ddl.sgml

<!-- $PostgreSQL: pgsql/doc/src/sgml/ddl.sgml,v 1.46 2005/11/01 23:19:05 neilc Exp $ -->

<chapter id="ddl">
 <title>Data Definition</title>

 <para>
  This chapter covers how one creates the database structures that
  will hold one's data.  In a relational database, the raw data is
  stored in tables, so the majority of this chapter is devoted to
  explaining how tables are created and modified and what features are
  available to control what data is stored in the tables.
  Subsequently, we discuss how tables can be organized into
  schemas, and how privileges can be assigned to tables.  Finally,
  we will briefly look at other features that affect the data storage,
  such as views, functions, and triggers.
 </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 -- it reflects how much data is stored at a given moment.
   SQL does not make any guarantees about the order of the rows in a
   table.  When a table is read, the rows will appear in random order,
   unless sorting is explicitly requested.  This is covered in <xref
   linkend="queries">.  Furthermore, SQL does not assign unique
   identifiers to rows, so it is possible to have several completely
   identical rows in a table.  This is a consequence of the
   mathematical model that underlies SQL but is usually not desirable.
   Later in this chapter we will see how to deal with this issue.
  </para>

  <para>
   Each column has a data type.  The data type constrains the set of
   possible values that can be assigned to a column and assigns
   semantics to the data stored in the column so that it can be used
   for computations.  For instance, a column declared to be of a
   numerical type will not accept arbitrary text strings, and the data
   stored in such a column can be used for mathematical computations.
   By contrast, a column declared to be of a character string type
   will accept almost any kind of data but it does not lend itself to
   mathematical calculations, although other operations such as string
   concatenation are available.
  </para>

  <para>
   <productname>PostgreSQL</productname> includes a sizable set of
   built-in data types that fit many applications.  Users can also
   define their own data types.  Most built-in data types have obvious
   names and semantics, so we defer a detailed explanation to <xref
   linkend="datatype">.  Some of the frequently used data types are
   <type>integer</type> for whole numbers, <type>numeric</type> for
   possibly fractional numbers, <type>text</type> for character
   strings, <type>date</type> for dates, <type>time</type> for
   time-of-day values, and <type>timestamp</type> for values
   containing both date and time.
  </para>

  <indexterm>
   <primary>table</primary>
   <secondary>creating</secondary>
  </indexterm>

  <para>
   To create a table, you use the aptly named <command>CREATE
   TABLE</command> 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
   <command>DROP TABLE</command> command.  For example:
<programlisting>
DROP TABLE my_first_table;
DROP TABLE products;
</programlisting>
   Attempting to drop a table that does not exist is an error.
   Nevertheless, it is common in SQL script files to unconditionally
   try to drop each table before creating it, ignoring the error
   messages.
  </para>

  <para>
   If you need to modify a table that already exists look into <xref
   linkend="ddl-alter"> later in this chapter.
  </para>

  <para>
   With the tools discussed so far you can create fully functional
   tables.  The remainder of this chapter is concerned with adding
   features to the table definition to ensure data integrity,
   security, or convenience.  If you are eager to fill your tables with
   data now you can skip ahead to <xref linkend="dml"> and read the
   rest of this chapter later.
  </para>
 </sect1>

 <sect1 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, the
   columns will be filled with their respective default values.  A
   data manipulation command can also request explicitly that a column
   be set to its default value, without 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 may 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 that a timestamp column may have a default of <literal>now()</>,
   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 only be one row for each product number.
  </para>

  <para>
   To that end, SQL allows you to define constraints on columns and
   tables.  Constraints give you as much control over the data in your
   tables as you wish.  If a user attempts to store data in a column
   that would violate a constraint, an error is raised.  This applies
   even if the value came from the default value definition.
  </para>

  <sect2>
   <title>Check Constraints</title>

   <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 just the same way as
    for 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>

    <para>
     Check constraints can also be used to enhance performance with
     very large tables, when used in conjunction with the <xref
     linkend="guc-constraint-exclusion"> parameter.  This is discussed
     in more detail in <xref linkend="ce-partitioning">.
   </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 may be null.
    The <literal>NULL</literal> constraint is not defined in the SQL
    standard and should not be used in portable applications.  (It was
    only added to <productname>PostgreSQL</productname> to be
    compatible with 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>
   <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 with respect to all the rows in the
    table.  The syntax is
<programlisting>
CREATE TABLE products (
    product_no integer <emphasis>UNIQUE</emphasis>,
    name text,
    price numeric
);
</programlisting>
    when written as a column constraint, and
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric,
    <emphasis>UNIQUE (product_no)</emphasis>
);
</programlisting>
    when written as a table constraint.
   </para>

