Embedded Python UDFs

You can define embedded Python UDFs, which will be executed in an embedded Python interpreter within RisingWave. The Python code is directly included within the CREATE FUNCTION statement.

Currently, embedded Python UDFs only support pure computational logic and do not support accessing external networks or file systems. If you need to access external services or resources, you can use Python UDFs as external functions.

Changed in v2.1.5, v2.2.4, v2.3.0: Creation of embedded Python UDFs is disabled by default. See UDF configurations for more information.

Define your functions

You can create Python UDFs using the CREATE FUNCTION command. Refer to the syntax below:

CREATE FUNCTION function_name (arg_name arg_type [, ...])
    [RETURNS return_type | RETURNS TABLE (column_name column_type [, ...])]
    LANGUAGE python
    AS [$$ function_body $$ | 'function_body'];

For example, the scalar function gcd can be defined as follows:

Create function

CREATE FUNCTION gcd(a int, b int) RETURNS int LANGUAGE python AS $$
def gcd(a, b):
    while b != 0:
        a, b = b, a % b
    return a
$$;

The Python code must contain a function that has the same name as declared in the CREATE FUNCTION statement. The function’s parameters and return type must match those declared in the CREATE FUNCTION statement, otherwise, an error may occur when the function is called.

See the correspondence between SQL types and Python types in the Data type mapping.

Due to the nature of Python, the correctness of the source code cannot be verified when creating a function. It is recommended to make sure your implementation is correct through batch query before using UDFs in materialized views. If an error occurs when executing UDF in materialized views, all output results will be NULL.

Call function

SELECT gcd(15, 25);

-----RESULT
   5
(1 row)

For table functions, your function needs to return an iterator using the yield statement. For example, to generate a sequence from 0 to n-1:

Create function

CREATE FUNCTION series(n int) RETURNS TABLE (x int) LANGUAGE python AS $$
def series(n):
    for i in range(n):
        yield i
$$;

Call function

SELECT * FROM series(5);

-----RESULT
 0
 1
 2
 3
 4
(5 rows)

If your function returns structured types, the Python function should return an object or dictionary containing structured data. For example, to parse key-value pairs in a string, both of the following implementations work:

Create function

CREATE FUNCTION key_value(varchar) RETURNS STRUCT<key varchar, value varchar> LANGUAGE python AS $$
def key_value(s: str):
    key, value = s.split('=')
    return {'key': key, 'value': value}
$$;

Create function

CREATE FUNCTION key_value(varchar) RETURNS STRUCT<key varchar, value varchar> LANGUAGE python AS $$
class KeyValue:
    def __init__(self, key, value):
        self.key = key
        self.value = value

def key_value(s: str):
    key, value = s.split('=')
    return KeyValue(key, value)
$$;

Define your aggregate functions

You can create aggregate functions using the CREATE AGGREGATE command. Refer to the syntax below:

CREATE AGGREGATE function_name ( argument_type [, ...] )
    RETURNS return_type
    LANGUAGE python
    AS $$ function_body $$

In the function_body, the code should define several functions to implement the aggregate function.

Required functions:

• create_state() -> state: Create a new state.
• accumulate(state, *args) -> state: Accumulate a new value into the state, returning the updated state.

Optional functions:

• finish(state) -> value: Get the result of the aggregate function. If not defined, the state is returned as the result.
• retract(state, *args) -> state: Retract a value from the state, returning the updated state. If not defined, the state can not be updated incrementally in materialized views and performance may be affected.

The following command creates an aggregate function named weighted_avg to calculate the weighted average.

Python UDAF

create aggregate weighted_avg(value int, weight int) returns float language python as $$
def create_state():
    return (0, 0)

def accumulate(state, value, weight):
    if value is None or weight is None:
        return state
    (s, w) = state
    s += value * weight
    w += weight
    return (s, w)

def retract(state, value, weight):
    if value is None or weight is None:
        return state
    (s, w) = state
    s -= value * weight
    w -= weight
    return (s, w)

def finish(state):
    (sum, weight) = state
    if weight == 0:
        return None
    else:
        return sum / weight
$$;

Limitations

Currently, embedded Python UDFs are only allowed to use the following standard libraries: json, decimal, re, math, datetime. Other third-party libraries are not supported. Embedded Python UDFs cannot access external resources, and the following built-in functions are also not allowed: breakpoint, exit, eval, help, input, open, print.

Data type mapping

The following table shows the data type mapping between SQL and Python:

SQL Type	Python Type	Notes
BOOLEAN	bool
SMALLINT	int
INT	int
BIGINT	int
REAL	float
DOUBLE PRECISION	float
DECIMAL	decimal.Decimal
DATE	datetime.date	Not supported yet.
TIME	datetime.time	Not supported yet.
TIMESTAMP	datetime.datetime	Not supported yet.
TIMESTAMPTZ	datetime.datetime	Not supported yet.
INTERVAL	MonthDayNano / (int, int, int)	Not supported yet.
VARCHAR	str
BYTEA	bytes
JSONB	bool, int, float, list, dict
T[]	list[T]
STRUCT<>	class or dict
…others	Not supported yet.