Catches when data prep cluster fails to start

llmfoundry/command_utils/data_prep/convert_delta_to_json.py

@@ -692,6 +692,7 @@ def fetch_DT(
 
     formatted_delta_table_name = format_tablename(delta_table_name)
     import grpc
+    import pyspark.errors.exceptions.connect as spark_errors
     try:
         fetch(
             method,
@@ -702,8 +703,16 @@ def fetch_DT(
             sparkSession,
             dbsql,
         )
-    except grpc.RpcError as e:
-        if e.code(
+    except (grpc.RpcError, spark_errors.SparkConnectGrpcException) as e:
+        if isinstance(
+            e,
+            spark_errors.SparkConnectGrpcException,
+        ) and 'Cannot start cluster' in str(e):
+            raise FaultyDataPrepCluster(
+                message=
+                f'The data preparation cluster you provided is terminated. Please retry with a cluster that is healthy and alive. {e}',
+            ) from e
+        if isinstance(e, grpc.RpcError) and e.code(
         ) == grpc.StatusCode.INTERNAL and 'Job aborted due to stage failure' in e.details(
         ):
             raise FaultyDataPrepCluster(

tests/a_scripts/data_prep/test_convert_delta_to_json.py

@@ -9,6 +9,7 @@
 
 import grpc
 from pyspark.errors import AnalysisException
+from pyspark.errors.exceptions.connect import SparkConnectGrpcException
 
 from llmfoundry.command_utils.data_prep.convert_delta_to_json import (
     FaultyDataPrepCluster,
@@ -584,6 +585,58 @@ def test_fetch_DT_grpc_error_handling(
         # Verify that fetch was called
         mock_fetch.assert_called_once()
 
+    @patch('llmfoundry.command_utils.data_prep.convert_delta_to_json.fetch')
+    @patch(
+        'llmfoundry.command_utils.data_prep.convert_delta_to_json.validate_and_get_cluster_info',
+    )
+    def test_fetch_DT_catches_cluster_failed_to_start(
+        self,
+        mock_validate_cluster_info: MagicMock,
+        mock_fetch: MagicMock,
+    ):
+        # Arrange
+        # Mock the validate_and_get_cluster_info to return test values
+        mock_validate_cluster_info.return_value = ('dbconnect', None, None)
+
+        # Create a SparkConnectGrpcException indicating that the cluster failed to start
+
+        grpc_error = SparkConnectGrpcException(
+            message='Cannot start cluster etc...',
+        )
+
+        # Configure the fetch function to raise the SparkConnectGrpcException
+        mock_fetch.side_effect = grpc_error
+
+        # Test inputs
+        delta_table_name = 'test_table'
+        json_output_folder = '/tmp/to/jsonl'
+        http_path = None
+        cluster_id = None
+        use_serverless = False
+        DATABRICKS_HOST = 'https://test-host'
+        DATABRICKS_TOKEN = 'test-token'
+
+        # Act & Assert
+        with self.assertRaises(FaultyDataPrepCluster) as context:
+            fetch_DT(
+                delta_table_name=delta_table_name,
+                json_output_folder=json_output_folder,
+                http_path=http_path,
+                cluster_id=cluster_id,
+                use_serverless=use_serverless,
+                DATABRICKS_HOST=DATABRICKS_HOST,
+                DATABRICKS_TOKEN=DATABRICKS_TOKEN,
+            )
+
+        # Verify that the FaultyDataPrepCluster contains the expected message
+        self.assertIn(
+            'The data preparation cluster you provided is terminated. Please retry with a cluster that is healthy and alive.',
+            str(context.exception),
+        )
+
+        # Verify that fetch was called
+        mock_fetch.assert_called_once()
+
     @patch(
         'llmfoundry.command_utils.data_prep.convert_delta_to_json.get_total_rows',
     )