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FeatureGroup#

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FeatureGroup#

hsfs.feature_group.FeatureGroup(
    name,
    version,
    featurestore_id,
    description="",
    partition_key=None,
    primary_key=None,
    hudi_precombine_key=None,
    featurestore_name=None,
    created=None,
    creator=None,
    id=None,
    features=None,
    location=None,
    online_enabled=False,
    time_travel_format=None,
    statistics_config=None,
    online_topic_name=None,
    event_time=None,
    stream=False,
    expectation_suite=None,
    parents=None,
    href=None,
    delta_streamer_job_conf=None,
)

Creation#

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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,
    event_time=None,
    stream=False,
    expectation_suite=None,
    parents=[],
)

Create a feature group metadata object.

Example

# connect to the Feature Store
fs = ...

fg = fs.create_feature_group(
        name='air_quality',
        description='Air Quality characteristics of each day',
        version=1,
        primary_key=['city','date'],
        online_enabled=True,
        event_time='date'
    )

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.

  • 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.

    Event time data type restriction

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

    • stream: Optionally, Define whether the feature group should support real time stream writing capabilities. Stream enabled Feature Groups have unified single API for writing streaming features transparently to both online and offline store.
    • expectation_suite Optional[Union[hsfs.expectation_suite.ExpectationSuite, great_expectations.core.expectation_suite.ExpectationSuite]]: Optionally, attach an expectation suite to the feature group which dataframes should be validated against upon insertion. Defaults to None.
    • parents Optional[List[hsfs.feature_group.FeatureGroup]]: Optionally, Define the parents of this feature group as the origin where the data is coming from.

Returns

FeatureGroup. The feature group metadata object.

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get_or_create_feature_group#

FeatureStore.get_or_create_feature_group(
    name,
    version,
    description="",
    online_enabled=False,
    time_travel_format="HUDI",
    partition_key=[],
    primary_key=[],
    hudi_precombine_key=None,
    features=[],
    statistics_config=None,
    expectation_suite=None,
    event_time=None,
    stream=False,
    parents=[],
)

Get feature group metadata object or create a new one if it doesn't exist. This method doesn't update existing feature group metadata object.

Example

# connect to the Feature Store
fs = ...

fg = fs.get_or_create_feature_group(
        name="electricity_prices",
        version=1,
        description="Electricity prices from NORD POOL",
        primary_key=["day", "area"],
        online_enabled=True,
        event_time="timestamp",
        )

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 insert() method with a DataFrame.

Arguments

  • name str: Name of the feature group to create.
  • version int: Version of the feature group to retrieve or create.
  • 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.
  • expectation_suite Optional[Union[hsfs.expectation_suite.ExpectationSuite, great_expectations.core.expectation_suite.ExpectationSuite]]: Optionally, attach an expectation suite to the feature group which dataframes should be validated against upon insertion. Defaults to None.

  • 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.

    Event time data type restriction

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

    • stream: Optionally, Define whether the feature group should support real time stream writing capabilities. Stream enabled Feature Groups have unified single API for writing streaming features transparently to both online and offline store.
    • parents Optional[List[hsfs.feature_group.FeatureGroup]]: Optionally, Define the parents of this feature group as the origin where the data is coming from.

Returns

FeatureGroup. The feature group metadata object.

Retrieval#

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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.

Example

# connect to the Feature Store
fs = ...

fg = fs.get_feature_group(
        name="electricity_prices",
        version=1,
    )

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

  • hsfs.client.exceptions.RestAPIError: If unable to retrieve feature group from the feature store.

Properties#

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avro_schema#

Avro schema representation of the feature group.

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backfill_job#

Get the Job object reference for the backfill job for this Feature Group.

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created#

Timestamp when the feature group was created.

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creator#

Username of the creator.

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description#

Description of the feature group contents.

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event_time#

Event time feature in the feature group.

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expectation_suite#

Expectation Suite configuration object defining the settings for data validation of the feature group.

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feature_store_id#

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feature_store_name#

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

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features#

Schema information.

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hudi_precombine_key#

Feature name that is the hudi precombine key.

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id#

Feature group id.

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location#

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name#

Name of the feature group.

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online_enabled#

Setting if the feature group is available in online storage.

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parents#

Parent feature groups as origin of the data in the current feature group. This is part of explicit provenance

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partition_key#

List of features building the partition key.

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primary_key#

List of features building the primary key.

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statistics#

Get the latest computed statistics for the feature group.

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statistics_config#

Statistics configuration object defining the settings for statistics computation of the feature group.

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stream#

Whether to enable real time stream writing capabilities.

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subject#

Subject of the feature group.

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time_travel_format#

Setting of the feature group time travel format.

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version#

Version number of the feature group.

Methods#

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add_tag#

FeatureGroup.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.

Example

# 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: Value of the tag to be added.

Raises

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

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append_features#

FeatureGroup.append_features(features)

Append features to the schema of the feature group.

