SpineGroup#

[source]

SpineGroup#

hsfs.feature_group.SpineGroup(
    storage_connector=None,
    query=None,
    data_format=None,
    path=None,
    options=None,
    name=None,
    version=None,
    description=None,
    primary_key=None,
    featurestore_id=None,
    featurestore_name=None,
    created=None,
    creator=None,
    id=None,
    features=None,
    location=None,
    statistics_config=None,
    event_time=None,
    expectation_suite=None,
    online_enabled=False,
    href=None,
    online_topic_name=None,
    topic_name=None,
    spine=True,
    dataframe=None,
    deprecated=False,
    online_config=None,
    **kwargs
)

Creation#

[source]

get_or_create_spine_group#

FeatureStore.get_or_create_spine_group(
    name,
    version=None,
    description="",
    primary_key=None,
    event_time=None,
    features=None,
    dataframe=None,
)

Create a spine group metadata object.

Instead of using a feature group to save a label/prediction target, you can use a spine together with a dataframe containing the labels. A Spine is essentially a metadata object similar to a feature group, however, the data is not materialized in the feature store. It only containes the needed metadata such as the relevant event time column and primary key columns to perform point-in-time correct joins.

# connect to the Feature Store
fs = ...

spine_df = pd.Dataframe()

spine_group = fs.get_or_create_spine_group(
                    name="sales",
                    version=1,
                    description="Physical shop sales features",
                    primary_key=['ss_store_sk'],
                    event_time='sale_date',
                    dataframe=spine_df
                    )

Note that you can inspect the dataframe in the spine group, or replace the dataframe:

spine_group.dataframe.show()

spine_group.dataframe = new_df

The spine can then be used to construct queries, with only one speciality:

If you want to use the query for a feature view to be used for online serving, you can only select the label or target feature from the spine. For the online lookup, the label is not required, therefore it is important to only select label from the left feature group, so that we don't need to provide a spine for online serving.

These queries can then be used to create feature views. Since the dataframe contained in the spine is not being materialized, every time you use a feature view created with spine to read data you will have to provide a dataframe with the same structure again.

For example, to generate training data:

X_train, X_test, y_train, y_test = feature_view_spine.train_test_split(0.2, spine=training_data_entities)

Or to get batches of fresh data for batch scoring:

feature_view_spine.get_batch_data(spine=scoring_entities_df).show()

Here you have the chance to pass a different set of entities to generate the training dataset.

Sometimes it might be handy to create a feature view with a regular feature group containing the label, but then at serving time to use a spine in order to fetch features for example only for a small set of primary key values. To do this, you can pass the spine group instead of a dataframe. Just make sure it contains the needed primary key, event time and label column.

feature_view.get_batch_data(spine=spine_group)

Arguments

• name str: Name of the spine group to create.
• version int | None: Version of the spine group to retrieve, defaults to None and will create the spine group with incremented version from the last version in the feature store.
• description str | None: A string describing the contents of the spine group to improve discoverability for Data Scientists, defaults to empty string "".
• primary_key List[str] | None: A list of feature names to be used as primary key for the spine 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 spine group won't have any primary key.
• event_time str | None: Optionally, provide the name of the feature containing the event time for the features in this spine group. If event_time is set the spine group can be used for point-in-time joins. Defaults to None.

• features List[hsfs.feature.Feature] | None: Optionally, define the schema of the spine 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.

  Event time data type restriction

  The supported data types for the event time column are: timestamp, date and bigint.

• __ dataframe__: DataFrame, RDD, Ndarray, list. Spine dataframe with primary key, event time and label column to use for point in time join when fetching features.

Returns

SpineGroup. The spine group metadata object.

Retrieval#

[source]

get_or_create_spine_group#

FeatureStore.get_or_create_spine_group(
    name,
    version=None,
    description="",
    primary_key=None,
    event_time=None,
    features=None,
    dataframe=None,
)

Create a spine group metadata object.

