[FEAT] add driver/executor pod in Spark

flytekit/models/task.py

@@ -1053,6 +1053,7 @@ def to_flyte_idl(self) -> _core_task.K8sPod:
             metadata=self._metadata.to_flyte_idl() if self.metadata else None,
             pod_spec=_json_format.Parse(_json.dumps(self.pod_spec), _struct.Struct()) if self.pod_spec else None,
             data_config=self.data_config.to_flyte_idl() if self.data_config else None,
+            primary_container_name=self.primary_container_name,
         )
 
     @classmethod
@@ -1081,6 +1082,7 @@ def from_pod_template(cls, pod_template: "PodTemplate") -> "K8sPod":
         return cls(
             metadata=K8sObjectMetadata(labels=pod_template.labels, annotations=pod_template.annotations),
             pod_spec=ApiClient().sanitize_for_serialization(pod_template.pod_spec),
+            primary_container_name=pod_template.primary_container_name,
         )
 
 

plugins/flytekit-spark/flytekitplugins/spark/models.py

@@ -7,6 +7,7 @@
 
 from flytekit.exceptions import user as _user_exceptions
 from flytekit.models import common as _common
+from flytekit.models.task import K8sPod
 
 
 class SparkType(enum.Enum):
@@ -27,6 +28,8 @@ def __init__(
         executor_path: str,
         databricks_conf: Optional[Dict[str, Dict[str, Dict]]] = None,
         databricks_instance: Optional[str] = None,
+        driver_pod: Optional[K8sPod] = None,
+        executor_pod: Optional[K8sPod] = None,
     ):
         """
         This defines a SparkJob target.  It will execute the appropriate SparkJob.
@@ -47,6 +50,8 @@ def __init__(
             databricks_conf = {}
         self._databricks_conf = databricks_conf
         self._databricks_instance = databricks_instance
+        self._driver_pod = driver_pod
+        self._executor_pod = executor_pod
 
     def with_overrides(
         self,
@@ -71,6 +76,8 @@ def with_overrides(
             hadoop_conf=new_hadoop_conf,
             databricks_conf=new_databricks_conf,
             databricks_instance=self.databricks_instance,
+            driver_pod=self.driver_pod,
+            executor_pod=self.executor_pod,
             executor_path=self.executor_path,
         )
 
@@ -139,6 +146,22 @@ def databricks_instance(self) -> str:
         """
         return self._databricks_instance
 
+    @property
+    def driver_pod(self) -> K8sPod:
+        """
+        Additional pod specs for driver pod.
+        :rtype: K8sPod
+        """
+        return self._driver_pod
+
+    @property
+    def executor_pod(self) -> K8sPod:
+        """
+        Additional pod specs for the worker node pods.
+        :rtype: K8sPod
+        """
+        return self._executor_pod
+
     def to_flyte_idl(self):
         """
         :rtype: flyteidl.plugins.spark_pb2.SparkJob
@@ -167,6 +190,8 @@ def to_flyte_idl(self):
             hadoopConf=self.hadoop_conf,
             databricksConf=databricks_conf,
             databricksInstance=self.databricks_instance,
+            driverPod=self.driver_pod.to_flyte_idl() if self.driver_pod else None,
+            executorPod=self.executor_pod.to_flyte_idl() if self.executor_pod else None,
         )
 
     @classmethod
@@ -193,4 +218,6 @@ def from_flyte_idl(cls, pb2_object):
             executor_path=pb2_object.executorPath,
             databricks_conf=json_format.MessageToDict(pb2_object.databricksConf),
             databricks_instance=pb2_object.databricksInstance,
+            driver_pod=pb2_object.driverPod,
+            executor_pod=pb2_object.executorPod,
         )

plugins/flytekit-spark/flytekitplugins/spark/task.py

@@ -10,9 +10,11 @@
 from flytekit import FlyteContextManager, PythonFunctionTask, lazy_module, logger
 from flytekit.configuration import DefaultImages, SerializationSettings
 from flytekit.core.context_manager import ExecutionParameters
+from flytekit.core.pod_template import PRIMARY_CONTAINER_DEFAULT_NAME, PodTemplate
 from flytekit.extend import ExecutionState, TaskPlugins
 from flytekit.extend.backend.base_agent import AsyncAgentExecutorMixin
 from flytekit.image_spec import DefaultImageBuilder, ImageSpec
+from flytekit.models.task import K8sPod
 
 from .models import SparkJob, SparkType
 
@@ -26,17 +28,21 @@ class Spark(object):
     Use this to configure a SparkContext for a your task. Task's marked with this will automatically execute
     natively onto K8s as a distributed execution of spark
 
-    Args:
-        spark_conf: Dictionary of spark config. The variables should match what spark expects
-        hadoop_conf: Dictionary of hadoop conf. The variables should match a typical hadoop configuration for spark
-        executor_path: Python binary executable to use for PySpark in driver and executor.
-        applications_path: MainFile is the path to a bundled JAR, Python, or R file of the application to execute.
+    Attributes:
+        spark_conf (Optional[Dict[str, str]]): Spark configuration dictionary.
+        hadoop_conf (Optional[Dict[str, str]]): Hadoop configuration dictionary.
+        executor_path (Optional[str]): Path to the Python binary for PySpark execution.
+        applications_path (Optional[str]): Path to the main application file.
+        driver_pod (Optional[PodTemplate]): The pod template for the Spark driver pod.
+        executor_pod (Optional[PodTemplate]): The pod template for the Spark executor pod.
     """
 
     spark_conf: Optional[Dict[str, str]] = None
     hadoop_conf: Optional[Dict[str, str]] = None
     executor_path: Optional[str] = None
     applications_path: Optional[str] = None
+    driver_pod: Optional[PodTemplate] = None
+    executor_pod: Optional[PodTemplate] = None
 
     def __post_init__(self):
         if self.spark_conf is None:
@@ -172,6 +178,8 @@ def get_custom(self, settings: SerializationSettings) -> Dict[str, Any]:
             executor_path=self._default_executor_path or settings.python_interpreter,
             main_class="",
             spark_type=SparkType.PYTHON,
+            driver_pod=self.to_k8s_pod(self.task_config.driver_pod),
+            executor_pod=self.to_k8s_pod(self.task_config.executor_pod),
         )
         if isinstance(self.task_config, (Databricks, DatabricksV2)):
             cfg = cast(DatabricksV2, self.task_config)
@@ -180,6 +188,27 @@ def get_custom(self, settings: SerializationSettings) -> Dict[str, Any]:
 
         return MessageToDict(job.to_flyte_idl())
 
+    def to_k8s_pod(self, pod_template: Optional[PodTemplate] = None) -> Optional[K8sPod]:
+        """
+        Convert the podTemplate to K8sPod
+        """
+        if pod_template is None:
+            return None
+
+        task_primary_container_name = (
+            self.pod_template.primary_container_name if self.pod_template else PRIMARY_CONTAINER_DEFAULT_NAME
+        )
+
+        if pod_template.primary_container_name != task_primary_container_name:
+            logger.warning(
+                "Primary container name ('%s') set in spark differs from the one in @task ('%s'). "
+                "The primary container name in @task will be overridden.",
+                pod_template.primary_container_name,
+                task_primary_container_name,
+            )
+
+        return K8sPod.from_pod_template(pod_template)
+
     def pre_execute(self, user_params: ExecutionParameters) -> ExecutionParameters:
         import pyspark as _pyspark