Example

# connect to the Feature Store
fs = ...

# define features to be inserted in the feature group
features = [
    Feature(name="id",type="int",online_type="int"),
    Feature(name="name",type="string",online_type="varchar(20)")
]

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

fg.append_features(features)

Safe append

This method appends the features to the feature group description safely. In case of failure your local metadata object will contain the correct schema.

It is only possible to append features to a feature group. Removing features is considered a breaking change.

Arguments

  • features Union[hsfs.feature.Feature, List[hsfs.feature.Feature]]: Feature or list. A feature object or list thereof to append to the schema of the feature group.

Returns

FeatureGroup. The updated feature group object.

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as_of#

FeatureGroup.as_of(wallclock_time=None, exclude_until=None)

Get Query object to retrieve all features of the group at a point in the past.

This method selects all features in the feature group and returns a Query object at the specified point in time. Optionally, commits before a specified point in time can be excluded from the query. The Query can then either be read into a Dataframe or used further to perform joins or construct a training dataset.

Reading features at a specific point in time:

# connect to the Feature Store
fs = ...

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

# get data at a specific point in time and show it
fg.as_of("2020-10-20 07:34:11").read().show()

Reading commits incrementally between specified points in time:

fg.as_of("2020-10-20 07:34:11", exclude_until="2020-10-19 07:34:11").read().show()

The first parameter is inclusive while the latter is exclusive. That means, in order to query a single commit, you need to query that commit time and exclude everything just before the commit.

Reading only the changes from a single commit

fg.as_of("2020-10-20 07:31:38", exclude_until="2020-10-20 07:31:37").read().show()

When no wallclock_time is given, the latest state of features is returned. Optionally, commits before a specified point in time can still be excluded.

Reading the latest state of features, excluding commits before a specified point in time:

fg.as_of(None, exclude_until="2020-10-20 07:31:38").read().show()

Note that the interval will be applied to all joins in the query. If you want to query different intervals for different feature groups in the query, you have to apply them in a nested fashion:

Example

# connect to the Feature Store
fs = ...

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

fg1.select_all().as_of("2020-10-20", exclude_until="2020-10-19")
    .join(fg2.select_all().as_of("2020-10-20", exclude_until="2020-10-19"))

If instead you apply another as_of selection after the join, all joined feature groups will be queried with this interval:

Example

fg1.select_all().as_of("2020-10-20", exclude_until="2020-10-19")  # as_of is not applied
    .join(fg2.select_all().as_of("2020-10-20", exclude_until="2020-10-15"))  # as_of is not applied
    .as_of("2020-10-20", exclude_until="2020-10-19")

Warning

This function only works for feature groups with time_travel_format='HUDI'.

Warning

Excluding commits via exclude_until is only possible within the range of the Hudi active timeline. By default, Hudi keeps the last 20 to 30 commits in the active timeline. If you need to keep a longer active timeline, you can overwrite the options: hoodie.keep.min.commits and hoodie.keep.max.commits when calling the insert() method.

Arguments

  • wallclock_time Optional[Union[str, int, datetime.datetime, datetime.date]]: Read data as of this point in time. Strings should be formatted in one of the following formats %Y-%m-%d, %Y-%m-%d %H, %Y-%m-%d %H:%M, or %Y-%m-%d %H:%M:%S.
  • exclude_until Optional[Union[str, int, datetime.datetime, datetime.date]]: Exclude commits until this point in time. String should be formatted in one of the following formats %Y-%m-%d, %Y-%m-%d %H, %Y-%m-%d %H:%M, or %Y-%m-%d %H:%M:%S.

Returns

Query. The query object with the applied time travel condition.

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commit_delete_record#

FeatureGroup.commit_delete_record(delete_df, write_options={})

Drops records present in the provided DataFrame and commits it as update to this Feature group. This method can only be used on time travel enabled feature groups.

Arguments

  • delete_df pyspark.sql.DataFrame: dataFrame containing records to be deleted.
  • write_options Optional[Dict[Any, Any]]: User provided write options. Defaults to {}.

Raises

hsfs.client.exceptions.RestAPIError.

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commit_details#

FeatureGroup.commit_details(wallclock_time=None, limit=None)

Retrieves commit timeline for this feature group. This method can only be used on time travel enabled feature groups

Example

# connect to the Feature Store
fs = ...

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

commit_details = fg.commit_details()

Arguments

  • wallclock_time Optional[Union[str, int, datetime.datetime, datetime.date]]: Commit details as of specific point in time. 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.
  • limit Optional[int]: Number of commits to retrieve. Defaults to None.

Returns

Dict[str, Dict[str, str]]. Dictionary object of commit metadata timeline, where Key is commit id and value is Dict[str, str] with key value pairs of date committed on, number of rows updated, inserted and deleted.

Raises

hsfs.client.exceptions.RestAPIError. hsfs.client.exceptions.FeatureStoreException. If the feature group does not have HUDI time travel format

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compute_statistics#

FeatureGroup.compute_statistics(wallclock_time=None)

Recompute the statistics for the feature group and save them to the feature store.

