import copy

import itertools

import logging

import os

import typing

import warnings

from collections import Counter, defaultdict

from datetime import datetime, timezone

from pathlib import Path

from typing import (

Any,

Dict,

Iterable,

List,

Mapping,

Optional,

Sequence,

Set,

Tuple,

Union,

cast,

)

import pandas as pd

import pyarrow

from dateutil.tz import tzlocal

from google.protobuf.timestamp_pb2 import Timestamp

from feast.constants import FEAST_FS_YAML_FILE_PATH_ENV_NAME

from feast.entity import Entity

from feast.errors import (

EntityNotFoundException,

FeatureNameCollisionError,

FeatureViewNotFoundException,

RequestDataNotFoundInEntityRowsException,

)

from feast.protos.feast.serving.ServingService_pb2 import (

FieldStatus,

GetOnlineFeaturesResponse,

)

from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto

from feast.protos.feast.types.Value_pb2 import RepeatedValue as RepeatedValueProto

from feast.protos.feast.types.Value_pb2 import Value as ValueProto

from feast.type_map import python_values_to_proto_values

from feast.value_type import ValueType

from feast.version import get_version

if typing.TYPE_CHECKING:

from feast.feature_service import FeatureService

from feast.feature_view import FeatureView

from feast.on_demand_feature_view import OnDemandFeatureView

APPLICATION_NAME = "feast-dev/feast"

USER_AGENT = "{}/{}".format(APPLICATION_NAME, get_version())

def get_user_agent():

return USER_AGENT

def make_tzaware(t: datetime) -> datetime:

"""We assume tz-naive datetimes are UTC"""

if t.tzinfo is None:

return t.replace(tzinfo=timezone.utc)

else:

return t

def make_df_tzaware(t: pd.DataFrame) -> pd.DataFrame:

"""Make all datetime type columns tzaware; leave everything else intact."""

df = t.copy() # don't modify incoming dataframe inplace

for column in df.columns:

if pd.api.types.is_datetime64_any_dtype(df[column]):

df[column] = pd.to_datetime(df[column], utc=True)

return df

def to_naive_utc(ts: datetime) -> datetime:

if ts.tzinfo is None:

return ts

else:

return ts.astimezone(timezone.utc).replace(tzinfo=None)

def maybe_local_tz(t: datetime) -> datetime:

if t.tzinfo is None:

return t.replace(tzinfo=tzlocal())

else:

return t

def get_default_yaml_file_path(repo_path: Path) -> Path:

if FEAST_FS_YAML_FILE_PATH_ENV_NAME in os.environ:

yaml_path = os.environ[FEAST_FS_YAML_FILE_PATH_ENV_NAME]

return Path(yaml_path)

else:

return repo_path / "feature_store.yaml"

def _get_requested_feature_views_to_features_dict(

feature_refs: List[str],

feature_views: List["FeatureView"],

on_demand_feature_views: List["OnDemandFeatureView"],

) -> Tuple[Dict["FeatureView", List[str]], Dict["OnDemandFeatureView", List[str]]]:

"""Create a dict of FeatureView -> List[Feature] for all requested features.

Set full_feature_names to True to have feature names prefixed by their feature view name."""

feature_views_to_feature_map: Dict["FeatureView", List[str]] = defaultdict(list)

on_demand_feature_views_to_feature_map: Dict["OnDemandFeatureView", List[str]] = (

defaultdict(list)

)

for ref in feature_refs:

ref_parts = ref.split(":")

feature_view_from_ref = ref_parts[0]

feature_from_ref = ref_parts[1]

found = False

for fv in feature_views:

if fv.projection.name_to_use() == feature_view_from_ref:

found = True

feature_views_to_feature_map[fv].append(feature_from_ref)

for odfv in on_demand_feature_views:

if odfv.projection.name_to_use() == feature_view_from_ref:

found = True

on_demand_feature_views_to_feature_map[odfv].append(feature_from_ref)

if not found:

raise ValueError(f"Could not find feature view from reference {ref}")

return feature_views_to_feature_map, on_demand_feature_views_to_feature_map

def _get_column_names(

feature_view: "FeatureView", entities: List[Entity]

) -> Tuple[List[str], List[str], str, Optional[str]]:

"""

If a field mapping exists, run it in reverse on the join keys,

feature names, event timestamp column, and created timestamp column

to get the names of the relevant columns in the offline feature store table.

