feat: push source pydantic model

sdk/python/feast/expediagroup/pydantic_models/data_source_model.py

@@ -12,7 +12,7 @@
 from pydantic import Field as PydanticField
 from typing_extensions import Annotated, Self
 
-from feast.data_source import KafkaSource, RequestSource
+from feast.data_source import KafkaSource, PushSource, RequestSource
 from feast.expediagroup.pydantic_models.field_model import FieldModel
 from feast.expediagroup.pydantic_models.stream_format_model import (
     AnyStreamFormat,
@@ -170,6 +170,66 @@ def from_data_source(
 ]
 
 
+SUPPORTED_PUSH_BATCH_SOURCES = [SparkSourceModel]
+
+
+class PushSourceModel(DataSourceModel):
+    """
+    Pydantic Model of a Feast PushSource.
+    """
+
+    name: str
+    model_type: Literal["PushSourceModel"] = "PushSourceModel"
+    batch_source: AnyBatchDataSource
+    description: Optional[str] = ""
+    tags: Optional[Dict[str, str]] = None
+    owner: Optional[str] = ""
+
+    def to_data_source(self) -> PushSource:
+        """
+        Given a Pydantic PushSourceModel, create and return a PushSource.
+
+        Returns:
+            A SparkSource.
+        """
+        return PushSource(
+            name=self.name,
+            batch_source=self.batch_source.to_data_source(),
+            description=self.description,
+            tags=self.tags,
+            owner=self.owner,
+        )
+
+    @classmethod
+    def from_data_source(
+        cls,
+        data_source,
+    ) -> Self:  # type: ignore
+        """
+        Converts a PushSource object to its pydantic model representation.
+
+        Returns:
+            A PushSourceModel.
+        """
+        class_ = getattr(
+            sys.modules[__name__],
+            type(data_source.batch_source).__name__ + "Model",
+        )
+        if class_ not in SUPPORTED_PUSH_BATCH_SOURCES:
+            raise ValueError(
+                "Push Source's batch source type is not a supported data source type."
+            )
+        batch_source = class_.from_data_source(data_source.batch_source)
+
+        return cls(
+            name=data_source.name,
+            batch_source=batch_source,
+            description=data_source.description,
+            tags=data_source.tags,
+            owner=data_source.owner,
+        )
+
+
 SUPPORTED_MESSAGE_FORMATS = [AvroFormatModel, JsonFormatModel, ProtoFormatModel]
 SUPPORTED_KAFKA_BATCH_SOURCES = [RequestSourceModel, SparkSourceModel]
 
@@ -278,6 +338,6 @@ def from_data_source(
 # https://blog.devgenius.io/deserialize-child-classes-with-pydantic-that-gonna-work-784230e1cf83
 # This lets us discriminate child classes of DataSourceModel with type hints.
 AnyDataSource = Annotated[
-    Union[RequestSourceModel, SparkSourceModel, KafkaSourceModel],
+    Union[RequestSourceModel, SparkSourceModel, KafkaSourceModel, PushSourceModel],
     PydanticField(discriminator="model_type"),
 ]

sdk/python/feast/infra/registry/http.py

@@ -11,7 +11,7 @@
 
 from feast import usage
 from feast.base_feature_view import BaseFeatureView
-from feast.data_source import DataSource, RequestSource
+from feast.data_source import DataSource, PushSource, RequestSource
 from feast.entity import Entity
 from feast.errors import (
     DataSourceObjectNotFoundException,
@@ -21,6 +21,7 @@
     ProjectMetadataNotFoundException,
 )
 from feast.expediagroup.pydantic_models.data_source_model import (
+    PushSourceModel,
     RequestSourceModel,
     SparkSourceModel,
 )
@@ -222,6 +223,10 @@ def apply_data_source(
                 data = RequestSourceModel.from_data_source(data_source).json()
                 response_data = self._send_request("PUT", url, params=params, data=data)
                 return RequestSourceModel.parse_obj(response_data).to_data_source()
+            elif isinstance(data_source, PushSource):
+                data = PushSourceModel.from_data_source(data_source).json()
+                response_data = self._send_request("PUT", url, params=params, data=data)
+                return PushSourceModel.parse_obj(response_data).to_data_source()
             else:
                 raise TypeError(
                     "Unsupported DataSource type. Please use either SparkSource or RequestSource only"

sdk/python/tests/unit/test_pydantic_models.py

@@ -19,11 +19,12 @@
 from pydantic import BaseModel
 
 from feast.data_format import AvroFormat
-from feast.data_source import KafkaSource, RequestSource
+from feast.data_source import KafkaSource, PushSource, RequestSource
 from feast.entity import Entity
 from feast.expediagroup.pydantic_models.data_source_model import (
     AnyDataSource,
     KafkaSourceModel,
+    PushSourceModel,
     RequestSourceModel,
     SparkSourceModel,
 )
@@ -209,6 +210,38 @@ def test_idempotent_sparksource_conversion():
     assert pydantic_obj == SparkSourceModel.parse_obj(pydantic_json)
 
 
+def test_idempotent_pushsource_conversion():
+    spark_source = SparkSource(
+        name="spark-source",
+        table="thingy",
+        description="desc",
+        tags={},
+        owner="feast",
+    )
+
+    python_obj = PushSource(
+        name="push-source",
+        batch_source=spark_source,
+        description="desc",
+        tags={},
+        owner="feast",
+    )
+
+    pydantic_obj = PushSourceModel.from_data_source(python_obj)
+    converted_python_obj = pydantic_obj.to_data_source()
+    assert python_obj == converted_python_obj
+
+    feast_proto = converted_python_obj.to_proto()
+    python_obj_from_proto = PushSource.from_proto(feast_proto)
+    assert python_obj == python_obj_from_proto
+
+    pydantic_json = pydantic_obj.json()
+    assert pydantic_obj == PushSourceModel.parse_raw(pydantic_json)
+
+    pydantic_json = pydantic_obj.dict()
+    assert pydantic_obj == PushSourceModel.parse_obj(pydantic_json)
+
+
 def test_idempotent_kafkasource_conversion():
     schema = [
         Field(name="f1", dtype=Float32),