<!-- $PostgreSQL: pgsql/doc/src/sgml/ddl.sgml,v 1.88 2009/10/23 05:24:52 petere Exp $ -->

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

 <para>
  This chapter covers how one creates the database structures that
  will hold one's data.  In a relational database, the raw data is
  stored in tables, so the majority of this chapter is devoted to
  explaining how tables are created and modified and what features are
  available to control what data is stored in the tables.
  Subsequently, we discuss how tables can be organized into
  schemas, and how privileges can be assigned to tables.  Finally,
  we will briefly look at other features that affect the data storage,
  such as inheritance, views, functions, and triggers.
 </para>

 <sect1 id="ddl-basics">
  <title>Table Basics</title>

  <indexterm zone="ddl-basics">
   <primary>table</primary>
  </indexterm>

  <indexterm>
   <primary>row</primary>
  </indexterm>

  <indexterm>
   <primary>column</primary>
  </indexterm>

  <para>
   A table in a relational database is much like a table on paper: It
   consists of rows and columns.  The number and order of the columns
   is fixed, and each column has a name.  The number of rows is
   variable &mdash; it reflects how much data is stored at a given moment.
   SQL does not make any guarantees about the order of the rows in a
   table.  When a table is read, the rows will appear in 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 <xref
   linkend="sql-createtable" endterm="sql-createtable-title"> command.
   In this command you specify at least a name for the new table, the
   names of the columns and the data type of each column.  For
   example:
<programlisting>
CREATE TABLE my_first_table (
    first_column text,
    second_column integer
);
</programlisting>
   This creates a table named <literal>my_first_table</literal> with
   two columns.  The first column is named
   <literal>first_column</literal> and has a data type of
   <type>text</type>; the second column has the name
   <literal>second_column</literal> and the type <type>integer</type>.
   The table and column names follow the identifier syntax explained
   in <xref linkend="sql-syntax-identifiers">.  The type names are
   usually also identifiers, but there are some exceptions.  Note that the
   column list is comma-separated and surrounded by parentheses.
  </para>

  <para>
   Of course, the previous example was heavily contrived.  Normally,
   you would give names to your tables and columns that convey what
   kind of data they store.  So let's look at a more realistic
   example:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
);
</programlisting>
   (The <type>numeric</type> type can store fractional components, as
   would be typical of monetary amounts.)
  </para>

  <tip>
   <para>
    When you create many interrelated tables it is wise to choose a
    consistent naming pattern for the tables and columns.  For
    instance, there is a choice of using singular or plural nouns for
    table names, both of which are favored by some theorist or other.
   </para>
  </tip>

  <para>
   There is a limit on how many columns a table can contain.
   Depending on the column types, it is between 250 and 1600.
   However, defining a table with anywhere near this many columns is
   highly unusual and often a questionable design.
  </para>

  <indexterm>
   <primary>table</primary>
   <secondary>removing</secondary>
  </indexterm>

  <para>
   If you no longer need a table, you can remove it using the <xref
   linkend="sql-droptable" endterm="sql-droptable-title"> command.
   For example:
<programlisting>
DROP TABLE my_first_table;
DROP TABLE products;
</programlisting>
   Attempting to drop a table that does not exist is an error.
   Nevertheless, it is common in SQL script files to unconditionally
   try to drop each table before creating it, ignoring any error
   messages, so that the script works whether or not the table exists.
   (If you like, you can use the <literal>DROP TABLE IF EXISTS</> variant
   to avoid the error messages, but this is not standard SQL.)
  </para>

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

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

 <sect1 id="ddl-default">
  <title>Default Values</title>

  <indexterm zone="ddl-default">
   <primary>default value</primary>
  </indexterm>

  <para>
   A column can be assigned a default value.  When a new row is
   created and no values are specified for some of the columns, those
   columns will be filled with their respective default values.  A
   data manipulation command can also request explicitly that a column
   be set to its default value, without having to know what that value is.
   (Details about data manipulation commands are in <xref linkend="dml">.)
  </para>

  <para>
   <indexterm><primary>null value</primary><secondary>default value</secondary></indexterm>
   If no default value is declared explicitly, the default value is the
   null value.  This usually makes sense because a null value can
   be considered to represent unknown data.
  </para>

  <para>
   In a table definition, default values are listed after the column
   data type.  For example:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric <emphasis>DEFAULT 9.99</emphasis>
);
</programlisting>
  </para>

  <para>
   The default value can be an expression, which will be
   evaluated whenever the default value is inserted
   (<emphasis>not</emphasis> when the table is created).  A common example
   is for a <type>timestamp</type> column to have a default of <literal>CURRENT_TIMESTAMP</>,
   so that it gets set to the time of row insertion.  Another common
   example is generating a <quote>serial number</> for each row.
   In <productname>PostgreSQL</productname> this is typically done by
   something like:
<programlisting>
CREATE TABLE products (
    product_no integer <emphasis>DEFAULT nextval('products_product_no_seq')</emphasis>,
    ...
);
</programlisting>
   where the <literal>nextval()</> function supplies successive values
   from a <firstterm>sequence object</> (see <xref
   linkend="functions-sequence">). This arrangement is sufficiently common
   that there's a special shorthand for it:
<programlisting>
CREATE TABLE products (
    product_no <emphasis>SERIAL</emphasis>,
    ...
);
</programlisting>
   The <literal>SERIAL</> shorthand is discussed further in <xref
   linkend="datatype-serial">.
  </para>
 </sect1>

 <sect1 id="ddl-constraints">
  <title>Constraints</title>

  <indexterm zone="ddl-constraints">
   <primary>constraint</primary>
  </indexterm>

  <para>
   Data types are a way to limit the kind of data that can be stored
   in a table.  For many applications, however, the constraint they
   provide is too coarse.  For example, a column containing a product
   price should probably only accept positive values.  But there is no
   standard data type that accepts only positive numbers.  Another issue is
   that you might want to constrain column data with respect to other
   columns or rows.  For example, in a table containing product
   information, there should be only one row for each product number.
  </para>

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

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

   <indexterm>
    <primary>check constraint</primary>
   </indexterm>

   <indexterm>
    <primary>constraint</primary>
    <secondary>check</secondary>
   </indexterm>

   <para>
    A check constraint is the most generic constraint type.  It allows
    you to specify that the value in a certain column must satisfy a
    Boolean (truth-value) expression.  For instance, to require positive
    product prices, you could use:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric <emphasis>CHECK (price &gt; 0)</emphasis>
);
</programlisting>
   </para>

   <para>
    As you see, the constraint definition comes after the data type,
    just like default value definitions.  Default values and
    constraints can be listed in any order.  A check constraint
    consists of the key word <literal>CHECK</literal> followed by an
    expression in parentheses.  The check constraint expression should
    involve the column thus constrained, otherwise the constraint
    would not make too much sense.
   </para>

   <indexterm>
    <primary>constraint</primary>
    <secondary>name</secondary>
   </indexterm>

   <para>
    You can also give the constraint a separate name.  This clarifies
    error messages and allows you to refer to the constraint when you
    need to change it.  The syntax is:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric <emphasis>CONSTRAINT positive_price</emphasis> CHECK (price &gt; 0)
);
</programlisting>
    So, to specify a named constraint, use the key word
    <literal>CONSTRAINT</literal> followed by an identifier followed
    by the constraint definition.  (If you don't specify a constraint
    name in this way, the system chooses a name for you.)
   </para>

