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feature_store.py
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3288 lines (2867 loc) · 129 KB
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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 os
import warnings
from datetime import datetime, timedelta
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Callable,
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
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.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
warnings.simplefilter("once", DeprecationWarning)
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
# Initialize feature service cache for performance optimization
self._feature_service_cache = {}
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 of this feature store."""
return self.config.project
@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
@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()
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_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 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:
The specified data source.
Raises:
DataSourceObjectNotFoundException: The data source could not be found.
"""
return self.registry.get_data_source(name, self.project)
def delete_feature_view(self, name: str):
"""
Deletes a feature view of any kind (FeatureView, OnDemandFeatureView, StreamFeatureView).
Args:
name: Name of feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return self.registry.delete_feature_view(name, self.project)
def delete_feature_service(self, name: str):
"""
Deletes a feature service.
Args:
name: Name of feature service.
Raises:
FeatureServiceNotFoundException: The feature view could not be found.
"""
return self.registry.delete_feature_service(name, self.project)
def _should_use_plan(self):
"""Returns True if plan and _apply_diffs should be used, False otherwise."""
# Currently only the local provider with sqlite online store supports plan and _apply_diffs.
return self.config.provider == "local" and (
self.config.online_store and self.config.online_store.type == "sqlite"
)
def _validate_all_feature_views(
self,
views_to_update: List[FeatureView],
odfvs_to_update: List[OnDemandFeatureView],
sfvs_to_update: List[StreamFeatureView],
):
"""Validates all feature views."""
if len(odfvs_to_update) > 0 and not flags_helper.is_test():
warnings.warn(
"On demand feature view is an experimental feature. "
"This API is stable, but the functionality does not scale well for offline retrieval",
RuntimeWarning,
)
_validate_feature_views(
[
*views_to_update,
*odfvs_to_update,
*sfvs_to_update,
]
)
def _make_inferences(
self,
data_sources_to_update: List[DataSource],
entities_to_update: List[Entity],
views_to_update: List[FeatureView],
odfvs_to_update: List[OnDemandFeatureView],
sfvs_to_update: List[StreamFeatureView],
feature_services_to_update: List[FeatureService],
):
"""Makes inferences for entities, feature views, odfvs, and feature services."""
update_data_sources_with_inferred_event_timestamp_col(
data_sources_to_update, self.config
)
update_data_sources_with_inferred_event_timestamp_col(
[view.batch_source for view in views_to_update], self.config
)
update_data_sources_with_inferred_event_timestamp_col(
[view.batch_source for view in sfvs_to_update], self.config
)
# New feature views may reference previously applied entities.
entities = self._list_entities()
provider = self._get_provider()
update_feature_views_with_inferred_features_and_entities(
provider,
views_to_update,
entities + entities_to_update,
self.config,
)
update_feature_views_with_inferred_features_and_entities(
provider,
sfvs_to_update,
entities + entities_to_update,
self.config,
)
# We need to attach the time stamp fields to the underlying data sources
# and cascade the dependencies
update_feature_views_with_inferred_features_and_entities(
provider, odfvs_to_update, entities + entities_to_update, self.config
)
# TODO(kevjumba): Update schema inference
for sfv in sfvs_to_update:
if not sfv.schema:
raise ValueError(
f"schema inference not yet supported for stream feature views. please define schema for stream feature view: {sfv.name}"
)
for odfv in odfvs_to_update:
odfv.infer_features()
odfvs_to_write = [
odfv for odfv in odfvs_to_update if odfv.write_to_online_store
]
# Update to include ODFVs with write to online store
fvs_to_update_map = {
view.name: view
for view in [*views_to_update, *sfvs_to_update, *odfvs_to_write]
}
for feature_service in feature_services_to_update:
feature_service.infer_features(fvs_to_update=fvs_to_update_map)
def _get_feature_views_to_materialize(
self,
feature_views: Optional[List[str]],
) -> List[Union[FeatureView, OnDemandFeatureView]]:
"""
Returns the list of feature views that should be materialized.
If no feature views are specified, all feature views will be returned.
Args:
feature_views: List of names of feature views to materialize.
