CREATE FUNCTION

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Creates a user-defined function (UDF) that extends Firebolt’s SQL capabilities with custom Python logic. UDFs allow you to encapsulate complex business logic, data transformations, or calculations that can be reused across queries. User-defined functions execute Python code within a secure, isolated environment. Each function invocation processes data row-by-row by default, or in batches when using vectorized execution with Pandas or PyArrow.

Syntax

CREATE FUNCTION <function_name>([<parameter_name> <data_type>, ...])
RETURNS <return_type>
LANGUAGE PYTHON
[PACKAGES ['<package>==<version>', ...]]
[SET <option>=<value>, ...]
AS '<python_code>'

You can also use dollar-quoted strings for the Python code to avoid escaping single quotes:

CREATE FUNCTION <function_name>([<parameter_name> <data_type>, ...])
RETURNS <return_type>
LANGUAGE PYTHON
[PACKAGES ['<package>[==<version>]', ...]]
[SET <option>=<value>, ...]
AS $$
<python_code>
$$;

Parameters

Parameter	Description
`<function_name>`	A unique identifier for the function. Function names are case-insensitive and cannot match existing built-in function names.
`<parameter_name>`	The name of a function parameter. Parameters are passed to the Python function by name.
`<data_type>`	The SQL data type of the parameter. See Supported data types for the list of supported types.
`<return_type>`	The SQL data type returned by the function. Must be one of the supported data types.
`LANGUAGE PYTHON`	Specifies Python as the implementation language. Currently, only Python is supported.
`PACKAGES`	An optional array of pip packages to install, specified as `'<package>[==<version>]'`. For example: `PACKAGES ['numpy', 'pandas==2.2.3']`.
`SET <option>=<value>`	Optional configuration settings. See Configuration options for available options.
`<python_code>`	The Python code implementing the function. Must contain either a single function or a function named `process`.

Configuration options

Option	Description
`memory_limit_mb`	Maximum memory in megabytes allocated to the UDF worker. Default is 1024 MB (1 GB).
`memory_limit`	Maximum memory in bytes allocated to the UDF worker. Cannot be used together with `memory_limit_mb`.
`description`	A text description of the function, visible in `information_schema.routines`.

Supported data types

The following SQL data types are supported for UDF parameters and return values:

SQL Type	Python Type	Notes
`INT` / `INTEGER`	`int`	32-bit signed integer
`BIGINT`	`int`	64-bit signed integer
`REAL`	`float`	32-bit floating point
`DOUBLE` / `DOUBLE PRECISION`	`float`	64-bit floating point
`DECIMAL(p, s)`	`decimal.Decimal`	Fixed-point decimal
`BOOLEAN`	`bool`	True or False
`TEXT`	`str`	Variable-length string
`BYTEA`	`bytes`	Binary data
`DATE`	`datetime.date`	Calendar date
`TIMESTAMP`	`datetime.datetime`	Date and time without timezone
`TIMESTAMPTZ`	`datetime.datetime`	Date and time with timezone
`ARRAY(<type>)`	`list`	Array of any supported type, including nested arrays

The following types are not supported in UDFs: GEOGRAPHY, STRUCT.

Python code requirements

The Python code must contain either a single function definition or a function named process. This function receives the SQL parameters as Python arguments and returns the result.

def process(param1, param2):
    # Your logic here
    return result

Key requirements:

Function naming: If your code contains multiple functions, the entry point must be named process. If there is only one function, it can have any name.
Parameter names: Python parameter names do not need to match SQL parameter names, but they are passed positionally in the order defined.
Return value: Return the computed value directly. Return None to represent SQL NULL.
NULL handling: NULL SQL values are passed as Python None. Your code should handle None values appropriately.

Examples

Basic arithmetic function

The following example creates a function that adds two integers:

CREATE FUNCTION add_numbers(a INT, b INT) RETURNS INT
LANGUAGE PYTHON
AS $$
def process(a, b):
    if a is None or b is None:
        return None
    return a + b
$$;

Use the function in a query:

SELECT add_numbers(5, 3);

Returns: 8

Function with no parameters

CREATE FUNCTION get_constant() RETURNS INT
LANGUAGE PYTHON
AS $$
def process():
    return 42
$$;

Using external packages

The following example uses NumPy to compute the percentile of an array:

CREATE FUNCTION array_percentile(arr ARRAY(DOUBLE), percentile DOUBLE) RETURNS DOUBLE
LANGUAGE PYTHON
PACKAGES ['numpy']
AS $$
import numpy as np

def process(arr, percentile):
    if arr is None or percentile is None or len(arr) == 0:
        return None
    return float(np.percentile(arr, percentile))
$$;

Use the function to find the median (50th percentile) of an array:

SELECT array_percentile([10.5, 20.3, 15.7, 8.2, 12.9], 50.0);

