feast/sdk/python/feast/utils.py at master · ExpediaGroup/feast · GitHub

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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 (

FeatureNameCollisionError,

FeatureViewNotFoundException,

RequestDataNotFoundInEntityRowsException,

)

from feast.field import Field

from feast.infra.key_encoding_utils import deserialize_entity_key

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 FloatList as FloatListProto

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_datetime_to_int_ms_timestamp,

python_values_to_proto_values,

)

from feast.types import ComplexFeastType, PrimitiveFeastType, from_feast_to_pyarrow_type

from feast.value_type import ValueType

from feast.version import get_version

if typing.TYPE_CHECKING:

from feast.base_feature_view import BaseFeatureView

from feast.feature_service import FeatureService

from feast.feature_view import FeatureView

from feast.infra.registry.base_registry import BaseRegistry

from feast.on_demand_feature_view import OnDemandFeatureView

logger = logging.getLogger(__name__)

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 _get_fields_with_aliases(

fields: List[str],

field_mappings: Dict[str, str],

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

"""

Get a list of fields with aliases based on the field mappings.

"""

for field in fields:

if "." in field and field not in field_mappings:

raise ValueError(

f"Feature {field} contains a '.' character, which is not allowed in field names. Use field mappings to rename fields."

)

fields_with_aliases = [

f"{field} AS {field_mappings[field]}" if field in field_mappings else field

for field in fields

]

aliases = [

field_mappings[field] if field in field_mappings else field for field in fields

]

return (fields_with_aliases, aliases)

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: Union["FeatureView", "BaseFeatureView", "OnDemandFeatureView"],

join_keys: Dict[str, ValueType],

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

# This is a workaround for isinstance(feature_view, OnDemandFeatureView), which triggers a circular import

# Check for source_request_sources or source_feature_view_projections attributes to identify ODFVs

if (

getattr(feature_view, "source_request_sources", None) is not None

or getattr(feature_view, "source_feature_view_projections", None) is not None

result = _convert_arrow_odfv_to_proto(table, feature_view, join_keys) # type: ignore[arg-type]

else:

result = _convert_arrow_fv_to_proto(table, feature_view, join_keys) # type: ignore[arg-type]

logger.info(

"[materialize] feature_view=%s rows=%d",

feature_view.name,

len(result),

)

return result

def _convert_arrow_fv_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]

# TODO: This will break if the feature view has aggregations or transformations

columns = [

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

] + list(join_keys.items())

# Sort key UNIX_TIMESTAMP columns use ms precision so that timestamps differing

# by < 1 second produce distinct sort key scores in the online store.

sort_key_ts_names = {

sk.name

for sk in getattr(feature_view, "sort_keys", [])

if sk.value_type == ValueType.UNIX_TIMESTAMP

}

proto_values_by_column = {}

for column, value_type in columns:

raw = table.column(column).to_numpy(zero_copy_only=False)

if column in sort_key_ts_names:

ms_vals = _python_datetime_to_int_ms_timestamp(raw)

proto_values_by_column[column] = [

ValueProto(unix_timestamp_val=int(ts)) for ts in ms_vals

]

else:

proto_values_by_column[column] = python_values_to_proto_values(

raw, value_type

)

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 _convert_arrow_odfv_to_proto(

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

feature_view: "OnDemandFeatureView",

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

if column in table.column_names

}

# Ensure join keys are included in proto_values_by_column, but check if they exist first

for join_key, value_type in join_keys.items():

if join_key not in proto_values_by_column:

# Check if the join key exists in the table before trying to access it

if join_key in table.column_names:

proto_values_by_column[join_key] = python_values_to_proto_values(

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

)

else:

# Create null/default values if the join key isn't in the table

null_column = [None] * table.num_rows

proto_values_by_column[join_key] = python_values_to_proto_values(

null_column, value_type

)

# Adding On Demand Features

for feature in feature_view.features:

if (

feature.name in [c[0] for c in columns]

and feature.name not in proto_values_by_column

# initializing the column as null

null_column = pyarrow.array(

[None] * table.num_rows,

type=from_feast_to_pyarrow_type(feature.dtype),

)

updated_table = pyarrow.RecordBatch.from_arrays(

table.columns + [null_column],

schema=table.schema.append(

pyarrow.field(feature.name, null_column.type) # type: ignore[attr-defined]

)

proto_values_by_column[feature.name] = python_values_to_proto_values(

updated_table.column(feature.name).to_numpy(zero_copy_only=False),

feature.dtype.to_value_type(),

)

entity_keys = [

EntityKeyProto(

join_keys=join_keys,

entity_values=[

proto_values_by_column[k][idx]

for k in join_keys

if k in proto_values_by_column

)

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

if feature.name in proto_values_by_column

}

if feature_view.write_to_online_store:

table_columns = [col.name for col in table.schema]

for feature in feature_view.schema:

if feature.name not in feature_dict and feature.name in table_columns:

feature_dict[feature.name] = proto_values_by_column[feature.name]

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

# We need to artificially add event_timestamps and created_timestamps

event_timestamps = []

timestamp_values = pd.to_datetime([_utc_now() for i in range(table.num_rows)])

for val in timestamp_values:

event_timestamps.append(_coerce_datetime(val))

