Revert "feat: don't force db connect if using serverless" (#3784)

Author: izeigerman

docs/integrations/engines/databricks.md

@@ -14,9 +14,9 @@ SQLMesh connects to Databricks with the [Databricks SQL Connector](https://docs.
 
 The SQL Connector is bundled with SQLMesh and automatically installed when you include the `databricks` extra in the command `pip install "sqlmesh[databricks]"`.
 
-The SQL Connector has all the functionality needed for SQLMesh to execute SQL models on Databricks and Python models that do not return PySpark DataFrames.
+The SQL Connector has all the functionality needed for SQLMesh to execute SQL models on Databricks and Python models locally (the default SQLMesh approach).
 
-If you have Python models returning PySpark DataFrames, check out the [Databricks Connect](#databricks-connect-1) section.
+The SQL Connector does not support Databricks Serverless Compute. If you require Serverless Compute then you must use the Databricks Connect library.
 
 ### Databricks Connect
 
@@ -229,9 +229,7 @@ If you want Databricks to process PySpark DataFrames in SQLMesh Python models, t
 
 SQLMesh **DOES NOT** include/bundle the Databricks Connect library. You must [install the version of Databricks Connect](https://docs.databricks.com/en/dev-tools/databricks-connect/python/install.html) that matches the Databricks Runtime used in your Databricks cluster.
 
-If SQLMesh detects that you have Databricks Connect installed, then it will automatically configure the connection and use it for all Python models that return a Pandas or PySpark DataFrame.
-
-To have databricks-connect installed but ignored by SQLMesh, set `disable_databricks_connect` to `true` in the connection configuration.
+SQLMesh's Databricks Connect implementation supports Databricks Runtime 13.0 or higher. If SQLMesh detects that you have Databricks Connect installed, then it will use it for all Python models (both Pandas and PySpark DataFrames).
 
 Databricks Connect can execute SQL and DataFrame operations on different clusters by setting the SQLMesh `databricks_connect_*` connection options. For example, these options could configure SQLMesh to run SQL on a [Databricks SQL Warehouse](https://docs.databricks.com/sql/admin/create-sql-warehouse.html) while still routing DataFrame operations to a normal Databricks Cluster.
 
@@ -261,7 +259,7 @@ The only relevant SQLMesh configuration parameter is the optional `catalog` para
 | `databricks_connect_server_hostname` | Databricks Connect Only: Databricks Connect server hostname. Uses `server_hostname` if not set.                                                                                                                                                     | string |    N     |
 | `databricks_connect_access_token`    | Databricks Connect Only: Databricks Connect access token. Uses `access_token` if not set.                                                                                                                                                           | string |    N     |
 | `databricks_connect_cluster_id`      | Databricks Connect Only: Databricks Connect cluster ID. Uses `http_path` if not set. Cannot be a Databricks SQL Warehouse.                                                                                                                          | string |    N     |
-| `databricks_connect_use_serverless`  | Databricks Connect Only: Use a serverless cluster for Databricks Connect instead of `databricks_connect_cluster_id`.                                                                                                                                |  bool  |    N     |
+| `databricks_connect_use_serverless`  | Databricks Connect Only: Use a serverless cluster for Databricks Connect. If using serverless then SQL connector is disabled since Serverless is not supported for SQL Connector                                                                    |  bool  |    N     |
 | `force_databricks_connect`           | When running locally, force the use of Databricks Connect for all model operations (so don't use SQL Connector for SQL models)                                                                                                                      |  bool  |    N     |
 | `disable_databricks_connect`         | When running locally, disable the use of Databricks Connect for all model operations (so use SQL Connector for all models)                                                                                                                          |  bool  |    N     |
 | `disable_spark_session`              | Do not use SparkSession if it is available (like when running in a notebook).                                                                                                                                                                       |  bool  |    N     |

sqlmesh/__init__.py

@@ -141,9 +141,6 @@ def configure_logging(
     log_limit: int = c.DEFAULT_LOG_LIMIT,
     log_file_dir: t.Optional[t.Union[str, Path]] = None,
 ) -> None:
-    # Remove noisy grpc logs that are not useful for users
-    os.environ["GRPC_VERBOSITY"] = os.environ.get("GRPC_VERBOSITY", "NONE")
-
     logger = logging.getLogger()
     debug = force_debug or debug_mode_enabled()
 

sqlmesh/core/config/connection.py

@@ -623,12 +623,6 @@ class DatabricksConnectionConfig(ConnectionConfig):
 