   <para>
    A check constraint can also refer to several columns.  Say you
    store a regular price and a discounted price, and you want to
    ensure that the discounted price is lower than the regular price:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric CHECK (price &gt; 0),
    discounted_price numeric CHECK (discounted_price &gt; 0),
    <emphasis>CHECK (price &gt; discounted_price)</emphasis>
);
</programlisting>
   </para>

   <para>
    The first two constraints should look familiar.  The third one
    uses a new syntax.  It is not attached to a particular column,
    instead it appears as a separate item in the comma-separated
    column list.  Column definitions and these constraint
    definitions can be listed in mixed order.
   </para>

   <para>
    We say that the first two constraints are column constraints, whereas the
    third one is a table constraint because it is written separately
    from any one column definition.  Column constraints can also be
    written as table constraints, while the reverse is not necessarily
    possible, since a column constraint is supposed to refer to only the
    column it is attached to.  (<productname>PostgreSQL</productname> doesn't
    enforce that rule, but you should follow it if you want your table
    definitions to work with other database systems.)  The above example could
    also be written as:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric,
    CHECK (price &gt; 0),
    discounted_price numeric,
    CHECK (discounted_price &gt; 0),
    CHECK (price &gt; discounted_price)
);
</programlisting>
    or even:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric CHECK (price &gt; 0),
    discounted_price numeric,
    CHECK (discounted_price &gt; 0 AND price &gt; discounted_price)
);
</programlisting>
    It's a matter of taste.
   </para>

   <para>
    Names can be assigned to table constraints in the same way as
    column constraints:
<programlisting>
CREATE TABLE products (
    product_no integer,
    name text,
    price numeric,
    CHECK (price &gt; 0),
    discounted_price numeric,
    CHECK (discounted_price &gt; 0),
    <emphasis>CONSTRAINT valid_discount</> CHECK (price &gt; discounted_price)
);
</programlisting>
   </para>

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

   <para>
    It should be noted that a check constraint is satisfied if the
    check expression evaluates to true or the null value.  Since most
    expressions will evaluate to the null value if any operand is null,
    they will not prevent null values in the constrained columns.  To
    ensure that a column does not contain null values, the not-null
    constraint described in the next section can be used.
   </para>
  </sect2>

  <sect2>
   <title>Not-Null Constraints</title>

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

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

   <para>
    A not-null constraint simply specifies that a column must not
    assume the null value.  A syntax example:
<programlisting>
CREATE TABLE products (
    product_no integer <emphasis>NOT NULL</emphasis>,
    name text <emphasis>NOT NULL</emphasis>,
    price numeric
);
</programlisting>
   </para>

   <para>
    A not-null constraint is always written as a column constraint.  A
    not-null constraint is functionally equivalent to creating a check
    constraint <literal>CHECK (<replaceable>column_name</replaceable>
    IS NOT NULL)</literal>, but in
    <productname>PostgreSQL</productname> creating an explicit
    not-null constraint is more efficient.  The drawback is that you
    cannot give explicit names to not-null constraints created this
    way.
   </para>

   <para>
    Of course, a column can have more than one constraint.  Just write
    the constraints one after another:
<programlisting>
CREATE TABLE products (
    product_no integer NOT NULL,
    name text NOT NULL,
    price numeric NOT NULL CHECK (price &gt; 0)
);
</programlisting>
    The order doesn't matter.  It does not necessarily determine in which
    order the constraints are checked.
   </para>

   <para>
    The <literal>NOT NULL</literal> constraint has an inverse: the
    <literal>NULL</literal> constraint.  This does not mean that the
    column must be null, which would surely be useless.  Instead, this
    simply selects the default behavior that the column might be null.
    The <literal>NULL</literal> constraint is not present in the SQL
    standard and should not be used in portable applications.  (It was
    only added to <productname>PostgreSQL</productname> to be
    compatible with some other database systems.)  Some users, however,
    like it because it makes it easy to toggle the constraint in a
    script file.  For example, you could start with:
<programlisting>
CREATE TABLE products (
    product_no integer NULL,
    name text NULL,
    price numeric NULL
);
</programlisting>
    and then insert the <literal>NOT</literal> key word where desired.
   </para>

   <tip>
    <para>
     In most database designs the majority of columns should be marked
     not null.
    </para>
   </tip>
  </sect2>

  <sect2>
   <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 is more than
    one row in the table where the values of all of the
    columns included in the constraint are equal.
    However, two null values are 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 might 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.  (There can be any number
    of unique and not-null constraints, which are functionally the same
    thing, but only one can be identified as the primary key.)
    Relational database theory
    dictates that every table must have a primary key.  This rule is
    not enforced by <productname>PostgreSQL</productname>, but it is
    usually best to follow it.
   </para>
  </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 at the time. This column is of type
      <type>oid</type> (same name as the column); see <xref
      linkend="datatype-oid"> for more information about the type.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>tableoid</></term>
    <listitem>
     <indexterm>
      <primary>tableoid</primary>
     </indexterm>

     <para>
      The OID of the table containing this row.  This column is
      particularly handy for queries that select from inheritance
      hierarchies (see <xref linkend="ddl-inherit">), since without it,
      it's difficult to tell which individual table a row came from.  The
      <structfield>tableoid</structfield> can be joined against the
      <structfield>oid</structfield> column of
      <structname>pg_class</structname> to obtain the table name.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>xmin</></term>
    <listitem>
     <indexterm>
      <primary>xmin</primary>
     </indexterm>

     <para>
      The identity (transaction ID) of the inserting transaction for
      this row version.  (A row version is an individual state of a
      row; each update of a row creates a new row version for the same
      logical row.)
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>cmin</></term>
    <listitem>
     <indexterm>
      <primary>cmin</primary>
     </indexterm>

     <para>
      The command identifier (starting at zero) within the inserting
      transaction.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>xmax</></term>
    <listitem>
     <indexterm>
      <primary>xmax</primary>
     </indexterm>

     <para>
      The identity (transaction ID) of the deleting transaction, or
      zero for an undeleted row version.  It is possible for this column to
      be nonzero in a visible row version. That usually indicates that the
      deleting transaction hasn't committed yet, or that an attempted
      deletion was rolled back.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>cmax</></term>
    <listitem>
     <indexterm>
      <primary>cmax</primary>
     </indexterm>

     <para>
      The command identifier within the deleting transaction, or zero.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><structfield>ctid</></term>
    <listitem>
     <indexterm>
      <primary>ctid</primary>
     </indexterm>

     <para>
      The physical location of the row version within its table.  Note that
      although the <structfield>ctid</structfield> can be used to
      locate the row version very quickly, a row's
      <structfield>ctid</structfield> will change if it is
      updated or moved by <command>VACUUM FULL</>.  Therefore
      <structfield>ctid</structfield> is useless as a long-term row
      identifier.  The OID, or even better a user-defined serial
      number, should be used to identify logical rows.
     </para>
    </listitem>
   </varlistentry>
  </variablelist>

   <para>
    OIDs are 32-bit quantities and are assigned from a single
    cluster-wide counter.  In a large or long-lived database, it is
    possible for the counter to wrap around.  Hence, it is bad
    practice to assume that OIDs are unique, unless you take steps to
    ensure that this is the case.  If you need to identify the rows in
    a table, using a sequence generator is strongly recommended.
    However, OIDs can be used as well, provided that a few additional
    precautions are taken:

