Feature Store#

FeatureStore#

hsfs.feature_store.FeatureStore(
    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,
)

Retrieval#

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get_feature_store#

Connection.get_feature_store(name=None)

Get a reference to a feature store to perform operations on.

Defaulting to the project name of default feature store. To get a Shared feature stores, the project name of the feature store is required.

Arguments

name str: The name of the feature store, defaults to None.

Returns

FeatureStore. A feature store handle object to perform operations on.

Properties#

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description#

Description of the feature store.

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hive_endpoint#

Hive endpoint for the offline feature store.

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id#

Id of the feature store.

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mysql_server_endpoint#

MySQL server endpoint for the online feature store.

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name#

Name of the feature store.

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offline_featurestore_name#

Name of the offline feature store database.

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online_enabled#

Indicator whether online feature store is enabled.

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online_featurestore_name#

Name of the online feature store database.

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project_id#

Id of the project in which the feature store is located.

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project_name#

Name of the project in which the feature store is located.

Methods#

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create_expectation#

FeatureStore.create_expectation(name, description="", features=[], rules=[])

Create an expectation metadata object.

Lazy

This method is lazy and does not persist the expectation in the feature store on its own. To materialize the expectation and save call the save() method of the expectation metadata object.

Arguments

name str: Name of the expectation to create.
description Optional[str]: A string describing the expectation that can describe its business logic and applications within the feature store.
features Optional[List[str]]: The features this expectation is applied on.
rules Optional[List[hsfs.rule.Rule]]: The validation rules this expectation will apply to the features.

Returns:

Expectation: The expectation metadata object.

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create_feature_group#

FeatureStore.create_feature_group(
    name,
    version=None,
    description="",
    online_enabled=False,
    time_travel_format="HUDI",
    partition_key=[],
    primary_key=[],
    hudi_precombine_key=None,
    features=[],
    statistics_config=None,
    validation_type="NONE",
    expectations=[],
    event_time=None,
)

Create a feature group metadata object.

Lazy

This method is lazy and does not persist any metadata or feature data in the feature store on its own. To persist the feature group and save feature data along the metadata in the feature store, call the save() method with a DataFrame.

Arguments

name str: Name of the feature group to create.
version Optional[int]: Version of the feature group to retrieve, defaults to None and will create the feature group with incremented version from the last version in the feature store.
description Optional[str]: A string describing the contents of the feature group to improve discoverability for Data Scientists, defaults to empty string "".
online_enabled Optional[bool]: Define whether the feature group should be made available also in the online feature store for low latency access, defaults to False.
time_travel_format Optional[str]: Format used for time travel, defaults to "HUDI".
partition_key Optional[List[str]]: A list of feature names to be used as partition key when writing the feature data to the offline storage, defaults to empty list [].
primary_key Optional[List[str]]: A list of feature names to be used as primary key for the feature group. This primary key can be a composite key of multiple features and will be used as joining key, if not specified otherwise. Defaults to empty list [], and the feature group won't have any primary key.
hudi_precombine_key Optional[str]: A feature name to be used as a precombine key for the "HUDI" feature group. Defaults to None. If feature group has time travel format "HUDI" and hudi precombine key was not specified then the first primary key of the feature group will be used as hudi precombine key.
features Optional[List[hsfs.feature.Feature]]: Optionally, define the schema of the feature group manually as a list of Feature objects. Defaults to empty list [] and will use the schema information of the DataFrame provided in the save method.
statistics_config Optional[Union[hsfs.StatisticsConfig, bool, dict]]: A configuration object, or a dictionary with keys "enabled" to generally enable descriptive statistics computation for this feature group, "correlations" to turn on feature correlation computation, "histograms" to compute feature value frequencies and "exact_uniqueness" to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass statistics_config=False. Defaults to None and will compute only descriptive statistics.
validation_type Optional[str]: Optionally, set the validation type to one of "NONE", "STRICT", "WARNING", "ALL". Determines the mode in which data validation is applied on ingested or already existing feature group data.
expectations Optional[List[hsfs.expectation.Expectation]]: Optionally, a list of expectations to be attached to the feature group. The expectations list contains Expectation metadata objects which can be retrieved with the get_expectation() and get_expectations() functions.
event_time Optional[str]: Optionally, provide the name of the feature containing the event time for the features in this feature group. If event_time is set the feature group can be used for point-in-time joins. Defaults to None.