   <para>
    If a unique constraint refers to a group of columns, the columns
    are listed separated by commas:
<programlisting>
CREATE TABLE example (
    a integer,
    b integer,
    c integer,
    <emphasis>UNIQUE (a, c)</emphasis>
);
</programlisting>
    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>

   <indexterm>
    <primary>null value</primary>
    <secondary sortas="unique constraints">with unique constraints</secondary>
   </indexterm>

   <para>
    In general, a unique constraint is violated when there are two or
    more rows in the table where the values of all of the
    columns included in the constraint are equal.
    However, null values are not 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 may not follow this rule.  So be
    careful when developing applications that are intended to be
    portable.
   </para>
  </sect2>

  <sect2>
   <title>Primary Keys</title>

   <indexterm>
    <primary>primary key</primary>
   </indexterm>

   <indexterm>
    <primary>constraint</primary>
    <secondary>primary key</secondary>
   </indexterm>

   <para>
    Technically, a primary key constraint is simply a combination of a
    unique constraint and a not-null constraint.  So, the following
    two table definitions accept the same data:
<programlisting>
CREATE TABLE products (
    product_no integer UNIQUE NOT NULL,
    name text,
    price numeric
);
</programlisting>

<programlisting>
CREATE TABLE products (
    product_no integer <emphasis>PRIMARY KEY</emphasis>,
    name text,
    price numeric
);
</programlisting>
   </para>

   <para>
    Primary keys can also constrain more than one column; the syntax
    is similar to unique constraints:
<programlisting>
CREATE TABLE example (
    a integer,
    b integer,
    c integer,
    <emphasis>PRIMARY KEY (a, c)</emphasis>
);
</programlisting>
   </para>

   <para>
    A primary key indicates that a column or group of columns can be
    used as a unique identifier for rows in the table.  (This is a
    direct consequence of the definition of a primary key.  Note that
    a unique constraint does not, by itself, provide a unique identifier
    because it does not exclude null values.)  This is useful both for
    documentation purposes and for client applications.  For example,
    a GUI application that allows modifying row values probably needs
    to know the primary key of a table to be able to identify rows
    uniquely.
   </para>

   <para>
    A table can have at most one primary key (while it can have many
    unique and not-null constraints).  Relational database theory
    dictates that every table must have a primary key.  This rule is
    not enforced by <productname>PostgreSQL</productname>, but it is
    usually best to follow it.
   </para>
  </sect2>

  <sect2 id="ddl-constraints-fk">
   <title>Foreign Keys</title>

   <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
    <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 contain more than one foreign key constraint.  This is
    used to implement many-to-many relationships between tables.  Say
    you have tables about products and orders, but now you want to
    allow one order to contain possibly many products (which the
    structure above did not allow).  You could use this table structure:
<programlisting>
CREATE TABLE products (
    product_no integer PRIMARY KEY,
    name text,
    price numeric
);

CREATE TABLE orders (
    order_id integer PRIMARY KEY,
    shipping_address text,
    ...
);

CREATE TABLE order_items (
    product_no integer REFERENCES products,
    order_id integer REFERENCES orders,
    quantity integer,
    PRIMARY KEY (product_no, order_id)
);
</programlisting>
    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 columns to be set to nulls or 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, 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.
   </para>

   <para>
    More information about updating and deleting data is in <xref
    linkend="dml">.
   </para>

   <para>
    Finally, we should mention that a foreign key must reference
    columns that either are a primary key or form a unique constraint.
    If the foreign key references a unique constraint, there are some
    additional possibilities regarding how null values are matched.
    These are explained in the reference documentation for
    <xref linkend="sql-createtable" endterm="sql-createtable-title">.
   </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. 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, 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 each time 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
       may 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>
       The tables in question should be created using <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 number of
    <acronym>SQL</acronym> commands, not number of rows processed.
   </para>
 </sect1>

 <sect1 id="ddl-inherit">
  <title>Inheritance</title>

   <indexterm>
    <primary>not-null constraint</primary>
   </indexterm>

   <indexterm>
    <primary>constraint</primary>
    <secondary>NOT NULL</secondary>
   </indexterm>

