tecton.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 a separate tecton apply. prevent_destroy can be used to prevent accidental changes such as inadvertantly 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. if prevent_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.

Returns

An object of type tecton.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,
    owner="matt@tecton.ai",
    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,
    owner="matt@tecton.ai",
    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