databend-udf 0.2.11

Databend UDF Server

Usage

1. Define your functions in a Python file

from databend_udf import *

# Define a function
@udf(input_types=["VARCHAR", "VARCHAR", "VARCHAR"], result_type="VARCHAR")
def split_and_join(s: str, split_s: str, join_s: str) -> str:
    return join_s.join(s.split(split_s))

# Define a function that accepts nullable values, and set skip_null to True to enable it returns NULL if any argument is NULL.
@udf(
    input_types=["INT", "INT"],
    result_type="INT",
    skip_null=True,
)
def gcd(x: int, y: int) -> int:
    while y != 0:
        (x, y) = (y, x % y)
    return x

# Define a function that accepts nullable values, and set skip_null to False to enable it handles NULL values inside the function.
@udf(
    input_types=["ARRAY(INT64 NULL)", "INT64"],
    result_type="INT NOT NULL",
    skip_null=False,
)
def array_index_of(array: List[int], item: int):
    if array is None:
        return 0

    try:
        return array.index(item) + 1
    except ValueError:
        return 0

# Define a function which is IO bound, and set io_threads to enable it can be executed concurrently.
@udf(input_types=["INT"], result_type="INT", io_threads=32)
def wait_concurrent(x):
    # assume an IO operation cost 2s
    time.sleep(2)
    return x

# Define a table-valued function that emits multiple columns per row.
@udf(
    input_types=["INT", "INT"],
    result_type=[("left", "INT"), ("right", "INT"), ("sum", "INT")],
    batch_mode=True,
)
def expand_pairs(left: List[int], right: List[int]):
    if len(left) != len(right):
        raise ValueError("Inputs must have the same length")
    return [
        {"left": l, "right": r, "sum": l + r} for l, r in zip(left, right)
    ]

if __name__ == '__main__':
    # create a UDF server listening at '0.0.0.0:8815'
    server = UDFServer("0.0.0.0:8815")
    # add defined functions
    server.add_function(split_and_join)
    server.add_function(gcd)
    server.add_function(array_index_of)
    server.add_function(wait_concurrent)
    # start the UDF server
    server.serve()

@udf is an annotation for creating a UDF. It supports following parameters:

• input_types: A list of strings or Arrow data types that specifies the input data types.
• result_type: A string or an Arrow data type that specifies the return value type.
• name: An optional string specifying the function name. If not provided, the original name will be used.
• io_threads: Number of I/O threads used per data chunk for I/O bound functions.
• skip_null: A boolean value specifying whether to skip NULL value. If it is set to True, NULL values will not be passed to the function, and the corresponding return value is set to NULL. Default to False.

2. Start the UDF Server

Then we can Start the UDF Server by running:

python3 udf_server.py

3. Update Databend query node config

Now, udf server is disabled by default in databend. You can enable it by setting 'enable_udf_server = true' in query node config.

In addition, for security reasons, only the address specified in the config can be accessed by databend. The list of allowed udf server addresses are specified through the udf_server_allowlist variable in the query node config.

Here is an example config:

[query]
...
enable_udf_server = true
udf_server_allow_list = [ "http://0.0.0.0:8815", "http://example.com" ]

4. Add the functions to Databend

We can use the CREATE FUNCTION command to add the functions you defined to Databend:

CREATE FUNCTION [IF NOT EXISTS] <udf_name> (<arg_type>, ...) RETURNS <return_type> LANGUAGE <language> HANDLER=<handler> ADDRESS=<udf_server_address>

The udf_name is the name of UDF you declared in Databend. The handler is the function name you defined in the python UDF server.

For example:

CREATE FUNCTION split_and_join (VARCHAR, VARCHAR, VARCHAR) RETURNS VARCHAR LANGUAGE python HANDLER = 'split_and_join' ADDRESS = 'http://0.0.0.0:8815';

NOTE: The udf_server_address you specify must appear in udf_server_allow_list explained in the previous step.

In step 2, when you starting the UDF server, the corresponding sql statement of each function will be printed out. You can use them directly.

5. Use the functions in Databend

mysql> select split_and_join('3,5,7', ',', ':');
+-----------------------------------+
| split_and_join('3,5,7', ',', ':') |
+-----------------------------------+
| 3:5:7                             |
+-----------------------------------+

Data Types

The data types supported by the Python UDF API and their corresponding python types are as follows :

SQL Type	Python Type
BOOLEAN	bool
TINYINT (UNSIGNED)	int
SMALLINT (UNSIGNED)	int
INT (UNSIGNED)	int
BIGINT (UNSIGNED)	int
FLOAT	float
DOUBLE	float
DECIMAL	decimal.Decimal
DATE	datetime.date
TIMESTAMP	datetime.datetime
VARCHAR	str
BINARY	bytes
VARIANT	any
MAP(K,V)	dict
ARRAY(T)	list[T]
TUPLE(T...)	tuple(T...)

The NULL in sql is represented by None in Python.

Databend UDF Server Tests

# start UDF server
python3 examples/server.py

./target/debug/databend-sqllogictests --run_dir udf_server

Acknowledgement

Databend Python UDF Server API is inspired by RisingWave Python API.

Code Formatting

Use Ruff to keep the Python sources consistent:

python -m pip install ruff  # once
python -m ruff format python/databend_udf python/tests