fix: Updating the batch field so that you can query create and event date.

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

@@ -267,7 +267,7 @@ def evaluate_historical_retrieval():
                 )
 
                 entity_df_with_features = _drop_columns(
-                    df_to_join, timestamp_field, created_timestamp_column
+                    df_to_join, features, timestamp_field, created_timestamp_column
                 )
 
                 # Ensure that we delete dataframes to free up memory
@@ -599,6 +599,11 @@ def _normalize_timestamp(
         created_timestamp_column_type = df_to_join_types[created_timestamp_column]
 
     if not hasattr(timestamp_field_type, "tz") or timestamp_field_type.tz != pytz.UTC:
+        # if you are querying for the event timestamp field, we have to deduplicate
+        if len(df_to_join[timestamp_field].shape) > 1:
+            df_to_join, dups = _df_column_uniquify(df_to_join)
+            df_to_join = df_to_join.drop(columns=dups)
+
         # Make sure all timestamp fields are tz-aware. We default tz-naive fields to UTC
         df_to_join[timestamp_field] = df_to_join[timestamp_field].apply(
             lambda x: x if x.tzinfo is not None else x.replace(tzinfo=pytz.utc),
@@ -609,6 +614,11 @@ def _normalize_timestamp(
         not hasattr(created_timestamp_column_type, "tz")
         or created_timestamp_column_type.tz != pytz.UTC
     ):
+        if len(df_to_join[created_timestamp_column].shape) > 1:
+            # if you are querying for the created timestamp field, we have to deduplicate
+            df_to_join, dups = _df_column_uniquify(df_to_join)
+            df_to_join = df_to_join.drop(columns=dups)
+
         df_to_join[created_timestamp_column] = df_to_join[
             created_timestamp_column
         ].apply(
@@ -701,14 +711,36 @@ def _drop_duplicates(
 
 def _drop_columns(
     df_to_join: dd.DataFrame,
+    features: List[str],
     timestamp_field: str,
     created_timestamp_column: str,
 ) -> dd.DataFrame:
-    entity_df_with_features = df_to_join.drop([timestamp_field], axis=1).persist()
-
-    if created_timestamp_column:
-        entity_df_with_features = entity_df_with_features.drop(
-            [created_timestamp_column], axis=1
-        ).persist()
+    entity_df_with_features = df_to_join
+    timestamp_columns = [
+        timestamp_field,
+        created_timestamp_column,
+    ]
+    for column in timestamp_columns:
+        if column and column not in features:
+            entity_df_with_features = entity_df_with_features.drop(
+                [column], axis=1
+            ).persist()
 
     return entity_df_with_features
+
+
+def _df_column_uniquify(df: dd.DataFrame) -> Tuple[dd.DataFrame, List[str]]:
+    df_columns = df.columns
+    new_columns = []
+    duplicate_cols = []
+    for item in df_columns:
+        counter = 0
+        newitem = item
+        while newitem in new_columns:
+            counter += 1
+            newitem = "{}_{}".format(item, counter)
+            if counter > 0:
+                duplicate_cols.append(newitem)
+        new_columns.append(newitem)
+    df.columns = new_columns
+    return df, duplicate_cols

sdk/python/feast/type_map.py

@@ -402,7 +402,12 @@ def _python_value_to_proto_value(
             valid_scalar_types,
         ) = PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE[feast_value_type]
         if valid_scalar_types:
-            assert type(sample) in valid_scalar_types
+            if sample == 0 or sample == 0.0:
+                # Numpy convert 0 to int. However, in the feature view definition, the type of column may be a float.
+                # So, if value is 0, type validation must pass if scalar_types are either int or float.
+                assert type(sample) in [np.int64, int, np.float64, float]
+            else:
+                assert type(sample) in valid_scalar_types
         if feast_value_type == ValueType.BOOL:
             # ProtoValue does not support conversion of np.bool_ so we need to convert it to support np.bool_.
             return [