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:
execute(query [, max-rows])
plpy.executewith 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.
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
The result object has these additional methods:
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).
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.
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.
prepare(query [, argtypes])
execute(plan [, arguments [, max-rows]])
plpy.prepareprepares 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.executeto 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.
Query parameters and result row fields are converted between PostgreSQL and Python data types as described in Section 43.3. 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 44) 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 43.4). 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 plpythonu;
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.
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 plpythonu; 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 plpythonu; CREATE FUNCTION count_odd_prepared() RETURNS integer AS $$ odd = 0 plan = plpy.prepare("select num from largetable where num % $1 <> 0", ["integer"]) rows = list(plpy.cursor(plan, )) return len(rows) $$ LANGUAGE plpythonu;
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.
Tip: 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.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 plpythonu;
The actual class of the exception being raised corresponds to the specific condition that caused the error. Refer to Table A-1 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 $$ from plpy import spiexceptions try: plan = plpy.prepare("INSERT INTO fractions (frac) VALUES ($1 / $2)", ["int", "int"]) plpy.execute(plan, [numerator, denominator]) except spiexceptions.DivisionByZero: return "denominator cannot equal zero" except spiexceptions.UniqueViolation: return "already have that fraction" except plpy.SPIError, e: return "other error, SQLSTATE %s" % e.sqlstate else: return "fraction inserted" $$ LANGUAGE plpythonu;
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