<!-- doc/src/sgml/plpython.sgml -->

<chapter id="plpython">
 <title>PL/Python - Python Procedural Language</title>

 <indexterm zone="plpython"><primary>PL/Python</></>
 <indexterm zone="plpython"><primary>Python</></>

 <para>
  The <application>PL/Python</application> procedural language allows
  <productname>PostgreSQL</productname> functions to be written in the
  <ulink url="http://www.python.org">Python language</ulink>.
 </para>

 <para>
  To install PL/Python in a particular database, use
  <literal>CREATE EXTENSION plpythonu</>, or from the shell command line use
  <literal>createlang plpythonu <replaceable>dbname</></literal> (but
  see also <xref linkend="plpython-python23">).
 </para>

  <tip>
   <para>
    If a language is installed into <literal>template1</>, all subsequently
    created databases will have the language installed automatically.
   </para>
  </tip>

 <para>
  As of <productname>PostgreSQL</productname> 7.4, PL/Python is only
  available as an <quote>untrusted</> language, meaning it does not
  offer any way of restricting what users can do in it.  It has
  therefore been renamed to <literal>plpythonu</>.  The trusted
  variant <literal>plpython</> might become available again in future,
  if a new secure execution mechanism is developed in Python.  The
  writer of a function in untrusted PL/Python must take care that the
  function cannot be used to do anything unwanted, since it will be
  able to do anything that could be done by a user logged in as the
  database administrator.  Only superusers can create functions in
  untrusted languages such as <literal>plpythonu</literal>.
 </para>

 <note>
  <para>
   Users of source packages must specially enable the build of
   PL/Python during the installation process.  (Refer to the
   installation instructions for more information.)  Users of binary
   packages might find PL/Python in a separate subpackage.
  </para>
 </note>

 <sect1 id="plpython-python23">
  <title>Python 2 vs. Python 3</title>

  <para>
   PL/Python supports both the Python 2 and Python 3 language
   variants.  (The PostgreSQL installation instructions might contain
   more precise information about the exact supported minor versions
   of Python.)  Because the Python 2 and Python 3 language variants
   are incompatible in some important aspects, the following naming
   and transitioning scheme is used by PL/Python to avoid mixing them:

   <itemizedlist>
    <listitem>
     <para>
      The PostgreSQL language named <literal>plpython2u</literal>
      implements PL/Python based on the Python 2 language variant.
     </para>
    </listitem>

    <listitem>
     <para>
      The PostgreSQL language named <literal>plpython3u</literal>
      implements PL/Python based on the Python 3 language variant.
     </para>
    </listitem>

    <listitem>
     <para>
      The language named <literal>plpythonu</literal> implements
      PL/Python based on the default Python language variant, which is
      currently Python 2.  (This default is independent of what any
      local Python installations might consider to be
      their <quote>default</quote>, for example,
      what <filename>/usr/bin/python</filename> might be.)  The
      default will probably be changed to Python 3 in a distant future
      release of PostgreSQL, depending on the progress of the
      migration to Python 3 in the Python community.
     </para>
    </listitem>
   </itemizedlist>

   It depends on the build configuration or the installed packages
   whether PL/Python for Python 2 or Python 3 or both are available.
  </para>

  <tip>
   <para>
    The built variant depends on which Python version was found during
    the installation or which version was explicitly set using
    the <envar>PYTHON</envar> environment variable;
    see <xref linkend="install-procedure">.  To make both variants of
    PL/Python available in one installation, the source tree has to be
    configured and built twice.
   </para>
  </tip>

  <para>
   This results in the following usage and migration strategy:

   <itemizedlist>
    <listitem>
     <para>
      Existing users and users who are currently not interested in
      Python 3 use the language name <literal>plpythonu</literal> and
      don't have to change anything for the foreseeable future.  It is
      recommended to gradually <quote>future-proof</quote> the code
      via migration to Python 2.6/2.7 to simplify the eventual
      migration to Python 3.
     </para>

     <para>
      In practice, many PL/Python functions will migrate to Python 3
      with few or no changes.
     </para>
    </listitem>

    <listitem>
     <para>
      Users who know that they have heavily Python 2 dependent code
      and don't plan to ever change it can make use of
      the <literal>plpython2u</literal> language name.  This will
      continue to work into the very distant future, until Python 2
      support might be completely dropped by PostgreSQL.
     </para>
    </listitem>

    <listitem>
     <para>
      Users who want to dive into Python 3 can use
      the <literal>plpython3u</literal> language name, which will keep
      working forever by today's standards.  In the distant future,
      when Python 3 might become the default, they might like to
      remove the <quote>3</quote> for aesthetic reasons.
     </para>
    </listitem>

    <listitem>
     <para>
      Daredevils, who want to build a Python-3-only operating system
      environment, can change the contents of
      <link linkend="catalog-pg-pltemplate"><structname>pg_pltemplate</structname></link>
      to make <literal>plpythonu</literal> be equivalent
      to <literal>plpython3u</literal>, keeping in mind that this
      would make their installation incompatible with most of the rest
      of the world.
     </para>
    </listitem>
   </itemizedlist>
  </para>

