SeaDatabricksClient: Add Metadata Commands

src/databricks/sql/backend/filters.py

@@ -0,0 +1,165 @@
+"""
+Client-side filtering utilities for Databricks SQL connector.
+
+This module provides filtering capabilities for result sets returned by different backends.
+"""
+
+import logging
+from typing import (
+    List,
+    Optional,
+    Any,
+    Callable,
+    cast,
+    TYPE_CHECKING,
+)
+
+from databricks.sql.backend.sea.backend import SeaDatabricksClient
+from databricks.sql.backend.types import ExecuteResponse
+
+if TYPE_CHECKING:
+    from databricks.sql.result_set import ResultSet, SeaResultSet
+
+logger = logging.getLogger(__name__)
+
+
+class ResultSetFilter:
+    """
+    A general-purpose filter for result sets that can be applied to any backend.
+
+    This class provides methods to filter result sets based on various criteria,
+    similar to the client-side filtering in the JDBC connector.
+    """
+
+    @staticmethod
+    def _filter_sea_result_set(
+        result_set: "SeaResultSet", filter_func: Callable[[List[Any]], bool]
+    ) -> "SeaResultSet":
+        """
+        Filter a SEA result set using the provided filter function.
+
+        Args:
+            result_set: The SEA result set to filter
+            filter_func: Function that takes a row and returns True if the row should be included
+
+        Returns:
+            A filtered SEA result set
+        """
+
+        # Get all remaining rows
+        all_rows = result_set.results.remaining_rows()
+
+        # Filter rows
+        filtered_rows = [row for row in all_rows if filter_func(row)]
+
+        # Import SeaResultSet here to avoid circular imports
+        from databricks.sql.result_set import SeaResultSet
+
+        # Reuse the command_id from the original result set
+        command_id = result_set.command_id
+
+        # Create an ExecuteResponse with the filtered data
+        execute_response = ExecuteResponse(
+            command_id=command_id,
+            status=result_set.status,
+            description=result_set.description,
+            has_been_closed_server_side=result_set.has_been_closed_server_side,
+            lz4_compressed=result_set.lz4_compressed,
+            arrow_schema_bytes=result_set._arrow_schema_bytes,
+            is_staging_operation=False,
+        )
+
+        # Create a new ResultData object with filtered data
+        from databricks.sql.backend.sea.models.base import ResultData
+
+        result_data = ResultData(data=filtered_rows, external_links=None)
+
+        # Create a new SeaResultSet with the filtered data
+        filtered_result_set = SeaResultSet(
+            connection=result_set.connection,
+            execute_response=execute_response,
+            sea_client=cast(SeaDatabricksClient, result_set.backend),
+            buffer_size_bytes=result_set.buffer_size_bytes,
+            arraysize=result_set.arraysize,
+            result_data=result_data,
+        )
+
+        return filtered_result_set
+
+    @staticmethod
+    def filter_by_column_values(
+        result_set: "ResultSet",
+        column_index: int,
+        allowed_values: List[str],
+        case_sensitive: bool = False,
+    ) -> "ResultSet":
+        """
+        Filter a result set by values in a specific column.
+
+        Args:
+            result_set: The result set to filter
+            column_index: The index of the column to filter on
+            allowed_values: List of allowed values for the column
+            case_sensitive: Whether to perform case-sensitive comparison
+
+        Returns:
+            A filtered result set
+        """
+
+        # Convert to uppercase for case-insensitive comparison if needed
+        if not case_sensitive:
+            allowed_values = [v.upper() for v in allowed_values]
+
+        # Determine the type of result set and apply appropriate filtering
+        from databricks.sql.result_set import SeaResultSet
+
+        if isinstance(result_set, SeaResultSet):
+            return ResultSetFilter._filter_sea_result_set(
+                result_set,
+                lambda row: (
+                    len(row) > column_index
+                    and isinstance(row[column_index], str)
+                    and (
+                        row[column_index].upper()
+                        if not case_sensitive
+                        else row[column_index]
+                    )
+                    in allowed_values
+                ),
+            )
+
+        # For other result set types, return the original (should be handled by specific implementations)
+        logger.warning(
+            f"Filtering not implemented for result set type: {type(result_set).__name__}"
+        )
+        return result_set
+
+    @staticmethod
+    def filter_tables_by_type(
+        result_set: "ResultSet", table_types: Optional[List[str]] = None
+    ) -> "ResultSet":
+        """
+        Filter a result set of tables by the specified table types.
+
+        This is a client-side filter that processes the result set after it has been
+        retrieved from the server. It filters out tables whose type does not match
+        any of the types in the table_types list.
+
+        Args:
+            result_set: The original result set containing tables
+            table_types: List of table types to include (e.g., ["TABLE", "VIEW"])
+
+        Returns:
+            A filtered result set containing only tables of the specified types
+        """
+
+        # Default table types if none specified
+        DEFAULT_TABLE_TYPES = ["TABLE", "VIEW", "SYSTEM TABLE"]
+        valid_types = (
+            table_types if table_types and len(table_types) > 0 else DEFAULT_TABLE_TYPES
+        )
+
+        # Table type is the 6th column (index 5)
+        return ResultSetFilter.filter_by_column_values(
+            result_set, 5, valid_types, case_sensitive=True
+        )