Returns:

Tuple containing the list of reverse-mapped join_keys,

reverse-mapped feature names, reverse-mapped event timestamp column,

and reverse-mapped created timestamp column that will be passed into

the query to the offline store.

"""

# if we have mapped fields, use the original field names in the call to the offline store

timestamp_field = feature_view.batch_source.timestamp_field

feature_names = [feature.name for feature in feature_view.features]

created_timestamp_column = feature_view.batch_source.created_timestamp_column

from feast.feature_view import DUMMY_ENTITY_ID

join_keys = [

entity.join_key for entity in entities if entity.join_key != DUMMY_ENTITY_ID

]

if feature_view.batch_source.field_mapping is not None:

reverse_field_mapping = {

v: k for k, v in feature_view.batch_source.field_mapping.items()

}

timestamp_field = (

reverse_field_mapping[timestamp_field]

if timestamp_field in reverse_field_mapping.keys()

else timestamp_field

)

created_timestamp_column = (

reverse_field_mapping[created_timestamp_column]

if created_timestamp_column

and created_timestamp_column in reverse_field_mapping.keys()

else created_timestamp_column

)

join_keys = [

reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col

for col in join_keys

]

feature_names = [

reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col

for col in feature_names

]

# We need to exclude join keys and timestamp columns from the list of features, after they are mapped to

# their final column names via the `field_mapping` field of the source.

feature_names = [

name

for name in feature_names

if name not in join_keys

and name != timestamp_field

and name != created_timestamp_column

]

return (

join_keys,

feature_names,

timestamp_field,

created_timestamp_column,

)

def _run_pyarrow_field_mapping(

table: pyarrow.Table,

field_mapping: Dict[str, str],

) -> pyarrow.Table:

# run field mapping in the forward direction

cols = table.column_names

mapped_cols = [

field_mapping[col] if col in field_mapping.keys() else col for col in cols

]

table = table.rename_columns(mapped_cols)

return table

def _coerce_datetime(ts):

"""

Depending on underlying time resolution, arrow to_pydict() sometimes returns pd

timestamp type (for nanosecond resolution), and sometimes you get standard python datetime

(for microsecond resolution).

While pd timestamp class is a subclass of python datetime, it doesn't always behave the

same way. We convert it to normal datetime so that consumers downstream don't have to deal

with these quirks.

"""

if isinstance(ts, pd.Timestamp):

return ts.to_pydatetime()

else:

return ts

def _convert_arrow_to_proto(

table: Union[pyarrow.Table, pyarrow.RecordBatch],

feature_view: "FeatureView",

join_keys: Dict[str, ValueType],

) -> List[Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]]:

# Avoid ChunkedArrays which guarantees `zero_copy_only` available.

if isinstance(table, pyarrow.Table):

table = table.to_batches()[0]

columns = [

(field.name, field.dtype.to_value_type()) for field in feature_view.features

] + list(join_keys.items())

proto_values_by_column = {

column: python_values_to_proto_values(

table.column(column).to_numpy(zero_copy_only=False), value_type

)

for column, value_type in columns

}

entity_keys = [

EntityKeyProto(

join_keys=join_keys,

entity_values=[proto_values_by_column[k][idx] for k in join_keys],

)

for idx in range(table.num_rows)

]

# Serialize the features per row

feature_dict = {

feature.name: proto_values_by_column[feature.name]

for feature in feature_view.features

}

features = [dict(zip(feature_dict, vars)) for vars in zip(*feature_dict.values())]