Instead of using a feature group to save a label/prediction target, you can use a spine together with a dataframe containing the labels. A Spine is essentially a metadata object similar to a feature group, however, the data is not materialized in the feature store. It only containes the needed metadata such as the relevant event time column and primary key columns to perform point-in-time correct joins.

# connect to the Feature Store
fs = ...

spine_df = pd.Dataframe()

spine_group = fs.get_or_create_spine_group(
                    name="sales",
                    version=1,
                    description="Physical shop sales features",
                    primary_key=['ss_store_sk'],
                    event_time='sale_date',
                    dataframe=spine_df
                    )

Note that you can inspect the dataframe in the spine group, or replace the dataframe:

spine_group.dataframe.show()

spine_group.dataframe = new_df

The spine can then be used to construct queries, with only one speciality:

If you want to use the query for a feature view to be used for online serving, you can only select the label or target feature from the spine. For the online lookup, the label is not required, therefore it is important to only select label from the left feature group, so that we don't need to provide a spine for online serving.

These queries can then be used to create feature views. Since the dataframe contained in the spine is not being materialized, every time you use a feature view created with spine to read data you will have to provide a dataframe with the same structure again.

For example, to generate training data:

X_train, X_test, y_train, y_test = feature_view_spine.train_test_split(0.2, spine=training_data_entities)

Or to get batches of fresh data for batch scoring:

feature_view_spine.get_batch_data(spine=scoring_entities_df).show()

Here you have the chance to pass a different set of entities to generate the training dataset.

Sometimes it might be handy to create a feature view with a regular feature group containing the label, but then at serving time to use a spine in order to fetch features for example only for a small set of primary key values. To do this, you can pass the spine group instead of a dataframe. Just make sure it contains the needed primary key, event time and label column.

feature_view.get_batch_data(spine=spine_group)

Arguments

• name str: Name of the spine group to create.
• version int | None: Version of the spine group to retrieve, defaults to None and will create the spine group with incremented version from the last version in the feature store.
• description str | None: A string describing the contents of the spine group to improve discoverability for Data Scientists, defaults to empty string "".
• primary_key List[str] | None: A list of feature names to be used as primary key for the spine 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 spine group won't have any primary key.
• event_time str | None: Optionally, provide the name of the feature containing the event time for the features in this spine group. If event_time is set the spine group can be used for point-in-time joins. Defaults to None.

• features List[hsfs.feature.Feature] | None: Optionally, define the schema of the spine 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.

  Event time data type restriction

  The supported data types for the event time column are: timestamp, date and bigint.

• __ dataframe__: DataFrame, RDD, Ndarray, list. Spine dataframe with primary key, event time and label column to use for point in time join when fetching features.

Returns

SpineGroup. The spine group metadata object.

Properties#

[source]

avro_schema#

Avro schema representation of the feature group.

[source]

dataframe#

Spine dataframe with primary key, event time and label column to use for point in time join when fetching features.

[source]

deprecated#

Setting if the feature group is deprecated.

[source]

embedding_index#

[source]

event_time#

Event time feature in the feature group.

[source]

feature_store#

[source]

feature_store_id#

[source]

features#

Feature Group schema (alias)

[source]

name#

Name of the feature group.

[source]

notification_topic_name#

The topic used for feature group notifications.

[source]

primary_key#

List of features building the primary key.

[source]

schema#

Feature Group schema

[source]

topic_name#

The topic used for feature group data ingestion.

[source]

version#

Version number of the feature group.

Methods#

[source]

add_tag#

SpineGroup.add_tag(name, value)

Attach a tag to a feature group.

A tag consists of a pair. Tag names are unique identifiers across the whole cluster. The value of a tag can be any valid json - primitives, arrays or json objects.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.add_tag(name="example_tag", value="42")

Arguments

• name str: Name of the tag to be added.
• value Any: Value of the tag to be added.

Raises

hsfs.client.exceptions.RestAPIError in case the backend fails to add the tag.

