@@ -17,7 +17,6 @@
 # under the License.
 
 from typing import Optional, Dict
-from kubernetes import client
 from airflow.exceptions import AirflowException
 from airflow.sensors.base import BaseSensorOperator
 from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook
@@ -34,7 +33,6 @@ def __init__(
         self,
         *,
         application_name: str,
-        attach_log: bool = False,
         namespace: Optional[str] = None,
         kubernetes_conn_id: str = "kubernetes_in_cluster",  # <2>
         api_group: str = "spark.stackable.tech",
@@ -44,44 +42,13 @@ def __init__(
     ) -> None:
         super().__init__(**kwargs)
         self.application_name = application_name
-        self.attach_log = attach_log
         self.namespace = namespace
         self.kubernetes_conn_id = kubernetes_conn_id
         self.hook = KubernetesHook(conn_id=self.kubernetes_conn_id)
         self.api_group = api_group
         self.api_version = api_version
         self.poke_interval = poke_interval
 
-    def _log_driver(self, application_state: str, response: dict) -> None:
-        if not self.attach_log:
-            return
-        status_info = response["status"]
-        if "driverInfo" not in status_info:
-            return
-        driver_info = status_info["driverInfo"]
-        if "podName" not in driver_info:
-            return
-        driver_pod_name = driver_info["podName"]
-        namespace = response["metadata"]["namespace"]
-        log_method = (
-            self.log.error
-            if application_state in self.FAILURE_STATES
-            else self.log.info
-        )
-        try:
-            log = ""
-            for line in self.hook.get_pod_logs(driver_pod_name, namespace=namespace):
-                log += line.decode()
-            log_method(log)
-        except client.rest.ApiException as e:
-            self.log.warning(
-                "Could not read logs for pod %s. It may have been disposed.\n"
-                "Make sure timeToLiveSeconds is set on your SparkApplication spec.\n"
-                "underlying exception: %s",
-                driver_pod_name,
-                e,
-            )
-
     def poke(self, context: Dict) -> bool:
         self.log.info("Poking: %s", self.application_name)
         response = self.hook.get_custom_object(
@@ -98,11 +65,6 @@ def poke(self, context: Dict) -> bool:
                 f"SparkApplication status could not be established: {response}"
             )
             return False
-        if (
-            self.attach_log
-            and application_state in self.FAILURE_STATES + self.SUCCESS_STATES
-        ):
-            self._log_driver(application_state, response)
         if application_state in self.FAILURE_STATES:
             raise AirflowException(
                 f"SparkApplication failed with state: {application_state}"
 
@@ -95,6 +95,13 @@ include::example$example-spark-dag.py[]
 <6> the subsequent task to monitor the job
 <7> the jobs are chained together in the correct order
 
+[NOTE]
+====
+The sensor only tracks the `status.phase` of the SparkApplication.
+It cannot copy the Spark driver log into the Airflow task log: the Spark operator deletes the driver Pod as soon as the application reaches `Succeeded` or `Failed`.
+To keep driver and executor logs, enable log aggregation for the SparkApplication as described in xref:spark-k8s:usage-guide/logging.adoc[], or use the xref:spark-k8s:usage-guide/history-server.adoc[Spark history server] for event logs.
+====
+
 Once this DAG is xref:usage-guide/mounting-dags.adoc[mounted] in the DAG folder it can be called and its progress viewed from within the Webserver UI:
 
 image::airflow_dag_graph.png[Airflow Connections]
@@ -109,7 +116,7 @@ TIP: A full example of the above is used as an integration test https://github.c
 
 == Logging
 
-As mentioned above, the logs are available from the webserver UI if the jobs run with the `celeryExecutor`.
+As mentioned above, the Airflow task logs are available from the webserver UI if the jobs run with the `celeryExecutor`.
 If the SDP logging mechanism has been deployed, log information can also be retrieved from the vector backend (e.g. Opensearch):
 
 image::airflow_dag_log_opensearch.png[Opensearch]