Aditya Vishwakarma created AIRFLOW-5660:
-------------------------------------------
Summary: Scheduler becomes responsive when processing large DAGs
on kubernetes.
Key: AIRFLOW-5660
URL: https://issues.apache.org/jira/browse/AIRFLOW-5660
Project: Apache Airflow
Issue Type: Bug
Components: executor-kubernetes
Affects Versions: 1.10.5
Reporter: Aditya Vishwakarma
Assignee: Daniel Imberman
For very large dags( 10,000+) and high parallelism, the scheduling loop can
take more 5-10 minutes.
It seems that `_labels_to_key` function in kubernetes_executor loads all tasks
with a given execution date into memory. It does it for every task in progress.
So, if 100 tasks are in progress of a dag with 10,000 tasks, it will load
million tasks on every tick of the scheduler from db.
[https://github.com/apache/airflow/blob/caf1f264b845153b9a61b00b1a57acb7c320e743/airflow/contrib/executors/kubernetes_executor.py#L598]
A quick fix is to search for task in the db directly before regressing to full
scan. I can submit a PR for it.
A proper fix requires persisting a mapping of (safe_dag_id, safe_task_id,
dag_id, task_id, execution_date) somewhere, probably in the metadatabase.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
