PL/Python Python The PL/Python procedural language allows PostgreSQL functions and procedures to be written in the Python language. To install PL/Python in a particular database, use CREATE EXTENSION plpython3u. If a language is installed into template1, all subsequently created databases will have the language installed automatically. PL/Python is only available as an untrusted language, meaning it does not offer any way of restricting what users can do in it and is therefore named plpython3u. A trusted variant plpython might become available in the future if a secure execution mechanism is developed in Python. The writer of a function in untrusted PL/Python must take care that the function cannot be used to do anything unwanted, since it will be able to do anything that could be done by a user logged in as the database administrator. Only superusers can create functions in untrusted languages such as plpython3u. 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 plpython3u; The body of a function is simply a Python script. When the function is called, its arguments are passed as elements of the list args; named arguments are also passed as ordinary variables to the Python script. Use of named arguments is usually more readable. The result is returned from the Python code in the usual way, with return or yield (in case of a result-set statement). If you do not provide a return value, Python returns the default None. PL/Python translates Python's None into the SQL null value. In a procedure, the result from the Python code must be None (typically achieved by ending the procedure without a return statement or by using a return statement without argument); otherwise, an error will be raised. 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 plpython3u; 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 arguments are set as global variables. Because of the scoping rules of Python, this has the subtle consequence that an argument variable cannot be reassigned inside the function to the value of an expression that involves the variable name itself, unless the variable is redeclared as global in the block. For example, the following won't work: CREATE FUNCTION pystrip(x text) RETURNS text AS $$ x = x.strip() # error return x $$ LANGUAGE plpython3u; because assigning to x makes x a local variable for the entire block, and so the x on the right-hand side of the assignment refers to a not-yet-assigned local variable x, not the PL/Python function parameter. Using the global statement, this can be made to work: CREATE FUNCTION pystrip(x text) RETURNS text AS $$ global x x = x.strip() # ok now return x $$ LANGUAGE plpython3u; But it is advisable not to rely on this implementation detail of PL/Python. It is better to treat the function parameters as read-only. Generally speaking, the aim of PL/Python is to provide a natural mapping between the PostgreSQL and the Python worlds. This informs the data mapping rules described below. When a PL/Python function is called, its arguments are converted from their PostgreSQL data type to a corresponding Python type: PostgreSQL boolean is converted to Python bool. PostgreSQL smallint, int, bigint and oid are converted to Python int. PostgreSQL real and double are converted to Python float. PostgreSQL numeric is converted to Python Decimal. This type is imported from the cdecimal package if that is available. Otherwise, decimal.Decimal from the standard library will be used. cdecimal is significantly faster than decimal. In Python 3.3 and up, however, cdecimal has been integrated into the standard library under the name decimal, so there is no longer any difference. PostgreSQL bytea is converted to Python bytes. All other data types, including the PostgreSQL character string types, are converted to a Python str (in Unicode like all Python strings). For nonscalar data types, see below. When a PL/Python function returns, its return value is converted to the function's declared PostgreSQL return data type as follows: When the PostgreSQL return type is boolean, the return value will be evaluated for truth according to the Python rules. That is, 0 and empty string are false, but notably 'f' is true. When the PostgreSQL return type is bytea, the return value will be converted to Python bytes using the respective Python built-ins, with the result being converted to bytea. For all other PostgreSQL return types, the return value is converted to a string using the Python built-in str, and the result is passed to the input function of the PostgreSQL data type. (If the Python value is a float, it is converted using the repr built-in instead of str, to avoid loss of precision.) Strings are automatically converted to the PostgreSQL server encoding when they are passed to PostgreSQL. For nonscalar data types, see below. Note that logical mismatches between the declared PostgreSQL return type and the Python data type of the actual return object are not flagged; the value will be converted in any case. If an SQL null valuenull valuein PL/Python is passed to a function, the argument value will appear as None in Python. For example, the function definition of pymax shown in 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 plpython3u; 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. SQL array values are passed into PL/Python as a Python list. To return an SQL array value out of a PL/Python function, return a Python list: CREATE FUNCTION return_arr() RETURNS int[] AS $$ return [1, 2, 3, 4, 5] $$ LANGUAGE plpython3u; SELECT return_arr(); return_arr ------------- {1,2,3,4,5} (1 row) Multidimensional arrays