# Copyright 2019 The Feast Authors

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# https://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import asyncio

import copy

import itertools

import logging

import os

import time

import warnings

from contextvars import ContextVar

from dataclasses import dataclass, field

from datetime import datetime, timedelta

from pathlib import Path

from typing import (

TYPE_CHECKING,

Any,

Dict,

Iterable,

List,

Mapping,

Optional,

Sequence,

Tuple,

Union,

cast,

)

if TYPE_CHECKING:

from feast.diff.apply_progress import ApplyProgressContext

from feast.embedder import EmbeddingProvider

import pandas as pd

import pyarrow as pa

from colorama import Fore, Style

from fastapi.concurrency import run_in_threadpool

from google.protobuf.timestamp_pb2 import Timestamp

from tqdm import tqdm

from feast import feature_server, flags_helper, ui_server, utils

from feast.base_feature_view import BaseFeatureView

from feast.batch_feature_view import BatchFeatureView

from feast.data_source import (

DataSource,

KafkaSource,

KinesisSource,

PushMode,

PushSource,

)

from feast.diff.infra_diff import InfraDiff, diff_infra_protos

from feast.diff.registry_diff import RegistryDiff, apply_diff_to_registry, diff_between

from feast.dqm.errors import ValidationFailed

from feast.entity import Entity

from feast.errors import (

ConflictingFeatureViewNames,

DataFrameSerializationError,

DataSourceRepeatNamesException,

FeatureViewNotFoundException,

PushSourceNotFoundException,

RequestDataNotFoundInEntityDfException,

)

from feast.feast_object import FeastObject

from feast.feature_service import FeatureService

from feast.feature_view import (

DUMMY_ENTITY,

DUMMY_ENTITY_ID,

DUMMY_ENTITY_NAME,

FeatureView,

FeatureViewState,

)

from feast.filter_models import ComparisonFilter, CompoundFilter, convert_dict_to_filter

from feast.inference import (

update_data_sources_with_inferred_event_timestamp_col,

update_feature_views_with_inferred_features_and_entities,

)

from feast.infra.infra_object import Infra

from feast.infra.offline_stores.offline_utils import (

DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,

)

from feast.infra.provider import Provider, RetrievalJob, get_provider

from feast.infra.registry.base_registry import BaseRegistry

from feast.infra.registry.registry import Registry

from feast.infra.registry.sql import SqlRegistry

from feast.labeling.label_view import LabelView

from feast.on_demand_feature_view import OnDemandFeatureView

from feast.online_response import OnlineResponse

from feast.permissions.permission import Permission

from feast.project import Project

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

FieldStatus,

GetOnlineFeaturesResponse,

)

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

from feast.protos.feast.types.Value_pb2 import RepeatedValue, Value

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

from feast.repo_config import RepoConfig, load_repo_config

from feast.repo_contents import RepoContents

from feast.saved_dataset import SavedDataset, SavedDatasetStorage, ValidationReference

from feast.ssl_ca_trust_store_setup import configure_ca_trust_store_env_variables

from feast.stream_feature_view import StreamFeatureView

from feast.transformation.pandas_transformation import PandasTransformation

from feast.transformation.python_transformation import PythonTransformation

from feast.utils import (

_distance_to_score,

_get_feature_view_vector_field_metadata,

_utc_now,

)

from feast.vector_store_utils import feature_view_to_vs_id

from feast.version_utils import parse_version

try:

from datetime import timezone as _timezone

except ImportError:

_timezone = None # type: ignore[assignment,misc]

_track_materialization = None # Lazy-loaded on first materialization call

_track_materialization_loaded = False

_logger = logging.getLogger(__name__)

def _get_track_materialization():

"""Lazy-import feast.metrics only when materialization tracking is needed.

Avoids importing the metrics module (and its prometheus_client /

psutil dependencies plus temp-dir creation) for every FeatureStore

usage such as ``feast apply`` or simple SDK reads.

