def __init__(
self,
feature_store_id,
feature_store_name,
feature_group,
spark_context,
spark_session,
):
self._feature_group = feature_group
self._spark_context = spark_context
self._spark_session = spark_session
self._feature_store_id = feature_store_id
self._feature_store_name = feature_store_name
self._base_path = self._feature_group.location
self._table_name = util.feature_group_name(feature_group)
self._primary_key = ",".join(feature_group.primary_key)
self._partition_key = (",".join(feature_group.partition_key) if
len(feature_group.partition_key) >= 1 else "")
self._partition_path = (":SIMPLE,".join(feature_group.partition_key) +
":SIMPLE" if
len(feature_group.partition_key) >= 1 else "")
self._pre_combine_key = (feature_group.hudi_precombine_key
if feature_group.hudi_precombine_key else
feature_group.primary_key[0])
self._feature_group_api = feature_group_api.FeatureGroupApi(
feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._feature_store_id)
self._connstr = self._storage_connector_api.get(
self._feature_store_name).connection_string
def __init__(self, feature_store_id):
self._tags_api = tags_api.TagsApi(feature_store_id, self.ENTITY_TYPE)
self._feature_group_api = feature_group_api.FeatureGroupApi(
feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
self._kafka_api = kafka_api.KafkaApi()
def __init__(
self,
name,
version,
description,
data_format,
location,
featurestore_id,
storage_connector=None,
splits=None,
seed=None,
cluster_analysis=None,
created=None,
creator=None,
descriptive_statistics=None,
feature_correlation_matrix=None,
features=None,
features_histogram=None,
featurestore_name=None,
id=None,
jobs=None,
inode_id=None,
storage_connector_name=None,
storage_connector_id=None,
storage_connector_type=None,
training_dataset_type=None,
):
self._id = id
self._name = name
self._version = version
self._description = description
self._data_format = data_format
self._seed = seed
self._location = location
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
featurestore_id)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
featurestore_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
featurestore_id)
# set up depending on user initialized or coming from backend response
if training_dataset_type is None:
# no type -> user init
self._features = features
self.storage_connector = storage_connector
self.splits = splits
else:
# type available -> init from backend response
# make rest call to get all connector information, description etc.
self._storage_connector = self._storage_connector_api.get_by_id(
storage_connector_id, storage_connector_type)
self._features = [
feature.Feature.from_response_json(feat) for feat in features
]
self._splits = splits
self._training_dataset_type = training_dataset_type
def __init__(
self,
feature_store_id,
feature_store_name,
feature_group,
spark_context,
spark_session,
):
self._feature_group = feature_group
self._spark_context = spark_context
self._spark_session = spark_session
self._feature_store_id = feature_store_id
self._feature_store_name = feature_store_name
self._base_path = self._feature_group.location
self._table_name = feature_group.name + "_" + str(
feature_group.version)
self._primary_key = ",".join(feature_group.primary_key)
self._partition_key = ",".join(feature_group.partition_key)
self._partition_path = ":SIMPLE,".join(
feature_group.partition_key) + ":SIMPLE"
self._pre_combine_key = feature_group.primary_key[0]
self._feature_group_api = feature_group_api.FeatureGroupApi(
feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._feature_store_id)
self._connstr = self._storage_connector_api.get(
self._feature_store_name,
storage_connector.StorageConnector.JDBC,
).connection_string
def __init__(self, feature_store_id, features=[], training_dataset_version=None):
self._training_dataset_version = training_dataset_version
self._features = features
self._prepared_statement_engine = None
self._prepared_statements = None
self._serving_keys = None
self._pkname_by_serving_index = None
self._prefix_by_serving_index = None
self._external = True
self._feature_store_id = feature_store_id
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
feature_store_id
)
self._feature_view_api = feature_view_api.FeatureViewApi(feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id
)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
feature_store_id
)
)
self._feature_view_engine = feature_view_engine.FeatureViewEngine(
feature_store_id
)
def __init__(self, id, name, description, featurestore_id):
self._id = id
self._name = name
self._description = description
self._featurestore_id = featurestore_id
