Source code for feast.saved_dataset

from abc import abstractmethod
from datetime import datetime
from typing import TYPE_CHECKING, Dict, List, Optional, Type, cast

import pandas as pd
import pyarrow
from google.protobuf.json_format import MessageToJson

from feast.data_source import DataSource
from feast.dqm.profilers.profiler import Profile, Profiler
from feast.protos.feast.core.SavedDataset_pb2 import SavedDataset as SavedDatasetProto
from feast.protos.feast.core.SavedDataset_pb2 import SavedDatasetMeta, SavedDatasetSpec
from feast.protos.feast.core.SavedDataset_pb2 import (
    SavedDatasetStorage as SavedDatasetStorageProto,
from feast.protos.feast.core.ValidationProfile_pb2 import (
    ValidationReference as ValidationReferenceProto,

    from feast.infra.offline_stores.offline_store import RetrievalJob

class _StorageRegistry(type):
    classes_by_proto_attr_name: Dict[str, Type["SavedDatasetStorage"]] = {}

    def __new__(cls, name, bases, dct):
        kls = type.__new__(cls, name, bases, dct)
        if dct.get("_proto_attr_name"):
            cls.classes_by_proto_attr_name[dct["_proto_attr_name"]] = kls
        return kls

[docs]class SavedDatasetStorage(metaclass=_StorageRegistry): _proto_attr_name: str
[docs] @staticmethod def from_proto(storage_proto: SavedDatasetStorageProto) -> "SavedDatasetStorage": proto_attr_name = cast(str, storage_proto.WhichOneof("kind")) return _StorageRegistry.classes_by_proto_attr_name[proto_attr_name].from_proto( storage_proto )
[docs] @abstractmethod def to_proto(self) -> SavedDatasetStorageProto: ...
[docs] @abstractmethod def to_data_source(self) -> DataSource: ...
[docs]class SavedDataset: name: str features: List[str] join_keys: List[str] full_feature_names: bool storage: SavedDatasetStorage tags: Dict[str, str] feature_service_name: Optional[str] = None created_timestamp: Optional[datetime] = None last_updated_timestamp: Optional[datetime] = None min_event_timestamp: Optional[datetime] = None max_event_timestamp: Optional[datetime] = None _retrieval_job: Optional["RetrievalJob"] = None def __init__( self, name: str, features: List[str], join_keys: List[str], storage: SavedDatasetStorage, full_feature_names: bool = False, tags: Optional[Dict[str, str]] = None, feature_service_name: Optional[str] = None, ): = name self.features = features self.join_keys = join_keys = storage self.full_feature_names = full_feature_names self.tags = tags or {} self.feature_service_name = feature_service_name self._retrieval_job = None def __repr__(self): items = (f"{k} = {v}" for k, v in self.__dict__.items()) return f"<{self.__class__.__name__}({', '.join(items)})>" def __str__(self): return str(MessageToJson(self.to_proto())) def __hash__(self): return hash(( def __eq__(self, other): if not isinstance(other, SavedDataset): raise TypeError( "Comparisons should only involve SavedDataset class objects." ) if ( != or sorted(self.features) != sorted(other.features) or sorted(self.join_keys) != sorted(other.join_keys) or != or self.full_feature_names != other.full_feature_names or self.tags != other.tags or self.feature_service_name != other.feature_service_name ): return False return True
[docs] @staticmethod def from_proto(saved_dataset_proto: SavedDatasetProto): """ Converts a SavedDatasetProto to a SavedDataset object. Args: saved_dataset_proto: A protobuf representation of a SavedDataset. """ ds = SavedDataset(, features=list(saved_dataset_proto.spec.features), join_keys=list(saved_dataset_proto.spec.join_keys), full_feature_names=saved_dataset_proto.spec.full_feature_names, storage=SavedDatasetStorage.from_proto(, tags=dict(saved_dataset_proto.spec.tags.items()), ) if saved_dataset_proto.spec.feature_service_name: ds.feature_service_name = saved_dataset_proto.spec.feature_service_name if saved_dataset_proto.meta.HasField("created_timestamp"): ds.created_timestamp = ( saved_dataset_proto.meta.created_timestamp.ToDatetime() ) if saved_dataset_proto.meta.HasField("last_updated_timestamp"): ds.last_updated_timestamp = ( saved_dataset_proto.meta.last_updated_timestamp.ToDatetime() ) if saved_dataset_proto.meta.HasField("min_event_timestamp"): ds.min_event_timestamp = ( saved_dataset_proto.meta.min_event_timestamp.ToDatetime() ) if saved_dataset_proto.meta.HasField("max_event_timestamp"): ds.max_event_timestamp = ( saved_dataset_proto.meta.max_event_timestamp.ToDatetime() ) return ds