Raises:
FeatureViewNotFoundException: One of the specified feature views could not be found.
ValueError: One of the specified feature views is not configured for materialization.
"""
feature_views_to_materialize: List[Union[FeatureView, OnDemandFeatureView]] = []
if feature_views is None:
regular_feature_views = utils._list_feature_views(
self.registry, self.project, hide_dummy_entity=False
)
feature_views_to_materialize.extend(
[fv for fv in regular_feature_views if fv.online]
)
stream_feature_views_to_materialize = self._list_stream_feature_views(
hide_dummy_entity=False
)
feature_views_to_materialize.extend(
[sfv for sfv in stream_feature_views_to_materialize if sfv.online]
)
on_demand_feature_views_to_materialize = self.list_on_demand_feature_views()
feature_views_to_materialize.extend(
[
odfv
for odfv in on_demand_feature_views_to_materialize
if odfv.write_to_online_store
]
)
else:
for name in feature_views:
feature_view: Union[FeatureView, OnDemandFeatureView]
try:
feature_view = self._get_feature_view(name, hide_dummy_entity=False)
except FeatureViewNotFoundException:
try:
feature_view = self._get_stream_feature_view(
name, hide_dummy_entity=False
)
except FeatureViewNotFoundException:
feature_view = self.get_on_demand_feature_view(name)
if hasattr(feature_view, "online") and not feature_view.online:
raise ValueError(
f"FeatureView {feature_view.name} is not configured to be served online."
)
elif (
hasattr(feature_view, "write_to_online_store")
and not feature_view.write_to_online_store
):
raise ValueError(
f"OnDemandFeatureView {feature_view.name} is not configured for write_to_online_store."
)
feature_views_to_materialize.append(feature_view)
return feature_views_to_materialize
def plan(
self,
desired_repo_contents: RepoContents,
skip_feature_view_validation: bool = False,
progress_ctx: Optional["ApplyProgressContext"] = None,
) -> Tuple[RegistryDiff, InfraDiff, Infra]:
"""Dry-run registering objects to metadata store.
The plan method dry-runs registering one or more definitions (e.g., Entity, FeatureView), and produces
a list of all the changes the that would be introduced in the feature repo. The changes computed by the plan
command are for informational purposes, and are not actually applied to the registry.
Args:
desired_repo_contents: The desired repo state.
skip_feature_view_validation: If True, skip validation of feature views. This can be useful when the validation
system is being overly strict. Use with caution and report any issues on GitHub. Default is False.
Raises:
ValueError: The 'objects' parameter could not be parsed properly.
Examples:
Generate a plan adding an Entity and a FeatureView.
>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from feast.feature_store import RepoContents
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
... path="data/driver_stats.parquet",
... timestamp_field="event_timestamp",
... created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
... name="driver_hourly_stats",
... entities=[driver],
... ttl=timedelta(seconds=86400 * 1),
... source=driver_hourly_stats,
... )
>>> registry_diff, infra_diff, new_infra = fs.plan(RepoContents(
... projects=[Project(name="project")],
... data_sources=[driver_hourly_stats],
... feature_views=[driver_hourly_stats_view],
... on_demand_feature_views=list(),
... stream_feature_views=list(),
... entities=[driver],
... feature_services=list(),
... permissions=list())) # register entity and feature view
"""
# Validate and run inference on all the objects to be registered.
if not skip_feature_view_validation:
self._validate_all_feature_views(
desired_repo_contents.feature_views,
desired_repo_contents.on_demand_feature_views,
desired_repo_contents.stream_feature_views,
)
_validate_data_sources(desired_repo_contents.data_sources)
self._make_inferences(
desired_repo_contents.data_sources,
desired_repo_contents.entities,
desired_repo_contents.feature_views,
desired_repo_contents.on_demand_feature_views,
desired_repo_contents.stream_feature_views,
desired_repo_contents.feature_services,
)
# Compute the desired difference between the current objects in the registry and
# the desired repo state.
registry_diff = diff_between(self.registry, self.project, desired_repo_contents)
if progress_ctx:
progress_ctx.update_phase_progress("Computing infrastructure diff")
