
<para>
To rename a table:
<programlisting>
ALTER TABLE products RENAME TO items;
</programlisting>
</para>
</sect2>
</sect1>
<sect1 id="ddl-priv">
<title>Privileges</title>
<indexterm zone="ddl-priv">
<primary>privilege</primary>
</indexterm>
<indexterm>
<primary>permission</primary>
<see>privilege</see>
</indexterm>
<indexterm zone="ddl-priv">
<primary>owner</primary>
</indexterm>
<indexterm zone="ddl-priv">
<primary>GRANT</primary>
</indexterm>
<indexterm zone="ddl-priv">
<primary>REVOKE</primary>
</indexterm>
<para>
When an object is created, it is assigned an owner. The
owner is normally the role that executed the creation statement.
For most kinds of objects, the initial state is that only the owner
(or a superuser) can do anything with the object. To allow
other roles to use it, <firstterm>privileges</firstterm> must be
granted.
</para>
<para>
There are different kinds of privileges: <literal>SELECT</>,
<literal>INSERT</>, <literal>UPDATE</>, <literal>DELETE</>,
<literal>TRUNCATE</>, <literal>REFERENCES</>, <literal>TRIGGER</>,
<literal>CREATE</>, <literal>CONNECT</>, <literal>TEMPORARY</>,
<literal>EXECUTE</>, and <literal>USAGE</>.
The privileges applicable to a particular
object vary depending on the object's type (table, function, etc).
For complete information on the different types of privileges
supported by <productname>PostgreSQL</productname>, refer to the
<xref linkend="sql-grant"> reference
page. The following sections and chapters will also show you how
those privileges are used.
</para>
<para>
The right to modify or destroy an object is always the privilege of
the owner only.
</para>
<para>
An object can be assigned to a new owner with an <command>ALTER</command>
command of the appropriate kind for the object, e.g. <xref
linkend="sql-altertable">. Superusers can always do
this; ordinary roles can only do it if they are both the current owner
of the object (or a member of the owning role) and a member of the new
owning role.
</para>
<para>
To assign privileges, the <command>GRANT</command> command is
used. For example, if <literal>joe</literal> is an existing role, and
<literal>accounts</literal> is an existing table, the privilege to
update the table can be granted with:
<programlisting>
GRANT UPDATE ON accounts TO joe;
</programlisting>
Writing <literal>ALL</literal> in place of a specific privilege grants all
privileges that are relevant for the object type.
</para>
<para>
The special <quote>role</quote> name <literal>PUBLIC</literal> can
be used to grant a privilege to every role on the system. Also,
<quote>group</> roles can be set up to help manage privileges when
there are many users of a database &mdash; for details see
<xref linkend="user-manag">.
</para>
<para>
To revoke a privilege, use the fittingly named
<command>REVOKE</command> command:
<programlisting>
REVOKE ALL ON accounts FROM PUBLIC;
</programlisting>
The special privileges of the object owner (i.e., the right to do
<command>DROP</>, <command>GRANT</>, <command>REVOKE</>, etc.)
are always implicit in being the owner,
and cannot be granted or revoked. But the object owner can choose
to revoke their own ordinary privileges, for example to make a
table read-only for themselves as well as others.
</para>
<para>
Ordinarily, only the object's owner (or a superuser) can grant or
revoke privileges on an object. However, it is possible to grant a
privilege <quote>with grant option</>, which gives the recipient
the right to grant it in turn to others. If the grant option is
subsequently revoked then all who received the privilege from that
recipient (directly or through a chain of grants) will lose the
privilege. For details see the <xref linkend="sql-grant"> and
<xref linkend="sql-revoke"> reference pages.
</para>
</sect1>
<sect1 id="ddl-rowsecurity">
<title>Row Security Policies</title>
<indexterm zone="ddl-rowsecurity">
<primary>row-level security</primary>
</indexterm>
<indexterm zone="ddl-rowsecurity">
<primary>policy</primary>
</indexterm>
<para>
In addition to the SQL-standard <link linkend="ddl-priv">privilege
system</link> available through <xref linkend="sql-grant">,
tables can have <firstterm>row security policies</> that restrict,
on a per-user basis, which rows can be returned by normal queries
or inserted, updated, or deleted by data modification commands.
This feature is also known as <firstterm>Row-Level Security</>.
By default, tables do not have any policies, so that if a user has
access privileges to a table according to the SQL privilege system,
all rows within it are equally available for querying or updating.
</para>
<para>
When row security is enabled on a table (with
<link linkend="sql-altertable">ALTER TABLE ... ENABLE ROW LEVEL
SECURITY</>), all normal access to the table for selecting rows or
modifying rows must be allowed by a row security policy. (However, the
table's owner is typically not subject to row security policies.) If no
policy exists for the table, a default-deny policy is used, meaning that
no rows are visible or can be modified. Operations that apply to the
whole table, such as <command>TRUNCATE</> and <literal>REFERENCES</>,
are not subject to row security.
</para>
<para>
Row security policies can be specific to commands, or to roles, or to
both. A policy can be specified to apply to <literal>ALL</literal>
commands, or to <literal>SELECT</>, <literal>INSERT</>, <literal>UPDATE</>,
or <literal>DELETE</>. Multiple roles can be assigned to a given
policy, and normal role membership and inheritance rules apply.
</para>
<para>
To specify which rows are visible or modifiable according to a policy,
an expression is required that returns a Boolean result. This
expression will be evaluated for each row prior to any conditions or
functions coming from the user's query. (The only exceptions to this
rule are <literal>leakproof</literal> functions, which are guaranteed to
not leak information; the optimizer may choose to apply such functions
ahead of the row-security check.) Rows for which the expression does
not return <literal>true</> will not be processed. Separate expressions
may be specified to provide independent control over the rows which are
visible and the rows which are allowed to be modified. Policy
expressions are run as part of the query and with the privileges of the
user running the query, although security-definer functions can be used
to access data not available to the calling user.
</para>
<para>
Superusers and roles with the <literal>BYPASSRLS</> attribute always
bypass the row security system when accessing a table. Table owners
normally bypass row security as well, though a table owner can choose to
be subject to row security with <link linkend="sql-altertable">ALTER
TABLE ... FORCE ROW LEVEL SECURITY</>.
</para>
<para>
Enabling and disabling row security, as well as adding policies to a
table, is always the privilege of the table owner only.
</para>
<para>
Policies are created using the <xref linkend="sql-createpolicy">
command, altered using the <xref linkend="sql-alterpolicy"> command,
and dropped using the <xref linkend="sql-droppolicy"> command. To
enable and disable row security for a given table, use the
<xref linkend="sql-altertable"> command.
</para>
<para>
Each policy has a name and multiple policies can be defined for a
table. As policies are table-specific, each policy for a table must
have a unique name. Different tables may have policies with the
same name.
</para>
<para>
When multiple policies apply to a given query, they are combined using
either <literal>OR</literal> (for permissive policies, which are the
default) or using <literal>AND</literal> (for restrictive policies).
This is similar to the rule that a given role has the privileges
of all roles that they are a member of. Permissive vs. restrictive
policies are discussed further below.
</para>
<para>
As a simple example, here is how to create a policy on
the <literal>account</> relation to allow only members of
the <literal>managers</> role to access rows, and only rows of their
accounts:
</para>
<programlisting>
CREATE TABLE accounts (manager text, company text, contact_email text);
ALTER TABLE accounts ENABLE ROW LEVEL SECURITY;
CREATE POLICY account_managers ON accounts TO managers
USING (manager = current_user);
</programlisting>
<para>
The policy above implicitly provides a <literal>WITH CHECK</literal>
clause identical to its <literal>USING</literal> clause, so that the
constraint applies both to rows selected by a command (so a manager
cannot <command>SELECT</command>, <command>UPDATE</command>,
or <command>DELETE</command> existing rows belonging to a different
manager) and to rows modified by a command (so rows belonging to a
different manager cannot be created via <command>INSERT</command>
or <command>UPDATE</command>).
</para>
<para>
If no role is specified, or the special user name
<literal>PUBLIC</literal> is used, then the policy applies to all
users on the system. To allow all users to access only their own row in
a <literal>users</literal> table, a simple policy can be used:
</para>
<programlisting>
CREATE POLICY user_policy ON users
USING (user_name = current_user);
</programlisting>
<para>
This works similarly to the previous example.
</para>
<para>
To use a different policy for rows that are being added to the table
compared to those rows that are visible, multiple policies can be
combined. This pair of policies would allow all users to view all rows
in the <literal>users</literal> table, but only modify their own:
</para>
<programlisting>
CREATE POLICY user_sel_policy ON users
FOR SELECT
USING (true);
CREATE POLICY user_mod_policy ON users
USING (user_name = current_user);
</programlisting>
<para>
In a <command>SELECT</command> command, these two policies are combined
using <literal>OR</literal>, with the net effect being that all rows
can be selected. In other command types, only the second policy applies,
so that the effects are the same as before.
</para>
<para>
Row security can also be disabled with the <command>ALTER TABLE</command>
command. Disabling row security does not remove any policies that are
defined on the table; they are simply ignored. Then all rows in the
table are visible and modifiable, subject to the standard SQL privileges
system.
</para>
<para>
Below is a larger example of how this feature can be used in production
environments. The table <literal>passwd</> emulates a Unix password
file:
</para>
<programlisting>
-- Simple passwd-file based example
CREATE TABLE passwd (
user_name text UNIQUE NOT NULL,
pwhash text,
uid int PRIMARY KEY,
gid int NOT NULL,
real_name text NOT NULL,
home_phone text,
extra_info text,
home_dir text NOT NULL,
shell text NOT NULL
);
CREATE ROLE admin; -- Administrator
CREATE ROLE bob; -- Normal user
CREATE ROLE alice; -- Normal user
-- Populate the table
INSERT INTO passwd VALUES
('admin','xxx',0,0,'Admin','111-222-3333',null,'/root','/bin/dash');
INSERT INTO passwd VALUES
('bob','xxx',1,1,'Bob','123-456-7890',null,'/home/bob','/bin/zsh');
INSERT INTO passwd VALUES
('alice','xxx',2,1,'Alice','098-765-4321',null,'/home/alice','/bin/zsh');
-- Be sure to enable row level security on the table
ALTER TABLE passwd ENABLE ROW LEVEL SECURITY;
-- Create policies
-- Administrator can see all rows and add any rows
CREATE POLICY admin_all ON passwd TO admin USING (true) WITH CHECK (true);
-- Normal users can view all rows
CREATE POLICY all_view ON passwd FOR SELECT USING (true);
-- Normal users can update their own records, but
-- limit which shells a normal user is allowed to set
CREATE POLICY user_mod ON passwd FOR UPDATE
USING (current_user = user_name)
WITH CHECK (
current_user = user_name AND
shell IN ('/bin/bash','/bin/sh','/bin/dash','/bin/zsh','/bin/tcsh')
);
-- Allow admin all normal rights
GRANT SELECT, INSERT, UPDATE, DELETE ON passwd TO admin;
-- Users only get select access on public columns
GRANT SELECT
(user_name, uid, gid, real_name, home_phone, extra_info, home_dir, shell)
ON passwd TO public;
-- Allow users to update certain columns
GRANT UPDATE
(pwhash, real_name, home_phone, extra_info, shell)
ON passwd TO public;
</programlisting>
<para>
As with any security settings, it's important to test and ensure that
the system is behaving as expected. Using the example above, this
demonstrates that the permission system is working properly.