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._featurestore_id)
def __init__(self, feature_store_id):
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
feature_store_id
)
self._tags_api = tags_api.TagsApi(feature_store_id, self.ENTITY_TYPE)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id
)
def __init__(self, feature_store_id):
self._feature_store_id = feature_store_id
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
feature_store_id)
self._tags_api = tags_api.TagsApi(feature_store_id, self.ENTITY_TYPE)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
feature_store_id))
def __init__(
self, feature_store_name, feature_store_id, left_feature_group, left_features
):
self._feature_store_name = feature_store_name
self._feature_store_id = feature_store_id
self._left_feature_group = left_feature_group
self._left_features = util.parse_features(left_features)
self._joins = []
self._query_constructor_api = query_constructor_api.QueryConstructorApi()
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id
)
def __init__(
self,
featurestore_id,
featurestore_name,
created,
hdfs_store_path,
project_name,
project_id,
featurestore_description,
inode_id,
offline_featurestore_name,
hive_endpoint,
online_enabled,
num_feature_groups=None,
num_training_datasets=None,
num_storage_connectors=None,
online_featurestore_name=None,
mysql_server_endpoint=None,
online_featurestore_size=None,
):
self._id = featurestore_id
self._name = featurestore_name
self._created = created
self._hdfs_store_path = hdfs_store_path
self._project_name = project_name
self._project_id = project_id
self._description = featurestore_description
self._inode_id = inode_id
self._online_feature_store_name = online_featurestore_name
self._online_feature_store_size = online_featurestore_size
self._offline_feature_store_name = offline_featurestore_name
self._hive_endpoint = hive_endpoint
self._mysql_server_endpoint = mysql_server_endpoint
self._online_enabled = online_enabled
self._num_feature_groups = num_feature_groups
self._num_training_datasets = num_training_datasets
self._num_storage_connectors = num_storage_connectors
self._feature_group_api = feature_group_api.FeatureGroupApi(self._id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._id)
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
self._id)
self._expectations_api = expectations_api.ExpectationsApi(self._id)
self._feature_group_engine = feature_group_engine.FeatureGroupEngine(
self._id)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
self._id))
self._feature_view_engine = feature_view_engine.FeatureViewEngine(
self._id)
def __init__(self, feature_store_id):
self._feature_store_id = feature_store_id
self._feature_view_api = feature_view_api.FeatureViewApi(
feature_store_id)
self._tags_api = tags_api.TagsApi(feature_store_id, self.ENTITY_TYPE)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
self._transformation_function_engine = (
transformation_function_engine.TransformationFunctionEngine(
feature_store_id))
self._td_code_engine = code_engine.CodeEngine(
feature_store_id, self._TRAINING_DATA_API_PATH)
self._statistics_engine = statistics_engine.StatisticsEngine(
feature_store_id, self._TRAINING_DATA_API_PATH)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
feature_store_id)
self._query_constructor_api = query_constructor_api.QueryConstructorApi(
)
def __init__(
self,
feature_store_id,
feature_store_name,
feature_group,
spark_context,
spark_session,
):
self._feature_group = feature_group
self._spark_context = spark_context
self._spark_session = spark_session
self._feature_store_id = feature_store_id
self._feature_store_name = feature_store_name
self._base_path = self._feature_group.location
self._table_name = feature_group._get_online_table_name()
self._primary_key = ",".join(feature_group.primary_key)
# add event time to primary key for upserts
if feature_group.event_time is not None:
self._primary_key = self._primary_key + "," + feature_group.event_time
self._partition_key = (",".join(feature_group.partition_key) if
len(feature_group.partition_key) >= 1 else "")
self._partition_path = (":SIMPLE,".join(feature_group.partition_key) +
":SIMPLE" if
len(feature_group.partition_key) >= 1 else "")
self._pre_combine_key = (feature_group.hudi_precombine_key
if feature_group.hudi_precombine_key else
feature_group.primary_key[0])
self._feature_group_api = feature_group_api.FeatureGroupApi(
feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._feature_store_id)
if self._feature_store_name:
# For read operations we don't actually need the connector
# Only to sync write operations
self._connstr = self._storage_connector_api.get(