Returns: 12.9

Working with dates

CREATE FUNCTION add_days(d DATE, n INT) RETURNS DATE
LANGUAGE PYTHON
AS $$
from datetime import timedelta

def process(d, n):
    if d is None or n is None:
        return None
    return d + timedelta(days=int(n))
$$;

Use the function to add days to a date:

SELECT add_days('2026-01-01', 32);

Returns: 2026-02-02

Setting memory limit and description

CREATE FUNCTION complex_calculation(a INT, b INT) RETURNS INT
LANGUAGE PYTHON
SET memory_limit_mb=2048, description='Performs a complex calculation'
AS $$
def process(a, b):
    return a * b + 100
$$;

SELECT complex_calculation(1, 2);

Vectorized UDFs

For improved performance when processing large datasets, you can write vectorized UDFs that process data in batches using PyArrow or Pandas. Vectorized UDFs receive entire columns of data at once instead of individual values. Unlike scalar UDFs, the Python function in a vectorized UDF must always have a single argument annotated with type pandas.DataFrame or pyarrow.RecordBatch. This type annotation is what distinguishes vectorized UDFs from scalar UDFs.

Pandas vectorized UDF

A Pandas vectorized UDF receives a pandas.DataFrame and must return a pandas.Series: Column names and positions in the DataFrame match the parameter names and order from the SQL function declaration. You can access columns by name (e.g., df['param_name']) or by position (e.g., df.iloc[:, 0]), where position 0 corresponds to the first parameter, position 1 to the second, and so on.

CREATE FUNCTION vec_uppercase(s TEXT) RETURNS TEXT
LANGUAGE PYTHON
PACKAGES ['pandas']
AS $$
import pandas as pd

def vec_uppercase(df: pd.DataFrame) -> pd.Series:
    return df['s'].str.upper()
$$;

SELECT vec_uppercase('aBc') FROM GENERATE_SERIES(0, 1);

PyArrow vectorized UDF

A PyArrow vectorized UDF receives a pyarrow.RecordBatch and must return a pyarrow.RecordBatch:

CREATE FUNCTION vec_add_one(a INT) RETURNS INT
LANGUAGE PYTHON
AS $$
import pyarrow as pa
import pyarrow.compute as pc

def process(batch: pa.RecordBatch) -> pa.RecordBatch:
    col = batch.column(0)
    result = pc.add(col, pa.scalar(1, type=col.type))
    return pa.RecordBatch.from_arrays([result], names=[batch.schema.names[0]])
$$;

SELECT vec_add_one(1) FROM GENERATE_SERIES(0, 1);

Vectorized UDFs automatically handle NULL values according to the library’s conventions.

Viewing user-defined functions

Query information_schema.routines to view all user-defined functions:

SELECT 
    routine_name,
    routine_schema,
    routine_parameters,
    data_type,
    description,
    memory_limit,
    created
FROM information_schema.routines 
WHERE routine_type = 'USER-DEFINED FUNCTION'
ORDER BY routine_name;

Limitations

Language: Only Python is supported. Other languages such as JavaScript or WebAssembly are not available.
Unsupported types: GEOGRAPHY and STRUCT types cannot be used as parameters or return types.
Schema restriction: User-defined functions can only be created in the public schema. Custom schemas are not supported.
Name conflicts: Function names cannot match existing built-in function names.
Overloading: Function overloading (multiple functions with the same name but different signatures) is not supported. Each function name must be unique.
Package availability: Only packages available on PyPI can be installed. Private or custom packages are not supported.
Execution context: UDFs run in isolated containers and cannot access external network resources, the file system, or other system resources.

What is Firebolt?

Overview

Performance and Observability

Guides

SQL reference

General reference

API reference

Syntax

Parameters

Configuration options

Supported data types

Python code requirements

Examples

Basic arithmetic function

Function with no parameters

Using external packages

Working with dates

Setting memory limit and description

Vectorized UDFs

Pandas vectorized UDF

PyArrow vectorized UDF

Viewing user-defined functions

Limitations

What is Firebolt?

Overview

Performance and Observability

Guides

SQL reference

General reference

API reference

Documentation Index

​Syntax

​Parameters

​Configuration options

​Supported data types

​Python code requirements

​Examples

​Basic arithmetic function

​Function with no parameters

​Using external packages

​Working with dates

​Setting memory limit and description

​Vectorized UDFs

​Pandas vectorized UDF

​PyArrow vectorized UDF

​Viewing user-defined functions

​Limitations

Syntax

Parameters

Configuration options

Supported data types

Python code requirements

Examples

Basic arithmetic function

Function with no parameters

Using external packages

Working with dates

Setting memory limit and description

Vectorized UDFs

Pandas vectorized UDF

PyArrow vectorized UDF

Viewing user-defined functions

Limitations