Statistics are only computed for data in the offline storage of the feature group.

Arguments

  • wallclock_time Optional[Union[str, int, datetime.datetime, datetime.date]]: If specified will recompute statistics on feature group as of specific point in time. If not specified then will compute statistics as of most recent time of this feature group. 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.

Returns

Statistics. The statistics metadata object.

Raises

hsfs.client.exceptions.RestAPIError. Unable to persist the statistics.

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delete#

FeatureGroup.delete()

Drop the entire feature group along with its feature data.

Example

# 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()

Potentially dangerous operation

This operation drops all metadata associated with this version of the feature group and all the feature data in offline and online storage associated with it.

Raises

hsfs.client.exceptions.RestAPIError.

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delete_expectation_suite#

FeatureGroup.delete_expectation_suite()

Delete the expectation suite attached to the Feature Group.

Example

# connect to the Feature Store
fs = ...

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

fg.delete_expectation_suite()

Raises

hsfs.client.exceptions.RestAPIError.

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delete_tag#

FeatureGroup.delete_tag(name)

Delete a tag attached to a feature group.

Example

# 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.

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filter#

FeatureGroup.filter(f)

Apply filter to the feature group.

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

Example

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:

Example

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

Composite filters require parenthesis:

Example

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

Arguments

  • f Union[hsfs.constructor.filter.Filter, hsfs.constructor.filter.Logic]: Filter object.

Returns

Query. The query object with the applied filter.

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finalize_multi_part_insert#

FeatureGroup.finalize_multi_part_insert()

Finalizes and exits the multi part insert context opened by multi_part_insert in a blocking fashion once all rows have been transmitted.

Multi part insert with manual context management

Instead of letting Python handle the entering and exiting of the multi part insert context, you can start and finalize the context manually. 

feature_group = fs.get_or_create_feature_group("fg_name", version=1)

while loop:
    small_batch_df = ...
    feature_group.multi_part_insert(small_batch_df)

# IMPORTANT: finalize the multi part insert to make sure all rows
# have been transmitted
feature_group.finalize_multi_part_insert()
Note that the first call to multi_part_insert initiates the context and be sure to finalize it. The finalize_multi_part_insert is a blocking call that returns once all rows have been transmitted.

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from_response_json#

FeatureGroup.from_response_json(json_dict)

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get_all_validation_reports#

FeatureGroup.get_all_validation_reports(ge_type=True)

Return the latest validation report attached to the feature group if it exists.

Example

# connect to the Feature Store
fs = ...

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

val_reports = fg.get_all_validation_reports()

Arguments

  • ge_type bool: If True returns a native Great Expectation type, Hopsworks custom type otherwise. Conversion can be performed via the to_ge_type() method on hopsworks type. Defaults to True.

Returns

Union[List[ValidationReport], ValidationReport]. All validation reports attached to the feature group.

Raises

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

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get_complex_features#

FeatureGroup.get_complex_features()

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

Example

complex_dtype_features = fg.get_complex_features()

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get_expectation_suite#

FeatureGroup.get_expectation_suite(ge_type=True)

Return the expectation suite attached to the feature group if it exists.

Example

# connect to the Feature Store
fs = ...

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

exp_suite = fg.get_expectation_suite()

Arguments

  • ge_type bool: If True returns a native Great Expectation type, Hopsworks custom type otherwise. Conversion can be performed via the to_ge_type() method on hopsworks type. Defaults to True.

Returns

ExpectationSuite. The expectation suite attached to the feature group.

Raises

hsfs.client.exceptions.RestAPIError.

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get_feature#

FeatureGroup.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:

Example

# 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")

Note

Attribute access to features works only for non-reserved names. For example features named id or name will not be accessible via fg.name, instead this will return the name of the feature group itself. Fall back on using the get_feature method.

Arguments:

name: The name of the feature to retrieve

Returns:

Feature: The feature object

Raises

hsfs.client.exceptions.FeatureStoreException.

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get_generated_feature_groups#

FeatureGroup.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.

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get_generated_feature_views#

FeatureGroup.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.

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get_latest_validation_report#

FeatureGroup.get_latest_validation_report(ge_type=True)

Return the latest validation report attached to the Feature Group if it exists.

Example

# connect to the Feature Store
fs = ...

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

latest_val_report = fg.get_latest_validation_report()

Arguments

  • ge_type bool: If True returns a native Great Expectation type, Hopsworks custom type otherwise. Conversion can be performed via the to_ge_type() method on hopsworks type. Defaults to True.

Returns

ValidationReport. The latest validation report attached to the Feature Group.

Raises

hsfs.client.exceptions.RestAPIError.

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get_parent_feature_groups#

FeatureGroup.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.

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get_statistics#

FeatureGroup.get_statistics(commit_time=None)

Returns the statistics for this feature group at a specific time.