    <itemizedlist>
     <listitem>
      <para>
       A unique constraint should be created on the OID column of each
       table for which the OID will be used to identify rows.  When such
       a unique constraint (or unique index) exists, the system takes
       care not to generate an OID matching an already-existing row.
       (Of course, this is only possible if the table contains fewer
       than 2<superscript>32</> (4 billion) rows, and in practice the
       table size had better be much less than that, or performance
       might suffer.)
      </para>
     </listitem>
     <listitem>
      <para>
       OIDs should never be assumed to be unique across tables; use
       the combination of <structfield>tableoid</> and row OID if you
       need a database-wide identifier.
      </para>
     </listitem>
     <listitem>
      <para>
       Of course, the tables in question must be created <literal>WITH
       OIDS</literal>.  As of <productname>PostgreSQL</productname> 8.1,
       <literal>WITHOUT OIDS</> is the default.
      </para>
     </listitem>
    </itemizedlist>
   </para>

   <para>
    Transaction identifiers are also 32-bit quantities.  In a
    long-lived database it is possible for transaction IDs to wrap
    around.  This is not a fatal problem given appropriate maintenance
    procedures; see <xref linkend="maintenance"> for details.  It is
    unwise, however, to depend on the uniqueness of transaction IDs
    over the long term (more than one billion transactions).
   </para>

   <para>
    Command identifiers are also 32-bit quantities.  This creates a hard limit
    of 2<superscript>32</> (4 billion) <acronym>SQL</acronym> commands
    within a single transaction.  In practice this limit is not a
    problem &mdash; note that the limit is on the number of
    <acronym>SQL</acronym> commands, not the number of rows processed.
    Also, as of <productname>PostgreSQL</productname> 8.3, only commands
    that actually modify the database contents will consume a command
    identifier.
   </para>
 </sect1>

 <sect1 id="ddl-alter">
  <title>Modifying Tables</title>

  <indexterm zone="ddl-alter">
   <primary>table</primary>
   <secondary>modifying</secondary>
  </indexterm>

  <para>
   When you create a table and you realize that you made a mistake, or
   the requirements of the application change, you can drop the
   table and create it again.  But this is not a convenient option if
   the table is already filled with data, or if the table is
   referenced by other database objects (for instance a foreign key
   constraint).  Therefore <productname>PostgreSQL</productname>
   provides a family of commands to make modifications to existing
   tables.  Note that this is conceptually distinct from altering
   the data contained in the table: here we are interested in altering
   the definition, or structure, of the table.
  </para>

  <para>
   You can:
   <itemizedlist spacing="compact">
    <listitem>
     <para>Add columns</para>
    </listitem>
    <listitem>
     <para>Remove columns</para>
    </listitem>
    <listitem>
     <para>Add constraints</para>
    </listitem>
    <listitem>
     <para>Remove constraints</para>
    </listitem>
    <listitem>
     <para>Change default values</para>
    </listitem>
    <listitem>
     <para>Change column data types</para>
    </listitem>
    <listitem>
     <para>Rename columns</para>
    </listitem>
    <listitem>
     <para>Rename tables</para>
    </listitem>
   </itemizedlist>

   All these actions are performed using the
   <xref linkend="sql-altertable" endterm="sql-altertable-title">
   command, whose reference page contains details beyond those given
   here.
  </para>

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

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

   <para>
    To add a column, use a command like:
<programlisting>
ALTER TABLE products ADD COLUMN description text;
</programlisting>
    The new column is initially filled with whatever default
    value is given (null if you don't specify a <literal>DEFAULT</> clause).
   </para>

   <para>
    You can also define constraints on the column at the same time,
    using the usual syntax:
<programlisting>
ALTER TABLE products ADD COLUMN description text CHECK (description &lt;&gt; '');
</programlisting>
    In fact all the options that can be applied to a column description
    in <command>CREATE TABLE</> can be used here.  Keep in mind however
    that the default value must satisfy the given constraints, or the
    <literal>ADD</> will fail.  Alternatively, you can add
    constraints later (see below) after you've filled in the new column
    correctly.
   </para>

  <tip>
   <para>
    Adding a column with a default requires updating each row of the
    table (to store the new column value).  However, if no default is
    specified, <productname>PostgreSQL</productname> is able to avoid
    the physical update.  So if you intend to fill the column with
    mostly nondefault values, it's best to add the column with no default,
    insert the correct values using <command>UPDATE</>, and then add any
    desired default as described below.
   </para>
  </tip>
  </sect2>

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

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

   <para>
    To remove a column, use a command like:
<programlisting>
ALTER TABLE products DROP COLUMN description;
</programlisting>
    Whatever data was in the column disappears.  Table constraints involving
    the column are dropped, too.  However, if the column is referenced by a
    foreign key constraint of another table,
    <productname>PostgreSQL</productname> will not silently drop that
    constraint.  You can authorize dropping everything that depends on
    the column by adding <literal>CASCADE</>:
<programlisting>
ALTER TABLE products DROP COLUMN description CASCADE;
</programlisting>
    See <xref linkend="ddl-depend"> for a description of the general
    mechanism behind this.
   </para>
  </sect2>

  <sect2>
   <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:
<programlisting>
ALTER TABLE products ALTER COLUMN price SET DEFAULT 7.77;
</programlisting>
    Note that this doesn't affect any existing rows in the table, it
    just changes the default for future <command>INSERT</> commands.
   </para>

   <para>
    To remove any default value, use:
<programlisting>
ALTER TABLE products ALTER COLUMN price DROP DEFAULT;
</programlisting>
    This is effectively the same as setting the default to null.
    As a consequence, it is not an error
    to drop a default where one hadn't been defined, because the
    default is implicitly the null value.
   </para>
  </sect2>

  <sect2>
   <title>Changing a Column's Data Type</title>

   <indexterm>
    <primary>column data type</primary>
    <secondary>changing</secondary>
   </indexterm>

   <para>
    To convert a column to a different data type, use a command like:
<programlisting>
ALTER TABLE products ALTER COLUMN price TYPE numeric(10,2);
</programlisting>
    This will succeed only if each existing entry in the column can be
    converted to the new type by an implicit cast.  If a more complex
    conversion is needed, you can add a <literal>USING</> clause that
    specifies how to compute the new values from the old.
   </para>

   <para>
    <productname>PostgreSQL</> will attempt to convert the column's
    default value (if any) to the new type, as well as any constraints
    that involve the column.  But these conversions might fail, or might
    produce surprising results.  It's often best to drop any constraints
    on the column before altering its type, and then add back suitably
    modified constraints afterwards.
   </para>
  </sect2>

  <sect2>
   <title>Renaming a Column</title>

   <indexterm>
    <primary>column</primary>
    <secondary>renaming</secondary>
   </indexterm>

   <para>
    To rename a column:
<programlisting>
ALTER TABLE products RENAME COLUMN product_no TO product_number;
</programlisting>
   </para>
  </sect2>

  <sect2>
   <title>Renaming a Table</title>

   <indexterm>
    <primary>table</primary>
    <secondary>renaming</secondary>
   </indexterm>

   <para>
    To rename a table:
<programlisting>
ALTER TABLE products RENAME TO items;
</programlisting>
   </para>
  </sect2>
 </sect1>

 <sect1 id="ddl-priv">
  <title>Privileges</title>

  <indexterm zone="ddl-priv">
   <primary>privilege</primary>
  </indexterm>

  <indexterm>
   <primary>permission</primary>
   <see>privilege</see>
  </indexterm>

  <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>TRUNCATE</>, <literal>REFERENCES</>, <literal>TRIGGER</>,
   <literal>CREATE</>, <literal>CONNECT</>, <literal>TEMPORARY</>,
   <literal>EXECUTE</>, and <literal>USAGE</>.
   The privileges applicable to a particular
   object vary depending on the object's type (table, function, etc).
   For complete information on the different types of privileges
   supported by <productname>PostgreSQL</productname>, refer to the
   <xref linkend="sql-grant" 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>
   Writing <literal>ALL</literal> in place of a specific privilege grants all
   privileges that are relevant for the object type.
  </para>

  <para>
   The special <quote>user</quote> name <literal>PUBLIC</literal> can
   be used to grant a privilege to every user on the system.  Also,
   <quote>group</> roles can be set up to help manage privileges when
   there are many users of a database &mdash; for details see
   <xref linkend="user-manag">.
  </para>

  <para>
   To revoke a privilege, use the fittingly named
   <command>REVOKE</command> command:
<programlisting>
REVOKE ALL ON accounts FROM PUBLIC;
</programlisting>
   The special privileges of the object owner (i.e., the right to do
   <command>DROP</>, <command>GRANT</>, <command>REVOKE</>, etc.)
   are always implicit in being the owner,
   and cannot be granted or revoked.  But the object owner can choose
   to revoke 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</> can
   contain tables named <literal>mytable</>.  Unlike databases,
   schemas are not rigidly separated: a user can access objects in any
   of the schemas in the database 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 do not collide with the names of other objects.
     </para>
    </listitem>
   </itemizedlist>

   Schemas are analogous to directories at the operating system level,
   except that schemas cannot be nested.
  </para>

  <sect2 id="ddl-schemas-create">
   <title>Creating a Schema</title>

   <indexterm zone="ddl-schemas-create">
    <primary>schema</primary>
    <secondary>creating</secondary>
   </indexterm>

   <para>
    To create a schema, use the <xref linkend="sql-createschema"
    endterm="sql-createschema-title"> command.  Give the schema a name
    of your choice.  For example:
<programlisting>
CREATE SCHEMA myschema;
</programlisting>
   </para>

   <indexterm>
    <primary>qualified name</primary>
   </indexterm>

   <indexterm>
    <primary>name</primary>
    <secondary>qualified</secondary>
   </indexterm>

   <para>
    To create or access objects in a schema, write a
    <firstterm>qualified name</> consisting of the schema name and
    table name separated by a dot:
<synopsis>
<replaceable>schema</><literal>.</><replaceable>table</>
</synopsis>
    This works anywhere a table name is expected, including the table
    modification commands and the data access commands discussed in
    the following chapters.
    (For brevity we will speak of tables only, but the same ideas apply
    to other kinds of named objects, such as types and functions.)
   </para>

   <para>
    Actually, the even more general syntax
<synopsis>
<replaceable>database</><literal>.</><replaceable>schema</><literal>.</><replaceable>table</>
</synopsis>
    can be used too, but at present this is just for <foreignphrase>pro
    forma</> compliance with the SQL standard.  If you write a database name,
    it must be the same as the database you are connected to.
   </para>

   <para>
    So to create a table in the new schema, use:
<programlisting>
CREATE TABLE myschema.mytable (
 ...
);
</programlisting>
   </para>

   <indexterm>
    <primary>schema</primary>
    <secondary>removing</secondary>
   </indexterm>

   <para>
    To drop a schema if it's empty (all objects in it have been
    dropped), use:
<programlisting>
DROP SCHEMA myschema;
</programlisting>
    To drop a schema including all contained objects, use:
<programlisting>
DROP SCHEMA myschema CASCADE;
</programlisting>
    See <xref linkend="ddl-depend"> for a description of the general
    mechanism behind this.
   </para>

   <para>
    Often you will want to create a schema owned by someone else
    (since this is one of the ways to restrict the activities of your
    users to well-defined namespaces).  The syntax for that is:
<programlisting>
CREATE SCHEMA <replaceable>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 cannot be created by users.
   </para>
  </sect2>

  <sect2 id="ddl-schemas-public">
   <title>The Public Schema</title>

   <indexterm zone="ddl-schemas-public">
    <primary>schema</primary>
    <secondary>public</secondary>
   </indexterm>

   <para>
    In the previous sections we created tables without specifying any
    schema names.  By default such tables (and other objects) are
    automatically put into a schema named <quote>public</quote>.  Every new
    database contains such a schema.  Thus, the following are equivalent:
<programlisting>
CREATE TABLE products ( ... );
</programlisting>
    and:
<programlisting>
CREATE TABLE public.products ( ... );
</programlisting>
   </para>
  </sect2>

  <sect2 id="ddl-schemas-path">
   <title>The Schema Search Path</title>

   <indexterm>
    <primary>search path</primary>
   </indexterm>

   <indexterm>
    <primary>unqualified name</primary>
   </indexterm>

   <indexterm>
    <primary>name</primary>
    <secondary>unqualified</secondary>
   </indexterm>

   <para>
    Qualified names are tedious to write, and it's often best not to
    wire a particular schema name into applications anyway.  Therefore
    tables are often referred to by <firstterm>unqualified names</>,
    which consist of just the table name.  The system determines which table
    is meant by following a <firstterm>search path</>, which is a list
    of schemas to look in.  The first matching table in the search path
    is taken to be the one wanted.  If there is no match in the search
    path, an error is reported, even if matching table names exist
    in other schemas in the database.
   </para>

   <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 must grant the
    <literal>USAGE</literal> privilege on the schema.  To allow users
    to make use of the objects in the schema, additional privileges
    might need to be granted, as appropriate for the object.
   </para>

   <para>
    A user can also be allowed to create objects in someone else's
    schema.  To allow that, the <literal>CREATE</literal> privilege on
    the schema needs to be granted.  Note that by default, everyone
    has <literal>CREATE</literal> and <literal>USAGE</literal> privileges on
    the schema
    <literal>public</literal>.  This allows all users that are able to
    connect to a given database to create objects in its
    <literal>public</literal> schema.  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 can explicitly place
    <literal>pg_catalog</> at the end of your search path if you
    prefer to have user-defined names override built-in names.
   </para>

   <para>
    In <productname>PostgreSQL</productname> versions before 7.3,
    table names beginning with <literal>pg_</> were reserved.  This is
    no longer true: you can 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 then be resolved as the system table instead.)
    System tables will continue to follow the convention of having
    names beginning with <literal>pg_</>, so that they will not
    conflict with unqualified user-table names so long as users avoid
    the <literal>pg_</> prefix.
   </para>
  </sect2>

  <sect2 id="ddl-schemas-patterns">
   <title>Usage Patterns</title>

   <para>
    Schemas can be used to organize your data in many ways.  There are
    a few usage patterns 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-inherit">
  <title>Inheritance</title>

  <indexterm>
   <primary>inheritance</primary>
  </indexterm>

  <indexterm>
   <primary>table</primary>
   <secondary>inheritance</secondary>
  </indexterm>

  <para>
   <productname>PostgreSQL</productname> implements table inheritance,
   which can be a useful tool for database designers.  (SQL:1999 and
   later define a type inheritance feature, which differs in many
   respects from the features described here.)
  </para>

  <para>
   Let's start with an example: suppose we are trying to build a data
   model for cities.  Each state has many cities, but only one
   capital. We want to be able to quickly retrieve the capital city
   for any particular state. This can be done by creating two tables,
   one for state capitals and one for cities that are not
   capitals. However, what happens when we want to ask for data about
   a city, regardless of whether it is a capital or not? The
   inheritance feature can help to resolve this problem. We define the
   <structname>capitals</structname> table so that it inherits from
   <structname>cities</structname>:

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

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

   In this case, the <structname>capitals</> table <firstterm>inherits</>
   all the columns of its parent table, <structname>cities</>. State
   capitals also have an extra column, <structfield>state</>, that shows
   their state.
  </para>

  <para>
   In <productname>PostgreSQL</productname>, a table can inherit from
   zero or more other tables, and a query can reference either all
   rows of a table or all rows of a table plus all of its descendant tables.
   The latter behavior is the default.
   For example, the following query finds the names of all cities,
   including state capitals, that are located at an altitude over
   500 feet:

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

   Given the sample data from the <productname>PostgreSQL</productname>
   tutorial (see <xref linkend="tutorial-sql-intro">), this returns:

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

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

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

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

  <para>
   Here the <literal>ONLY</literal> keyword indicates that the query
   should apply only to <structname>cities</structname>, and not any tables
   below <structname>cities</structname> in the inheritance hierarchy.  Many
   of the commands that we have already discussed &mdash;
   <command>SELECT</command>, <command>UPDATE</command> and
   <command>DELETE</command> &mdash; support the
   <literal>ONLY</literal> keyword.
  </para>

  <para>
   In some cases you might wish to know which table a particular row
   originated from. There is a system column called
   <structfield>tableoid</structfield> in each table which can tell you the
   originating table:

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

   which returns:

<programlisting>
 tableoid |   name    | altitude
----------+-----------+----------
   139793 | Las Vegas |     2174
   139793 | Mariposa  |     1953
   139798 | Madison   |      845
</programlisting>

   (If you try to reproduce this example, you will probably get
   different numeric OIDs.)  By doing a join with
   <structname>pg_class</> you can see the actual table names:

<programlisting>
SELECT p.relname, c.name, c.altitude
FROM cities c, pg_class p
WHERE c.altitude &gt; 500 AND c.tableoid = p.oid;
</programlisting>

   which returns:

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

  <para>
   Inheritance does not automatically propagate data from
   <command>INSERT</command> or <command>COPY</command> commands to
   other tables in the inheritance hierarchy. In our example, the
   following <command>INSERT</command> statement will fail:
<programlisting>
INSERT INTO cities (name, population, altitude, state)
VALUES ('New York', NULL, NULL, 'NY');
</programlisting>
   We might hope that the data would somehow be routed to the
   <structname>capitals</structname> table, but this does not happen:
   <command>INSERT</command> always inserts into exactly the table
   specified.  In some cases it is possible to redirect the insertion
   using a rule (see <xref linkend="rules">).  However that does not
   help for the above case because the <structname>cities</> table
   does not contain the column <structfield>state</>, and so the
   command will be rejected before the rule can be applied.
  </para>

  <para>
   All check constraints and not-null constraints on a parent table are
   automatically inherited by its children.  Other types of constraints
   (unique, primary key, and foreign key constraints) are not inherited.
  </para>

  <para>
   A table can inherit from more than one parent table, in which case it has
   the union of the columns defined by the parent tables.  Any columns
   declared in the child table's definition are added to these.  If the
   same column name appears in multiple parent tables, or in both a parent
   table and the child's definition, then these columns are <quote>merged</>
   so that there is only one such column in the child table.  To be merged,
   columns must have the same data types, else an error is raised.  The
   merged column will have copies of all the check constraints coming from
   any one of the column definitions it came from, and will be marked not-null
   if any of them are.
  </para>

  <para>
   Table inheritance is typically established when the child table is
   created, using the <literal>INHERITS</> clause of the
   <xref linkend="sql-createtable" endterm="sql-createtable-title">
   statement.
   Alternatively, a table which is already defined in a compatible way can
   have a new parent relationship added, using the <literal>INHERIT</literal>
   variant of <xref linkend="sql-altertable" endterm="sql-altertable-title">.
   To do this the new child table must already include columns with
   the same names and types as the columns of the parent. It must also include
   check constraints with the same names and check expressions as those of the
   parent. Similarly an inheritance link can be removed from a child using the
   <literal>NO INHERIT</literal> variant of <command>ALTER TABLE</>.
   Dynamically adding and removing inheritance links like this can be useful
   when the inheritance relationship is being used for table
   partitioning (see <xref linkend="ddl-partitioning">).
  </para>

  <para>
   One convenient way to create a compatible table that will later be made
   a new child is to use the <literal>LIKE</literal> clause in <command>CREATE
   TABLE</command>. This creates a new table with the same columns as
   the source table. If there are any <literal>CHECK</literal>
   constraints defined on the source table, the <literal>INCLUDING
   CONSTRAINTS</literal> option to <literal>LIKE</literal> should be
   specified, as the new child must have constraints matching the parent
   to be considered compatible.
  </para>

  <para>
   A parent table cannot be dropped while any of its children remain. Neither
   can columns or check constraints of child tables be dropped or altered
   if they are inherited
   from any parent tables. If you wish to remove a table and all of its
   descendants, one easy way is to drop the parent table with the
   <literal>CASCADE</literal> option.
  </para>

  <para>
   <xref linkend="sql-altertable" endterm="sql-altertable-title"> will
   propagate any changes in column data definitions and check
   constraints down the inheritance hierarchy.  Again, dropping
   columns that are depended on by other tables is only possible when using
   the <literal>CASCADE</literal> option. <command>ALTER
   TABLE</command> follows the same rules for duplicate column merging
   and rejection that apply during <command>CREATE TABLE</command>.
  </para>

  <para>
   Note how table access permissions are handled.  Querying a parent
   table can automatically access data in child tables without further
   access privilege checking.  This preserves the appearance that the
   data is (also) in the parent table.  Accessing the child tables
   directly is, however, not automatically allowed and would require
   further privileges to be granted.
  </para>

 <sect2 id="ddl-inherit-caveats">
  <title>Caveats</title>

  <para>
   Note that not all SQL commands are able to work on
   inheritance hierarchies.  Commands that are used for data querying,
   data modification, or schema modification
   (e.g., <literal>SELECT</literal>, <literal>UPDATE</literal>, <literal>DELETE</literal>,
   most variants of <literal>ALTER TABLE</literal>, but
   not <literal>INSERT</literal> and <literal>ALTER TABLE ...
   RENAME</literal>) typically default to including child tables and
   support the <literal>ONLY</literal> notation to exclude them.
   Commands that do database maintenance and tuning
   (e.g., <literal>REINDEX</literal>, <literal>VACUUM</literal>)
   typically only work on individual, physical tables and do no
   support recursing over inheritance hierarchies.  The respective
   behavior of each individual command is documented in the reference
   part (<xref linkend="sql-commands">).
  </para>

  <para>
   A serious limitation of the inheritance feature is that indexes (including
   unique constraints) and foreign key constraints only apply to single
   tables, not to their inheritance children. This is true on both the
   referencing and referenced sides of a foreign key constraint. Thus,
   in the terms of the above example:

   <itemizedlist>
    <listitem>
     <para>
      If we declared <structname>cities</>.<structfield>name</> to be
      <literal>UNIQUE</> or a <literal>PRIMARY KEY</>, this would not stop the
      <structname>capitals</> table from having rows with names duplicating
      rows in <structname>cities</>.  And those duplicate rows would by
      default show up in queries from <structname>cities</>.  In fact, by
      default <structname>capitals</> would have no unique constraint at all,
      and so could contain multiple rows with the same name.
      You could add a unique constraint to <structname>capitals</>, but this
      would not prevent duplication compared to <structname>cities</>.
     </para>
    </listitem>

