Skip to content
Navigation Menu
{{ message }}
forked from apache/datafusion-python
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_udaf.py
More file actions
296 lines (220 loc) · 8.36 KB
/
Copy pathtest_udaf.py
File metadata and controls
296 lines (220 loc) · 8.36 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
# 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
from datetime import datetime, timezone
import pyarrow as pa
import pyarrow.compute as pc
import pytest
from datafusion import Accumulator, column, udaf
class Summarize(Accumulator):
"""Interface of a user-defined accumulation."""
def __init__(self, initial_value: float = 0.0, as_scalar: bool = False):
self._sum = initial_value
self.as_scalar = as_scalar
def state(self) -> list[pa.Scalar]:
if self.as_scalar:
return [pa.scalar(self._sum)]
return [self._sum]
def update(self, values: pa.Array) -> None:
# Not nice since pyarrow scalars can't be summed yet.
# This breaks on `None`
self._sum = self._sum + pc.sum(values).as_py()
def merge(self, states: list[pa.Array]) -> None:
# Not nice since pyarrow scalars can't be summed yet.
# This breaks on `None`
self._sum = self._sum + pc.sum(states[0]).as_py()
def evaluate(self) -> pa.Scalar:
if self.as_scalar:
return pa.scalar(self._sum)
return self._sum
class NotSubclassOfAccumulator:
pass
class MissingMethods(Accumulator):
def __init__(self):
self._sum = pa.scalar(0)
def state(self) -> list[pa.Scalar]:
return [self._sum]
class CollectTimestamps(Accumulator):
def __init__(self, wrap_in_scalar: bool):
self._values: list[datetime] = []
self.wrap_in_scalar = wrap_in_scalar
def state(self) -> list[pa.Scalar]:
if self.wrap_in_scalar:
return [pa.scalar(self._values, type=pa.list_(pa.timestamp("ns")))]
return [pa.array(self._values, type=pa.timestamp("ns"))]
def update(self, values: pa.Array) -> None:
self._values.extend(values.to_pylist())
def merge(self, states: list[pa.Array]) -> None:
for state in states[0].to_pylist():
if state is not None:
self._values.extend(state)
def evaluate(self) -> pa.Scalar:
if self.wrap_in_scalar:
return pa.scalar(self._values, type=pa.list_(pa.timestamp("ns")))
return pa.array(self._values, type=pa.timestamp("ns"))
@pytest.fixture
def df(ctx):
# create a RecordBatch and a new DataFrame from it
batch = pa.RecordBatch.from_arrays(
[pa.array([1, 2, 3]), pa.array([4, 4, 6])],
names=["a", "b"],
)
return ctx.create_dataframe([[batch]], name="test_table")
def test_errors(df):
with pytest.raises(TypeError):
udaf(
NotSubclassOfAccumulator,
pa.float64(),
pa.float64(),
[pa.float64()],
volatility="immutable",
)
msg = (
"Can't instantiate abstract class MissingMethods (without an implementation "
"for abstract methods 'evaluate', 'merge', 'update'|with abstract methods "
"evaluate, merge, update)"
)
with pytest.raises(Exception, match=msg):
accum = udaf( # noqa: F841
MissingMethods,
pa.int64(),
pa.int64(),
[pa.int64()],
volatility="immutable",
)
def test_udaf_aggregate(df):
summarize = udaf(
Summarize,
pa.float64(),
pa.float64(),
[pa.float64()],
volatility="immutable",
)
df1 = df.aggregate([], [summarize(column("a"))])
# execute and collect the first (and only) batch
result = df1.collect()[0]
assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
df2 = df.aggregate([], [summarize(column("a"))])
# Run a second time to ensure the state is properly reset
result = df2.collect()[0]
assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
def test_udaf_decorator_aggregate(df):
@udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable")
def summarize():
return Summarize()
df1 = df.aggregate([], [summarize(column("a"))])
# execute and collect the first (and only) batch
result = df1.collect()[0]
assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
df2 = df.aggregate([], [summarize(column("a"))])
# Run a second time to ensure the state is properly reset
result = df2.collect()[0]
assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
@pytest.mark.parametrize("as_scalar", [True, False])
def test_udaf_aggregate_with_arguments(df, as_scalar):
bias = 10.0
summarize = udaf(
lambda: Summarize(initial_value=bias, as_scalar=as_scalar),
pa.float64(),
pa.float64(),
[pa.float64()],
volatility="immutable",
)
df1 = df.aggregate([], [summarize(column("a"))])
# execute and collect the first (and only) batch
result = df1.collect()[0]
assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
df2 = df.aggregate([], [summarize(column("a"))])
# Run a second time to ensure the state is properly reset
result = df2.collect()[0]
assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
def test_udaf_decorator_aggregate_with_arguments(df):
bias = 10.0
@udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable")
def summarize():
return Summarize(bias)
df1 = df.aggregate([], [summarize(column("a"))])
# execute and collect the first (and only) batch
result = df1.collect()[0]
assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
df2 = df.aggregate([], [summarize(column("a"))])
# Run a second time to ensure the state is properly reset
result = df2.collect()[0]
assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
def test_group_by(df):
summarize = udaf(
Summarize,
pa.float64(),
pa.float64(),
[pa.float64()],
volatility="immutable",
)
df = df.aggregate([column("b")], [summarize(column("a"))])
batches = df.collect()
arrays = [batch.column(1) for batch in batches]
joined = pa.concat_arrays(arrays)
assert joined == pa.array([1.0 + 2.0, 3.0])
def test_register_udaf(ctx, df) -> None:
summarize = udaf(
Summarize,
pa.float64(),
pa.float64(),
[pa.float64()],
volatility="immutable",
)
ctx.register_udaf(summarize)
df_result = ctx.sql("select summarize(b) from test_table")
assert df_result.collect()[0][0][0].as_py() == 14.0
@pytest.mark.parametrize("wrap_in_scalar", [True, False])
def test_udaf_list_timestamp_return(ctx, wrap_in_scalar) -> None:
timestamps1 = [
datetime(2024, 1, 1, tzinfo=timezone.utc),
datetime(2024, 1, 2, tzinfo=timezone.utc),
]
timestamps2 = [
datetime(2024, 1, 3, tzinfo=timezone.utc),
datetime(2024, 1, 4, tzinfo=timezone.utc),
]
batch1 = pa.RecordBatch.from_arrays(
[pa.array(timestamps1, type=pa.timestamp("ns"))],
names=["ts"],
)
batch2 = pa.RecordBatch.from_arrays(
[pa.array(timestamps2, type=pa.timestamp("ns"))],
names=["ts"],
)
df = ctx.create_dataframe([[batch1], [batch2]], name="timestamp_table")
list_type = pa.list_(
pa.field("item", type=pa.timestamp("ns"), nullable=wrap_in_scalar)
)
collect = udaf(
lambda: CollectTimestamps(wrap_in_scalar),
pa.timestamp("ns"),
list_type,
[list_type],
volatility="immutable",
)
result = df.aggregate([], [collect(column("ts"))]).collect()[0]
# There is no guarantee about the ordering of the batches, so perform a sort
# to get consistent results. Alternatively we could sort on evaluate().
assert (
result.column(0).values.sort()
== pa.array(
[[*timestamps1, *timestamps2]],
type=list_type,
).values
)
You can’t perform that action at this time.