tecton.FeatureTable
A Tecton Feature Table.
Feature Tables are used to batch push features into Tecton from external feature computation systems.
Attributesβ
Name | Data Type | Description |
---|---|---|
description | str | Returns the description of the Tecton object. |
entities | List[specs.EntitySpec] The Entities for this Feature View. | |
id | str | Returns the unique id of the Tecton object. |
info | A dataclass containing basic info about this Tecton object. | |
join_keys | List[str] | The join key column names. |
name | str | Returns the name of the Tecton object. |
online_serving_index | List[str] | The set of join keys that will be indexed and queryable during online serving. |
owner | Optional[str] | Returns the owner of the Tecton object. |
tags | Dict[str, str] | Returns the tags of the Tecton object. |
url | str | Returns a link to the Tecton Web UI. |
wildcard_join_key | Optional[set] | Returns a wildcard join key column name if it exists; Otherwise returns None. |
workspace | Optional[str] | Returns the workspace that this Tecton object belongs to. |
Methodsβ
Name | Description |
---|---|
__init__(...) | Instantiate a new FeatureTable. |
cancel_materialization_job(...) | Cancels the scheduled or running batch materialization job for this Feature View specified by the job identifier. |
delete_keys(...) | Deletes any materialized data that matches the specified join keys from the FeatureTable. |
deletion_status(...) | Displays information for deletion jobs created with the delete_keys() method,which may include past jobs, scheduled jobs, and job failures. |
get_feature_columns() | The features produced by this FeatureView. |
get_historical_features(...) | Returns a TectonDataFrame of historical values for this feature table. |
get_materialization_job(...) | Retrieves data about the specified materialization job for this Feature View. |
get_online_features(...) | Returns a single Tecton FeatureVector from the Online Store. |
get_timestamp_field() | Returns the name of the timestamp field of this Feature Table. |
ingest() | Ingests a Dataframe into the FeatureTable. |
list_materialization_jobs() | Retrieves the list of all materialization jobs for this Feature View. |
materialization_status(...) | Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures. |
summary() | Displays a human readable summary. |
validate() | Validate this Tecton object and its dependencies (if any). |
with_join_key_map(...) | Rebind join keys for a Feature View used in a Feature Service. |
with_name(...) | Rename a Feature View used in a Feature Service. |
__init__(...)β
Instantiate a new FeatureTable.
Parametersβ
-
name
(str
) β Unique, human friendly name that identifies the FeatureTable. -
description
(Optional
[str
]) β A human readable description. (Default:None
) -
owner
(Optional
[str
]) β Owner name (typically the email of the primary maintainer). (Default:None
) -
tags
(Optional
[Dict
[str
,str
) β Tags associated with this Tecton Object (key-value pairs of arbitrary metadata). (Default:None
) -
prevent_destroy
(bool
) β If True, this Tecton object will be blocked from being deleted or re-created (i.e. a destructive update) during tecton plan/apply. To remove or update this object,prevent_destroy
must be first set to False via the same tecton apply or a separate tecton apply.prevent_destroy
can be used to prevent accidental changes such as inadvertently deleting a Feature Service used in production or recreating a Feature View that triggers expensive rematerialization jobs.prevent_destroy
also blocks changes to dependent Tecton objects that would trigger a recreate of the tagged object, e.g. ifprevent_destroy
is set on a Feature Service, that will also prevent deletions or re-creates of Feature Views used in that service.prevent_destroy
is only enforced in live (i.e. non-dev) workspaces. (Default:False
) -
entities
(List
[Entity
]) β A list of Entity objects, used to organize features. -
schema
(List
[Field
]) β A schema for the FeatureTable. Supported types are: Int64, Float64, String, Bool and Array with Int64, Float32, Float64 and String typed elements. Additionally you must have exactly one Timestamp typed column for the feature timestamp. -
ttl
(timedelta
