There are sample functions in plpython_function.sql. The Python code you write gets transformed into a function. E.g.,
CREATE FUNCTION myfunc(text) RETURNS text AS 'return args' LANGUAGE 'plpython';
gets transformed into
def __plpython_procedure_myfunc_23456(): return args
where 23456 is the OID of the function.
If you do not provide a return value, Python returns the default None which may or may not be what you want. The language module translates Python's None into SQL NULL.
PostgreSQL function variables are available in the global args list. In the myfunc example, args contains whatever was passed in as the text argument. For myfunc2(text, integer), args would contain the text variable and args the integer variable.
The global dictionary SD is available to store data between function calls. This variable is private static data. The global dictionary GD is public data, available to all python functions within a backend. Use with care.
Each function gets its own restricted execution object in the Python interpreter, so that global data and function arguments from myfunc are not available to myfunc2. The exception is the data in the GD dictionary, as mentioned above.
When a function is used in a trigger, the dictionary TD contains transaction related values. The trigger tuples are in TD["new"] and/or TD["old"] depending on the trigger event. TD["event"] contains the event as a string (INSERT, UPDATE, DELETE, or UNKNOWN). TD["when"] contains one of (BEFORE, AFTER, or UNKNOWN). TD["level"] contains one of ROW, STATEMENT, or UNKNOWN. TD["name"] contains the trigger name, and TD["relid"] contains the relation id of the table on which the trigger occurred. If the trigger was called with arguments they are available in TD["args"] to TD["args"][(n -1)].
If the trigger "when" is BEFORE, you may return None or "OK" from the Python function to indicate the tuple is unmodified, "SKIP" to abort the event, or "MODIFIED" to indicate you've modified the tuple.
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. At present plpy implements the functions plpy.debug("msg"), plpy.log("msg"), plpy.info("msg"), plpy.notice("msg"), plpy.warning("msg"), plpy.error("msg"), and plpy.fatal("msg"). They are mostly equivalent to calling elog(LEVEL, "msg"). plpy.error and plpy.fatal actually raise a Python exception which, if uncaught, causes the PL/Python module to call elog(ERROR, msg) when the function handler returns from the Python interpreter. Long jumping out of the Python interpreter is probably not good. raise plpy.ERROR("msg") and raise plpy.FATAL("msg") are equivalent to calling plpy.error or plpy.fatal.
Additionally, the plpy module provides two functions called execute and prepare. Calling plpy.execute with a query string, and an optional limit argument, causes that query to be run, and the result returned in a result object. The result object emulates a list or dictionary object. The result object can be accessed by row number, and field name. It has these additional methods: nrows() which returns the number of rows returned by the query, and status which is the SPI_exec return variable. The result object can be modified.
rv = plpy.execute("SELECT * FROM my_table", 5)
returns up to 5 rows from my_table. Ff my_table has a column my_field it would be accessed as
foo = rv[i]["my_field"]
The second function plpy.prepare is called with a query string, and a list of argument types if you have bind variables in the query.
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 as $1. After preparing you use the function plpy.execute to run it.
rv = plpy.execute(plan, [ "name" ], 5)
The limit argument is optional in the call to plpy.execute.
When you prepare a plan using the PL/Python module it is automatically saved. Read the SPI documentation (Chapter 21) for a description of what this means. The take home message is if you do
plan = plpy.prepare("SOME QUERY") plan = plpy.prepare("SOME OTHER QUERY")
you are leaking memory, as I know of no way to free a saved plan. The alternative of using unsaved plans it even more painful (for me).