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From "George Roldugin (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (AIRFLOW-2145) Deadlock after clearing a running task
Date Fri, 23 Feb 2018 21:31:00 GMT

     [ https://issues.apache.org/jira/browse/AIRFLOW-2145?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

George Roldugin updated AIRFLOW-2145:
-------------------------------------
    Priority: Minor  (was: Major)

> Deadlock after clearing a running task
> --------------------------------------
>
>                 Key: AIRFLOW-2145
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-2145
>             Project: Apache Airflow
>          Issue Type: Bug
>    Affects Versions: 1.9.0
>            Reporter: George Roldugin
>            Priority: Minor
>         Attachments: image-2018-02-23-18-59-11-828.png, image-2018-02-23-19-00-37-741.png,
image-2018-02-23-19-00-55-630.png, image-2018-02-23-19-01-45-012.png, image-2018-02-23-19-01-57-498.png,
image-2018-02-23-19-02-18-837.png
>
>
> TL;DR The essense of the issue is that whenever a currently running ask is cleared, the
dagrun enters a deadlocked state and fails.
>  
> We see this in production with Celery executors and {{TimeDeltaSensor}}, and I've been
able to reproduce it locally with both {{TimeDeltaSensor}} and {{WebHDFSSensor}}.
> Here's the minimal example:
> {code:java}
> from datetime import datetime, timedelta
> import airflow
> from airflow.operators.sensors import TimeDeltaSensor
> from airflow.operators.dummy_operator import DummyOperator
> with airflow.DAG(
>     'foo',
>     schedule_interval='@daily',
>     start_date=datetime(2018, 1, 1)) as dag:
>     wait_for_upstream_sla = TimeDeltaSensor(
>         task_id="wait_for_upstream_sla",
>         delta=timedelta(days=365*10)
>     )
>     do_work = DummyOperator(task_id='do_work')
>     dag >> wait_for_upstream_sla >> do_work
> {code}
>  
> Sequence of actions, relevant DEBUG level logs, and some UI screenshots
> {code:java}
> airflow clear foo -e 2018-02-22 --no_confirm && airflow backfill foo -s 2018-02-22
-e 2018-02-22{code}
> {code:java}
> [2018-02-23 17:17:45,983] {__init__.py:45} INFO - Using executor SequentialExecutor
> [2018-02-23 17:17:46,069] {models.py:189} INFO - Filling up the DagBag from /Users/grol/Drive/dev/reporting/dags
> ...
> [2018-02-23 17:17:47,563] {jobs.py:2180} DEBUG - Task instance to run <TaskInstance:
foo.wait_for_upstream_sla 2018-02-22 00:00:00 [scheduled]> state scheduled
> ...
> {code}
> !image-2018-02-23-18-59-11-828.png|width=418,height=87!
> Now we clear all DAG's tasks externally:
> {code:java}
> airflow clear foo -e 2018-02-22 --no_confirm
> {code}
> This causes the following:
> {code:java}
> [2018-02-23 17:17:55,258] {base_task_runner.py:98} INFO - Subtask: [2018-02-23 17:17:55,258]
{sensors.py:629} INFO - Checking if the time (2018-02-23 16:19:00) has come
> [2018-02-23 17:17:58,844] {jobs.py:184} DEBUG - [heart] Boom.
> [2018-02-23 17:18:03,848] {jobs.py:184} DEBUG - [heart] Boom.
> [2018-02-23 17:18:08,856] {jobs.py:2585} WARNING - State of this instance has been externally
set to shutdown. Taking the poison pill.
> [2018-02-23 17:18:08,874] {helpers.py:266} DEBUG - There are no descendant processes
to kill
> [2018-02-23 17:18:08,875] {jobs.py:184} DEBUG - [heart] Boom.
> [2018-02-23 17:18:08,900] {helpers.py:266} DEBUG - There are no descendant processes
to kill
> [2018-02-23 17:18:08,922] {helpers.py:266} DEBUG - There are no descendant processes
to kill
> [2018-02-23 17:18:09,005] {sequential_executor.py:47} ERROR - Failed to execute task
Command 'airflow run foo wait_for_upstream_sla 2018-02-22T00:00:00 --local -sd DAGS_FOLDER/foo.py'
returned non-zero exit status 1.
> [2018-02-23 17:18:09,012] {jobs.py:2004} DEBUG - Executor state: failed task <TaskInstance:
foo.wait_for_upstream_sla 2018-02-22 00:00:00 [shutdown]>
> [2018-02-23 17:18:09,018] {models.py:4584} INFO - Updating state for <DagRun foo @
