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# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements. See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership. The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing,

# software distributed under the License is distributed on an

# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

# KIND, either express or implied. See the License for the

# specific language governing permissions and limitations

# under the License.

from uuid import UUID

import pyarrow as pa

import pyarrow.compute as pc

import pytest

from datafusion import SessionContext, column, udf

from datafusion import functions as f

@pytest.fixture

def df(ctx):

# create a RecordBatch and a new DataFrame from it

batch = pa.RecordBatch.from_arrays(

[pa.array([1, 2, 3]), pa.array([4, 4, None])],

names=["a", "b"],

)

return ctx.create_dataframe([[batch]], name="test_table")

def test_udf(df):

# is_null is a pa function over arrays

is_null = udf(

lambda x: x.is_null(),

[pa.int64()],

pa.bool_(),

volatility="immutable",

)

df = df.select(is_null(column("b")))

result = df.collect()[0].column(0)

assert result == pa.array([False, False, True])

def test_udf_decorator(df):

@udf([pa.int64()], pa.bool_(), "immutable")

def is_null(x: pa.Array) -> pa.Array:

return x.is_null()

df = df.select(is_null(column("b")))

result = df.collect()[0].column(0)

assert result == pa.array([False, False, True])

def test_register_udf(ctx, df) -> None:

is_null = udf(

lambda x: x.is_null(),

[pa.float64()],

pa.bool_(),

volatility="immutable",

name="is_null",

)

ctx.register_udf(is_null)

df_result = ctx.sql("select is_null(b) from test_table")

result = df_result.collect()[0].column(0)

assert result == pa.array([False, False, True])

class OverThresholdUDF:

def __init__(self, threshold: int = 0) -> None:

self.threshold = threshold

def __call__(self, values: pa.Array) -> pa.Array:

return pa.array(v.as_py() >= self.threshold for v in values)

def test_udf_with_parameters_function(df) -> None:

udf_no_param = udf(

OverThresholdUDF(),

pa.int64(),

pa.bool_(),

volatility="immutable",

)

df1 = df.select(udf_no_param(column("a")))

result = df1.collect()[0].column(0)

assert result == pa.array([True, True, True])

udf_with_param = udf(

OverThresholdUDF(2),

pa.int64(),

pa.bool_(),

volatility="immutable",

)

df2 = df.select(udf_with_param(column("a")))

result = df2.collect()[0].column(0)

assert result == pa.array([False, True, True])

def test_udf_with_parameters_decorator(df) -> None:

@udf([pa.int64()], pa.bool_(), "immutable")

def udf_no_param(values: pa.Array) -> pa.Array:

return OverThresholdUDF()(values)

df1 = df.select(udf_no_param(column("a")))

result = df1.collect()[0].column(0)

assert result == pa.array([True, True, True])

@udf([pa.int64()], pa.bool_(), "immutable")

def udf_with_param(values: pa.Array) -> pa.Array:

return OverThresholdUDF(2)(values)

df2 = df.select(udf_with_param(column("a")))

result = df2.collect()[0].column(0)

assert result == pa.array([False, True, True])

def test_udf_with_metadata(ctx) -> None:

@udf([pa.string()], pa.uuid(), "stable")

def uuid_from_string(uuid_string):

return pa.array((UUID(s).bytes for s in uuid_string.to_pylist()), pa.uuid())

@udf([pa.uuid()], pa.int64(), "stable")

def uuid_version(uuid):

return pa.array(s.version for s in uuid.to_pylist())

batch = pa.record_batch({"idx": pa.array(range(5))})

results = (

ctx.create_dataframe([[batch]])

.with_column("uuid_string", f.uuid())

.with_column("uuid", uuid_from_string(column("uuid_string")))

.select(uuid_version(column("uuid").alias("uuid_version")))

.collect()

)

assert results[0][0].to_pylist() == [4, 4, 4, 4, 4]

def test_udf_with_nullability(ctx: SessionContext) -> None:

field_nullable_i64 = pa.field("with_nulls", type=pa.int64(), nullable=True)

field_non_nullable_i64 = pa.field("no_nulls", type=pa.int64(), nullable=False)

@udf([field_nullable_i64], field_nullable_i64, "stable")

def nullable_abs(input_col):

return pc.abs(input_col)

@udf([field_non_nullable_i64], field_non_nullable_i64, "stable")

def non_nullable_abs(input_col):

return pc.abs(input_col)

batch = pa.record_batch(

{

"with_nulls": pa.array([-2, None, 0, 1, 2]),

"no_nulls": pa.array([-2, -1, 0, 1, 2]),

},

schema=pa.schema(

[

field_nullable_i64,

field_non_nullable_i64,

]

),

)

ctx.register_record_batches("t", [[batch]])

df = ctx.table("t")

# Input matches expected, nullable

df_result = df.select(nullable_abs(column("with_nulls")))

returned_field = df_result.schema().field(0)

assert returned_field.nullable

results = df_result.collect()

assert results[0][0].to_pylist() == [2, None, 0, 1, 2]

# Input coercible to expected, nullable

df_result = df.select(nullable_abs(column("no_nulls")))

returned_field = df_result.schema().field(0)

assert returned_field.nullable

results = df_result.collect()

assert results[0][0].to_pylist() == [2, 1, 0, 1, 2]

# Input matches expected, no nulls

df_result = df.select(non_nullable_abs(column("no_nulls")))

returned_field = df_result.schema().field(0)

assert not returned_field.nullable

results = df_result.collect()

assert results[0][0].to_pylist() == [2, 1, 0, 1, 2]

# Invalid - requires non-nullable input but that is not possible

df_result = df.select(non_nullable_abs(column("with_nulls")))

returned_field = df_result.schema().field(0)

assert not returned_field.nullable

with pytest.raises(Exception) as e_info:

_results = df_result.collect()

assert "Invalid argument error" in str(e_info)