snowflake.ml.feature_store.FeatureStore¶

class snowflake.ml.feature_store.FeatureStore(session: Session, database: str, name: str, default_warehouse: str, creation_mode: CreationMode = CreationMode.FAIL_IF_NOT_EXIST)¶

Bases: object

FeatureStore provides APIs to create, materialize, retrieve and manage feature pipelines.

Creates a FeatureStore instance.

Parameters:

session – Snowpark Session to interact with Snowflake backend.
database – Database to create the FeatureStore instance.
name – Target FeatureStore name, maps to a schema in the database.
default_warehouse – Default warehouse for feature store compute.
creation_mode – If FAIL_IF_NOT_EXIST, feature store throws when required resources not already exist; If CREATE_IF_NOT_EXIST, feature store will create required resources if they not already exist. Required resources include schema and tags. Note database must already exist in either mode.

Raises:

SnowflakeMLException – [ValueError] default_warehouse does not exist.
SnowflakeMLException – [ValueError] Required resources not exist when mode is FAIL_IF_NOT_EXIST.
SnowflakeMLException – [RuntimeError] Failed to find resources.
SnowflakeMLException – [RuntimeError] Failed to create feature store.

Methods

delete_entity(name: str) → None¶

Delete a previously registered Entity.

Parameters:

name – Entity name.

Raises:

SnowflakeMLException – [ValueError] Entity with given name not exists.
SnowflakeMLException – [RuntimeError] Failed to alter schema or drop tag.
SnowflakeMLException – [RuntimeError] Failed to find resources.

delete_feature_view(feature_view: FeatureView) → None¶

Delete a FeatureView.

Parameters:: feature_view – FeatureView to delete.
Raises:: SnowflakeMLException – [ValueError] FeatureView is not registered.

generate_dataset(name: str, spine_df: DataFrame, features: List[Union[FeatureView, FeatureViewSlice]], version: Optional[str] = None, spine_timestamp_col: Optional[str] = None, spine_label_cols: Optional[List[str]] = None, exclude_columns: Optional[List[str]] = None, include_feature_view_timestamp_col: bool = False, desc: str = '', output_type: Literal['dataset'] = 'dataset') → Dataset¶

generate_dataset(name: str, spine_df: DataFrame, features: List[Union[FeatureView, FeatureViewSlice]], output_type: Literal['table'], version: Optional[str] = None, spine_timestamp_col: Optional[str] = None, spine_label_cols: Optional[List[str]] = None, exclude_columns: Optional[List[str]] = None, include_feature_view_timestamp_col: bool = False, desc: str = '') → DataFrame

Generate dataset by given source table and feature views.

Parameters:

name – The name of the Dataset to be generated. Datasets are uniquely identified within a schema by their name and version.
spine_df – The fact table contains the raw dataset.
features – A list of FeatureView or FeatureViewSlice which contains features to be joined.
version – The version of the Dataset to be generated. If none specified, the current timestamp will be used instead.
spine_timestamp_col – Name of timestamp column in spine_df that will be used to join time-series features. If spine_timestamp_col is not none, the input features also must have timestamp_col.
spine_label_cols – Name of column(s) in spine_df that contains labels.
exclude_columns – Column names to exclude from the result dataframe. The underlying storage will still contain the columns.
include_feature_view_timestamp_col – Generated dataset will include timestamp column of feature view (if feature view has timestamp column) if set true. Default to false.
desc – A description about this dataset.
output_type – The type of Snowflake storage to use for the generated training data.

Returns:

If output_type is “dataset” (default), returns a Dataset object. If output_type is “table”, returns a Snowpark DataFrame representing the table.

Raises:

SnowflakeMLException – [ValueError] Dataset name/version already exists
SnowflakeMLException – [ValueError] Snapshot creation failed.
SnowflakeMLException – [ValueError] Invalid output_type specified.
SnowflakeMLException – [RuntimeError] Failed to create clone from table.
SnowflakeMLException – [RuntimeError] Failed to find resources.

get_entity(name: str) → Entity¶

Retrieve previously registered Entity object.

Parameters:

name – Entity name.

Returns:

Entity object.

Raises:

SnowflakeMLException – [ValueError] Entity is not found.
SnowflakeMLException – [RuntimeError] Failed to retrieve tag reference information.
SnowflakeMLException – [RuntimeError] Failed to find resources.

get_feature_view(name: str, version: str) → FeatureView¶

Retrieve previously registered FeatureView.

Parameters:

name – FeatureView name.
version – FeatureView version.

Returns:

FeatureView object.

Raises:

SnowflakeMLException – [ValueError] FeatureView with name and version is not found, or incurred exception when reconstructing the FeatureView object.

list_entities() → DataFrame¶

List all Entities in the FeatureStore.