Returns

FeatureGroup. The feature group metadata object.

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create_on_demand_feature_group#

FeatureStore.create_on_demand_feature_group(
    name,
    storage_connector,
    query=None,
    data_format=None,
    path="",
    options={},
    version=None,
    description="",
    primary_key=[],
    features=[],
    statistics_config=None,
    event_time=None,
    validation_type="NONE",
    expectations=[],
)

Create a on-demand feature group metadata object.

Lazy

This method is lazy and does not persist any metadata in the feature store on its own. To persist the feature group metadata in the feature store, call the save() method.

Arguments

name str: Name of the on-demand feature group to create.
query Optional[str]: A string containing a SQL query valid for the target data source. the query will be used to pull data from the data sources when the feature group is used.
data_format Optional[str]: If the on-demand feature groups refers to a directory with data, the data format to use when reading it
path Optional[str]: The location within the scope of the storage connector, from where to read the data for the on-demand feature group
storage_connector hsfs.StorageConnector: the storage connector to use to establish connectivity with the data source.
version Optional[int]: Version of the on-demand feature group to retrieve, defaults to None and will create the feature group with incremented version from the last version in the feature store.
description Optional[str]: A string describing the contents of the on-demand feature group to improve discoverability for Data Scientists, defaults to empty string "".
primary_key Optional[List[str]]: A list of feature names to be used as primary key for the feature group. This primary key can be a composite key of multiple features and will be used as joining key, if not specified otherwise. Defaults to empty list [], and the feature group won't have any primary key.
features Optional[List[hsfs.feature.Feature]]: Optionally, define the schema of the on-demand feature group manually as a list of Feature objects. Defaults to empty list [] and will use the schema information of the DataFrame resulting by executing the provided query against the data source.
statistics_config Optional[Union[hsfs.StatisticsConfig, bool, dict]]: A configuration object, or a dictionary with keys "enabled" to generally enable descriptive statistics computation for this on-demand feature group, "correlations" to turn on feature correlation computation, "histograms" to compute feature value frequencies and "exact_uniqueness" to compute uniqueness, distinctness and entropy. The values should be booleans indicating the setting. To fully turn off statistics computation pass statistics_config=False. Defaults to None and will compute only descriptive statistics.
event_time Optional[str]: Optionally, provide the name of the feature containing the event time for the features in this feature group. If event_time is set the feature group can be used for point-in-time joins. Defaults to None.
validation_type Optional[str]: Optionally, set the validation type to one of "NONE", "STRICT", "WARNING", "ALL". Determines the mode in which data validation is applied on ingested or already existing feature group data.
expectations Optional[List[hsfs.expectation.Expectation]]: Optionally, a list of expectations to be attached to the feature group. The expectations list contains Expectation metadata objects which can be retrieved with the get_expectation() and get_expectations() functions.

Returns

OnDemandFeatureGroup. The on-demand feature group metadata object.

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create_training_dataset#

FeatureStore.create_training_dataset(
    name,
    version=None,
    description="",
    data_format="tfrecords",
    coalesce=False,
    storage_connector=None,
    splits={},
    location="",
    seed=None,
    statistics_config=None,
    label=[],
    transformation_functions={},
    train_split=None,
)

Create a training dataset metadata object.

Lazy

This method is lazy and does not persist any metadata or feature data in the feature store on its own. To materialize the training dataset and save feature data along the metadata in the feature store, call the save() method with a DataFrame or Query.

Data Formats

The feature store currently supports the following data formats for training datasets:

tfrecord
csv
tsv
parquet
avro
orc

Currently not supported petastorm, hdf5 and npy file formats.

