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GeezerVison is showing github.com — feast/sdk/python/feast/feature_view.py at master · iamcodingcat/feast Expand file tree/ Copy pathfeature_view.pyMore file actions 666 lines (590 loc) · 26.7 KB / Copy pathfeature_view.pyFile metadata and controls666 lines (590 loc) · 26.7 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 # 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 google.protobuf.message import Message from typeguard import typechecked from feast import utils from feast.base_feature_view import BaseFeatureView from feast.data_source import DataSource, KafkaSource, KinesisSource, PushSource from feast.entity import Entity from feast.feature_view_projection import FeatureViewProjection from feast.field import Field from feast.proto_utils import ( mode_to_string, serialize_data_source, transformation_to_proto, ) 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.transformation.mode import TransformationMode from feast.types import from_value_type 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_keys=[DUMMY_ENTITY_ID], value_type=ValueType.UNKNOWN, ) DUMMY_ENTITY_FIELD = Field( name=DUMMY_ENTITY_ID, dtype=from_value_type(ValueType.STRING), ) @typechecked class FeatureView(BaseFeatureView): """ A FeatureView defines a logical group of features. Attributes: name: The unique name of the feature view. entities: The list of names of entities that this feature view is associated with. 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. This is optional ONLY if a push source is specified as the stream_source, since push sources contain their own batch sources. stream_source: The stream source of data where this group of features is stored. schema: The schema of the feature view, including feature, timestamp, and entity columns. If not specified, can be inferred from the underlying data source. entity_columns: The list of entity columns contained in the schema. If not specified, can be inferred from the underlying data source. features: The list of feature columns contained in the schema. If not specified, can be inferred from the underlying data source. 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. mode: The transformation mode for feature transformations. Only meaningful when transformations are applied. Choose from TransformationMode enum values (e.g., PYTHON, PANDAS, RAY, SQL, SPARK, SUBSTRAIT). """ name: str entities: List[str] ttl: Optional[timedelta] batch_source: DataSource stream_source: Optional[DataSource] source_views: Optional[List["FeatureView"]] entity_columns: List[Field] features: List[Field] online: bool offline: bool description: str tags: Dict[str, str] owner: str materialization_intervals: List[Tuple[datetime, datetime]] mode: Optional[Union["TransformationMode", str]] enable_validation: bool def __init__( self, *, name: str, source: Union[DataSource, "FeatureView", List["FeatureView"]], sink_source: Optional[DataSource] = None, schema: Optional[List[Field]] = None, entities: Optional[List[Entity]] = None, ttl: Optional[timedelta] = timedelta(days=0), online: bool = True, offline: bool = False, description: str = "", tags: Optional[Dict[str, str]] = None, owner: str = "", mode: Optional[Union["TransformationMode", str]] = None, enable_validation: bool = False, ): """ Creates a FeatureView object. Args: name: The unique name of the feature view. source: The source of data for this group of features. May be a stream source, or a batch source. If a stream source, the source should contain a batch_source for backfills & batch materialization. schema (optional): The schema of the feature view, including feature, timestamp, and entity columns. # TODO: clarify that schema is only useful here... entities (optional): The list of entities with which this group of features is associated. ttl (optional): 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. online (optional): A boolean indicating whether online retrieval is enabled for this feature view. offline (optional): A boolean indicating whether write to offline store is enabled for this feature view. description (optional): A human-readable description. tags (optional): A dictionary of key-value pairs to store arbitrary metadata. owner (optional): The owner of the feature view, typically the email of the primary maintainer. mode (optional): The transformation mode for feature transformations. Only meaningful when transformations are applied. Choose from TransformationMode enum values. enable_validation (optional): If True, enables schema validation during materialization to check that data conforms to the declared feature types. Default is False. Raises: ValueError: A field mapping conflicts with an Entity or a Feature. """ self.name = name self.enable_validation = enable_validation self.entities = [e.name for e in entities] if entities else [DUMMY_ENTITY_NAME] self.ttl = ttl schema = schema or [] self.mode = mode # Normalize source self.stream_source = None self.data_source: Optional[DataSource] = None self.source_views: List[FeatureView] = [] if isinstance(source, DataSource): self.data_source = source elif isinstance(source, FeatureView): self.source_views = [source] elif isinstance(source, list) and all( isinstance(sv, FeatureView) for sv in source ): self.source_views = source else: raise TypeError( "source must be a DataSource, a FeatureView, or a list of FeatureView." ) # Set up stream, batch and