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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>45.7. Database Access</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets V1.79.1" /><link rel="prev" href="plpython-trigger.html" title="45.6. Trigger Functions" /><link rel="next" href="plpython-subtransaction.html" title="45.8. Explicit Subtransactions" /></head><body><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">45.7. Database Access</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="plpython-trigger.html" title="45.6. Trigger Functions">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="plpython.html" title="Chapter 45. PL/Python - Python Procedural Language">Up</a></td><th width="60%" align="center">Chapter 45. PL/Python - Python Procedural Language</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 12.4 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="plpython-subtransaction.html" title="45.8. Explicit Subtransactions">Next</a></td></tr></table><hr></hr></div><div class="sect1" id="PLPYTHON-DATABASE"><div class="titlepage"><div><div><h2 class="title" style="clear: both">45.7. Database Access</h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="plpython-database.html#id-1.8.11.15.3">45.7.1. Database Access Functions</a></span></dt><dt><span class="sect2"><a href="plpython-database.html#PLPYTHON-TRAPPING">45.7.2. Trapping Errors</a></span></dt></dl></div><p>
   The PL/Python language module automatically imports a Python module
   called <code class="literal">plpy</code>.  The functions and constants in
   this module are available to you in the Python code as
   <code class="literal">plpy.<em class="replaceable"><code>foo</code></em></code>.
  </p><div class="sect2" id="id-1.8.11.15.3"><div class="titlepage"><div><div><h3 class="title">45.7.1. Database Access Functions</h3></div></div></div><p>
   The <code class="literal">plpy</code> module provides several functions to execute
   database commands:
  </p><div class="variablelist"><dl class="variablelist"><dt><span class="term"><code class="literal">plpy.<code class="function">execute</code>(<em class="replaceable"><code>query</code></em> [, <em class="replaceable"><code>max-rows</code></em>])</code></span></dt><dd><p>
      Calling <code class="function">plpy.execute</code> with a query string and an
      optional row limit argument causes that query to be run and the result to
      be returned in a result object.
     </p><p>
      The result object emulates a list or dictionary object.  The result
      object can be accessed by row number and column name.  For example:
</p><pre class="programlisting">
rv = plpy.execute("SELECT * FROM my_table", 5)
</pre><p>
      returns up to 5 rows from <code class="literal">my_table</code>.  If
      <code class="literal">my_table</code> has a column
      <code class="literal">my_column</code>, it would be accessed as:
</p><pre class="programlisting">
foo = rv[i]["my_column"]
</pre><p>
      The number of rows returned can be obtained using the built-in
      <code class="function">len</code> function.
     </p><p>
      The result object has these additional methods:
      </p><div class="variablelist"><dl class="variablelist"><dt><span class="term"><code class="literal"><code class="function">nrows</code>()</code></span></dt><dd><p>
          Returns the number of rows processed by the command.  Note that this
          is not necessarily the same as the number of rows returned.  For
          example, an <code class="command">UPDATE</code> command will set this value but
          won't return any rows (unless <code class="literal">RETURNING</code> is used).
         </p></dd><dt><span class="term"><code class="literal"><code class="function">status</code>()</code></span></dt><dd><p>
          The <code class="function">SPI_execute()</code> return value.
         </p></dd><dt><span class="term"><code class="literal"><code class="function">colnames</code>()</code><br /></span><span class="term"><code class="literal"><code class="function">coltypes</code>()</code><br /></span><span class="term"><code class="literal"><code class="function">coltypmods</code>()</code></span></dt><dd><p>
          Return a list of column names, list of column type OIDs, and list of
          type-specific type modifiers for the columns, respectively.
         </p><p>
          These methods raise an exception when called on a result object from
          a command that did not produce a result set, e.g.,
          <code class="command">UPDATE</code> without <code class="literal">RETURNING</code>, or
          <code class="command">DROP TABLE</code>.  But it is OK to use these methods on
          a result set containing zero rows.
