Concat tables to be backward compatible

src/databricks/sql/result_set.py

@@ -20,7 +20,12 @@
 
 from databricks.sql.types import Row
 from databricks.sql.exc import RequestError, CursorAlreadyClosedError
-from databricks.sql.utils import ExecuteResponse, ColumnTable, ColumnQueue
+from databricks.sql.utils import (
+    ExecuteResponse,
+    ColumnTable,
+    ColumnQueue,
+    concat_table_chunks,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -251,23 +256,6 @@ def _convert_arrow_table(self, table):
         res = df.to_numpy(na_value=None, dtype="object")
         return [ResultRow(*v) for v in res]
 
-    def merge_columnar(self, result1, result2) -> "ColumnTable":
-        """
-        Function to merge / combining the columnar results into a single result
-        :param result1:
-        :param result2:
-        :return:
-        """
-
-        if result1.column_names != result2.column_names:
-            raise ValueError("The columns in the results don't match")
-
-        merged_result = [
-            result1.column_table[i] + result2.column_table[i]
-            for i in range(result1.num_columns)
-        ]
-        return ColumnTable(merged_result, result1.column_names)
-
     def fetchmany_arrow(self, size: int) -> "pyarrow.Table":
         """
         Fetch the next set of rows of a query result, returning a PyArrow table.
@@ -292,7 +280,7 @@ def fetchmany_arrow(self, size: int) -> "pyarrow.Table":
             n_remaining_rows -= partial_results.num_rows
             self._next_row_index += partial_results.num_rows
 
-        return pyarrow.concat_tables(partial_result_chunks, use_threads=True)
+        return concat_table_chunks(partial_result_chunks)
 
     def fetchmany_columnar(self, size: int):
         """
@@ -305,19 +293,19 @@ def fetchmany_columnar(self, size: int):
         results = self.results.next_n_rows(size)
         n_remaining_rows = size - results.num_rows
         self._next_row_index += results.num_rows
-
+        partial_result_chunks = [results]
         while (
             n_remaining_rows > 0
             and not self.has_been_closed_server_side
             and self.has_more_rows
         ):
             self._fill_results_buffer()
             partial_results = self.results.next_n_rows(n_remaining_rows)
-            results = self.merge_columnar(results, partial_results)
+            partial_result_chunks.append(partial_results)
             n_remaining_rows -= partial_results.num_rows
             self._next_row_index += partial_results.num_rows
 
-        return results
+        return concat_table_chunks(partial_result_chunks)
 
     def fetchall_arrow(self) -> "pyarrow.Table":
         """Fetch all (remaining) rows of a query result, returning them as a PyArrow table."""
@@ -327,36 +315,34 @@ def fetchall_arrow(self) -> "pyarrow.Table":
         while not self.has_been_closed_server_side and self.has_more_rows:
             self._fill_results_buffer()
             partial_results = self.results.remaining_rows()
-            if isinstance(results, ColumnTable) and isinstance(
-                partial_results, ColumnTable
-            ):
-                results = self.merge_columnar(results, partial_results)
-            else:
-                partial_result_chunks.append(partial_results)
+            partial_result_chunks.append(partial_results)
             self._next_row_index += partial_results.num_rows
 
+        result_table = concat_table_chunks(partial_result_chunks)
         # If PyArrow is installed and we have a ColumnTable result, convert it to PyArrow Table
         # Valid only for metadata commands result set
-        if isinstance(results, ColumnTable) and pyarrow:
+        if isinstance(result_table, ColumnTable) and pyarrow:
             data = {
                 name: col
-                for name, col in zip(results.column_names, results.column_table)
+                for name, col in zip(
+                    result_table.column_names, result_table.column_table
+                )
             }
             return pyarrow.Table.from_pydict(data)
-        return pyarrow.concat_tables(partial_result_chunks, use_threads=True)
+        return result_table
 
     def fetchall_columnar(self):
         """Fetch all (remaining) rows of a query result, returning them as a Columnar table."""
         results = self.results.remaining_rows()
         self._next_row_index += results.num_rows
-
+        partial_result_chunks = [results]
         while not self.has_been_closed_server_side and self.has_more_rows:
             self._fill_results_buffer()
             partial_results = self.results.remaining_rows()
-            results = self.merge_columnar(results, partial_results)
+            partial_result_chunks.append(partial_results)
             self._next_row_index += partial_results.num_rows
 
