from datetime import datetime
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import pandas
from tqdm import tqdm
from feast import FeatureTable
from feast.entity import Entity
from feast.feature_view import FeatureView
from feast.infra.offline_stores.offline_utils import get_offline_store_from_config
from feast.infra.online_stores.helpers import get_online_store_from_config
from feast.infra.provider import (
Provider,
RetrievalJob,
_convert_arrow_to_proto,
_get_column_names,
_run_field_mapping,
)
from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
from feast.protos.feast.types.Value_pb2 import Value as ValueProto
from feast.registry import Registry
from feast.repo_config import RepoConfig
[docs]class GcpProvider(Provider):
_gcp_project_id: Optional[str]
_namespace: Optional[str]
def __init__(self, config: RepoConfig):
self.repo_config = config
self.offline_store = get_offline_store_from_config(config.offline_store)
self.online_store = get_online_store_from_config(config.online_store)
[docs] def update_infra(
self,
project: str,
tables_to_delete: Sequence[Union[FeatureTable, FeatureView]],
tables_to_keep: Sequence[Union[FeatureTable, FeatureView]],
entities_to_delete: Sequence[Entity],
entities_to_keep: Sequence[Entity],
partial: bool,
):
self.online_store.update(
config=self.repo_config,
tables_to_delete=tables_to_delete,
tables_to_keep=tables_to_keep,
entities_to_keep=entities_to_keep,
entities_to_delete=entities_to_delete,
partial=partial,
)
[docs] def teardown_infra(
self,
project: str,
tables: Sequence[Union[FeatureTable, FeatureView]],
entities: Sequence[Entity],
) -> None:
self.online_store.teardown(self.repo_config, tables, entities)
[docs] def online_write_batch(
self,
config: RepoConfig,
table: Union[FeatureTable, FeatureView],
data: List[
Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
],
progress: Optional[Callable[[int], Any]],
) -> None:
self.online_store.online_write_batch(config, table, data, progress)
[docs] def online_read(
self,
config: RepoConfig,
table: Union[FeatureTable, FeatureView],
entity_keys: List[EntityKeyProto],
requested_features: List[str] = None,
) -> List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]]:
result = self.online_store.online_read(config, table, entity_keys)
return result
[docs] def materialize_single_feature_view(
self,
config: RepoConfig,
feature_view: FeatureView,
start_date: datetime,
end_date: datetime,
registry: Registry,
project: str,
tqdm_builder: Callable[[int], tqdm],
) -> None:
entities = []
for entity_name in feature_view.entities:
entities.append(registry.get_entity(entity_name, project))
(
join_key_columns,
feature_name_columns,
event_timestamp_column,
created_timestamp_column,
) = _get_column_names(feature_view, entities)
offline_job = self.offline_store.pull_latest_from_table_or_query(
config=config,
data_source=feature_view.batch_source,
join_key_columns=join_key_columns,
feature_name_columns=feature_name_columns,
event_timestamp_column=event_timestamp_column,
created_timestamp_column=created_timestamp_column,
start_date=start_date,
end_date=end_date,
)
table = offline_job.to_arrow()
if feature_view.batch_source.field_mapping is not None:
table = _run_field_mapping(table, feature_view.batch_source.field_mapping)
join_keys = [entity.join_key for entity in entities]
rows_to_write = _convert_arrow_to_proto(table, feature_view, join_keys)
with tqdm_builder(len(rows_to_write)) as pbar:
self.online_write_batch(
self.repo_config, feature_view, rows_to_write, lambda x: pbar.update(x)
)
[docs] def get_historical_features(
self,
config: RepoConfig,
feature_views: List[FeatureView],
feature_refs: List[str],
entity_df: Union[pandas.DataFrame, str],
registry: Registry,
project: str,
full_feature_names: bool,
) -> RetrievalJob:
job = self.offline_store.get_historical_features(
config=config,
feature_views=feature_views,
feature_refs=feature_refs,
entity_df=entity_df,
registry=registry,
project=project,
full_feature_names=full_feature_names,
)
return job