Arguments

name str: Name of the training dataset to create.
version Optional[int]: Version of the training dataset to retrieve, defaults to None and will create the training dataset with incremented version from the last version in the feature store.
description Optional[str]: A string describing the contents of the training dataset to improve discoverability for Data Scientists, defaults to empty string "".
data_format Optional[str]: The data format used to save the training dataset, defaults to "tfrecords"-format.
coalesce Optional[bool]: If true the training dataset data will be coalesced into a single partition before writing. The resulting training dataset will be a single file per split. Default False.
storage_connector Optional[hsfs.StorageConnector]: Storage connector defining the sink location for the training dataset, defaults to None, and materializes training dataset on HopsFS.
splits Optional[Dict[str, float]]: A dictionary defining training dataset splits to be created. Keys in the dictionary define the name of the split as str, values represent percentage of samples in the split as float. Currently, only random splits are supported. Defaults to empty dict{}, creating only a single training dataset without splits.
location Optional[str]: Path to complement the sink storage connector with, e.g if the storage connector points to an S3 bucket, this path can be used to define a sub-directory inside the bucket to place the training dataset. Defaults to "", saving the training dataset at the root defined by the storage connector.
seed Optional[int]: Optionally, define a seed to create the random splits with, in order to guarantee reproducability, defaults to None.
statistics_config Optional[Union[hsfs.StatisticsConfig, bool, dict]]: A configuration object, or a dictionary with keys "enabled" to generally enable descriptive statistics computation for this feature group, "correlations" to turn on feature correlation computation and "histograms" to compute feature value frequencies. The values should be booleans indicating the setting. To fully turn off statistics computation pass statistics_config=False. Defaults to None and will compute only descriptive statistics.
label Optional[List[str]]: A list of feature names constituting the prediction label/feature of the training dataset. When replaying a Query during model inference, the label features can be omitted from the feature vector retrieval. Defaults to [], no label.
transformation_functions Optional[Dict[str, hsfs.transformation_function.TransformationFunction]]: A dictionary mapping tansformation functions to to the features they should be applied to before writing out the training data and at inference time. Defaults to {}, no transformations.
train_split Optional[str]: If splits is set, provide the name of the split that is going to be used for training. The statistics of this split will be used for transformation functions if necessary. Defaults to None.

Returns:

TrainingDataset: The training dataset metadata object.

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create_transformation_function#

FeatureStore.create_transformation_function(transformation_function, output_type, version=None)

Create a transformation function metadata object.

Lazy

This method is lazy and does not persist the transformation function in the feature store on its own. To materialize the transformation function and save call the save() method of the transformation function metadata object.

Arguments

transformation_function callable: callable object.
output_type Union[str, str, string, bytes, numpy.int8, int8, byte, numpy.int16, int16, short, int, int, numpy.int32, numpy.int64, int64, long, bigint, float, float, numpy.float64, float64, double, datetime.datetime, numpy.datetime64, datetime.date, bool, boolean, bool]: python or numpy output type that will be inferred as pyspark.sql.types type.

Returns:

TransformationFunction: The TransformationFunction metadata object.

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delete_expectation#

FeatureStore.delete_expectation(name)

Delete an expectation from the feature store.

Arguments

name str: Name of the training dataset to create.

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from_response_json#

FeatureStore.from_response_json(json_dict)

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get_expectation#

FeatureStore.get_expectation(name)

Get an expectation entity from the feature store.

Getting an expectation from the Feature Store means getting its metadata handle so you can subsequently add features and/or rules and save it which will overwrite the previous instance.

Arguments

name str: Name of the training dataset to get.

Returns

Expectation: The expectation metadata object.

Raises

RestAPIError: If unable to retrieve the expectation from the feature store.

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get_expectations#

FeatureStore.get_expectations()

Get all expectation entities from the feature store.

Getting expectations from the Feature Store means getting their metadata handles so you can subsequently add features and/or rules and save it which will overwrite the previous instance.

Returns

Expectation: The expectation metadata object.

Raises

RestAPIError: If unable to retrieve the expectations from the feature store.

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get_feature_group#

FeatureStore.get_feature_group(name, version=None)

Get a feature group entity from the feature store.

Getting a feature group from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame or use the Query-API to perform joins between feature groups.

Arguments

name str: Name of the feature group to get.
version Optional[int]: Version of the feature group to retrieve, defaults to None and will return the version=1.

Returns

FeatureGroup: The feature group metadata object.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_feature_groups#

FeatureStore.get_feature_groups(name)

Get a list of all versions of a feature group entity from the feature store.