[source]

check_deprecated#

SpineGroup.check_deprecated()

[source]

create_feature_monitoring#

SpineGroup.create_feature_monitoring(
    name,
    feature_name,
    description=None,
    start_date_time=None,
    end_date_time=None,
    cron_expression="0 0 12 ? * * *",
)

Enable feature monitoring to compare statistics on snapshots of feature data over time.

# fetch feature group
fg = fs.get_feature_group(name="my_feature_group", version=1)

# enable feature monitoring
my_config = fg.create_feature_monitoring(
    name="my_monitoring_config",
    feature_name="my_feature",
    description="my monitoring config description",
    cron_expression="0 0 12 ? * * *",
).with_detection_window(
    # Data inserted in the last day
    time_offset="1d",
    window_length="1d",
).with_reference_window(
    # Data inserted last week on the same day
    time_offset="1w1d",
    window_length="1d",
).compare_on(
    metric="mean",
    threshold=0.5,
).save()

Arguments

• name str: Name of the feature monitoring configuration. name must be unique for all configurations attached to the feature group.
• feature_name str: Name of the feature to monitor.
• description str | None: Description of the feature monitoring configuration.
• start_date_time int | str | datetime.datetime | datetime.date | pandas._libs.tslibs.timestamps.Timestamp | None: Start date and time from which to start computing statistics.
• end_date_time int | str | datetime.datetime | datetime.date | pandas._libs.tslibs.timestamps.Timestamp | None: End date and time at which to stop computing statistics.
• cron_expression str | None: Cron expression to use to schedule the job. The cron expression must be in UTC and follow the Quartz specification. Default is '0 0 12 ? * * *', every day at 12pm UTC.

Raises

hsfs.client.exceptions.FeatureStoreException.

Return

FeatureMonitoringConfig Configuration with minimal information about the feature monitoring. Additional information are required before feature monitoring is enabled.

[source]

create_statistics_monitoring#

SpineGroup.create_statistics_monitoring(
    name,
    feature_name=None,
    description=None,
    start_date_time=None,
    end_date_time=None,
    cron_expression="0 0 12 ? * * *",
)

Run a job to compute statistics on snapshot of feature data on a schedule.

# fetch feature group
fg = fs.get_feature_group(name="my_feature_group", version=1)

# enable statistics monitoring
my_config = fg.create_statistics_monitoring(
    name="my_config",
    start_date_time="2021-01-01 00:00:00",
    description="my description",
    cron_expression="0 0 12 ? * * *",
).with_detection_window(
    # Statistics computed on 10% of the last week of data
    time_offset="1w",
    row_percentage=0.1,
).save()

Arguments

• name str: Name of the feature monitoring configuration. name must be unique for all configurations attached to the feature group.
• feature_name str | None: Name of the feature to monitor. If not specified, statistics will be computed for all features.
• description str | None: Description of the feature monitoring configuration.
• start_date_time int | str | datetime.datetime | datetime.date | pandas._libs.tslibs.timestamps.Timestamp | None: Start date and time from which to start computing statistics.
• end_date_time int | str | datetime.datetime | datetime.date | pandas._libs.tslibs.timestamps.Timestamp | None: End date and time at which to stop computing statistics.
• cron_expression str | None: Cron expression to use to schedule the job. The cron expression must be in UTC and follow the Quartz specification. Default is '0 0 12 ? * * *', every day at 12pm UTC.

Raises

hsfs.client.exceptions.FeatureStoreException.

Return

FeatureMonitoringConfig Configuration with minimal information about the feature monitoring. Additional information are required before feature monitoring is enabled.

[source]

delete#

SpineGroup.delete()

Drop the entire feature group along with its feature data.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(
        name='bitcoin_price',
        version=1
        )

# delete the feature group
fg.delete()

Raises

hsfs.client.exceptions.RestAPIError.

