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GeezerVison is showing github.com — feast/sdk/python/feast/stream_feature_view.py at master · iamcodingcat/feast Expand file tree/ Copy pathstream_feature_view.pyMore file actions 463 lines (420 loc) · 17.9 KB / Copy pathstream_feature_view.pyFile metadata and controls463 lines (420 loc) · 17.9 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 import copy import functools import warnings from datetime import datetime, timedelta from types import FunctionType from typing import Any, Dict, List, Optional, Tuple, Type, Union import dill from google.protobuf.duration_pb2 import Duration from google.protobuf.message import Message from typeguard import typechecked from feast import flags_helper, utils from feast.aggregation import Aggregation from feast.data_source import DataSource from feast.entity import Entity from feast.feature_view import FeatureView from feast.field import Field from feast.proto_utils import mode_to_string, serialize_data_source from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto from feast.protos.feast.core.OnDemandFeatureView_pb2 import ( UserDefinedFunction as UserDefinedFunctionProto, ) from feast.protos.feast.core.StreamFeatureView_pb2 import ( StreamFeatureView as StreamFeatureViewProto, ) from feast.protos.feast.core.StreamFeatureView_pb2 import ( StreamFeatureViewSpec as StreamFeatureViewSpecProto, ) from feast.protos.feast.core.Transformation_pb2 import ( FeatureTransformationV2 as FeatureTransformationProto, ) from feast.protos.feast.core.Transformation_pb2 import ( UserDefinedFunctionV2 as UserDefinedFunctionProtoV2, ) from feast.transformation.base import Transformation from feast.transformation.mode import TransformationMode warnings.simplefilter("once", RuntimeWarning) SUPPORTED_STREAM_SOURCES = {"KafkaSource", "PushSource"} @typechecked class StreamFeatureView(FeatureView): """ A stream feature view defines a logical group of features that has both a stream data source and a batch data source. Attributes: name: The unique name of the stream feature view. mode: The transformation mode to use for the stream feature view. This can be one of TransformationMode. entities: List of entities or entity join keys. 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. schema: The schema of the feature view, including feature, timestamp, and entity columns. If not specified, can be inferred from the underlying data source. source: The stream source of data where this group of features is stored. aggregations: List of aggregations registered with the stream feature view. timestamp_field: Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows. enable_tiling: Enable tiling optimization for efficient windowed aggregations. tiling_hop_size: Time interval between tiles (e.g., 5 minutes). Defaults to 5 minutes. online: A boolean indicating whether online retrieval, and write to online store is enabled for this feature view. offline: A boolean indicating whether offline retrieval, and write to offline store 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 stream feature view, typically the email of the primary maintainer. udf: The user defined transformation function. This transformation function should have all of the corresponding imports imported within the function. udf_string: The string representation of the user defined transformation function. feature_transformation: The transformation to apply to the features. Note, feature_transformation has precedence over udf and udf_string. stream_engine: Optional dictionary containing stream engine specific configurations. Note, it will override the repo-level default stream engine config defined in the yaml file. """ name: str entities: List[str] ttl: Optional[timedelta] source: DataSource sink_source: Optional[DataSource] = None schema: List[Field] entity_columns: List[Field] features: List[Field] online: bool offline: bool description: str tags: Dict[str, str] owner: str mode: Union[TransformationMode, str] materialization_intervals: List[Tuple[datetime, datetime]] udf: Optional[FunctionType] udf_string: Optional[str] feature_transformation: Optional[Transformation] stream_engine: Optional[Dict[str, Any]] = None aggregations: List[Aggregation] timestamp_field: str enable_tiling: bool tiling_hop_size: Optional[timedelta] def __init__( self, *, name: str, source: Union[DataSource, "StreamFeatureView", List["StreamFeatureView"]], sink_source: Optional[DataSource] = None, entities: Optional[List[Entity]] = None, ttl: timedelta = timedelta(days=0), tags: Optional[Dict[str, str]] = None, online: bool = True, offline: bool = False, description: str = "", owner: str = "", schema: Optional[List[Field]] = None, aggregations: Optional[List[Aggregation]] = None, mode: Union[str, TransformationMode] = TransformationMode.PYTHON, timestamp_field: Optional[str] = "", udf: Optional[FunctionType] = None, udf_string: Optional[str] = "", feature_transformation: Optional[Transformation] = None, stream_engine: Optional[Dict[str, Any]] = None, enable_tiling: bool = False, tiling_hop_size: Optional[timedelta] = None, enable_validation: bool = False, ): if not flags_helper.is_test(): warnings.warn( "Stream feature views are experimental features in alpha development. " "Some functionality may still be unstable so functionality can change in the future.", RuntimeWarning, ) if isinstance(source, DataSource) and ( type(source).