Getting a feature group from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame or use the Query-API to perform joins between feature groups.

Arguments

name str: Name of the feature group to get.

Returns

FeatureGroup: List of feature group metadata objects.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_on_demand_feature_group#

FeatureStore.get_on_demand_feature_group(name, version=None)

Get a on-demand feature group entity from the feature store.

Getting a on-demand feature group from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame or use the Query-API to perform joins between feature groups.

Arguments

name str: Name of the on-demand feature group to get.
version Optional[int]: Version of the on-demand feature group to retrieve, defaults to None and will return the version=1.

Returns

OnDemandFeatureGroup: The on-demand feature group metadata object.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_on_demand_feature_groups#

FeatureStore.get_on_demand_feature_groups(name)

Get a list of all versions of an on-demand feature group entity from the feature store.

Getting a on-demand feature group from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame or use the Query-API to perform joins between feature groups.

Arguments

name str: Name of the on-demand feature group to get.

Returns

OnDemandFeatureGroup: List of on-demand feature group metadata objects.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_online_storage_connector#

FeatureStore.get_online_storage_connector()

Get the storage connector for the Online Feature Store of the respective project's feature store.

The returned storage connector depends on the project that you are connected to.

Returns

StorageConnector. JDBC storage connector to the Online Feature Store.

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get_storage_connector#

FeatureStore.get_storage_connector(name)

Get a previously created storage connector from the feature store.

Storage connectors encapsulate all information needed for the execution engine to read and write to specific storage. This storage can be S3, a JDBC compliant database or the distributed filesystem HOPSFS.

If you want to connect to the online feature store, see the get_online_storage_connector method to get the JDBC connector for the Online Feature Store.

Getting a Storage Connector

sc = fs.get_storage_connector("demo_fs_meb10000_Training_Datasets")

td = fs.create_training_dataset(..., storage_connector=sc, ...)

Arguments

name str: Name of the storage connector to retrieve.

Returns

StorageConnector. Storage connector object.

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get_training_dataset#

FeatureStore.get_training_dataset(name, version=None)

Get a training dataset entity from the feature store.

Getting a training dataset from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame.

Arguments

name str: Name of the training dataset to get.
version Optional[int]: Version of the training dataset to retrieve, defaults to None and will return the version=1.

Returns

TrainingDataset: The training dataset metadata object.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_training_datasets#

FeatureStore.get_training_datasets(name)

Get a list of all versions of a training dataset entity from the feature store.

Getting a training dataset from the Feature Store means getting its metadata handle so you can subsequently read the data into a Spark or Pandas DataFrame.

Arguments

name str: Name of the training dataset to get.

Returns

TrainingDataset: List of training dataset metadata objects.

Raises

RestAPIError: If unable to retrieve feature group from the feature store.

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get_transformation_function#

FeatureStore.get_transformation_function(name, version=None)

Get transformation function metadata object.

Arguments

name str: name of transformation function.
version Optional[int]: version of transformation function. Optional, if not provided all functions that match to provided name will be retrieved .

Returns:

TransformationFunction: The TransformationFunction metadata object.

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get_transformation_functions#

FeatureStore.get_transformation_functions()

Get all transformation functions metadata objects.

Returns:

List[TransformationFunction]. List of transformation function instances.

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register_builtin_transformation_functions#

FeatureStore.register_builtin_transformation_functions()

Deprecated

Register hsfs built-in transformation functions.

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sql#

FeatureStore.sql(query, dataframe_type="default", online=False, read_options={})

Execute SQL command on the offline or online feature store database

Arguments

query str: The SQL query to execute.
dataframe_type Optional[str]: The type of the returned dataframe. Defaults to "default". which maps to Spark dataframe for the Spark Engine and Pandas dataframe for the Hive engine.
online Optional[bool]: Set to true to execute the query against the online feature store. Defaults to False.
read_options Optional[dict]: Additional options to pass to the execution engine. Defaults to {}. If running queries on the online feature store, users can provide an entry {'external': True}, this instructs the library to use the host parameter in the hsfs.connection() to establish the connection to the online feature store. If not set, or set to False, the online feature store storage connector is used which relies on the private ip.

Returns

DataFrame: DataFrame depending on the chosen type.