PL/Python Python The PL/Python procedural language allows PostgreSQL functions to be written in the Python language. To install PL/Python in a particular database, use createlang plpythonu dbname. If a language is installed into template1, all subsequently created databases will have the language installed automatically. As of PostgreSQL 7.4, PL/Python is only available as an untrusted language (meaning it does not offer any way of restricting what users can do in it). It has therefore been renamed to plpythonu. The trusted variant plpython might become available again in future, if a new secure execution mechanism is developed in Python. 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. Functions in PL/Python are declared via the standard syntax: CREATE FUNCTION funcname (argument-list) RETURNS return-type AS $$ # PL/Python function body $$ LANGUAGE plpythonu; The body of a function is simply a Python script. When the function is called, its arguments are passed as elements of the array args[]; named arguments are also passed as ordinary variables to the Python script. The result is returned from the Python code in the usual way, with return or yield (in case of a result-set statement). For example, a function to return the greater of two integers can be defined as: CREATE FUNCTION pymax (a integer, b integer) RETURNS integer AS $$ if a > b: return a return b $$ LANGUAGE plpythonu; 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: def __plpython_procedure_pymax_23456(): if a > b: return a return b assuming that 23456 is the OID assigned to the function by PostgreSQL. The PostgreSQL function parameters are available in the global args list. In the pymax example, args[0] contains whatever was passed in as the first argument and args[1] contains the second argument's value. Alternatively, one can use named parameters as shown in the example above. Use of named parameters is usually more readable. If an SQL null valuenull valuePL/Python is passed to a function, the argument value will appear as None in Python. The above function definition will return the wrong answer for null inputs. We could add STRICT to the function definition to make PostgreSQL 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: CREATE FUNCTION pymax (a integer, b integer) RETURNS integer AS $$ if (a is None) or (b is None): return None if a > b: return a return b $$ LANGUAGE plpythonu; As shown above, to return an SQL null value from a PL/Python function, return the value None. This can be done whether the function is strict or not. 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 None in the mapping. Here is an example: CREATE TABLE employee ( name text, salary integer, age integer ); CREATE FUNCTION overpaid (e employee) RETURNS boolean AS $$ if e["salary"] > 200000: return True if (e["age"] < 30) and (e["salary"] > 100000): return True return False $$ LANGUAGE plpythonu; There are multiple ways to return row or composite types from a Python function. The following examples assume we have: CREATE TYPE named_value AS ( name text, value integer ); A composite result can be returned as a: Sequence type (a tuple or list, but not a set because it is not indexable) 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: CREATE FUNCTION make_pair (name text, value integer) RETURNS named_value AS $$ return [ name, value ] # or alternatively, as tuple: return ( name, value ) $$ LANGUAGE plpythonu; To return a SQL null for any column, insert None at the corresponding position. Mapping (dictionary) The value for each result type column is retrieved from the mapping with the column name as key. Example: CREATE FUNCTION make_pair (name text, value integer) RETURNS named_value AS $$ return { "name": name, "value": value } $$ LANGUAGE plpythonu; Any extra dictionary key/value pairs are ignored. Missing keys are treated as errors. To return a SQL null value for any column, insert None with the corresponding column name as the key. Object (any object providing method __getattr__) This works the same as a mapping. Example: 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; If you do not provide a return value, Python returns the default None. PL/Python translates Python's None into the SQL null value. A PL/Python 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: CREATE TYPE greeting AS ( how text, who text ); A set result can be returned from a: Sequence type (tuple, list, set) 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; Iterator (any object providing __iter__ and next methods) 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; Generator (yield) CREATE FUNCTION greet (how text) RETURNS SETOF greeting AS $$ for who in [ "World", "PostgreSQL", "PL/Python" ]: yield ( how, who ) $$ LANGUAGE plpythonu; Currently, due to Python bug #1483133, some debug versions of Python 2.4 (configured and compiled with option --with-pydebug) are known to crash the PostgreSQL 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. 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 session. Use with care.global datain PL/Python Each function gets its own execution environment 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. trigger in PL/Python When a function is used as a trigger, the dictionary TD contains trigger-related values. TD["event"] contains the event as a string (INSERT, UPDATE, DELETE, TRUNCATE, or UNKNOWN). TD["when"] contains one of BEFORE, AFTER, or UNKNOWN. TD["level"] contains one of ROW, STATEMENT, or UNKNOWN. For a row-level trigger, the trigger rows are in TD["new"] and/or TD["old"] depending on the trigger event. TD["name"] contains the trigger name, TD["table_name"] contains the name of the table on which the trigger occurred, TD["table_schema"] contains the schema of the table on which the trigger occurred, and TD["relid"] contains the OID of the table on which the trigger occurred. If the CREATE TRIGGER command included arguments, they are available in TD["args"][0] to TD["args"][n-1]. If TD["when"] is BEFORE and TD["level"] is ROW, you can return None or "OK" from the Python function to indicate the row is unmodified, "SKIP" to abort the event, or "MODIFY" to indicate you've modified the row. Otherwise the return value is ignored. 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).elogin PL/Python plpy.error and plpy.fatal actually raise a Python exception which, if uncaught, propagates out to the calling query, causing the current transaction or subtransaction to be aborted. raise plpy.ERROR(msg) and raise plpy.FATAL(msg) are equivalent to calling plpy.error and plpy.fatal, 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 and configuration variables. See for more information. 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 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. For example: 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"] preparing a queryin PL/Python The second function, plpy.prepare, 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: 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. When you prepare a plan using the PL/Python module it is automatically saved. Read the SPI documentation () 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 ). 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; The current version of PL/Python functions as a trusted language only; access to the file system and other local resources is disabled. Specifically, PL/Python uses the Python restricted execution environment, further restricts it to prevent the use of the file open call, and allows only modules from a specific list to be imported. Presently, that list includes: array, bisect, binascii, calendar, cmath, codecs, errno, marshal, math, md5, mpz, operator, pcre, pickle, random, re, regex, sre, sha, string, StringIO, struct, time, whrandom, and zlib. ]]>