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# 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 pyarrow as pa

from datafusion import Accumulator, SessionContext, udaf

# Define a user-defined aggregation function (UDAF)

class MyAccumulator(Accumulator):

"""

Interface of a user-defined accumulation.

"""

def __init__(self) -> None:

self._sum = pa.scalar(0.0)

def update(self, values: list[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[0]).as_py())

def state(self) -> list[pa.Array]:

return [self._sum]

def evaluate(self) -> pa.Scalar:

return self._sum

my_udaf = udaf(

MyAccumulator,

pa.float64(),

pa.float64(),

[pa.float64()],

"stable",

# This will be the name of the UDAF in SQL

# If not specified it will by default the same as accumulator class name

name="my_accumulator",

)

# Create a context

ctx = SessionContext()

# Create a datafusion DataFrame from a Python dictionary

source_df = ctx.from_pydict({"a": [1, 1, 3], "b": [4, 5, 6]}, name="t")

# Dataframe:

# +---+---+

# | a | b |

# +---+---+

# | 1 | 4 |

# | 1 | 5 |

# | 3 | 6 |

# +---+---+

# Register UDF for use in SQL

ctx.register_udaf(my_udaf)

# Query the DataFrame using SQL

result_df = ctx.sql(

"select a, my_accumulator(b) as b_aggregated from t group by a order by a"

)

# Dataframe:

# +---+--------------+

# | a | b_aggregated |

# +---+--------------+

# | 1 | 9 |

# | 3 | 6 |

# +---+--------------+

assert result_df.to_pydict()["a"] == [1, 3]

assert result_df.to_pydict()["b_aggregated"] == [9, 6]