"""

global _track_materialization, _track_materialization_loaded

if not _track_materialization_loaded:

_track_materialization_loaded = True

try:

from feast.metrics import track_materialization

_track_materialization = track_materialization

except Exception: # pragma: no cover

_track_materialization = None

return _track_materialization

warnings.simplefilter("once", DeprecationWarning)

_UNSET = object()

@dataclass

class _MaterializationDateRange:

"""Per-batch start dates plus shared end date for materialization watermarks."""

end_date: datetime

fv_start_dates: dict = field(default_factory=dict)

class FeatureStore:

"""

A FeatureStore object is used to define, create, and retrieve features.

Attributes:

config: The config for the feature store.

repo_path: The path to the feature repo.

_registry: The registry for the feature store.

_provider: The provider for the feature store.

_openlineage_emitter: Optional OpenLineage emitter for lineage tracking.

"""

config: RepoConfig

repo_path: Path

_registry: Optional[BaseRegistry]

_provider: Optional[Provider]

_openlineage_emitter: Optional[Any] = None

_embedding_provider: Optional["EmbeddingProvider"]

_feature_service_cache: Dict[str, List[str]]

def __init__(

self,

repo_path: Optional[str] = None,

config: Optional[RepoConfig] = None,

fs_yaml_file: Optional[Path] = None,

embedding_provider: Optional["EmbeddingProvider"] = None,

):

"""

Creates a FeatureStore object.

Args:

repo_path (optional): Path to the feature repo. Defaults to the current working directory.

config (optional): Configuration object used to configure the feature store.

fs_yaml_file (optional): Path to the `feature_store.yaml` file used to configure the feature store.

At most one of 'fs_yaml_file' and 'config' can be set.

embedding_provider (optional): Custom embedding provider implementing

the :class:`~feast.embedder.EmbeddingProvider` protocol. When not

supplied, a :class:`~feast.embedder.SentenceTransformersEmbeddingProvider` is

created from ``embedding_model`` in ``feature_store.yaml``.

Raises:

ValueError: If both or neither of repo_path and config are specified.

"""

if fs_yaml_file is not None and config is not None:

raise ValueError("You cannot specify both fs_yaml_file and config.")

configure_ca_trust_store_env_variables()

if repo_path:

self.repo_path = Path(repo_path)

else:

self.repo_path = Path(os.getcwd())

# If config is specified, or fs_yaml_file is specified, those take precedence over

# the default feature_store.yaml location under repo_path.

if config is not None:

self.config = config

elif fs_yaml_file is not None:

self.config = load_repo_config(self.repo_path, fs_yaml_file)

else:

self.config = load_repo_config(

self.repo_path, utils.get_default_yaml_file_path(self.repo_path)

)

# Initialize lazy-loaded components as None

self._registry = None

self._provider = None

self._openlineage_emitter = None

self._current_project: ContextVar[Optional[str]] = ContextVar(

"current_project", default=None

)

self._embedding_provider = embedding_provider

# Initialize feature service cache for performance optimization

self._feature_service_cache = {}

# Cache for _resolve_feature_service_name lookups

self._fs_name_cache: Dict[frozenset, Optional[str]] = {}

self._fs_name_index: Dict[frozenset, str] = {}

self._fs_name_index_ts: float = -self._FS_NAME_INDEX_TTL_SECONDS

self._mlflow_client: Any = _UNSET

def _init_mlflow(self) -> Optional[Any]:

"""Bootstrap MLflow integration on first access.

Checks the config, imports the module, and creates the integration

client. Returns the client or ``None`` if MLflow is disabled or

unavailable.

"""

try:

mlflow_cfg = getattr(self.config, "mlflow", None)

if mlflow_cfg is None or not mlflow_cfg.enabled:

return None

from feast.mlflow import _register_store

_register_store(self)

from feast.mlflow_integration.client import FeastMlflowClient

return FeastMlflowClient(self)

except ImportError:

return None

except Exception as e:

warnings.warn(f"Failed to configure MLflow tracking: {e}")

return None

@property

def mlflow(self) -> Any:

"""Access the Feast–MLflow integration client.