If commit_time is None, the most recent statistics are returned.

Example

# connect to the Feature Store
fs = ...

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

fg_statistics = fg.get_statistics(commit_time=None)

Arguments

  • commit_time Optional[Union[str, int, datetime.datetime, datetime.date]]: Date and time of the commit. 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.

Returns

Statistics. Statistics object.

Raises

hsfs.client.exceptions.RestAPIError.

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get_tag#

FeatureGroup.get_tag(name)

Get the tags of a feature group.

Example

# 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.

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get_tags#

FeatureGroup.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.

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get_validation_history#

FeatureGroup.get_validation_history(
    expectation_id, start_validation_time=None, end_validation_time=None, filter_by=[], ge_type=True
)

Fetch validation history of an Expectation specified by its id.

Example

validation_history = fg.get_validation_history(
    expectation_id=1,
    filter_by=["REJECTED", "UNKNOWN"],
    start_validation_time="2022-01-01 00:00:00",
    end_validation_time=datetime.datetime.now(),
    ge_type=False
)

Arguments

  • expectation_id int: id of the Expectation for which to fetch the validation history
  • filter_by List[str]: list of ingestion_result category to keep. Ooptions are "INGESTED", "REJECTED", "FG_DATA", "EXPERIMENT", "UNKNOWN".
  • start_validation_time Optional[Union[str, int, datetime.datetime, datetime.date]]: fetch only validation result posterior to the provided time, inclusive. Supported format include timestamps(int), datetime, date or string formatted to be datutils parsable. See examples above.
  • end_validation_time Optional[Union[str, int, datetime.datetime, datetime.date]]: fetch only validation result prior to the provided time, inclusive. Supported format include timestamps(int), datetime, date or string formatted to be datutils parsable. See examples above.

Raises

hsfs.client.exceptions.RestAPIError.

Return

Union[List[ValidationResult], List[ExpectationValidationResult]] A list of validation result connected to the expectation_id

[source]

insert#

FeatureGroup.insert(
    features,
    overwrite=False,
    operation="upsert",
    storage=None,
    write_options={},
    validation_options={},
    save_code=True,
    wait=False,
)

Persist the metadata and materialize the feature group to the feature store or insert data from a dataframe into the existing feature group.

Incrementally insert data to a feature group or overwrite all data contained in the feature group. By default, the data is inserted into the offline storage as well as the online storage if the feature group is online_enabled=True. To insert only into the online or offline storage set storage="online" or storage="offline" respectively.

The features dataframe can be a Spark DataFrame or RDD, a Pandas DataFrame, or a two-dimensional Numpy array or a two-dimensional Python nested list. If statistics are enabled, statistics are recomputed for the entire feature group. If feature group's time travel format is HUDI then operation argument can be either insert or upsert.

If feature group doesn't exists the insert method will create the necessary metadata the first time it is invoked and writes the specified features dataframe as feature group to the online/offline feature store.

Changed in 3.3.0

insert and save methods are now async by default in non-spark clients. To achieve the old behaviour, set wait argument to True.

Upsert new feature data with time travel format HUDI

# connect to the Feature Store
fs = ...

fg = fs.get_or_create_feature_group(
    name='bitcoin_price',
    description='Bitcoin price aggregated for days',
    version=1,
    primary_key=['unix'],
    online_enabled=True,
    event_time='unix'
)

fg.insert(df_bitcoin_processed)

Async insert

# connect to the Feature Store
fs = ...

fg1 = fs.get_or_create_feature_group(
    name='feature_group_name1',
    description='Description of the first FG',
    version=1,
    primary_key=['unix'],
    online_enabled=True,
    event_time='unix'
)
# async insertion in order not to wait till finish of the job
fg.insert(df_for_fg1, write_options={"wait_for_job" : False})

fg2 = fs.get_or_create_feature_group(
    name='feature_group_name2',
    description='Description of the second FG',
    version=1,
    primary_key=['unix'],
    online_enabled=True,
    event_time='unix'
)
fg.insert(df_for_fg2)