</para>
<programlisting>
-- admin can view all rows and fields
postgres=&gt; set role admin;
SET
postgres=&gt; table passwd;
user_name | pwhash | uid | gid | real_name | home_phone | extra_info | home_dir | shell
-----------+--------+-----+-----+-----------+--------------+------------+-------------+-----------
admin | xxx | 0 | 0 | Admin | 111-222-3333 | | /root | /bin/dash
bob | xxx | 1 | 1 | Bob | 123-456-7890 | | /home/bob | /bin/zsh
alice | xxx | 2 | 1 | Alice | 098-765-4321 | | /home/alice | /bin/zsh
(3 rows)
-- Test what Alice is able to do
postgres=&gt; set role alice;
SET
postgres=&gt; table passwd;
ERROR: permission denied for relation passwd
postgres=&gt; select user_name,real_name,home_phone,extra_info,home_dir,shell from passwd;
user_name | real_name | home_phone | extra_info | home_dir | shell
-----------+-----------+--------------+------------+-------------+-----------
admin | Admin | 111-222-3333 | | /root | /bin/dash
bob | Bob | 123-456-7890 | | /home/bob | /bin/zsh
alice | Alice | 098-765-4321 | | /home/alice | /bin/zsh
(3 rows)
postgres=&gt; update passwd set user_name = 'joe';
ERROR: permission denied for relation passwd
-- Alice is allowed to change her own real_name, but no others
postgres=&gt; update passwd set real_name = 'Alice Doe';
UPDATE 1
postgres=&gt; update passwd set real_name = 'John Doe' where user_name = 'admin';
UPDATE 0
postgres=&gt; update passwd set shell = '/bin/xx';
ERROR: new row violates WITH CHECK OPTION for "passwd"
postgres=&gt; delete from passwd;
ERROR: permission denied for relation passwd
postgres=&gt; insert into passwd (user_name) values ('xxx');
ERROR: permission denied for relation passwd
-- Alice can change her own password; RLS silently prevents updating other rows
postgres=&gt; update passwd set pwhash = 'abc';
UPDATE 1
</programlisting>
<para>
All of the policies constructed thus far have been permissive policies,
meaning that when multiple policies are applied they are combined using
the <quote>OR</quote> Boolean operator. While permissive policies can be constructed
to only allow access to rows in the intended cases, it can be simpler to
combine permissive policies with restrictive policies (which the records
must pass and which are combined using the <quote>AND</quote> Boolean operator).
Building on the example above, we add a restrictive policy to require
the administrator to be connected over a local Unix socket to access the
records of the <literal>passwd</literal> table:
</para>
<programlisting>
CREATE POLICY admin_local_only ON passwd AS RESTRICTIVE TO admin
USING (pg_catalog.inet_client_addr() IS NULL);
</programlisting>
<para>
We can then see that an administrator connecting over a network will not
see any records, due to the restrictive policy:
</para>
<programlisting>
=&gt; SELECT current_user;
current_user
--------------
admin
(1 row)
=&gt; select inet_client_addr();
inet_client_addr
------------------
127.0.0.1
(1 row)
=&gt; SELECT current_user;
current_user
--------------
admin
(1 row)
=&gt; TABLE passwd;
user_name | pwhash | uid | gid | real_name | home_phone | extra_info | home_dir | shell
-----------+--------+-----+-----+-----------+------------+------------+----------+-------
(0 rows)
=&gt; UPDATE passwd set pwhash = NULL;
UPDATE 0
</programlisting>
<para>
Referential integrity checks, such as unique or primary key constraints
and foreign key references, always bypass row security to ensure that
data integrity is maintained. Care must be taken when developing
schemas and row level policies to avoid <quote>covert channel</> leaks of
information through such referential integrity checks.
</para>
<para>
In some contexts it is important to be sure that row security is
not being applied. For example, when taking a backup, it could be
disastrous if row security silently caused some rows to be omitted
from the backup. In such a situation, you can set the
<xref linkend="guc-row-security"> configuration parameter
to <literal>off</>. This does not in itself bypass row security;
what it does is throw an error if any query's results would get filtered
by a policy. The reason for the error can then be investigated and
fixed.
</para>
<para>
In the examples above, the policy expressions consider only the current
values in the row to be accessed or updated. This is the simplest and
best-performing case; when possible, it's best to design row security
applications to work this way. If it is necessary to consult other rows
or other tables to make a policy decision, that can be accomplished using
sub-<command>SELECT</>s, or functions that contain <command>SELECT</>s,
in the policy expressions. Be aware however that such accesses can
create race conditions that could allow information leakage if care is
not taken. As an example, consider the following table design:
</para>
<programlisting>
-- definition of privilege groups
CREATE TABLE groups (group_id int PRIMARY KEY,
group_name text NOT NULL);
INSERT INTO groups VALUES
(1, 'low'),
(2, 'medium'),
(5, 'high');
GRANT ALL ON groups TO alice; -- alice is the administrator
GRANT SELECT ON groups TO public;
-- definition of users' privilege levels
CREATE TABLE users (user_name text PRIMARY KEY,
group_id int NOT NULL REFERENCES groups);
INSERT INTO users VALUES
('alice', 5),
('bob', 2),
('mallory', 2);
GRANT ALL ON users TO alice;
GRANT SELECT ON users TO public;
-- table holding the information to be protected
CREATE TABLE information (info text,
group_id int NOT NULL REFERENCES groups);
INSERT INTO information VALUES
('barely secret', 1),
('slightly secret', 2),
('very secret', 5);
ALTER TABLE information ENABLE ROW LEVEL SECURITY;
-- a row should be visible to/updatable by users whose security group_id is
-- greater than or equal to the row's group_id
CREATE POLICY fp_s ON information FOR SELECT
USING (group_id &lt;= (SELECT group_id FROM users WHERE user_name = current_user));
CREATE POLICY fp_u ON information FOR UPDATE
USING (group_id &lt;= (SELECT group_id FROM users WHERE user_name = current_user));
-- we rely only on RLS to protect the information table
GRANT ALL ON information TO public;
</programlisting>
<para>
Now suppose that <literal>alice</> wishes to change the <quote>slightly
secret</> information, but decides that <literal>mallory</> should not
be trusted with the new content of that row, so she does:
</para>
<programlisting>
BEGIN;
UPDATE users SET group_id = 1 WHERE user_name = 'mallory';
UPDATE information SET info = 'secret from mallory' WHERE group_id = 2;
COMMIT;
</programlisting>
<para>
That looks safe; there is no window wherein <literal>mallory</> should be
able to see the <quote>secret from mallory</> string. However, there is
a race condition here. If <literal>mallory</> is concurrently doing,
say,
<programlisting>
SELECT * FROM information WHERE group_id = 2 FOR UPDATE;
</programlisting>
and her transaction is in <literal>READ COMMITTED</> mode, it is possible
for her to see <quote>secret from mallory</>. That happens if her
transaction reaches the <structname>information</> row just
after <literal>alice</>'s does. It blocks waiting
for <literal>alice</>'s transaction to commit, then fetches the updated
row contents thanks to the <literal>FOR UPDATE</> clause. However, it
does <emphasis>not</> fetch an updated row for the
implicit <command>SELECT</> from <structname>users</>, because that
sub-<command>SELECT</> did not have <literal>FOR UPDATE</>; instead
the <structname>users</> row is read with the snapshot taken at the start
of the query. Therefore, the policy expression tests the old value
of <literal>mallory</>'s privilege level and allows her to see the
updated row.
</para>
<para>
There are several ways around this problem. One simple answer is to use
<literal>SELECT ... FOR SHARE</> in sub-<command>SELECT</>s in row
security policies. However, that requires granting <literal>UPDATE</>
privilege on the referenced table (here <structname>users</>) to the
affected users, which might be undesirable. (But another row security
policy could be applied to prevent them from actually exercising that
privilege; or the sub-<command>SELECT</> could be embedded into a security
definer function.) Also, heavy concurrent use of row share locks on the
referenced table could pose a performance problem, especially if updates
of it are frequent. Another solution, practical if updates of the
referenced table are infrequent, is to take an exclusive lock on the
referenced table when updating it, so that no concurrent transactions
could be examining old row values. Or one could just wait for all
concurrent transactions to end after committing an update of the
referenced table and before making changes that rely on the new security
situation.
</para>
<para>
For additional details see <xref linkend="sql-createpolicy">
and <xref linkend="sql-altertable">.
</para>
</sect1>
<sect1 id="ddl-schemas">
<title>Schemas</title>
<indexterm zone="ddl-schemas">
<primary>schema</primary>
</indexterm>
<para>
A <productname>PostgreSQL</productname> database cluster
contains one or more named databases. Users and groups of users are
shared across the entire cluster, but no other data is shared across
databases. Any given client connection to the server can access
only the data in a single database, the one specified in the connection
request.
</para>
<note>
<para>
Users of a cluster do not necessarily have the privilege to access every
database in the cluster. Sharing of user names means that there
cannot be different users named, say, <literal>joe</> in two databases
in the same cluster; but the system can be configured to allow
<literal>joe</> access to only some of the databases.
</para>
</note>
<para>
A database contains one or more named <firstterm>schemas</>, which
in turn contain tables. Schemas also contain other kinds of named
objects, including data types, functions, and operators. The same
object name can be used in different schemas without conflict; for
example, both <literal>schema1</> and <literal>myschema</> can
contain tables named <literal>mytable</>. Unlike databases,
schemas are not rigidly separated: a user can access objects in any
of the schemas in the database they are connected to, if they have
privileges to do so.
