Reusing Entities across FeatureViews with different source data column names

[martinpella]

Hello,
I want to define an Entity that can be reused across multiple FeatureViews. Here is an example:

customer_entity = Entity(
    name="customer_id",
    join_keys=["customer_id"],
    value_type=ValueType.INT64,
)

The challenge arises because not all source tables have a uniform column name for customer_id; for instance, some tables might use user_id, customer, etc.

Consider a scenario where we want to utilize the above-defined entity for a FeatureView with a BigQuery table that uses user_id as a column name instead of customer_id:

fv = FeatureView(
    name="test__features",
    entities=[customer_entity],
    ttl=timedelta(0),
    schema=[
        Field(name="customer_id", dtype=Int64),
        Field(name="feature1", dtype=Float64),
        Field(name="feature2", dtype=Float64),
    ],
    online=False,
    source=BigQuerySource(
        table="catalog.schema.table1",
        timestamp_field="timestamp_column",
        field_mapping={
            "feature1_column_name": "feature1",
            "feature2_column_name": "feature2",
            "user_id": "customer_id",
        },
    ),
)

The specific issue occurs in the subpart of the generated query here, where the query tries to select customer_id rather than user_id leading to the following error: google.api_core.exceptions.BadRequest: 400 Query error: Unrecognized name: customer_id

test__features__subquery AS (
        SELECT
                timestamp_column as event_timestamp,
            
                customer_id AS customer_id,
            
                feature1_column_name as feature1, 
            
                feature2_column_name as feature2
            
        FROM `catalog.schema.table1`
        WHERE P_CREATION_DATE <= '2024-12-09T00:00:00'
    ),

I've also explored the option of Entity aliasing:

fs = FeatureService(
    name="test_service",
    features=[fv.with_name("test").with_join_key_map({"customer_id": "user_id"})],
)

Although this approach results in a different subquery, the issue persists where the query incorrectly attempts to select customer_id from the table:

test__subquery AS (
        SELECT
                timestamp_column as event_timestamp,
            
                customer_id AS user_id,
            
                feature1_column_name as feature1, 
            
                feature2_column_name as feature2
            
        FROM `catalog.schema.table1`
        WHERE P_CREATION_DATE <= '2024-12-09T00:00:00'

I experimented with reversing the key/value pair in the join map, but unfortunately, the problem still remains.

Any suggestions or insights on how to address this mismatch would be greatly appreciated.