Arguments

  • features Union[pandas.DataFrame, pyspark.sql.DataFrame, pyspark.RDD, numpy.ndarray, List[list]]: DataFrame, RDD, Ndarray, list. Features to be saved.
  • overwrite Optional[bool]: Drop all data in the feature group before inserting new data. This does not affect metadata, defaults to False.
  • operation Optional[str]: Apache Hudi operation type "insert" or "upsert". Defaults to "upsert".
  • storage Optional[str]: Overwrite default behaviour, write to offline storage only with "offline" or online only with "online", defaults to None.
  • write_options Optional[Dict[str, Any]]: Additional write options as key-value pairs, defaults to {}. When using the python engine, write_options can contain the following entries:
    • key spark and value an object of type hsfs.core.job_configuration.JobConfiguration to configure the Hopsworks Job used to write data into the feature group.
    • key wait_for_job and value True or False to configure whether or not to the insert call should return only after the Hopsworks Job has finished. By default it waits.
    • key start_offline_backfill and value True or False to configure whether or not to start the backfill job to write data to the offline storage. By default the backfill job gets started immediately.
    • key internal_kafka and value True or False in case you established connectivity from you Python environment to the internal advertised listeners of the Hopsworks Kafka Cluster. Defaults to False and will use external listeners when connecting from outside of Hopsworks.
  • validation_options Optional[Dict[str, Any]]: Additional validation options as key-value pairs, defaults to {}.
    • key run_validation boolean value, set to False to skip validation temporarily on ingestion.
    • key save_report boolean value, set to False to skip upload of the validation report to Hopsworks.
    • key ge_validate_kwargs a dictionary containing kwargs for the validate method of Great Expectations.
    • key fetch_expectation_suite a boolean value, by default True, to control whether the expectation suite of the feature group should be fetched before every insert.
  • save_code Optional[bool]: When running HSFS on Hopsworks or Databricks, HSFS can save the code/notebook used to create the feature group or used to insert data to it. When calling the insert method repeatedly with small batches of data, this can slow down the writes. Use this option to turn off saving code. Defaults to True.
  • wait bool: Wait for job to finish before returning, defaults to False. Shortcut for read_options {"wait_for_job": False}.

Returns

(Job, ValidationReport) A tuple with job information if python engine is used and the validation report if validation is enabled.

[source]

insert_stream#

FeatureGroup.insert_stream(
    features,
    query_name=None,
    output_mode="append",
    await_termination=False,
    timeout=None,
    checkpoint_dir=None,
    write_options={},
)

Ingest a Spark Structured Streaming Dataframe to the online feature store.

This method creates a long running Spark Streaming Query, you can control the termination of the query through the arguments.

It is possible to stop the returned query with the .stop() and check its status with .isActive.

To get a list of all active queries, use:

sqm = spark.streams

# get the list of active streaming queries
[q.name for q in sqm.active]

Engine Support

Spark only

Stream ingestion using Pandas/Python as engine is currently not supported. Python/Pandas has no notion of streaming.

Data Validation Support

insert_stream does not perform any data validation using Great Expectations even when a expectation suite is attached.

Arguments

  • features pyspark.sql.DataFrame: Features in Streaming Dataframe to be saved.
  • query_name Optional[str]: It is possible to optionally specify a name for the query to make it easier to recognise in the Spark UI. Defaults to None.
  • output_mode Optional[str]: Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. (1) "append": Only the new rows in the streaming DataFrame/Dataset will be written to the sink. (2) "complete": All the rows in the streaming DataFrame/Dataset will be written to the sink every time there is some update. (3) "update": only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn’t contain aggregations, it will be equivalent to append mode. Defaults to "append".
  • await_termination Optional[bool]: Waits for the termination of this query, either by query.stop() or by an exception. If the query has terminated with an exception, then the exception will be thrown. If timeout is set, it returns whether the query has terminated or not within the timeout seconds. Defaults to False.
  • timeout Optional[int]: Only relevant in combination with await_termination=True. Defaults to None.
  • checkpoint_dir Optional[str]: Checkpoint directory location. This will be used to as a reference to from where to resume the streaming job. If None then hsfs will construct as "insert_stream_" + online_topic_name. Defaults to None. write_options: Additional write options for Spark as key-value pairs. Defaults to {}.

Returns

StreamingQuery: Spark Structured Streaming Query object.

[source]

json#

FeatureGroup.json()

Get specific Feature Group metadata in json format.

Example

fg.json()

[source]

multi_part_insert#

FeatureGroup.multi_part_insert(
    features=None,
    overwrite=False,
    operation="upsert",
    storage=None,
    write_options={},
    validation_options={},
)

Get FeatureGroupWriter for optimized multi part inserts or call this method to start manual multi part optimized inserts.

In use cases where very small batches (1 to 1000) rows per Dataframe need to be written to the feature store repeatedly, it might be inefficient to use the standard feature_group.insert() method as it performs some background actions to update the metadata of the feature group object first.

For these cases, the feature group provides the multi_part_insert API, which is optimized for writing many small Dataframes after another.

There are two ways to use this API:

Python Context Manager

Using the Python with syntax you can acquire a FeatureGroupWriter object that implements the same multi_part_insert API. 

feature_group = fs.get_or_create_feature_group("fg_name", version=1)

with feature_group.multi_part_insert() as writer:
    # run inserts in a loop:
    while loop:
        small_batch_df = ...
        writer.insert(small_batch_df)
The writer batches the small Dataframes and transmits them to Hopsworks efficiently. When exiting the context, the feature group writer is sure to exit only once all the rows have been transmitted.