  <para>
   <productname>PostgreSQL</productname> implements table inheritance
   which can be a useful tool for database designers.  The SQL:2003
   standard optionally defines type inheritance 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
   <literal>capitals</literal> table so that it inherits from
   <literal>cities</literal>:

<programlisting>
CREATE TABLE cities (
    name            text,
    population      float,
    altitude        int     -- in feet
);

CREATE TABLE capitals (
    state           char(2)
) INHERITS (cities);
</programlisting>

   In this case, a row of <literal>capitals</> <firstterm>inherits</>
   all the columns of its parent table, <literal>cities</>. State
   capitals have an extra attribute, <literal>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 descendants.
   For example, the following query finds the names of all cities,
   including state capitals, that are located at an altitude over
   500ft:

<programlisting>
SELECT name, altitude
    FROM cities
    WHERE altitude &gt; 500;
</programlisting>

   which returns:

<programlisting>
   name    | altitude
-----------+----------
 Las Vegas |     2174
 Mariposa  |     1953
 Madison   |      845
</programlisting>
  </para>

  <para>
   On the other hand, the following query finds all the cities that
   are not state capitals and are situated at an altitude over 500ft:

<programlisting>
SELECT name, altitude
    FROM ONLY cities
    WHERE altitude &gt; 500;

   name    | altitude
-----------+----------
 Las Vegas |     2174
 Mariposa  |     1953
</programlisting>
  </para>

  <para>
   Here the <literal>ONLY</literal> keyword indicates that the query
   should apply only to <literal>cities</literal>, and not any tables
   below <literal>cities</literal> 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>

  <note>
   <title>Inheritance and Permissions</title>
   <para>
    Because permissions are not inherited automatically, a user
    attempting to access a parent table must either have at least the
    same permission for the child table or must use the
    <quote>ONLY</quote> notation. If creating a new inheritance
    relationship in an existing system be careful that this does not
    create problems.
   </para>
  </note>

  <para>
   Inheritance does not automatically propogate 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 ('New York', NULL, NULL, 'NY');
</programlisting>
   We might hope that the data would be somehow routed to the
   <literal>capitals</literal> table, though this does not happen. If
   the child has no locally defined columns, then it is possible to
   route data from the parent to the child using a rule, see <xref
   linkend="rules-update">.  This is not possible with the above
   <command>INSERT</> statement because the <literal>state</> column
   does not exist on both parent and child tables.
  </para>

  <para>
   In some cases you may 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>
   As shown above, a child table may locally define columns as well as
   inheriting them from their parents.  However, a locally defined
   column cannot override the datatype of an inherited column of the
   same name.  A table can inherit from a table that has itself
   inherited from other tables. A table can also inherit from more
   than one parent table, in which case it inherits the union of the
   columns defined by the parent tables.  Inherited columns with
   duplicate names and datatypes will be merged so that only a single
   column is stored.
  </para>

  <para>
   Table inheritance can currently only be defined using the <xref
   linkend="sql-createtable" endterm="sql-createtable-title">
   statement.  The related statement <literal>CREATE TABLE ... AS
   SELECT</literal> does not allow inheritance to be specified. There
   is no way to add an inheritance link to make an existing table into
   a child table. Similarly, there is no way to remove an inheritance
   link from a child table once it has been defined, other than using
   <literal>DROP TABLE</literal>.  A parent table cannot be dropped
   while any of its children remain. If you wish to remove a table and
   all of its descendants, then you can do so using the
   <literal>CASCADE</literal> option of the <xref
   linkend="sql-droptable" endterm="sql-droptable-title"> statement.
  </para>

  <para>
   Check constraints can be defined on tables within an inheritance
   hierarchy. All check constraints on a parent table are
   automatically inherited by all of their children. It is currently
   possible to inherit mutually exclusive check constraints, but that
   definition quickly shows itself since all attempted row inserts
   will be rejected.
  </para>

  <para>
   <xref linkend="sql-altertable" endterm="sql-altertable-title"> will
   propogate any changes in data definition on columns or check
   constraints down the inheritance hierarchy.  Again, dropping
   columns or constraints on parent 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>
   Both parent and child tables can have primary and foreign keys, so
   that they can take part normally on both the referencing and
   referenced sides of a foreign key constraint. Indexes may be
   defined on any of these columns whether or not they are inherited.
   However, a serious current limitation of the inheritance feature is
   that indexes (including unique constraints) and foreign key
   constraints only apply to single tables and do not also index their
   inheritance children.  This is true on both 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</>.  However, it is possible to set up a
      foreign key such as <structname>capitals</>.<structfield>name</>
      <literal>REFERENCES</> <structname>states</>.<structfield>name</>.
      So it is possible to workaround this restriction by manually adding
      foreign keys to each child table.
     </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>

   These deficiencies will probably be fixed in some future release,
   but in the meantime considerable care is needed in deciding whether
   inheritance is useful for your problem.

