Fix: empty schema and schema name parsing (#1550)

* fix: empty schema and schema name parsing

* fix: empty schema and schema name parsing

Author: eakmanrq

sqlmesh/core/engine_adapter/base.py

@@ -652,7 +652,7 @@ def drop_schema(
         """Drop a schema from a name or qualified table name."""
         self.execute(
             exp.Drop(
-                this=exp.to_identifier(schema_name.split(".")[0]),
+                this=exp.table_(schema_name.split(".")[0]),
                 kind="SCHEMA",
                 exists=ignore_if_not_exists,
                 cascade=cascade,

sqlmesh/core/engine_adapter/bigquery.py

@@ -541,21 +541,24 @@ def _get_data_objects(
         """
         Returns all the data objects that exist in the given schema and optionally catalog.
         """
+        from google.api_core.exceptions import NotFound
         from google.cloud.bigquery import DatasetReference
 
         dataset_ref = DatasetReference(
             project=catalog_name or self.client.project, dataset_id=schema_name
         )
-        all_tables = self._db_call(self.client.list_tables, dataset=dataset_ref)
-        return [
-            DataObject(
-                catalog=table.project,
-                schema=table.dataset_id,
-                name=table.table_id,
-                type=DataObjectType.from_str(table.table_type),
-            )
-            for table in all_tables
-        ]
+        try:
+            return [
+                DataObject(
+                    catalog=table.project,
+                    schema=table.dataset_id,
+                    name=table.table_id,
+                    type=DataObjectType.from_str(table.table_type),
+                )
+                for table in self._db_call(self.client.list_tables, dataset=dataset_ref)
+            ]
+        except NotFound:
+            return []
 
     @property
     def _query_data(self) -> t.Any:

sqlmesh/core/engine_adapter/snowflake.py

@@ -89,9 +89,16 @@ def _get_data_objects(
         """
         Returns all the data objects that exist in the given schema and optionally catalog.
         """
+        from snowflake.connector.errors import ProgrammingError
+
         target = nullsafe_join(".", catalog_name, schema_name)
         sql = f"SHOW TERSE OBJECTS IN {target}"
-        df = self.fetchdf(sql, quote_identifiers=True)
+        try:
+            df = self.fetchdf(sql, quote_identifiers=True)
+        except ProgrammingError as e:
+            if "Object does not exist" in str(e):
+                return []
+            raise e
         if df.empty:
             return []
         return [

sqlmesh/core/engine_adapter/spark.py

@@ -271,11 +271,17 @@ def _get_data_objects(
     ) -> t.List[DataObject]:
         target = nullsafe_join(".", catalog_name, schema_name)
         sql = f"SHOW TABLE EXTENDED IN {target} LIKE '*'"
-        results = (
-            self.fetch_pyspark_df(sql).collect()
-            if self._use_spark_session
-            else self.fetchdf(sql).to_dict("records")
-        )
+        try:
+            results = (
+                self.fetch_pyspark_df(sql).collect()
+                if self._use_spark_session
+                else self.fetchdf(sql).to_dict("records")
+            )
+        # Improvement: Figure out all the different exceptions we could get from executing a query either with or
+        # without a Spark Session. In addition Databricks would need to be updated to handle it's own exceptions.
+        # Therefore just doing except Exception for now.
+        except Exception:
+            return []
         return [
             DataObject(
                 catalog=catalog_name,

tests/core/engine_adapter/test_integration.py

@@ -317,14 +317,23 @@ def test_materialized_view(ctx: TestContext):
 
 def test_drop_schema(ctx: TestContext):
     ctx.columns_to_types = {"one": "int"}
-    ctx.init()
-    ctx.engine_adapter.create_schema("test_schema")
+    schema = normalize_identifiers(
+        exp.to_identifier(TEST_SCHEMA), dialect=ctx.engine_adapter.dialect
+    ).sql(dialect=ctx.engine_adapter.dialect)
+    ctx.engine_adapter.drop_schema(schema, cascade=True)
+    results = ctx.get_metadata_results()
+    assert len(results.tables) == 0
+    assert len(results.views) == 0
 
+    ctx.engine_adapter.create_schema(schema)
     view = ctx.table("test_view")
     view_query = exp.Select().select(exp.Literal.number(1).as_("one"))
     ctx.engine_adapter.create_view(view, view_query, ctx.columns_to_types)
+    results = ctx.get_metadata_results(schema)
+    assert len(results.tables) == 0
+    assert len(results.views) == 1
 
-    ctx.engine_adapter.drop_schema("test_schema", cascade=True)
+    ctx.engine_adapter.drop_schema(schema, cascade=True)
     results = ctx.get_metadata_results()
     assert len(results.tables) == 0
     assert len(results.views) == 0