from datetime import timedelta

from feast import BigQuerySource, Entity, Feature, FeatureView, ValueType

# Define an entity for the driver. Entities can be thought of as primary keys used to

# retrieve features. Entities are also used to join multiple tables/views during the

# construction of feature vectors

driver = Entity(

# Name of the entity. Must be unique within a project

name="driver_id",

# The join key of an entity describes the storage level field/column on which

# features can be looked up. The join key is also used to join feature

# tables/views when building feature vectors

join_key="driver_id",

# The storage level type for an entity

value_type=ValueType.INT64,

)

# Indicates a data source from which feature values can be retrieved. Sources are queried when building training

# datasets or materializing features into an online store.

driver_stats_source = BigQuerySource(

# The BigQuery table where features can be found

table_ref="feast-oss.demo_data.driver_hourly_stats",

# The event timestamp is used for point-in-time joins and for ensuring only

# features within the TTL are returned

event_timestamp_column="datetime",

# The (optional) created timestamp is used to ensure there are no duplicate

# feature rows in the offline store or when building training datasets

created_timestamp_column="created",

)

# Feature views are a grouping based on how features are stored in either the

# online or offline store.

driver_stats_fv = FeatureView(

# The unique name of this feature view. Two feature views in a single

# project cannot have the same name

name="driver_hourly_stats",

# The list of entities specifies the keys required for joining or looking

# up features from this feature view. The reference provided in this field

# correspond to the name of a defined entity (or entities)

entities=["driver_id"],

# The timedelta is the maximum age that each feature value may have

# relative to its lookup time. For historical features (used in training),

# TTL is relative to each timestamp provided in the entity dataframe.

# TTL also allows for eviction of keys from online stores and limits the

# amount of historical scanning required for historical feature values

# during retrieval

ttl=timedelta(weeks=52),

# The list of features defined below act as a schema to both define features

# for both materialization of features into a store, and are used as references

# during retrieval for building a training dataset or serving features

features=[

Feature(name="conv_rate", dtype=ValueType.FLOAT),

Feature(name="acc_rate", dtype=ValueType.FLOAT),

Feature(name="avg_daily_trips", dtype=ValueType.INT64),

],

# Inputs are used to find feature values. In the case of this feature

# view we will query a source table on BigQuery for driver statistics

# features

input=driver_stats_source,

# Tags are user defined key/value pairs that are attached to each

# feature view

tags={"team": "driver_performance"},

)