-
Notifications
You must be signed in to change notification settings - Fork 13.7k
/
result_set.py
222 lines (189 loc) · 8.21 KB
/
result_set.py
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
# 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.
""" Superset wrapper around pyarrow.Table.
"""
import datetime
import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Type
import numpy as np
import pandas as pd
import pyarrow as pa
from superset import db_engine_specs
from superset.typing import DbapiDescription, DbapiResult
from superset.utils import core as utils
logger = logging.getLogger(__name__)
def dedup(l: List[str], suffix: str = "__", case_sensitive: bool = True) -> List[str]:
"""De-duplicates a list of string by suffixing a counter
Always returns the same number of entries as provided, and always returns
unique values. Case sensitive comparison by default.
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
foo,bar,bar__1,bar__2,Bar
>>> print(
','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))
)
foo,bar,bar__1,bar__2,Bar__3
"""
new_l: List[str] = []
seen: Dict[str, int] = {}
for item in l:
s_fixed_case = item if case_sensitive else item.lower()
if s_fixed_case in seen:
seen[s_fixed_case] += 1
item += suffix + str(seen[s_fixed_case])
else:
seen[s_fixed_case] = 0
new_l.append(item)
return new_l
def stringify(obj: Any) -> str:
return json.dumps(obj, default=utils.json_iso_dttm_ser)
def stringify_values(array: np.ndarray) -> np.ndarray:
vstringify = np.vectorize(stringify)
return vstringify(array)
class SupersetResultSet:
def __init__( # pylint: disable=too-many-locals,too-many-branches
self,
data: DbapiResult,
cursor_description: DbapiDescription,
db_engine_spec: Type[db_engine_specs.BaseEngineSpec],
):
self.db_engine_spec = db_engine_spec
data = data or []
column_names: List[str] = []
pa_data: List[pa.Array] = []
deduped_cursor_desc: List[Tuple[Any, ...]] = []
numpy_dtype: List[Tuple[str, ...]] = []
stringified_arr: np.ndarray
if cursor_description:
# get deduped list of column names
column_names = dedup([col[0] for col in cursor_description])
# fix cursor descriptor with the deduped names
deduped_cursor_desc = [
tuple([column_name, *list(description)[1:]])
for column_name, description in zip(column_names, cursor_description)
]
# generate numpy structured array dtype
numpy_dtype = [(column_name, "object") for column_name in column_names]
# only do expensive recasting if datatype is not standard list of tuples
if data and (not isinstance(data, list) or not isinstance(data[0], tuple)):
data = [tuple(row) for row in data]
array = np.array(data, dtype=numpy_dtype)
if array.size > 0:
for column in column_names:
try:
pa_data.append(pa.array(array[column].tolist()))
except (
pa.lib.ArrowInvalid,
pa.lib.ArrowTypeError,
pa.lib.ArrowNotImplementedError,
TypeError, # this is super hackey,
# https://issues.apache.org/jira/browse/ARROW-7855
):
# attempt serialization of values as strings
stringified_arr = stringify_values(array[column])
pa_data.append(pa.array(stringified_arr.tolist()))
if pa_data: # pylint: disable=too-many-nested-blocks
for i, column in enumerate(column_names):
if pa.types.is_nested(pa_data[i].type):
# TODO: revisit nested column serialization once nested types
# are added as a natively supported column type in Superset
# (superset.utils.core.DbColumnType).
stringified_arr = stringify_values(array[column])
pa_data[i] = pa.array(stringified_arr.tolist())
elif pa.types.is_temporal(pa_data[i].type):
# workaround for bug converting
# `psycopg2.tz.FixedOffsetTimezone` tzinfo values.
# related: https://issues.apache.org/jira/browse/ARROW-5248
sample = self.first_nonempty(array[column])
if sample and isinstance(sample, datetime.datetime):
try:
if sample.tzinfo:
tz = sample.tzinfo
series = pd.Series(
array[column], dtype="datetime64[ns]"
)
series = pd.to_datetime(series).dt.tz_localize(tz)
pa_data[i] = pa.Array.from_pandas(
series, type=pa.timestamp("ns", tz=tz)
)
except Exception as ex: # pylint: disable=broad-except
logger.exception(ex)
self.table = pa.Table.from_arrays(pa_data, names=column_names)
self._type_dict: Dict[str, Any] = {}
try:
# The driver may not be passing a cursor.description
self._type_dict = {
col: db_engine_spec.get_datatype(deduped_cursor_desc[i][1])
for i, col in enumerate(column_names)
if deduped_cursor_desc
}
except Exception as ex: # pylint: disable=broad-except
logger.exception(ex)
@staticmethod
def convert_pa_dtype(pa_dtype: pa.DataType) -> Optional[str]:
if pa.types.is_boolean(pa_dtype):
return "BOOL"
if pa.types.is_integer(pa_dtype):
return "INT"
if pa.types.is_floating(pa_dtype):
return "FLOAT"
if pa.types.is_string(pa_dtype):
return "STRING"
if pa.types.is_temporal(pa_dtype):
return "DATETIME"
return None
@staticmethod
def convert_table_to_df(table: pa.Table) -> pd.DataFrame:
return table.to_pandas(integer_object_nulls=True)
@staticmethod
def first_nonempty(items: List[Any]) -> Any:
return next((i for i in items if i), None)
def is_temporal(self, db_type_str: Optional[str]) -> bool:
return self.db_engine_spec.is_db_column_type_match(
db_type_str, utils.DbColumnType.TEMPORAL
)
def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]:
"""Given a pyarrow data type, Returns a generic database type"""
set_type = self._type_dict.get(col_name)
if set_type:
return set_type
mapped_type = self.convert_pa_dtype(pa_dtype)
if mapped_type:
return mapped_type
return None
def to_pandas_df(self) -> pd.DataFrame:
return self.convert_table_to_df(self.table)
@property
def pa_table(self) -> pa.Table:
return self.table
@property
def size(self) -> int:
return self.table.num_rows
@property
def columns(self) -> List[Dict[str, Any]]:
if not self.table.column_names:
return []
columns = []
for col in self.table.schema:
db_type_str = self.data_type(col.name, col.type)
column = {
"name": col.name,
"type": db_type_str,
"is_date": self.is_temporal(db_type_str),
}
columns.append(column)
return columns