  <para>
   See also the
   document <ulink url="http://docs.python.org/py3k/whatsnew/3.0.html">What's
   New In Python 3.0</ulink> for more information about porting to
   Python 3.
  </para>

  <para>
   It is not allowed to use PL/Python based on Python 2 and PL/Python
   based on Python 3 in the same session, because the symbols in the
   dynamic modules would clash, which could result in crashes of the
   PostgreSQL server process.  There is a check that prevents mixing
   Python major versions in a session, which will abort the session if
   a mismatch is detected.  It is possible, however, to use both
   PL/Python variants in the same database, from separate sessions.
  </para>
 </sect1>

 <sect1 id="plpython-funcs">
  <title>PL/Python Functions</title>

  <para>
   Functions in PL/Python are declared via the
   standard <xref linkend="sql-createfunction"> syntax:

<programlisting>
CREATE FUNCTION <replaceable>funcname</replaceable> (<replaceable>argument-list</replaceable>)
  RETURNS <replaceable>return-type</replaceable>
AS $$
  # PL/Python function body
$$ LANGUAGE plpythonu;
</programlisting>
  </para>

  <para>
   The body of a function is simply a Python script. When the function
   is called, its arguments are passed as elements of the list
   <varname>args</varname>; named arguments are also passed as
   ordinary variables to the Python script.  Use of named arguments is
   usually more readable.  The result is returned from the Python code
   in the usual way, with <literal>return</literal> or
   <literal>yield</literal> (in case of a result-set statement).  If
   you do not provide a return value, Python returns the default
   <symbol>None</symbol>. <application>PL/Python</application> translates
   Python's <symbol>None</symbol> into the SQL null value.
  </para>

  <para>
   For example, a function to return the greater of two integers can be
   defined as:

<programlisting>
CREATE FUNCTION pymax (a integer, b integer)
  RETURNS integer
AS $$
  if a &gt; b:
    return a
  return b
$$ LANGUAGE plpythonu;
</programlisting>

   The Python code that is given as the body of the function definition
   is transformed into a Python function. For example, the above results in:

<programlisting>
def __plpython_procedure_pymax_23456():
  if a &gt; b:
    return a
  return b
</programlisting>

   assuming that 23456 is the OID assigned to the function by
   <productname>PostgreSQL</productname>.
  </para>

  <para>
   The arguments are set as global variables.  Because of the scoping
   rules of Python, this has the subtle consequence that an argument
   variable cannot be reassigned inside the function to the value of
   an expression that involves the variable name itself, unless the
   variable is redeclared as global in the block.  For example, the
   following won't work:
<programlisting>
CREATE FUNCTION pystrip(x text)
  RETURNS text
AS $$
  x = x.strip()  # error
  return x
$$ LANGUAGE plpythonu;
</programlisting>
   because assigning to <varname>x</varname>
   makes <varname>x</varname> a local variable for the entire block,
   and so the <varname>x</varname> on the right-hand side of the
   assignment refers to a not-yet-assigned local
   variable <varname>x</varname>, not the PL/Python function
   parameter.  Using the <literal>global</literal> statement, this can
   be made to work:
<programlisting>
CREATE FUNCTION pystrip(x text)
  RETURNS text
AS $$
  global x
  x = x.strip()  # ok now
  return x
$$ LANGUAGE plpythonu;
</programlisting>
   But it is advisable not to rely on this implementation detail of
   PL/Python.  It is better to treat the function parameters as
   read-only.
  </para>
 </sect1>

 <sect1 id="plpython-data">
  <title>Data Values</title>
  <para>
   Generally speaking, the aim of PL/Python is to provide
   a <quote>natural</quote> mapping between the PostgreSQL and the
   Python worlds.  This informs the data mapping rules described
   below.
  </para>

  <sect2>
   <title>Data Type Mapping</title>
   <para>
    Function arguments are converted from their PostgreSQL type to a
    corresponding Python type:
    <itemizedlist>
     <listitem>
      <para>
       PostgreSQL <type>boolean</type> is converted to Python <type>bool</type>.
      </para>
     </listitem>

     <listitem>
      <para>
       PostgreSQL <type>smallint</type> and <type>int</type> are
       converted to Python <type>int</type>.
       PostgreSQL <type>bigint</type> is converted
       to <type>long</type> in Python 2 and to <type>int</type> in
       Python 3.
      </para>
     </listitem>

     <listitem>
      <para>
       PostgreSQL <type>real</type>, <type>double</type>,
       and <type>numeric</type> are converted to
       Python <type>float</type>.  Note that for
       the <type>numeric</type> this loses information and can lead to
       incorrect results.  This might be fixed in a future
       release.
      </para>
     </listitem>

     <listitem>
      <para>
       PostgreSQL <type>bytea</type> is converted to
       Python <type>str</type> in Python 2 and to <type>bytes</type>
       in Python 3.  In Python 2, the string should be treated as a
       byte sequence without any character encoding.
      </para>
     </listitem>

     <listitem>
      <para>
       All other data types, including the PostgreSQL character string
       types, are converted to a Python <type>str</type>.  In Python
       2, this string will be in the PostgreSQL server encoding; in
       Python 3, it will be a Unicode string like all strings.
      </para>
     </listitem>

