happy to report that the “fix” worked. thanks Alex. btw, wondering why was it there in the first place? how does it help — saves time, early termination — what?
> On Nov 23, 2018, at 8:18 AM, Alex Guziel <alex.guz...@airbnb.com> wrote: > > Yup. > > On Thu, Nov 22, 2018 at 3:16 PM soma dhavala <soma.dhav...@gmail.com > <mailto:soma.dhav...@gmail.com>> wrote: > > >> On Nov 23, 2018, at 3:28 AM, Alex Guziel <alex.guz...@airbnb.com >> <mailto:alex.guz...@airbnb.com>> wrote: >> >> It’s because of this >> >> “When searching for DAGs, Airflow will only consider files where the string >> “airflow” and “DAG” both appear in the contents of the .py file.” >> > > Have not noticed it. From airflow/models.py, in process_file — (both in 1.9 > and 1.10) > .. > if not all([s in content for s in (b'DAG', b'airflow')]): > .. > is looking for those strings and if they are not found, it is returning > without loading the DAGs. > > > So having “airflow” and “DAG” dummy strings placed somewhere will make it > work? > > >> On Thu, Nov 22, 2018 at 2:27 AM soma dhavala <soma.dhav...@gmail.com >> <mailto:soma.dhav...@gmail.com>> wrote: >> >> >>> On Nov 22, 2018, at 3:37 PM, Alex Guziel <alex.guz...@airbnb.com >>> <mailto:alex.guz...@airbnb.com>> wrote: >>> >>> I think this is what is going on. The dags are picked by local variables. >>> I.E. if you do >>> dag = Dag(...) >>> dag = Dag(…) >> >> from my_module import create_dag >> >> for file in yaml_files: >> dag = create_dag(file) >> globals()[dag.dag_id] = dag >> >> You notice that create_dag is in a different module. If it is in the same >> scope (file), it will be fine. >> >>> >> >>> Only the second dag will be picked up. >>> >>> On Thu, Nov 22, 2018 at 2:04 AM Soma S Dhavala <soma.dhav...@gmail.com >>> <mailto:soma.dhav...@gmail.com>> wrote: >>> Hey AirFlow Devs: >>> In our organization, we build a Machine Learning WorkBench with AirFlow as >>> an orchestrator of the ML Work Flows, and have wrapped AirFlow python >>> operators to customize the behaviour. These work flows are specified in >>> YAML. >>> >>> We drop a DAG loader (written python) in the default location airflow >>> expects the DAG files. This DAG loader reads the specified YAML files and >>> converts them into airflow DAG objects. Essentially, we are >>> programmatically creating the DAG objects. In order to support muliple >>> parsers (yaml, json etc), we separated the DAG creation from loading. But >>> when a DAG is created (in a separate module) and made available to the DAG >>> loaders, airflow does not pick it up. As an example, consider that I >>> created a DAG picked it, and will simply unpickle the DAG and give it to >>> airflow. >>> >>> However, in current avatar of airfow, the very creation of DAG has to >>> happen in the loader itself. As far I am concerned, airflow should not care >>> where and how the DAG object is created, so long as it is a valid DAG >>> object. The workaround for us is to mix parser and loader in the same file >>> and drop it in the airflow default dags folder. During dag_bag creation, >>> this file is loaded up with import_modules utility and shows up in the UI. >>> While this is a solution, but it is not clean. >>> >>> What do DEVs think about a solution to this problem? Will saving the DAG to >>> the db and reading it from the db work? Or some core changes need to happen >>> in the dag_bag creation. Can dag_bag take a bunch of "created" DAGs. >>> >>> thanks, >>> -soma >> >
