|PostgreSQL 9.0.0 Documentation|
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The PL/Python language module automatically imports a Python module called plpy. The functions and constants in this module are available to you in the Python code as plpy.foo.
The plpy module provides two
plpy.execute 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:
nrows which returns the number of rows
returned by the query, and
status which is the
SPI_execute() return value. The result object
can be modified.
rv = plpy.execute("SELECT * FROM my_table", 5)
returns up to 5 rows from my_table. If my_table has a column my_column, it would be accessed as:
foo = rv[i]["my_column"]
The second function,
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:
plan = plpy.prepare("SELECT last_name FROM my_users WHERE first_name = $1", [ "text" ])
text is the type of the variable you will be
passing for $1. After preparing a statement, you
use the function
plpy.execute to run it:
rv = plpy.execute(plan, [ "name" ], 5)
The third argument is the limit and is optional.
Query parameters and result row fields are converted between PostgreSQL and Python data types as described in Section 42.3. 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.
When you prepare a plan using the PL/Python module it is automatically saved. Read the SPI documentation (Chapter 43) 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 SD or GD (see Section 42.4). For example:
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;