Fix failing tests

cpp/arcticdb/python/normalization_checks.cpp

@@ -196,7 +196,7 @@ void fix_normalization_or_throw(
     auto &old_norm = existing_isr.tsd().proto().normalization();
     auto &new_norm = new_frame.norm_meta;
     if (check_pandas_like(old_norm, new_norm)) {
-        const IndexDescriptor::Type old_index_type = existing_isr.seg().descriptor().index().type();
+        const IndexDescriptor::Type old_index_type = existing_isr.tsd().proto().stream_descriptor().index().kind();
         const IndexDescriptor::Type new_index_type = new_frame.desc.index().type();
         if (old_index_type == new_index_type && old_index_type == IndexDescriptor::ROWCOUNT) {
             update_rowcount_normalization_data(old_norm, new_norm, existing_isr.tsd().proto().total_rows());

cpp/arcticdb/stream/index.hpp

@@ -313,19 +313,19 @@ class EmptyIndex : public BaseIndex<EmptyIndex> {
     }
 
     [[nodiscard]] static IndexValue start_value_for_segment(const SegmentInMemory& segment) {
-        return static_cast<timestamp>(segment.offset());
+        return static_cast<NumericIndex>(segment.offset());
     }
 
     [[nodiscard]] static IndexValue end_value_for_segment(const SegmentInMemory& segment) {
-        return static_cast<timestamp>(segment.offset());
+        return static_cast<NumericIndex>(segment.offset());
     }
 
     [[nodiscard]] static IndexValue start_value_for_keys_segment(const SegmentInMemory& segment) {
-        return static_cast<timestamp>(segment.offset());
+        return static_cast<NumericIndex>(segment.offset());
     }
 
     [[nodiscard]] static IndexValue end_value_for_keys_segment(const SegmentInMemory& segment) {
-        return static_cast<timestamp>(segment.offset());
+        return static_cast<NumericIndex>(segment.offset());
     }
 };
 

python/arcticdb/version_store/_normalization.py

@@ -292,20 +292,19 @@ def _normalize_single_index(index, index_names, index_norm, dynamic_strings=None
     # index: pd.Index or np.ndarray -> np.ndarray
     index_tz = None
 
-    if not index_norm.is_physically_stored:
+    if isinstance(index_norm, NormalizationMetadata.PandasIndex) and not index_norm.is_physically_stored:
+        if index.name:
+            if not isinstance(index.name, int) and not isinstance(index.name, str):
+                raise NormalizationException(
+                    f"Index name must be a string or an int, received {index.name} of type {type(index.name)}"
+                )
+            if isinstance(index.name, int):
+                index_norm.is_int = True
+            index_norm.name = str(index.name)
         if isinstance(index, RangeIndex):
             # skip index since we can reconstruct it, so no need to actually store it
-            if index.name:
-                if not isinstance(index.name, int) and not isinstance(index.name, str):
-                    raise NormalizationException(
-                        f"Index name must be a string or an int, received {index.name} of type {type(index.name)}"
-                    )
-                if isinstance(index.name, int):
-                    index_norm.is_int = True
-                index_norm.name = str(index.name)
             index_norm.start = index.start if _range_index_props_are_public else index._start
             index_norm.step = index.step if _range_index_props_are_public else index._step
-            return [], []
         return [], []
     else:
         coerce_type = DTN64_DTYPE if len(index) == 0 else None
@@ -550,7 +549,7 @@ def _index_to_records(self, df, pd_norm, dynamic_strings, string_max_len):
             is_not_range_index = not isinstance(index, RangeIndex)
             df_has_rows = not(len(index) == 0 and len(df.select_dtypes(include="category").columns) == 0)
             index_norm = pd_norm.index
-            index_norm.is_physically_stored = is_not_range_index or df_has_rows
+            index_norm.is_physically_stored = is_not_range_index and df_has_rows
             if not df_has_rows:
                 index = Index([])
 
@@ -844,7 +843,6 @@ def normalize(self, item, string_max_len=None, dynamic_strings=False, coerce_col
         # type: (DataFrame, Optional[int])->NormalizedInput
         norm_meta = NormalizationMetadata()
         norm_meta.df.common.mark = True
-
         if isinstance(item.columns, RangeIndex):
             norm_meta.df.has_synthetic_columns = True
 
@@ -1097,6 +1095,7 @@ def normalize(self, item, string_max_len=None, dynamic_strings=False, coerce_col
         norm_meta = NormalizationMetadata()
         norm_meta.ts.mark = True
         index_norm = norm_meta.ts.common.index
+        index_norm.is_physically_stored = len(item.times) > 0 and not isinstance(item.times, RangeIndex)
         index_names, ix_vals = _normalize_single_index(
             item.times, ["times"], index_norm, dynamic_strings, string_max_len
         )

python/tests/unit/arcticdb/version_store/test_empty_column_type.py

@@ -816,10 +816,4 @@ def test_cannot_append_different_index_type_after_first_non_empty(self, lmdb_ver
         lmdb_version_store_static_and_dynamic.write("sym", pd.DataFrame({"col": []}))
         lmdb_version_store_static_and_dynamic.append("sym", pd.DataFrame({"col": [1,2,3]}, index=incompatible_indexes[0]))
         with pytest.raises(Exception):
-            lmdb_version_store_static_and_dynamic.append("sym", pd.DataFrame({"col": [4, 5, 6]}, index=incompatible_indexes[1]))
-
-
-def test_foo(lmdb_version_store_v2):
-    lmdb_version_store_v2.write("sym", pd.DataFrame({"col": []}, index=pd.DatetimeIndex([])))
-    print(lmdb_version_store_v2.read("sym").data)
-    print(lmdb_version_store_v2.read("sym").data.index)
+            lmdb_version_store_static_and_dynamic.append("sym", pd.DataFrame({"col": [4, 5, 6]}, index=incompatible_indexes[1]))