     @model_validator(mode="before")
     def _databricks_connect_validator(cls, data: t.Any) -> t.Any:
-        # SQLQueryContextLogger will output any error SQL queries even if they are in a try/except block.
-        # Disabling this allows SQLMesh to determine what should be shown to the user.
-        # Ex: We describe a table to see if it exists and therefore that execution can fail but we don't need to show
-        # the user since it is expected if the table doesn't exist. Without this change the user would see the error.
-        logging.getLogger("SQLQueryContextLogger").setLevel(logging.CRITICAL)
-
         if not isinstance(data, dict):
             return data
 
@@ -647,6 +641,10 @@ def _databricks_connect_validator(cls, data: t.Any) -> t.Any:
             data.get("auth_type"),
         )
 
+        if databricks_connect_use_serverless:
+            data["force_databricks_connect"] = True
+            data["disable_databricks_connect"] = False
+
         if (not server_hostname or not http_path or not access_token) and (
             not databricks_connect_use_serverless and not auth_type
         ):
@@ -668,12 +666,11 @@ def _databricks_connect_validator(cls, data: t.Any) -> t.Any:
                 data["databricks_connect_access_token"] = access_token
             if not data.get("databricks_connect_server_hostname"):
                 data["databricks_connect_server_hostname"] = f"https://{server_hostname}"
-            if not databricks_connect_use_serverless and not data.get(
-                "databricks_connect_cluster_id"
-            ):
-                if t.TYPE_CHECKING:
-                    assert http_path is not None
-                data["databricks_connect_cluster_id"] = http_path.split("/")[-1]
+            if not databricks_connect_use_serverless:
+                if not data.get("databricks_connect_cluster_id"):
+                    if t.TYPE_CHECKING:
+                        assert http_path is not None
+                    data["databricks_connect_cluster_id"] = http_path.split("/")[-1]
 
         if auth_type:
             from databricks.sql.auth.auth import AuthType

sqlmesh/core/engine_adapter/base.py

@@ -43,11 +43,7 @@
 from sqlmesh.utils import columns_to_types_all_known, random_id
 from sqlmesh.utils.connection_pool import create_connection_pool
 from sqlmesh.utils.date import TimeLike, make_inclusive, to_time_column
-from sqlmesh.utils.errors import (
-    SQLMeshError,
-    UnsupportedCatalogOperationError,
-    MissingDefaultCatalogError,
-)
+from sqlmesh.utils.errors import SQLMeshError, UnsupportedCatalogOperationError
 from sqlmesh.utils.pandas import columns_to_types_from_df
 
 if t.TYPE_CHECKING:
@@ -190,9 +186,7 @@ def default_catalog(self) -> t.Optional[str]:
             return None
         default_catalog = self._default_catalog or self.get_current_catalog()
         if not default_catalog:
-            raise MissingDefaultCatalogError(
-                "Could not determine a default catalog despite it being supported."
-            )
+            raise SQLMeshError("Could not determine a default catalog despite it being supported.")
         return default_catalog
 
     @property

sqlmesh/core/engine_adapter/databricks.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import logging
+import os
 import typing as t
 
 import pandas as pd
@@ -16,7 +17,7 @@
 from sqlmesh.core.engine_adapter.spark import SparkEngineAdapter
 from sqlmesh.core.node import IntervalUnit
 from sqlmesh.core.schema_diff import SchemaDiffer
-from sqlmesh.utils.errors import SQLMeshError, MissingDefaultCatalogError
+from sqlmesh.utils.errors import SQLMeshError
 
 if t.TYPE_CHECKING:
     from sqlmesh.core._typing import SchemaName, TableName
@@ -91,6 +92,17 @@ def _use_spark_session(self) -> bool:
             )
         )
 
+    @property
+    def use_serverless(self) -> bool:
+        from sqlmesh import RuntimeEnv
+        from sqlmesh.utils import str_to_bool
+
+        if not self._use_spark_session:
+            return False
+        return (
+            RuntimeEnv.get().is_databricks and str_to_bool(os.environ.get("IS_SERVERLESS", "False"))
+        ) or bool(self._extra_config["databricks_connect_use_serverless"])
+
     @property
     def is_spark_session_cursor(self) -> bool:
         from sqlmesh.engines.spark.db_api.spark_session import SparkSessionCursor
@@ -112,17 +124,12 @@ def spark(self) -> PySparkSession:
         from databricks.connect import DatabricksSession
 
         if self._spark is None:
-            connect_kwargs = dict(
-                host=self._extra_config["databricks_connect_server_hostname"],
-                token=self._extra_config["databricks_connect_access_token"],
-            )
-            if "databricks_connect_use_serverless" in self._extra_config:
-                connect_kwargs["serverless"] = True
-            else:
-                connect_kwargs["cluster_id"] = self._extra_config["databricks_connect_cluster_id"]
-
             self._spark = (
-                DatabricksSession.builder.remote(**connect_kwargs)
+                DatabricksSession.builder.remote(
+                    host=self._extra_config["databricks_connect_server_hostname"],
+                    token=self._extra_config["databricks_connect_access_token"],
+                    cluster_id=self._extra_config["databricks_connect_cluster_id"],
+                )
                 .userAgent("sqlmesh")
                 .getOrCreate()
             )
@@ -150,8 +157,14 @@ def _df_to_source_queries(
 