Lazily initializes on first access. Returns ``None`` when MLflow

integration is not enabled, allowing callers to guard with

``if store.mlflow:``.

"""

if self._mlflow_client is _UNSET:

self._mlflow_client = self._init_mlflow()

return self._mlflow_client

@staticmethod

def _count_entities(entity_rows: Any) -> int:

"""Count entities from either a list or columnar mapping."""

if isinstance(entity_rows, list):

return len(entity_rows)

if isinstance(entity_rows, Mapping):

try:

_first_col = next(iter(entity_rows.values()))

if isinstance(_first_col, RepeatedValue):

return len(_first_col.val)

return len(_first_col)

except Exception:

return 0

return 0

_FS_NAME_INDEX_TTL_SECONDS = 300

def _rebuild_fs_name_index(self) -> None:

"""Rebuild the {frozenset(refs) → service_name} index from the registry."""

index: Dict[frozenset, str] = {}

for fs in self.registry.list_feature_services(self.project, allow_cache=True):

fs_refs = frozenset(

f"{p.name_to_use()}:{f.name}"

for p in fs.feature_view_projections

for f in p.features

)

index[fs_refs] = fs.name

self._fs_name_index = index

self._fs_name_cache = {}

self._fs_name_index_ts = time.monotonic()

def _resolve_feature_service_name(self, feature_refs: List[str]) -> Optional[str]:

"""Find the best-matching feature service for the given feature refs.

Resolution: exact match wins immediately; otherwise the smallest

superset (fewest extra features) is returned. The full index is

rebuilt from the registry every _FS_NAME_INDEX_TTL_SECONDS and

per-query results are cached for O(1) repeated lookups.

"""

try:

now = time.monotonic()

if (now - self._fs_name_index_ts) >= self._FS_NAME_INDEX_TTL_SECONDS:

self._rebuild_fs_name_index()

ref_key = frozenset(feature_refs)

if ref_key in self._fs_name_cache:

return self._fs_name_cache[ref_key]

if ref_key in self._fs_name_index:

self._fs_name_cache[ref_key] = self._fs_name_index[ref_key]

return self._fs_name_index[ref_key]

best_match = None

best_extra = float("inf")

for fs_refs, fs_name in self._fs_name_index.items():

if ref_key.issubset(fs_refs):

extra = len(fs_refs) - len(ref_key)

if extra < best_extra:

best_match = fs_name

best_extra = extra

self._fs_name_cache[ref_key] = best_match

return best_match

except Exception as e:

_logger.debug("Failed to resolve feature service name: %s", e)

return None

def _log_entity_df_metadata(self, entity_df, start_date=None, end_date=None):

"""Log lightweight entity_df metadata to MLflow."""

try:

if self.mlflow is not None:

self.mlflow.log_entity_df_metadata(entity_df, start_date, end_date)

except Exception as e:

_logger.debug("Failed to log entity_df metadata to MLflow: %s", e)

def _log_entity_df_artifact(self, entity_df):

"""Upload entity DataFrame as a parquet artifact to MLflow."""

try:

if self.mlflow is not None:

self.mlflow.log_entity_df_artifact(entity_df)

except Exception as e:

_logger.debug("Failed to log entity_df artifact to MLflow: %s", e)

def _init_openlineage_emitter(self) -> Optional[Any]:

"""Initialize OpenLineage emitter if configured and enabled."""

try:

if (

hasattr(self.config, "openlineage")

and self.config.openlineage is not None

and self.config.openlineage.enabled

):

from feast.openlineage import FeastOpenLineageEmitter

ol_config = self.config.openlineage.to_openlineage_config()

emitter = FeastOpenLineageEmitter(ol_config)

if emitter.is_enabled:

self._wire_local_processor(emitter)

return emitter

except ImportError:

# OpenLineage not installed, silently skip

pass

except Exception as e:

warnings.warn(f"Failed to initialize OpenLineage emitter: {e}")

return None

def _wire_local_processor(self, emitter: Any) -> None:

"""Wire the local OL consumer processor into the emitter so

Feast-produced events are also stored in the consumer DB."""

try:

from feast.api.registry.rest import get_ol_processor

processor = get_ol_processor()

if processor and hasattr(emitter, "_client") and emitter._client:

emitter._client.set_local_processor(processor)

_logger.info(

"Feast OL emitter wired to local consumer processor (lazy)"

)

except Exception as e:

_logger.debug(f"Could not wire emitter to local processor: {e}")

def __repr__(self) -> str:

# Show lazy loading status without triggering initialization

registry_status = "not loaded" if self._registry is None else "loaded"

provider_status = "not loaded" if self._provider is None else "loaded"

return (

f"FeatureStore(\n"

f" repo_path={self.repo_path!r},\n"

f" config={self.config!r},\n"

f" registry={registry_status},\n"

f" provider={provider_status}\n"

f")"

)

@property

def embedding_provider(self) -> "EmbeddingProvider":

"""Return the active embedding provider, creating one from config if needed."""

if self._embedding_provider is None:

from feast.embedder import get_embedding_provider

embed_cfg = self.config.embedding_model

if embed_cfg is None:

raise ValueError(

"No embedding provider set and embedding_model is not "

"configured in feature_store.yaml. Either pass an "

"embedding_provider to FeatureStore() or add an "

"'embedding_model' section to feature_store.yaml.\n"

"Example:\n"

" embedding_model:\n"

" provider: sentence_transformers\n"

" model: all-MiniLM-L6-v2"

)

self._embedding_provider = get_embedding_provider(embed_cfg)

return self._embedding_provider

@embedding_provider.setter

def embedding_provider(self, provider: "EmbeddingProvider") -> None:

self._embedding_provider = provider

@property

def registry(self) -> BaseRegistry:

"""Gets the registry of this feature store."""

if self._registry is None:

self._registry = self._create_registry()

# Add feature service cache to registry for performance optimization

if self._registry and not hasattr(self._registry, "_feature_service_cache"):

setattr(

self._registry,

"_feature_service_cache",

self._feature_service_cache,

)

if self._registry is None:

raise RuntimeError("Registry failed to initialize properly")

return self._registry

def _create_registry(self) -> BaseRegistry:

"""Create and initialize the registry."""

registry_config = self.config.registry

if registry_config.registry_type == "sql":

return SqlRegistry(registry_config, self.config.project, None)

elif registry_config.registry_type == "snowflake.registry":

from feast.infra.registry.snowflake import SnowflakeRegistry

return SnowflakeRegistry(registry_config, self.config.project, None)

elif registry_config and registry_config.registry_type == "remote":

from feast.infra.registry.remote import RemoteRegistry

return RemoteRegistry(

registry_config, self.config.project, None, self.config.auth_config

)

else:

return Registry(

self.config.project,

registry_config,

repo_path=self.repo_path,

auth_config=self.config.auth_config,

)

@property

def project(self) -> str:

"""Gets the project for the current request context, falling back to the configured project."""

return self._current_project.get() or self.config.project

def set_current_project(self, project: Optional[str]):

return self._current_project.set(project)

def reset_current_project(self, token):

self._current_project.reset(token)

@property

def provider(self) -> Provider:

"""Gets the provider of this feature store."""

if self._provider is None:

self._provider = get_provider(self.config)

return self._provider

def _get_provider(self) -> Provider:

# TODO: Bake self.repo_path into self.config so that we dont only have one interface to paths

return self.provider

def _rollback_fv_states(

self,

feature_views: list,

previous_states: dict,

) -> None:

"""Restore feature views to their pre-materialization states."""

for fv in feature_views:

prev = previous_states.get(fv.name)

if (

hasattr(fv, "state")

and prev is not None

and prev != FeatureViewState.STATE_UNSPECIFIED

):

fv.state = prev

self.registry.apply_feature_view(fv, self.project, commit=True)

def _transition_fv_to_materializing(

self,

feature_view,

already_transitioned: list,

previous_states: dict,

) -> None:

"""

Transition a feature view to MATERIALIZING state.

