| # 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(), | |
| [pa.float64()], | |
| "stable", | |
| ) | |
| df = df.aggregate([], [my_udaf(col("a"))]) | |
| result = df.collect()[0] | |
| assert result.column(0) == pa.array([6.0]) |