  </para>

  <note>
   <title>Deprecated</title>
   <para>
     In previous versions of <productname>PostgreSQL</productname>, the
     default behavior was not to include child tables in queries. This was
     found to be error prone and is also in violation of the SQL:2003
     standard. Under the old syntax, to get the sub-tables you append
     <literal>*</literal> to the table name. For example:
<programlisting>
SELECT * from cities*;
</programlisting>
     You can still explicitly specify scanning child tables by
     appending <literal>*</literal>, as well as explicitly specify not
     scanning child tables by writing <quote>ONLY</quote>.  But
     beginning in version 7.1, the default behavior for an undecorated
     table name is to scan its child tables too, whereas before the
     default was not to do so.  To get the old default behavior,
     disable the <xref linkend="guc-sql-inheritance"> configuration
     option.
   </para>
  </note>

  </sect1>

  <sect1 id="ce-partitioning">
   <title>Constraint Exclusion and Partitioning</title>

   <indexterm>
    <primary>partitioning</primary>
   </indexterm>

   <indexterm>
    <primary>constraint exclusion</primary>
   </indexterm>

   <para>
    <productname>PostgreSQL</productname> supports basic table
    partitioning. This section describes why and how you can implement
    this as part of your database design.
   </para>

   <sect2 id="ce-partitioning-overview">
     <title>Overview</title>

   <para>
    Currently, partitioning is implemented in conjunction with table
    inheritance only, though using fully SQL:2003 compliant syntax.
    Table inheritance allows tables to be split into partitions, and
    constraint exclusion allows partitions to be selectively combined
    as needed to satisfy a particular <command>SELECT</command>
    statement. You should be familiar with inheritance (see <xref
    linkend="ddl-inherit">) before attempting to implement
    partitioning.
   </para>

   <para>
    Partitioning can provide several benefits:
   <itemizedlist>
    <listitem>
     <para>
      Query performance can be improved dramatically for certain kinds
      of queries without the need to maintain costly indexes.
     </para>
    </listitem>

    <listitem>
     <para>
      Insert performance can be improved by breaking down a large
      index into multiple pieces. When an index no longer fits easily
      in memory, both read and write operations on the index take
      progressively more disk accesses.
     </para>
    </listitem>

    <listitem>
     <para>
      Bulk deletes may be avoided altogether by simply removing one of the
      partitions, if that requirement is planned into the partitioning design.
     </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 data table would
    otherwise be very large. That is for you to judge, though would not
    usually be lower than the size of physical RAM on the database server.
   </para>

   <para>
    In <productname>PostgreSQL</productname> &version;, the following
    partitioning types are supported:

    <itemizedlist>
     <listitem>
      <para>
       "Range Partitioning" where the table is partitioned along a
       "range" defined by a single column or set of columns, with no
       overlap between partitions. Examples might be a date range or a
       range of identifiers for particular business objects.
      </para>
     </listitem>

     <listitem>
      <para>
       "List Partitioning" where the table is partitioned by
       explicitly listing which values relate to each partition.
      </para>
     </listitem>
    </itemizedlist>

    Hash partitioning is not currently supported.
   </para>
   </sect2>

   <sect2 id="ce-partitioning-implementation">
     <title>Implementing Partitioning</title>

    <para>
     Partitioning a table is a straightforward process.  There
     are a wide range of options for you to consider, so judging exactly
     when and how to implement partitioning is a more complex topic. We
     will address that complexity primarily through the examples in this
     section.
    </para>

    <para>
     To use partitioning, do the following:
     <orderedlist spacing=compact>
      <listitem>
       <para>
        Create the <quote>master</quote> table, from which all of the
        partitions will inherit.
       </para>
       <para>
        This table will contain no data.  Do not define any
        constraints or keys on this table, unless you intend them to
        be applied equally to all partitions.
       </para>
      </listitem>

