Datasets:

Partha117

/

oss_bugs

like 0

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/sql_to_s3.py", "tests/providers/amazon/aws/transfers/test_sql_to_s3.py"]

Changes to SqlToS3Operator Breaking CSV formats

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers `apache-airflow-providers-amazon==5.1.0` ### Apache Airflow version 2.3.4 ### Operating System Linux ### Deployment Astronomer ### Deployment details _No response_ ### What happened Once https://github.com/apache/airflow/pull/25083 was merged, when using CSV as the output format on the `SqlToS3Operator`, null strings started appearing as `"None"` in the actual CSV export. This will cause unintended behavior in most use cases for reading the CSV including uploading to databases. Certain databases such as Snowflake allow for things like `NULL_IF` on import however there are times where you would want the actual string "None" to be in the field and there would be no way at that point to distinguish. Before:  After:  ### What you think should happen instead The strings should be empty as they did previously. I understand the implementation of the recent PR for parquet and propose that we add an additional condition to line 138 of the `sql_to_s3.py` file restricting that to only if the chosen output is parquet. ### How to reproduce Run the `SqlToS3Operator` with the default output format of `CSV` on any query that selects a column of type string that allows null. Look the outputted CSV in S3. ### Anything else Every time we select a nullable column with the `SqlToS3Operator` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26567

https://github.com/apache/airflow/pull/26676

fa0cb363b860b553af2ef9530ea2de706bd16e5d

9c59312fbcf113d56ee0a61e018dfd7cef725af7

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/concepts/tasks.rst"]

Have SLA docs reflect reality

### What do you see as an issue? The [SLA documentation](https://airflow.apache.org/docs/apache-airflow/stable/concepts/tasks.html#slas) currently states the following: > An SLA, or a Service Level Agreement, is an expectation for the maximum time a Task should take. If a task takes longer than this to run... However this is not how SLAs currently work in Airflow, the SLA time is calculated from the start of the DAG not from the start of the task. For example if you have a DAG like this the SLA will always trigger after the DAG has started for 5 minutes even though the task never takes 5 minutes to run: ```python import datetime from airflow import DAG from airflow.sensors.time_sensor import TimeSensor from airflow.operators.python import PythonOperator with DAG(dag_id="my_dag", schedule_interval="0 0 * * *") as dag: wait_time_mins = TimeSensor(target_time=datetime.time(minute=10)) run_fast = PythonOperator( python_callable=lambda *a, **kw: True, sla=datetime.timedelta(minutes=5), ) run_fast.set_upstream(wait_time_mins) ``` ### Solving the problem Update the docs to explain how SLAs work in reality. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26566

https://github.com/apache/airflow/pull/27111

671029bebc33a52d96f9513ae997e398bd0945c1

639210a7e0bfc3f04f28c7d7278292d2cae7234b

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/core-concepts/executor/local.rst"]

Documentation unclear about multiple LocalExecutors on HA Scheduler deployment

### What do you see as an issue? According to Airflow documentation, it's now possible to run multiple Airflow Schedulers starting with Airflow 2.x. What's not clear from the documentation is what happens if each of the machines running the Scheduler has executor = LocalExecutor in the [core] section of airflow.cfg. In this context, if I have Airflow Scheduler running on 3 machines, does this mean that there will also be 3 LocalExecutors processing tasks in a distributed fashion? ### Solving the problem Enhancing documentation to clarify the details about multiple LocalExecutors on HA Scheduler deployment ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26565

https://github.com/apache/airflow/pull/32310

61f33304d587b3b0a48a876d3bfedab82e42bacc

e53320d62030a53c6ffe896434bcf0fc85803f31

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "tests/cli/commands/test_task_command.py"]