Multi part insert with manual context management

Instead of letting Python handle the entering and exiting of the multi part insert context, you can start and finalize the context manually. 

feature_group = fs.get_or_create_feature_group("fg_name", version=1)

while loop:
    small_batch_df = ...
    feature_group.multi_part_insert(small_batch_df)

# IMPORTANT: finalize the multi part insert to make sure all rows
# have been transmitted
feature_group.finalize_multi_part_insert()
Note that the first call to multi_part_insert initiates the context and be sure to finalize it. The finalize_multi_part_insert is a blocking call that returns once all rows have been transmitted.

Once you are done with the multi part insert, it is good practice to start the backfill job in order to write the data to the offline storage: 

feature_group.backfill_job.run(await_termination=True)

Arguments

  • features Optional[Union[pandas.DataFrame, pyspark.sql.DataFrame, pyspark.RDD, numpy.ndarray, List[list]]]: DataFrame, RDD, Ndarray, list. Features to be saved.
  • overwrite Optional[bool]: Drop all data in the feature group before inserting new data. This does not affect metadata, defaults to False.
  • operation Optional[str]: Apache Hudi operation type "insert" or "upsert". Defaults to "upsert".
  • storage Optional[str]: Overwrite default behaviour, write to offline storage only with "offline" or online only with "online", defaults to None.
  • write_options Optional[Dict[str, Any]]: Additional write options as key-value pairs, defaults to {}. When using the python engine, write_options can contain the following entries:
    • key spark and value an object of type hsfs.core.job_configuration.JobConfiguration to configure the Hopsworks Job used to write data into the feature group.
    • key wait_for_job and value True or False to configure whether or not to the insert call should return only after the Hopsworks Job has finished. By default it waits.
    • key start_offline_backfill and value True or False to configure whether or not to start the backfill job to write data to the offline storage. By default the backfill job does not get started automatically for multi part inserts.
    • key internal_kafka and value True or False in case you established connectivity from you Python environment to the internal advertised listeners of the Hopsworks Kafka Cluster. Defaults to False and will use external listeners when connecting from outside of Hopsworks.
  • validation_options Optional[Dict[str, Any]]: Additional validation options as key-value pairs, defaults to {}.
    • key run_validation boolean value, set to False to skip validation temporarily on ingestion.
    • key save_report boolean value, set to False to skip upload of the validation report to Hopsworks.
    • key ge_validate_kwargs a dictionary containing kwargs for the validate method of Great Expectations.
    • key fetch_expectation_suite a boolean value, by default False for multi part inserts, to control whether the expectation suite of the feature group should be fetched before every insert.

Returns

(Job, ValidationReport) A tuple with job information if python engine is used and the validation report if validation is enabled. FeatureGroupWriter When used as a context manager with Python with statement.

[source]

read#

FeatureGroup.read(wallclock_time=None, online=False, dataframe_type="default", read_options={})

Read the feature group into a dataframe.

Reads the feature group by default from the offline storage as Spark DataFrame on Hopsworks and Databricks, and as Pandas dataframe on AWS Sagemaker and pure Python environments.

Set online to True to read from the online storage, or change dataframe_type to read as a different format.

Read feature group as of latest state:

# connect to the Feature Store
fs = ...

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

Read feature group as of specific point in time:

fg = fs.get_or_create_feature_group(...)
fg.read("2020-10-20 07:34:11")

Arguments

  • wallclock_time Optional[Union[str, int, datetime.datetime, datetime.date]]: If specified will retrieve feature group as of specific point in time. Defaults to None. If not specified, will return as of most recent time. 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.
  • online Optional[bool]: bool, optional. If True read from online feature store, defaults to False.
  • dataframe_type Optional[str]: str, optional. Possible values are "default", "spark", "pandas", "numpy" or "python", defaults to "default".
  • read_options Optional[dict]: Additional options as key/value pairs to pass to the execution engine. For spark engine: Dictionary of read options for Spark. For python engine:
    • key "use_hive" and value True to read feature group with Hive instead of ArrowFlight Server.
    • key "hive_config" to pass a dictionary of hive or tez configurations. For example: {"hive_config": {"hive.tez.cpu.vcores": 2, "tez.grouping.split-count": "3"}}
    • key "pandas_types" and value True to retrieve columns as Pandas nullable types rather than numpy/object(string) types (experimental). Defaults to {}.

Returns

DataFrame: The spark dataframe containing the feature data. pyspark.DataFrame. A Spark DataFrame. pandas.DataFrame. A Pandas DataFrame. numpy.ndarray. A two-dimensional Numpy array. list. A two-dimensional Python list.

Raises

hsfs.client.exceptions.RestAPIError. No data is available for feature group with this commit date, If time travel enabled.

[source]

read_changes#

FeatureGroup.read_changes(start_wallclock_time, end_wallclock_time, read_options={})

Reads updates of this feature that occurred between specified points in time.

Deprecated

`read_changes` method is deprecated. Use
`as_of(end_wallclock_time, exclude_until=start_wallclock_time).read(read_options=read_options)`
instead.

This function only works on feature groups with HUDI time travel format.