derived view sources if ( isinstance(self.data_source, PushSource) or isinstance(self.data_source, KafkaSource) or isinstance(self.data_source, KinesisSource) ): # Stream source definition self.stream_source = self.data_source if not self.data_source.batch_source: raise ValueError( f"A batch_source needs to be specified for stream source `{self.data_source.name}`" ) self.batch_source = self.data_source.batch_source elif self.data_source: # Batch source definition self.batch_source = self.data_source else: # Derived view source definition if not sink_source: raise ValueError("Derived FeatureView must specify `sink_source`.") self.batch_source = sink_source # Initialize features and entity columns. features: List[Field] = [] self.entity_columns = [] join_keys: List[str] = [] if entities: for entity in entities: join_keys.append(entity.join_key) # Ensure that entities have unique join keys. if len(set(join_keys)) < len(join_keys): raise ValueError( "A feature view should not have entities that share a join key." ) for field in schema: if field.name in join_keys: self.entity_columns.append(field) # Confirm that the inferred type matches the specified entity type, if it exists. matching_entities = ( [e for e in entities if e.join_key == field.name] if entities else [] ) assert len(matching_entities) == 1 entity = matching_entities[0] if entity.value_type != ValueType.UNKNOWN: if from_value_type(entity.value_type) != field.dtype: raise ValueError( f"Entity {entity.name} has type {entity.value_type}, which does not match the inferred type {field.dtype}." ) else: features.append(field) assert len([f for f in features if f.vector_index]) < 2, ( f"Only one vector feature is allowed per feature view. Please update {self.name}." ) # TODO(felixwang9817): Add more robust validation of features. if self.batch_source is not None: cols = [field.name for field in schema] 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, source=self.batch_source, ) self.online = online self.offline = offline self.mode = mode self.materialization_intervals = [] def __hash__(self): return super().__hash__() def __copy__(self): fv = FeatureView( name=self.name, ttl=self.ttl, source=self.source_views if self.source_views else (self.stream_source if self.stream_source else self.batch_source), schema=self.schema, tags=self.tags, online=self.online, offline=self.offline, sink_source=self.batch_source if self.source_views else None, enable_validation=self.enable_validation, ) # This is deliberately set outside of the FV initialization as we do not have the Entity objects. fv.entities = self.entities fv.features = copy.copy(self.features) fv.entity_columns = copy.copy(self.entity_columns) 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 ( sorted(self.entities) != sorted(other.entities) or self.ttl != other.ttl or self.online != other.online or self.offline != other.offline or self.batch_source != other.batch_source or self.stream_source != other.stream_source or sorted(self.entity_columns) != sorted(other.entity_columns) or self.source_views != other.source_views or self.materialization_intervals != other.materialization_intervals or self.enable_validation != other.enable_validation ): return False return True @property def join_keys(self) -> List[str]: """Returns a list of all the join keys.""" return [entity.name for entity in self.entity_columns] @property def schema(self) -> List[Field]: return list(set(self.entity_columns + self.features)) 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[Message]: 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 update_materialization_intervals( self, existing_materialization_intervals: List[Tuple[datetime, datetime]] ): if ( len(existing_materialization_intervals) > 0 and len(self.materialization_intervals) == 0 ): for interval in existing_materialization_intervals: self.materialization_intervals.append((interval[0], interval[1])) def to_proto(self) -> FeatureViewProto: """ Converts a feature view object to its protobuf representation. Returns: A FeatureViewProto protobuf. """ return self._to_proto_internal(seen={}) def _to_proto_internal( self, seen: Dict[str, Union[None, FeatureViewProto]] ) -> FeatureViewProto: if self.name in seen: if seen[self.name] is None: raise ValueError( f"Cycle detected during serialization of FeatureView: {self.name}" ) return seen[self.name] # type: ignore[return-value] seen[self.name] = None spec = self.to_proto_spec(seen) meta = self.to_proto_meta() proto = FeatureViewProto(spec=spec, meta=meta) seen[self.name] = proto return proto def to_proto_spec( self, seen: Dict[str, Union[None, FeatureViewProto]] ) -> FeatureViewSpecProto: ttl_duration = self.get_ttl_duration() batch_source_proto = serialize_data_source(self.batch_source) stream_source_proto = serialize_data_source(self.stream_source) source_view_protos = None if self.source_views: source_view_protos = [ view._to_proto_internal(seen).spec for view in self.source_views ] feature_transformation_proto = None if hasattr(self, "feature_transformation") and self.feature_transformation: feature_transformation_proto = transformation_to_proto( self.feature_transformation ) return FeatureViewSpecProto( name=self.name, entities=self.entities, entity_columns=[field.to_proto() for field in self.entity_columns], 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, offline=self.offline, batch_source=batch_source_proto, stream_source=stream_source_proto, source_views=source_view_protos, feature_transformation=feature_transformation_proto, mode=mode_to_string(self.mode), enable_validation=self.enable_validation, ) def