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From "Robert Kozikowski (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (AIRFLOW-515) Airflow system is stuck without signs of life
Date Mon, 19 Sep 2016 19:38:21 GMT

     [ https://issues.apache.org/jira/browse/AIRFLOW-515?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Robert Kozikowski updated AIRFLOW-515:
--------------------------------------
    Environment: 
airflow==1.7.1.3

My airflow Dockerfile: https://gist.github.com/kozikow/9290c44f03fba8d0f1299f355ef96b51

Airflow running locally in kubernetes minikube, with config https://gist.github.com/kozikow/49c812f11d1e4fd43e465e8090619274
.

  was:
airflow==1.7.1.3
My airflow Dockerfile: https://gist.github.com/kozikow/9290c44f03fba8d0f1299f355ef96b51
Airflow running locally in kubernetes minikube, with config https://gist.github.com/kozikow/49c812f11d1e4fd43e465e8090619274
.


> Airflow system is stuck without signs of life
> ---------------------------------------------
>
>                 Key: AIRFLOW-515
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-515
>             Project: Apache Airflow
>          Issue Type: Bug
>         Environment: airflow==1.7.1.3
> My airflow Dockerfile: https://gist.github.com/kozikow/9290c44f03fba8d0f1299f355ef96b51
> Airflow running locally in kubernetes minikube, with config https://gist.github.com/kozikow/49c812f11d1e4fd43e465e8090619274
.
>            Reporter: Robert Kozikowski
>
> h1. Symptoms
> Airflow system is stuck without response. I trigger a dag on the web node via the trigger_dag.
Same dag worked previously.
> Trigger the dag only adds it to postgres database dag_run, but does not have effect otherwise.
It does not appear in the flower. 
> Only restarting schedluler and worker helped.
> h1. Recent logs
> Logs were collected at 19:10, the system was stuck for almost an hour.
> h2. scheduler
> {noformat}
> /usr/lib/python3.5/site-packages/sqlalchemy/sql/default_comparator.py:153: SAWarning:
The IN-predicate on "task_instance.execution_date" was invoked with an empty sequence. This
results in a contradiction, which nonetheless can be expensive to evaluate.  Consider alternative
strategies for improved performance.
>   'strategies for improved performance.' % expr)
> [2016-09-19 18:15:41,171] {jobs.py:741} INFO - Done queuing tasks, calling the executor's
heartbeat
> [2016-09-19 18:15:41,172] {jobs.py:744} INFO - Loop took: 0.37502 seconds
> [2016-09-19 18:15:41,189] {models.py:305} INFO - Finding 'running' jobs without a recent
heartbeat
> [2016-09-19 18:15:41,189] {models.py:311} INFO - Failing jobs without heartbeat after
2016-09-19 18:13:26.189579
> [2016-09-19 18:15:41,212] {celery_executor.py:64} INFO - [celery] queuing ('repos_to_process_dag',
'update_repos_to_process', datetime.datetime(2016, 9, 19, 18, 15, 35, 200082)) through celery,
queue=default
> [2016-09-19 18:15:45,817] {jobs.py:574} INFO - Prioritizing 0 queued jobs
> [2016-09-19 18:15:45,831] {models.py:154} INFO - Filling up the DagBag from /usr/local/airflow/dags
> [2016-09-19 18:15:45,881] {jobs.py:726} INFO - Starting 2 scheduler jobs
> {noformat}
> h2. web
> {noformat}
> [2016-09-19 18:10:12,213] {__init__.py:36} INFO - Using executor CeleryExecutor
> [2016-09-19 18:10:12,597] {__init__.py:36} INFO - Using executor CeleryExecutor
> [2016-09-19 18:10:12,663] {__init__.py:36} INFO - Using executor CeleryExecutor
> [2016-09-19 18:10:12,790] {__init__.py:36} INFO - Using executor CeleryExecutor
> [2016-09-19 18:10:13,210] {models.py:154} INFO - Filling up the DagBag from /usr/local/airflow/dags
> [2016-09-19 18:10:13,742] {models.py:154} INFO - Filling up the DagBag from /usr/local/airflow/dags
> [2016-09-19 18:10:13,815] {models.py:154} INFO - Filling up the DagBag from /usr/local/airflow/dags
> [2016-09-19 18:10:13,912] {models.py:154} INFO - Filling up the DagBag from /usr/local/airflow/dags
> {noformat}
> h2. worker
> {noformat}
> Logging into: /usr/local/airflow/logs/repos_to_process_dag/update_repos_to_process/2016-09-19T18:15:35.200082
> {noformat}



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