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
[, max-rows
])
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.
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
[, max-rows
]])
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 Section 46.2.
When you prepare a plan using the PL/Python module it is automatically
saved. Read the SPI documentation (Chapter 47) 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 46.3). 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 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 $$ from plpy import spiexceptions try: plan = plpy.prepare("INSERT INTO fractions (frac) VALUES ($1 / $2)", ["int", "int"]) plpy.execute(plan, [numerator, denominator]) except spiexceptions.DivisionByZero: return "denominator cannot equal zero" except spiexceptions.UniqueViolation: return "already have that fraction" except plpy.SPIError as e: return "other error, SQLSTATE %s" % e.sqlstate else: return "fraction inserted" $$ 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