fix: override is_string/is_number/is_float type classification for Databricks types

CHANGELOG.md

@@ -10,6 +10,7 @@
 - Fix `workflow_job` Python model submission method failing with dictionary attribute error ([#1360](https://github.com/databricks/dbt-databricks/issues/1360))
 - Fix `TestWorkflowJob` functional test that was unreachable on all profiles due to incorrect skip list, wrong model fixture, and invalid `max_retries` parameter ([#1360](https://github.com/databricks/dbt-databricks/issues/1360))
 - Fix column order mismatch in microbatch and replace_where incremental strategies by using INSERT BY NAME syntax ([#1338](https://github.com/databricks/dbt-databricks/issues/1338))
+- Fix `is_string()`, `is_number()`, `is_float()`, `is_integer()`, and `is_numeric()` returning `False` for Databricks/Spark column types by overriding them in `DatabricksColumn` with the correct Spark type names ([#1380](https://github.com/databricks/dbt-databricks/issues/1380))
 - Fix `dbt run --empty` failing with inline `ref()` / `source()` aliases ([dbt-labs/dbt-adapters#660](https://github.com/dbt-labs/dbt-adapters/issues/660))
 
 ### Under the Hood

dbt/adapters/databricks/column.py

@@ -28,6 +28,53 @@ def create(cls, name: str, label_or_dtype: str) -> "DatabricksColumn":
         column_type = cls.translate_type(label_or_dtype)
         return cls(name, column_type)
 
+    def is_string(self) -> bool:
+        return self.dtype.lower() in {
+            "string",
+            "varchar",
+            "char",
+            "text",
+            "character varying",
+            "character",
+            "nchar",
+            "nvarchar",
+        }
+
+    def is_number(self) -> bool:
+        return self.dtype.lower() in {
+            "tinyint",
+            "smallint",
+            "int",
+            "integer",
+            "bigint",
+            "long",
+            "float",
+            "double",
+            "decimal",
+            "numeric",
+            "real",
+        } or self.dtype.lower().startswith("decimal(")
+
+    def is_float(self) -> bool:
+        return self.dtype.lower() in {
+            "float",
+            "double",
+            "real",
+        }
+
+    def is_integer(self) -> bool:
+        return self.dtype.lower() in {
+            "tinyint",
+            "smallint",
+            "int",
+            "integer",
+            "bigint",
+            "long",
+        }
+
+    def is_numeric(self) -> bool:
+        return self.is_number()
+
     @classmethod
     def from_json_metadata(cls, json_metadata: str) -> list["DatabricksColumn"]:
         """

tests/unit/test_column.py

@@ -290,3 +290,80 @@ def test_parse_type_from_json_primitive_types(self):
             type_info = {"name": type_name}
             result = DatabricksColumn._parse_type_from_json(type_info)
             assert result == type_name
+
+
+class TestTypeClassification:
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "string",
+            "varchar",
+            "char",
+            "text",
+            "character varying",
+            "character",
+            "nchar",
+            "nvarchar",
+            "STRING",
+            "VARCHAR",
+            "CHAR",
+        ],
+    )
+    def test_is_string_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_string() is True
+
+    @pytest.mark.parametrize("dtype", ["int", "bigint", "double", "decimal(10,2)"])
+    def test_is_string_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_string() is False
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["tinyint", "smallint", "int", "integer", "bigint", "long", "INT", "BIGINT"],
+    )
+    def test_is_integer_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_integer() is True
+
+    @pytest.mark.parametrize("dtype", ["float", "double", "string", "decimal(10,2)"])
+    def test_is_integer_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_integer() is False
+
+    @pytest.mark.parametrize("dtype", ["float", "double", "real", "FLOAT", "DOUBLE"])
+    def test_is_float_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_float() is True
+
+    @pytest.mark.parametrize("dtype", ["int", "bigint", "string", "decimal(10,2)"])
+    def test_is_float_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_float() is False
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "tinyint",
+            "smallint",
+            "int",
+            "integer",
+            "bigint",
+            "long",
+            "float",
+            "double",
+            "decimal",
+            "numeric",
+            "real",
+            "decimal(10,2)",
+            "decimal(38,0)",
+            "DECIMAL(10,2)",
+        ],
+    )
+    def test_is_number_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_number() is True
+
+    @pytest.mark.parametrize("dtype", ["string", "varchar", "boolean", "date", "timestamp"])
+    def test_is_number_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_number() is False
+
+    def test_is_numeric_delegates_to_is_number(self):
+        col = DatabricksColumn("col", "bigint")
+        assert col.is_numeric() == col.is_number()
+
+    def test_is_numeric_false_for_string(self):
+        assert DatabricksColumn("col", "string").is_numeric() is False