Rolls back all already-transitioned FVs if this one can't transition.

Already MATERIALIZING is a no-op (async server may have reserved the state

before returning 202); rollback target is GENERATED in that case.

"""

current = getattr(feature_view, "state", None)

if current == FeatureViewState.MATERIALIZING:

previous_states[feature_view.name] = FeatureViewState.GENERATED

return

previous_states[feature_view.name] = current

if (

hasattr(feature_view, "state")

and feature_view.state != FeatureViewState.STATE_UNSPECIFIED

):

if not feature_view.state.can_transition_to(FeatureViewState.MATERIALIZING):

self._rollback_fv_states(already_transitioned, previous_states)

raise ValueError(

f"FeatureView {feature_view.name} cannot transition "

f"from {feature_view.state.name} to MATERIALIZING."

)

feature_view.state = FeatureViewState.MATERIALIZING

self.registry.apply_feature_view(feature_view, self.project, commit=True)

def _submit_and_process_materialization_jobs(

self,

provider,

tasks: list,

regular_fvs: list,

previous_states: dict,

date_range: "_MaterializationDateRange",

openlineage_run_id: Optional[str] = None,

) -> None:

"""

Submit all tasks to the engine in one call and process the results.

For each returned job: record watermark on success, roll back state on

error. If the engine itself raises, all states are rolled back.

"""

from feast.infra.common.materialization_job import (

MaterializationJobStatus,

)

batch_start = time.monotonic()

materialize_kwargs: Dict[str, Any] = {}

if openlineage_run_id and self.openlineage_emitter is not None:

from feast.openlineage.identity import (

LineageParentContext,

materialize_job_name,

)

materialize_kwargs["lineage_parent"] = LineageParentContext(

job_namespace=self.openlineage_emitter.namespace_for(self.project),

job_name=materialize_job_name(self.project),

run_id=openlineage_run_id,

)

try:

jobs = provider.batch_engine.materialize(

self.registry, tasks, **materialize_kwargs

)

except Exception:

self._rollback_fv_states(regular_fvs, previous_states)

raise

if len(jobs) != len(regular_fvs):

self._rollback_fv_states(regular_fvs, previous_states)

raise RuntimeError(

f"Engine returned {len(jobs)} jobs for {len(regular_fvs)} tasks"

)

first_error = None

succeeded_fvs = []

failed_fvs = []

for fv, job in zip(regular_fvs, jobs):

fv_status = job.status()

if fv_status == MaterializationJobStatus.ERROR:

failed_fvs.append(fv)

if first_error is None and job.error():

first_error = job.error()

else:

succeeded_fvs.append(fv)

if failed_fvs:

self._rollback_fv_states(failed_fvs, previous_states)

# Engines that apply watermarks themselves (e.g. SparkApplication pod)

# must not get a second apply_materialization — that duplicates intervals.

if not getattr(provider.batch_engine, "applies_materialization", False):

for fv in succeeded_fvs:

self.registry.apply_materialization(

fv,

self.project,

date_range.fv_start_dates[fv.name],

date_range.end_date,

)

_tracker = _get_track_materialization()

if _tracker is not None:

elapsed = time.monotonic() - batch_start

for fv in succeeded_fvs:

_tracker(fv.name, True, elapsed)

for fv in failed_fvs:

_tracker(fv.name, False, elapsed)

if first_error:

raise first_error

def _materialize_fvs_batch(

self,

provider,

fv_with_dates: list,

end_date: datetime,

tqdm_builder,

disable_event_timestamp: bool = False,

openlineage_run_id: Optional[str] = None,

) -> None:

"""Batch path: collect all FVs, submit to engine in one call.