     <listitem>
      <para>
       For nonscalar data types, see below.
      </para>
     </listitem>
    </itemizedlist>
   </para>

   <para>
    Function return values are converted to the declared PostgreSQL
    return data type as follows:
    <itemizedlist>
     <listitem>
      <para>
       When the PostgreSQL return type is <type>boolean</type>, the
       return value will be evaluated for truth according to the
       <emphasis>Python</emphasis> rules.  That is, 0 and empty string
       are false, but notably <literal>'f'</literal> is true.
      </para>
     </listitem>

     <listitem>
      <para>
       When the PostgreSQL return type is <type>bytea</type>, the
       return value will be converted to a string (Python 2) or bytes
       (Python 3) using the respective Python built-ins, with the
       result being converted <type>bytea</type>.
      </para>
     </listitem>

     <listitem>
      <para>
       For all other PostgreSQL return types, the returned Python
       value is converted to a string using the Python
       built-in <literal>str</literal>, and the result is passed to the
       input function of the PostgreSQL data type.
      </para>

      <para>
       Strings in Python 2 are required to be in the PostgreSQL server
       encoding when they are passed to PostgreSQL.  Strings that are
       not valid in the current server encoding will raise an error,
       but not all encoding mismatches can be detected, so garbage
       data can still result when this is not done correctly.  Unicode
       strings are converted to the correct encoding automatically, so
       it can be safer and more convenient to use those.  In Python 3,
       all strings are Unicode strings.
      </para>
     </listitem>

     <listitem>
      <para>
       For nonscalar data types, see below.
      </para>
     </listitem>
    </itemizedlist>

    Note that logical mismatches between the declared PostgreSQL
    return type and the Python data type of the actual return object
    are not flagged; the value will be converted in any case.
   </para>
  </sect2>

  <sect2>
   <title>Null, None</title>
  <para>
   If an SQL null value<indexterm><primary>null value</primary><secondary
   sortas="PL/Python">in PL/Python</secondary></indexterm> is passed to a
   function, the argument value will appear as <symbol>None</symbol> in
   Python. For example, the function definition of <function>pymax</function>
   shown in <xref linkend="plpython-funcs"> will return the wrong answer for null
   inputs. We could add <literal>STRICT</literal> to the function definition
   to make <productname>PostgreSQL</productname> do something more reasonable:
   if a null value is passed, the function will not be called at all,
   but will just return a null result automatically. Alternatively,
   we could check for null inputs in the function body:

<programlisting>
CREATE FUNCTION pymax (a integer, b integer)
  RETURNS integer
AS $$
  if (a is None) or (b is None):
    return None
  if a &gt; b:
    return a
  return b
$$ LANGUAGE plpythonu;
</programlisting>

   As shown above, to return an SQL null value from a PL/Python
   function, return the value <symbol>None</symbol>. This can be done whether the
   function is strict or not.
  </para>
  </sect2>

  <sect2 id="plpython-arrays">
   <title>Arrays, Lists</title>
  <para>
   SQL array values are passed into PL/Python as a Python list.  To
   return an SQL array value out of a PL/Python function, return a
   Python sequence, for example a list or tuple:

<programlisting>
CREATE FUNCTION return_arr()
  RETURNS int[]
AS $$
return (1, 2, 3, 4, 5)
$$ LANGUAGE plpythonu;

SELECT return_arr();
 return_arr  
-------------
 {1,2,3,4,5}
(1 row)
</programlisting>

   Note that in Python, strings are sequences, which can have
   undesirable effects that might be familiar to Python programmers:

<programlisting>
CREATE FUNCTION return_str_arr()
  RETURNS varchar[]
AS $$
return "hello"
$$ LANGUAGE plpythonu;

SELECT return_str_arr();
 return_str_arr
----------------
 {h,e,l,l,o}
(1 row)
</programlisting>
  </para>
  </sect2>

  <sect2>
   <title>Composite Types</title>
  <para>
   Composite-type arguments are passed to the function as Python mappings. The
   element names of the mapping are the attribute names of the composite type.
   If an attribute in the passed row has the null value, it has the value
   <symbol>None</symbol> in the mapping. Here is an example:

<programlisting>
CREATE TABLE employee (
  name text,
  salary integer,
  age integer
);

CREATE FUNCTION overpaid (e employee)
  RETURNS boolean
AS $$
  if e["salary"] &gt; 200000:
    return True
  if (e["age"] &lt; 30) and (e["salary"] &gt; 100000):
    return True
  return False
$$ LANGUAGE plpythonu;
</programlisting>
  </para>

  <para>
   There are multiple ways to return row or composite types from a Python
   function. The following examples assume we have:

<programlisting>
CREATE TYPE named_value AS (
  name   text,
  value  integer
);
</programlisting>

   A composite result can be returned as a:

   <variablelist>
    <varlistentry>
     <term>Sequence type (a tuple or list, but not a set because
     it is not indexable)</term>
     <listitem>
      <para>
       Returned sequence objects must have the same number of items as the
       composite result type has fields. The item with index 0 is assigned to
       the first field of the composite type, 1 to the second and so on. For
       example:

<programlisting>
CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  return [ name, value ]
  # or alternatively, as tuple: return ( name, value )
$$ LANGUAGE plpythonu;
</programlisting>

       To return a SQL null for any column, insert <symbol>None</symbol> at
       the corresponding position.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term>Mapping (dictionary)</term>
     <listitem>
      <para>
       The value for each result type column is retrieved from the mapping
       with the column name as key. Example:

<programlisting>
CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  return { "name": name, "value": value }
$$ LANGUAGE plpythonu;
</programlisting>

       Any extra dictionary key/value pairs are ignored. Missing keys are
       treated as errors.
       To return a SQL null value for any column, insert
       <symbol>None</symbol> with the corresponding column name as the key.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term>Object (any object providing method <literal>__getattr__</literal>)</term>
     <listitem>
      <para>
       This works the same as a mapping.
       Example:

<programlisting>
CREATE FUNCTION make_pair (name text, value integer)
  RETURNS named_value
AS $$
  class named_value:
    def __init__ (self, n, v):
      self.name = n
      self.value = v
  return named_value(name, value)

  # or simply
  class nv: pass
  nv.name = name
  nv.value = value
  return nv
$$ LANGUAGE plpythonu;
</programlisting>
      </para>
     </listitem>
    </varlistentry>
   </variablelist>
  </para>

   <para>
    Functions with <literal>OUT</literal> parameters are also supported.  For example:
<programlisting>
CREATE FUNCTION multiout_simple(OUT i integer, OUT j integer) AS $$
return (1, 2)
$$ LANGUAGE plpythonu;

SELECT * FROM multiout_simple();
</programlisting>
   </para>
  </sect2>

  <sect2>
   <title>Set-returning Functions</title>
  <para>
   A <application>PL/Python</application> function can also return sets of
   scalar or composite types. There are several ways to achieve this because
   the returned object is internally turned into an iterator. The following
   examples assume we have composite type:

<programlisting>
CREATE TYPE greeting AS (
  how text,
  who text
);
</programlisting>

   A set result can be returned from a:

   <variablelist>
    <varlistentry>
     <term>Sequence type (tuple, list, set)</term>
     <listitem>
      <para>
<programlisting>
CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  # return tuple containing lists as composite types
  # all other combinations work also
  return ( [ how, "World" ], [ how, "PostgreSQL" ], [ how, "PL/Python" ] )
$$ LANGUAGE plpythonu;
</programlisting>
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term>Iterator (any object providing <symbol>__iter__</symbol> and
      <symbol>next</symbol> methods)</term>
     <listitem>
      <para>
<programlisting>
CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  class producer:
    def __init__ (self, how, who):
      self.how = how
      self.who = who
      self.ndx = -1

    def __iter__ (self):
      return self

    def next (self):
      self.ndx += 1
      if self.ndx == len(self.who):
        raise StopIteration
      return ( self.how, self.who[self.ndx] )

  return producer(how, [ "World", "PostgreSQL", "PL/Python" ])
$$ LANGUAGE plpythonu;
</programlisting>
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term>Generator (<literal>yield</literal>)</term>
     <listitem>
      <para>
<programlisting>
CREATE FUNCTION greet (how text)
  RETURNS SETOF greeting
AS $$
  for who in [ "World", "PostgreSQL", "PL/Python" ]:
    yield ( how, who )
$$ LANGUAGE plpythonu;
</programlisting>

       <warning>
        <para>
         Due to Python
         <ulink url="http://bugs.python.org/issue1483133">bug #1483133</ulink>,
         some debug versions of Python 2.4
         (configured and compiled with option <literal>--with-pydebug</literal>)
         are known to crash the <productname>PostgreSQL</productname> server
         when using an iterator to return a set result.
         Unpatched versions of Fedora 4 contain this bug.
         It does not happen in production versions of Python or on patched
         versions of Fedora 4.
        </para>
       </warning>
      </para>
     </listitem>
    </varlistentry>
   </variablelist>
  </para>

   <para>
    Set-returning functions with <literal>OUT</literal> parameters
    (using <literal>RETURNS SETOF record</literal>) are also
    supported.  For example:
<programlisting>
CREATE FUNCTION multiout_simple_setof(n integer, OUT integer, OUT integer) RETURNS SETOF record AS $$
return [(1, 2)] * n
$$ LANGUAGE plpythonu;

SELECT * FROM multiout_simple_setof(3);
</programlisting>
   </para>
  </sect2>
 </sect1>

 <sect1 id="plpython-sharing">
  <title>Sharing Data</title>
  <para>
   The global dictionary <varname>SD</varname> is available to store
   data between function calls.  This variable is private static data.
   The global dictionary <varname>GD</varname> is public data,
   available to all Python functions within a session.  Use with
   care.<indexterm><primary>global data</>
   <secondary>in PL/Python</></indexterm>
  </para>

  <para>
   Each function gets its own execution environment in the
   Python interpreter, so that global data and function arguments from
   <function>myfunc</function> are not available to
   <function>myfunc2</function>.  The exception is the data in the
   <varname>GD</varname> dictionary, as mentioned above.
  </para>
 </sect1>