__name__ not in SUPPORTED_STREAM_SOURCES and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE ): raise ValueError( f"Stream feature views need a stream source, expected one of {SUPPORTED_STREAM_SOURCES} " f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead " ) if aggregations and not timestamp_field: raise ValueError( "aggregations must have a timestamp field associated with them to perform the aggregations" ) self.mode = mode self.udf = udf self.udf_string = udf_string self.aggregations = aggregations or [] self.timestamp_field = timestamp_field or "" self.feature_transformation = ( feature_transformation or self.get_feature_transformation() ) self.stream_engine = stream_engine self.enable_tiling = enable_tiling self.tiling_hop_size = tiling_hop_size if enable_tiling and self.aggregations: effective_hop_size = tiling_hop_size or timedelta(minutes=5) time_windows = [ agg.time_window for agg in self.aggregations if agg.time_window is not None ] if time_windows: min_window_size = min(time_windows) if effective_hop_size >= min_window_size: raise ValueError( f"tiling_hop_size ({effective_hop_size}) must be smaller than " f"the minimum aggregation time_window ({min_window_size}). " f"If hop_size >= window_size, the tiling algorithm will produce incorrect results." ) super().__init__( name=name, entities=entities, ttl=ttl, tags=tags, online=online, offline=offline, description=description, owner=owner, schema=schema, source=source, # type: ignore[arg-type] mode=mode, sink_source=sink_source, enable_validation=enable_validation, ) def get_feature_transformation(self) -> Optional[Transformation]: if not self.udf: # TODO: Currently StreamFeatureView allow no transformation, but this should be removed in the future return None if self.mode in ( TransformationMode.PANDAS, TransformationMode.PYTHON, TransformationMode.SPARK_SQL, TransformationMode.SPARK, ) or self.mode in ("pandas", "python", "spark_sql", "spark"): return Transformation( mode=self.mode, udf=self.udf, udf_string=self.udf_string or "" ) else: raise ValueError( f"Unsupported transformation mode: {self.mode} for StreamFeatureView" ) def __eq__(self, other): if not isinstance(other, StreamFeatureView): raise TypeError("Comparisons should only involve StreamFeatureViews") if not super().__eq__(other): return False if not self.udf: return not other.udf if not other.udf: return False if ( self.mode != other.mode or self.timestamp_field != other.timestamp_field or self.udf.__code__.co_code != other.udf.__code__.co_code or self.udf_string != other.udf_string or self.aggregations != other.aggregations ): return False return True def __hash__(self) -> int: return super().__hash__() def to_proto(self): meta = self.to_proto_meta() ttl_duration = self.get_ttl_duration() batch_source_proto = serialize_data_source(self.batch_source) stream_source_proto = serialize_data_source(self.stream_source) udf_proto, feature_transformation = None, None if self.udf: udf_proto = UserDefinedFunctionProto( name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True), body_text=self.udf_string, ) udf_proto_v2 = UserDefinedFunctionProtoV2( name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True), body_text=self.udf_string, ) feature_transformation = FeatureTransformationProto( user_defined_function=udf_proto_v2, ) # Serialize tiling configuration tiling_hop_size_duration = None if self.tiling_hop_size is not None: tiling_hop_size_duration = Duration() tiling_hop_size_duration.FromTimedelta(self.tiling_hop_size) spec = StreamFeatureViewSpecProto( name=self.name, entities=self.entities, entity_columns=[field.to_proto() for field in self.entity_columns], features=[field.to_proto() for field in self.schema], user_defined_function=udf_proto, feature_transformation=feature_transformation, description=self.description, tags=self.tags, owner=self.owner, ttl=ttl_duration, online=self.online, batch_source=batch_source_proto, stream_source=stream_source_proto, timestamp_field=self.timestamp_field, aggregations=[agg.to_proto() for agg in self.aggregations], mode=mode_to_string(self.mode), enable_tiling=self.enable_tiling, tiling_hop_size=tiling_hop_size_duration, enable_validation=self.enable_validation, ) return