"airflow tasks render/state" cli commands do not work for mapped task instances

### Apache Airflow version Other Airflow 2 version ### What happened Running following cli command: ``` airflow tasks render test-dynamic-mapping consumer scheduled__2022-09-18T15:14:15.107780+00:00 --map-index ``` fails with exception: ``` Traceback (most recent call last): File "/opt/python3.8/bin/airflow", line 8, in <module> sys.exit(main()) File "/opt/python3.8/lib/python3.8/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/opt/python3.8/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/cli.py", line 101, in wrapper return f(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/cli.py", line 337, in _wrapper f(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 576, in task_render for attr in task.__class__.template_fields: TypeError: 'member_descriptor' object is not iterable ``` Running following cli command: ``` airflow tasks state test-dynamic-mapping consumer scheduled__2022-09-18T15:14:15.107780+00:00 --map-index ``` fails with exception: ``` Traceback (most recent call last): File "/opt/python3.8/bin/airflow", line 8, in <module> sys.exit(main()) File "/opt/python3.8/lib/python3.8/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/opt/python3.8/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/cli.py", line 101, in wrapper return f(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/cli.py", line 337, in _wrapper f(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 422, in task_state print(ti.current_state()) File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 849, in current_state session.query(TaskInstance.state) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2879, in scalar ret = self.one() File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2856, in one return self._iter().one() File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/engine/result.py", line 1190, in one return self._only_one_row( File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/engine/result.py", line 613, in _only_one_row raise exc.MultipleResultsFound( sqlalchemy.exc.MultipleResultsFound: Multiple rows were found when exactly one was required ``` ### What you think should happen instead Command successfully executed ### How to reproduce _No response_ ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26555

https://github.com/apache/airflow/pull/28698

a7e1cb2fbfc684508f4b832527ae2371f99ad37d

1da17be37627385fed7fc06584d72e0abda6a1b5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/renderedtifields.py", "airflow/utils/sqlalchemy.py"]

Resolve warning about renderedtifields query

### Body This warning is emitted when running a task instance, at least on mysql: ``` [2022-09-21, 05:22:56 UTC] {logging_mixin.py:117} WARNING - /home/airflow/.local/lib/python3.8/site-packages/airflow/models/renderedtifields.py:258 SAWarning: Coercing Subquery object into a select() for use in IN(); please pass a select() construct explicitly ``` Need to resolve. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/26548

https://github.com/apache/airflow/pull/26667

22d52c00f6397fde8d97cf2479c0614671f5b5ba

0e79dd0b1722a610c898da0ba8557b8a94da568c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db.py"]

Choose setting for sqlalchemy SQLALCHEMY_TRACK_MODIFICATIONS

### Body We need to determine what to do about this warning: ``` /Users/dstandish/.virtualenvs/2.4.0/lib/python3.8/site-packages/flask_sqlalchemy/__init__.py:872 FSADeprecationWarning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be disabled by default in the future. Set it to True or False to suppress this warning. ``` Should we set to true or false? @ashb @potiuk @jedcunningham @uranusjr ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/26544

https://github.com/apache/airflow/pull/26617

3396d1f822caac7cbeb14e1e67679b8378a84a6c

051ba159e54b992ca0111107df86b8abfd8b7279

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/serialization/serialized_objects.py", "docs/apache-airflow/best-practices.rst", "docs/apache-airflow/concepts/timetable.rst", "tests/serialization/test_dag_serialization.py"]

Variable.get inside of a custom Timetable breaks the Scheduler

### Apache Airflow version 2.3.4 ### What happened If you try to use `Variable.get` from inside of a custom Timetable, the Scheduler will break with errors like: ``` scheduler | [2022-09-20 10:19:36,104] {variable.py:269} ERROR - Unable to retrieve variable from secrets backend (MetastoreBackend). Checking subsequent secrets backend. scheduler | Traceback (most recent call last): scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/variable.py", line 265, in get_variable_from_secrets scheduler | var_val = secrets_backend.get_variable(key=key) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper scheduler | return func(*args, session=session, **kwargs) scheduler | File "/opt/conda/envs/production/lib/python3.9/contextlib.py", line 126, in __exit__ scheduler | next(self.gen) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 33, in create_session scheduler | session.commit() scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 1435, in commit scheduler | self._transaction.commit(_to_root=self.future) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 829, in commit scheduler | self._prepare_impl() scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 797, in _prepare_impl scheduler | self.session.dispatch.before_commit(self.session) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/sqlalchemy/event/attr.py", line 343, in __call__ scheduler | fn(*args, **kw) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/sqlalchemy.py", line 341, in _validate_commit scheduler | raise RuntimeError("UNEXPECTED COMMIT - THIS WILL BREAK HA LOCKS!") scheduler | RuntimeError: UNEXPECTED COMMIT - THIS WILL BREAK HA LOCKS! scheduler | [2022-09-20 10:19:36,105] {plugins_manager.py:264} ERROR - Failed to import plugin /home/tsanders/airflow_standalone_sqlite/plugins/custom_timetable.py scheduler | Traceback (most recent call last): scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/plugins_manager.py", line 256, in load_plugins_from_plugin_directory scheduler | loader.exec_module(mod) scheduler | File "<frozen importlib._bootstrap_external>", line 850, in exec_module scheduler | File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed scheduler | File "/home/tsanders/airflow_standalone_sqlite/plugins/custom_timetable.py", line 9, in <module> scheduler | class CustomTimetable(CronDataIntervalTimetable): scheduler | File "/home/tsanders/airflow_standalone_sqlite/plugins/custom_timetable.py", line 10, in CustomTimetable scheduler | def __init__(self, *args, something=Variable.get('something'), **kwargs): scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/variable.py", line 138, in get scheduler | raise KeyError(f'Variable {key} does not exist') scheduler | KeyError: 'Variable something does not exist' scheduler | [2022-09-20 10:19:36,179] {scheduler_job.py:769} ERROR - Exception when executing SchedulerJob._run_scheduler_loop scheduler | Traceback (most recent call last): scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 752, in _execute scheduler | self._run_scheduler_loop() scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 840, in _run_scheduler_loop scheduler | num_queued_tis = self._do_scheduling(session) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 914, in _do_scheduling scheduler | self._start_queued_dagruns(session) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1086, in _start_queued_dagruns scheduler | dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/dagbag.py", line 179, in get_dag scheduler | self._add_dag_from_db(dag_id=dag_id, session=session) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/dagbag.py", line 254, in _add_dag_from_db scheduler | dag = row.dag scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/serialized_dag.py", line 209, in dag scheduler | dag = SerializedDAG.from_dict(self.data) # type: Any scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1099, in from_dict scheduler | return cls.deserialize_dag(serialized_obj['dag']) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1021, in deserialize_dag scheduler | v = _decode_timetable(v) scheduler | File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 189, in _decode_timetable scheduler | raise _TimetableNotRegistered(importable_string) scheduler | airflow.serialization.serialized_objects._TimetableNotRegistered: Timetable class 'custom_timetable.CustomTimetable' is not registered ``` Note that in this case, the Variable in question *does* exist, and the `KeyError` is a red herring. If you add a `default_var`, things seem to work, though I wouldn't trust it since there is clearly some context where it will fail to load the Variable and will always fall back to the default. Additionally, this still raises the `UNEXPECTED COMMIT - THIS WILL BREAK HA LOCKS!` error, which I assume is a bad thing. ### What you think should happen instead I'm not sure whether or not this should be allowed. In my case, I was able to work around the error by making all Timetable initializer args required (no default values) and pulling the `Variable.get` out into a wrapper function. ### How to reproduce `custom_timetable.py` ``` #!/usr/bin/env python3 from __future__ import annotations from airflow.models.variable import Variable from airflow.plugins_manager import AirflowPlugin from airflow.timetables.interval import CronDataIntervalTimetable class CustomTimetable(CronDataIntervalTimetable): def __init__(self, *args, something=Variable.get('something'), **kwargs): self._something = something super().__init__(*args, **kwargs) class CustomTimetablePlugin(AirflowPlugin): name = 'custom_timetable_plugin' timetables = [CustomTimetable] ``` `test_custom_timetable.py` ``` #!/usr/bin/env python3 import datetime import pendulum from airflow.decorators import dag, task from custom_timetable import CustomTimetable @dag( start_date=datetime.datetime(2022, 9, 19), timetable=CustomTimetable(cron='0 0 * * *', timezone=pendulum.UTC), ) def test_custom_timetable(): @task def a_task(): print('hello') a_task() dag = test_custom_timetable() if __name__ == '__main__': dag.cli() ``` ``` airflow variables set something foo airflow dags trigger test_custom_timetable ``` ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers None ### Deployment Other ### Deployment details I was able to reproduce this with: * Standalone mode, SQLite DB, SequentialExecutor * Self-hosted deployment, Postgres DB, CeleryExecutor ### Anything else Related: #21895 ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26529