 <sect1 id="plpython-do">
  <title>Anonymous Code Blocks</title>

  <para>
   PL/Python also supports anonymous code blocks called with the
   <xref linkend="sql-do"> statement:

<programlisting>
DO $$
    # PL/Python code
$$ LANGUAGE plpythonu;
</programlisting>

   An anonymous code block receives no arguments, and whatever value it
   might return is discarded.  Otherwise it behaves just like a function.
  </para>
 </sect1>

 <sect1 id="plpython-trigger">
  <title>Trigger Functions</title>

  <indexterm zone="plpython-trigger">
   <primary>trigger</primary>
   <secondary>in PL/Python</secondary>
  </indexterm>

  <para>
   When a function is used as a trigger, the dictionary
   <literal>TD</literal> contains trigger-related values:
   <variablelist>
    <varlistentry>
     <term><literal>TD["event"]</></term>
     <listitem>
      <para>
       contains the event as a string:
       <literal>INSERT</>, <literal>UPDATE</>,
       <literal>DELETE</>, or <literal>TRUNCATE</>.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["when"]</></term>
     <listitem>
      <para>
       contains one of <literal>BEFORE</>, <literal>AFTER</>, or
       <literal>INSTEAD OF</>.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["level"]</></term>
     <listitem>
      <para>
       contains <literal>ROW</> or <literal>STATEMENT</>.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["new"]</></term>
     <term><literal>TD["old"]</></term>
     <listitem>
      <para>
       For a row-level trigger, one or both of these fields contain
       the respective trigger rows, depending on the trigger event.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["name"]</></term>
     <listitem>
      <para>
       contains the trigger name.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["table_name"]</></term>
     <listitem>
      <para>
       contains the name of the table on which the trigger occurred.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["table_schema"]</></term>
     <listitem>
      <para>
       contains the schema of the table on which the trigger occurred.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["relid"]</></term>
     <listitem>
      <para>
       contains the OID of the table on which the trigger occurred.
      </para>
     </listitem>
    </varlistentry>

    <varlistentry>
     <term><literal>TD["args"]</></term>
     <listitem>
      <para>
       If the <command>CREATE TRIGGER</> command
       included arguments, they are available in <literal>TD["args"][0]</> to
       <literal>TD["args"][<replaceable>n</>-1]</>.
      </para>
     </listitem>
    </varlistentry>
   </variablelist>
  </para>

  <para>
   If <literal>TD["when"]</literal> is <literal>BEFORE</> or
   <literal>INSTEAD OF</> and
   <literal>TD["level"]</literal> is <literal>ROW</>, you can
   return <literal>None</literal> or <literal>"OK"</literal> from the
   Python function to indicate the row is unmodified,
   <literal>"SKIP"</> to abort the event, or if <literal>TD["event"]</>
   is <command>INSERT</> or <command>UPDATE</> you can return
   <literal>"MODIFY"</> to indicate you've modified the new row.
   Otherwise the return value is ignored.
  </para>
 </sect1>

 <sect1 id="plpython-database">
  <title>Database Access</title>

  <para>
   The PL/Python language module automatically imports a Python module
   called <literal>plpy</literal>.  The functions and constants in
   this module are available to you in the Python code as
   <literal>plpy.<replaceable>foo</replaceable></literal>.
  </para>

  <sect2>
    <title>Database Access Functions</title>

  <para>
   The <literal>plpy</literal> module provides two
   functions called <function>execute</function> and
   <function>prepare</function>.  Calling
   <function>plpy.execute</function> with a query string and an
   optional limit argument causes that query to be run and the result
   to be returned in a result object.  The result object emulates a
   list or dictionary object.  The result object can be accessed by
   row number and column name.  It has these additional methods:
   <function>nrows</function> which returns the number of rows
   returned by the query, and <function>status</function> which is the
   <function>SPI_execute()</function> return value.  The result object
   can be modified.
  </para>

  <para>
   Note that calling <literal>plpy.execute</literal> will cause the entire
   result set to be read into memory.  Only use that function when you are sure
   that the result set will be relatively small.  If you don't want to risk
   excessive memory usage when fetching large results,
   use <literal>plpy.cursor</literal> rather
   than <literal>plpy.execute</literal>.
  </para>

  <para>
   For example:
<programlisting>
rv = plpy.execute("SELECT * FROM my_table", 5)
</programlisting>
   returns up to 5 rows from <literal>my_table</literal>.  If
   <literal>my_table</literal> has a column
   <literal>my_column</literal>, it would be accessed as:
<programlisting>
foo = rv[i]["my_column"]
</programlisting>
  </para>

  <para>
   <indexterm><primary>preparing a query</><secondary>in PL/Python</></indexterm>
   The second function, <function>plpy.prepare</function>, prepares
   the execution plan for a query.  It is called with a query string
   and a list of parameter types, if you have parameter references in
   the query.  For example:
<programlisting>
plan = plpy.prepare("SELECT last_name FROM my_users WHERE first_name = $1", [ "text" ])
</programlisting>
   <literal>text</literal> is the type of the variable you will be
   passing for <literal>$1</literal>.  After preparing a statement, you
   use the function <function>plpy.execute</function> to run it:
<programlisting>
rv = plpy.execute(plan, [ "name" ], 5)
</programlisting>
   The third argument is the limit and is optional.
  </para>