) β The TTL (or βlook back windowβ) for features defined by this feature table. This parameter determines how long features will live in the online store and how far to βlook backβ relative to a training exampleβs timestamp when generating offline training sets. Shorter TTLs improve performance and reduce costs. -
online
(bool
) β Enable writing to online feature store. (Default:False
) -
offline
(bool
) β Enable writing to offline feature store. (Default:False
) -
offline_store
(DeltaConfig
) β Configuration for how data is written to the offline feature store. (Default:DeltaConfig(time_partition_size=datetime.timedelta(days=1), subdirectory_override=None
) -
online_store
(Union[DynamoConfig, RedisConfig,
None
]
) β Configuration for how data is written to the online feature store. (Default:None
) -
batch_compute
([Union[DatabricksClusterConfig, EMRClusterConfig, DatabricksJsonClusterConfig, EMRJsonClusterConfig
,None
]
) β Configuration for batch materialization clusters. Should be one of: [EMRClusterConfig
,DatabricksClusterConfig
,EMRJsonClusterConfig
,DatabricksJsonClusterConfig
] (Default:None
) -
online_serving_index
(Optional
[List
[str
) β (Advanced) Defines the set of join keys that will be indexed and queryable during online serving. Defaults to the complete set of join keys. Up to one join key may be omitted. If one key is omitted, online requests to a Feature Service will return all feature vectors that match the specified join keys. (Default:None
) -
alert_email
(Optional
[str
]) β Email that alerts for this FeatureTable will be sent to. (Default: None) -
tecton_materialization_runtime
(Optional
[str
]) - Version of Tecton Materialization libraries to be used for your jobs. Only available in 0.7.11 and later patches. (Default:None
)
Exampleβ
from tecton import Entity, FeatureTable
from tecton.types import Field, String, Timestamp, Int64
import datetime
# Declare your user Entity instance here or import it if defined elsewhere in
# your Tecton repo.
user = ...
schema = [
Field("user_id", String),
Field("timestamp", Timestamp),
Field("user_login_count_7d", Int64),
Field("user_login_count_30d", Int64),
]
user_login_counts = FeatureTable(
name="user_login_counts",
entities=[user],
schema=schema,
online=True,
offline=True,
ttl=datetime.timedelta(days=30),
alert_email="xxx@yyy.com",
)
cancel_materialization_job(...)β
Cancels the scheduled or running batch materialization job for this Feature View specified by the job identifier. Once cancelled, a job will not be retried further.
Job run state will be set to MANUAL_CANCELLATION_REQUESTED
. Note that
cancellation is asynchronous, so it may take some time for the cancellation to
complete. If job run is already in MANUAL_CANCELLATION_REQUESTED
or in a
terminal state then itβll return the job.
Parametersβ
job_id
(str
) β ID string of the materialization job.
Returnsβ
MaterializationJobData
object for the cancelled job.
delete_keys(...)β
Deletes any materialized data that matches the specified join keys from the FeatureTable.
This method kicks off a job to delete the data in the offline and online stores. If a FeatureTable has multiple entities, the full set of join keys must be specified. Only supports Dynamo online store. Maximum 500,000 keys can be deleted per request.
Parametersβ
-
keys
(Union[
DataFrame
,DataFrame
]
) β The Dataframe to be deleted. Must conform to the FeatureTable join keys. -
online
(bool
) β (Optional, default=True) Whether or not to delete from the online store. (Default:True
) -
offline
(bool
) β (Optional, default=True) Whether or not to delete from the offline store. (Default:True
)
Returnsβ
None if deletion job was created successfully.
deletion_status(...)β
Displays information for deletion jobs created with the delete_keys() method,which may include past jobs, scheduled jobs, and job failures.
Parametersβ
-
verbose
β If set to true, method will display additional low level deletion information, useful for debugging. (Default:False
) -
limit
β Maximum number of jobs to return. (Default:1000
) -
sort_columns
β A comma-separated list of column names by which to sort the rows. (Default:None
) -
errors_only
: If set to true, method will only return jobs that failed with an error. (Default:False
)
get_feature_columns()β
The features produced by this FeatureView.
get_historical_features(...)β
Returns a
TectonDataFrame
of historical values for this feature table.
If no arguments are passed in, all feature values for this feature table will be returned in a TectonDataFrame.
The timestamp_key parameter is only applicable when a spine is passed in. Parameters start_time, end_time, and entities are only applicable when a spine is not passed in.
Parametersβ
-
spine
(Union[
pyspark.sql.DataFrame
,pandas.DataFrame
,TectonDataFrame
]
) β The spine to join against, as a dataframe. If present, the returned DataFrame will contain rollups for all (join key, temporal key) combinations that are required to compute a full frame from the spine. To distinguish between spine columns and feature columns, feature columns are labeled as feature_view_name.feature_name in the returned DataFrame. If spine is not specified, itβll return a DataFrame of feature values in the specified time range. (Default:None
) -
timestamp_key
(str
) β Name of the time column in spine. This method will fetch the latest features computed before the specified timestamps in this column. If unspecified, will default to the time column of the spine if there is only one present. (Default:None
) -
entities
(Union[
pyspark.sql.DataFrame
,pandas.DataFrame
,TectonDataFrame
]
) β A DataFrame that is used to filter down feature values. If specified, this DataFrame should only contain join key columns. (Default:None
) -
start_time
(Union[pendulum.DateTime
,datetime.datetime
]
) β The interval start time from when we want to retrieve features. If no timezone is specified, will default to using UTC. (Default:None
) -
end_time
(Union[pendulum.DateTime
,datetime.datetime
]
) β The interval end time until when we want to retrieve features. If no timezone is specified, will default to using UTC. (Default:None
) -
save
(bool
) β Whether to persist the DataFrame as a Dataset object. (Default:False
). -
save_as
(str
) β name to save the DataFrame as. If unspecified and save=True, a name will be generated. (Default:None
)
Returnsβ
A
TectonDataFrame
with features values.
Examplesβ
A FeatureTable ft
with join key user_id
.
-
ft.get_historical_features(spine)
wherespine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]})
Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine. -
ft.get_historical_features(spine, save_as='my_dataset)
wherespine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]})
Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine. Save the DataFrame as dataset with the namemy_dataset
. -
ft.get_historical_features(spine, timestamp_key='date_1')
wherespine=pandas.Dataframe({'user_id': [1,2,3], 'date_1': [datetime(...), datetime(...), datetime(...)], 'date_2': [datetime(...), datetime(...), datetime(...)]})
Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the βdate_1β column in the spine. -
ft.get_historical_features(start_time=datetime(...), end_time=datetime(...))
Fetch all historical features from the offline store in the time range specified by start_time and end_time.
get_materialization_job(...)β
Retrieves data about the specified materialization job for this Feature View.
This data includes information about job attempts.
Parametersβ
job_id
(str
) β ID string of the materialization job.
Returnsβ
MaterializationJobData
object for the job.
get_online_features(...)β
Returns a single Tecton FeatureVector from the Online Store.
Parametersβ
-
join_keys
(Mapping
[str
,Union
[int
,int64
,str
,bytes
]])
β Join keys of the enclosed FeatureTable. -
include_join_keys_in_response
(bool
) β Whether to include join keys as part of the response FeatureVector. (Default:False
)
Returnsβ
A FeatureVector of the results.
Examplesβ
A FeatureTable ft
with join key user_id
.
-
ft.get_online_features(join_keys={'user_id': 1})
Fetch the latest features from the online store for user 1. -
ft.get_online_features(join_keys={'user_id': 1}, include_join_keys_in_respone=True)
Fetch the latest features from the online store for user 1 and include the join key information (user_id=1) in the returned FeatureVector.
get_timestamp_field()β
Returns the name of the timestamp field of this Feature Table.
ingest()β
Ingests a Dataframe into the FeatureTable.