    <listitem>
     <para>
      Similarly, if we were to specify that
      <structname>cities</>.<structfield>name</> <literal>REFERENCES</> some
      other table, this constraint would not automatically propagate to
      <structname>capitals</>.  In this case you could work around it by
      manually adding the same <literal>REFERENCES</> constraint to
      <structname>capitals</>.
     </para>
    </listitem>

    <listitem>
     <para>
      Specifying that another table's column <literal>REFERENCES
      cities(name)</> would allow the other table to contain city names, but
      not capital names.  There is no good workaround for this case.
     </para>
    </listitem>
   </itemizedlist>

   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 application.
  </para>

  <note>
   <title>Deprecated</title>
   <para>
     In releases of <productname>PostgreSQL</productname> prior to 7.1, the
     default behavior was not to include child tables in queries. This was
     found to be error prone and also in violation of the SQL
     standard.  You can get the pre-7.1 behavior by turning off the
     <xref linkend="guc-sql-inheritance"> configuration
     option.
   </para>
  </note>

   </sect2>
  </sect1>

  <sect1 id="ddl-partitioning">
   <title>Partitioning</title>

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

   <indexterm>
    <primary>table</primary>
    <secondary>partitioning</secondary>
   </indexterm>

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

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

   <para>
    Partitioning refers to splitting what is logically one large table
    into smaller physical pieces.
    Partitioning can provide several benefits:
   <itemizedlist>
    <listitem>
     <para>
      Query performance can be improved dramatically in certain situations,
      particularly when most of the heavily accessed rows of the table are in a
      single partition or a small number of partitions.  The partitioning
      substitutes for leading columns of indexes, reducing index size and
      making it more likely that the heavily-used parts of the indexes
      fit in memory.
     </para>
    </listitem>

    <listitem>
     <para>
      When queries or updates access a large percentage of a single
      partition, performance can be improved by taking advantage
      of sequential scan of that partition instead of using an
      index and random access reads scattered across the whole table.
     </para>
    </listitem>

    <listitem>
     <para>
      Bulk loads and deletes can be accomplished by adding or removing
      partitions, if that requirement is planned into the partitioning design.
      <command>ALTER TABLE</> is far faster than a bulk operation.
      It also entirely avoids the <command>VACUUM</command>
      overhead caused by a bulk <command>DELETE</>.
     </para>
    </listitem>

    <listitem>
     <para>
      Seldom-used data can be migrated to cheaper and slower storage media.
     </para>
    </listitem>
   </itemizedlist>

    The benefits will normally be worthwhile only when a table would
    otherwise be very large. The exact point at which a table will
    benefit from partitioning depends on the application, although a
    rule of thumb is that the size of the table should exceed the physical
    memory of the database server.
   </para>

   <para>
    Currently, <productname>PostgreSQL</productname> supports partitioning
    via table inheritance.  Each partition must be created as a child
    table of a single parent table.  The parent table itself is normally
    empty; it exists just to represent the entire data set.  You should be
    familiar with inheritance (see <xref linkend="ddl-inherit">) before
    attempting to set up partitioning.
   </para>

   <para>
    The following forms of partitioning can be implemented in
    <productname>PostgreSQL</productname>:

    <variablelist>
     <varlistentry>
      <term>Range Partitioning</term>

      <listitem>
       <para>
        The table is partitioned into <quote>ranges</quote> defined
        by a key column or set of columns, with no overlap between
        the ranges of values assigned to different partitions.  For
        example one might partition by date ranges, or by ranges of
        identifiers for particular business objects.
       </para>
      </listitem>
     </varlistentry>

     <varlistentry>
      <term>List Partitioning</term>

      <listitem>
       <para>
        The table is partitioned by explicitly listing which key values
        appear in each partition.
       </para>
      </listitem>
     </varlistentry>
    </variablelist>
   </para>
   </sect2>

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

    <para>
     To set up a partitioned table, 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 check
        constraints on this table, unless you intend them to
        be applied equally to all partitions.  There is no point
        in defining any indexes or unique constraints on it, either.
       </para>
      </listitem>

      <listitem>
       <para>
        Create several <quote>child</quote> tables that each inherit from
        the master table.  Normally, these tables will not add any columns
        to the set inherited from the master.
       </para>

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

      <listitem>
       <para>
        Add table constraints to the partition tables to define the
        allowed key values in each partition.
       </para>

       <para>
        Typical examples would be:
<programlisting>
CHECK ( x = 1 )
CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire', 'Warwickshire' ))
CHECK ( outletID &gt;= 100 AND outletID &lt; 200 )
</programlisting>
        Ensure that the constraints guarantee that there is no overlap
        between the key values permitted in different partitions.  A common
        mistake is to set up range constraints like:
<programlisting>
CHECK ( outletID BETWEEN 100 AND 200 )
CHECK ( outletID BETWEEN 200 AND 300 )
</programlisting>
        This is wrong since it is not clear which partition the key value
        200 belongs in.
       </para>

       <para>
        Note that there is no difference in
        syntax between range and list partitioning; those terms are
        descriptive only.
       </para>
      </listitem>

      <listitem>
       <para>
        For each partition, create an index on the key column(s),
        as well as any other indexes you might want.  (The key index is
        not strictly necessary, but in most scenarios it is helpful.
        If you intend the key values to be unique then you should
        always create a unique or primary-key constraint for each
        partition.)
       </para>
      </listitem>

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

      <listitem>
       <para>
        Ensure that the <xref linkend="guc-constraint-exclusion">
        configuration parameter is not disabled in
        <filename>postgresql.conf</>.
        If it is, queries will not be optimized as desired.
       </para>
      </listitem>

     </orderedlist>
    </para>

    <para>
     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. Conceptually,
     we want a table like:

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

     We know that most queries will access just the last week's, month's or
     quarter's data, since the main use of this table will be to prepare
     online reports for management.
     To reduce the amount of old data that needs to be stored, we
     decide to only keep the most recent 3 years worth of data. At the
     beginning of each month we will remove the oldest month's data.
    </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 set up as follows:
    </para>

    <para>
     <orderedlist spacing="compact">
      <listitem>
       <para>
        The master table is the <structname>measurement</> table, declared
        exactly as above.
       </para>
      </listitem>

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

<programlisting>
CREATE TABLE measurement_y2006m02 ( ) INHERITS (measurement);
CREATE TABLE measurement_y2006m03 ( ) INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 ( ) INHERITS (measurement);
CREATE TABLE measurement_y2007m12 ( ) INHERITS (measurement);
CREATE TABLE measurement_y2008m01 ( ) INHERITS (measurement);
</programlisting>

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

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

      <listitem>
       <para>
        We must provide non-overlapping table constraints.  Rather than
        just creating the partition tables as above, the table creation
        script should really be:

 <programlisting>
CREATE TABLE measurement_y2006m02 (
    CHECK ( logdate &gt;= DATE '2006-02-01' AND logdate &lt; DATE '2006-03-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2006m03 (
    CHECK ( logdate &gt;= DATE '2006-03-01' AND logdate &lt; DATE '2006-04-01' )
) INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 (
    CHECK ( logdate &gt;= DATE '2007-11-01' AND logdate &lt; DATE '2007-12-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2007m12 (
    CHECK ( logdate &gt;= DATE '2007-12-01' AND logdate &lt; DATE '2008-01-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2008m01 (
    CHECK ( logdate &gt;= DATE '2008-01-01' AND logdate &lt; DATE '2008-02-01' )
) INHERITS (measurement);
</programlisting>
       </para>
      </listitem>

