🧬 BatchFeatureView in Feast

BatchFeatureView is a flexible abstraction in Feast that allows users to define features derived from batch data sources or even other FeatureViews, enabling composable and reusable feature pipelines. It is an extension of the FeatureView class, with support for user-defined transformations, aggregations, and recursive chaining of feature logic.

Supported Compute Engines

• LocalComputeEngine
• SparkComputeEngine
• LambdaComputeEngine
• KubernetesComputeEngine

✅ Key Capabilities

• Composable DAG of FeatureViews: Supports defining a BatchFeatureView on top of one or more other FeatureViews.
• Transformations: Apply transformation logic (feature_transformation or udf) to raw data source, can also be used to deal with multiple data sources.
• Aggregations: Define time-windowed aggregations (e.g. sum, avg) over event-timestamped data.
• Feature resolution & execution: Automatically resolves and executes DAGs of dependent views during materialization or retrieval. More details in the Compute engine documentation.
• Materialization Sink Customization: Specify a custom sink_source to define where derived feature data should be persisted.

📐 Class Signature

class BatchFeatureView(FeatureView):
    def __init__(
        *,
        name: str,
        source: Optional[Union[DataSource, FeatureView, List[FeatureView]]] = None,
        sink_source: Optional[DataSource] = None,
        schema: Optional[List[Field]] = None,
        entities: Optional[List[Entity]] = None,
        aggregations: Optional[List[Aggregation]] = None,
        udf: Optional[Callable[[DataFrame], DataFrame]] = None,
        udf_string: Optional[str] = None,
        ttl: Optional[timedelta] = timedelta(days=0),
        online: bool = True,
        offline: bool = False,
        description: str = "",
        tags: Optional[Dict[str, str]] = None,
        owner: str = "",
    )

🧠 Usage

1. Simple Feature View from Data Source

from feast import BatchFeatureView, Field
from feast.types import Float32, Int32
from feast import FileSource
from feast.aggregation import Aggregation
from datetime import timedelta

source = FileSource(
    path="s3://bucket/path/data.parquet",
    timestamp_field="event_timestamp",
    created_timestamp_column="created",
)

driver_fv = BatchFeatureView(
    name="driver_hourly_stats",
    entities=["driver_id"],
    schema=[
        Field(name="driver_id", dtype=Int32),
        Field(name="conv_rate", dtype=Float32),
    ],
    aggregations=[
        Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1), name="total_conv_rate_1d"),
    ],
    source=source,
)

2. Derived Feature View from Another View

You can build feature views on top of other features by deriving a feature view from another view. Let's take a look at an example.

from feast import BatchFeatureView, Field
from pyspark.sql import DataFrame
from feast.types import Float32, Int32
from feast import FileSource

def transform(df: DataFrame) -> DataFrame:
    return df.withColumn("conv_rate", df["conv_rate"] * 2)

daily_driver_stats = BatchFeatureView(
    name="daily_driver_stats",
    entities=["driver_id"],
    schema=[
        Field(name="driver_id", dtype=Int32),
        Field(name="conv_rate", dtype=Float32),
    ],
    udf=transform,
    source=driver_fv,
    sink_source=FileSource(  # Required to specify where to sink the derived view
        name="daily_driver_stats_sink",
        path="s3://bucket/daily_stats/",
        file_format="parquet",
        timestamp_field="event_timestamp",
        created_timestamp_column="created",
    ),
)

🔄 Execution Flow

Feast automatically resolves the DAG of BatchFeatureView dependencies during:

• materialize(): recursively resolves and executes the feature view graph.
• get_historical_features(): builds the execution plan for retrieving point-in-time correct features.
• apply(): registers the feature view DAG structure to the registry.

Each transformation and aggregation is turned into a DAG node (e.g., SparkTransformationNode, SparkAggregationNode) executed by the compute engine (e.g., SparkComputeEngine).

⚙️ How Materialization Works

• If the BatchFeatureView is backed by a base source (FileSource, BigQuerySource, SparkSource etc), the batch_source is used directly.
• If the source is another feature view (i.e., chained views), the sink_source must be provided to define the materialization target data source.
• During DAG planning, SparkWriteNode uses the sink_source as the batch sink.

🧪 Example Tests

See:

• test_spark_dag_materialize_recursive_view(): Validates chaining of two feature views and output validation.
• test_spark_compute_engine_materialize(): Validates transformation and write of features into offline and online stores.

🛑 Gotchas

• sink_source is required when chaining views (i.e., source is another FeatureView or list of them).
• source is optional; if omitted (None), the feature view has no associated batch data source.
• Schema fields must be consistent with sink_source, batch_source.field_mapping if field mappings exist.
• Aggregation logic must reference columns present in the raw source or transformed inputs.
• The output feature name for an aggregation defaults to {function}_{column} (e.g., sum_conv_rate). Use the name parameter to override it (e.g., name="total_conv_rate_1d").

🔮 Future Directions

• Support additional offline stores (e.g., Snowflake, Redshift) with auto-generated sink sources.
• Enable fully declarative transform logic (SQL + UDF mix).
• Introduce optimization passes for DAG pruning and fusion.