tecton.declarative.Aggregation

class tecton.declarative.Aggregation(column, function, time_window, name=None)

This class describes a single aggregation that is applied in a batch or stream feature view.

Parameters

  • column (str) – Column name of the feature we are aggregating.

  • function (Union[str, AggregationFunction]) – One of the built-in aggregation functions.

  • time_window (datetime.timedelta) – Duration to aggregate over. Example: datetime.timedelta(days=30).

  • name (str) – The name of this feature. Defaults to an autogenerated name, e.g. transaction_count_7d_1d.

function can be one of predefined numeric aggregation functions, namely "count", "sum", "mean", "min", "max". For these numeric aggregations, you can pass the name of it as a string. Nulls are handled like Spark SQL Function(column), e.g. SUM/MEAN/MIN/MAX of all nulls is null and COUNT of all nulls is 0.

In addition to numeric aggregations, Aggregation supports “last-n” aggregations that will compute the last N distinct values for the column by timestamp. Right now only string column types are supported as inputs to this aggregation, i.e., the resulting feature value will be a list of strings. Nulls are not included in the aggregated list.

You can use it via the last_distinct() helper function like this:

from tecton.aggregation_functions import last_distinct

@batch_feature_view(
...
aggregations=[Aggregation(
    column='my_column',
    function=last_distinct(15),
    time_window=datetime.timedelta(days=7))],
...
)
def my_fv(data_source):
    pass

Methods

__init__

Method generated by attrs for class Aggregation.
__init__(column, function, time_window, name=None)

Method generated by attrs for class Aggregation.