[source]

delete_tag#

SpineGroup.delete_tag(name)

Delete a tag attached to a feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.delete_tag("example_tag")

Arguments

• name str: Name of the tag to be removed.

Raises

hsfs.client.exceptions.RestAPIError in case the backend fails to delete the tag.

[source]

filter#

SpineGroup.filter(f)

Apply filter to the feature group.

Selects all features and returns the resulting Query with the applied filter.

from hsfs.feature import Feature

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.filter(Feature("weekly_sales") > 1000)

If you are planning to join the filtered feature group later on with another feature group, make sure to select the filtered feature explicitly from the respective feature group:

fg.filter(fg.feature1 == 1).show(10)

Composite filters require parenthesis and symbols for logical operands (e.g. &, |, ...):

fg.filter((fg.feature1 == 1) | (fg.feature2 >= 2))

Arguments

• f hsfs.constructor.filter.Filter | hsfs.constructor.filter.Logic: Filter object.

Returns

Query. The query object with the applied filter.

[source]

get_all_statistics#

SpineGroup.get_all_statistics(computation_time=None, feature_names=None)

Returns all the statistics metadata computed before a specific time for the current feature group.

If computation_time is None, all the statistics metadata are returned.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg_statistics = fg.get_statistics(computation_time=None)

Arguments

• computation_time str | int | float | datetime.datetime | datetime.date | None: Date and time when statistics were computed. Defaults to None. Strings should be formatted in one of the following formats %Y-%m-%d, %Y-%m-%d %H, %Y-%m-%d %H:%M, %Y-%m-%d %H:%M:%S, or %Y-%m-%d %H:%M:%S.%f.
• feature_names List[str] | None: List of feature names of which statistics are retrieved.

Returns

Statistics. Statistics object.

Raises

hsfs.client.exceptions.RestAPIError hsfs.client.exceptions.FeatureStoreException.

[source]

get_complex_features#

SpineGroup.get_complex_features()

Returns the names of all features with a complex data type in this feature group.

complex_dtype_features = fg.get_complex_features()

[source]

get_feature#

SpineGroup.get_feature(name)

Retrieve a Feature object from the schema of the feature group.

There are several ways to access features of a feature group:

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

# get Feature instanse
fg.feature1
fg["feature1"]
fg.get_feature("feature1")

Arguments:

name: The name of the feature to retrieve

Returns:

Feature: The feature object

Raises

hsfs.client.exceptions.FeatureStoreException.

[source]

get_feature_monitoring_configs#

SpineGroup.get_feature_monitoring_configs(name=None, feature_name=None, config_id=None)

Fetch all feature monitoring configs attached to the feature group, or fetch by name or feature name only. If no arguments is provided the method will return all feature monitoring configs attached to the feature group, meaning all feature monitoring configs that are attach to a feature in the feature group. If you wish to fetch a single config, provide the its name. If you wish to fetch all configs attached to a particular feature, provide the feature name.

# fetch your feature group
fg = fs.get_feature_group(name="my_feature_group", version=1)

# fetch all feature monitoring configs attached to the feature group
fm_configs = fg.get_feature_monitoring_configs()

# fetch a single feature monitoring config by name
fm_config = fg.get_feature_monitoring_configs(name="my_config")

# fetch all feature monitoring configs attached to a particular feature
fm_configs = fg.get_feature_monitoring_configs(feature_name="my_feature")

# fetch a single feature monitoring config with a given id
fm_config = fg.get_feature_monitoring_configs(config_id=1)

Arguments

• name str | None: If provided fetch only the feature monitoring config with the given name. Defaults to None.
• feature_name str | None: If provided, fetch only configs attached to a particular feature. Defaults to None.
• config_id int | None: If provided, fetch only the feature monitoring config with the given id. Defaults to None.

Raises

hsfs.client.exceptions.RestAPIError. hsfs.client.exceptions.FeatureStoreException. - ValueError: if both name and feature_name are provided. - TypeError: if name or feature_name are not string or None.