# Compute the desired difference between the current infra, as stored in the registry,
# and the desired infra.
self.registry.refresh(project=self.project)
self._clear_feature_service_cache()
current_infra_proto = self.registry.get_infra(self.project).to_proto()
desired_registry_proto = desired_repo_contents.to_registry_proto()
new_infra = self.provider.plan_infra(self.config, desired_registry_proto)
new_infra_proto = new_infra.to_proto()
infra_diff = diff_infra_protos(
current_infra_proto, new_infra_proto, project=self.project
)
return registry_diff, infra_diff, new_infra
def _apply_diffs(
self,
registry_diff: RegistryDiff,
infra_diff: InfraDiff,
new_infra: Infra,
progress_ctx: Optional["ApplyProgressContext"] = None,
):
"""Applies the given diffs to the metadata store and infrastructure.
Args:
registry_diff: The diff between the current registry and the desired registry.
infra_diff: The diff between the current infra and the desired infra.
new_infra: The desired infra.
progress_ctx: Optional progress context for tracking apply progress.
"""
try:
# Infrastructure phase
if progress_ctx:
infra_ops_count = len(infra_diff.infra_object_diffs)
progress_ctx.start_phase("Updating infrastructure", infra_ops_count)
infra_diff.update(progress_ctx=progress_ctx)
if progress_ctx:
progress_ctx.complete_phase()
progress_ctx.start_phase("Updating registry", 2)
# Registry phase
apply_diff_to_registry(
self.registry, registry_diff, self.project, commit=False
)
if progress_ctx:
progress_ctx.update_phase_progress("Committing registry changes")
self.registry.update_infra(new_infra, self.project, commit=True)
if progress_ctx:
progress_ctx.update_phase_progress("Registry update complete")
progress_ctx.complete_phase()
finally:
# Always cleanup progress bars
if progress_ctx:
progress_ctx.cleanup()
# Emit OpenLineage events for applied objects
self._emit_openlineage_apply_diffs(registry_diff)
def _emit_openlineage_apply_diffs(self, registry_diff: RegistryDiff):
"""Emit OpenLineage events for objects applied via diffs."""
if self.openlineage_emitter is None:
return
# Collect all objects that were added or updated
objects: List[Any] = []
for feast_object_diff in registry_diff.feast_object_diffs:
if feast_object_diff.new_feast_object is not None:
objects.append(feast_object_diff.new_feast_object)
if objects:
self._emit_openlineage_apply(objects)
def apply(
self,
objects: Union[
Project,
DataSource,
Entity,
FeatureView,
OnDemandFeatureView,
BatchFeatureView,
StreamFeatureView,
FeatureService,
ValidationReference,
Permission,
List[FeastObject],
],
objects_to_delete: Optional[List[FeastObject]] = None,
partial: bool = True,
skip_feature_view_validation: bool = False,
):
"""Register objects to metadata store and update related infrastructure.
The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these
objects in the Feast registry. Once the apply method has updated the infrastructure (e.g., create tables in
an online store), it will commit the updated registry. All operations are idempotent, meaning they can safely
be rerun.
Note: The apply method does NOT delete objects that are removed from the provided list. To delete objects
from the registry, use explicit delete methods like delete_feature_view(), delete_feature_service(), or
pass objects to the objects_to_delete parameter with partial=False.
Args:
objects: A single object, or a list of objects that should be registered with the Feature Store.
objects_to_delete: A list of objects to be deleted from the registry and removed from the
provider's infrastructure. This deletion will only be performed if partial is set to False.
partial: If True, apply will only handle the specified objects; if False, apply will also delete
all the objects in objects_to_delete, and tear down any associated cloud resources.
skip_feature_view_validation: If True, skip validation of feature views. This can be useful when the validation
system is being overly strict. Use with caution and report any issues on GitHub. Default is False.
Raises:
ValueError: The 'objects' parameter could not be parsed properly.
Examples:
Register an Entity and a FeatureView.
>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
... path="data/driver_stats.parquet",
... timestamp_field="event_timestamp",
... created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
... name="driver_hourly_stats",