https://github.com/apache/airflow/pull/26649

26f94c5370587f73ebd935cecf208c6a36bdf9b6

37c0cb6d3240062106388449cf8eed9c948fb539

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/json.py", "airflow/www/app.py", "airflow/www/utils.py", "airflow/www/views.py", "tests/www/test_app.py"]

UI error when clicking on graph view when a task has pod overrides

### Apache Airflow version 2.4.0 ### What happened When I click on the graph view or the gaant view for a DAG that has a task with pod_overrides, I get ``` Something bad has happened. Airflow is used by many users, and it is very likely that others had similar problems and you can easily find a solution to your problem. Consider following these steps: * gather the relevant information (detailed logs with errors, reproduction steps, details of your deployment) * find similar issues using: * [GitHub Discussions](https://github.com/apache/airflow/discussions) * [GitHub Issues](https://github.com/apache/airflow/issues) * [Stack Overflow](https://stackoverflow.com/questions/tagged/airflow) * the usual search engine you use on a daily basis * if you run Airflow on a Managed Service, consider opening an issue using the service support channels * if you tried and have difficulty with diagnosing and fixing the problem yourself, consider creating a [bug report](https://github.com/apache/airflow/issues/new/choose). Make sure however, to include all relevant details and results of your investigation so far. Python version: 3.8.14 Airflow version: 2.4.0 Node: airflow-webserver-6d4d7d5ccd-qc2x5 ------------------------------------------------------------------------------- Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/decorators.py", line 118, in view_func return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/decorators.py", line 81, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 2810, in graph return self.render_template( File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 541, in render_template return super().render_template( File "/home/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/baseviews.py", line 322, in render_template return render_template( File "/home/airflow/.local/lib/python3.8/site-packages/flask/templating.py", line 147, in render_template return _render(app, template, context) File "/home/airflow/.local/lib/python3.8/site-packages/flask/templating.py", line 130, in _render rv = template.render(context) File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/environment.py", line 1301, in render self.environment.handle_exception() File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/environment.py", line 936, in handle_exception raise rewrite_traceback_stack(source=source) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/templates/airflow/graph.html", line 21, in top-level template code {% from 'appbuilder/loading_dots.html' import loading_dots %} File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/templates/airflow/dag.html", line 37, in top-level template code {% set execution_date_arg = request.args.get('execution_date') %} File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/templates/airflow/main.html", line 21, in top-level template code {% from 'airflow/_messages.html' import show_message %} File "/home/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 2, in top-level template code {% import 'appbuilder/baselib.html' as baselib %} File "/home/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/templates/appbuilder/init.html", line 50, in top-level template code {% block tail %} File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/templates/airflow/graph.html", line 137, in block 'tail' let taskInstances = {{ task_instances|tojson }}; File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/filters.py", line 1688, in do_tojson return htmlsafe_json_dumps(value, dumps=dumps, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/utils.py", line 658, in htmlsafe_json_dumps dumps(obj, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/flask/json/provider.py", line 230, in dumps return json.dumps(obj, **kwargs) File "/usr/local/lib/python3.8/json/__init__.py", line 234, in dumps return cls( File "/usr/local/lib/python3.8/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/usr/local/lib/python3.8/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/home/airflow/.local/lib/python3.8/site-packages/flask/json/provider.py", line 122, in _default raise TypeError(f"Object of type {type(o).__name__} is not JSON serializable") TypeError: Object of type V1Pod is not JSON serializable ``` ### What you think should happen instead The UI should render the dag visualization. ### How to reproduce * Add a `pod_override` to a task * Run the task * click on the graph view ### Operating System Debian GNU/Linux 11 (bullseye) docker image ### Versions of Apache Airflow Providers apache-airflow-providers-airbyte==3.1.0 apache-airflow-providers-amazon==5.1.0 apache-airflow-providers-apache-spark==3.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.3.0 apache-airflow-providers-common-sql==1.2.0 apache-airflow-providers-datadog==3.0.0 apache-airflow-providers-docker==3.1.0 apache-airflow-providers-elasticsearch==4.2.0 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-google==8.3.0 apache-airflow-providers-grpc==3.0.0 apache-airflow-providers-hashicorp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-jira==3.0.1 apache-airflow-providers-microsoft-azure==4.2.0 apache-airflow-providers-odbc==3.1.1 apache-airflow-providers-pagerduty==3.0.0 apache-airflow-providers-postgres==5.2.1 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-salesforce==5.1.0 apache-airflow-providers-sendgrid==3.0.0 apache-airflow-providers-sftp==4.0.0 apache-airflow-providers-slack==5.1.0 apache-airflow-providers-sqlite==3.2.1 apache-airflow-providers-ssh==3.1.0 apache-airflow-providers-tableau==3.0.1 ### Deployment Official Apache Airflow Helm Chart ### Deployment details official helm ### Anything else every time after the dag is run ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26527