</para>
<para>
There are several reasons why one might want to use schemas:
<itemizedlist>
<listitem>
<para>
To allow many users to use one database without interfering with
each other.
</para>
</listitem>
<listitem>
<para>
To organize database objects into logical groups to make them
more manageable.
</para>
</listitem>
<listitem>
<para>
Third-party applications can be put into separate schemas so
they do not collide with the names of other objects.
</para>
</listitem>
</itemizedlist>
Schemas are analogous to directories at the operating system level,
except that schemas cannot be nested.
</para>
<sect2 id="ddl-schemas-create">
<title>Creating a Schema</title>
<indexterm zone="ddl-schemas-create">
<primary>schema</primary>
<secondary>creating</secondary>
</indexterm>
<para>
To create a schema, use the <xref linkend="sql-createschema">
command. Give the schema a name
of your choice. For example:
<programlisting>
CREATE SCHEMA myschema;
</programlisting>
</para>
<indexterm>
<primary>qualified name</primary>
</indexterm>
<indexterm>
<primary>name</primary>
<secondary>qualified</secondary>
</indexterm>
<para>
To create or access objects in a schema, write a
<firstterm>qualified name</> consisting of the schema name and
table name separated by a dot:
<synopsis>
<replaceable>schema</><literal>.</><replaceable>table</>
</synopsis>
This works anywhere a table name is expected, including the table
modification commands and the data access commands discussed in
the following chapters.
(For brevity we will speak of tables only, but the same ideas apply
to other kinds of named objects, such as types and functions.)
</para>
<para>
Actually, the even more general syntax
<synopsis>
<replaceable>database</><literal>.</><replaceable>schema</><literal>.</><replaceable>table</>
</synopsis>
can be used too, but at present this is just for <foreignphrase>pro
forma</> compliance with the SQL standard. If you write a database name,
it must be the same as the database you are connected to.
</para>
<para>
So to create a table in the new schema, use:
<programlisting>
CREATE TABLE myschema.mytable (
...
);
</programlisting>
</para>
<indexterm>
<primary>schema</primary>
<secondary>removing</secondary>
</indexterm>
<para>
To drop a schema if it's empty (all objects in it have been
dropped), use:
<programlisting>
DROP SCHEMA myschema;
</programlisting>
To drop a schema including all contained objects, use:
<programlisting>
DROP SCHEMA myschema CASCADE;
</programlisting>
See <xref linkend="ddl-depend"> for a description of the general
mechanism behind this.
</para>
<para>
Often you will want to create a schema owned by someone else
(since this is one of the ways to restrict the activities of your
users to well-defined namespaces). The syntax for that is:
<programlisting>
CREATE SCHEMA <replaceable>schema_name</replaceable> AUTHORIZATION <replaceable>user_name</replaceable>;
</programlisting>
You can even omit the schema name, in which case the schema name
will be the same as the user name. See <xref
linkend="ddl-schemas-patterns"> for how this can be useful.
</para>
<para>
Schema names beginning with <literal>pg_</> are reserved for
system purposes and cannot be created by users.
</para>
</sect2>
<sect2 id="ddl-schemas-public">
<title>The Public Schema</title>
<indexterm zone="ddl-schemas-public">
<primary>schema</primary>
<secondary>public</secondary>
</indexterm>
<para>
In the previous sections we created tables without specifying any
schema names. By default such tables (and other objects) are
automatically put into a schema named <quote>public</quote>. Every new
database contains such a schema. Thus, the following are equivalent:
<programlisting>
CREATE TABLE products ( ... );
</programlisting>
and:
<programlisting>
CREATE TABLE public.products ( ... );
</programlisting>
</para>
</sect2>
<sect2 id="ddl-schemas-path">
<title>The Schema Search Path</title>
<indexterm>
<primary>search path</primary>
</indexterm>
<indexterm>
<primary>unqualified name</primary>
</indexterm>
<indexterm>
<primary>name</primary>
<secondary>unqualified</secondary>
</indexterm>
<para>
Qualified names are tedious to write, and it's often best not to
wire a particular schema name into applications anyway. Therefore
tables are often referred to by <firstterm>unqualified names</>,
which consist of just the table name. The system determines which table
is meant by following a <firstterm>search path</>, which is a list
of schemas to look in. The first matching table in the search path
is taken to be the one wanted. If there is no match in the search
path, an error is reported, even if matching table names exist
in other schemas in the database.
</para>
<para>
The ability to create like-named objects in different schemas complicates
writing a query that references precisely the same objects every time. It
also opens up the potential for users to change the behavior of other
users' queries, maliciously or accidentally. Due to the prevalence of
unqualified names in queries and their use
in <productname>PostgreSQL</productname> internals, adding a schema
to <varname>search_path</varname> effectively trusts all users having
<literal>CREATE</literal> privilege on that schema. When you run an
ordinary query, a malicious user able to create objects in a schema of
your search path can take control and execute arbitrary SQL functions as
though you executed them.
</para>
<indexterm>
<primary>schema</primary>
<secondary>current</secondary>
</indexterm>
<para>
The first schema named in the search path is called the current schema.
Aside from being the first schema searched, it is also the schema in
which new tables will be created if the <command>CREATE TABLE</>
command does not specify a schema name.
</para>
<indexterm>
<primary><varname>search_path</varname> configuration parameter</primary>
</indexterm>
<para>
To show the current search path, use the following command:
<programlisting>
SHOW search_path;
</programlisting>
In the default setup this returns:
<screen>
search_path
--------------
"$user", public
</screen>
The first element specifies that a schema with the same name as
the current user is to be searched. If no such schema exists,
the entry is ignored. The second element refers to the
public schema that we have seen already.
</para>
<para>
The first schema in the search path that exists is the default
location for creating new objects. That is the reason that by
default objects are created in the public schema. When objects
are referenced in any other context without schema qualification
(table modification, data modification, or query commands) the
search path is traversed until a matching object is found.
Therefore, in the default configuration, any unqualified access
again can only refer to the public schema.
</para>
<para>
To put our new schema in the path, we use:
<programlisting>
SET search_path TO myschema,public;
</programlisting>
(We omit the <literal>$user</literal> here because we have no
immediate need for it.) And then we can access the table without
schema qualification:
<programlisting>
DROP TABLE mytable;
</programlisting>
Also, since <literal>myschema</literal> is the first element in
the path, new objects would by default be created in it.
</para>
<para>
We could also have written:
<programlisting>
SET search_path TO myschema;
</programlisting>
Then we no longer have access to the public schema without
explicit qualification. There is nothing special about the public
schema except that it exists by default. It can be dropped, too.
</para>
<para>
See also <xref linkend="functions-info"> for other ways to manipulate
the schema search path.
</para>
<para>
The search path works in the same way for data type names, function names,
and operator names as it does for table names. Data type and function
names can be qualified in exactly the same way as table names. If you
need to write a qualified operator name in an expression, there is a
special provision: you must write
<synopsis>
<literal>OPERATOR(</><replaceable>schema</><literal>.</><replaceable>operator</><literal>)</>
</synopsis>
This is needed to avoid syntactic ambiguity. An example is:
<programlisting>
SELECT 3 OPERATOR(pg_catalog.+) 4;
</programlisting>
In practice one usually relies on the search path for operators,
so as not to have to write anything so ugly as that.
</para>
</sect2>
<sect2 id="ddl-schemas-priv">
<title>Schemas and Privileges</title>
<indexterm zone="ddl-schemas-priv">
<primary>privilege</primary>
<secondary sortas="schemas">for schemas</secondary>
</indexterm>
<para>
By default, users cannot access any objects in schemas they do not
own. To allow that, the owner of the schema must grant the
<literal>USAGE</literal> privilege on the schema. To allow users
to make use of the objects in the schema, additional privileges
might need to be granted, as appropriate for the object.
</para>
<para>
A user can also be allowed to create objects in someone else's
schema. To allow that, the <literal>CREATE</literal> privilege on
the schema needs to be granted. Note that by default, everyone
has <literal>CREATE</literal> and <literal>USAGE</literal> privileges on
the schema
<literal>public</literal>. This allows all users that are able to
connect to a given database to create objects in its
<literal>public</literal> schema.
Some <link linkend="ddl-schemas-patterns">usage patterns</link> call for
revoking that privilege:
<programlisting>
REVOKE CREATE ON SCHEMA public FROM PUBLIC;
</programlisting>
(The first <quote>public</quote> is the schema, the second
<quote>public</quote> means <quote>every user</quote>. In the
first sense it is an identifier, in the second sense it is a
key word, hence the different capitalization; recall the
guidelines from <xref linkend="sql-syntax-identifiers">.)
</para>
</sect2>
<sect2 id="ddl-schemas-catalog">
<title>The System Catalog Schema</title>
<indexterm zone="ddl-schemas-catalog">
<primary>system catalog</primary>
<secondary>schema</secondary>
</indexterm>
<para>
In addition to <literal>public</> and user-created schemas, each
database contains a <literal>pg_catalog</> schema, which contains
the system tables and all the built-in data types, functions, and
operators. <literal>pg_catalog</> is always effectively part of
the search path. If it is not named explicitly in the path then
it is implicitly searched <emphasis>before</> searching the path's
schemas. This ensures that built-in names will always be
findable. However, you can explicitly place
<literal>pg_catalog</> at the end of your search path if you
prefer to have user-defined names override built-in names.
</para>
<para>
Since system table names begin with <literal>pg_</>, it is best to
avoid such names to ensure that you won't suffer a conflict if some
future version defines a system table named the same as your
table. (With the default search path, an unqualified reference to
your table name would then be resolved as the system table instead.)
System tables will continue to follow the convention of having
names beginning with <literal>pg_</>, so that they will not
conflict with unqualified user-table names so long as users avoid
the <literal>pg_</> prefix.
</para>
</sect2>
<sect2 id="ddl-schemas-patterns">
<title>Usage Patterns</title>
<para>
Schemas can be used to organize your data in many ways. There are a few
usage patterns easily supported by the default configuration, only one of
which suffices when database users mistrust other database users:
<itemizedlist>
<listitem>
<!-- "DROP SCHEMA public" is inferior to this REVOKE, because pg_dump
doesn't preserve that DROP. -->
<para>
Constrain ordinary users to user-private schemas. To implement this,
issue <literal>REVOKE CREATE ON SCHEMA public FROM PUBLIC</literal>,
and create a schema for each user with the same name as that user. If
affected users had logged in before this, consider auditing the public
schema for objects named like objects in
schema <literal>pg_catalog</literal>. Recall that the default search
path starts with <literal>$user</literal>, which resolves to the user
name. Therefore, if each user has a separate schema, they access their
own schemas by default.