are passed into PL/Python as nested Python lists. A 2-dimensional array is a list of lists, for example. When returning a multi-dimensional SQL array out of a PL/Python function, the inner lists at each level must all be of the same size. For example: CREATE FUNCTION test_type_conversion_array_int4(x int4[]) RETURNS int4[] AS $$ plpy.info(x, type(x)) return x $$ LANGUAGE plpython3u; SELECT * FROM test_type_conversion_array_int4(ARRAY[[1,2,3],[4,5,6]]); INFO: ([[1, 2, 3], [4, 5, 6]], <type 'list'>) test_type_conversion_array_int4 --------------------------------- {{1,2,3},{4,5,6}} (1 row) Other Python sequences, like tuples, are also accepted for backwards-compatibility with PostgreSQL versions 9.6 and below, when multi-dimensional arrays were not supported. However, they are always treated as one-dimensional arrays, because they are ambiguous with composite types. For the same reason, when a composite type is used in a multi-dimensional array, it must be represented by a tuple, rather than a list. Note that in Python, strings are sequences, which can have undesirable effects that might be familiar to Python programmers: CREATE FUNCTION return_str_arr() RETURNS varchar[] AS $$ return "hello" $$ LANGUAGE plpython3u; SELECT return_str_arr(); return_str_arr ---------------- {h,e,l,l,o} (1 row) 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 plpython3u; 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 $$ return ( name, value ) # or alternatively, as list: return [ name, value ] $$ LANGUAGE plpython3u; To return an SQL null for any column, insert None at the corresponding position. When an array of composite types is returned, it cannot be returned as a list, because it is ambiguous whether the Python list represents a composite type, or another array dimension. 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 plpython3u; Any extra dictionary key/value pairs are ignored. Missing keys are treated as errors. To return an 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 plpython3u; Functions with OUT parameters are also supported. For example: CREATE FUNCTION multiout_simple(OUT i integer, OUT j integer) AS $$ return (1, 2) $$ LANGUAGE plpython3u; SELECT * FROM multiout_simple(); Output parameters of procedures are passed back the same way. For example: CREATE PROCEDURE python_triple(INOUT a integer, INOUT b integer) AS $$ return (a * 3, b * 3) $$ LANGUAGE plpython3u; CALL python_triple(5, 10); 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 plpython3u; 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 plpython3u; Generator (yield) CREATE FUNCTION greet (how text) RETURNS SETOF greeting AS $$ for who in [ "World", "PostgreSQL", "PL/Python" ]: yield ( how, who ) $$ LANGUAGE plpython3u; Set-returning functions with OUT parameters (using RETURNS SETOF record) are also supported. For example: CREATE FUNCTION multiout_simple_setof(n integer, OUT integer, OUT integer) RETURNS SETOF record AS $$ return [(1, 2)] * n $$ LANGUAGE plpython3u; SELECT * FROM multiout_simple_setof(3); The global dictionary SD is available to store private data between repeated calls to the same function. The global dictionary GD is public data, that is available to all Python functions within a session; use with care.global data in 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. PL/Python also supports anonymous code blocks called with the statement: DO $$ # PL/Python code $$ LANGUAGE plpython3u; An anonymous code block receives no arguments, and whatever value it might return is discarded. Otherwise it behaves just like a function. 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, or TRUNCATE. TD["when"] contains one of BEFORE, AFTER, or INSTEAD OF. TD["level"] contains ROW or STATEMENT. TD["new"] TD["old"] For a row-level trigger, one or both of these fields contain the respective trigger rows, 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. TD["relid"] contains the OID of the table on which the trigger occurred. TD["args"] If the CREATE TRIGGER command included arguments, they are available in TD["args"][0] to TD["args"][n-1]. If TD["when"] is BEFORE or INSTEAD OF 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 if TD["event"] is INSERT or UPDATE you can return "MODIFY" to indicate you've modified the new 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. The plpy module provides several functions to execute database commands: plpy.execute(query [, limit]) Calling plpy.execute 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. If limit is specified and is greater than zero, then plpy.execute retrieves at most limit rows, much as if the query included a LIMIT clause. Omitting limit or specifying it as zero results in no row limit. The result object emulates a list or dictionary object. The result object can be accessed by row number and column name. 