Only used when ``provider.batch_engine.supports_batch`` is True.

"""

from feast.infra.common.materialization_job import MaterializationTask

tasks: list = []

regular_fvs: list = []

previous_states: dict = {}

date_range = _MaterializationDateRange(end_date=end_date)

for feature_view, fv_start in fv_with_dates:

self._transition_fv_to_materializing(

feature_view, regular_fvs, previous_states

)

regular_fvs.append(feature_view)

date_range.fv_start_dates[feature_view.name] = fv_start

tasks.append(

MaterializationTask(

project=self.project,

feature_view=feature_view,

start_time=fv_start,

end_time=end_date,

tqdm_builder=tqdm_builder,

disable_event_timestamp=disable_event_timestamp,

)

)

if tasks:

self._submit_and_process_materialization_jobs(

provider,

tasks,

regular_fvs,

previous_states,

date_range,

openlineage_run_id=openlineage_run_id,

)

@property

def openlineage_emitter(self) -> Optional[Any]:

"""Gets the OpenLineage emitter of this feature store."""

if self._openlineage_emitter is None:

self._openlineage_emitter = self._init_openlineage_emitter()

return self._openlineage_emitter

def _clear_feature_service_cache(self):

"""Clear feature service cache to avoid stale data after registry refresh."""

self._feature_service_cache.clear()

if hasattr(self.registry, "_feature_service_cache"):

getattr(self.registry, "_feature_service_cache").clear()

self._fs_name_cache.clear()

self._fs_name_index.clear()

self._fs_name_index_ts = -self._FS_NAME_INDEX_TTL_SECONDS

def refresh_registry(self):

"""Fetches and caches a copy of the feature registry in memory.

Explicitly calling this method allows for direct control of the state of the registry cache. Every time this

method is called the complete registry state will be retrieved from the remote registry store backend

(e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features()

is called, then get_online_features() will use the cached registry instead of retrieving (and caching) the

registry itself.

Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that

refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value

greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be

downloaded synchronously, which may increase latencies if the triggering method is get_online_features().

"""

self.registry.refresh(self.project)

self._clear_feature_service_cache()

def list_entities(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[Entity]:

"""

Retrieves the list of entities from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

tags: Filter by tags.

Returns:

A list of entities.

"""

return self._list_entities(allow_cache, tags=tags)

def _list_entities(

self,

allow_cache: bool = False,

hide_dummy_entity: bool = True,

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

) -> List[Entity]:

all_entities = self.registry.list_entities(

self.project, allow_cache=allow_cache, tags=tags

)

return [

entity

for entity in all_entities

if entity.name != DUMMY_ENTITY_NAME or not hide_dummy_entity

]

def list_feature_services(

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

) -> List[FeatureService]:

"""

Retrieves the list of feature services from the registry.

Args:

tags: Filter by tags.

Returns:

A list of feature services.

"""

return self.registry.list_feature_services(self.project, tags=tags)

def _list_all_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[BaseFeatureView]:

feature_views = []

for fv in self.registry.list_all_feature_views(

self.project, allow_cache=allow_cache, tags=tags

):

if (

isinstance(fv, FeatureView)

and fv.entities

and fv.entities[0] == DUMMY_ENTITY_NAME

):

fv.entities = []

fv.entity_columns = []

feature_views.append(fv)

return feature_views

def list_all_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[BaseFeatureView]:

"""

Retrieves the list of feature views from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

Returns:

A list of feature views.

"""

return self._list_all_feature_views(allow_cache, tags=tags)

def list_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[FeatureView]:

"""

Retrieves the list of feature views from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

tags: Filter by tags.

Returns:

A list of feature views.

