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From "Pramiti (JIRA)" <>
Subject [jira] [Commented] (AIRFLOW-2229) Scheduler cannot retry abrupt task failures within factory-generated DAGs
Date Thu, 13 Sep 2018 04:05:00 GMT


Pramiti commented on AIRFLOW-2229:

We are also facing the same issue. If we change the worker queue, the same dag starts to run.
What is wrong with worker ?

> Scheduler cannot retry abrupt task failures within factory-generated DAGs
> -------------------------------------------------------------------------
>                 Key: AIRFLOW-2229
>                 URL:
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: scheduler
>    Affects Versions: 1.9.0
>            Reporter: James Meickle
>            Priority: Major
> We had an issue where one of our tasks failed without the worker updating state (unclear
why, but let's assume it was an OOM), resulting in this series of error messages:
> {{Mar 20 14:27:05 airflow_scheduler-stdout.log:
[2018-03-20 14:27:04,993] \{{ ERROR - Executor reports task instance %s finished
(%s) although the task says its %s. Was the task killed externally?}}}}
> {{ Mar 20 14:27:05 airflow_scheduler-stdout.log:
> {{ Mar 20 14:27:05 airflow_scheduler-stdout.log:
[2018-03-20 14:27:04,994] {{ ERROR - Cannot load the dag bag to handle failure
for <TaskInstance: nightly_dataload.dummy_operator 2018-03-19 00:00:00 [queued]>. Setting
task to FAILED without callbacks or retries. Do you have enough resources?}}}}
> Mysterious failures are not unexpected, because we are in the cloud, after all. The concern
is the last line: ignoring callbacks and retries, implying that it's a lack of resources.
However, the machine was totally underutilized at the time.
> I dug into this code a bit more and as far as I can tell this error is happening in this
code path: []
> {{self.log.error(msg)}}
>  {{try:}}
>  {{    simple_dag = simple_dag_bag.get_dag(dag_id)}}
>  {{    dagbag = models.DagBag(simple_dag.full_filepath)}}
>  {{    dag = dagbag.get_dag(dag_id)}}
>  {{    ti.task = dag.get_task(task_id)}}
>  {{    ti.handle_failure(msg)}}
>  {{except Exception:}}
>  {{    self.log.error("Cannot load the dag bag to handle failure for %s"}}
>  {{    ". Setting task to FAILED without callbacks or "}}
>  {{    "retries. Do you have enough resources?", ti)}}
>  {{    ti.state = State.FAILED}}
>  {{    session.merge(ti)}}
>  {{    session.commit()}}{{}}
> I am not very familiar with this code, nor do I have time to attach a debugger at the
moment, but I think what is happening here is:
>  * I have a factory Python file, which imports and instantiates DAG code from other files.
>  * The scheduler loads the DAGs from the factory file on the filesystem. It gets a fileloc
(as represented in the DB) not of the factory file, but of the file it loaded code from.
>  * The scheduler makes a simple DAGBag from the instantiated DAGs.
>  * This line of code uses the simple DAG, which references the original DAG object's
fileloc, to create a new DAGBag object.
>  * This DAGBag looks for the original DAG in the fileloc, which is the file containing
that DAG's _code_, but is not actually importable by Airflow.
>  * An exception is raised trying to load the DAG from the DAGBag, which found nothing.
>  * Handling of the task failure never occurs.
>  * The over-broad Exception code swallows all of the above occurring.
>  * There's just a generic error message that is not helpful to a system operator.
> If this is the case, at minimum, the try/except should be rewritten to be more graceful
and to have a better error message. But I question whether this level of DAGBag abstraction/indirection
isn't making this failure case worse than it needs to be; under normal conditions the scheduler
is definitely able to find the relevant factory-generated DAGs and execute tasks within them
as expected, even with the fileloc set to the code path and not the import path.

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