fix: Clean up snowflake to_spark_df() (feast-dev#3607)

Signed-off-by: Miles Adkins <[email protected]>

Author: sfc-gh-madkins

docs/reference/offline-stores/overview.md

@@ -46,11 +46,11 @@ Below is a matrix indicating which `RetrievalJob`s support what functionality.
 | --------------------------------- | --- | --- | --- | --- | --- | --- | --- |
 | export to dataframe                                   | yes | yes | yes | yes | yes | yes | yes |
 | export to arrow table                                 | yes | yes | yes | yes | yes | yes | yes |
-| export to arrow batches                               | no  | no  | yes  | yes | no  | no  | no  |
-| export to SQL                                         | no  | yes | yes  | yes | yes | no  | yes |
+| export to arrow batches                               | no  | no  | no  | yes | no  | no  | no  |
+| export to SQL                                         | no  | yes | yes  | yes | yes | no | yes |
 | export to data lake (S3, GCS, etc.)                   | no  | no  | yes | no  | yes | no  | no  |
 | export to data warehouse                              | no  | yes | yes | yes | yes | no  | no  |
-| export as Spark dataframe                             | no  | no  | yes  | no  | no  | yes | no  |
+| export as Spark dataframe                             | no  | no  | yes | no  | no  | yes | no  |
 | local execution of Python-based on-demand transforms  | yes | yes | yes | yes | yes | no  | yes |
 | remote execution of Python-based on-demand transforms | no  | no  | no  | no  | no  | no  | no  |
 | persist results in the offline store                  | yes | yes | yes | yes | yes | yes | no  |

docs/reference/offline-stores/snowflake.md

@@ -53,7 +53,7 @@ Below is a matrix indicating which functionality is supported by `SnowflakeRetri
 | ----------------------------------------------------- | --------- |
 | export to dataframe                                   | yes       |
 | export to arrow table                                 | yes       |
-| export to arrow batches                               | yes        |
+| export to arrow batches                               | yes       |
 | export to SQL                                         | yes       |
 | export to data lake (S3, GCS, etc.)                   | yes       |
 | export to data warehouse                              | yes       |

sdk/python/feast/errors.py

@@ -56,14 +56,6 @@ def __init__(self, name, project=None):
             super().__init__(f"Feature view {name} does not exist")
 
 
-class InvalidSparkSessionException(Exception):
-    def __init__(self, spark_arg):
-        super().__init__(
-            f" Need Spark Session to convert results to spark data frame\
-               recieved {type(spark_arg)} instead. "
-        )
-
-
 class OnDemandFeatureViewNotFoundException(FeastObjectNotFoundException):
     def __init__(self, name, project=None):
         if project:

sdk/python/feast/infra/offline_stores/snowflake.py

@@ -28,11 +28,7 @@
 
 from feast import OnDemandFeatureView
 from feast.data_source import DataSource
-from feast.errors import (
-    EntitySQLEmptyResults,
-    InvalidEntityType,
-    InvalidSparkSessionException,
-)
+from feast.errors import EntitySQLEmptyResults, InvalidEntityType
 from feast.feature_logging import LoggingConfig, LoggingSource
 from feast.feature_view import DUMMY_ENTITY_ID, DUMMY_ENTITY_VAL, FeatureView
 from feast.infra.offline_stores import offline_utils
@@ -528,28 +524,22 @@ def to_spark_df(self, spark_session: "SparkSession") -> "DataFrame":
         """
 
         try:
-            from pyspark.sql import DataFrame, SparkSession
+            from pyspark.sql import DataFrame
         except ImportError as e:
             from feast.errors import FeastExtrasDependencyImportError
 
             raise FeastExtrasDependencyImportError("spark", str(e))
 
-        if isinstance(spark_session, SparkSession):
-            arrow_batches = self.to_arrow_batches()
+        spark_session.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
 
-            if arrow_batches:
-                spark_df = reduce(
-                    DataFrame.unionAll,
-                    [
-                        spark_session.createDataFrame(batch.to_pandas())
-                        for batch in arrow_batches
-                    ],
-                )
-                return spark_df
-            else:
-                raise EntitySQLEmptyResults(self.to_sql())
-        else:
-            raise InvalidSparkSessionException(spark_session)
+        # This can be improved by parallelizing the read of chunks
+        pandas_batches = self.to_pandas_batches()
+
+        spark_df = reduce(
+            DataFrame.unionAll,
+            [spark_session.createDataFrame(batch) for batch in pandas_batches],
+        )
+        return spark_df
 
     def persist(
         self,