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# 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
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_NAME,
FeatureView,
FeatureViewState,
)
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 _get_feature_view_vector_field_metadata, _utc_now
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
_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,
):
"""
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.
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
)
# 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:
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 __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 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.
"""
previous_state = getattr(feature_view, "state", None)
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)
previous_states[feature_view.name] = previous_state
def _submit_and_process_materialization_jobs(
self,
provider,
tasks: list,
regular_fvs: list,
previous_states: dict,
date_range: "_MaterializationDateRange",
) -> 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()
try:
jobs = provider.batch_engine.materialize(self.registry, tasks)
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,
) -> 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,
)
@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:
feature_view = self.registry.get_feature_view(
name, self.project, allow_cache=allow_registry_cache
)
if hide_dummy_entity and feature_view.entities[0] == DUMMY_ENTITY_NAME:
feature_view.entities = []
return feature_view
def list_feature_view_versions(self, name: str) -> List[Dict[str, Any]]:
"""
List version history for a feature view.
Args:
name: Name of feature view.
Returns:
List of version records.
"""
return self.registry.list_feature_view_versions(name, self.project)
def get_stream_feature_view(
self, name: str, allow_registry_cache: bool = False
) -> StreamFeatureView:
"""
Retrieves a stream feature view.
Args:
name: Name of stream feature view.
allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
Returns:
The specified stream feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return self._get_stream_feature_view(
name, allow_registry_cache=allow_registry_cache
)
def _get_stream_feature_view(
self,
name: str,
hide_dummy_entity: bool = True,
allow_registry_cache: bool = False,
) -> StreamFeatureView:
stream_feature_view = self.registry.get_stream_feature_view(
name, self.project, allow_cache=allow_registry_cache
)
if hide_dummy_entity and stream_feature_view.entities[0] == DUMMY_ENTITY_NAME:
stream_feature_view.entities = []
return stream_feature_view
def get_on_demand_feature_view(
self, name: str, allow_registry_cache: bool = False
) -> OnDemandFeatureView:
"""
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.registry.get_on_demand_feature_view(
name, self.project, allow_cache=allow_registry_cache
)
def get_data_source(self, name: str) -> DataSource:
"""
Retrieves the list of data sources from the registry.
Args:
name: Name of the data source.
Returns: