nailo2c commented on code in PR #65991:
URL: https://github.com/apache/airflow/pull/65991#discussion_r3254120552
##########
providers/apache/spark/src/airflow/providers/apache/spark/hooks/spark_submit.py:
##########
@@ -611,6 +629,28 @@ def submit(self, application: str = "", **kwargs: Any) ->
None:
)
self._process_spark_submit_log(iter(self._submit_sp.stdout)) # type:
ignore
+
+ if (
+ self._yarn_track_via_application_status
+ and self._is_yarn
+ and self._connection["deploy_mode"] == "cluster"
+ and self._yarn_application_id
+ and self._yarn_application_submitted
+ ):
+ # JVM is idle waiting for its stdout to be drained; terminate and
reap it,
+ # then switch to polling ResourceManager via `yarn application
-status`.
+ self._submit_sp.terminate()
+ try:
+ self._submit_sp.wait(timeout=30)
+ except subprocess.TimeoutExpired:
+ self._submit_sp.kill()
+ self._submit_sp.wait()
+ try:
+ self._track_yarn_application(self._yarn_application_id)
Review Comment:
The method body is YARN-specific (shells out to `yarn` CLI, parses
`Final-State`), so the `_yarn` prefix reflects what it does. Following the same
yarn/k8s parallel pattern as discussed above.
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