Similarly, it's been a while since I touched the backfill code -- my last commit was more than 2 years ago apparently!! -- so it's certainly progressed considerably from my early changes. However, to echo Bolke, the biggest issue with Backfill was that it behaved differently than the scheduler, which was especially problematic because it was actually used by the scheduler to run SubDagOperators. And I agree, the behavior you're seeing probably was a consequence of introducing DagRuns as a unifying concept.
So if an "automaticly clear failed operators" option is desirable (as in the PR), then I would suggest making it an option that is False by default. That way SubDagOperators will continue to operate properly in the scheduler, but users who want to take advantage of that functionality can simply enable it for manual backfills. On Wed, Jun 6, 2018 at 3:33 PM Maxime Beauchemin <maximebeauche...@gmail.com> wrote: > Thanks for the input, this is helpful. > > To add to the list, there's some complexity around concurrency management > and multiple executors: > I just hit this thing where backfill doesn't check DAG-level concurrency, > fires up 32 tasks, and `airlfow run` double-checks DAG-level concurrency > limit and exits. Right after backfill reschedules right away and so on, > burning a bunch of CPU doing nothing. In this specific case it seems like > `airflow run` should skip that specific check when in the context of a > backfill. > > Max > > On Tue, Jun 5, 2018 at 9:23 PM Bolke de Bruin <bdbr...@gmail.com> wrote: > > > Thinking out loud here, because it is a while back that I did work on > > backfills. There were some real issues with backfills: > > > > 1. Tasks were running in non deterministic order ending up in regular > > deadlocks > > 2. Didn’t create dag runs, making behavior inconsistent. Max dag runs > > could not be enforced. Ui could really display it, lots of minor other > > issues because of it. > > 3. Behavior was different from the scheduler, while subdagoperators > > particularly make use of backfills at the moment. > > > > I think with 3 the behavior you are observing crept in. And given 3 I > > would argue a consistent behavior between the scheduler and the backfill > > mechanism is still paramount. Thus we should explicitly clear tasks from > > failed if we want to rerun them. This at least until we move the > > subdagoperator out of backfill and into the scheduler (which is actually > > not too hard). Also we need those command line options anyway. > > > > Bolke > > > > Verstuurd vanaf mijn iPad > > > > > Op 6 jun. 2018 om 01:27 heeft Scott Halgrim <scott.halg...@zapier.com > .INVALID> > > het volgende geschreven: > > > > > > The request was for opposition, but I’d like to weigh in on the side of > > “it’s a better behavior [to have failed tasks re-run when cleared in a > > backfill" > > >> On Jun 5, 2018, 4:16 PM -0700, Maxime Beauchemin < > > maximebeauche...@gmail.com>, wrote: > > >> @Jeremiah Lowin <jlo...@gmail.com> & @Bolke de Bruin < > bdbr...@gmail.com> > > I > > >> think you may have some context on why this may have changed at some > > point. > > >> I'm assuming that when DagRun handling was added to the backfill > logic, > > the > > >> behavior just happened to change to what it is now. > > >> > > >> Any opposition in moving back towards re-running failed tasks when > > starting > > >> a backfill? I think it's a better behavior, though it's a change in > > >> behavior that we should mention in UPDATE.md. > > >> > > >> One of our goals is to make sure that a failed or killed backfill can > be > > >> restarted and just seamlessly pick up where it left off. > > >> > > >> Max > > >> > > >>> On Tue, Jun 5, 2018 at 3:25 PM Tao Feng <fengta...