PostgreSQL 9.1beta2 Documentation
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# 42.7. Database Access

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``.

## 42.7.1. Database Access Functions

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"]

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.

Query parameters and result row fields are converted between PostgreSQL and Python data types as described in Section 42.3. 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 43) 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 42.4). 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;

## 42.7.2. Trapping Errors

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 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

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