# Convert event_timestamps

event_timestamps = [

_coerce_datetime(val)

for val in pd.to_datetime(

table.column(feature_view.batch_source.timestamp_field).to_numpy(

zero_copy_only=False

)

)

]

# Convert created_timestamps if they exist

if feature_view.batch_source.created_timestamp_column:

created_timestamps = [

_coerce_datetime(val)

for val in pd.to_datetime(

table.column(

feature_view.batch_source.created_timestamp_column

).to_numpy(zero_copy_only=False)

)

]

else:

created_timestamps = [None] * table.num_rows

return list(zip(entity_keys, features, event_timestamps, created_timestamps))

def _validate_entity_values(join_key_values: Dict[str, List[ValueProto]]):

set_of_row_lengths = {len(v) for v in join_key_values.values()}

if len(set_of_row_lengths) > 1:

raise ValueError("All entity rows must have the same columns.")

return set_of_row_lengths.pop()

def _validate_feature_refs(feature_refs: List[str], full_feature_names: bool = False):

"""

Validates that there are no collisions among the feature references.

Args:

feature_refs: List of feature references to validate. Feature references must have format

"feature_view:feature", e.g. "customer_fv:daily_transactions".

full_feature_names: If True, the full feature references are compared for collisions; if False,

only the feature names are compared.

Raises:

FeatureNameCollisionError: There is a collision among the feature references.

"""

collided_feature_refs = []

if full_feature_names:

collided_feature_refs = [

ref for ref, occurrences in Counter(feature_refs).items() if occurrences > 1

]

else:

feature_names = [ref.split(":")[1] for ref in feature_refs]

collided_feature_names = [

ref

for ref, occurrences in Counter(feature_names).items()

if occurrences > 1

]

for feature_name in collided_feature_names:

collided_feature_refs.extend(

[ref for ref in feature_refs if ref.endswith(":" + feature_name)]

)

if len(collided_feature_refs) > 0:

raise FeatureNameCollisionError(collided_feature_refs, full_feature_names)

def _group_feature_refs(

features: List[str],

all_feature_views: List["FeatureView"],

all_on_demand_feature_views: List["OnDemandFeatureView"],

) -> Tuple[

List[Tuple["FeatureView", List[str]]], List[Tuple["OnDemandFeatureView", List[str]]]

]:

"""Get list of feature views and corresponding feature names based on feature references"""

# view name to view proto

view_index = {view.projection.name_to_use(): view for view in all_feature_views}

# on demand view to on demand view proto

on_demand_view_index = {

view.projection.name_to_use(): view for view in all_on_demand_feature_views

}

# view name to feature names

views_features = defaultdict(set)

# on demand view name to feature names

on_demand_view_features = defaultdict(set)

for ref in features:

view_name, feat_name = ref.split(":")

if view_name in view_index:

view_index[view_name].projection.get_feature(feat_name) # For validation

views_features[view_name].add(feat_name)

elif view_name in on_demand_view_index:

on_demand_view_index[view_name].projection.get_feature(

feat_name

) # For validation

on_demand_view_features[view_name].add(feat_name)

# Let's also add in any FV Feature dependencies here.

for input_fv_projection in on_demand_view_index[

view_name

].source_feature_view_projections.values():

for input_feat in input_fv_projection.features:

views_features[input_fv_projection.name].add(input_feat.name)

else:

raise FeatureViewNotFoundException(view_name)

fvs_result: List[Tuple["FeatureView", List[str]]] = []

odfvs_result: List[Tuple["OnDemandFeatureView", List[str]]] = []

for view_name, feature_names in views_features.items():

fvs_result.append((view_index[view_name], list(feature_names)))

for view_name, feature_names in on_demand_view_features.items():

odfvs_result.append((on_demand_view_index[view_name], list(feature_names)))

return fvs_result, odfvs_result

def apply_list_mapping(

lst: Iterable[Any], mapping_indexes: Iterable[List[int]]

) -> Iterable[Any]:

output_len = sum(len(item) for item in mapping_indexes)

output = [None] * output_len

for elem, destinations in zip(lst, mapping_indexes):

for idx in destinations:

output[idx] = elem

return output

def _augment_response_with_on_demand_transforms(

online_features_response: GetOnlineFeaturesResponse,

feature_refs: List[str],

requested_on_demand_feature_views: List["OnDemandFeatureView"],

full_feature_names: bool,

):

"""Computes on demand feature values and adds them to the result rows.