</para>
</listitem>
<listitem>
<para>
Remove the public schema from each user's default search path
using <literal>ALTER ROLE <replaceable>user</replaceable> SET
search_path = "$user"</literal>. Everyone retains the ability to
create objects in the public schema, but only qualified names will
choose those objects. While qualified table references are fine, calls
to functions in the public schema <link linkend="typeconv-func">will be
unsafe or unreliable</link>. Also, a user holding
the <literal>CREATEROLE</literal> privilege can undo this setting and
issue arbitrary queries under the identity of users relying on the
setting. If you create functions or extensions in the public schema or
grant <literal>CREATEROLE</literal> to users not warranting this
almost-superuser ability, use the first pattern instead.
</para>
</listitem>
<listitem>
<para>
Remove the public schema from <varname>search_path</varname> in
<link linkend="config-setting-configuration-file"><filename>postgresql.conf</filename></link>.
The ensuing user experience matches the previous pattern. In addition
to that pattern's implications for functions
and <literal>CREATEROLE</literal>, this trusts database owners
like <literal>CREATEROLE</literal>. If you create functions or
extensions in the public schema or assign
the <literal>CREATEROLE</literal>
privilege, <literal>CREATEDB</literal> privilege or individual database
ownership to users not warranting almost-superuser access, use the
first pattern instead.
</para>
</listitem>
<listitem>
<para>
Keep the default. All users access the public schema implicitly. This
simulates the situation where schemas are not available at all, giving
a smooth transition from the non-schema-aware world. However, any user
can issue arbitrary queries under the identity of any user not electing
to protect itself individually. This pattern is acceptable only when
the database has a single user or a few mutually-trusting users.
</para>
</listitem>
</itemizedlist>
</para>
<para>
For any pattern, to install shared applications (tables to be used by
everyone, additional functions provided by third parties, etc.), put them
into separate schemas. Remember to grant appropriate privileges to allow
the other users to access them. Users can then refer to these additional
objects by qualifying the names with a schema name, or they can put the
additional schemas into their search path, as they choose.
</para>
</sect2>
<sect2 id="ddl-schemas-portability">
<title>Portability</title>
<para>
In the SQL standard, the notion of objects in the same schema
being owned by different users does not exist. Moreover, some
implementations do not allow you to create schemas that have a
different name than their owner. In fact, the concepts of schema
and user are nearly equivalent in a database system that
implements only the basic schema support specified in the
standard. Therefore, many users consider qualified names to
really consist of
<literal><replaceable>user_name</>.<replaceable>table_name</></literal>.
This is how <productname>PostgreSQL</productname> will effectively
behave if you create a per-user schema for every user.
</para>
<para>
Also, there is no concept of a <literal>public</> schema in the
SQL standard. For maximum conformance to the standard, you should
not use the <literal>public</> schema.
</para>
<para>
Of course, some SQL database systems might not implement schemas
at all, or provide namespace support by allowing (possibly
limited) cross-database access. If you need to work with those
systems, then maximum portability would be achieved by not using
schemas at all.
</para>
</sect2>
</sect1>
<sect1 id="ddl-inherit">
<title>Inheritance</title>
<indexterm>
<primary>inheritance</primary>
</indexterm>
<indexterm>
<primary>table</primary>
<secondary>inheritance</secondary>
</indexterm>
<para>
<productname>PostgreSQL</productname> implements table inheritance,
which can be a useful tool for database designers. (SQL:1999 and
later define a type inheritance feature, which differs in many
respects from the features described here.)
</para>
<para>
Let's start with an example: suppose we are trying to build a data
model for cities. Each state has many cities, but only one
capital. We want to be able to quickly retrieve the capital city
for any particular state. This can be done by creating two tables,
one for state capitals and one for cities that are not
capitals. However, what happens when we want to ask for data about
a city, regardless of whether it is a capital or not? The
inheritance feature can help to resolve this problem. We define the
<structname>capitals</structname> table so that it inherits from
<structname>cities</structname>:
<programlisting>
CREATE TABLE cities (
name text,
population float,
altitude int -- in feet
);
CREATE TABLE capitals (
state char(2)
) INHERITS (cities);
</programlisting>
In this case, the <structname>capitals</> table <firstterm>inherits</>
all the columns of its parent table, <structname>cities</>. State
capitals also have an extra column, <structfield>state</>, that shows
their state.
</para>
<para>
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 descendant tables.
The latter behavior is the default.
For example, the following query finds the names of all cities,
including state capitals, that are located at an altitude over
500 feet:
<programlisting>
SELECT name, altitude
FROM cities
WHERE altitude &gt; 500;
</programlisting>
Given the sample data from the <productname>PostgreSQL</productname>
tutorial (see <xref linkend="tutorial-sql-intro">), this 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 500 feet:
<programlisting>
SELECT name, altitude
FROM ONLY cities
WHERE altitude &gt; 500;
name | altitude
-----------+----------
Las Vegas | 2174
Mariposa | 1953
</programlisting>
</para>
<para>
Here the <literal>ONLY</literal> keyword indicates that the query
should apply only to <structname>cities</structname>, and not any tables
below <structname>cities</structname> in the inheritance hierarchy. Many
of the commands that we have already discussed &mdash;
<command>SELECT</command>, <command>UPDATE</command> and
<command>DELETE</command> &mdash; support the
<literal>ONLY</literal> keyword.
</para>
<para>
You can also write the table name with a trailing <literal>*</>
to explicitly specify that descendant tables are included:
<programlisting>
SELECT name, altitude
FROM cities*
WHERE altitude &gt; 500;
</programlisting>
Writing <literal>*</> is not necessary, since this behavior is always
the default. However, this syntax is still supported for
compatibility with older releases where the default could be changed.
</para>
<para>
In some cases you might wish to know which table a particular row
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
<structname>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>
<para>
Another way to get the same effect is to use the <type>regclass</>
alias type, which will print the table OID symbolically:
<programlisting>
SELECT c.tableoid::regclass, c.name, c.altitude
FROM cities c
WHERE c.altitude &gt; 500;
</programlisting>
</para>
<para>
Inheritance does not automatically propagate data from
<command>INSERT</command> or <command>COPY</command> commands to
other tables in the inheritance hierarchy. In our example, the
following <command>INSERT</command> statement will fail:
<programlisting>
INSERT INTO cities (name, population, altitude, state)
VALUES ('Albany', NULL, NULL, 'NY');
</programlisting>
We might hope that the data would somehow be routed to the
<structname>capitals</structname> table, but this does not happen:
<command>INSERT</command> always inserts into exactly the table
specified. In some cases it is possible to redirect the insertion
using a rule (see <xref linkend="rules">). However that does not
help for the above case because the <structname>cities</> table
does not contain the column <structfield>state</>, and so the
command will be rejected before the rule can be applied.
</para>
<para>
All check constraints and not-null constraints on a parent table are
automatically inherited by its children, unless explicitly specified
otherwise with <literal>NO INHERIT</> clauses. Other types of constraints
(unique, primary key, and foreign key constraints) are not inherited.
</para>
<para>
A table can inherit from more than one parent table, in which case it has
the union of the columns defined by the parent tables. Any columns
declared in the child table's definition are added to these. If the
same column name appears in multiple parent tables, or in both a parent
table and the child's definition, then these columns are <quote>merged</>
so that there is only one such column in the child table. To be merged,
columns must have the same data types, else an error is raised.
Inheritable check constraints and not-null constraints are merged in a
similar fashion. Thus, for example, a merged column will be marked
not-null if any one of the column definitions it came from is marked
not-null. Check constraints are merged if they have the same name,
and the merge will fail if their conditions are different.
</para>
<para>
Table inheritance is typically established when the child table is
created, using the <literal>INHERITS</> clause of the
<xref linkend="sql-createtable">
statement.
Alternatively, a table which is already defined in a compatible way can
have a new parent relationship added, using the <literal>INHERIT</literal>
variant of <xref linkend="sql-altertable">.
To do this the new child table must already include columns with
the same names and types as the columns of the parent. It must also include
check constraints with the same names and check expressions as those of the
parent. Similarly an inheritance link can be removed from a child using the
<literal>NO INHERIT</literal> variant of <command>ALTER TABLE</>.
Dynamically adding and removing inheritance links like this can be useful
when the inheritance relationship is being used for table
partitioning (see <xref linkend="ddl-partitioning">).
</para>
<para>
One convenient way to create a compatible table that will later be made
a new child is to use the <literal>LIKE</literal> clause in <command>CREATE
TABLE</command>. This creates a new table with the same columns as
the source table. If there are any <literal>CHECK</literal>
constraints defined on the source table, the <literal>INCLUDING
CONSTRAINTS</literal> option to <literal>LIKE</literal> should be
specified, as the new child must have constraints matching the parent
to be considered compatible.
</para>
<para>
A parent table cannot be dropped while any of its children remain. Neither
can columns or check constraints of child tables be dropped or altered
if they are inherited
from any parent tables. If you wish to remove a table and all of its
descendants, one easy way is to drop the parent table with the
<literal>CASCADE</literal> option (see <xref linkend="ddl-depend">).
</para>
<para>
<xref linkend="sql-altertable"> will
propagate any changes in column data definitions and check
constraints down the inheritance hierarchy. Again, dropping
columns that are depended on by other tables is only possible when using
the <literal>CASCADE</literal> option. <command>ALTER
TABLE</command> follows the same rules for duplicate column merging
and rejection that apply during <command>CREATE TABLE</command>.
</para>
<para>
Inherited queries perform access permission checks on the parent table
only. Thus, for example, granting <literal>UPDATE</> permission on
the <structname>cities</> table implies permission to update rows in
the <structname>capitals</structname> table as well, when they are
accessed through <structname>cities</>. This preserves the appearance
that the data is (also) in the parent table. But
the <structname>capitals</structname> table could not be updated directly
without an additional grant. In a similar way, the parent table's row
security policies (see <xref linkend="ddl-rowsecurity">) are applied to
rows coming from child tables during an inherited query. A child table's
policies, if any, are applied only when it is the table explicitly named
in the query; and in that case, any policies attached to its parent(s) are
ignored.
</para>
<para>
Foreign tables (see <xref linkend="ddl-foreign-data">) can also
be part of inheritance hierarchies, either as parent or child
tables, just as regular tables can be. If a foreign table is part
of an inheritance hierarchy then any operations not supported by
the foreign table are not supported on the whole hierarchy either.
