BShraman commented on code in PR #31996:
URL: https://github.com/apache/airflow/pull/31996#discussion_r1271662083
##########
airflow/jobs/job.py:
##########
@@ -207,7 +207,16 @@ def heartbeat(
self.log.debug("[heartbeat]")
except OperationalError:
Stats.incr(convert_camel_to_snake(self.__class__.__name__) +
"_heartbeat_failure", 1, 1)
- self.log.exception("%s heartbeat got an exception",
self.__class__.__name__)
+ if self.is_alive():
+ self.log.error(
+ "%s heartbeat failed with error. Scheduler may go into
unhealthy state",
+ self.__class__.__name__,
+ )
+ else:
+ self.log.error(
+ "%s heartbeat failed with error. Scheduler is in unhealthy
state", self.__class__.__name__
+ )
Review Comment:
@Bowrna
We rely on the Splunk engine to set up alerts and monitor Airflow. Having
detailed STDOUT log messages will undoubtedly aid in identifying the root cause
of any issues. While we can develop custom applications to address various
behaviors in Airflow, such as handling database connection errors, heavy
workload, service unavailability, external dependencies, and resource
misconfiguration/utilization, having built-in or similar mechanisms would
definitely be a valuable addition.
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