Arguments

  • start_wallclock_time Union[str, int, datetime.datetime, datetime.date]: Start time of the time travel query. 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.
  • end_wallclock_time Union[str, int, datetime.datetime, datetime.date]: End time of the time travel query. 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.
  • read_options Optional[dict]: Additional options as key/value pairs to pass to the execution engine. For spark engine: Dictionary of read options for Spark. For python engine:
    • key "hive_config" to pass a dictionary of hive or tez configurations. For example: {"hive_config": {"hive.tez.cpu.vcores": 2, "tez.grouping.split-count": "3"}} Defaults to {}.

Returns

DataFrame. The spark dataframe containing the incremental changes of feature data.

Raises

hsfs.client.exceptions.RestAPIError. No data is available for feature group with this commit date. hsfs.client.exceptions.FeatureStoreException. If the feature group does not have HUDI time travel format

[source]

save#

FeatureGroup.save(features=None, write_options={}, validation_options={}, wait=False)

Persist the metadata and materialize the feature group to the feature store.

Changed in 3.3.0

insert and save methods are now async by default in non-spark clients. To achieve the old behaviour, set wait argument to True.

Calling save creates the metadata for the feature group in the feature store. If a DataFrame, RDD or Ndarray is provided, the data is written to the online/offline feature store as specified. By default, this writes the feature group to the offline storage, and if online_enabled for the feature group, also to the online feature store. The features dataframe can be a Spark DataFrame or RDD, a Pandas DataFrame, or a two-dimensional Numpy array or a two-dimensional Python nested list. Arguments

  • features Optional[Union[pandas.DataFrame, pyspark.sql.DataFrame, pyspark.RDD, numpy.ndarray, List[hsfs.feature.Feature]]]: DataFrame, RDD, Ndarray or a list of features. Features to be saved. This argument is optional if the feature list is provided in the create_feature_group or in the get_or_create_feature_group method invokation.
  • write_options Optional[Dict[Any, Any]]: Additional write options as key-value pairs, defaults to {}. When using the python engine, write_options can contain the following entries:
    • key spark and value an object of type hsfs.core.job_configuration.JobConfiguration to configure the Hopsworks Job used to write data into the feature group.
    • key wait_for_job and value True or False to configure whether or not to the save call should return only after the Hopsworks Job has finished. By default it does not wait.
    • key start_offline_backfill and value True or False to configure whether or not to start the backfill job to write data to the offline storage. By default the backfill job gets started immediately.
    • key internal_kafka and value True or False in case you established connectivity from you Python environment to the internal advertised listeners of the Hopsworks Kafka Cluster. Defaults to False and will use external listeners when connecting from outside of Hopsworks.
  • validation_options Optional[Dict[Any, Any]]: Additional validation options as key-value pairs, defaults to {}.
    • key run_validation boolean value, set to False to skip validation temporarily on ingestion.
    • key save_report boolean value, set to False to skip upload of the validation report to Hopsworks.
    • key ge_validate_kwargs a dictionary containing kwargs for the validate method of Great Expectations.
  • wait bool: Wait for job to finish before returning, defaults to False. Shortcut for read_options {"wait_for_job": False}.

Returns

Job: When using the python engine, it returns the Hopsworks Job that was launched to ingest the feature group data.

Raises

hsfs.client.exceptions.RestAPIError. Unable to create feature group.

[source]

save_expectation_suite#

FeatureGroup.save_expectation_suite(
    expectation_suite, run_validation=True, validation_ingestion_policy="ALWAYS", overwrite=False
)

Attach an expectation suite to a feature group and saves it for future use. If an expectation suite is already attached, it is replaced. Note that the provided expectation suite is modified inplace to include expectationId fields.

Example

# connect to the Feature Store
fs = ...

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

fg.save_expectation_suite(expectation_suite, run_validation=True)

Arguments

  • expectation_suite Union[hsfs.expectation_suite.ExpectationSuite, great_expectations.core.expectation_suite.ExpectationSuite]: The expectation suite to attach to the Feature Group.
  • overwrite bool: If an Expectation Suite is already attached, overwrite it. The new suite will have its own validation history, but former reports are preserved.
  • run_validation bool: Set whether the expectation_suite will run on ingestion
  • validation_ingestion_policy str: Set the policy for ingestion to the Feature Group.
    • "STRICT" only allows DataFrame passing validation to be inserted into Feature Group.
    • "ALWAYS" always insert the DataFrame to the Feature Group, irrespective of overall validation result.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

save_validation_report#

FeatureGroup.save_validation_report(
    validation_report, ingestion_result="UNKNOWN", ge_type=True
)

Save validation report to hopsworks platform along previous reports of the same Feature Group.

