def from_proto(data_source: DataSourceProto):
deprecated_schema = data_source.request_data_options.deprecated_schema
schema_pb = data_source.request_data_options.schema
if deprecated_schema and not schema_pb:
warnings.warn(
"Schema in RequestSource is changing type. The schema data type Dict[str, ValueType] is being deprecated in Feast 0.23. "
"Please use List[Field] instead for the schema",
DeprecationWarning,
)
dict_schema = {}
for key, val in deprecated_schema.items():
dict_schema[key] = ValueType(val)
return RequestSource(
name=data_source.name,
schema=dict_schema,
description=data_source.description,
tags=dict(data_source.tags),
owner=data_source.owner,
)
else:
list_schema = []
for field_proto in schema_pb:
list_schema.append(Field.from_proto(field_proto))
return RequestSource(
name=data_source.name,
schema=list_schema,
description=data_source.description,
tags=dict(data_source.tags),
owner=data_source.owner,
)
def from_proto(proto: FeatureViewProjectionProto):
feature_view_projection = FeatureViewProjection(
name=proto.feature_view_name,
name_alias=proto.feature_view_name_alias,
features=[],
join_key_map=dict(proto.join_key_map),
)
for feature_column in proto.feature_columns:
feature_view_projection.features.append(Field.from_proto(feature_column))
return feature_view_projection
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],
schema=[
Field.from_proto(field_proto)
for field_proto 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()),
source=batch_source,
)
if stream_source:
feature_view.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