feat: Add Entity df in format of a Spark Dataframe instead of just pd…

….DataFrame or string for SparkOfflineStore (feast-dev#3988)

* remove unused parameter when init sparksource

Signed-off-by: tanlocnguyen <tanlocnguyen296@gmail.com>

* feat: add entity df to SparkOfflineStore when get_historical_features

Signed-off-by: tanlocnguyen <tanlocnguyen296@gmail.com>

* fix: lint error

Signed-off-by: tanlocnguyen <tanlocnguyen296@gmail.com>

---------

Signed-off-by: tanlocnguyen <tanlocnguyen296@gmail.com>
Co-authored-by: tanlocnguyen <tanlocnguyen296@gmail.com>

sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark.py

@@ -125,7 +125,7 @@ def get_historical_features(
         config: RepoConfig,
         feature_views: List[FeatureView],
         feature_refs: List[str],
-        entity_df: Union[pandas.DataFrame, str],
+        entity_df: Union[pandas.DataFrame, str, pyspark.sql.DataFrame],
         registry: Registry,
         project: str,
         full_feature_names: bool = False,
@@ -473,15 +473,16 @@ def _get_entity_df_event_timestamp_range(
             entity_df_event_timestamp.min().to_pydatetime(),
             entity_df_event_timestamp.max().to_pydatetime(),
         )
-    elif isinstance(entity_df, str):
+    elif isinstance(entity_df, str) or isinstance(entity_df, pyspark.sql.DataFrame):
         # If the entity_df is a string (SQL query), determine range
         # from table
-        df = spark_session.sql(entity_df).select(entity_df_event_timestamp_col)
-
-        # Checks if executing entity sql resulted in any data
-        if df.rdd.isEmpty():
-            raise EntitySQLEmptyResults(entity_df)
-
+        if isinstance(entity_df, str):
+            df = spark_session.sql(entity_df).select(entity_df_event_timestamp_col)
+            # Checks if executing entity sql resulted in any data
+            if df.rdd.isEmpty():
+                raise EntitySQLEmptyResults(entity_df)
+        else:
+            df = entity_df
         # TODO(kzhang132): need utc conversion here.
 
         entity_df_event_timestamp_range = (
@@ -499,8 +500,11 @@ def _get_entity_schema(
 ) -> Dict[str, np.dtype]:
     if isinstance(entity_df, pd.DataFrame):
         return dict(zip(entity_df.columns, entity_df.dtypes))
-    elif isinstance(entity_df, str):
-        entity_spark_df = spark_session.sql(entity_df)
+    elif isinstance(entity_df, str) or isinstance(entity_df, pyspark.sql.DataFrame):
+        if isinstance(entity_df, str):
+            entity_spark_df = spark_session.sql(entity_df)
+        else:
+            entity_spark_df = entity_df
         return dict(
             zip(
                 entity_spark_df.columns,
@@ -526,6 +530,9 @@ def _upload_entity_df(
     elif isinstance(entity_df, str):
         spark_session.sql(entity_df).createOrReplaceTempView(table_name)
         return
+    elif isinstance(entity_df, pyspark.sql.DataFrame):
+        entity_df.createOrReplaceTempView(table_name)
+        return
     else:
         raise InvalidEntityType(type(entity_df))