self._feature_store_name).connection_string
else:
self._connstr = None
def __init__(
self,
featurestore_id,
featurestore_name,
created,
hdfs_store_path,
project_name,
project_id,
featurestore_description,
inode_id,
offline_featurestore_name,
hive_endpoint,
online_enabled,
online_featurestore_name=None,
mysql_server_endpoint=None,
online_featurestore_size=None,
):
self._id = featurestore_id
self._name = featurestore_name
self._created = created
self._hdfs_store_path = hdfs_store_path
self._project_name = project_name
self._project_id = project_id
self._description = featurestore_description
self._inode_id = inode_id
self._online_feature_store_name = online_featurestore_name
self._online_feature_store_size = online_featurestore_size
self._offline_feature_store_name = offline_featurestore_name
self._hive_endpoint = hive_endpoint
self._mysql_server_endpoint = mysql_server_endpoint
self._online_enabled = online_enabled
self._feature_group_api = feature_group_api.FeatureGroupApi(self._id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
self._id)
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
self._id)
self._feature_group_engine = feature_group_engine.FeatureGroupEngine(
self._id)
def __init__(
self,
left_feature_group,
left_features,
feature_store_name=None,
feature_store_id=None,
left_feature_group_start_time=None,
left_feature_group_end_time=None,
joins=None,
filter=None,
):
self._feature_store_name = feature_store_name
self._feature_store_id = feature_store_id
self._left_feature_group = left_feature_group
self._left_features = util.parse_features(left_features)
self._left_feature_group_start_time = left_feature_group_start_time
self._left_feature_group_end_time = left_feature_group_end_time
self._joins = joins or []
self._filter = filter
self._query_constructor_api = query_constructor_api.QueryConstructorApi(
)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
def __init__(
self,
left_feature_group,
left_features,
feature_store_name=None,
feature_store_id=None,
left_feature_group_start_time=None,
left_feature_group_end_time=None,
joins=None,
filter=None,
):
self._feature_store_name = feature_store_name
self._feature_store_id = feature_store_id
self._left_feature_group = left_feature_group
self._left_features = util.parse_features(left_features)
self._left_feature_group_start_time = left_feature_group_start_time
self._left_feature_group_end_time = left_feature_group_end_time
self._joins = joins or []
self._filter = Logic.from_response_json(filter)
self._python_engine = True if engine.get_type() == "python" else False
self._query_constructor_api = query_constructor_api.QueryConstructorApi()
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id
)
def __init__(self, feature_store_id):
self._feature_group_api = feature_group_api.FeatureGroupApi(
feature_store_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
feature_store_id)
self._tags_api = tags_api.TagsApi(feature_store_id, "featuregroups")
def __init__(
self,
name,
version,
data_format,
location,
featurestore_id,
description=None,
storage_connector=None,
splits=None,
seed=None,
created=None,
creator=None,
features=None,
statistics_config=None,
featurestore_name=None,
id=None,
jobs=None,
inode_id=None,
storage_connector_name=None,
storage_connector_id=None,
storage_connector_type=None,
training_dataset_type=None,
from_query=None,
querydto=None,
):
self._id = id
self._name = name
self._version = version
self._description = description
self._data_format = data_format
self._seed = seed
self._location = location
self._from_query = from_query
self._querydto = querydto
self._training_dataset_api = training_dataset_api.TrainingDatasetApi(
featurestore_id)
self._training_dataset_engine = training_dataset_engine.TrainingDatasetEngine(
featurestore_id)
self._storage_connector_api = storage_connector_api.StorageConnectorApi(
featurestore_id)
self._statistics_engine = statistics_engine.StatisticsEngine(
featurestore_id, self.ENTITY_TYPE)
# set up depending on user initialized or coming from backend response
if training_dataset_type is None:
# no type -> user init
self._features = features
self.storage_connector = storage_connector
self.splits = splits
self.statistics_config = statistics_config
else:
# type available -> init from backend response
# make rest call to get all connector information, description etc.
self._storage_connector = self._storage_connector_api.get_by_id(
storage_connector_id, storage_connector_type)
self._features = [
training_dataset_feature.TrainingDatasetFeature.
from_response_json(feat) for feat in features
]
self._splits = splits
self._training_dataset_type = training_dataset_type
self.statistics_config = None