Example

# connect to the Feature Store
fs = ...

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

validation_report = great_expectations.from_pandas(
    my_experimental_features_df,
    fg.get_expectation_suite()).validate()

fg.save_validation_report(validation_report, ingestion_result="EXPERIMENT")

Arguments

  • validation_report Union[dict, hsfs.validation_report.ValidationReport, great_expectations.core.expectation_validation_result.ExpectationSuiteValidationResult]: The validation report to attach to the Feature Group.
  • ingestion_result str: Specify the fate of the associated data, defaults to "UNKNOWN". Supported options are "UNKNOWN", "INGESTED", "REJECTED", "EXPERIMENT", "FG_DATA". Use "INGESTED" or "REJECTED" for validation of DataFrames to be inserted in the Feature Group. Use "EXPERIMENT" for testing and development and "FG_DATA" when validating data already in the Feature Group.
  • ge_type bool: If True returns a native Great Expectation type, Hopsworks custom type otherwise. Conversion can be performed via the to_ge_type() method on hopsworks type. Defaults to True.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

select#

FeatureGroup.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.

Example

# 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 Optional[List[Union[str, hsfs.feature.Feature]]]: A list of Feature objects or feature names as strings to be selected, defaults to [].

Returns

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

[source]

select_all#

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

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

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

Example

# 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 Optional[bool]: If True, include primary key of the feature group to the feature list. Defaults to True.
  • include_event_time Optional[bool]: 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#

FeatureGroup.select_except(features=[])

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.

Example

# 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 Optional[List[Union[str, hsfs.feature.Feature]]]: 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]

show#

FeatureGroup.show(n, online=False)

Show the first n rows of the feature group.

Example

# connect to the Feature Store
fs = ...

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

# make a query and show top 5 rows
fg.select(['date','weekly_sales','is_holiday']).show(5)

Arguments

  • n int: int. Number of rows to show.
  • online Optional[bool]: bool, optional. If True read from online feature store, defaults to False.

[source]

to_dict#

FeatureGroup.to_dict()

Get structured info about specific Feature Group in python dictionary format.

Example

# connect to the Feature Store
fs = ...

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

fg.to_dict()

[source]

update_description#

FeatureGroup.update_description(description)

Update the description of the feature group.

Example

# 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.")

Safe update

This method updates the feature group description safely. In case of failure your local metadata object will keep the old description.

Arguments

  • description str: New description string.

Returns

FeatureGroup. The updated feature group object.

[source]

update_feature_description#

FeatureGroup.update_feature_description(feature_name, description)

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

Example

# 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.")

Safe update

This method updates the feature description safely. In case of failure your local metadata object will keep the old 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#

FeatureGroup.update_features(features)

Update metadata of features in this feature group.

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

Unsafe update

Note that if you use an existing Feature object of the schema in the feature group metadata object, this might leave your metadata object in a corrupted state if the update fails.

Arguments

  • features Union[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_from_response_json#

FeatureGroup.update_from_response_json(json_dict)

[source]

update_statistics_config#

FeatureGroup.update_statistics_config()

Update the statistics configuration of the feature group.

Change the statistics_config object and persist the changes by calling this method.

Example

# connect to the Feature Store
fs = ...

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

fg.update_statistics_config()

Returns

FeatureGroup. The updated metadata object of the feature group.

Raises

hsfs.client.exceptions.RestAPIError.

[source]

validate#

FeatureGroup.validate(
    dataframe=None,
    expectation_suite=None,
    save_report=False,
    validation_options={},
    ingestion_result="UNKNOWN",
    ge_type=True,
)

Run validation based on the attached expectations.

Runs any expectation attached with Deequ. But also runs attached Great Expectation Suites.

Example

# connect to the Feature Store
fs = ...

# get feature group instance
fg = fs.get_or_create_feature_group(...)

ge_report = fg.validate(df, save_report=False)

Arguments

  • dataframe Optional[Union[pandas.DataFrame, pyspark.sql.DataFrame]]: The dataframe to run the data validation expectations against.
  • expectation_suite Optional[hsfs.expectation_suite.ExpectationSuite]: Optionally provide an Expectation Suite to override the one that is possibly attached to the feature group. This is useful for testing new Expectation suites. When an extra suite is provided, the results will never be persisted. Defaults to None.
  • validation_options Optional[Dict[Any, Any]]: Additional validation options as key-value pairs, defaults to {}.
    • key run_validation boolean value, set to False to skip validation temporarily on ingestion.
    • key ge_validate_kwargs a dictionary containing kwargs for the validate method of Great Expectations.
  • ingestion_result str: Specify the fate of the associated data, defaults to "UNKNOWN". Supported options are "UNKNOWN", "INGESTED", "REJECTED", "EXPERIMENT", "FG_DATA". Use "INGESTED" or "REJECTED" for validation of DataFrames to be inserted in the Feature Group. Use "EXPERIMENT" for testing and development and "FG_DATA" when validating data already in the Feature Group.
  • save_report Optional[bool]: Whether to save the report to the backend. This is only possible if the Expectation suite is initialised and attached to the Feature Group. Defaults to False.
  • ge_type bool: Whether to return a Great Expectations object or Hopsworks own abstraction. Defaults to True.

Returns

A Validation Report produced by Great Expectations.