mirror of
https://github.com/langchain-ai/datafusion.git
synced 2026-07-16 12:04:27 -04:00
Jiayu Liu
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import List
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import pyarrow as pa
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import pyarrow.compute as pc
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import pytest
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from datafusion import ExecutionContext
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from datafusion import functions as f
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class Accumulator:
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"""
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Interface of a user-defined accumulation.
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"""
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def __init__(self):
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self._sum = pa.scalar(0.0)
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def to_scalars(self) -> List[pa.Scalar]:
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return [self._sum]
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def update(self, values: pa.Array) -> None:
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# Not nice since pyarrow scalars can't be summed yet.
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# This breaks on `None`
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self._sum = pa.scalar(self._sum.as_py() + pc.sum(values).as_py())
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def merge(self, states: pa.Array) -> None:
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# Not nice since pyarrow scalars can't be summed yet.
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# This breaks on `None`
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self._sum = pa.scalar(self._sum.as_py() + pc.sum(states).as_py())
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def evaluate(self) -> pa.Scalar:
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return self._sum
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@pytest.fixture
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def df():
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ctx = ExecutionContext()
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# create a RecordBatch and a new DataFrame from it
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batch = pa.RecordBatch.from_arrays(
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[pa.array([1, 2, 3]), pa.array([4, 4, 6])],
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names=["a", "b"],
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)
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return ctx.create_dataframe([[batch]])
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def test_aggregate(df):
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udaf = f.udaf(Accumulator, pa.float64(), pa.float64(), [pa.float64()])
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df = df.aggregate([], [udaf(f.col("a"))])
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# execute and collect the first (and only) batch
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result = df.collect()[0]
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assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
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def test_group_by(df):
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udaf = f.udaf(Accumulator, pa.float64(), pa.float64(), [pa.float64()])
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df = df.aggregate([f.col("b")], [udaf(f.col("a"))])
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batches = df.collect()
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arrays = [batch.column(1) for batch in batches]
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joined = pa.concat_arrays(arrays)
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assert joined == pa.array([1.0 + 2.0, 3.0])
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