</para>
<sect2 id="ddl-inherit-caveats">
<title>Caveats</title>
<para>
Note that not all SQL commands are able to work on
inheritance hierarchies. Commands that are used for data querying,
data modification, or schema modification
(e.g., <literal>SELECT</literal>, <literal>UPDATE</literal>, <literal>DELETE</literal>,
most variants of <literal>ALTER TABLE</literal>, but
not <literal>INSERT</literal> or <literal>ALTER TABLE ...
RENAME</literal>) typically default to including child tables and
support the <literal>ONLY</literal> notation to exclude them.
Commands that do database maintenance and tuning
(e.g., <literal>REINDEX</literal>, <literal>VACUUM</literal>)
typically only work on individual, physical tables and do not
support recursing over inheritance hierarchies. The respective
behavior of each individual command is documented in its reference
page (<xref linkend="sql-commands">).
</para>
<para>
A serious 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. This is true on both the
referencing and referenced sides of a foreign key constraint. Thus,
in the terms of the above example:
<itemizedlist>
<listitem>
<para>
If we declared <structname>cities</>.<structfield>name</> to be
<literal>UNIQUE</> or a <literal>PRIMARY KEY</>, this would not stop the
<structname>capitals</> table from having rows with names duplicating
rows in <structname>cities</>. And those duplicate rows would by
default show up in queries from <structname>cities</>. In fact, by
default <structname>capitals</> would have no unique constraint at all,
and so could contain multiple rows with the same name.
You could add a unique constraint to <structname>capitals</>, but this
would not prevent duplication compared to <structname>cities</>.
</para>
</listitem>
<listitem>
<para>
Similarly, if we were to specify that
<structname>cities</>.<structfield>name</> <literal>REFERENCES</> some
other table, this constraint would not automatically propagate to
<structname>capitals</>. In this case you could work around it by
manually adding the same <literal>REFERENCES</> constraint to
<structname>capitals</>.
</para>
</listitem>
<listitem>
<para>
Specifying that another table's column <literal>REFERENCES
cities(name)</> would allow the other table to contain city names, but
not capital names. There is no good workaround for this case.
</para>
</listitem>
</itemizedlist>
Some functionality not implemented for inheritance hierarchies is
implemented for declarative partitioning.
Considerable care is needed in deciding whether partitioning with legacy
inheritance is useful for your application.
</para>
</sect2>
</sect1>
<sect1 id="ddl-partitioning">
<title>Table Partitioning</title>
<indexterm>
<primary>partitioning</primary>
</indexterm>
<indexterm>
<primary>table</primary>
<secondary>partitioning</secondary>
</indexterm>
<indexterm>
<primary>partitioned table</primary>
</indexterm>
<para>
<productname>PostgreSQL</productname> supports basic table
partitioning. This section describes why and how to implement
partitioning as part of your database design.
</para>
<sect2 id="ddl-partitioning-overview">
<title>Overview</title>
<para>
Partitioning refers to splitting what is logically one large table into
smaller physical pieces. Partitioning can provide several benefits:
<itemizedlist>
<listitem>
<para>
Query performance can be improved dramatically in certain situations,
particularly when most of the heavily accessed rows of the table are in a
single partition or a small number of partitions. The partitioning
substitutes for leading columns of indexes, reducing index size and
making it more likely that the heavily-used parts of the indexes
fit in memory.
</para>
</listitem>
<listitem>
<para>
When queries or updates access a large percentage of a single
partition, performance can be improved by taking advantage
of sequential scan of that partition instead of using an
index and random access reads scattered across the whole table.
</para>
</listitem>
<listitem>
<para>
Bulk loads and deletes can be accomplished by adding or removing
partitions, if that requirement is planned into the partitioning design.
Doing <command>ALTER TABLE DETACH PARTITION</> or dropping an individual
partition using <command>DROP TABLE</> is far faster than a bulk
operation. These commands also entirely avoid the
<command>VACUUM</command> overhead caused by a bulk <command>DELETE</>.
</para>
</listitem>
<listitem>
<para>
Seldom-used data can be migrated to cheaper and slower storage media.
</para>
</listitem>
</itemizedlist>
The benefits will normally be worthwhile only when a table would
otherwise be very large. The exact point at which a table will
benefit from partitioning depends on the application, although a
rule of thumb is that the size of the table should exceed the physical
memory of the database server.
</para>
<para>
<productname>PostgreSQL</productname> offers built-in support for the
following forms of partitioning:
<variablelist>
<varlistentry>
<term>Range Partitioning</term>
<listitem>
<para>
The table is partitioned into <quote>ranges</quote> defined
by a key column or set of columns, with no overlap between
the ranges of values assigned to different partitions. For
example, one might partition by date ranges, or by ranges of
identifiers for particular business objects.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>List Partitioning</term>
<listitem>
<para>
The table is partitioned by explicitly listing which key values
appear in each partition.
</para>
</listitem>
</varlistentry>
</variablelist>
If your application needs to use other forms of partitioning not listed
above, alternative methods such as inheritance and
<literal>UNION ALL</literal> views can be used instead. Such methods
offer flexibility but do not have some of the performance benefits
of built-in declarative partitioning.
</para>
</sect2>
<sect2 id="ddl-partitioning-declarative">
<title>Declarative Partitioning</title>
<para>
<productname>PostgreSQL</productname> offers a way to specify how to
divide a table into pieces called partitions. The table that is divided
is referred to as a <firstterm>partitioned table</firstterm>. The
specification consists of the <firstterm>partitioning method</firstterm>
and a list of columns or expressions to be used as the
<firstterm>partition key</firstterm>.
</para>
<para>
All rows inserted into a partitioned table will be routed to one of the
<firstterm>partitions</firstterm> based on the value of the partition
key. Each partition has a subset of the data defined by its
<firstterm>partition bounds</firstterm>. Currently supported
partitioning methods include range and list, where each partition is
assigned a range of keys and a list of keys, respectively.
</para>
<para>
Partitions may themselves be defined as partitioned tables, using what is
called <firstterm>sub-partitioning</firstterm>. Partitions may have their
own indexes, constraints and default values, distinct from those of other
partitions. Indexes must be created separately for each partition. See
<xref linkend="sql-createtable"> for more details on creating partitioned
tables and partitions.
</para>
<para>
It is not possible to turn a regular table into a partitioned table or
vice versa. However, it is possible to add a regular or partitioned table
containing data as a partition of a partitioned table, or remove a
partition from a partitioned table turning it into a standalone table;
see <xref linkend="sql-altertable"> to learn more about the
<command>ATTACH PARTITION</> and <command>DETACH PARTITION</>
sub-commands.
</para>
<para>
Individual partitions are linked to the partitioned table with inheritance
behind-the-scenes; however, it is not possible to use some of the
inheritance features discussed in the previous section with partitioned
tables and partitions. For example, a partition cannot have any parents
other than the partitioned table it is a partition of, nor can a regular
table inherit from a partitioned table making the latter its parent.
That means partitioned tables and partitions do not participate in
inheritance with regular tables. Since a partition hierarchy consisting
of the partitioned table and its partitions is still an inheritance
hierarchy, all the normal rules of inheritance apply as described in
<xref linkend="ddl-inherit"> with some exceptions, most notably:
<itemizedlist>
<listitem>
<para>
Both <literal>CHECK</literal> and <literal>NOT NULL</literal>
constraints of a partitioned table are always inherited by all its
partitions. <literal>CHECK</literal> constraints that are marked
<literal>NO INHERIT</literal> are not allowed to be created on
partitioned tables.
</para>
</listitem>
<listitem>
<para>
Using <literal>ONLY</literal> to add or drop a constraint on only the
partitioned table is supported when there are no partitions. Once
partitions exist, using <literal>ONLY</literal> will result in an error
as adding or dropping constraints on only the partitioned table, when
partitions exist, is not supported. Instead, constraints can be added
or dropped, when they are not present in the parent table, directly on
the partitions. As a partitioned table does not have any data
directly, attempts to use <command>TRUNCATE</command>
<literal>ONLY</literal> on a partitioned table will always return an
error.
</para>
</listitem>
<listitem>
<para>
Partitions cannot have columns that are not present in the parent. It
is neither possible to specify columns when creating partitions with
<command>CREATE TABLE</> nor is it possible to add columns to
partitions after-the-fact using <command>ALTER TABLE</>. Tables may be
added as a partition with <command>ALTER TABLE ... ATTACH PARTITION</>
only if their columns exactly match the parent, including any
<literal>oid</literal> column.
</para>
</listitem>
<listitem>
<para>
You cannot drop the <literal>NOT NULL</literal> constraint on a
partition's column if the constraint is present in the parent table.
</para>
</listitem>
</itemizedlist>
</para>
<para>
Partitions can also be foreign tables
(see <xref linkend="sql-createforeigntable">),
although these have some limitations that normal tables do not. For
example, data inserted into the partitioned table is not routed to
foreign table partitions.
</para>
<sect3 id="ddl-partitioning-declarative-example">
<title>Example</title>
<para>
Suppose we are constructing a database for a large ice cream company.
The company measures peak temperatures every day as well as ice cream
sales in each region. Conceptually, we want a table like:
<programlisting>
CREATE TABLE measurement (
city_id int not null,
logdate date not null,
peaktemp int,
unitsales int
);
</programlisting>
We know that most queries will access just the last week's, month's or
quarter's data, since the main use of this table will be to prepare
online reports for management. To reduce the amount of old data that
needs to be stored, we decide to only keep the most recent 3 years
worth of data. At the beginning of each month we will remove the oldest
month's data. In this situation we can use partitioning to help us meet
all of our different requirements for the measurements table.
</para>
<para>
To use declarative partitioning in this case, use the following steps:
<orderedlist spacing="compact">
<listitem>
<para>
Create <structname>measurement</structname> table as a partitioned
table by specifying the <literal>PARTITION BY</literal> clause, which
includes the partitioning method (<literal>RANGE</literal> in this
case) and the list of column(s) to use as the partition key.
<programlisting>
CREATE TABLE measurement (
city_id int not null,
logdate date not null,
peaktemp int,
unitsales int
) PARTITION BY RANGE (logdate);
</programlisting>
</para>
<para>
You may decide to use multiple columns in the partition key for range
partitioning, if desired. Of course, this will often result in a larger
number of partitions, each of which is individually smaller. On the
other hand, using fewer columns may lead to a coarser-grained
partitioning criteria with smaller number of partitions. A query
accessing the partitioned table will have to scan fewer partitions if
the conditions involve some or all of these columns.
For example, consider a table range partitioned using columns
<structfield>lastname</> and <structfield>firstname</> (in that order)
as the partition key.