"""

return utils._list_feature_views(

self.registry, self.project, allow_cache, tags=tags

)

def list_batch_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[FeatureView]:

"""

Retrieves the list of feature views from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

tags: Filter by tags.

Returns:

A list of feature views.

"""

return self._list_batch_feature_views(allow_cache=allow_cache, tags=tags)

def _list_batch_feature_views(

self,

allow_cache: bool = False,

hide_dummy_entity: bool = True,

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

) -> List[FeatureView]:

feature_views = []

for fv in self.registry.list_feature_views(

self.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 _list_stream_feature_views(

self,

allow_cache: bool = False,

hide_dummy_entity: bool = True,

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

) -> List[StreamFeatureView]:

stream_feature_views = []

for sfv in self.registry.list_stream_feature_views(

self.project, allow_cache=allow_cache, tags=tags

):

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

sfv.entities = []

sfv.entity_columns = []

stream_feature_views.append(sfv)

return stream_feature_views

def list_on_demand_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[OnDemandFeatureView]:

"""

Retrieves the list of on demand feature views from the registry.

Args:

allow_cache: Whether to allow returning entities from a cached registry.

tags: Filter by tags.

Returns:

A list of on demand feature views.

"""

return self.registry.list_on_demand_feature_views(

self.project, allow_cache=allow_cache, tags=tags

)

def list_stream_feature_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[StreamFeatureView]:

"""

Retrieves the list of stream feature views from the registry.

Returns:

A list of stream feature views.

"""

return self._list_stream_feature_views(allow_cache, tags=tags)

def list_label_views(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[LabelView]:

"""

Retrieves the list of label views from the registry.

Args:

allow_cache: Whether to allow returning label views from a cached registry.

tags: Filter by tags.

Returns:

A list of label views.

"""

return self.registry.list_label_views(

self.project, allow_cache=allow_cache, tags=tags

)

def get_label_view(

self, name: str, allow_registry_cache: bool = False

) -> LabelView:

"""

Retrieves a label view by name.

Args:

name: Name of the label view.

allow_registry_cache: Whether to allow returning the label view from a cached registry.

Returns:

The specified label view.

Raises:

FeatureViewNotFoundException: The label view could not be found.

"""

return self.registry.get_label_view(

name, self.project, allow_cache=allow_registry_cache

)

def list_data_sources(

self, allow_cache: bool = False, tags: Optional[dict[str, str]] = None

) -> List[DataSource]:

"""

Retrieves the list of data sources from the registry.

Args:

allow_cache: Whether to allow returning data sources from a cached registry.

tags: Filter by tags.

Returns:

A list of data sources.

"""

return self.registry.list_data_sources(

self.project, allow_cache=allow_cache, tags=tags

)

def get_entity(self, name: str, allow_registry_cache: bool = False) -> Entity:

"""

Retrieves an entity.

Args:

name: Name of entity.

allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry

Returns:

The specified entity.

Raises:

EntityNotFoundException: The entity could not be found.

"""

return self.registry.get_entity(

name, self.project, allow_cache=allow_registry_cache

)

def get_feature_service(

self, name: str, allow_cache: bool = False

) -> FeatureService:

"""

Retrieves a feature service.

Args:

name: Name of feature service.

allow_cache: Whether to allow returning feature services from a cached registry.

Returns:

The specified feature service.

Raises:

FeatureServiceNotFoundException: The feature service could not be found.

"""

return self.registry.get_feature_service(name, self.project, allow_cache)

def get_feature_view(

self, name: str, allow_registry_cache: bool = False

) -> FeatureView:

"""

Retrieves a feature view.

Args:

name: Name of feature view.

allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry

Returns:

The specified feature view.

Raises:

FeatureViewNotFoundException: The feature view could not be found.

"""

return self._get_feature_view(name, allow_registry_cache=allow_registry_cache)

def _get_feature_view(

self,

name: str,

hide_dummy_entity: bool = True,

allow_registry_cache: bool = False,

) -> FeatureView: