forked from feast-dev/feast
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfeature_view.py
More file actions
427 lines (374 loc) · 16.2 KB
/
feature_view.py
File metadata and controls
427 lines (374 loc) · 16.2 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
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
# Copyright 2019 The Feast Authors
#
# 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
#
# https://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.
import copy
import warnings
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Type, Union
from google.protobuf.duration_pb2 import Duration
from feast import utils
from feast.base_feature_view import BaseFeatureView
from feast.data_source import DataSource, PushSource
from feast.entity import Entity
from feast.feature import Feature
from feast.feature_view_projection import FeatureViewProjection
from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto
from feast.protos.feast.core.FeatureView_pb2 import (
FeatureViewMeta as FeatureViewMetaProto,
)
from feast.protos.feast.core.FeatureView_pb2 import (
FeatureViewSpec as FeatureViewSpecProto,
)
from feast.protos.feast.core.FeatureView_pb2 import (
MaterializationInterval as MaterializationIntervalProto,
)
from feast.usage import log_exceptions
from feast.value_type import ValueType
warnings.simplefilter("once", DeprecationWarning)
# DUMMY_ENTITY is a placeholder entity used in entityless FeatureViews
DUMMY_ENTITY_ID = "__dummy_id"
DUMMY_ENTITY_NAME = "__dummy"
DUMMY_ENTITY_VAL = ""
DUMMY_ENTITY = Entity(
name=DUMMY_ENTITY_NAME, join_key=DUMMY_ENTITY_ID, value_type=ValueType.STRING,
)
class FeatureView(BaseFeatureView):
"""
A FeatureView defines a logical group of features.
Attributes:
name: The unique name of the feature view.
entities: The list of entities with which this group of features is associated.
ttl: The amount of time this group of features lives. A ttl of 0 indicates that
this group of features lives forever. Note that large ttl's or a ttl of 0
can result in extremely computationally intensive queries.
batch_source (optional): The batch source of data where this group of features is stored.
This is optional ONLY a push source is specified as the stream_source, since push sources
contain their own batch sources.
stream_source (optional): The stream source of data where this group of features
is stored.
features: The list of features defined as part of this feature view.
online: A boolean indicating whether online retrieval is enabled for this feature
view.
description: A human-readable description.
tags: A dictionary of key-value pairs to store arbitrary metadata.
owner: The owner of the feature view, typically the email of the primary
maintainer.
"""
name: str
entities: List[str]
ttl: Optional[timedelta]
batch_source: DataSource
stream_source: Optional[DataSource]
features: List[Feature]
online: bool
description: str
tags: Dict[str, str]
owner: str
materialization_intervals: List[Tuple[datetime, datetime]]
@log_exceptions
def __init__(
self,
*args,
name: Optional[str] = None,
entities: Optional[List[str]] = None,
ttl: Optional[Union[Duration, timedelta]] = None,
batch_source: Optional[DataSource] = None,
stream_source: Optional[DataSource] = None,
features: Optional[List[Feature]] = None,
tags: Optional[Dict[str, str]] = None,
online: bool = True,
description: str = "",
owner: str = "",
):
"""
Creates a FeatureView object.
Args:
name: The unique name of the feature view.
entities: The list of entities with which this group of features is associated.
ttl: The amount of time this group of features lives. A ttl of 0 indicates that
this group of features lives forever. Note that large ttl's or a ttl of 0
can result in extremely computationally intensive queries.
batch_source: The batch source of data where this group of features is stored.
stream_source (optional): The stream source of data where this group of features
is stored.
features (optional): The list of features defined as part of this feature view.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
online (optional): A boolean indicating whether online retrieval is enabled for
this feature view.
description (optional): A human-readable description.
owner (optional): The owner of the feature view, typically the email of the
primary maintainer.
Raises:
ValueError: A field mapping conflicts with an Entity or a Feature.
"""
positional_attributes = ["name, entities, ttl"]
_name = name
_entities = entities
_ttl = ttl
if args:
warnings.warn(
(
"feature view parameters should be specified as a keyword argument instead of a positional arg."
"Feast 0.23+ will not support positional arguments to construct feature views"
),
DeprecationWarning,
)
if len(args) > len(positional_attributes):
raise ValueError(
f"Only {', '.join(positional_attributes)} are allowed as positional args when defining "
f"feature views, for backwards compatibility."
)
if len(args) >= 1:
_name = args[0]
if len(args) >= 2:
_entities = args[1]
if len(args) >= 3:
_ttl = args[2]
if not _name:
raise ValueError("feature view name needs to be specified")
self.name = _name
self.entities = _entities if _entities else [DUMMY_ENTITY_NAME]
if isinstance(_ttl, Duration):
self.ttl = timedelta(seconds=int(_ttl.seconds))
warnings.warn(
(
"The option to pass a Duration object to the ttl parameter is being deprecated. "
"Please pass a timedelta object instead. Feast 0.21 and onwards will not support "
"Duration objects."
),
DeprecationWarning,
)
elif isinstance(_ttl, timedelta) or _ttl is None:
self.ttl = _ttl
else:
raise ValueError(f"unknown value type specified for ttl {type(_ttl)}")
_features = features or []
if stream_source is not None and isinstance(stream_source, PushSource):
if stream_source.batch_source is None or not isinstance(
stream_source.batch_source, DataSource
):
raise ValueError(
f"A batch_source needs to be specified for feature view `{name}`"
)
self.batch_source = stream_source.batch_source
else:
if batch_source is None:
raise ValueError(
f"A batch_source needs to be specified for feature view `{name}`"
)
self.batch_source = batch_source
cols = [entity for entity in self.entities] + [feat.name for feat in _features]
for col in cols:
if (
self.batch_source.field_mapping is not None
and col in self.batch_source.field_mapping.keys()
):
raise ValueError(
f"The field {col} is mapped to {self.batch_source.field_mapping[col]} for this data source. "
f"Please either remove this field mapping or use {self.batch_source.field_mapping[col]} as the "
f"Entity or Feature name."
)
super().__init__(
name=name,
features=_features,
description=description,
tags=tags,
owner=owner,
)
self.online = online
self.stream_source = stream_source
self.materialization_intervals = []
# Note: Python requires redefining hash in child classes that override __eq__
def __hash__(self):
return super().__hash__()
def __copy__(self):
fv = FeatureView(
name=self.name,
entities=self.entities,
ttl=self.ttl,
batch_source=self.batch_source,
stream_source=self.stream_source,
features=self.features,
tags=self.tags,
online=self.online,
)
fv.projection = copy.copy(self.projection)
return fv
def __eq__(self, other):
if not isinstance(other, FeatureView):
raise TypeError(
"Comparisons should only involve FeatureView class objects."
)
if not super().__eq__(other):
return False
if (
self.tags != other.tags
or self.ttl != other.ttl
or self.online != other.online
):
return False
if sorted(self.entities) != sorted(other.entities):
return False
if self.batch_source != other.batch_source:
return False
if self.stream_source != other.stream_source:
return False
return True
def ensure_valid(self):
"""
Validates the state of this feature view locally.
Raises:
ValueError: The feature view does not have a name or does not have entities.
"""
super().ensure_valid()
if not self.entities:
raise ValueError("Feature view has no entities.")
@property
def proto_class(self) -> Type[FeatureViewProto]:
return FeatureViewProto
def with_join_key_map(self, join_key_map: Dict[str, str]):
"""
Returns a copy of this feature view with the join key map set to the given map.
This join_key mapping operation is only used as part of query operations and will
not modify the underlying FeatureView.
Args:
join_key_map: A map of join keys in which the left is the join_key that
corresponds with the feature data and the right corresponds with the entity data.