</para>
</listitem>
<listitem>
<para>
Create partitions. Each partition's definition must specify the bounds
that correspond to the partitioning method and partition key of the
parent. Note that specifying bounds such that the new partition's
values will overlap with those in one or more existing partitions will
cause an error. Inserting data into the parent table that does not map
to one of the existing partitions will cause an error; an appropriate
partition must be added manually.
</para>
<para>
Partitions thus created are in every way normal
<productname>PostgreSQL</>
tables (or, possibly, foreign tables). It is possible to specify a
tablespace and storage parameters for each partition separately.
</para>
<para>
It is not necessary to create table constraints describing partition
boundary condition for partitions. Instead, partition constraints are
generated implicitly from the partition bound specification whenever
there is need to refer to them.
<programlisting>
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');
CREATE TABLE measurement_y2006m03 PARTITION OF measurement
FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');
...
CREATE TABLE measurement_y2007m11 PARTITION OF measurement
FOR VALUES FROM ('2007-11-01') TO ('2007-12-01');
CREATE TABLE measurement_y2007m12 PARTITION OF measurement
FOR VALUES FROM ('2007-12-01') TO ('2008-01-01')
TABLESPACE fasttablespace;
CREATE TABLE measurement_y2008m01 PARTITION OF measurement
FOR VALUES FROM ('2008-01-01') TO ('2008-02-01')
WITH (parallel_workers = 4)
TABLESPACE fasttablespace;
</programlisting>
</para>
<para>
To implement sub-partitioning, specify the
<literal>PARTITION BY</literal> clause in the commands used to create
individual partitions, for example:
<programlisting>
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01')
PARTITION BY RANGE (peaktemp);
</programlisting>
After creating partitions of <structname>measurement_y2006m02</>,
any data inserted into <structname>measurement</> that is mapped to
<structname>measurement_y2006m02</> (or data that is directly inserted
into <structname>measurement_y2006m02</>, provided it satisfies its
partition constraint) will be further redirected to one of its
partitions based on the <structfield>peaktemp</> column. The partition
key specified may overlap with the parent's partition key, although
care should be taken when specifying the bounds of a sub-partition
such that the set of data it accepts constitutes a subset of what
the partition's own bounds allows; the system does not try to check
whether that's really the case.
</para>
</listitem>
<listitem>
<para>
Create an index on the key column(s), as well as any other indexes you
might want for every partition. (The key index is not strictly
necessary, but in most scenarios it is helpful. If you intend the key
values to be unique then you should always create a unique or
primary-key constraint for each partition.)
<programlisting>
CREATE INDEX ON measurement_y2006m02 (logdate);
CREATE INDEX ON measurement_y2006m03 (logdate);
...
CREATE INDEX ON measurement_y2007m11 (logdate);
CREATE INDEX ON measurement_y2007m12 (logdate);
CREATE INDEX ON measurement_y2008m01 (logdate);
</programlisting>
</para>
</listitem>
<listitem>
<para>
Ensure that the <xref linkend="guc-constraint-exclusion">
configuration parameter is not disabled in <filename>postgresql.conf</>.
If it is, queries will not be optimized as desired.
</para>
</listitem>
</orderedlist>
</para>
<para>
In the above example we would be creating a new partition each month, so
it might be wise to write a script that generates the required DDL
automatically.
</para>
</sect3>
<sect3 id="ddl-partitioning-declarative-maintenance">
<title>Partition Maintenance</title>
<para>
Normally the set of partitions established when initially defining the
table are not intended to remain static. It is common to want to
remove old partitions of data and periodically add new partitions for
new data. One of the most important advantages of partitioning is
precisely that it allows this otherwise painful task to be executed
nearly instantaneously by manipulating the partition structure, rather
than physically moving large amounts of data around.
</para>
<para>
The simplest option for removing old data is to drop the partition that
is no longer necessary:
<programlisting>
DROP TABLE measurement_y2006m02;
</programlisting>
This can very quickly delete millions of records because it doesn't have
to individually delete every record. Note however that the above command
requires taking an <literal>ACCESS EXCLUSIVE</literal> lock on the parent
table.
</para>
<para>
Another option that is often preferable is to remove the partition from
the partitioned table but retain access to it as a table in its own
right:
<programlisting>
ALTER TABLE measurement DETACH PARTITION measurement_y2006m02;
</programlisting>
This allows further operations to be performed on the data before
it is dropped. For example, this is often a useful time to back up
the data using <command>COPY</>, <application>pg_dump</>, or
similar tools. It might also be a useful time to aggregate data
into smaller formats, perform other data manipulations, or run
reports.
</para>
<para>
Similarly we can add a new partition to handle new data. We can create an
empty partition in the partitioned table just as the original partitions
were created above:
<programlisting>
CREATE TABLE measurement_y2008m02 PARTITION OF measurement
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01')
TABLESPACE fasttablespace;
</programlisting>
As an alternative, it is sometimes more convenient to create the
new table outside the partition structure, and make it a proper
partition later. This allows the data to be loaded, checked, and
transformed prior to it appearing in the partitioned table:
<programlisting>
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS)
TABLESPACE fasttablespace;
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate &gt;= DATE '2008-02-01' AND logdate &lt; DATE '2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement ATTACH PARTITION measurement_y2008m02
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01' );
</programlisting>
</para>
<para>
Before running the <command>ATTACH PARTITION</> command, it is
recommended to create a <literal>CHECK</> constraint on the table to
be attached matching the desired partition constraint. That way,
the system will be able to skip the scan to validate the implicit
partition constraint. Without the <literal>CHECK</> constraint,
the table will be scanned to validate the partition constraint while
holding an <literal>ACCESS EXCLUSIVE</literal> lock on the parent table.
It may be desired to drop the redundant <literal>CHECK</> constraint
after <command>ATTACH PARTITION</> is finished.
</para>
</sect3>
<sect3 id="ddl-partitioning-declarative-limitations">
<title>Limitations</title>
<para>
The following limitations apply to partitioned tables:
<itemizedlist>
<listitem>
<para>
There is no facility available to create the matching indexes on all
partitions automatically. Indexes must be added to each partition with
separate commands. This also means that there is no way to create a
primary key, unique constraint, or exclusion constraint spanning all
partitions; it is only possible to constrain each leaf partition
individually.
</para>
</listitem>
<listitem>
<para>
Since primary keys are not supported on partitioned tables, foreign
keys referencing partitioned tables are not supported, nor are foreign
key references from a partitioned table to some other table.
</para>
</listitem>
<listitem>
<para>
Using the <literal>ON CONFLICT</literal> clause with partitioned tables
will cause an error, because unique or exclusion constraints can only be
created on individual partitions. There is no support for enforcing
uniqueness (or an exclusion constraint) across an entire partitioning
hierarchy.
</para>
</listitem>
<listitem>
<para>
An <command>UPDATE</> that causes a row to move from one partition to
another fails, because the new value of the row fails to satisfy the
implicit partition constraint of the original partition.
</para>
</listitem>
<listitem>
<para>
Row triggers, if necessary, must be defined on individual partitions,
not the partitioned table.
</para>
</listitem>
<listitem>
<para>
Mixing temporary and permanent relations in the same partition tree is
not allowed. Hence, if the partitioned table is permanent, so must be
its partitions and likewise if the partitioned table is temporary. When
using temporary relations, all members of the partition tree have to be
from the same session.
</para>
</listitem>
</itemizedlist>
</para>
</sect3>
</sect2>
<sect2 id="ddl-partitioning-implementation-inheritance">
<title>Implementation Using Inheritance</title>
<para>
While the built-in declarative partitioning is suitable for most
common use cases, there are some circumstances where a more flexible
approach may be useful. Partitioning can be implemented using table
inheritance, which allows for several features which are not supported
by declarative partitioning, such as:
<itemizedlist>
<listitem>
<para>
Partitioning enforces a rule that all partitions must have exactly
the same set of columns as the parent, but table inheritance allows
children to have extra columns not present in the parent.
</para>
</listitem>
<listitem>
<para>
Table inheritance allows for multiple inheritance.
</para>
</listitem>
<listitem>
<para>
Declarative partitioning only supports list and range partitioning,
whereas table inheritance allows data to be divided in a manner of
the user's choosing. (Note, however, that if constraint exclusion is
unable to prune partitions effectively, query performance will be very
poor.)
</para>
</listitem>
<listitem>
<para>
Some operations require a stronger lock when using declarative
partitioning than when using table inheritance. For example, adding
or removing a partition to or from a partitioned table requires taking
an <literal>ACCESS EXCLUSIVE</literal> lock on the parent table,
whereas a <literal>SHARE UPDATE EXCLUSIVE</literal> lock is enough
in the case of regular inheritance.
</para>
</listitem>
</itemizedlist>
</para>
<sect3 id="ddl-partitioning-inheritance-example">
<title>Example</title>
<para>
We use the same <structname>measurement</structname> table we used
above. To implement it as a partitioned table using inheritance, use
the following steps:
<orderedlist spacing="compact">
<listitem>
<para>
Create the <quote>master</quote> table, from which all of the
partitions will inherit. This table will contain no data. Do not
define any check constraints on this table, unless you intend them
to be applied equally to all partitions. There is no point in
defining any indexes or unique constraints on it, either. For our
example, the master table is the <structname>measurement</structname>
table as originally defined.
</para>
</listitem>
<listitem>
<para>
Create several <quote>child</quote> tables that each inherit from
the master table. Normally, these tables will not add any columns
to the set inherited from the master. Just as with declarative
partitioning, these partitions are in every way normal
<productname>PostgreSQL</> tables (or foreign tables).
</para>
<para>
<programlisting>
CREATE TABLE measurement_y2006m02 () INHERITS (measurement);
CREATE TABLE measurement_y2006m03 () INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 () INHERITS (measurement);
CREATE TABLE measurement_y2007m12 () INHERITS (measurement);
CREATE TABLE measurement_y2008m01 () INHERITS (measurement);
</programlisting>
</para>
</listitem>
<listitem>
<para>
Add non-overlapping table constraints to the partition tables to
define the allowed key values in each partition.
</para>
<para>
Typical examples would be:
<programlisting>
CHECK ( x = 1 )
CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire', 'Warwickshire' ))
CHECK ( outletID &gt;= 100 AND outletID &lt; 200 )
</programlisting>
Ensure that the constraints guarantee that there is no overlap
between the key values permitted in different partitions. A common
mistake is to set up range constraints like:
<programlisting>
CHECK ( outletID BETWEEN 100 AND 200 )
CHECK ( outletID BETWEEN 200 AND 300 )
</programlisting>
This is wrong since it is not clear which partition the key value
200 belongs in.