src/databricks/sql/backend/sea/backend.py

@@ -40,6 +40,11 @@
     GetStatementResponse,
     CreateSessionResponse,
 )
+from databricks.sql.backend.sea.models.responses import (
+    _parse_status,
+    _parse_manifest,
+    _parse_result,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -627,9 +632,22 @@ def get_catalogs(
         max_rows: int,
         max_bytes: int,
         cursor: "Cursor",
-    ):
-        """Not implemented yet."""
-        raise NotImplementedError("get_catalogs is not yet implemented for SEA backend")
+    ) -> "ResultSet":
+        """Get available catalogs by executing 'SHOW CATALOGS'."""
+        result = self.execute_command(
+            operation="SHOW CATALOGS",
+            session_id=session_id,
+            max_rows=max_rows,
+            max_bytes=max_bytes,
+            lz4_compression=False,
+            cursor=cursor,
+            use_cloud_fetch=False,
+            parameters=[],
+            async_op=False,
+            enforce_embedded_schema_correctness=False,
+        )
+        assert result is not None, "execute_command returned None in synchronous mode"
+        return result
 
     def get_schemas(
         self,
@@ -639,9 +657,30 @@ def get_schemas(
         cursor: "Cursor",
         catalog_name: Optional[str] = None,
         schema_name: Optional[str] = None,
-    ):
-        """Not implemented yet."""
-        raise NotImplementedError("get_schemas is not yet implemented for SEA backend")
+    ) -> "ResultSet":
+        """Get schemas by executing 'SHOW SCHEMAS IN catalog [LIKE pattern]'."""
+        if not catalog_name:
+            raise ValueError("Catalog name is required for get_schemas")
+
+        operation = f"SHOW SCHEMAS IN {catalog_name}"
+
+        if schema_name:
+            operation += f" LIKE '{schema_name}'"
+
+        result = self.execute_command(
+            operation=operation,
+            session_id=session_id,
+            max_rows=max_rows,
+            max_bytes=max_bytes,
+            lz4_compression=False,
+            cursor=cursor,
+            use_cloud_fetch=False,
+            parameters=[],
+            async_op=False,
+            enforce_embedded_schema_correctness=False,
+        )
+        assert result is not None, "execute_command returned None in synchronous mode"
+        return result
 
     def get_tables(
         self,
@@ -653,9 +692,43 @@ def get_tables(
         schema_name: Optional[str] = None,
         table_name: Optional[str] = None,
         table_types: Optional[List[str]] = None,
-    ):
-        """Not implemented yet."""
-        raise NotImplementedError("get_tables is not yet implemented for SEA backend")
+    ) -> "ResultSet":
+        """Get tables by executing 'SHOW TABLES IN catalog [SCHEMA LIKE pattern] [LIKE pattern]'."""
+        if not catalog_name:
+            raise ValueError("Catalog name is required for get_tables")
+
+        operation = "SHOW TABLES IN " + (
+            "ALL CATALOGS"
+            if catalog_name in [None, "*", "%"]
+            else f"CATALOG {catalog_name}"
+        )
+
+        if schema_name:
+            operation += f" SCHEMA LIKE '{schema_name}'"
+
+        if table_name:
+            operation += f" LIKE '{table_name}'"
+
+        result = self.execute_command(
+            operation=operation,
+            session_id=session_id,
+            max_rows=max_rows,
+            max_bytes=max_bytes,
+            lz4_compression=False,
+            cursor=cursor,
+            use_cloud_fetch=False,
+            parameters=[],
+            async_op=False,
+            enforce_embedded_schema_correctness=False,
+        )
+        assert result is not None, "execute_command returned None in synchronous mode"
+
+        # Apply client-side filtering by table_types
+        from databricks.sql.backend.filters import ResultSetFilter
+
+        result = ResultSetFilter.filter_tables_by_type(result, table_types)
+
+        return result
 
     def get_columns(
         self,
@@ -667,6 +740,33 @@ def get_columns(
         schema_name: Optional[str] = None,
         table_name: Optional[str] = None,
         column_name: Optional[str] = None,
-    ):
-        """Not implemented yet."""
-        raise NotImplementedError("get_columns is not yet implemented for SEA backend")
+    ) -> "ResultSet":
+        """Get columns by executing 'SHOW COLUMNS IN CATALOG catalog [SCHEMA LIKE pattern] [TABLE LIKE pattern] [LIKE pattern]'."""
+        if not catalog_name:
+            raise ValueError("Catalog name is required for get_columns")
+
+        operation = f"SHOW COLUMNS IN CATALOG {catalog_name}"
+
+        if schema_name:
+            operation += f" SCHEMA LIKE '{schema_name}'"
+
+        if table_name:
+            operation += f" TABLE LIKE '{table_name}'"
+
+        if column_name:
+            operation += f" LIKE '{column_name}'"
+
+        result = self.execute_command(
+            operation=operation,
+            session_id=session_id,
+            max_rows=max_rows,
+            max_bytes=max_bytes,
+            lz4_compression=False,
+            cursor=cursor,
+            use_cloud_fetch=False,
+            parameters=[],
+            async_op=False,
+            enforce_embedded_schema_correctness=False,
+        )
+        assert result is not None, "execute_command returned None in synchronous mode"
+        return result