on_demand_feature_view
Summary​
Declare an On-Demand Feature View
Parameters​
-
name
(Optional
[str
]) – Unique, human friendly name that identifies the FeatureView. Defaults to the function name. (Default:None
) -
description
(Optional
[str
]) – A human readable description. (Default:None
) -
owner
(Optional
[str
]) – Owner name (typically the email of the primary maintainer). (Default:None
) -
tags
(Optional
[Dict
[str
,str
]]) – Tags associated with this Tecton Object (key-value pairs of arbitrary metadata). (Default:None
) -
prevent_destroy
(bool
) – 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
) -
mode
(str
) – Whether the annotated function is a pipeline function (“pipeline” mode) or a transformation function (“python” or “pandas” mode). For the non-pipeline mode, an inferred transformation will also be registered. -
sources
(List
[Union
[RequestSource
,FeatureView
,FeatureReference
]]) – The data source inputs to the feature view. An input can be a RequestSource or a materialized Feature View. -
schema
(List
[Field
]) – Tecton schema matching the expected output of the transformation. -
environments
(Optional
[List
[str
]]) – The environments in which this feature view can run. Defaults to aNone
, which means the feature view can run in any environment. If specified, the feature view will only run in the specified environments. Learn more about environments at Environments. (Default:None
)
Returns​
An object of type OnDemandFeatureView
.
Examples​
An example declaration of an on-demand feature view using Python mode. With Python mode, the function sources will be dictionaries, and the function is expected to return a dictionary matching the schema from output_schema. Tecton recommends using Python mode for improved online serving performance.
from tecton import RequestSource, on_demand_feature_view
from tecton.types import Field, Float64, Int64
# Define the request schema
transaction_request = RequestSource(schema=[Field("amount", Float64)])
# Define the output schema
output_schema = [Field("transaction_amount_is_high", Int64)]
# This On-Demand Feature View evaluates a transaction amount and declares it as "high", if it's higher than 10,000
@on_demand_feature_view(
sources=[transaction_request],
mode="python",
schema=output_schema,
description="Whether the transaction amount is considered high (over $10000)",
)
def transaction_amount_is_high(transaction_request):
result = {}
result["transaction_amount_is_high"] = int(transaction_request["amount"] >= 10000)
return result
An example declaration of an on-demand feature view using Pandas mode. With Pandas mode, the function sources will be Pandas Dataframes, and the function is expected to return a Dataframe matching the schema from output_schema.
from tecton import RequestSource, on_demand_feature_view
from tecton.types import Field, Float64, Int64
import pandas
# Define the request schema
transaction_request = RequestSource(schema=[Field("amount", Float64)])
# Define the output schema
output_schema = [Field("transaction_amount_is_high", Int64)]
# This On-Demand Feature View evaluates a transaction amount and declares it as "high", if it's higher than 10,000
@on_demand_feature_view(
sources=[transaction_request],
mode="pandas",
schema=output_schema,
description="Whether the transaction amount is considered high (over $10000)",
)
def transaction_amount_is_high(transaction_request):
import pandas as pd
df = pd.DataFrame()
df["transaction_amount_is_high"] = (transaction_request["amount"] >= 10000).astype("int64")
return df