[docs] def to_proto(self) -> SavedDatasetProto: """ Converts a SavedDataset to its protobuf representation. Returns: A SavedDatasetProto protobuf. """ meta = SavedDatasetMeta() if self.created_timestamp: meta.created_timestamp.FromDatetime(self.created_timestamp) if self.min_event_timestamp: meta.min_event_timestamp.FromDatetime(self.min_event_timestamp) if self.max_event_timestamp: meta.max_event_timestamp.FromDatetime(self.max_event_timestamp) spec = SavedDatasetSpec(, features=self.features, join_keys=self.join_keys, full_feature_names=self.full_feature_names,, tags=self.tags, ) if self.feature_service_name: spec.feature_service_name = self.feature_service_name saved_dataset_proto = SavedDatasetProto(spec=spec, meta=meta) return saved_dataset_proto
[docs] def with_retrieval_job(self, retrieval_job: "RetrievalJob") -> "SavedDataset": self._retrieval_job = retrieval_job return self
[docs] def to_df(self) -> pd.DataFrame: if not self._retrieval_job: raise RuntimeError( "To load this dataset use FeatureStore.get_saved_dataset() " "instead of instantiating it directly." ) return self._retrieval_job.to_df()
[docs] def to_arrow(self) -> pyarrow.Table: if not self._retrieval_job: raise RuntimeError( "To load this dataset use FeatureStore.get_saved_dataset() " "instead of instantiating it directly." ) return self._retrieval_job.to_arrow()
[docs] def as_reference(self, name: str, profiler: "Profiler") -> "ValidationReference": return ValidationReference.from_saved_dataset( name=name, profiler=profiler, dataset=self )
[docs] def get_profile(self, profiler: Profiler) -> Profile: return profiler.analyze_dataset(self.to_df())
[docs]class ValidationReference: name: str dataset_name: str description: str tags: Dict[str, str] profiler: Profiler _profile: Optional[Profile] = None _dataset: Optional[SavedDataset] = None def __init__( self, name: str, dataset_name: str, profiler: Profiler, description: str = "", tags: Optional[Dict[str, str]] = None, ): """ Validation reference combines a reference dataset (currently only a saved dataset object can be used as a reference) and a profiler function to generate a validation profile. The validation profile can be cached in this object, and in this case the saved dataset retrieval and the profiler call will happen only once. Validation reference is being stored in the Feast registry and can be retrieved by its name, which must be unique within one project. Args: name: the unique name for validation reference dataset_name: the name of the saved dataset used as a reference description: a human-readable description tags: a dictionary of key-value pairs to store arbitrary metadata profiler: the profiler function used to generate profile from the saved dataset """ = name self.dataset_name = dataset_name self.profiler = profiler self.description = description self.tags = tags or {}
[docs] @classmethod def from_saved_dataset(cls, name: str, dataset: SavedDataset, profiler: Profiler): """ Internal constructor to create validation reference object with actual saved dataset object (regular constructor requires only its name). """ ref = ValidationReference(name,, profiler) ref._dataset = dataset return ref
@property def profile(self) -> Profile: if not self._profile: if not self._dataset: raise RuntimeError( "In order to calculate a profile validation reference must be instantiated from a saved dataset. " "Use ValidationReference.from_saved_dataset constructor or FeatureStore.get_validation_reference " "to get validation reference object." ) self._profile = self.profiler.analyze_dataset(self._dataset.to_df()) return self._profile
[docs] @classmethod def from_proto(cls, proto: ValidationReferenceProto) -> "ValidationReference": profiler_attr = proto.WhichOneof("profiler") if profiler_attr == "ge_profiler": from feast.dqm.profilers.ge_profiler import GEProfiler profiler = GEProfiler.from_proto(proto.ge_profiler) else: raise RuntimeError("Unrecognized profiler") profile_attr = proto.WhichOneof("cached_profile") if profile_attr == "ge_profile": from feast.dqm.profilers.ge_profiler import GEProfile profile = GEProfile.from_proto(proto.ge_profile) elif not profile_attr: profile = None else: raise RuntimeError("Unrecognized profile") ref = ValidationReference(, dataset_name=proto.reference_dataset_name, profiler=profiler, description=proto.description, tags=dict(proto.tags), ) ref._profile = profile return ref
[docs] def to_proto(self) -> ValidationReferenceProto: from feast.dqm.profilers.ge_profiler import GEProfile, GEProfiler proto = ValidationReferenceProto(, reference_dataset_name=self.dataset_name, tags=self.tags, description=self.description, ge_profiler=self.profiler.to_proto() if isinstance(self.profiler, GEProfiler) else None, ge_profile=self._profile.to_proto() if isinstance(self._profile, GEProfile) else None, ) return proto