      <listitem>
       <para>
        Create several <quote>child</quote> tables that inherit from
        the master table.
       </para>

       <para>
        We will refer to the child tables as partitions, though they
        are in every way just normal <productname>PostgreSQL</>
        tables.
       </para>
      </listitem>

      <listitem>
       <para>
        Add table constraints to define the allowed values in each partition.
       </para>
       <para>
        Only clauses of the form [COLUMN] [OPERATOR] [CONSTANT(s)] will be used
        for constraint exclusion. Simple examples would be:
<programlisting>
CHECK ( x = 1 )
CHECK ( county IN ('Oxfordshire','Buckinghamshire','Warwickshire'))
CHECK ( outletID BETWEEN 1 AND 99 )
</programlisting>

        These can be linked together with boolean operators AND and OR to
        form complex constraints. Note that there is no difference in syntax
        between Range and List Partitioning mechanisms; those terms are
        descriptive only. Ensure that the set of values in each child table
        do not overlap.
       </para>
      </listitem>

      <listitem>
       <para>
        Add any other indexes you want to the partitions, bearing in
        mind that it is always more efficient to add indexes after
        data has been bulk loaded.
       </para>
      </listitem>

      <listitem>
       <para>
        Optionally, define a rule or trigger to redirect modifications
        of the master table to the appropriate partition.
       </para>
      </listitem>

     </orderedlist>
    </para>

    <para>
     For example, 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. They have two
     tables:

<programlisting>
CREATE TABLE cities (
    id              int not null,
    name            text not null,
    altitude        int            -- in feet
);

CREATE TABLE measurement (
    city_id         int not null,
    logdate         date not null,
    peaktemp        int,
    unitsales       int
);
</programlisting>

     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 remove the oldest month's data.
    </para>

    <para>
     Most queries just access the last week, month or quarter's data,
     since we need to keep track of sales. As a result we have a large table,
     yet only the most frequent 10% is accessed. Most of these queries
     are online reports for various levels of management. These queries access
     much of the table, so it is difficult to build enough indexes and at
     the same time allow us to keep loading all of the data fast enough.
     Yet, the reports are online so we need to respond quickly.
    </para>

    <para>
     In this situation we can use partitioning to help us meet all of our
     different requirements for the measurements table. Following the
     steps outlined above, partitioning can be enabled as follows:
    </para>

    <para>
     <orderedlist spacing=compact>
      <listitem>
       <para>
        The measurement table is our master table.
       </para>
      </listitem>

      <listitem>
       <para>
        Next we create one partition for each month using inheritance:

<programlisting>
CREATE TABLE measurement_yy04mm02 ( ) INHERITS (measurement);
CREATE TABLE measurement_yy04mm03 ( ) INHERITS (measurement);
...
CREATE TABLE measurement_yy05mm11 ( ) INHERITS (measurement);
CREATE TABLE measurement_yy05mm12 ( ) INHERITS (measurement);
CREATE TABLE measurement_yy06mm01 ( ) INHERITS (measurement);
</programlisting>

        Each of the partitions are complete tables in their own right,
        but they inherit their definition from the measurement table.
       </para>

       <para>
        This solves one of our problems: deleting old data. Each
        month, all we need to do is perform a <command>DROP
        TABLE</command> on the oldest table and create a new table to
        insert into.
       </para>
      </listitem>

      <listitem>
       <para>
        We now add non-overlapping table constraints, so that our
        table creation script becomes:

 <programlisting>
CREATE TABLE measurement_yy04mm02 (
    CHECK ( logdate >= DATE '2004-02-01' AND logdate < DATE '2004-03-01' )
                                    ) INHERITS (measurement);
CREATE TABLE measurement_yy04mm03 (
    CHECK ( logdate >= DATE '2004-03-01' AND logdate < DATE '2004-04-01' )
                                    ) INHERITS (measurement);
...
CREATE TABLE measurement_yy05mm11 (
    CHECK ( logdate >= DATE '2005-11-01' AND logdate < DATE '2005-12-01' )
                                    ) INHERITS (measurement);
CREATE TABLE measurement_yy05mm12 (
    CHECK ( logdate >= DATE '2005-12-01' AND logdate < DATE '2006-01-01' )
                                    ) INHERITS (measurement);
CREATE TABLE measurement_yy06mm01 (
    CHECK ( logdate >= DATE '2006-01-01' AND logdate < DATE '2006-02-01' )
                                    ) INHERITS (measurement);
</programlisting>
       </para>
      </listitem>

      <listitem>
       <para>
        We choose not to add further indexes at this time.
       </para>
      </listitem>

      <listitem>
       <para>
        Data will be added each day to the latest partition. This
        allows us to set up a very simple rule to insert data. We must
        redefine this each month so that it always points to the
        current partition.