This method kicks off a materialization job to write the data into the offline and online store, depending on the Feature Table configuration.
Parametersβ
df
(Union[
DataFrame
,DataFrame
]
) β The Dataframe to be ingested. Has to conform to the FeatureTable schema.
list_materialization_jobs()β
Retrieves the list of all materialization jobs for this Feature View.
Returnsβ
List of
MaterializationJobData
objects.
materialization_status(...)β
Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures.
This method returns different information depending on the type of FeatureView.
Parametersβ
verbose
β If set to true, method will display additional low level materialization information, useful for debugging. (Default:False
)limit
β Maximum number of jobs to return. (Default:1000
)sort_columns
β A comma-separated list of column names by which to sort the rows. (Default:None
)errors_only
β If set to true, method will only return jobs that failed with an error. (Default:False
)
summary()β
Displays a human readable summary of this data source.
validate()β
Validate this Tecton object and its dependencies (if any).
Validation performs most of the same checks and operations as tecton plan
.
-
Check for invalid object configurations, e.g. setting conflicting fields.
-
For Data Sources and Feature Views, test query code and derive schemas. e.g. test that a Data Sourceβs specified s3 path exists or that a Feature Viewβs SQL code executes and produces supported feature data types.
Objects already applied to Tecton do not need to be re-validated on retrieval
(e.g. fv = tecton.get_workspace('prod').get_feature_view('my_fv')
) since they
have already been validated during tecton plan
. Locally defined objects (e.g.
my_ds = BatchSource(name="my_ds", ...)
) may need to be validated before some
of their methods can be called, e.g.
my_feature_view.get_historical_features()
.
with_join_key_map(...)β
Rebind join keys for a Feature View used in a Feature Service.
The keys in join_key_map
should be the feature view join keys, and the values
should be the feature service overrides.
Parametersβ
join_key_map
Exampleβ
from tecton import FeatureService
# The join key for this feature service will be "feature_service_user_id".
feature_service = FeatureService(
name="feature_service",
features=[
my_feature_view.with_join_key_map({"user_id": "feature_service_user_id"}),
],
)
# Here is a more sophisticated example. The join keys for this feature service will be "transaction_id",
# "sender_id", and "recipient_id" and will contain three feature views named "transaction_features",
# "sender_features", and "recipient_features".
transaction_fraud_service = FeatureService(
name="transaction_fraud_service",
features=[
# Select a subset of features from a feature view.
transaction_features[["amount"]],
# Rename a feature view and/or rebind its join keys. In this example, we want user features for both the
# transaction sender and recipient, so include the feature view twice and bind it to two different feature
# service join keys.
user_features.with_name("sender_features").with_join_key_map({"user_id": "sender_id"}),
user_features.with_name("recipient_features").with_join_key_map({"user_id": "recipient_id"}),
],
)
with_name(...)β
Rename a Feature View used in a Feature Service.
Parametersβ
namespace
Exampleβ
from tecton import FeatureService
# The feature view in this feature service will be named "new_named_feature_view" in training data dataframe
# columns and other metadata.
feature_service = FeatureService(
name="feature_service",
features=[my_feature_view.with_name("new_named_feature_view")],
)
# Here is a more sophisticated example. The join keys for this feature service will be "transaction_id",
# "sender_id", and "recipient_id" and will contain three feature views named "transaction_features",
# "sender_features", and "recipient_features".
transaction_fraud_service = FeatureService(
name="transaction_fraud_service",
features=[
# Select a subset of features from a feature view.
transaction_features[["amount"]],
# Rename a feature view and/or rebind its join keys. In this example, we want user features for both the
# transaction sender and recipient, so include the feature view twice and bind it to two different feature
# service join keys.
user_features.with_name("sender_features").with_join_key_map({"user_id": "sender_id"}),
user_features.with_name("recipient_features").with_join_key_map({"user_id": "recipient_id"}),
],
)