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saved_dataset.py
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352 lines (289 loc) · 12.2 KB
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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.importer import import_class
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,
)
if TYPE_CHECKING:
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
_DATA_SOURCE_TO_SAVED_DATASET_STORAGE = {
"FileSource": "feast.infra.offline_stores.file_source.SavedDatasetFileStorage",
}
def get_saved_dataset_storage_class_from_path(saved_dataset_storage_path: str):
module_name, class_name = saved_dataset_storage_path.rsplit(".", 1)
return import_class(module_name, class_name, "SavedDatasetStorage")
class SavedDatasetStorage(metaclass=_StorageRegistry):
_proto_attr_name: str
@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
)
@abstractmethod
def to_proto(self) -> SavedDatasetStorageProto:
pass
@abstractmethod
def to_data_source(self) -> DataSource:
pass
@staticmethod
def from_data_source(data_source: DataSource) -> "SavedDatasetStorage":
data_source_type = type(data_source).__name__
if data_source_type in _DATA_SOURCE_TO_SAVED_DATASET_STORAGE:
cls = get_saved_dataset_storage_class_from_path(
_DATA_SOURCE_TO_SAVED_DATASET_STORAGE[data_source_type]
)
return cls.from_data_source(data_source)
else:
raise ValueError(
f"This method currently does not support {data_source_type}."
)
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,
):
self.name = name
self.features = features
self.join_keys = join_keys
self.storage = 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((self.name))
def __eq__(self, other):
if not isinstance(other, SavedDataset):
raise TypeError(
"Comparisons should only involve SavedDataset class objects."
)
if (
self.name != other.name
or sorted(self.features) != sorted(other.features)
or sorted(self.join_keys) != sorted(other.join_keys)
or self.storage != other.storage
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
@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(
name=saved_dataset_proto.spec.name,
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(saved_dataset_proto.spec.storage),
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
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(
name=self.name,
features=self.features,
join_keys=self.join_keys,
full_feature_names=self.full_feature_names,
storage=self.storage.to_proto(),
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
def with_retrieval_job(self, retrieval_job: "RetrievalJob") -> "SavedDataset":
self._retrieval_job = retrieval_job
return self
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()
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()
def as_reference(self, name: str, profiler: "Profiler") -> "ValidationReference":
return ValidationReference.from_saved_dataset(
name=name, profiler=profiler, dataset=self
)
def get_profile(self, profiler: Profiler) -> Profile:
return profiler.analyze_dataset(self.to_df())
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
"""
self.name = name
self.dataset_name = dataset_name
self.profiler = profiler
self.description = description
self.tags = tags or {}
@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, dataset.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
@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(
name=proto.name,
dataset_name=proto.reference_dataset_name,
profiler=profiler,
description=proto.description,
tags=dict(proto.tags),
)
ref._profile = profile
return ref
def to_proto(self) -> ValidationReferenceProto:
from feast.dqm.profilers.ge_profiler import GEProfile, GEProfiler
proto = ValidationReferenceProto(
name=self.name,
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