      <listitem>
       <para>
        We probably need indexes on the key columns too:

 <programlisting>
CREATE INDEX measurement_y2006m02_logdate ON measurement_y2006m02 (logdate);
CREATE INDEX measurement_y2006m03_logdate ON measurement_y2006m03 (logdate);
...
CREATE INDEX measurement_y2007m11_logdate ON measurement_y2007m11 (logdate);
CREATE INDEX measurement_y2007m12_logdate ON measurement_y2007m12 (logdate);
CREATE INDEX measurement_y2008m01_logdate ON measurement_y2008m01 (logdate);
</programlisting>

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

      <listitem>
       <para>
        We want our application to be able to say <literal>INSERT INTO
        measurement ...</> and have the data be redirected into the
        appropriate partition table.  We can arrange that by attaching
        a suitable trigger function to the master table.
        If data will be added only to the latest partition, we can
        use a very simple trigger function:

<programlisting>
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
    INSERT INTO measurement_y2008m01 VALUES (NEW.*);
    RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</programlisting>

        After creating the function, we create a trigger which
        calls the trigger function:

<programlisting>
CREATE TRIGGER insert_measurement_trigger
    BEFORE INSERT ON measurement
    FOR EACH ROW EXECUTE PROCEDURE measurement_insert_trigger();
</programlisting>

        We must redefine the trigger function each month so that it always
        points to the current partition.  The trigger definition does
        not need to be updated, however.
       </para>

       <para>
        We might want to insert data and have the server automatically
        locate the partition into which the row should be added. We
        could do this with a more complex trigger function, for example:

<programlisting>
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
    IF ( NEW.logdate &gt;= DATE '2006-02-01' AND
         NEW.logdate &lt; DATE '2006-03-01' ) THEN
        INSERT INTO measurement_y2006m02 VALUES (NEW.*);
    ELSIF ( NEW.logdate &gt;= DATE '2006-03-01' AND
            NEW.logdate &lt; DATE '2006-04-01' ) THEN
        INSERT INTO measurement_y2006m03 VALUES (NEW.*);
    ...
    ELSIF ( NEW.logdate &gt;= DATE '2008-01-01' AND
            NEW.logdate &lt; DATE '2008-02-01' ) THEN
        INSERT INTO measurement_y2008m01 VALUES (NEW.*);
    ELSE
        RAISE EXCEPTION 'Date out of range.  Fix the measurement_insert_trigger() function!';
    END IF;
    RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</programlisting>

        The trigger definition is the same as before.
        Note that each <literal>IF</literal> test must exactly match the
        <literal>CHECK</literal> constraint for its partition.
       </para>

       <para>
        While this function is more complex than the single-month case,
        it doesn't need to be updated as often, since branches can be
        added in advance of being needed.
       </para>

       <note>
        <para>
         In practice it might be best to check the newest partition first,
         if most inserts go into that partition.  For simplicity we have
         shown the trigger's tests in the same order as in other parts
         of this example.
        </para>
       </note>
      </listitem>
     </orderedlist>
    </para>

    <para>
     As we can see, a complex partitioning scheme could require a
     substantial amount of DDL. In the above example we would be
     creating a new partition each month, so it might be wise to write a
     script that generates the required DDL automatically.
    </para>

   </sect2>

   <sect2 id="ddl-partitioning-managing-partitions">
   <title>Managing Partitions</title>

   <para>
     Normally the set of partitions established when initially
     defining the table are not intended to remain static. It is
     common to want to remove old partitions of data and periodically
     add new partitions for new data. One of the most important
     advantages of partitioning is precisely that it allows this
     otherwise painful task to be executed nearly instantaneously by
     manipulating the partition structure, rather than physically moving large
     amounts of data around.
   </para>

   <para>
     The simplest option for removing old data is simply to drop the partition
     that is no longer necessary:
<programlisting>
DROP TABLE measurement_y2006m02;
</programlisting>
     This can very quickly delete millions of records because it doesn't have
     to individually delete every record.
   </para>

   <para>
     Another option that is often preferable is to remove the partition from
     the partitioned table but retain access to it as a table in its own
     right:
<programlisting>
ALTER TABLE measurement_y2006m02 NO INHERIT measurement;
</programlisting>
     This allows further operations to be performed on the data before
     it is dropped. For example, this is often a useful time to back up
     the data using <command>COPY</>, <application>pg_dump</>, or
     similar tools. It might also be a useful time to aggregate data
     into smaller formats, perform other data manipulations, or run
     reports.
   </para>

   <para>
     Similarly we can add a new partition to handle new data. We can create an
     empty partition in the partitioned table just as the original partitions
     were created above:

<programlisting>
CREATE TABLE measurement_y2008m02 (
    CHECK ( logdate &gt;= DATE '2008-02-01' AND logdate &lt; DATE '2008-03-01' )
) INHERITS (measurement);
</programlisting>

     As an alternative, it is sometimes more convenient to create the
     new table outside the partition structure, and make it a proper
     partition later. This allows the data to be loaded, checked, and
     transformed prior to it appearing in the partitioned table:

<programlisting>
CREATE TABLE measurement_y2008m02
  (LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
   CHECK ( logdate &gt;= DATE '2008-02-01' AND logdate &lt; DATE '2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement_y2008m02 INHERIT measurement;
</programlisting>
    </para>
   </sect2>

   <sect2 id="ddl-partitioning-constraint-exclusion">
   <title>Partitioning and Constraint Exclusion</title>

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

   <para>
    <firstterm>Constraint exclusion</> is a query optimization technique
    that improves performance for partitioned tables defined in the
    fashion described above.  As an example:

<programlisting>
SET constraint_exclusion = on;
SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
</programlisting>

    Without constraint exclusion, the above query would scan each of
    the partitions of the <structname>measurement</> table. With constraint
    exclusion enabled, the planner will examine the constraints of each
    partition and try to prove that the partition need not
    be scanned because it could not contain any rows meeting the query's
    <literal>WHERE</> clause.  When the planner can prove this, it
    excludes the partition from the query plan.
   </para>

   <para>
    You can use the <command>EXPLAIN</> command to show the difference
    between a plan with <varname>constraint_exclusion</> on and a plan
    with it off.  A typical unoptimized plan for this type of table setup is:

<programlisting>
SET constraint_exclusion = off;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';

                                          QUERY PLAN
-----------------------------------------------------------------------------------------------
 Aggregate  (cost=158.66..158.68 rows=1 width=0)
   -&gt;  Append  (cost=0.00..151.88 rows=2715 width=0)
         -&gt;  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
         -&gt;  Seq Scan on measurement_y2006m02 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
         -&gt;  Seq Scan on measurement_y2006m03 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
...
         -&gt;  Seq Scan on measurement_y2007m12 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
         -&gt;  Seq Scan on measurement_y2008m01 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
</programlisting>

    Some or all of the partitions might use index scans instead of
    full-table sequential scans, but the point here is that there
    is no need to scan the older partitions at all to answer this query.
    When we enable constraint exclusion, we get a significantly
    cheaper plan that will deliver the same answer:

<programlisting>
SET constraint_exclusion = on;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
                                          QUERY PLAN
-----------------------------------------------------------------------------------------------
 Aggregate  (cost=63.47..63.48 rows=1 width=0)
   -&gt;  Append  (cost=0.00..60.75 rows=1086 width=0)
         -&gt;  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
         -&gt;  Seq Scan on measurement_y2008m01 measurement  (cost=0.00..30.38 rows=543 width=0)
               Filter: (logdate &gt;= '2008-01-01'::date)
</programlisting>
   </para>

