Resolve review comments

cpp/arcticdb/python/python_to_tensor_frame.cpp

@@ -116,7 +116,7 @@ NativeTensor obj_to_tensor(PyObject *ptr, bool empty_types) {
     const auto arr = pybind11::detail::array_proxy(ptr);
     const auto descr = pybind11::detail::array_descriptor_proxy(arr->descr);
     auto ndim = arr->nd;
-    ssize_t size = ndim == 1 ? arr->dimensions[0] : arr->dimensions[0] * arr->dimensions[1];
+    const ssize_t size = ndim == 1 ? arr->dimensions[0] : arr->dimensions[0] * arr->dimensions[1];
     // In Pandas < 2, empty series dtype is `"float"`, but as of Pandas 2.0, empty series dtype is `"object"`
     // The Normalizer in Python cast empty `"float"` series to `"object"` so `EMPTY` is used here.
     // See: https://github.com/man-group/ArcticDB/pull/1049

cpp/arcticdb/version/schema_checks.hpp

@@ -67,7 +67,7 @@ inline void check_normalization_index_match(
             );
         } else {
             // (old_idx_kind == IndexDescriptor::TIMESTAMP && new_idx_kind == IndexDescriptor::ROWCOUNT) is left to preserve
-            // pre-empty index behavior with pandas 2, see test_empty_writes.cpp::test_append_empty_series. Empty pd.Series
+            // pre-empty index behavior with pandas 2, see test_empty_writes.py::test_append_empty_series. Empty pd.Series
             // have Rowrange index, but due to: https://github.com/man-group/ArcticDB/blob/bd1776291fe402d8b18af9fea865324ebd7705f1/python/arcticdb/version_store/_normalization.py#L545
             // it gets converted to DatetimeIndex (all empty indexes except categorical and multiindex are converted to datetime index
             // in pandas 2 if empty index type is disabled), however we still want to be able to append pd.Series to empty pd.Series.
@@ -87,25 +87,23 @@ inline void check_normalization_index_match(
 
 inline bool columns_match(
     const StreamDescriptor& df_in_store_descriptor,
-    const StreamDescriptor& new_df_descriptor,
-    bool empty_types
+    const StreamDescriptor& new_df_descriptor
 ) {
-    const int right_fields_offset = (empty_types && df_in_store_descriptor.index().type() == IndexDescriptor::EMPTY)
-        ? new_df_descriptor.index().field_count()
-        : 0;
+    const int index_field_size =
+        df_in_store_descriptor.index().type() == IndexDescriptor::EMPTY ? new_df_descriptor.index().field_count() : 0;
     // The empty index is compatible with all other index types. Differences in the index fields in this case is
     // allowed. The index fields are always the first in the list.
-    if (df_in_store_descriptor.fields().size() + right_fields_offset != new_df_descriptor.fields().size()) {
+    if (df_in_store_descriptor.fields().size() + index_field_size != new_df_descriptor.fields().size()) {
         return false;
     }
     // In case the left index is empty index we want to skip name/type checking of the index fields which are always
     // the first fields.
     for (auto i = 0; i < int(df_in_store_descriptor.fields().size()); ++i) {
-        if (df_in_store_descriptor.fields(i).name() != new_df_descriptor.fields(i + right_fields_offset).name())
+        if (df_in_store_descriptor.fields(i).name() != new_df_descriptor.fields(i + index_field_size).name())
             return false;
 
         const TypeDescriptor& left_type = df_in_store_descriptor.fields(i).type();
-        const TypeDescriptor& right_type = new_df_descriptor.fields(i + right_fields_offset).type();
+        const TypeDescriptor& right_type = new_df_descriptor.fields(i + index_field_size).type();
 
         if (!trivially_compatible_types(left_type, right_type) &&
             !(is_empty_type(left_type.data_type()) || is_empty_type(right_type.data_type())))
@@ -128,7 +126,7 @@ inline void fix_descriptor_mismatch_or_throw(
 
     fix_normalization_or_throw(operation == APPEND, existing_isr, new_frame);
 
-    if (!columns_match(old_sd, new_frame.desc, empty_types)) {
+    if (!columns_match(old_sd, new_frame.desc)) {
         throw StreamDescriptorMismatch(
             "The columns (names and types) in the argument are not identical to that of the existing version",
             StreamDescriptor{old_sd},

python/arcticdb/version_store/_store.py

@@ -332,9 +332,7 @@ def _try_normalize(
         dynamic_schema = self.resolve_defaults(
             "dynamic_schema", self._lib_cfg.lib_desc.version.write_options, False, **kwargs
         )
-        empty_types = self.resolve_defaults(
-            "empty_types", self._lib_cfg.lib_desc.version.write_options, False, **kwargs
-        )
+        empty_types = self.resolve_defaults("empty_types", self._lib_cfg.lib_desc.version.write_options, False)
         try:
             udm = normalize_metadata(metadata) if metadata is not None else None
             opt_custom = self._custom_normalizer.normalize(dataframe)