  <para>
   Query parameters and result row fields are converted between
   PostgreSQL and Python data types as described
   in <xref linkend="plpython-data">.  The exception is that composite
   types are currently not supported: They will be rejected as query
   parameters and are converted to strings when appearing in a query
   result.  As a workaround for the latter problem, the query can
   sometimes be rewritten so that the composite type result appears as
   a result row rather than as a field of the result row.
   Alternatively, the resulting string could be parsed apart by hand,
   but this approach is not recommended because it is not
   future-proof.
  </para>

  <para>
   When you prepare a plan using the PL/Python module it is
   automatically saved.  Read the SPI documentation (<xref
   linkend="spi">) for a description of what this means.
   In order to make effective use of this across function calls
   one needs to use one of the persistent storage dictionaries
   <literal>SD</literal> or <literal>GD</literal> (see
   <xref linkend="plpython-sharing">). For example:
<programlisting>
CREATE FUNCTION usesavedplan() RETURNS trigger AS $$
    if SD.has_key("plan"):
        plan = SD["plan"]
    else:
        plan = plpy.prepare("SELECT 1")
        SD["plan"] = plan
    # rest of function
$$ LANGUAGE plpythonu;
</programlisting>
  </para>

  </sect2>

  <sect2>
   <title>Accessing Data with Cursors</title>

   <para>
    The <literal>plpy.cursor</literal> function accepts the same arguments
    as <literal>plpy.execute</literal> (except for <literal>limit</literal>)
    and returns a cursor object, which allows you to process large result sets
    in smaller chunks.  As with <literal>plpy.execute</literal>, either a query
    string or a plan object along with a list of arguments can be used.  The
    cursor object provides a <literal>fetch</literal> method that accepts an
    integer parameter and returns a result object.  Each time you
    call <literal>fetch</literal>, the returned object will contain the next
    batch of rows, never larger than the parameter value.  Once all rows are
    exhausted, <literal>fetch</literal> starts returning an empty result
    object.  Cursor objects also provide an
    <ulink url="http://docs.python.org/library/stdtypes.html#iterator-types">iterator
    interface</ulink>, yielding one row at a time until all rows are exhausted.
    Data fetched that way is not returned as result objects, but rather as
    dictionaries, each dictionary corresponding to a single result row.
   </para>

   <para>
    Cursors are automatically disposed of.  But if you want to explicitly
    release all resources held by a cursor, use the <literal>close</literal>
    method.  Once closed, a cursor cannot be fetched from anymore.
   </para>

   <tip>
    <para>
      Do not confuse objects created by <literal>plpy.cursor</literal> with
      DB-API cursors as defined by
      the <ulink url="http://www.python.org/dev/peps/pep-0249/">Python Database
      API specification</ulink>.  They don't have anything in common except for
      the name.
    </para>
   </tip>

   <para>
    An example of two ways of processing data from a large table is:
<programlisting>
CREATE FUNCTION count_odd_iterator() RETURNS integer AS $$
odd = 0
for row in plpy.cursor("select num from largetable"):
    if row['num'] % 2:
         odd += 1
return odd
$$ LANGUAGE plpythonu;

CREATE FUNCTION count_odd_fetch(batch_size integer) RETURNS integer AS $$
odd = 0
cursor = plpy.cursor("select num from largetable")
while True:
    rows = cursor.fetch(batch_size)
    if not rows:
        break
    for row in rows:
        if row['num'] % 2:
            odd += 1
return odd
$$ LANGUAGE plpythonu;

CREATE FUNCTION count_odd_prepared() RETURNS integer AS $$
odd = 0
plan = plpy.prepare("select num from largetable where num % $1 &lt;&gt; 0", ["integer"])
rows = list(plpy.cursor(plan, [2]))

return len(rows)
$$ LANGUAGE plpythonu;
</programlisting>
   </para>
  </sect2>

  <sect2 id="plpython-trapping">
   <title>Trapping Errors</title>

   <para>
    Functions accessing the database might encounter errors, which
    will cause them to abort and raise an exception.  Both
    <function>plpy.execute</function> and
    <function>plpy.prepare</function> can raise an instance of a subclass of
    <literal>plpy.SPIError</literal>, which by default will terminate
    the function.  This error can be handled just like any other
    Python exception, by using the <literal>try/except</literal>
    construct.  For example:
<programlisting>
CREATE FUNCTION try_adding_joe() RETURNS text AS $$
    try:
        plpy.execute("INSERT INTO users(username) VALUES ('joe')")
    except plpy.SPIError:
        return "something went wrong"
    else:
        return "Joe added"
$$ LANGUAGE plpythonu;
</programlisting>
   </para>