2018-02-22 00:00:00: backfill_2018-02-22T00:00:00, externally triggered: False> considering
2 task(s)
> [2018-02-23 17:18:09,021] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [None]> dependency 'Previous Dagrun State' PASSED: True, The task did not have
depends_on_past set.
> [2018-02-23 17:18:09,021] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [None]> dependency 'Not In Retry Period' PASSED: True, The context specified that
being in a retry period was permitted.
> [2018-02-23 17:18:09,027] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [None]> dependency 'Trigger Rule' PASSED: False, Task's trigger rule 'all_success'
requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_tasks_state={'skipped':
0, 'successes': 0, 'failed': 0, 'upstream_failed': 0, 'done': 0, 'total': 1}, upstream_task_ids=['wait_for_upstream_sla']
> [2018-02-23 17:18:09,029] {models.py:4643} INFO - Deadlock; marking run <DagRun foo
@ 2018-02-22 00:00:00: backfill_2018-02-22T00:00:00, externally triggered: False> failed
> [2018-02-23 17:18:09,045] {jobs.py:2125} INFO - [backfill progress] | finished run 1
of 1 | tasks waiting: 1 | succeeded: 0 | kicked_off: 1 | failed: 0 | skipped: 0 | deadlocked:
0 | not ready: 1
> [2018-02-23 17:18:09,045] {jobs.py:2129} DEBUG - Finished dag run loop iteration. Remaining
tasks [<TaskInstance: foo.do_work 2018-02-22 00:00:00 [scheduled]>]
> [2018-02-23 17:18:09,045] {jobs.py:2160} DEBUG - *** Clearing out not_ready list ***
> [2018-02-23 17:18:09,048] {jobs.py:2180} DEBUG - Task instance to run <TaskInstance:
foo.do_work 2018-02-22 00:00:00 [None]> state None
> [2018-02-23 17:18:09,049] {jobs.py:2186} WARNING - FIXME: task instance {} state was
set to None externally. This should not happen
> [2018-02-23 17:18:09,053] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [scheduled]> dependency 'Task Instance State' PASSED: True, Task state scheduled
was valid.
> [2018-02-23 17:18:09,053] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [scheduled]> dependency 'Previous Dagrun State' PASSED: True, The task did not
have depends_on_past set.
> [2018-02-23 17:18:09,056] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [scheduled]> dependency 'Task Concurrency' PASSED: True, Task concurrency is not
set.
> [2018-02-23 17:18:09,056] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [scheduled]> dependency 'Not In Retry Period' PASSED: True, The task instance
was not marked for retrying.
> [2018-02-23 17:18:09,061] {models.py:1215} DEBUG - <TaskInstance: foo.do_work 2018-02-22
00:00:00 [scheduled]> dependency 'Trigger Rule' PASSED: False, Task's trigger rule 'all_success'
requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_tasks_state={'skipped':
0, 'successes': 0, 'failed': 0, 'upstream_failed': 0, 'done': 0, 'total': 1}, upstream_task_ids=['wait_for_upstream_sla']
> [2018-02-23 17:18:09,061] {models.py:1190} INFO - Dependencies not met for <TaskInstance:
foo.do_work 2018-02-22 00:00:00 [scheduled]>, dependency 'Trigger Rule' FAILED: Task's
trigger rule 'all_success' requires all upstream tasks to have succeeded, but found 1 non-success(es).
upstream_tasks_state={'skipped': 0, 'successes': 0, 'failed': 0, 'upstream_failed': 0, 'done':
0, 'total': 1}, upstream_task_ids=['wait_for_upstream_sla']
> [2018-02-23 17:18:09,061] {jobs.py:2274} DEBUG - Adding <TaskInstance: foo.do_work
2018-02-22 00:00:00 [scheduled]> to not_ready
> [2018-02-23 17:18:09,067] {jobs.py:184} DEBUG - [heart] Boom.
> {code}
> !image-2018-02-23-19-00-37-741.png|width=375,height=78!
> !image-2018-02-23-19-01-57-498.png|width=374,height=77!
> Interestingly, once the success condition of the {{TimeDeltaSensor}} is met, in production
we see the following final state in the UI: DAG failed, while the {{TimeDeltaSensor}} task
succeeded, though there's no evidence of success in the celery executors logs.
>   !image-2018-02-23-19-02-18-837.png|width=563,height=87!



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