Return

Union[FeatureMonitoringConfig, List[FeatureMonitoringConfig], None] A list of feature monitoring configs. If name provided, returns either a single config or None if not found.

[source]

get_feature_monitoring_history#

SpineGroup.get_feature_monitoring_history(
    config_name=None, config_id=None, start_time=None, end_time=None, with_statistics=True
)

Fetch feature monitoring history for a given feature monitoring config.

# fetch your feature group
fg = fs.get_feature_group(name="my_feature_group", version=1)

# fetch feature monitoring history for a given feature monitoring config
fm_history = fg.get_feature_monitoring_history(
    config_name="my_config",
    start_time="2020-01-01",
)

# fetch feature monitoring history for a given feature monitoring config id
fm_history = fg.get_feature_monitoring_history(
    config_id=1,
    start_time=datetime.now() - timedelta(weeks=2),
    end_time=datetime.now() - timedelta(weeks=1),
    with_statistics=False,
)

Arguments

• config_name str | None: The name of the feature monitoring config to fetch history for. Defaults to None.
• config_id int | None: The id of the feature monitoring config to fetch history for. Defaults to None.
• start_time int | str | datetime.datetime | datetime.date | None: The start date of the feature monitoring history to fetch. Defaults to None.
• end_time int | str | datetime.datetime | datetime.date | None: The end date of the feature monitoring history to fetch. Defaults to None.
• with_statistics bool | None: Whether to include statistics in the feature monitoring history. Defaults to True. If False, only metadata about the monitoring will be fetched.

Raises

hsfs.client.exceptions.RestAPIError. hsfs.client.exceptions.FeatureStoreException. - ValueError: if both config_name and config_id are provided. - TypeError: if config_name or config_id are not respectively string, int or None.

Return

List[FeatureMonitoringResult] A list of feature monitoring results containing the monitoring metadata as well as the computed statistics for the detection and reference window if requested.

[source]

get_fg_name#

SpineGroup.get_fg_name()

[source]

get_generated_feature_groups#

SpineGroup.get_generated_feature_groups()

Get the generated feature groups using this feature group, based on explicit provenance. These feature groups can be accessible or inaccessible. Explicit provenance does not track deleted generated feature group links, so deleted will always be empty. For inaccessible feature groups, only a minimal information is returned.

Returns

ProvenanceLinks: Object containing the section of provenance graph requested.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

get_generated_feature_views#

SpineGroup.get_generated_feature_views()

Get the generated feature view using this feature group, based on explicit provenance. These feature views can be accessible or inaccessible. Explicit provenance does not track deleted generated feature view links, so deleted will always be empty. For inaccessible feature views, only a minimal information is returned.

Returns

ProvenanceLinks: Object containing the section of provenance graph requested.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

get_parent_feature_groups#

SpineGroup.get_parent_feature_groups()

Get the parents of this feature group, based on explicit provenance. Parents are feature groups or external feature groups. These feature groups can be accessible, deleted or inaccessible. For deleted and inaccessible feature groups, only a minimal information is returned.

Returns

ProvenanceLinks: Object containing the section of provenance graph requested.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

get_storage_connector#

SpineGroup.get_storage_connector()

Get the storage connector using this feature group, based on explicit provenance. Only the accessible storage connector is returned. For more items use the base method - get_storage_connector_provenance

Returns

`StorageConnector: Storage connector.

[source]

get_storage_connector_provenance#

SpineGroup.get_storage_connector_provenance()

Get the parents of this feature group, based on explicit provenance. Parents are storage connectors. These storage connector can be accessible, deleted or inaccessible. For deleted and inaccessible storage connector, only a minimal information is returned.

Returns

ExplicitProvenance.Links: the storage connector used to generated this feature group

Raises

hsfs.client.exceptions.RestAPIError.

[source]

get_tag#

SpineGroup.get_tag(name)

Get the tags of a feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg_tag_value = fg.get_tag("example_tag")

Arguments

• name str: Name of the tag to get.

