[
https://issues.apache.org/jira/browse/AIRFLOW-515?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Robert Kozikowski updated AIRFLOW-515:
--------------------------------------
Description:
h1. Symptoms
Triggering a dag on the web node via the trigger_dag does not start it. Trigger
the same dag worked a moment before and I did not apply any config changes in
the mean time.
Triggering the dag (airflow trigger_dag on the web node) only adds it to
postgres table dag_run. The run does not appear in the flower or the main web
gui. System was stuck for 50 minutes without any signs of life, and no logs
coming from either scheduler, worker or webserver.
In the end, system got unblocked after force-restarting scheduler and worker.
Is there any way I should health-check my scheduler to avoid similar situation
in production?
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}
was:
h1. Symptoms
Triggering a dag on the web node via the trigger_dag does not start it. Trigger
the same dag worked a moment before and I did not apply any config changes in
the mean time.
Triggering the dag only adds it to postgres table dag_run. The run does not
appear in the flower or the main web gui. System was stuck for 50 minutes
without any signs of life, and no logs coming from either scheduler, worker or
webserver.
In the end, system got unblocked after force-restarting scheduler and worker.
Is there any way I should health-check my scheduler to avoid similar situation
in production?
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}
> 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 Dockerfile:
> https://gist.github.com/kozikow/9290c44f03fba8d0f1299f355ef96b51
> kubernetes config (running within minikube):
> https://gist.github.com/kozikow/49c812f11d1e4fd43e465e8090619274
> Reporter: Robert Kozikowski
>
> h1. Symptoms
> Triggering a dag on the web node via the trigger_dag does not start it.
> Trigger the same dag worked a moment before and I did not apply any config
> changes in the mean time.
> Triggering the dag (airflow trigger_dag on the web node) only adds it to
> postgres table dag_run. The run does not appear in the flower or the main web
> gui. System was stuck for 50 minutes without any signs of life, and no logs
> coming from either scheduler, worker or webserver.
> In the end, system got unblocked after force-restarting scheduler and worker.
> Is there any way I should health-check my scheduler to avoid similar
> situation in production?
> 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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