Examples:
Join a location feature data table to both the origin column and destination
column of the entity data.
temperatures_feature_service = FeatureService(
name="temperatures",
features=[
location_stats_feature_view
.with_name("origin_stats")
.with_join_key_map(
{"location_id": "origin_id"}
),
location_stats_feature_view
.with_name("destination_stats")
.with_join_key_map(
{"location_id": "destination_id"}
),
],
)
"""
cp = self.__copy__()
cp.projection.join_key_map = join_key_map
return cp
def to_proto(self) -> FeatureViewProto:
"""
Converts a feature view object to its protobuf representation.
Returns:
A FeatureViewProto protobuf.
"""
meta = FeatureViewMetaProto(materialization_intervals=[])
if self.created_timestamp:
meta.created_timestamp.FromDatetime(self.created_timestamp)
if self.last_updated_timestamp:
meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)
for interval in self.materialization_intervals:
interval_proto = MaterializationIntervalProto()
interval_proto.start_time.FromDatetime(interval[0])
interval_proto.end_time.FromDatetime(interval[1])
meta.materialization_intervals.append(interval_proto)
ttl_duration = None
if self.ttl is not None:
ttl_duration = Duration()
ttl_duration.FromTimedelta(self.ttl)
batch_source_proto = self.batch_source.to_proto()
batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}"
stream_source_proto = None
if self.stream_source:
stream_source_proto = self.stream_source.to_proto()
stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}"
spec = FeatureViewSpecProto(
name=self.name,
entities=self.entities,
features=[feature.to_proto() for feature in self.features],
description=self.description,
tags=self.tags,
owner=self.owner,
ttl=(ttl_duration if ttl_duration is not None else None),
online=self.online,
batch_source=batch_source_proto,
stream_source=stream_source_proto,
)
return FeatureViewProto(spec=spec, meta=meta)
@classmethod
def from_proto(cls, feature_view_proto: FeatureViewProto):
"""
Creates a feature view from a protobuf representation of a feature view.
Args:
feature_view_proto: A protobuf representation of a feature view.
Returns:
A FeatureViewProto object based on the feature view protobuf.
"""
batch_source = DataSource.from_proto(feature_view_proto.spec.batch_source)
stream_source = (
DataSource.from_proto(feature_view_proto.spec.stream_source)
if feature_view_proto.spec.HasField("stream_source")
else None
)
feature_view = cls(
name=feature_view_proto.spec.name,
entities=[entity for entity in feature_view_proto.spec.entities],
features=[
Feature(
name=feature.name,
dtype=ValueType(feature.value_type),
labels=dict(feature.labels),
)
for feature in feature_view_proto.spec.features
],
description=feature_view_proto.spec.description,
tags=dict(feature_view_proto.spec.tags),
owner=feature_view_proto.spec.owner,
online=feature_view_proto.spec.online,
ttl=(
timedelta(days=0)
if feature_view_proto.spec.ttl.ToNanoseconds() == 0
else feature_view_proto.spec.ttl.ToTimedelta()
),
batch_source=batch_source,
stream_source=stream_source,
)
# FeatureViewProjections are not saved in the FeatureView proto.
# Create the default projection.
feature_view.projection = FeatureViewProjection.from_definition(feature_view)
if feature_view_proto.meta.HasField("created_timestamp"):
feature_view.created_timestamp = (
feature_view_proto.meta.created_timestamp.ToDatetime()
)
if feature_view_proto.meta.HasField("last_updated_timestamp"):
feature_view.last_updated_timestamp = (
feature_view_proto.meta.last_updated_timestamp.ToDatetime()
)
for interval in feature_view_proto.meta.materialization_intervals:
feature_view.materialization_intervals.append(
(
utils.make_tzaware(interval.start_time.ToDatetime()),
utils.make_tzaware(interval.end_time.ToDatetime()),
)
)
return feature_view
@property
def most_recent_end_time(self) -> Optional[datetime]:
"""
Retrieves the latest time up to which the feature view has been materialized.
Returns:
The latest time, or None if the feature view has not been materialized.
"""
if len(self.materialization_intervals) == 0:
return None
return max([interval[1] for interval in self.materialization_intervals])