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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 __future__ import annotations
import datafusion
import pyarrow as pa
from datafusion import col, lit, udwf
from datafusion import functions as f
from datafusion.expr import WindowFrame
from datafusion.user_defined import WindowEvaluator
# This example creates five different examples of user defined window functions in order
# to demonstrate the variety of ways a user may need to implement.
class ExponentialSmoothDefault(WindowEvaluator):
"""Create a running smooth operation across an entire partition at once."""
def __init__(self, alpha: float) -> None:
self.alpha = alpha
def evaluate_all(self, values: list[pa.Array], num_rows: int) -> pa.Array:
results = []
curr_value = 0.0
values = values[0]
for idx in range(num_rows):
if idx == 0:
curr_value = values[idx].as_py()
else:
curr_value = values[idx].as_py() * self.alpha + curr_value * (
1.0 - self.alpha
)
results.append(curr_value)
return pa.array(results)
class SmoothBoundedFromPreviousRow(WindowEvaluator):
"""Smooth over from the previous to current row only."""
def __init__(self, alpha: float) -> None:
self.alpha = alpha
def supports_bounded_execution(self) -> bool:
return True
def get_range(self, idx: int, num_rows: int) -> tuple[int, int]: # noqa: ARG002
# Override the default range of current row since uses_window_frame is False
# So for the purpose of this test we just smooth from the previous row to
# current.
if idx == 0:
return (0, 0)
return (idx - 1, idx)
def evaluate(
self, values: list[pa.Array], eval_range: tuple[int, int]
) -> pa.Scalar:
(start, stop) = eval_range
curr_value = 0.0
values = values[0]
for idx in range(start, stop + 1):
if idx == start:
curr_value = values[idx].as_py()
else:
curr_value = values[idx].as_py() * self.alpha + curr_value * (
1.0 - self.alpha
)
return pa.scalar(curr_value).cast(pa.float64())
class SmoothAcrossRank(WindowEvaluator):
"""Smooth over the rank from the previous rank to current."""
def __init__(self, alpha: float) -> None:
self.alpha = alpha
def include_rank(self) -> bool:
return True
def evaluate_all_with_rank(
self, num_rows: int, ranks_in_partition: list[tuple[int, int]]
) -> pa.Array:
results = []
for idx in range(num_rows):
if idx == 0:
prior_value = 1.0
matching_row = [
i
for i in range(len(ranks_in_partition))
if ranks_in_partition[i][0] <= idx and ranks_in_partition[i][1] > idx
][0] + 1
curr_value = matching_row * self.alpha + prior_value * (1.0 - self.alpha)
results.append(curr_value)
prior_value = matching_row
return pa.array(results)
class ExponentialSmoothFrame(WindowEvaluator):
"Find the value across an entire frame using exponential smoothing"
def __init__(self, alpha: float) -> None:
self.alpha = alpha
def uses_window_frame(self) -> bool:
return True
def evaluate(
self, values: list[pa.Array], eval_range: tuple[int, int]
) -> pa.Scalar:
(start, stop) = eval_range
curr_value = 0.0
if len(values) > 1:
order_by = values[1] # noqa: F841
values = values[0]
else:
values = values[0]
for idx in range(start, stop):
if idx == start:
curr_value = values[idx].as_py()
else:
curr_value = values[idx].as_py() * self.alpha + curr_value * (
1.0 - self.alpha
)
return pa.scalar(curr_value).cast(pa.float64())
class SmoothTwoColumn(WindowEvaluator):
"""Smooth once column based on a condition of another column.
If the second column is above a threshold, then smooth over the first column from
the previous and next rows.
"""
def __init__(self, alpha: float) -> None:
self.alpha = alpha
def evaluate_all(self, values: list[pa.Array], num_rows: int) -> pa.Array:
results = []
values_a = values[0]
values_b = values[1]
for idx in range(num_rows):
if not values_b[idx].is_valid:
if idx == 0:
results.append(values_a[1].cast(pa.float64()))
elif idx == num_rows - 1:
results.append(values_a[num_rows - 2].cast(pa.float64()))
else:
results.append(
pa.scalar(
values_a[idx - 1].as_py() * self.alpha
+ values_a[idx + 1].as_py() * (1.0 - self.alpha)
)
)
else:
results.append(values_a[idx].cast(pa.float64()))
return pa.array(results)
# create a context
ctx = datafusion.SessionContext()
# create a RecordBatch and a new DataFrame from it
batch = pa.RecordBatch.from_arrays(
[
pa.array([1.0, 2.1, 2.9, 4.0, 5.1, 6.0, 6.9, 8.0]),
pa.array([1, 2, None, 4, 5, 6, None, 8]),
pa.array(["A", "A", "A", "A", "A", "B", "B", "B"]),
],
names=["a", "b", "c"],
)
df = ctx.create_dataframe([[batch]])
exp_smooth = udwf(
lambda: ExponentialSmoothDefault(0.9),
pa.float64(),
pa.float64(),
volatility="immutable",
)
smooth_two_row = udwf(
lambda: SmoothBoundedFromPreviousRow(0.9),
pa.float64(),
pa.float64(),
volatility="immutable",
)
smooth_rank = udwf(
lambda: SmoothAcrossRank(0.9),
pa.float64(),
pa.float64(),
volatility="immutable",
)
smooth_frame = udwf(
lambda: ExponentialSmoothFrame(0.9),
pa.float64(),
pa.float64(),
volatility="immutable",
name="smooth_frame",
)
smooth_two_col = udwf(
lambda: SmoothTwoColumn(0.9),
[pa.float64(), pa.int64()],
pa.float64(),
volatility="immutable",
)
# These are done with separate statements instead of one large `select` because that will
# attempt to combine the window operations and our defined UDFs do not all support that.
(
df.with_column("exp_smooth", exp_smooth(col("a")))
.with_column("smooth_prior_row", smooth_two_row(col("a")))
.with_column("smooth_rank", smooth_rank(col("a")).order_by(col("c")).build())
.with_column("smooth_two_col", smooth_two_col(col("a"), col("b")))
.with_column(
"smooth_frame",
smooth_frame(col("a")).window_frame(WindowFrame("rows", None, 0)).build(),
)
.select(
"a",
"b",
"c",
"exp_smooth",
"smooth_prior_row",
"smooth_rank",
"smooth_two_col",
"smooth_frame",
)
).show()
assert df.select(f.round(exp_smooth(col("a")), lit(3))).collect()[0].column(
0
) == pa.array([1, 1.99, 2.809, 3.881, 4.978, 5.898, 6.8, 7.88])
assert df.select(f.round(smooth_two_row(col("a")), lit(3))).collect()[0].column(
0
) == pa.array([1.0, 1.99, 2.82, 3.89, 4.99, 5.91, 6.81, 7.89])
assert df.select(smooth_rank(col("a")).order_by(col("c")).build()).collect()[0].column(
0
) == pa.array([1, 1, 1, 1, 1, 1.9, 2.0, 2.0])
assert df.select(smooth_two_col(col("a"), col("b"))).collect()[0].column(0) == pa.array(
[1, 2.1, 2.29, 4, 5.1, 6, 6.2, 8.0]
)
assert df.select(
f.round(
smooth_frame(col("a")).window_frame(WindowFrame("rows", None, 0)).build(),
lit(3),
)
).collect()[0].column(0) == pa.array([1, 1.99, 2.809, 3.881, 4.978, 5.898, 6.8, 7.88])
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