Assumes that 'online_features_response' already contains the necessary request data and input feature

views for the on demand feature views. Unneeded feature values such as request data and

unrequested input feature views will be removed from 'online_features_response'.

Args:

online_features_response: Protobuf object to populate

feature_refs: List of all feature references to be returned.

requested_on_demand_feature_views: List of all odfvs that have been requested.

full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,

changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to

"customer_fv__daily_transactions").

"""

from feast.online_response import OnlineResponse

requested_odfv_map = {odfv.name: odfv for odfv in requested_on_demand_feature_views}

requested_odfv_feature_names = requested_odfv_map.keys()

odfv_feature_refs = defaultdict(list)

for feature_ref in feature_refs:

view_name, feature_name = feature_ref.split(":")

if view_name in requested_odfv_feature_names:

odfv_feature_refs[view_name].append(

f"{requested_odfv_map[view_name].projection.name_to_use()}__{feature_name}"

if full_feature_names

else feature_name

)

initial_response = OnlineResponse(online_features_response)

initial_response_arrow: Optional[pyarrow.Table] = None

initial_response_dict: Optional[Dict[str, List[Any]]] = None

# Apply on demand transformations and augment the result rows

odfv_result_names = set()

for odfv_name, _feature_refs in odfv_feature_refs.items():

odfv = requested_odfv_map[odfv_name]

if odfv.mode == "python":

if initial_response_dict is None:

initial_response_dict = initial_response.to_dict()

transformed_features_dict: Dict[str, List[Any]] = odfv.transform_dict(

initial_response_dict

)

elif odfv.mode in {"pandas", "substrait"}:

if initial_response_arrow is None:

initial_response_arrow = initial_response.to_arrow()

transformed_features_arrow = odfv.transform_arrow(

initial_response_arrow, full_feature_names

)

else:

raise Exception(

f"Invalid OnDemandFeatureMode: {odfv.mode}. Expected one of 'pandas', 'python', or 'substrait'."

)

transformed_features = (

transformed_features_dict

if odfv.mode == "python"

else transformed_features_arrow

)

transformed_columns = (

transformed_features.column_names

if isinstance(transformed_features, pyarrow.Table)

else transformed_features

)

selected_subset = [f for f in transformed_columns if f in _feature_refs]

proto_values = []

for selected_feature in selected_subset:

feature_vector = transformed_features[selected_feature]

proto_values.append(

python_values_to_proto_values(feature_vector, ValueType.UNKNOWN)

if odfv.mode == "python"

else python_values_to_proto_values(

feature_vector.to_numpy(), ValueType.UNKNOWN

)

)

odfv_result_names |= set(selected_subset)

online_features_response.metadata.feature_names.val.extend(selected_subset)

for feature_idx in range(len(selected_subset)):

online_features_response.results.append(

GetOnlineFeaturesResponse.FeatureVector(

values=proto_values[feature_idx],

statuses=[FieldStatus.PRESENT] * len(proto_values[feature_idx]),

event_timestamps=[Timestamp()] * len(proto_values[feature_idx]),

)

)

def _get_entity_maps(

registry,

project,

feature_views,

) -> Tuple[Dict[str, str], Dict[str, ValueType], Set[str]]:

# TODO(felixwang9817): Support entities that have different types for different feature views.

entities = registry.list_entities(project, allow_cache=True)

entity_name_to_join_key_map: Dict[str, str] = {}

entity_type_map: Dict[str, ValueType] = {}

for entity in entities:

entity_name_to_join_key_map[entity.name] = entity.join_key

for feature_view in feature_views:

for entity_name in feature_view.entities:

entity = registry.get_entity(entity_name, project, allow_cache=True)

# User directly uses join_key as the entity reference in the entity_rows for the

# entity mapping case.

entity_name = feature_view.projection.join_key_map.get(

entity.join_key, entity.name

)

join_key = feature_view.projection.join_key_map.get(

entity.join_key, entity.join_key

)

entity_name_to_join_key_map[entity_name] = join_key

for entity_column in feature_view.entity_columns:

entity_type_map[entity_column.name] = entity_column.dtype.to_value_type()

return (

entity_name_to_join_key_map,

entity_type_map,

set(entity_name_to_join_key_map.values()),

)

def _get_table_entity_values(

table: "FeatureView",

entity_name_to_join_key_map: Dict[str, str],

join_key_proto_values: Dict[str, List[ValueProto]],

) -> Dict[str, List[ValueProto]]:

# The correct join_keys expected by the OnlineStore for this Feature View.

table_join_keys = [

entity_name_to_join_key_map[entity_name] for entity_name in table.entities

]

# If the FeatureView has a Projection then the join keys may be aliased.

alias_to_join_key_map = {v: k for k, v in table.projection.join_key_map.items()}

# Subset to columns which are relevant to this FeatureView and

# give them the correct names.

entity_values = {

alias_to_join_key_map.get(k, k): v

for k, v in join_key_proto_values.items()

if alias_to_join_key_map.get(k, k) in table_join_keys

}

return entity_values

def _get_unique_entities(

table: "FeatureView",

join_key_values: Dict[str, List[ValueProto]],

entity_name_to_join_key_map: Dict[str, str],

) -> Tuple[Tuple[Dict[str, ValueProto], ...], Tuple[List[int], ...]]:

"""Return the set of unique composite Entities for a Feature View and the indexes at which they appear.

This method allows us to query the OnlineStore for data we need only once

rather than requesting and processing data for the same combination of

Entities multiple times.

"""

# Get the correct set of entity values with the correct join keys.

table_entity_values = _get_table_entity_values(

table,

entity_name_to_join_key_map,

join_key_values,

)

# Convert back to rowise.

keys = table_entity_values.keys()

# Sort the rowise data to allow for grouping but keep original index. This lambda is

# sufficient as Entity types cannot be complex (ie. lists).

rowise = list(enumerate(zip(*table_entity_values.values())))

rowise.sort(key=lambda row: tuple(getattr(x, x.WhichOneof("val")) for x in row[1]))

# Identify unique entities and the indexes at which they occur.

unique_entities: Tuple[Dict[str, ValueProto], ...]

indexes: Tuple[List[int], ...]

unique_entities, indexes = tuple(

zip(

*[

(dict(zip(keys, k)), [_[0] for _ in g])

for k, g in itertools.groupby(rowise, key=lambda x: x[1])

]

)

)

return unique_entities, indexes

def _drop_unneeded_columns(

online_features_response: GetOnlineFeaturesResponse,

requested_result_row_names: Set[str],

):

"""

Unneeded feature values such as request data and unrequested input feature views will

be removed from 'online_features_response'.

Args:

online_features_response: Protobuf object to populate

requested_result_row_names: Fields from 'result_rows' that have been requested, and

therefore should not be dropped.

"""

# Drop values that aren't needed

unneeded_feature_indices = [

idx

for idx, val in enumerate(online_features_response.metadata.feature_names.val)

if val not in requested_result_row_names

]

for idx in reversed(unneeded_feature_indices):

del online_features_response.metadata.feature_names.val[idx]

del online_features_response.results[idx]

def _populate_result_rows_from_columnar(

online_features_response: GetOnlineFeaturesResponse,

data: Dict[str, List[ValueProto]],

):

timestamp = Timestamp() # Only initialize this timestamp once.