StreamFeatureViewProto(spec=spec, meta=meta) @classmethod def from_proto(cls, sfv_proto): batch_source = ( DataSource.from_proto(sfv_proto.spec.batch_source) if sfv_proto.spec.HasField("batch_source") else None ) stream_source = ( DataSource.from_proto(sfv_proto.spec.stream_source) if sfv_proto.spec.HasField("stream_source") else None ) udf = ( dill.loads(sfv_proto.spec.user_defined_function.body) if sfv_proto.spec.HasField("user_defined_function") else None ) udf_string = ( sfv_proto.spec.user_defined_function.body_text if sfv_proto.spec.HasField("user_defined_function") else None ) # feature_transformation = ( # sfv_proto.spec.feature_transformation.user_defined_function.body_text # if sfv_proto.spec.HasField("feature_transformation") # else None # ) stream_feature_view = cls( name=sfv_proto.spec.name, description=sfv_proto.spec.description, tags=dict(sfv_proto.spec.tags), owner=sfv_proto.spec.owner, online=sfv_proto.spec.online, schema=[ Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features ], ttl=( timedelta(days=0) if sfv_proto.spec.ttl.ToNanoseconds() == 0 else sfv_proto.spec.ttl.ToTimedelta() ), source=stream_source, mode=sfv_proto.spec.mode, udf=udf, udf_string=udf_string, aggregations=[ Aggregation.from_proto(agg_proto) for agg_proto in sfv_proto.spec.aggregations ], timestamp_field=sfv_proto.spec.timestamp_field, enable_tiling=sfv_proto.spec.enable_tiling, tiling_hop_size=( sfv_proto.spec.tiling_hop_size.ToTimedelta() if sfv_proto.spec.HasField("tiling_hop_size") and sfv_proto.spec.tiling_hop_size.ToNanoseconds() != 0 else None ), enable_validation=sfv_proto.spec.enable_validation, ) if batch_source: stream_feature_view.batch_source = batch_source if stream_source: stream_feature_view.stream_source = stream_source stream_feature_view.entities = list(sfv_proto.spec.entities) stream_feature_view.features = [ Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features ] stream_feature_view.entity_columns = [ Field.from_proto(field_proto) for field_proto in sfv_proto.spec.entity_columns ] if sfv_proto.meta.HasField("created_timestamp"): stream_feature_view.created_timestamp = ( sfv_proto.meta.created_timestamp.ToDatetime() ) if sfv_proto.meta.HasField("last_updated_timestamp"): stream_feature_view.last_updated_timestamp = ( sfv_proto.meta.last_updated_timestamp.ToDatetime() ) for interval in sfv_proto.meta.materialization_intervals: stream_feature_view.materialization_intervals.append( ( utils.make_tzaware(interval.start_time.ToDatetime()), utils.make_tzaware(interval.end_time.ToDatetime()), ) ) return stream_feature_view def __copy__(self): fv = StreamFeatureView( name=self.name, schema=self.schema, ttl=self.ttl, tags=self.tags, online=self.online, description=self.description, owner=self.owner, aggregations=self.aggregations, mode=self.mode, timestamp_field=self.timestamp_field, source=self.stream_source if self.stream_source else self.batch_source, udf=self.udf, udf_string=self.udf_string, feature_transformation=self.feature_transformation, enable_validation=self.enable_validation, ) 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 @property def proto_class(self) -> Type[Message]: return StreamFeatureViewProto def stream_feature_view( *, entities: Optional[Union[List[Entity], List[str]]] = None, ttl: Optional[timedelta] = None, tags: Optional[Dict[str, str]] = None, online: Optional[bool] = True, description: Optional[str] = "", owner: Optional[str] = "", schema: Optional[List[Field]] = None, source: Optional[DataSource] = None, aggregations: Optional[List[Aggregation]] = None, mode: Optional[str] = "spark", timestamp_field: Optional[str] = "", enable_validation: bool = False, ): """ Creates an StreamFeatureView object with the given user function as udf. Please make sure that the udf contains all non-built in imports within the function to ensure that the execution of a deserialized function does not miss imports. """ def mainify(obj): # Needed to allow dill to properly serialize the udf. Otherwise, clients will need to have a file with the same # name as the original file defining the sfv. if obj.__module__ != "__main__": obj.__module__ = "__main__" def decorator(user_function): udf_string = dill.source.getsource(user_function) mainify(user_function) stream_feature_view_obj = StreamFeatureView( name=user_function.__name__, entities=entities, ttl=ttl, source=source, schema=schema, udf=user_function, udf_string=udf_string, description=description, tags=tags, online=online, owner=owner, aggregations=aggregations, mode=mode, timestamp_field=timestamp_field, enable_validation=enable_validation, ) functools.update_wrapper(wrapper=stream_feature_view_obj, wrapped=user_function) return stream_feature_view_obj return decorator |
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