   <para>
    The actual class of the exception being raised corresponds to the
    specific condition that caused the error.  Refer
    to <xref linkend="errcodes-table"> for a list of possible
    conditions.  The module
    <literal>plpy.spiexceptions</literal> defines an exception class
    for each <productname>PostgreSQL</productname> condition, deriving
    their names from the condition name.  For
    instance, <literal>division_by_zero</literal>
    becomes <literal>DivisionByZero</literal>, <literal>unique_violation</literal>
    becomes <literal>UniqueViolation</literal>, <literal>fdw_error</literal>
    becomes <literal>FdwError</literal>, and so on.  Each of these
    exception classes inherits from <literal>SPIError</literal>.  This
    separation makes it easier to handle specific errors, for
    instance:
<programlisting>
CREATE FUNCTION insert_fraction(numerator int, denominator int) RETURNS text AS $$
from plpy import spiexceptions
try:
    plan = plpy.prepare("INSERT INTO fractions (frac) VALUES ($1 / $2)", ["int", "int"])
    plpy.execute(plan, [numerator, denominator])
except spiexceptions.DivisionByZero:
    return "denominator cannot equal zero"
except spiexceptions.UniqueViolation:
    return "already have that fraction"
except plpy.SPIError, e:
    return "other error, SQLSTATE %s" % e.sqlstate
else:
    return "fraction inserted"
$$ LANGUAGE plpythonu;
</programlisting>
    Note that because all exceptions from
    the <literal>plpy.spiexceptions</literal> module inherit
    from <literal>SPIError</literal>, an <literal>except</literal>
    clause handling it will catch any database access error.
   </para>

   <para>
    As an alternative way of handling different error conditions, you
    can catch the <literal>SPIError</literal> exception and determine
    the specific error condition inside the <literal>except</literal>
    block by looking at the <literal>sqlstate</literal> attribute of
    the exception object.  This attribute is a string value containing
    the <quote>SQLSTATE</quote> error code.  This approach provides
    approximately the same functionality
   </para>
  </sect2>
 </sect1>

 <sect1 id="plpython-subtransaction">
  <title>Explicit Subtransactions</title>

  <para>
   Recovering from errors caused by database access as described in
   <xref linkend="plpython-trapping"> can lead to an undesirable
   situation where some operations succeed before one of them fails,
   and after recovering from that error the data is left in an
   inconsistent state.  PL/Python offers a solution to this problem in
   the form of explicit subtransactions.
  </para>

  <sect2>
   <title>Subtransaction Context Managers</title>

   <para>
    Consider a function that implements a transfer between two
    accounts:
<programlisting>
CREATE FUNCTION transfer_funds() RETURNS void AS $$
try:
    plpy.execute("UPDATE accounts SET balance = balance - 100 WHERE account_name = 'joe'")
    plpy.execute("UPDATE accounts SET balance = balance + 100 WHERE account_name = 'mary'")
except plpy.SPIError, e:
    result = "error transferring funds: %s" % e.args
else:
    result = "funds transferred correctly"
plan = plpy.prepare("INSERT INTO operations (result) VALUES ($1)", ["text"])
plpy.execute(plan, [result])
$$ LANGUAGE plpythonu;
</programlisting>
    If the second <literal>UPDATE</literal> statement results in an
    exception being raised, this function will report the error, but
    the result of the first <literal>UPDATE</literal> will
    nevertheless be committed.  In other words, the funds will be
    withdrawn from Joe's account, but will not be transferred to
    Mary's account.
   </para>

   <para>
    To avoid such issues, you can wrap your
    <literal>plpy.execute</literal> calls in an explicit
    subtransaction.  The <literal>plpy</literal> module provides a
    helper object to manage explicit subtransactions that gets created
    with the <literal>plpy.subtransaction()</literal> function.
    Objects created by this function implement the
    <ulink url="http://docs.python.org/library/stdtypes.html#context-manager-types">
    context manager interface</ulink>.  Using explicit subtransactions
    we can rewrite our function as:
<programlisting>
CREATE FUNCTION transfer_funds2() RETURNS void AS $$
try:
    with plpy.subtransaction():
        plpy.execute("UPDATE accounts SET balance = balance - 100 WHERE account_name = 'joe'")
        plpy.execute("UPDATE accounts SET balance = balance + 100 WHERE account_name = 'mary'")
except plpy.SPIError, e:
    result = "error transferring funds: %s" % e.args
else:
    result = "funds transferred correctly"
plan = plpy.prepare("INSERT INTO operations (result) VALUES ($1)", ["text"])
plpy.execute(plan, [result])
$$ LANGUAGE plpythonu;
</programlisting>
    Note that the use of <literal>try/catch</literal> is still
    required.  Otherwise the exception would propagate to the top of
    the Python stack and would cause the whole function to abort with
    a <productname>PostgreSQL</productname> error, so that the
    <literal>operations</literal> table would not have any row
    inserted into it.  The subtransaction context manager does not
    trap errors, it only assures that all database operations executed
    inside its scope will be atomically committed or rolled back.  A
    rollback of the subtransaction block occurs on any kind of
    exception exit, not only ones caused by errors originating from
    database access.  A regular Python exception raised inside an
    explicit subtransaction block would also cause the subtransaction
    to be rolled back.
   </para>
  </sect2>

