This repository was archived by the owner on Apr 1, 2026. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 68
Expand file tree
/
Copy pathpruning.py
More file actions
86 lines (76 loc) · 3.03 KB
/
pruning.py
File metadata and controls
86 lines (76 loc) · 3.03 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
# Copyright 2024 Google LLC
#
# Licensed 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 typing import Set, TYPE_CHECKING
import bigframes.core.expression as ex
import bigframes.core.identifiers as ids
import bigframes.core.nodes
import bigframes.dtypes
import bigframes.operations as ops
if TYPE_CHECKING:
import bigframes.core.nodes
LOW_CARDINALITY_TYPES = [bigframes.dtypes.BOOL_DTYPE]
COMPARISON_OP_TYPES = tuple(
type(i)
for i in (
ops.eq_op,
ops.eq_null_match_op,
ops.ne_op,
ops.gt_op,
ops.ge_op,
ops.lt_op,
ops.le_op,
)
)
def cluster_cols_for_predicate(
predicate: ex.Expression, clusterable_cols: Set[ids.ColumnId]
) -> list[ids.ColumnId]:
"""Try to determine cluster col candidates that work with given predicates."""
# TODO: Prioritize based on predicted selectivity (eg. equality conditions are probably very selective)
if isinstance(predicate, ex.DerefOp):
cols = [predicate.id]
elif isinstance(predicate, ex.OpExpression):
op = predicate.op
# TODO: Support geo predicates, which support pruning if clustered (other than st_disjoint)
# https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions
if isinstance(op, COMPARISON_OP_TYPES):
cols = cluster_cols_for_comparison(predicate.inputs[0], predicate.inputs[1])
elif isinstance(op, (type(ops.invert_op))):
cols = cluster_cols_for_predicate(predicate.inputs[0], clusterable_cols)
elif isinstance(op, (type(ops.and_op), type(ops.or_op))):
left_cols = cluster_cols_for_predicate(
predicate.inputs[0], clusterable_cols
)
right_cols = cluster_cols_for_predicate(
predicate.inputs[1], clusterable_cols
)
cols = [*left_cols, *[col for col in right_cols if col not in left_cols]]
else:
cols = []
else:
# Constant
cols = []
return [col for col in cols if col in clusterable_cols]
def cluster_cols_for_comparison(
left_ex: ex.Expression, right_ex: ex.Expression
) -> list[ids.ColumnId]:
# TODO: Try to normalize expressions such that one side is a single variable.
# eg. Convert -cola>=3 to cola<-3 and colb+3 < 4 to colb < 1
if left_ex.is_const:
# There are some invertible ops that would also be ok
if isinstance(right_ex, ex.DerefOp):
return [right_ex.id]
elif right_ex.is_const:
if isinstance(left_ex, ex.DerefOp):
return [left_ex.id]
return []