to_proto_meta(self): 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) return meta def get_ttl_duration(self): ttl_duration = None if self.ttl is not None: ttl_duration = Duration() ttl_duration.FromTimedelta(self.ttl) return ttl_duration @classmethod def from_proto(cls, feature_view_proto: FeatureViewProto) -> "FeatureView": return cls._from_proto_internal(feature_view_proto, seen={}) @classmethod def _from_proto_internal( cls, feature_view_proto: FeatureViewProto, seen: Dict[str, Union[None, "FeatureView"]], ) -> "FeatureView": """ Creates a feature view from a protobuf representation of a feature view. Args: feature_view_proto: A protobuf representation of a feature view. seen: A dictionary to keep track of already seen feature views to avoid recursion. Returns: A FeatureViewProto object based on the feature view protobuf. """ feature_view_name = feature_view_proto.spec.name if feature_view_name in seen: if seen[feature_view_name] is None: raise ValueError( f"Cycle detected while deserializing FeatureView: {feature_view_name}" ) return seen[feature_view_name] # type: ignore[return-value] seen[feature_view_name] = None batch_source = ( DataSource.from_proto(feature_view_proto.spec.batch_source) if feature_view_proto.spec.HasField("batch_source") else None ) stream_source = ( DataSource.from_proto(feature_view_proto.spec.stream_source) if feature_view_proto.spec.HasField("stream_source") else None ) source_views = [ FeatureView._from_proto_internal( FeatureViewProto(spec=view_spec, meta=None), seen ) for view_spec in feature_view_proto.spec.source_views ] has_transformation = feature_view_proto.spec.HasField("feature_transformation") if has_transformation and cls == FeatureView: from feast.batch_feature_view import BatchFeatureView from feast.transformation.factory import get_transformation_class_from_type from feast.transformation.python_transformation import PythonTransformation from feast.transformation.substrait_transformation import ( SubstraitTransformation, ) feature_transformation_proto = ( feature_view_proto.spec.feature_transformation ) transformation = None if feature_transformation_proto.HasField("user_defined_function"): udf_proto = feature_transformation_proto.user_defined_function if udf_proto.mode: try: transformation_class = get_transformation_class_from_type( udf_proto.mode ) transformation = transformation_class.from_proto(udf_proto) except (ValueError, KeyError): transformation = PythonTransformation.from_proto(udf_proto) else: transformation = PythonTransformation.from_proto(udf_proto) elif feature_transformation_proto.HasField("substrait_transformation"): transformation = SubstraitTransformation.from_proto( feature_transformation_proto.substrait_transformation ) mode: Union[TransformationMode, str] if feature_view_proto.spec.mode: mode = feature_view_proto.spec.mode elif transformation and hasattr(transformation, "mode"): mode = transformation.mode else: mode = TransformationMode.PYTHON feature_view: FeatureView = BatchFeatureView( # type: ignore[assignment] name=feature_view_proto.spec.name, 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, offline=feature_view_proto.spec.offline, ttl=( timedelta(days=0) if feature_view_proto.spec.ttl.ToNanoseconds() == 0 else feature_view_proto.spec.ttl.ToTimedelta() ), source=source_views if source_views else batch_source, # type: ignore[arg-type] sink_source=batch_source if source_views else None, mode=mode, feature_transformation=transformation, ) else: mode_from_spec = ( feature_view_proto.spec.mode if feature_view_proto.spec.mode else None ) feature_view = cls( # type: ignore[assignment] name=feature_view_proto.spec.name, 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, offline=feature_view_proto.spec.offline, ttl=( timedelta(days=0) if feature_view_proto.spec.ttl.ToNanoseconds() == 0 else feature_view_proto.spec.ttl.ToTimedelta() ), source=source_views if source_views else batch_source, sink_source=batch_source if source_views else None, mode=mode_from_spec, ) if stream_source: feature_view.stream_source = stream_source # This avoids the deprecation warning. feature_view.entities = list(feature_view_proto.spec.entities) # Instead of passing in a schema, we set the features and entity columns. feature_view.features = [ Field.from_proto(field_proto) for field_proto in feature_view_proto.spec.features ] feature_view.entity_columns = [ Field.from_proto(field_proto) for field_proto in feature_view_proto.spec.entity_columns ] if len(feature_view.entities) != len(feature_view.entity_columns): warnings.warn( f"There are some mismatches in your feature view: {feature_view.name} registered entities. Please check if you have applied your entities correctly." f"Entities: {feature_view.entities} vs Entity Columns: {feature_view.entity_columns}" ) # Restore enable_validation from proto field. feature_view.enable_validation = feature_view_proto.spec.enable_validation # FeatureViewProjections are not saved in the FeatureView proto. # Create the default projection. feature_view.projection = FeatureViewProjection.from_feature_view_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()), ) ) seen[feature_view_name] = feature_view 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]) |
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