Using PL/Python

PostgreSQL 7.2 Documentation
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# 26.3. Using PL/Python

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[0]'
    LANGUAGE 'plpython';

gets transformed into

    def __plpython_procedure_myfunc_23456():
    	return args[0]

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[0]` contains whatever was passed in as the text argument. For `myfunc2(text, integer)`, `args[0]` would contain the `text` variable and `args[1]` 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"][0]` 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.error("msg")`, `plpy.fatal("msg")`, `plpy.debug("msg")`, and `plpy.notice("msg")`. They are mostly equivalent to calling `elog(`LEVEL`, "msg")`, where `LEVEL` is DEBUG, ERROR, FATAL or NOTICE. `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).

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