Returns

tag value

Raises

hsfs.client.exceptions.RestAPIError in case the backend fails to retrieve the tag.

[source]

get_tags#

SpineGroup.get_tags()

Retrieves all tags attached to a feature group.

Returns

Dict[str, obj] of tags.

Raises

hsfs.client.exceptions.RestAPIError in case the backend fails to retrieve the tags.

[source]

json#

SpineGroup.json()

[source]

select#

SpineGroup.select(features)

Select a subset of features of the feature group and return a query object.

The query can be used to construct joins of feature groups or create a feature view with a subset of features of the feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
from hsfs.feature import Feature
fg = fs.create_feature_group(
        "fg",
        features=[
                Feature("id", type="string"),
                Feature("ts", type="bigint"),
                Feature("f1", type="date"),
                Feature("f2", type="double")
                ],
        primary_key=["id"],
        event_time="ts")

# construct query
query = fg.select(["id", "f1"])
query.features
# [Feature('id', ...), Feature('f1', ...)]

Arguments

• features List[str | hsfs.feature.Feature]: A list of Feature objects or feature names as strings to be selected.

Returns

Query: A query object with the selected features of the feature group.

[source]

select_all#

SpineGroup.select_all(include_primary_key=True, include_event_time=True)

Select all features along with primary key and event time from the feature group and return a query object.

The query can be used to construct joins of feature groups or create a feature view.

# connect to the Feature Store
fs = ...

# get the Feature Group instances
fg1 = fs.get_or_create_feature_group(...)
fg2 = fs.get_or_create_feature_group(...)

# construct the query
query = fg1.select_all().join(fg2.select_all())

# show first 5 rows
query.show(5)


# select all features exclude primary key and event time
from hsfs.feature import Feature
fg = fs.create_feature_group(
        "fg",
        features=[
                Feature("id", type="string"),
                Feature("ts", type="bigint"),
                Feature("f1", type="date"),
                Feature("f2", type="double")
                ],
        primary_key=["id"],
        event_time="ts")

query = fg.select_all()
query.features
# [Feature('id', ...), Feature('ts', ...), Feature('f1', ...), Feature('f2', ...)]

query = fg.select_all(include_primary_key=False, include_event_time=False)
query.features
# [Feature('f1', ...), Feature('f2', ...)]

Arguments

• include_primary_key bool | None: If True, include primary key of the feature group to the feature list. Defaults to True.
• include_event_time bool | None: If True, include event time of the feature group to the feature list. Defaults to True.

Returns

Query. A query object with all features of the feature group.

[source]

select_except#

SpineGroup.select_except(features=None)

Select all features including primary key and event time feature of the feature group except provided features and return a query object.

The query can be used to construct joins of feature groups or create a feature view with a subset of features of the feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
from hsfs.feature import Feature
fg = fs.create_feature_group(
        "fg",
        features=[
                Feature("id", type="string"),
                Feature("ts", type="bigint"),
                Feature("f1", type="date"),
                Feature("f2", type="double")
                ],
        primary_key=["id"],
        event_time="ts")

# construct query
query = fg.select_except(["ts", "f1"])
query.features
# [Feature('id', ...), Feature('f1', ...)]

Arguments

• features List[str | hsfs.feature.Feature] | None: A list of Feature objects or feature names as strings to be excluded from the selection. Defaults to [], selecting all features.

Returns

Query: A query object with the selected features of the feature group.

[source]

select_features#

SpineGroup.select_features()

Select all the features in the feature group and return a query object.

Queries define the schema of Feature View objects which can be used to create Training Datasets, read from the Online Feature Store, and more. They can also be composed to create more complex queries using the join method.

This method does not select the primary key and event time of the feature group. Use select_all to include them. Note that primary keys do not need to be included in the query to allow joining on them.