         </p></dd><dt><span class="term"><code class="literal"><code class="function">__str__</code>()</code></span></dt><dd><p>
          The standard <code class="literal">__str__</code> method is defined so that it
          is possible for example to debug query execution results
          using <code class="literal">plpy.debug(rv)</code>.
         </p></dd></dl></div><p>
     </p><p>
      The result object can be modified.
     </p><p>
      Note that calling <code class="literal">plpy.execute</code> 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 <code class="literal">plpy.cursor</code> rather
      than <code class="literal">plpy.execute</code>.
     </p></dd><dt><span class="term"><code class="literal">plpy.<code class="function">prepare</code>(<em class="replaceable"><code>query</code></em> [, <em class="replaceable"><code>argtypes</code></em>])</code><br /></span><span class="term"><code class="literal">plpy.<code class="function">execute</code>(<em class="replaceable"><code>plan</code></em> [, <em class="replaceable"><code>arguments</code></em> [, <em class="replaceable"><code>max-rows</code></em>]])</code></span></dt><dd><p>
      <a id="id-1.8.11.15.3.3.2.3.1.1" class="indexterm"></a>
      <code class="function">plpy.prepare</code> 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:
</p><pre class="programlisting">
plan = plpy.prepare("SELECT last_name FROM my_users WHERE first_name = $1", ["text"])
</pre><p>
      <code class="literal">text</code> is the type of the variable you will be passing
      for <code class="literal">$1</code>.  The second argument is optional if you don't
      want to pass any parameters to the query.
     </p><p>
      After preparing a statement, you use a variant of the
      function <code class="function">plpy.execute</code> to run it:
</p><pre class="programlisting">
rv = plpy.execute(plan, ["name"], 5)
</pre><p>
      Pass the plan as the first argument (instead of the query string), and a
      list of values to substitute into the query as the second argument.  The
      second argument is optional if the query does not expect any parameters.
      The third argument is the optional row limit as before.
     </p><p>
      Alternatively, you can call the <code class="function">execute</code> method on
      the plan object:
</p><pre class="programlisting">
rv = plan.execute(["name"], 5)
</pre><p>
     </p><p>
      Query parameters and result row fields are converted between PostgreSQL
      and Python data types as described in <a class="xref" href="plpython-data.html" title="45.3. Data Values">Section 45.3</a>.
     </p><p>
      When you prepare a plan using the PL/Python module it is automatically
      saved.  Read the SPI documentation (<a class="xref" href="spi.html" title="Chapter 46. Server Programming Interface">Chapter 46</a>) 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 <code class="literal">SD</code> or <code class="literal">GD</code> (see
      <a class="xref" href="plpython-sharing.html" title="45.4. Sharing Data">Section 45.4</a>). For example:
</p><pre class="programlisting">
CREATE FUNCTION usesavedplan() RETURNS trigger AS $$
    if "plan" in SD:
        plan = SD["plan"]
    else:
        plan = plpy.prepare("SELECT 1")
        SD["plan"] = plan
    # rest of function
$$ LANGUAGE plpythonu;
</pre><p>
     </p></dd><dt><span class="term"><code class="literal">plpy.<code class="function">cursor</code>(<em class="replaceable"><code>query</code></em>)</code><br /></span><span class="term"><code class="literal">plpy.<code class="function">cursor</code>(<em class="replaceable"><code>plan</code></em> [, <em class="replaceable"><code>arguments</code></em>])</code></span></dt><dd><p>
      The <code class="literal">plpy.cursor</code> function accepts the same arguments
      as <code class="literal">plpy.execute</code> (except for the row limit) and returns
      a cursor object, which allows you to process large result sets in smaller
      chunks.  As with <code class="literal">plpy.execute</code>, either a query string
      or a plan object along with a list of arguments can be used, or
      the <code class="function">cursor</code> function can be called as a method of
      the plan object.
     </p><p>
      The cursor object provides a <code class="literal">fetch</code> method that accepts
      an integer parameter and returns a result object.  Each time you
      call <code class="literal">fetch</code>, the returned object will contain the next
      batch of rows, never larger than the parameter value.  Once all rows are
      exhausted, <code class="literal">fetch</code> starts returning an empty result
      object.  Cursor objects also provide an
      <a class="ulink" href="https://docs.python.org/library/stdtypes.html#iterator-types" target="_top">iterator
      interface</a>, 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.