   <para>
    Note that constraint exclusion is driven only by <literal>CHECK</>
    constraints, not by the presence of indexes.  Therefore it isn't
    necessary to define indexes on the key columns.  Whether an index
    needs to be created for a given partition depends on whether you
    expect that queries that scan the partition will generally scan
    a large part of the partition or just a small part.  An index will
    be helpful in the latter case but not the former.
   </para>

   <para>
    The default (and recommended) setting of
    <xref linkend="guc-constraint-exclusion"> is actually neither
    <literal>on</> nor <literal>off</>, but an intermediate setting
    called <literal>partition</>, which causes the technique to be
    applied only to queries that are likely to be working on partitioned
    tables.  The <literal>on</> setting causes the planner to examine
    <literal>CHECK</> constraints in all queries, even simple ones that
    are unlikely to benefit.
   </para>

   </sect2>

   <sect2 id="ddl-partitioning-alternatives">
   <title>Alternative Partitioning Methods</title>

    <para>
     A different approach to redirecting inserts into the appropriate
     partition table is to set up rules, instead of a trigger, on the
     master table.  For example:

<programlisting>
CREATE RULE measurement_insert_y2006m02 AS
ON INSERT TO measurement WHERE
    ( logdate &gt;= DATE '2006-02-01' AND logdate &lt; DATE '2006-03-01' )
DO INSTEAD
    INSERT INTO measurement_y2006m02 VALUES (NEW.*);
...
CREATE RULE measurement_insert_y2008m01 AS
ON INSERT TO measurement WHERE
    ( logdate &gt;= DATE '2008-01-01' AND logdate &lt; DATE '2008-02-01' )
DO INSTEAD
    INSERT INTO measurement_y2008m01 VALUES (NEW.*);
</programlisting>

     A rule has significantly more overhead than a trigger, but the overhead
     is paid once per query rather than once per row, so this method might be
     advantageous for bulk-insert situations.  In most cases, however, the
     trigger method will offer better performance.
    </para>

    <para>
     Be aware that <command>COPY</> ignores rules.  If you want to
     use <command>COPY</> to insert data, you'll need to copy into the correct
     partition table rather than into the master.  <command>COPY</> does fire
     triggers, so you can use it normally if you use the trigger approach.
    </para>

    <para>
     Another disadvantage of the rule approach is that there is no simple
     way to force an error if the set of rules doesn't cover the insertion
     date; the data will silently go into the master table instead.
    </para>

    <para>
     Partitioning can also be arranged using a <literal>UNION ALL</literal>
     view, instead of table inheritance.  For example,

<programlisting>
CREATE VIEW measurement AS
          SELECT * FROM measurement_y2006m02
UNION ALL SELECT * FROM measurement_y2006m03
...
UNION ALL SELECT * FROM measurement_y2007m11
UNION ALL SELECT * FROM measurement_y2007m12
UNION ALL SELECT * FROM measurement_y2008m01;
</programlisting>

     However, the need to recreate the view adds an extra step to adding and
     dropping individual partitions of the data set.  In practice this
     method has little to recommend it compared to using inheritance.
    </para>

   </sect2>

   <sect2 id="ddl-partitioning-caveats">
   <title>Caveats</title>

   <para>
    The following caveats apply to partitioned tables:
   <itemizedlist>
    <listitem>
     <para>
      There is no automatic way to verify that all of the
      <literal>CHECK</literal> constraints are mutually
      exclusive.  It is safer to create code that generates
      partitions and creates and/or modifies associated objects than
      to write each by hand.
     </para>
    </listitem>

    <listitem>
     <para>
      The schemes shown here assume that the partition key column(s)
      of a row never change, or at least do not change enough to require
      it to move to another partition.  An <command>UPDATE</> that attempts
      to do that will fail because of the <literal>CHECK</> constraints.
      If you need to handle such cases, you can put suitable update triggers
      on the partition tables, but it makes management of the structure
      much more complicated.
     </para>
    </listitem>

    <listitem>
     <para>
      If you are using manual <command>VACUUM</command> or
      <command>ANALYZE</command> commands, don't forget that
      you need to run them on each partition individually. A command like:
<programlisting>
ANALYZE measurement;
</programlisting>
      will only process the master table.
     </para>
    </listitem>

   </itemizedlist>
   </para>

   <para>
    The following caveats apply to constraint exclusion:

   <itemizedlist>
    <listitem>
     <para>
      Constraint exclusion only works when the query's <literal>WHERE</>
      clause contains constants.  A parameterized query will not be
      optimized, since the planner cannot know which partitions the
      parameter value might select at run time.  For the same reason,
      <quote>stable</> functions such as <function>CURRENT_DATE</function>
      must be avoided.
     </para>
    </listitem>

    <listitem>
     <para>
      Keep the partitioning constraints simple, else the planner may not be
      able to prove that partitions don't need to be visited.  Use simple
      equality conditions for list partitioning, or simple
      range tests for range partitioning, as illustrated in the preceding
      examples.  A good rule of thumb is that partitioning constraints should
      contain only comparisons of the partitioning column(s) to constants
      using B-tree-indexable operators.
     </para>
    </listitem>

    <listitem>
     <para>
      All constraints on all partitions of the master table are examined
      during constraint exclusion, so large numbers of partitions are likely
      to increase query planning time considerably.  Partitioning using
      these techniques will work well with up to perhaps a hundred partitions;
      don't try to use many thousands of partitions.
     </para>
    </listitem>

   </itemizedlist>
   </para>
  </sect2>
 </sect1>

 <sect1 id="ddl-others">
  <title>Other Database Objects</title>

  <para>
   Tables are the central objects in a relational database structure,
   because they hold your data.  But they are not the only objects
   that exist in a database.  Many other kinds of objects can be
   created to make the use and management of the data more efficient
   or convenient.  They are not discussed in this chapter, but we give
   you a list here so that you are aware of what is possible:
  </para>

  <itemizedlist>
   <listitem>
    <para>
     Views
    </para>
   </listitem>

   <listitem>
    <para>
     Functions and operators
    </para>
   </listitem>

   <listitem>
    <para>
     Data types and domains
    </para>
   </listitem>

   <listitem>
    <para>
     Triggers and rewrite rules
    </para>
   </listitem>
  </itemizedlist>

  <para>
   Detailed information on
   these topics appears in <xref linkend="server-programming">.
  </para>
 </sect1>

 <sect1 id="ddl-depend">
  <title>Dependency Tracking</title>

  <indexterm zone="ddl-depend">
   <primary>CASCADE</primary>
   <secondary sortas="DROP">with DROP</secondary>
  </indexterm>

  <indexterm zone="ddl-depend">
   <primary>RESTRICT</primary>
   <secondary sortas="DROP">with DROP</secondary>
  </indexterm>

  <para>
   When you create complex database structures involving many tables
   with foreign key constraints, views, triggers, functions, etc. you
   implicitly create a net of dependencies between the objects.
   For instance, a table with a foreign key constraint depends on the
   table it references.
  </para>

  <para>
   To ensure the integrity of the entire database structure,
   <productname>PostgreSQL</productname> makes sure that you cannot
   drop objects that other objects still depend on.  For example,
   attempting to drop the products table we 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 <command>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 the dropping 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>