<programlisting>
CREATE OR REPLACE RULE measurement_current_partition AS
ON INSERT
TO measurement
DO INSTEAD
    INSERT INTO measurement_yy06mm01 VALUES ( NEW.city_id,
                                              NEW.logdate,
                                              NEW.peaktemp,
                                              NEW.unitsales );
</programlisting>

        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 set of rules as shown below.

<programlisting>
CREATE RULE measurement_insert_yy04mm02 AS
ON INSERT
TO measurement WHERE
    ( logdate >= DATE '2004-02-01' AND logdate < DATE '2004-03-01' )
DO INSTEAD
    INSERT INTO measurement_yy04mm02 VALUES ( NEW.city_id,
                                              NEW.logdate,
                                              NEW.peaktemp,
                                              NEW.unitsales );
...
CREATE RULE measurement_insert_yy05mm12 AS
ON INSERT
TO measurement WHERE
    ( logdate >= DATE '2005-12-01' AND logdate < DATE '2006-01-01' )
DO INSTEAD
    INSERT INTO measurement_yy05mm12 VALUES ( NEW.city_id,
                                              NEW.logdate,
                                              NEW.peaktemp,
                                              NEW.unitsales );
CREATE RULE measurement_insert_yy06mm01 AS
ON INSERT
TO measurement WHERE
    ( logdate >= DATE '2006-01-01' AND logdate < DATE '2006-02-01' )
DO INSTEAD
    INSERT INTO measurement_yy06mm01 VALUES ( NEW.city_id,
                                              NEW.logdate,
                                              NEW.peaktemp,
                                              NEW.unitsales );
</programlisting>

        Note that the <literal>WHERE</literal> clause in each rule
        exactly matches those used for the <literal>CHECK</literal>
        constraints on each partition.
       </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 may be wise to write a
     script that generates the required DDL automatically.
    </para>

   <para>
    The following caveats apply:
   <itemizedlist>
    <listitem>
     <para>
      There is currently no way to specify that all of the
      <literal>CHECK</literal> constraints are mutually
      exclusive. Care is required by the database designer.
     </para>
    </listitem>

    <listitem>
     <para>
      There is currently no way to specify that rows may not be
      inserted into the master table. A <literal>CHECK</literal>
      constraint on the master table will be inherited by all child
      tables, so that cannot not be used for this purpose.
     </para>
    </listitem>

    <listitem>
     <para>
      For some datatypes you must explicitly coerce the constant values
      into the datatype of the column. The following constraint will
      work if x is an INTEGER datatype, but not if x is BIGINT datatype.
<programlisting>
CHECK ( x = 1 )
</programlisting>
      For BIGINT we must use a constraint like:
<programlisting>
CHECK ( x = 1::bigint )
</programlisting>
      The issue is not restricted to BIGINT datatypes but can occur whenever
      the default datatype of the constant does not match the datatype of
      the column to which it is being compared.
     </para>
    </listitem>

    <listitem>
     <para>
      Partitioning can also be arranged using a <literal>UNION
      ALL</literal> view:

<programlisting>
CREATE VIEW measurement AS
          SELECT * FROM measurement_yy04mm02
UNION ALL SELECT * FROM measurement_yy04mm03
...
UNION ALL SELECT * FROM measurement_yy05mm11
UNION ALL SELECT * FROM measurement_yy05mm12
UNION ALL SELECT * FROM measurement_yy06mm01;
</programlisting>

      However, constraint exclusion is currently not supported for
      partitioned tables defined in this manner.
     </para>
    </listitem>
   </itemizedlist>
   </para>
   </sect2>

   <sect2 id="constraint-exclusion-queries">
    <title>Constraint Exclusion in Queries</title>

   <para>
    Partitioning can be used to improve query performance when used in
    conjunction with constraint exclusion. As an example:

<programlisting>
SET constraint_exclusion=true;
SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';
</programlisting>

    Without constraint exclusion, the above query would scan each of
    the partitions of the measurement table. With constraint
    exclusion, the planner will examine each of the constraints and
    try to prove that each of the partitions needs to be involved in
    the query. If the planner is able to refute that for any
    partition, 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.