         def query_factory() -> Query:
             temp_table = self._get_temp_table(target_table or "spark", table_only=True)
-            df.createOrReplaceTempView(temp_table.sql(dialect=self.dialect))
-            self._connection_pool.set_attribute("requires_spark_session_temp_objects", True)
+            if self.use_serverless:
+                # Global temp views are not supported on Databricks Serverless
+                # This also means we can't mix Python SQL Connection and DB Connect since they wouldn't
+                # share the same temp objects.
+                df.createOrReplaceTempView(temp_table.sql(dialect=self.dialect))  # type: ignore
+            else:
+                df.createOrReplaceGlobalTempView(temp_table.sql(dialect=self.dialect))  # type: ignore
+                temp_table.set("db", "global_temp")
             return exp.select(*self._casted_columns(columns_to_types)).from_(temp_table)
 
         if self._use_spark_session:
@@ -186,50 +199,28 @@ def fetchdf(
             return df.toPandas()
         return df
 
-    def _execute(
-        self,
-        sql: str,
-        **kwargs: t.Any,
-    ) -> None:
-        if self._connection_pool.get_attribute("requires_spark_session_temp_objects"):
-            self._fetch_native_df(sql)
-        else:
-            super()._execute(sql, **kwargs)
-
-    def _end_session(self) -> None:
-        """End the existing session."""
-        self._connection_pool.set_attribute("requires_spark_session_temp_objects", False)
-
     def get_current_catalog(self) -> t.Optional[str]:
-        pyspark_catalog = None
-        sql_connector_catalog = None
+        # Update the Dataframe API if we have a spark session
         if self._use_spark_session:
             from py4j.protocol import Py4JError
             from pyspark.errors.exceptions.connect import SparkConnectGrpcException
 
             try:
                 # Note: Spark 3.4+ Only API
-                pyspark_catalog = self.spark.catalog.currentCatalog()
+                return self.spark.catalog.currentCatalog()
             except (Py4JError, SparkConnectGrpcException):
                 pass
-        if not self.is_spark_session_cursor:
-            result = self.fetchone(exp.select(self.CURRENT_CATALOG_EXPRESSION))
-            sql_connector_catalog = result[0] if result else None
-        if (
-            self._use_spark_session
-            and not self.is_spark_session_cursor
-            and pyspark_catalog != sql_connector_catalog
-        ):
-            raise SQLMeshError(
-                f"Current catalog mismatch between Databricks SQL Connector and Databricks-Connect: `{sql_connector_catalog}` != `{pyspark_catalog}`. Set `catalog` connection property to make them the same."
-            )
-        return pyspark_catalog or sql_connector_catalog
+        result = self.fetchone(exp.select(self.CURRENT_CATALOG_EXPRESSION))
+        if result:
+            return result[0]
+        return None
 
     def set_current_catalog(self, catalog_name: str) -> None:
         # Since Databricks splits commands across the Dataframe API and the SQL Connector
         # (depending if databricks-connect is installed and a Dataframe is used) we need to ensure both
-        # are set to the same catalog since they maintain their default catalog separately
+        # are set to the same catalog since they maintain their default catalog seperately
         self.execute(exp.Use(this=exp.to_identifier(catalog_name), kind="CATALOG"))
+        # Update the Dataframe API is we have a spark session
         if self._use_spark_session:
             from py4j.protocol import Py4JError
             from pyspark.errors.exceptions.connect import SparkConnectGrpcException
@@ -266,15 +257,6 @@ def clone_table(
     def wap_supported(self, table_name: TableName) -> bool:
         return False
 
-    @property
-    def default_catalog(self) -> t.Optional[str]:
-        try:
-            return super().default_catalog
-        except MissingDefaultCatalogError as e:
-            raise MissingDefaultCatalogError(
-                "Could not determine default catalog. Define the connection property `catalog` since it can't be inferred from your connection. See SQLMesh Databricks documentation for details"
-            ) from e
-
     def _build_table_properties_exp(
         self,
         catalog_name: t.Optional[str] = None,

sqlmesh/utils/errors.py

@@ -159,10 +159,6 @@ class PythonModelEvalError(SQLMeshError):
     pass
 
 
-class MissingDefaultCatalogError(SQLMeshError):
-    pass
-
-
 def raise_config_error(
     msg: str,
     location: t.Optional[str | Path] = None,