  <sect2>
   <title>Older Python Versions</title>

   <para>
    Context managers syntax using the <literal>with</literal> keyword
    is available by default in Python 2.6.  If using PL/Python with an
    older Python version, it is still possible to use explicit
    subtransactions, although not as transparently.  You can call the
    subtransaction manager's <literal>__enter__</literal> and
    <literal>__exit__</literal> functions using the
    <literal>enter</literal> and <literal>exit</literal> convenience
    aliases.  The example function that transfers funds could be
    written as:
<programlisting>
CREATE FUNCTION transfer_funds_old() RETURNS void AS $$
try:
    subxact = plpy.subtransaction()
    subxact.enter()
    try:
        plpy.execute("UPDATE accounts SET balance = balance - 100 WHERE account_name = 'joe'")
        plpy.execute("UPDATE accounts SET balance = balance + 100 WHERE account_name = 'mary'")
    except:
        import sys
        subxact.exit(*sys.exc_info())
        raise
    else:
        subxact.exit(None, None, None)
except plpy.SPIError, e:
    result = "error transferring funds: %s" % e.args
else:
    result = "funds transferred correctly"

plan = plpy.prepare("INSERT INTO operations (result) VALUES ($1)", ["text"])
plpy.execute(plan, [result])
$$ LANGUAGE plpythonu;
</programlisting>
   </para>

   <note>
    <para>
     Although context managers were implemented in Python 2.5, to use
     the <literal>with</literal> syntax in that version you need to
     use a <ulink
     url="http://docs.python.org/release/2.5/ref/future.html">future
     statement</ulink>.  Because of implementation details, however,
     you cannot use future statements in PL/Python functions.
    </para>
   </note>
  </sect2>
 </sect1>

 <sect1 id="plpython-util">
  <title>Utility Functions</title>
  <para>
   The <literal>plpy</literal> module also provides the functions
   <literal>plpy.debug(<replaceable>msg</>)</literal>,
   <literal>plpy.log(<replaceable>msg</>)</literal>,
   <literal>plpy.info(<replaceable>msg</>)</literal>,
   <literal>plpy.notice(<replaceable>msg</>)</literal>,
   <literal>plpy.warning(<replaceable>msg</>)</literal>,
   <literal>plpy.error(<replaceable>msg</>)</literal>, and
   <literal>plpy.fatal(<replaceable>msg</>)</literal>.<indexterm><primary>elog</><secondary>in PL/Python</></indexterm>
   <function>plpy.error</function> and
   <function>plpy.fatal</function> actually raise a Python exception
   which, if uncaught, propagates out to the calling query, causing
   the current transaction or subtransaction to be aborted.
   <literal>raise plpy.Error(<replaceable>msg</>)</literal> and
   <literal>raise plpy.Fatal(<replaceable>msg</>)</literal> are
   equivalent to calling
   <function>plpy.error</function> and
   <function>plpy.fatal</function>, respectively.
   The other functions only generate messages of different
   priority levels.
   Whether messages of a particular priority are reported to the client,
   written to the server log, or both is controlled by the
   <xref linkend="guc-log-min-messages"> and
   <xref linkend="guc-client-min-messages"> configuration
   variables. See <xref linkend="runtime-config"> for more information.
  </para>

  <para>
   Another set of utility functions are
   <literal>plpy.quote_literal(<replaceable>string</>)</literal>,
   <literal>plpy.quote_nullable(<replaceable>string</>)</literal>, and
   <literal>plpy.quote_ident(<replaceable>string</>)</literal>.  They
   are equivalent to the built-in quoting functions described in <xref
   linkend="functions-string">.  They are useful when constructing
   ad-hoc queries.  A PL/Python equivalent of dynamic SQL from <xref
   linkend="plpgsql-quote-literal-example"> would be:
<programlisting>
plpy.execute("UPDATE tbl SET %s = %s WHERE key = %s" % (
    plpy.quote_ident(colname),
    plpy.quote_nullable(newvalue),
    plpy.quote_literal(keyvalue)))
</programlisting>
  </para>
 </sect1>

 <sect1 id="plpython-envar">
  <title>Environment Variables</title>

  <para>
   Some of the environment variables that are accepted by the Python
   interpreter can also be used to affect PL/Python behavior.  They
   would need to be set in the environment of the main PostgreSQL
   server process, for example in a start script.  The available
   environment variables depend on the version of Python; see the
   Python documentation for details.  At the time of this writing, the
   following environment variables have an affect on PL/Python,
   assuming an adequate Python version:
   <itemizedlist>
    <listitem>
     <para><envar>PYTHONHOME</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONPATH</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONY2K</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONOPTIMIZE</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONDEBUG</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONVERBOSE</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONCASEOK</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONDONTWRITEBYTECODE</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONIOENCODING</envar></para>
    </listitem>

    <listitem>
     <para><envar>PYTHONUSERBASE</envar></para>
    </listitem>
   </itemizedlist>

   (It appears to be a Python implementation detail beyond the control
   of PL/Python that some of the environment variables listed on
   the <command>python</command> man page are only effective in a
   command-line interpreter and not an embedded Python interpreter.)
  </para>
 </sect1>
</chapter>