<programlisting>
SET constraint_exclusion=false;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';

                                          QUERY PLAN
-----------------------------------------------------------------------------------------------
 Aggregate  (cost=158.66..158.68 rows=1 width=0)
   ->  Append  (cost=0.00..151.88 rows=2715 width=0)
         ->  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
         ->  Seq Scan on measurement_yy04mm02 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
         ->  Seq Scan on measurement_yy04mm03 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
...
         ->  Seq Scan on measurement_yy05mm12 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
         ->  Seq Scan on measurement_yy06mm01 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
</programlisting>

    Now when we enable constraint exclusion, we get a significantly
    reduced plan but the same result set:

<programlisting>
SET constraint_exclusion=true;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';
                                          QUERY PLAN
-----------------------------------------------------------------------------------------------
 Aggregate  (cost=63.47..63.48 rows=1 width=0)
   ->  Append  (cost=0.00..60.75 rows=1086 width=0)
         ->  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
         ->  Seq Scan on measurement_yy06mm01 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate >= '2006-01-01'::date)
</programlisting>

    Don't forget that you still need to run <command>ANALYZE</command>
    on each partition individually. A command like this
<programlisting>
ANALYZE measurement;
</programlisting>

    only affects the master table.
   </para>

   <para>
    No indexes are required to use constraint exclusion. The
    partitions should be defined with appropriate <literal>CHECK</>
    constraints. These are then compared with the predicates of the
    <command>SELECT</> query to determine which partitions must be
    scanned.
   </para>

   <para>
    The following caveats apply to this release:
   <itemizedlist>
    <listitem>
     <para>
      Constraint exclusion only works when the query directly matches
      a constant. A constant bound to a parameterised query will not
      work in the same way since the plan is fixed and would need to
      vary with each execution.  Also, stable constants such as
      <literal>CURRENT_DATE</literal> may not be used, since these are
      constant only for during the execution of a single query.  Join
      conditions will not allow constraint exclusion to work either.
     </para>
    </listitem>

    <listitem>
     <para>
      UPDATEs and DELETEs against the master table do not perform
      constraint exclusion.
     </para>
    </listitem>

    <listitem>
     <para>
      All constraints on all partitions of the master table are considered for
      constraint exclusion, so large numbers of partitions are likely to
      increase query planning time considerably.
     </para>
    </listitem>

   </itemizedlist>
   </para>

   </sect2>

 </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, then you can drop the
   table and create it again.  But this is not a convenient option if
   the table is already filled with data, or if the table is
   referenced by other database objects (for instance a foreign key
   constraint).  Therefore <productname>PostgreSQL</productname>
   provides a family of commands to make modifications 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" endterm="sql-altertable-title">
   command.
  </para>

  <sect2>
   <title>Adding a Column</title>

   <indexterm>
    <primary>column</primary>
    <secondary>adding</secondary>
   </indexterm>

   <para>
    To add a column, use a command like this:
<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>
  </sect2>

  <sect2>
   <title>Removing a Column</title>

   <indexterm>
    <primary>column</primary>
    <secondary>removing</secondary>
   </indexterm>

   <para>
    To remove a column, use a command like this:
<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>
   <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>
   <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 this:
<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 this:
<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 may fail, or may
    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>

  <para>
   When you create a database object, you become its owner.  By
   default, only the owner of an object can do anything with the
   object. In order to allow other users to use it,
   <firstterm>privileges</firstterm> must be granted.  (However,
   users that have the superuser attribute can always
   access any object.)
  </para>

  <para>
   There are several different privileges: <literal>SELECT</>,
   <literal>INSERT</>, <literal>UPDATE</>, <literal>DELETE</>,
   <literal>RULE</>, <literal>REFERENCES</>, <literal>TRIGGER</>,
   <literal>CREATE</>, <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" endterm="sql-grant-title"> 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>

  <note>
   <para>
    To change the owner of a table, index, sequence, or view, use the
    <xref linkend="sql-altertable" endterm="sql-altertable-title">
    command.  There are corresponding <literal>ALTER</> commands for
    other object types.
   </para>
  </note>

