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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.

import datafusion

import pyarrow as pa

import pyarrow.compute

from datafusion import Accumulator, col, udaf

class MyAccumulator(Accumulator):

"""

Interface of a user-defined accumulation.

"""

def __init__(self) -> None:

self._sum = pa.scalar(0.0)

def update(self, values: pa.Array) -> None:

# not nice since pyarrow scalars can't be summed yet. This breaks on `None`

self._sum = pa.scalar(self._sum.as_py() + pa.compute.sum(values).as_py())

def merge(self, states: pa.Array) -> None:

# not nice since pyarrow scalars can't be summed yet. This breaks on `None`

self._sum = pa.scalar(self._sum.as_py() + pa.compute.sum(states).as_py())

def state(self) -> pa.Array:

return pa.array([self._sum.as_py()])

def evaluate(self) -> pa.Scalar:

return self._sum

# create a context

ctx = datafusion.SessionContext()

# create a RecordBatch and a new DataFrame from it

batch = pa.RecordBatch.from_arrays(

[pa.array([1, 2, 3]), pa.array([4, 5, 6])],

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

)

df = ctx.create_dataframe([[batch]])

my_udaf = udaf(

MyAccumulator,

pa.float64(),

[pa.float64()],

"stable",

)

df = df.aggregate([], [my_udaf(col("a"))])

result = df.collect()[0]

assert result.column(0) == pa.array([6.0])