@gmail.com> wrote: > > >>> > > >>> After discussing with Max, we think it would be great if `airflow > > backfill` > > >>> could be able to auto pick up and rerun those failed tasks. > Currently, > > it > > >>> will throw exceptions( > > >>> > > >>> > > > https://github.com/apache/incubator-airflow/blob/master/airflow/jobs.py#L2489 > > >>> ) > > >>> without rerunning the failed tasks. > > >>> > > >>> But since it broke some of the previous assumptions for backfill, we > > would > > >>> like to get some feedback and see if anyone has any concerns(pr could > > be > > >>> found at https://github.com/apache/incubator-airflow/pull/3464/files > ). > > >>> > > >>> Thanks, > > >>> -Tao > > >>> > > >>> On Thu, May 24, 2018 at 10:26 AM, Maxime Beauchemin < > > >>> maximebeauche...@gmail.com> wrote: > > >>> > > >>>> So I'm running a backfill for what feels like the first time in > years > > >>> using > > >>>> a simple `airflow backfill --local` commands. > > >>>> > > >>>> First I start getting a ton of `logging.info` of each tasks that > > cannot > > >>> be > > >>>> started just yet at every tick flooding my terminal with the keyword > > >>>> `FAILED` in it, looking like a million of lines like this one: > > >>>> > > >>>> [2018-05-24 14:33:07,852] {models.py:1123} INFO - Dependencies not > met > > >>> for > > >>>> <TaskInstance: some_dag.some_task_id 2018-01-28 00:00:00 > [scheduled]>, > > >>>> dependency 'Trigger Rule' FAILED: Task's trigger rule 'all_success' > re > > >>>> quires all upstream tasks to have succeeded, but found 1 > > non-success(es). > > >>>> upstream_tasks_state={'successes': 0L, 'failed': 0L, > > 'upstream_failed': > > >>>> 0L, > > >>>> 'skipped': 0L, 'done': 0L}, upstream_task_ids=['some_other_task_id'] > > >>>> > > >>>> Good thing I triggered 1 month and not 2 years like I actually need, > > just > > >>>> the logs here would be "big data". Now I'm unclear whether there's > > >>> anything > > >>>> actually running or if I did something wrong, so I decide to kill > the > > >>>> process so I can set a smaller date range and get a better picture > of > > >>>> what's up. > > >>>> > > >>>> I check my logging level, am I in DEBUG? Nope. Just INFO. So I take > a > > >>> note > > >>>> that I'll need to find that log-flooding line and demote it to DEBUG > > in a > > >>>> quick PR, no biggy. > > >>>> > > >>>> Now I restart with just a single schedule, and get an error `Dag > > >>> {some_dag} > > >>>> has reached maximum amount of 3 dag runs`. Hmmm, I wish backfill > could > > >>> just > > >>>> pickup where it left off. Maybe I need to run an `airflow clear` > > command > > >>>> and restart? Ok, ran my clear command, same error is showing up. > Dead > > >>> end. > > >>>> > > >>>> Maybe there is some new `airflow clear --reset-dagruns` option? > > Doesn't > > >>>> look like it... Maybe `airflow backfill` has some new switches to > > pick up > > >>>> where it left off? Can't find it. Am I supposed to clear the DAG > Runs > > >>>> manually in the UI? This is a pre-production, in-development DAG, so > > >>> it's > > >>>> not on the production web server. Am I supposed to fire up my own > web > > >>>> server to go and manually handle the backfill-related DAG Runs? > > Cannot to > > >>>> my staging MySQL and do manually clear some DAG runs? > > >>>> > > >>>> So. Fire up a web server, navigate to my dag_id, delete the DAG > runs, > > it > > >>>> appears I can finally start over. > > >>>> > > >>>> Next thought was: "Alright looks like I need to go Linus on the > > mailing > > >>>> list". > > >>>> > > >>>> What am I missing? I'm really hoping these issues specific to 1.8.2! > > >>>> > > >>>> Backfilling is core to Airflow and should work very well. I want to > > >>> restate > > >>>> some reqs for Airflow backfill: > > >>>> * when failing / interrupted, it should seamlessly be able to pickup > > >>> where > > >>>> it left off > > >>>> * terminal logging at the INFO level should be a clear, human > > consumable, > > >>>> indicator of progress > > >>>> * backfill-related operations (including restarts) should be doable > > >>> through > > >>>> CLI interactions, and not require web server interactions as the > > typical > > >>>> sandbox (dev environment) shouldn't assume the existence of a web > > server > > >>>> > > >>>> Let's fix this. > > >>>> > > >>>> Max > > >>>> > > >>> > > >