  <para>
   To assign privileges, the <command>GRANT</command> command is
   used. For example, if <literal>joe</literal> is an existing user, 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>
   To grant a privilege to a group, use this syntax:
<programlisting>
GRANT SELECT ON accounts TO GROUP staff;
</programlisting>
   The special <quote>user</quote> name <literal>PUBLIC</literal> can
   be used to grant a privilege to every user on the system. Writing
   <literal>ALL</literal> in place of a specific privilege grants all
   privileges that are relevant for the object type.
  </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 his own ordinary privileges, for example to make a
   table read-only for himself 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"
   endterm="sql-grant-title"> and <xref linkend="sql-revoke"
   endterm="sql-revoke-title"> reference pages.
  </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</> may
   contain tables named <literal>mytable</>.  Unlike databases,
   schemas are not rigidly separated: a user may access objects in any
   of the schemas in the database he is connected to, if he has
   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 cannot 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 command <literal>CREATE
    SCHEMA</literal>.  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>schemaname</replaceable> AUTHORIZATION <replaceable>username</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 may not 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>

   <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>search_path</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 needs to grant the
    <literal>USAGE</literal> privilege on the schema.  To allow users
    to make use of the objects in the schema, additional privileges
    may 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.  If you do
    not want to allow that, you can revoke 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 may 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>
    In <productname>PostgreSQL</productname> versions before 7.3,
    table names beginning with <literal>pg_</> were reserved.  This is
    no longer true: you may create such a table name if you wish, in
    any non-system schema.  However, it's best to continue 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 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 that are recommended and are easily supported by
    the default configuration:
    <itemizedlist>
     <listitem>
      <para>
       If you do not create any schemas then all users access the
       public schema implicitly.  This simulates the situation where
       schemas are not available at all.  This setup is mainly
       recommended when there is only a single user or a few cooperating
       users in a database.  This setup also allows smooth transition
       from the non-schema-aware world.
      </para>
     </listitem>

     <listitem>
      <para>
       You can create a schema for each user with the same name as
       that user.  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>

      <para>
       If you use this setup then you might also want to revoke access
       to the public schema (or drop it altogether), so users are
       truly constrained to their own schemas.
      </para>
     </listitem>

     <listitem>
      <para>
       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>
     </listitem>
    </itemizedlist>
   </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>username</>.<replaceable>tablename</></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 (perhaps even remove) 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-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
   will implicitly create a net of dependencies between the objects.
   For instance, a table with a foreign key constraint depends on the
   table it references.
  </para>

  <para>
   To ensure the integrity of the entire database structure,
   <productname>PostgreSQL</productname> makes sure that you cannot
   drop objects that other objects still depend on.  For example,
   attempting to drop the products table we had considered in <xref
   linkend="ddl-constraints-fk">, with the orders table depending on
   it, would result in an error message such as this:
<screen>
DROP TABLE products;

NOTICE:  constraint orders_product_no_fkey on table orders depends on table products
ERROR:  cannot drop table products because other objects depend on it
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.  In this case, it
   doesn't remove the orders table, it only removes the foreign key
   constraint.  (If you want to check what <literal>DROP ... CASCADE</> will do,
   run <command>DROP</> without <literal>CASCADE</> and read the <literal>NOTICE</> messages.)
  </para>

  <para>
   All drop commands in <productname>PostgreSQL</productname> support
   specifying <literal>CASCADE</literal>.  Of course, the nature of
   the possible dependencies varies with the type of the object.  You
   can also write <literal>RESTRICT</literal> instead of
   <literal>CASCADE</literal> to get the default behavior, which is to
   prevent drops of objects that other objects depend on.
  </para>

  <note>
   <para>
    According to the SQL standard, specifying either
    <literal>RESTRICT</literal> or <literal>CASCADE</literal> is
    required.  No database system actually enforces that rule, but
    whether the default behavior is <literal>RESTRICT</literal> or
    <literal>CASCADE</literal> varies across systems.
   </para>
  </note>

  <note>
   <para>
    Foreign key constraint dependencies and serial column dependencies
    from <productname>PostgreSQL</productname> versions prior to 7.3
    are <emphasis>not</emphasis> maintained or created during the
    upgrade process.  All other dependency types will be properly
    created during an upgrade from a pre-7.3 database.
   </para>
  </note>
 </sect1>

</chapter>