https://github.com/apache/airflow/pull/26554

e61d823f18238a82570203b62fe986bd0bc91b51

378dfbe2fe266f17859dbabd34b9bc8cd5c904ab

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/xcom_arg.py"]

Dynamic task mapping zip() iterates unexpected number of times

### Apache Airflow version 2.4.0 ### What happened When running `zip()` with different-length lists, I get an unexpected result: ```python from datetime import datetime from airflow import DAG from airflow.decorators import task with DAG( dag_id="demo_dynamic_task_mapping_zip", start_date=datetime(2022, 1, 1), schedule=None, ): @task def push_letters(): return ["a", "b", "c"] @task def push_numbers(): return [1, 2, 3, 4] @task def pull(value): print(value) pull.expand(value=push_letters().zip(push_numbers())) ``` Iterates over `[("a", 1), ("b", 2), ("c", 3), ("a", 1)]`, so it iterates for the length of the longest collection, but restarts iterating elements when reaching the length of the shortest collection. I would expect it to behave like Python's builtin `zip` and iterate for the length of the shortest collection, so 3x in the example above, i.e. `[("a", 1), ("b", 2), ("c", 3)]`. Additionally, I went digging in the source code and found the `fillvalue` argument which works as expected: ```python pull.expand(value=push_letters().zip(push_numbers(), fillvalue="foo")) ``` Iterates over `[("a", 1), ("b", 2), ("c", 3), ("foo", 4)]`. However, with `fillvalue` not set, I would expect it to iterate only for the length of the shortest collection. ### What you think should happen instead I expect `zip()` to iterate over the number of elements of the shortest collection (without `fillvalue` set). ### How to reproduce See above. ### Operating System MacOS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details OSS Airflow ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26499

https://github.com/apache/airflow/pull/26636

df3bfe3219da340c566afc9602278e2751889c70

f219bfbe22e662a8747af19d688bbe843e1a953d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/env.py", "airflow/migrations/versions/0118_2_4_2_add_missing_autoinc_fab.py", "airflow/migrations/versions/0119_2_5_0_add_updated_at_to_dagrun_and_ti.py", "airflow/settings.py", "airflow/utils/db.py", "docs/apache-airflow/img/airflow_erd.sha256", "docs/apache-airflow/migrations-ref.rst"]