# Add more values to the existing result rows

for feature_name, feature_values in data.items():

online_features_response.metadata.feature_names.val.append(feature_name)

online_features_response.results.append(

GetOnlineFeaturesResponse.FeatureVector(

values=feature_values,

statuses=[FieldStatus.PRESENT] * len(feature_values),

event_timestamps=[timestamp] * len(feature_values),

)

)

def get_needed_request_data(

grouped_odfv_refs: List[Tuple["OnDemandFeatureView", List[str]]],

) -> Set[str]:

needed_request_data: Set[str] = set()

for odfv, _ in grouped_odfv_refs:

odfv_request_data_schema = odfv.get_request_data_schema()

needed_request_data.update(odfv_request_data_schema.keys())

return needed_request_data

def ensure_request_data_values_exist(

needed_request_data: Set[str],

request_data_features: Dict[str, List[Any]],

):

if len(needed_request_data) != len(request_data_features.keys()):

missing_features = [

x for x in needed_request_data if x not in request_data_features

]

raise RequestDataNotFoundInEntityRowsException(feature_names=missing_features)

def _populate_response_from_feature_data(

feature_data: Iterable[

Tuple[

Iterable[Timestamp], Iterable["FieldStatus.ValueType"], Iterable[ValueProto]

]

],

indexes: Iterable[List[int]],

online_features_response: GetOnlineFeaturesResponse,

full_feature_names: bool,

requested_features: Iterable[str],

table: "FeatureView",

):

"""Populate the GetOnlineFeaturesResponse with feature data.

This method assumes that `_read_from_online_store` returns data for each

combination of Entities in `entity_rows` in the same order as they

are provided.

Args:

feature_data: A list of data in Protobuf form which was retrieved from the OnlineStore.

indexes: A list of indexes which should be the same length as `feature_data`. Each list

of indexes corresponds to a set of result rows in `online_features_response`.

online_features_response: The object to populate.

full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,

changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to

"customer_fv__daily_transactions").

requested_features: The names of the features in `feature_data`. This should be ordered in the same way as the

data in `feature_data`.

table: The FeatureView that `feature_data` was retrieved from.

"""

# Add the feature names to the response.

requested_feature_refs = [

f"{table.projection.name_to_use()}__{feature_name}"

if full_feature_names

else feature_name

for feature_name in requested_features

]

online_features_response.metadata.feature_names.val.extend(requested_feature_refs)

timestamps, statuses, values = zip(*feature_data)

# Populate the result with data fetched from the OnlineStore

# which is guaranteed to be aligned with `requested_features`.

for (

feature_idx,

(timestamp_vector, statuses_vector, values_vector),

) in enumerate(zip(zip(*timestamps), zip(*statuses), zip(*values))):

online_features_response.results.append(

GetOnlineFeaturesResponse.FeatureVector(

values=apply_list_mapping(values_vector, indexes),

statuses=apply_list_mapping(statuses_vector, indexes),

event_timestamps=apply_list_mapping(timestamp_vector, indexes),

)

)

def _get_features(

registry,

project,

features: Union[List[str], "FeatureService"],

allow_cache: bool = False,

) -> List[str]:

from feast.feature_service import FeatureService

_features = features

if not _features:

raise ValueError("No features specified for retrieval")

_feature_refs = []

if isinstance(_features, FeatureService):

feature_service_from_registry = registry.get_feature_service(

_features.name, project, allow_cache

)

if feature_service_from_registry != _features:

warnings.warn(

"The FeatureService object that has been passed in as an argument is "

"inconsistent with the version from the registry. Potentially a newer version "

"of the FeatureService has been applied to the registry."