Returns:: Snowpark DataFrame containing the results.

list_feature_views(entity_name: Optional[str] = None, feature_view_name: Optional[str] = None) → DataFrame¶

List FeatureViews in the FeatureStore. If entity_name is specified, FeatureViews associated with that Entity will be listed. If feature_view_name is specified, further reducing the results to only match the specified name.

Parameters:

entity_name – Entity name.
feature_view_name – FeatureView name.

Returns:

FeatureViews information as a Snowpark DataFrame.

load_feature_views_from_dataset(ds: Dataset) → List[Union[FeatureView, FeatureViewSlice]]¶

Retrieve FeatureViews used during Dataset construction.

Parameters:: ds – Dataset object created from feature store.
Returns:: List of FeatureViews used during Dataset construction.
Raises:: ValueError – if dataset object is not generated from feature store.

read_feature_view(feature_view: FeatureView) → DataFrame¶

Read FeatureView data.

Parameters:: feature_view – FeatureView to retrieve data from.
Returns:: Snowpark DataFrame(lazy mode) containing the FeatureView data.
Raises:: SnowflakeMLException – [ValueError] FeatureView is not registered.

register_entity(entity: Entity) → Entity¶

Parameters:: entity – Entity object to register.
Returns:: A registered entity object.
Raises:: SnowflakeMLException – [RuntimeError] Failed to find resources.

register_feature_view(feature_view: FeatureView, version: str, block: bool = True, overwrite: bool = False) → FeatureView¶

Materialize a FeatureView to Snowflake backend. Incremental maintenance for updates on the source data will be automated if refresh_freq is set. NOTE: Each new materialization will trigger a full FeatureView history refresh for the data included in the

FeatureView.

Examples

… draft_fv = FeatureView(name=”my_fv”, entities=[entities], feature_df) registered_fv = fs.register_feature_view(feature_view=draft_fv, version=”v1”) …

Parameters:

feature_view – FeatureView instance to materialize.
version – version of the registered FeatureView. NOTE: Version only accepts letters, numbers and underscore. Also version will be capitalized.
block – Specify whether the FeatureView backend materialization should be blocking or not. If blocking then the API will wait until the initial FeatureView data is generated. Default to true.
overwrite – Overwrite the existing FeatureView with same version. This is the same as dropping the FeatureView first then recreate. NOTE: there will be backfill cost associated if the FeatureView is being continuously maintained.

Returns:

A materialized FeatureView object.

Raises:

SnowflakeMLException – [ValueError] FeatureView entity has not been registered.
SnowflakeMLException – [ValueError] Warehouse or default warehouse is not specified.
SnowflakeMLException – [RuntimeError] Failed to create dynamic table, task, or view.
SnowflakeMLException – [RuntimeError] Failed to find resources.

resume_feature_view(feature_view: FeatureView) → FeatureView¶

Resume a previously suspended FeatureView.

Parameters:: feature_view – FeatureView to resume.
Returns:: A new feature view with updated status.

retrieve_feature_values(spine_df: DataFrame, features: Union[List[Union[FeatureView, FeatureViewSlice]], List[str]], spine_timestamp_col: Optional[str] = None, exclude_columns: Optional[List[str]] = None, include_feature_view_timestamp_col: bool = False) → DataFrame¶

Enrich spine dataframe with feature values. Mainly used to generate inference data input. If spine_timestamp_col is specified, point-in-time feature values will be fetched.

Parameters:

spine_df – Snowpark DataFrame to join features into.
features – List of features to join into the spine_df. Can be a list of FeatureView or FeatureViewSlice, or a list of serialized feature objects from Dataset.
spine_timestamp_col – Timestamp column in spine_df for point-in-time feature value lookup.
exclude_columns – Column names to exclude from the result dataframe.
include_feature_view_timestamp_col – Generated dataset will include timestamp column of feature view (if feature view has timestamp column) if set true. Default to false.

Returns:

Snowpark DataFrame containing the joined results.

Raises:

ValueError – if features is empty.

suspend_feature_view(feature_view: FeatureView) → FeatureView¶

Suspend an active FeatureView.

Parameters:: feature_view – FeatureView to suspend.
Returns:: A new feature view with updated status.

update_default_warehouse(warehouse_name: str) → None¶

Update default warehouse for feature store.

Parameters:: warehouse_name – Name of warehouse.
Raises:: SnowflakeMLException – If warehouse does not exists.

update_feature_view(name: str, version: str, refresh_freq: Optional[str] = None, warehouse: Optional[str] = None) → FeatureView¶

Update a registered feature view.: Check feature_view.py for which fields are allowed to be updated after registration.

Parameters:

name – name of the FeatureView to be updated.
version – version of the FeatureView to be updated.
refresh_freq – updated refresh frequency.
warehouse – updated warehouse.

Returns:

Updated FeatureView.

Raises:

SnowflakeMLException – [RuntimeError] If FeatureView is not managed and refresh_freq is defined.
SnowflakeMLException – [RuntimeError] Failed to update feature view.