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https://issues.apache.org/jira/browse/AIRFLOW-3746?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16751712#comment-16751712
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ASF GitHub Bot commented on AIRFLOW-3746:
-----------------------------------------
ashwiniadiga commented on pull request #4583: [AIRFLOW-3746] Fix to prevent
missing container exit
URL: https://github.com/apache/airflow/pull/4583
My PR addresses the following [Airflow
Jira](https://issues.apache.org/jira/browse/AIRFLOW-3746)
switch from cli.logs to cli.attach to prevent missing container exit
Make sure you have checked _all_ steps below.
### Jira
- [ ] My PR addresses the following [Airflow
Jira](https://issues.apache.org/jira/browse/AIRFLOW/) issues and references
them in the PR title. For example, "\[AIRFLOW-XXX\] My Airflow PR"
- https://issues.apache.org/jira/browse/AIRFLOW-XXX
- In case you are fixing a typo in the documentation you can prepend your
commit with \[AIRFLOW-XXX\], code changes always need a Jira issue.
### Description
- [ ] Here are some details about my PR, including screenshots of any UI
changes:
### Tests
- [ ] My PR adds the following unit tests __OR__ does not need testing for
this extremely good reason:
### Commits
- [ ] My commits all reference Jira issues in their subject lines, and I
have squashed multiple commits if they address the same issue. In addition, my
commits follow the guidelines from "[How to write a good git commit
message](http://chris.beams.io/posts/git-commit/)":
1. Subject is separated from body by a blank line
1. Subject is limited to 50 characters (not including Jira issue reference)
1. Subject does not end with a period
1. Subject uses the imperative mood ("add", not "adding")
1. Body wraps at 72 characters
1. Body explains "what" and "why", not "how"
### Documentation
- [ ] In case of new functionality, my PR adds documentation that describes
how to use it.
- When adding new operators/hooks/sensors, the autoclass documentation
generation needs to be added.
- All the public functions and the classes in the PR contain docstrings
that explain what it does
### Code Quality
- [ ] Passes `flake8`
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> DockerOperator tasks in Airflow celery worker are stuck in "Running" state
> ---------------------------------------------------------------------------
>
> Key: AIRFLOW-3746
> URL: https://issues.apache.org/jira/browse/AIRFLOW-3746
> Project: Apache Airflow
> Issue Type: Bug
> Components: celery
> Reporter: Ashwini
> Priority: Major
>
> With the following DAG and task and using the celery executor, the task runs
> but never completes.
> *from* *airflow* *import* DAG
> *from* *airflow.operators.bash_operator* *import* BashOperator
> *from* *airflow.operators.docker_operator* *import* DockerOperator
> *from* *datetime* *import* datetime, timedelta
>
>
> default_args = {
> "owner": "airflow",
> "depends_on_past": False,
> "start_date": datetime(2018, 12, 31),
> "email": ["airf...@airflow.com"],
> "email_on_failure": False,
> "email_on_retry": False,
> "retries": 1,
> "retry_delay": timedelta(minutes=5),
> }
>
> dag = DAG("celery_test", default_args=default_args,
> schedule_interval=timedelta(1))
> DockerOperator(task_id ="test_docker", image =
> "gitlab-registry.nordstrom.com/merchprice/airflow:hello_python", retries=0,
> xcom_all=True , dag = dag)
>
> t2.set_upstream(t1)
> ------------------------
> This is verison of airfow and celery and using
> python 3.6.
> apache-airflow 1.10.1
> celery 4.1.1
> docker 3.7.0
>
> ------------------------------
> Here is the logs:
> *** Log file does not exist:
> /home/x9eu/airflow/logs/celery_test/test_docker/2019-01-16T00:00:00+00:00/1.log
> *** Fetching from:
> http://test.com:8793/log/celery_test/test_docker/2019-01-16T00:00:00+00:00/1.log
>
> [2019-01-21 20:49:26,260] \{models.py:1361} INFO - Dependencies all met for
> <TaskInstance: celery_test.test_docker 2019-01-16T00:00:00+00:00 [queued]>
> [2019-01-21 20:49:26,742] \{models.py:1361} INFO - Dependencies all met for
> <TaskInstance: celery_test.test_docker 2019-01-16T00:00:00+00:00 [queued]>
> [2019-01-21 20:49:26,742] \{models.py:1573} INFO -
> --------------------------------------------------------------------------------
> Starting attempt 1 of 1
> --------------------------------------------------------------------------------
>
> [2019-01-21 20:49:26,925] \{models.py:1595} INFO - Executing
> <Task(DockerOperator): test_docker> on 2019-01-16T00:00:00+00:00
> [2019-01-21 20:49:26,925] \{base_task_runner.py:118} INFO - Running: ['bash',
> '-c', 'airflow run celery_test test_docker 2019-01-16T00:00:00+00:00 --pickle
> 20 --job_id 59 --raw --cfg_path /tmp/tmps0u9a_e0']
> [2019-01-21 20:49:27,524] \{base_task_runner.py:101} INFO - Job 59: Subtask
> test_docker [2019-01-21 20:49:27,523] \{settings.py:174} INFO -
> setting.configure_orm(): Using pool settings. pool_size=5, pool_recycle=1800
> [2019-01-21 20:49:28,187] \{base_task_runner.py:101} INFO - Job 59: Subtask
> test_docker [2019-01-21 20:49:28,185] \{__init__.py:51} INFO - Using executor
> CeleryExecutor
> [2019-01-21 20:49:29,544] \{base_task_runner.py:101} INFO - Job 59: Subtask
> test_docker [2019-01-21 20:49:29,542] \{cli.py:470} INFO - Loading pickle id
> 20
> [2019-01-21 20:49:31,140] \{base_task_runner.py:101} INFO - Job 59: Subtask
> test_docker [2019-01-21 20:49:31,137] \{cli.py:484} INFO - Running
> <TaskInstance: celery_test.test_docker 2019-01-16T00:00:00+00:00 [running]>
> on host test.com
> [2019-01-21 20:49:32,603] \{docker_operator.py:182} INFO - Starting docker
> container from image registry/airflow:hello_python
> [2019-01-21 20:49:48,770] \{docker_operator.py:228} INFO - Hello, %d 0
> Hello, %d 1
> Hello, %d 2
> Hello, %d 3
> Hello, %d 4
> Hello, %d 5
> Hello, %d 6
> Hello, %d 7
> Hello, %d 8
> Hello, %d 9
> Hello, %d 10
> Hello, %d 11
> Hello, %d 12
> Hello, %d 13
> Hello, %d 14
>
>
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