)

for projection in feature_service_from_registry.feature_view_projections:

_feature_refs.extend(

[f"{projection.name_to_use()}:{f.name}" for f in projection.features]

)

else:

assert isinstance(_features, list)

_feature_refs = _features

return _feature_refs

def _list_feature_views(

registry,

project,

allow_cache: bool = False,

hide_dummy_entity: bool = True,

tags: Optional[dict[str, str]] = None,

) -> List["FeatureView"]:

from feast.feature_view import DUMMY_ENTITY_NAME

logging.warning(

"_list_feature_views will make breaking changes. Please use _list_batch_feature_views instead. "

"_list_feature_views will behave like _list_all_feature_views in the future."

)

feature_views = []

for fv in registry.list_feature_views(project, allow_cache=allow_cache, tags=tags):

if hide_dummy_entity and fv.entities and fv.entities[0] == DUMMY_ENTITY_NAME:

fv.entities = []

fv.entity_columns = []

feature_views.append(fv)

return feature_views

def _get_feature_views_to_use(

registry,

project,

features: Optional[Union[List[str], "FeatureService"]],

allow_cache=False,

hide_dummy_entity: bool = True,

) -> Tuple[List["FeatureView"], List["OnDemandFeatureView"]]:

from feast.feature_service import FeatureService

fvs = {

fv.name: fv

for fv in [

*_list_feature_views(registry, project, allow_cache, hide_dummy_entity),

*registry.list_stream_feature_views(

project=project, allow_cache=allow_cache

),

]

}

od_fvs = {

fv.name: fv

for fv in registry.list_on_demand_feature_views(

project=project, allow_cache=allow_cache

)

}

if isinstance(features, FeatureService):

fvs_to_use, od_fvs_to_use = [], []

for fv_name, projection in [

(projection.name, projection)

for projection in features.feature_view_projections

]:

if fv_name in fvs:

fvs_to_use.append(fvs[fv_name].with_projection(copy.copy(projection)))

elif fv_name in od_fvs:

odfv = od_fvs[fv_name].with_projection(copy.copy(projection))

od_fvs_to_use.append(odfv)

# Let's make sure to include an FVs which the ODFV requires Features from.

for projection in odfv.source_feature_view_projections.values():

fv = fvs[projection.name].with_projection(copy.copy(projection))

if fv not in fvs_to_use:

fvs_to_use.append(fv)

else:

raise ValueError(

f"The provided feature service {features.name} contains a reference to a feature view"

f"{fv_name} which doesn't exist. Please make sure that you have created the feature view"

f'{fv_name} and that you have registered it by running "apply".'

)

views_to_use = (fvs_to_use, od_fvs_to_use)

else:

views_to_use = (

[*fvs.values()],

[*od_fvs.values()],

)

return views_to_use

def _get_online_request_context(

registry,

project,

features: Union[List[str], "FeatureService"],

full_feature_names: bool,

):

from feast.feature_view import DUMMY_ENTITY_NAME

_feature_refs = _get_features(registry, project, features, allow_cache=True)

(

requested_feature_views,

requested_on_demand_feature_views,

) = _get_feature_views_to_use(

registry=registry,

project=project,

features=features,

allow_cache=True,

hide_dummy_entity=False,

)

(

entity_name_to_join_key_map,

entity_type_map,

join_keys_set,

) = _get_entity_maps(registry, project, requested_feature_views)

_validate_feature_refs(_feature_refs, full_feature_names)

(

grouped_refs,

grouped_odfv_refs,

) = _group_feature_refs(

_feature_refs,

requested_feature_views,

requested_on_demand_feature_views,

)

requested_result_row_names = {

feat_ref.replace(":", "__") for feat_ref in _feature_refs

}

if not full_feature_names:

requested_result_row_names = {

name.rpartition("__")[-1] for name in requested_result_row_names

}

feature_views = list(view for view, _ in grouped_refs)

needed_request_data = get_needed_request_data(grouped_odfv_refs)

entityless_case = DUMMY_ENTITY_NAME in [

entity_name

for feature_view in feature_views

for entity_name in feature_view.entities

]

return (

_feature_refs,

requested_on_demand_feature_views,

entity_name_to_join_key_map,

entity_type_map,

join_keys_set,

grouped_refs,

requested_result_row_names,

needed_request_data,

entityless_case,

)

def _prepare_entities_to_read_from_online_store(

registry,

project,

features: Union[List[str], "FeatureService"],

entity_values: Mapping[

str, Union[Sequence[Any], Sequence[ValueProto], RepeatedValueProto]