</para>
<para>
It would be better to instead create partitions as follows:
<programlisting>
CREATE TABLE measurement_y2006m02 (
CHECK ( logdate &gt;= DATE '2006-02-01' AND logdate &lt; DATE '2006-03-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2006m03 (
CHECK ( logdate &gt;= DATE '2006-03-01' AND logdate &lt; DATE '2006-04-01' )
) INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 (
CHECK ( logdate &gt;= DATE '2007-11-01' AND logdate &lt; DATE '2007-12-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2007m12 (
CHECK ( logdate &gt;= DATE '2007-12-01' AND logdate &lt; DATE '2008-01-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2008m01 (
CHECK ( logdate &gt;= DATE '2008-01-01' AND logdate &lt; DATE '2008-02-01' )
) INHERITS (measurement);
</programlisting>
</para>
</listitem>
<listitem>
<para>
For each partition, create an index on the key column(s),
as well as any other indexes you might want.
<programlisting>
CREATE INDEX measurement_y2006m02_logdate ON measurement_y2006m02 (logdate);
CREATE INDEX measurement_y2006m03_logdate ON measurement_y2006m03 (logdate);
CREATE INDEX measurement_y2007m11_logdate ON measurement_y2007m11 (logdate);
CREATE INDEX measurement_y2007m12_logdate ON measurement_y2007m12 (logdate);
CREATE INDEX measurement_y2008m01_logdate ON measurement_y2008m01 (logdate);
</programlisting>
</para>
</listitem>
<listitem>
<para>
We want our application to be able to say <literal>INSERT INTO
measurement ...</> and have the data be redirected into the
appropriate partition table. We can arrange that by attaching
a suitable trigger function to the master table.
If data will be added only to the latest partition, we can
use a very simple trigger function:
<programlisting>
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</programlisting>
</para>
<para>
After creating the function, we create a trigger which
calls the trigger function:
<programlisting>
CREATE TRIGGER insert_measurement_trigger
BEFORE INSERT ON measurement
FOR EACH ROW EXECUTE PROCEDURE measurement_insert_trigger();
</programlisting>
We must redefine the trigger function each month so that it always
points to the current partition. The trigger definition does
not need to be updated, however.
</para>
<para>
We might want to insert data and have the server automatically
locate the partition into which the row should be added. We
could do this with a more complex trigger function, for example:
<programlisting>
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
IF ( NEW.logdate &gt;= DATE '2006-02-01' AND
NEW.logdate &lt; DATE '2006-03-01' ) THEN
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
ELSIF ( NEW.logdate &gt;= DATE '2006-03-01' AND
NEW.logdate &lt; DATE '2006-04-01' ) THEN
INSERT INTO measurement_y2006m03 VALUES (NEW.*);
...
ELSIF ( NEW.logdate &gt;= DATE '2008-01-01' AND
NEW.logdate &lt; DATE '2008-02-01' ) THEN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
ELSE
RAISE EXCEPTION 'Date out of range. Fix the measurement_insert_trigger() function!';
END IF;
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</programlisting>
The trigger definition is the same as before.
Note that each <literal>IF</literal> test must exactly match the
<literal>CHECK</literal> constraint for its partition.
</para>
<para>
While this function is more complex than the single-month case,
it doesn't need to be updated as often, since branches can be
added in advance of being needed.
</para>
<note>
<para>
In practice it might be best to check the newest partition first,
if most inserts go into that partition. For simplicity we have
shown the trigger's tests in the same order as in other parts
of this example.
</para>
</note>
<para>
A different approach to redirecting inserts into the appropriate
partition table is to set up rules, instead of a trigger, on the
master table. For example:
<programlisting>
CREATE RULE measurement_insert_y2006m02 AS
ON INSERT TO measurement WHERE
( logdate &gt;= DATE '2006-02-01' AND logdate &lt; DATE '2006-03-01' )
DO INSTEAD
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
...
CREATE RULE measurement_insert_y2008m01 AS
ON INSERT TO measurement WHERE
( logdate &gt;= DATE '2008-01-01' AND logdate &lt; DATE '2008-02-01' )
DO INSTEAD
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
</programlisting>
A rule has significantly more overhead than a trigger, but the
overhead is paid once per query rather than once per row, so this
method might be advantageous for bulk-insert situations. In most
cases, however, the trigger method will offer better performance.
</para>
<para>
Be aware that <command>COPY</> ignores rules. If you want to
use <command>COPY</> to insert data, you'll need to copy into the
correct partition table rather than into the master. <command>COPY</>
does fire triggers, so you can use it normally if you use the trigger
approach.
</para>
<para>
Another disadvantage of the rule approach is that there is no simple
way to force an error if the set of rules doesn't cover the insertion
date; the data will silently go into the master table instead.
</para>
</listitem>
<listitem>
<para>
Ensure that the <xref linkend="guc-constraint-exclusion">
configuration parameter is not disabled in
<filename>postgresql.conf</>.
If it is, queries will not be optimized as desired.
</para>
</listitem>
</orderedlist>
</para>
<para>
As we can see, a complex partitioning scheme could require a
substantial amount of DDL. In the above example we would be creating
a new partition each month, so it might be wise to write a script that
generates the required DDL automatically.
</para>
</sect3>
<sect3 id="ddl-partitioning-inheritance-maintenance">
<title>Partition Maintenance</title>
<para>
To remove old data quickly, simply drop the partition that is no longer
necessary:
<programlisting>
DROP TABLE measurement_y2006m02;
</programlisting>
</para>
<para>
To remove the partition from the partitioned table but retain access to
it as a table in its own right:
<programlisting>
ALTER TABLE measurement_y2006m02 NO INHERIT measurement;
</programlisting>
</para>
<para>
To add a new partition to handle new data, create an empty partition
just as the original partitions were created above:
<programlisting>
CREATE TABLE measurement_y2008m02 (
CHECK ( logdate &gt;= DATE '2008-02-01' AND logdate &lt; DATE '2008-03-01' )
) INHERITS (measurement);
</programlisting>
Alternatively, one may want to create the new table outside the partition
structure, and make it a partition after the data is loaded, checked,
and transformed.
<programlisting>
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate &gt;= DATE '2008-02-01' AND logdate &lt; DATE '2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement_y2008m02 INHERIT measurement;
</programlisting>
</para>
</sect3>
<sect3 id="ddl-partitioning-inheritance-caveats">
<title>Caveats</title>
<para>
The following caveats apply to partitioned tables implemented using
inheritance:
<itemizedlist>
<listitem>
<para>
There is no automatic way to verify that all of the
<literal>CHECK</literal> constraints are mutually
exclusive. It is safer to create code that generates
partitions and creates and/or modifies associated objects than
to write each by hand.
</para>
</listitem>
<listitem>
<para>
The schemes shown here assume that the partition key column(s)
of a row never change, or at least do not change enough to require
it to move to another partition. An <command>UPDATE</> that attempts
to do that will fail because of the <literal>CHECK</> constraints.
If you need to handle such cases, you can put suitable update triggers
on the partition tables, but it makes management of the structure
much more complicated.
</para>
</listitem>
<listitem>
<para>
If you are using manual <command>VACUUM</command> or
<command>ANALYZE</command> commands, don't forget that
you need to run them on each partition individually. A command like:
<programlisting>
ANALYZE measurement;
</programlisting>
will only process the master table.
</para>
</listitem>
<listitem>
<para>
<command>INSERT</command> statements with <literal>ON CONFLICT</>
clauses are unlikely to work as expected, as the <literal>ON CONFLICT</>
action is only taken in case of unique violations on the specified
target relation, not its child relations.
</para>
</listitem>
<listitem>
<para>
Triggers or rules will be needed to route rows to the desired
partition, unless the application is explicitly aware of the
partitioning scheme. Triggers may be complicated to write, and will
be much slower than the tuple routing performed internally by
declarative partitioning.
</para>
</listitem>
</itemizedlist>
</para>
</sect3>
</sect2>
<sect2 id="ddl-partitioning-constraint-exclusion">
<title>Partitioning and Constraint Exclusion</title>
<indexterm>
<primary>constraint exclusion</primary>
</indexterm>
<para>
<firstterm>Constraint exclusion</> is a query optimization technique
that improves performance for partitioned tables defined in the
fashion described above (both declaratively partitioned tables and those
implemented using inheritance). As an example:
<programlisting>
SET constraint_exclusion = on;
SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
</programlisting>
Without constraint exclusion, the above query would scan each of
the partitions of the <structname>measurement</> table. With constraint
exclusion enabled, the planner will examine the constraints of each
partition and try to prove that the partition need not
be scanned because it could not contain any rows meeting the query's
<literal>WHERE</> clause. When the planner can prove this, it
excludes the partition from the query plan.
</para>
<para>
You can use the <command>EXPLAIN</> command to show the difference
between a plan with <varname>constraint_exclusion</> on and a plan
with it off. A typical unoptimized plan for this type of table setup is:
<programlisting>
SET constraint_exclusion = off;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
QUERY PLAN
-----------------------------------------------------------------------------------------------
Aggregate (cost=158.66..158.68 rows=1 width=0)
-&gt; Append (cost=0.00..151.88 rows=2715 width=0)
-&gt; Seq Scan on measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
-&gt; Seq Scan on measurement_y2006m02 measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
-&gt; Seq Scan on measurement_y2006m03 measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
...
-&gt; Seq Scan on measurement_y2007m12 measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
-&gt; Seq Scan on measurement_y2008m01 measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
</programlisting>
Some or all of the partitions might use index scans instead of
full-table sequential scans, but the point here is that there
is no need to scan the older partitions at all to answer this query.
When we enable constraint exclusion, we get a significantly
cheaper plan that will deliver the same answer:
<programlisting>
SET constraint_exclusion = on;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
QUERY PLAN
-----------------------------------------------------------------------------------------------
Aggregate (cost=63.47..63.48 rows=1 width=0)
-&gt; Append (cost=0.00..60.75 rows=1086 width=0)
-&gt; Seq Scan on measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
-&gt; Seq Scan on measurement_y2008m01 measurement (cost=0.00..30.38 rows=543 width=0)
Filter: (logdate &gt;= '2008-01-01'::date)
</programlisting>
</para>
<para>
Note that constraint exclusion is driven only by <literal>CHECK</>
constraints, not by the presence of indexes. Therefore it isn't
necessary to define indexes on the key columns. Whether an index
needs to be created for a given partition depends on whether you
expect that queries that scan the partition will generally scan
a large part of the partition or just a small part. An index will
be helpful in the latter case but not the former.