# setting them equivalent

created_timestamps = event_timestamps

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[Union["FeatureView", "OnDemandFeatureView"], 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: Dict[str, Union["FeatureView", "OnDemandFeatureView"]] = {

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

}

# on demand view to on demand view proto

on_demand_view_index: Dict[str, "OnDemandFeatureView"] = {}

for view in all_on_demand_feature_views:

if view.projection and not view.write_to_online_store:

on_demand_view_index[view.projection.name_to_use()] = view

elif view.projection and view.write_to_online_store:

# we insert the ODFV view to FVs for ones that are written to the online store

view_index[view.projection.name_to_use()] = view

# 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:

if hasattr(view_index[view_name], "write_to_online_store"):

tmp_feat_name = [

f for f in view_index[view_name].schema if f.name == feat_name

]

if len(tmp_feat_name) > 0:

feat_name = tmp_feat_name[0].name

else:

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[Union["FeatureView", "OnDemandFeatureView"], 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 construct_response_feature_vector(

values_vector: Iterable[Any],

statuses_vector: Iterable[Any],

timestamp_vector: Iterable[Any],

mapping_indexes: Iterable[List[int]],

output_len: int,

) -> GetOnlineFeaturesResponse.FeatureVector:

values_output: Iterable[Any] = [None] * output_len

statuses_output: Iterable[Any] = [None] * output_len

timestamp_output: Iterable[Any] = [None] * output_len

for i, destinations in enumerate(mapping_indexes):

for idx in destinations:

values_output[idx] = values_vector[i] # type: ignore[index]

statuses_output[idx] = statuses_vector[i] # type: ignore[index]

timestamp_output[idx] = timestamp_vector[i] # type: ignore[index]

return GetOnlineFeaturesResponse.FeatureVector(

values=values_output,

statuses=statuses_output,

event_timestamps=timestamp_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 not odfv.write_to_online_store:

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 = []

schema_dict = {k.name: k.dtype for k in odfv.schema}

for selected_feature in selected_subset:

feature_vector = transformed_features[selected_feature]

selected_feature_type = schema_dict.get(selected_feature, None)

feature_type: ValueType = ValueType.UNKNOWN

if selected_feature_type is not None:

if isinstance(

selected_feature_type, (ComplexFeastType, PrimitiveFeastType)

feature_type = selected_feature_type.to_value_type()

elif not isinstance(selected_feature_type, ValueType):

raise TypeError(

f"Unexpected type for feature_type: {type(feature_type)}"

)

proto_values.append(

python_values_to_proto_values(

feature_vector

if isinstance(feature_vector, list)

else [feature_vector]

if odfv.mode == "python"

else feature_vector.to_numpy(),

feature_type,

)

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:

dtype = entity_column.dtype.to_value_type()

entity_join_key_column_name = feature_view.projection.join_key_map.get(

entity_column.name, entity_column.name

)

entity_type_map[entity_join_key_column_name] = dtype

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], ...], 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,

)

# Validate that all expected join keys exist and have non-empty values.

expected_keys = set(entity_name_to_join_key_map.values())

expected_keys.discard("__dummy_id")

missing_keys = sorted(

list(set([key for key in expected_keys if key not in table_entity_values]))

)

empty_keys = sorted(

list(set([key for key in expected_keys if not table_entity_values.get(key)]))

)

if missing_keys or empty_keys:

if not any(table_entity_values.values()):

raise KeyError(

f"Missing join key values for keys: {missing_keys}. "

f"No values provided for keys: {empty_keys}. "

f"Provided join_key_values: {list(join_key_values.keys())}"

)

# Convert the column-oriented table_entity_values into row-wise data.

keys = list(table_entity_values.keys())

# Each row is a tuple of ValueProto objects corresponding to the join keys.

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

# If there are no rows, return empty tuples.

if not rowise:

return (), (), 0

# Sort rowise so that rows with the same join key values are adjacent.

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

# Group rows by their composite join key value.

groups = [

(dict(zip(keys, key_tuple)), [idx for idx, _ in group])

for key_tuple, group in itertools.groupby(rowise, key=lambda row: row[1])

]

# If no groups were formed (should not happen for valid input), return empty tuples.

if not groups:

return (), (), 0

# Unpack the unique entities and their original row indexes.

unique_entities, indexes = tuple(zip(*groups))

return unique_entities, indexes, len(rowise)

def _get_unique_entities_from_values(

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

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

"""

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, len(rowise)

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()

# if odfv.write_to_online_store, we should not pass in the request data

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",

output_len: int,

"""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.

output_len: The number of result rows in `online_features_response`.

"""

# Add the feature names to the response.

table_name = table.projection.name_to_use()

requested_feature_refs = [

(f"{table_name}__{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)

# Process each feature vector in a single pass

for timestamp_vector, statuses_vector, values_vector in feature_data:

response_vector = construct_response_feature_vector(

values_vector, statuses_vector, timestamp_vector, indexes, output_len

)

online_features_response.results.append(response_vector)

def _populate_response_from_feature_data_v2(

feature_data: Iterable[

Tuple[

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

]

indexes: Iterable[List[int]],

online_features_response: GetOnlineFeaturesResponse,

requested_features: Iterable[str],

output_len: int,

"""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`.

output_len: The number of result rows in `online_features_response`.

"""

# Add the feature names to the response.

requested_feature_refs = [(feature_name) for feature_name in requested_features]

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

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