# connect to the Feature Store
fs = hopsworks.login().get_feature_store()

# Some dataframe to create the feature group with
# both an event time and a primary key column
my_df.head()
+------------+------------+------------+------------+
|    id      | feature_1  |    ...     |    ts      |
+------------+------------+------------+------------+
|     8      |     8      |            |    15      |
|     3      |     3      |    ...     |    6       |
|     1      |     1      |            |    18      |
+------------+------------+------------+------------+

# Create the Feature Group instances
fg1 = fs.create_feature_group(
        name = "fg1",
        version=1,
        primary_key=["id"],
        event_time="ts",
    )

# Insert data to the feature group.
fg1.insert(my_df)

# select all features from `fg1` excluding primary key and event time
query = fg1.select_features()

# show first 3 rows
query.show(3)

# Output, no id or ts columns
+------------+------------+------------+
| feature_1  | feature_2  | feature_3  |
+------------+------------+------------+
|     8      |     7      |    15      |
|     3      |     1      |     6      |
|     1      |     2      |    18      |
+------------+------------+------------+

# connect to the Feature Store
fs = hopsworks.login().get_feature_store()

# Get the Feature Group from the previous example
fg1 = fs.get_feature_group("fg1", 1)

# Some dataframe to create another feature group
# with a primary key column
+------------+------------+------------+
|    id_2    | feature_6  | feature_7  |
+------------+------------+------------+
|     8      |     11     |            |
|     3      |     4      |    ...     |
|     1      |     9      |            |
+------------+------------+------------+

# join the two feature groups on their indexes, `id` and `id_2`
# but does not include them in the query
query = fg1.select_features().join(fg2.select_features(), left_on="id", right_on="id_2")

# show first 5 rows
query.show(3)

# Output
+------------+------------+------------+------------+------------+
| feature_1  | feature_2  | feature_3  | feature_6  | feature_7  |
+------------+------------+------------+------------+------------+
|     8      |     7      |    15      |    11      |    15      |
|     3      |     1      |     6      |     4      |     3      |
|     1      |     2      |    18      |     9      |    20      |
+------------+------------+------------+------------+------------+

Returns

Query. A query object with all features of the feature group.

[source]

to_dict#

SpineGroup.to_dict()

[source]

update_deprecated#

SpineGroup.update_deprecated(deprecate=True)

Deprecate the feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.update_deprecated(deprecate=True)

Arguments

• deprecate bool: Boolean value identifying if the feature group should be deprecated. Defaults to True.

Returns

FeatureGroup. The updated feature group object.

[source]

update_description#

SpineGroup.update_description(description)

Update the description of the feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.update_description(description="Much better description.")

Arguments

• description str: New description string.

Returns

FeatureGroup. The updated feature group object.

[source]

update_feature_description#

SpineGroup.update_feature_description(feature_name, description)

Update the description of a single feature in this feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.update_feature_description(feature_name="min_temp",
                              description="Much better feature description.")

Arguments

• feature_name str: Name of the feature to be updated.
• description str: New description string.

Returns

FeatureGroup. The updated feature group object.

[source]

update_features#

SpineGroup.update_features(features)

Update metadata of features in this feature group.

Currently it's only supported to update the description of a feature.

Arguments

• features hsfs.feature.Feature | List[hsfs.feature.Feature]: Feature or list of features. A feature object or list thereof to be updated.

Returns

FeatureGroup. The updated feature group object.

[source]

update_notification_topic_name#

SpineGroup.update_notification_topic_name(notification_topic_name)

Update the notification topic name of the feature group.

# connect to the Feature Store
fs = ...

# get the Feature Group instance
fg = fs.get_or_create_feature_group(...)

fg.update_notification_topic_name(notification_topic_name="notification_topic_name")

Arguments

• notification_topic_name str: Name of the topic used for sending notifications when entries are inserted or updated on the online feature store. If set to None no notifications are sent.

Returns

FeatureGroup. The updated feature group object.