</para>
<para>
The default (and recommended) setting of
<xref linkend="guc-constraint-exclusion"> is actually neither
<literal>on</> nor <literal>off</>, but an intermediate setting
called <literal>partition</>, which causes the technique to be
applied only to queries that are likely to be working on partitioned
tables. The <literal>on</> setting causes the planner to examine
<literal>CHECK</> constraints in all queries, even simple ones that
are unlikely to benefit.
</para>
<para>
The following caveats apply to constraint exclusion, which is used by
both inheritance and partitioned tables:
<itemizedlist>
<listitem>
<para>
Constraint exclusion only works when the query's <literal>WHERE</>
clause contains constants (or externally supplied parameters).
For example, a comparison against a non-immutable function such as
<function>CURRENT_TIMESTAMP</function> cannot be optimized, since the
planner cannot know which partition the function value might fall
into at run time.
</para>
</listitem>
<listitem>
<para>
Keep the partitioning constraints simple, else the planner may not be
able to prove that partitions don't need to be visited. Use simple
equality conditions for list partitioning, or simple
range tests for range partitioning, as illustrated in the preceding
examples. A good rule of thumb is that partitioning constraints should
contain only comparisons of the partitioning column(s) to constants
using B-tree-indexable operators, which applies even to partitioned
tables, because only B-tree-indexable column(s) are allowed in the
partition key. (This is not a problem when using declarative
partitioning, since the automatically generated constraints are simple
enough to be understood by the planner.)
</para>
</listitem>
<listitem>
<para>
All constraints on all partitions of the master table are examined
during constraint exclusion, so large numbers of partitions are likely
to increase query planning time considerably. Partitioning using
these techniques will work well with up to perhaps a hundred partitions;
don't try to use many thousands of partitions.
</para>
</listitem>
</itemizedlist>
</para>
</sect2>
<sect2 id="ddl-partitioning-declarative-best-practices">
<title>Declarative Partitioning Best Practices</title>
<para>
The choice of how to partition a table should be made carefully as the
performance of query planning and execution can be negatively affected by
poor design.
</para>
<para>
One of the most critical design decisions will be the column or columns
by which you partition your data. Often the best choice will be to
partition by the column or set of columns which most commonly appear in
<literal>WHERE</literal> clauses of queries being executed on the
partitioned table. <literal>WHERE</literal> clause items that match and
are compatible with the partition key can be used to prune unneeded
partitions. Removal of unwanted data is also a factor to consider when
planning your partitioning strategy. An entire partition can be detached
fairly quickly, so it may be beneficial to design the partition strategy
in such a way that all data to be removed at once is located in a single
partition.
</para>
<para>
Choosing the target number of partitions that the table should be divided
into is also a critical decision to make. Not having enough partitions
may mean that indexes remain too large and that data locality remains poor
which could result in low cache hit ratios. However, dividing the table
into too many partitions can also cause issues. Too many partitions can
mean longer query planning times and higher memory consumption during both
query planning and execution. When choosing how to partition your table,
it's also important to consider what changes may occur in the future. For
example, if you choose to have one partition per customer and you
currently have a small number of large customers, consider the
implications if in several years you instead find yourself with a large
number of small customers. In this case, it may be better to choose to
partition by <literal>RANGE</literal> and choose a reasonable number of
partitions, each containing a fixed number of customers, rather than
trying to partition by <literal>LIST</literal> and hoping that the number
of customers does not increase beyond what it is practical to partition
the data by.
</para>
<para>
Sub-partitioning can be useful to further divide partitions that are
expected to become larger than other partitions, although excessive
sub-partitioning can easily lead to large numbers of partitions and can
cause the same problems mentioned in the preceding paragraph.
</para>
<para>
It is also important to consider the overhead of partitioning during
query planning and execution. The query planner is generally able to
handle partition hierarchies with up to a few hundred partitions.
Planning times become longer and memory consumption becomes higher as more
partitions are added. This is particularly true for the
<command>UPDATE</command> and <command>DELETE</command> commands. Another
reason to be concerned about having a large number of partitions is that
the server's memory consumption may grow significantly over a period of
time, especially if many sessions touch large numbers of partitions.
That's because each partition requires its metadata to be loaded into the
local memory of each session that touches it.
</para>
<para>
With data warehouse type workloads, it can make sense to use a larger
number of partitions than with an <acronym>OLTP</acronym> type workload.
Generally, in data warehouses, query planning time is less of a concern as
the majority of processing time is spent during query execution. With
either of these two types of workload, it is important to make the right
decisions early, as re-partitioning large quantities of data can be
painfully slow. Simulations of the intended workload are often beneficial
for optimizing the partitioning strategy. Never assume that more
partitions are better than fewer partitions and vice-versa.
</para>
</sect2>
</sect1>
<sect1 id="ddl-foreign-data">
<title>Foreign Data</title>
<indexterm>
<primary>foreign data</primary>
</indexterm>
<indexterm>
<primary>foreign table</primary>
</indexterm>
<indexterm>
<primary>user mapping</primary>
</indexterm>
<para>
<productname>PostgreSQL</productname> implements portions of the SQL/MED
specification, allowing you to access data that resides outside
PostgreSQL using regular SQL queries. Such data is referred to as
<firstterm>foreign data</>. (Note that this usage is not to be confused
with foreign keys, which are a type of constraint within the database.)
</para>
<para>
Foreign data is accessed with help from a
<firstterm>foreign data wrapper</firstterm>. A foreign data wrapper is a
library that can communicate with an external data source, hiding the
details of connecting to the data source and obtaining data from it.
There are some foreign data wrappers available as <filename>contrib</>
modules; see <xref linkend="contrib">. Other kinds of foreign data
wrappers might be found as third party products. If none of the existing
foreign data wrappers suit your needs, you can write your own; see <xref
linkend="fdwhandler">.
</para>
<para>
To access foreign data, you need to create a <firstterm>foreign server</>
object, which defines how to connect to a particular external data source
according to the set of options used by its supporting foreign data
wrapper. Then you need to create one or more <firstterm>foreign
tables</firstterm>, which define the structure of the remote data. A
foreign table can be used in queries just like a normal table, but a
foreign table has no storage in the PostgreSQL server. Whenever it is
used, <productname>PostgreSQL</productname> asks the foreign data wrapper
to fetch data from the external source, or transmit data to the external
source in the case of update commands.
</para>
<para>
Accessing remote data may require authenticating to the external
data source. This information can be provided by a
<firstterm>user mapping</>, which can provide additional data
such as user names and passwords based
on the current <productname>PostgreSQL</productname> role.
</para>
<para>
For additional information, see
<xref linkend="sql-createforeigndatawrapper">,
<xref linkend="sql-createserver">,
<xref linkend="sql-createusermapping">,
<xref linkend="sql-createforeigntable">, and
<xref linkend="sql-importforeignschema">.
</para>
</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 and operators
</para>
</listitem>
<listitem>
<para>
Data types and domains
</para>
</listitem>
<listitem>
<para>
Triggers and rewrite rules
</para>
</listitem>
</itemizedlist>
<para>
Detailed information on
these topics appears in <xref linkend="server-programming">.
</para>
</sect1>
<sect1 id="ddl-depend">
<title>Dependency Tracking</title>
<indexterm zone="ddl-depend">
<primary>CASCADE</primary>
<secondary sortas="DROP">with DROP</secondary>
</indexterm>
<indexterm zone="ddl-depend">
<primary>RESTRICT</primary>
<secondary sortas="DROP">with DROP</secondary>
</indexterm>
<para>
When you create complex database structures involving many tables
with foreign key constraints, views, triggers, functions, etc. you
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 considered in <xref
linkend="ddl-constraints-fk">, with the orders table depending on
it, would result in an error message like this:
<screen>
DROP TABLE products;
ERROR: cannot drop table products because other objects depend on it
DETAIL: constraint orders_product_no_fkey on table orders depends on table products
HINT: Use DROP ... CASCADE to drop the dependent objects too.
</screen>
The error message contains a useful hint: if you do not 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, as will any objects
that depend on them, recursively. In this case, it doesn't remove
the orders table, it only removes the foreign key constraint.
It stops there because nothing depends on the foreign key constraint.
(If you want to check what <command>DROP ... CASCADE</> will do,
run <command>DROP</> without <literal>CASCADE</> and read the
<literal>DETAIL</> output.)
</para>
<para>
Almost all <command>DROP</> commands in <productname>PostgreSQL</> 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
prevent dropping objects that any other objects depend on.
</para>
<note>
<para>
According to the SQL standard, specifying either
<literal>RESTRICT</literal> or <literal>CASCADE</literal> is
required in a <command>DROP</> command. No database system actually
enforces that rule, but whether the default behavior
is <literal>RESTRICT</literal> or <literal>CASCADE</literal> varies
across systems.
</para>
</note>
<para>
If a <command>DROP</> command lists multiple
objects, <literal>CASCADE</literal> is only required when there are
dependencies outside the specified group. For example, when saying
<literal>DROP TABLE tab1, tab2</literal> the existence of a foreign
key referencing <literal>tab1</> from <literal>tab2</> would not mean
that <literal>CASCADE</literal> is needed to succeed.
</para>
<para>
For user-defined functions, <productname>PostgreSQL</productname> tracks
dependencies associated with a function's externally-visible properties,
such as its argument and result types, but <emphasis>not</> dependencies
that could only be known by examining the function body. As an example,
consider this situation:
<programlisting>
CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow',
'green', 'blue', 'purple');
CREATE TABLE my_colors (color rainbow, note text);
CREATE FUNCTION get_color_note (rainbow) RETURNS text AS
'SELECT note FROM my_colors WHERE color = $1'
LANGUAGE SQL;
</programlisting>
(See <xref linkend="xfunc-sql"> for an explanation of SQL-language
functions.) <productname>PostgreSQL</productname> will be aware that
the <function>get_color_note</> function depends on the <type>rainbow</>
type: dropping the type would force dropping the function, because its
argument type would no longer be defined. But <productname>PostgreSQL</>
will not consider <function>get_color_note</> to depend on
the <structname>my_colors</> table, and so will not drop the function if
the table is dropped. While there are disadvantages to this approach,
there are also benefits. The function is still valid in some sense if the
table is missing, though executing it would cause an error; creating a new
table of the same name would allow the function to work again.
</para>
</sect1>
</chapter>
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