     </p><p>
      An example of two ways of processing data from a large table is:
</p><pre class="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]))  # or: = list(plan.cursor([2]))

return len(rows)
$$ LANGUAGE plpythonu;
</pre><p>
     </p><p>
      Cursors are automatically disposed of.  But if you want to explicitly
      release all resources held by a cursor, use the <code class="literal">close</code>
      method.  Once closed, a cursor cannot be fetched from anymore.
     </p><div class="tip"><h3 class="title">Tip</h3><p>
        Do not confuse objects created by <code class="literal">plpy.cursor</code> with
        DB-API cursors as defined by
        the <a class="ulink" href="https://www.python.org/dev/peps/pep-0249/" target="_top">Python
        Database API specification</a>.  They don't have anything in common
        except for the name.
      </p></div></dd></dl></div></div><div class="sect2" id="PLPYTHON-TRAPPING"><div class="titlepage"><div><div><h3 class="title">45.7.2. Trapping Errors</h3></div></div></div><p>
    Functions accessing the database might encounter errors, which
    will cause them to abort and raise an exception.  Both
    <code class="function">plpy.execute</code> and
    <code class="function">plpy.prepare</code> can raise an instance of a subclass of
    <code class="literal">plpy.SPIError</code>, which by default will terminate
    the function.  This error can be handled just like any other
    Python exception, by using the <code class="literal">try/except</code>
    construct.  For example:
</p><pre class="programlisting">
CREATE FUNCTION try_adding_joe() RETURNS text AS $$
    try:
        plpy.execute("INSERT INTO users(username) VALUES ('joe')")
    except plpy.SPIError:
        return "something went wrong"
    else:
        return "Joe added"
$$ LANGUAGE plpythonu;
</pre><p>
   </p><p>
    The actual class of the exception being raised corresponds to the
    specific condition that caused the error.  Refer
    to <a class="xref" href="errcodes-appendix.html#ERRCODES-TABLE" title="Table A.1. PostgreSQL Error Codes">Table A.1</a> for a list of possible
    conditions.  The module
    <code class="literal">plpy.spiexceptions</code> defines an exception class
    for each <span class="productname">PostgreSQL</span> condition, deriving
    their names from the condition name.  For
    instance, <code class="literal">division_by_zero</code>
    becomes <code class="literal">DivisionByZero</code>, <code class="literal">unique_violation</code>
    becomes <code class="literal">UniqueViolation</code>, <code class="literal">fdw_error</code>
    becomes <code class="literal">FdwError</code>, and so on.  Each of these
    exception classes inherits from <code class="literal">SPIError</code>.  This
    separation makes it easier to handle specific errors, for
    instance:
</p><pre class="programlisting">
CREATE FUNCTION insert_fraction(numerator int, denominator int) RETURNS text AS $$
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;
</pre><p>
    Note that because all exceptions from
    the <code class="literal">plpy.spiexceptions</code> module inherit
    from <code class="literal">SPIError</code>, an <code class="literal">except</code>
    clause handling it will catch any database access error.
   </p><p>
    As an alternative way of handling different error conditions, you
    can catch the <code class="literal">SPIError</code> exception and determine
    the specific error condition inside the <code class="literal">except</code>
    block by looking at the <code class="literal">sqlstate</code> attribute of
    the exception object.  This attribute is a string value containing
    the <span class="quote">“<span class="quote">SQLSTATE</span>”</span> error code.  This approach provides
    approximately the same functionality
   </p></div></div><div class="navfooter"><hr /><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="plpython-trigger.html">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="plpython.html">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="plpython-subtransaction.html">Next</a></td></tr><tr><td width="40%" align="left" valign="top">45.6. Trigger Functions </td><td width="20%" align="center"><a accesskey="h" href="index.html">Home</a></td><td width="40%" align="right" valign="top"> 45.8. Explicit Subtransactions</td></tr></table></div></body></html>