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"] The number of rows returned can be obtained using the built-in len function. The result object has these additional methods: nrows() 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 UPDATE command will set this value but won't return any rows (unless RETURNING is used). status() The SPI_execute() return value. colnames() coltypes() coltypmods() Return a list of column names, list of column type OIDs, and list of type-specific type modifiers for the columns, respectively. These methods raise an exception when called on a result object from a command that did not produce a result set, e.g., UPDATE without RETURNING, or DROP TABLE. But it is OK to use these methods on a result set containing zero rows. __str__() The standard __str__ method is defined so that it is possible for example to debug query execution results using plpy.debug(rv). The result object can be modified. Note that calling plpy.execute 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 plpy.cursor rather than plpy.execute. plpy.prepare(query [, argtypes]) plpy.execute(plan [, arguments [, limit]]) preparing a queryin PL/Python 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. The second argument is optional if you don't want to pass any parameters to the query. After preparing a statement, you use a variant of the function plpy.execute to run it: rv = plpy.execute(plan, ["name"], 5) 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. Alternatively, you can call the execute method on the plan object: rv = plan.execute(["name"], 5) Query parameters and result row fields are converted between PostgreSQL and Python data types as described in . 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 $$ if "plan" in SD: plan = SD["plan"] else: plan = plpy.prepare("SELECT 1") SD["plan"] = plan # rest of function $$ LANGUAGE plpython3u; plpy.cursor(query) plpy.cursor(plan [, arguments]) The plpy.cursor function accepts the same arguments as plpy.execute (except for the row limit) and returns a cursor object, which allows you to process large result sets in smaller chunks. As with plpy.execute, either a query string or a plan object along with a list of arguments can be used, or the cursor function can be called as a method of the plan object. The cursor object provides a fetch method that accepts an integer parameter and returns a result object. Each time you call fetch, the returned object will contain the next batch of rows, never larger than the parameter value. Once all rows are exhausted, fetch starts returning an empty result object. Cursor objects also provide an iterator interface, 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. An example of two ways of processing data from a large table is: 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 plpython3u; 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 plpython3u; CREATE FUNCTION count_odd_prepared() RETURNS integer AS $$ odd = 0 plan = plpy.prepare("select num from largetable where num % $1 <> 0", ["integer"]) rows = list(plpy.cursor(plan, [2])) # or: = list(plan.cursor([2])) return len(rows) $$ LANGUAGE plpython3u; Cursors are automatically disposed of. But if you want to explicitly release all resources held by a cursor, use the close method. Once closed, a cursor cannot be fetched from anymore. Do not confuse objects created by plpy.cursor with DB-API cursors as defined by the Python Database API specification. They don't have anything in common except for the name. Functions accessing the database might encounter errors, which will cause them to abort and raise an exception. Both plpy.execute and plpy.prepare can raise an instance of a subclass of plpy.SPIError, which by default will terminate the function. This error can be handled just like any other Python exception, by using the try/except construct. For example: 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 plpython3u; The actual class of the exception being raised corresponds to the specific condition that caused the error. Refer to for a list of possible conditions. The module plpy.spiexceptions defines an exception class for each PostgreSQL condition, deriving their names from the condition name. For instance, division_by_zero becomes DivisionByZero, unique_violation becomes UniqueViolation, fdw_error becomes FdwError, and so on. Each of these exception classes inherits from SPIError. This separation makes it easier to handle specific errors, for instance: CREATE FUNCTION insert_fraction(numerator int, denominator int) RETURNS text AS $$ from plpy import spiexceptions try: plan = plpy.prepare("INSERT INTO fractions (frac) VALUES ($1 / $2)", ["int", "int"]) plpy.execute(plan, [numerator, denominator]) except spiexceptions.DivisionByZero: return "denominator cannot equal zero" except spiexceptions.UniqueViolation: return "already have that fraction" except plpy.SPIError as e: return "other error, SQLSTATE %s" % e.sqlstate else: return "fraction inserted" $$ LANGUAGE plpython3u; Note that because all exceptions from the plpy.spiexceptions module inherit from SPIError, an except clause handling it will catch any database access error. As an alternative way of handling different error conditions, you can catch the SPIError exception and determine the specific error condition inside the except block by looking at the sqlstate attribute of the exception object. This attribute is a string value containing the SQLSTATE error code. This approach provides approximately the same functionality Recovering from errors caused by database access as described in can lead to an undesirable situation where some operations succeed before one of them fails, and after recovering from that error the data is left in an inconsistent state. PL/Python offers a solution to this problem in the form of explicit subtransactions. Consider a function that implements a transfer between two accounts: CREATE FUNCTION transfer_funds() RETURNS void AS $$ try: plpy.execute("UPDATE accounts SET balance = balance - 100 WHERE account_name = 'joe'") plpy.execute("UPDATE accounts SET balance = balance + 100 WHERE account_name = 'mary'") except plpy.SPIError as e: result = "error transferring funds: %s" % e.args else: result = "funds transferred correctly" plan = plpy.prepare("INSERT INTO operations (result) VALUES ($1)", ["text"]) plpy.execute(plan, [result]) $$ LANGUAGE plpython3u; If the second UPDATE statement results in an exception being raised, this function will report the error, but the result of the first UPDATE will nevertheless be committed. In other words, the funds will be withdrawn from Joe's account, but will not be transferred to Mary's account. To avoid such issues, you can wrap your plpy.execute calls in an explicit subtransaction. The plpy module provides a helper object to manage explicit subtransactions that gets created with the plpy.subtransaction() function. Objects created by this function implement the context manager interface. Using explicit subtransactions we can rewrite our function as: CREATE FUNCTION transfer_funds2() RETURNS void AS $$ try: with plpy.subtransaction(): plpy.execute("UPDATE accounts SET balance = balance - 100 WHERE account_name = 'joe'") plpy.execute("UPDATE accounts SET balance = balance + 100 WHERE account_name = 'mary'") except plpy.SPIError as e: result = "error transferring funds: %s" % e.args else: result = "funds transferred correctly" plan = plpy.prepare("INSERT INTO operations (result) VALUES ($1)", ["text"]) plpy.execute(plan, [result]) $$ LANGUAGE plpython3u; Note that the use of try/catch is still required. Otherwise the exception would propagate to the top of the Python stack and would cause the whole function to abort with a PostgreSQL error, so that the operations table would not have any row inserted into it. The subtransaction context manager does not trap errors, it only assures that all database operations executed inside its scope will be atomically committed or rolled back. A rollback of the subtransaction block occurs on any kind of exception exit, not only ones caused by errors originating from database access. A regular Python exception raised inside an explicit subtransaction block would also cause the subtransaction to be rolled back. In a procedure called from the top level or an anonymous code block (DO command) called from the top level it is possible to control transactions. To commit the current transaction, call plpy.commit(). To roll back the current transaction, call plpy.rollback(). (Note that it is not possible to run the SQL commands COMMIT or ROLLBACK via plpy.execute or similar. It has to be done using these functions.) After a transaction is ended, a new transaction is automatically started, so there is no separate function for that. Here is an example: CREATE PROCEDURE transaction_test1() LANGUAGE plpython3u AS $$ for i in range(0, 10): plpy.execute("INSERT INTO test1 (a) VALUES (%d)" % i) if i % 2 == 0: plpy.commit() else: plpy.rollback() $$; CALL transaction_test1(); Transactions cannot be ended when an explicit subtransaction is active. The plpy module also provides the functions plpy.debug(msg, **kwargs) plpy.log(msg, **kwargs) plpy.info(msg, **kwargs) plpy.notice(msg, **kwargs) plpy.warning(msg, **kwargs) plpy.error(msg, **kwargs) plpy.fatal(msg, **kwargs) 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(msg) and plpy.fatal(msg), respectively but the raise form does not allow passing keyword arguments. 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. The msg argument is given as a positional argument. For backward compatibility, more than one positional argument can be given. In that case, the string representation of the tuple of positional arguments becomes the message reported to the client. The following keyword-only arguments are accepted: detail hint sqlstate schema_name table_name column_name datatype_name constraint_name The string representation of the objects passed as keyword-only arguments is used to enrich the messages reported to the client. For example: CREATE FUNCTION raise_custom_exception() RETURNS void AS $$ plpy.error("custom exception message", detail="some info about exception", hint="hint for users") $$ LANGUAGE plpython3u; =# SELECT raise_custom_exception(); ERROR: plpy.Error: custom exception message DETAIL: some info about exception HINT: hint for users CONTEXT: Traceback (most recent call last): PL/Python function "raise_custom_exception", line 4, in <module> hint="hint for users") PL/Python function "raise_custom_exception" Another set of utility functions are plpy.quote_literal(string), plpy.quote_nullable(string), and plpy.quote_ident(string). They are equivalent to the built-in quoting functions described in . They are useful when constructing ad-hoc queries. A PL/Python equivalent of dynamic SQL from would be: plpy.execute("UPDATE tbl SET %s = %s WHERE key = %s" % ( plpy.quote_ident(colname), plpy.quote_nullable(newvalue), plpy.quote_literal(keyvalue))) PL/Python supports only Python 3. Past versions of PostgreSQL supported Python 2, using the plpythonu and plpython2u language names. Some of the environment variables that are accepted by the Python interpreter can also be used to affect PL/Python behavior. They would need to be set in the environment of the main PostgreSQL server process, for example in a start script. The available environment variables depend on the version of Python; see the Python documentation for details. At the time of this writing, the following environment variables have an affect on PL/Python, assuming an adequate Python version: PYTHONHOME PYTHONPATH PYTHONY2K PYTHONOPTIMIZE PYTHONDEBUG PYTHONVERBOSE PYTHONCASEOK PYTHONDONTWRITEBYTECODE PYTHONIOENCODING PYTHONUSERBASE PYTHONHASHSEED (It appears to be a Python implementation detail beyond the control of PL/Python that some of the environment variables listed on the python man page are only effective in a command-line interpreter and not an embedded Python interpreter.)