-        return results
+        return concat_table_chunks(partial_result_chunks)
 
     def fetchone(self) -> Optional[Row]:
         """

src/databricks/sql/utils.py

@@ -785,3 +785,25 @@ def _create_python_tuple(t_col_value_wrapper):
             result[i] = None
 
     return tuple(result)
+
+
+def concat_table_chunks(
+    table_chunks: List[Union["pyarrow.Table", ColumnTable]]
+) -> Union["pyarrow.Table", ColumnTable]:
+    if len(table_chunks) == 0:
+        return table_chunks
+
+    if isinstance(table_chunks[0], ColumnTable):
+        ## Check if all have the same column names
+        if not all(
+            table.column_names == table_chunks[0].column_names for table in table_chunks
+        ):
+            raise ValueError("The columns in the results don't match")
+
+        result_table = table_chunks[0].column_table
+        for i in range(1, len(table_chunks)):
+            for j in range(table_chunks[i].num_columns):
+                result_table[j].extend(table_chunks[i].column_table[j])
+        return ColumnTable(result_table, table_chunks[0].column_names)
+    else:
+        return pyarrow.concat_tables(table_chunks, use_threads=True)

tests/unit/test_util.py

@@ -1,8 +1,17 @@
 import decimal
 import datetime
 from datetime import timezone, timedelta
+import pytest
+from databricks.sql.utils import (
+    convert_to_assigned_datatypes_in_column_table,
+    ColumnTable,
+    concat_table_chunks,
+)
 
-from databricks.sql.utils import convert_to_assigned_datatypes_in_column_table
+try:
+    import pyarrow
+except ImportError:
+    pyarrow = None
 
 
 class TestUtils:
@@ -122,3 +131,33 @@ def test_convert_to_assigned_datatypes_in_column_table(self):
         for index, entry in enumerate(converted_column_table):
             assert entry[0] == expected_convertion[index][0]
             assert isinstance(entry[0], expected_convertion[index][1])
+
+    def test_concat_table_chunks_column_table(self):
+        column_table1 = ColumnTable([[1, 2], [5, 6]], ["col1", "col2"])
+        column_table2 = ColumnTable([[3, 4], [7, 8]], ["col1", "col2"])
+
+        result_table = concat_table_chunks([column_table1, column_table2])
+
+        assert result_table.column_table == [[1, 2, 3, 4], [5, 6, 7, 8]]
+        assert result_table.column_names == ["col1", "col2"]
+
+    @pytest.mark.skipif(pyarrow is None, reason="PyArrow is not installed")
+    def test_concat_table_chunks_arrow_table(self):
+        arrow_table1 = pyarrow.Table.from_pydict({"col1": [1, 2], "col2": [5, 6]})
+        arrow_table2 = pyarrow.Table.from_pydict({"col1": [3, 4], "col2": [7, 8]})
+
+        result_table = concat_table_chunks([arrow_table1, arrow_table2])
+        assert result_table.column_names == ["col1", "col2"]
+        assert result_table.column("col1").to_pylist() == [1, 2, 3, 4]
+        assert result_table.column("col2").to_pylist() == [5, 6, 7, 8]
+
+    def test_concat_table_chunks_empty(self):
+        result_table = concat_table_chunks([])
+        assert result_table == []
+
+    def test_concat_table_chunks__incorrect_column_names_error(self):
+        column_table1 = ColumnTable([[1, 2], [5, 6]], ["col1", "col2"])
+        column_table2 = ColumnTable([[3, 4], [7, 8]], ["col1", "col3"])
+
+        with pytest.raises(ValueError):
+            concat_table_chunks([column_table1, column_table2])