],

full_feature_names: bool = False,

native_entity_values: bool = True,

):

from feast.feature_view import DUMMY_ENTITY, DUMMY_ENTITY_ID, DUMMY_ENTITY_VAL

(

feature_refs,

requested_on_demand_feature_views,

entity_name_to_join_key_map,

entity_type_map,

join_keys_set,

grouped_refs,

requested_result_row_names,

needed_request_data,

entityless_case,

) = _get_online_request_context(registry, project, features, full_feature_names)

# Extract Sequence from RepeatedValue Protobuf.

entity_value_lists: Dict[str, Union[List[Any], List[ValueProto]]] = {

k: list(v) if isinstance(v, Sequence) else list(v.val)

for k, v in entity_values.items()

}

entity_proto_values: Dict[str, List[ValueProto]]

if native_entity_values:

# Convert values to Protobuf once.

entity_proto_values = {

k: python_values_to_proto_values(

v, entity_type_map.get(k, ValueType.UNKNOWN)

)

for k, v in entity_value_lists.items()

}

else:

entity_proto_values = entity_value_lists

num_rows = _validate_entity_values(entity_proto_values)

join_key_values: Dict[str, List[ValueProto]] = {}

request_data_features: Dict[str, List[ValueProto]] = {}

# Entity rows may be either entities or request data.

for join_key_or_entity_name, values in entity_proto_values.items():

# Found request data

if join_key_or_entity_name in needed_request_data:

request_data_features[join_key_or_entity_name] = values

else:

if join_key_or_entity_name in join_keys_set:

join_key = join_key_or_entity_name

else:

try:

join_key = entity_name_to_join_key_map[join_key_or_entity_name]

except KeyError:

raise EntityNotFoundException(join_key_or_entity_name, project)

else:

warnings.warn(

"Using entity name is deprecated. Use join_key instead."

)

# All join keys should be returned in the result.

requested_result_row_names.add(join_key)

join_key_values[join_key] = values

ensure_request_data_values_exist(needed_request_data, request_data_features)

# Populate online features response proto with join keys and request data features

online_features_response = GetOnlineFeaturesResponse(results=[])

_populate_result_rows_from_columnar(

online_features_response=online_features_response,

data=dict(**join_key_values, **request_data_features),

)

# Add the Entityless case after populating result rows to avoid having to remove

# it later.

if entityless_case:

join_key_values[DUMMY_ENTITY_ID] = python_values_to_proto_values(

[DUMMY_ENTITY_VAL] * num_rows, DUMMY_ENTITY.value_type

)

return (

join_key_values,

grouped_refs,

entity_name_to_join_key_map,

requested_on_demand_feature_views,

feature_refs,

requested_result_row_names,

online_features_response,

)

def _get_entity_key_protos(

entity_rows: Iterable[Mapping[str, ValueProto]],

) -> List[EntityKeyProto]:

# Instantiate one EntityKeyProto per Entity.

entity_key_protos = [

EntityKeyProto(join_keys=row.keys(), entity_values=row.values())

for row in entity_rows

]

return entity_key_protos

def _convert_rows_to_protobuf(

requested_features: List[str],

read_rows: List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]],

) -> List[Tuple[List[Timestamp], List["FieldStatus.ValueType"], List[ValueProto]]]:

# Each row is a set of features for a given entity key.

# We only need to convert the data to Protobuf once.

null_value = ValueProto()