Upgrading to airflow 2.4.0 from 2.3.4 causes NotNullViolation error

### Apache Airflow version 2.4.0 ### What happened Stopped existing processes, upgraded from airflow 2.3.4 to 2.4.0, and ran airflow db upgrade successfully. Upon restarting the services, I'm not seeing any dag runs from the past 10 days. I kick off a new job, and I don't see it show up in the grid view. Upon checking the systemd logs, I see that there are a lot of postgress errors with webserver. Below is a sample of such errors. ``` [SQL: INSERT INTO ab_view_menu (name) VALUES (%(name)s) RETURNING ab_view_menu.id] [parameters: {'name': 'Datasets'}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,183] {manager.py:511} ERROR - Creation of Permission View Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, 13, null). [SQL: INSERT INTO ab_permission_view (permission_id, view_menu_id) VALUES (%(permission_id)s, %(view_menu_id)s) RETURNING ab_permission_view.id] [parameters: {'permission_id': 13, 'view_menu_id': None}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,209] {manager.py:420} ERROR - Add View Menu Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, Datasets). [SQL: INSERT INTO ab_view_menu (name) VALUES (%(name)s) RETURNING ab_view_menu.id] [parameters: {'name': 'Datasets'}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,212] {manager.py:511} ERROR - Creation of Permission View Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, 17, null). [SQL: INSERT INTO ab_permission_view (permission_id, view_menu_id) VALUES (%(permission_id)s, %(view_menu_id)s) RETURNING ab_permission_view.id] [parameters: {'permission_id': 17, 'view_menu_id': None}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,229] {manager.py:420} ERROR - Add View Menu Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, DAG Warnings). [SQL: INSERT INTO ab_view_menu (name) VALUES (%(name)s) RETURNING ab_view_menu.id] [parameters: {'name': 'DAG Warnings'}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,232] {manager.py:511} ERROR - Creation of Permission View Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, 17, null). [SQL: INSERT INTO ab_permission_view (permission_id, view_menu_id) VALUES (%(permission_id)s, %(view_menu_id)s) RETURNING ab_permission_view.id] [parameters: {'permission_id': 17, 'view_menu_id': None}] (Background on this error at: https://sqlalche.me/e/14/gkpj) [2022-09-19 14:03:16,250] {manager.py:511} ERROR - Creation of Permission View Error: (psycopg2.errors.NotNullViolation) null value in column "id" violates not-null constraint DETAIL: Failing row contains (null, 13, 23). ``` I tried running airflow db check, init, check-migration, upgrade without any errors, but the errors still remain. Please let me know if I missed any steps during the upgrade, or if this is a known issue with a workaround. ### What you think should happen instead All dag runs should be visible ### How to reproduce upgrade airflow, upgrade db, restart the services ### Operating System Ubuntu 18.04.6 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26497

https://github.com/apache/airflow/pull/26885

2f326a6c03efed8788fe0263df96b68abb801088

7efdeed5eccbf5cb709af40c8c66757e59c957ed

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/log/file_task_handler.py"]

Cannot fetch log from Celery worker

### Discussed in https://github.com/apache/airflow/discussions/26490 <div type='discussions-op-text'> ^{Originally posted by **emredjan** September 19, 2022} ### Apache Airflow version 2.4.0 ### What happened When running tasks on a remote celery worker, webserver fails to fetch logs from the machine, giving a '403 - Forbidden' error on version 2.4.0. This behavior does not happen on 2.3.3, where the remote logs are retrieved and displayed successfully. The `webserver / secret_key` configuration is the same in all nodes (the config files are synced), and their time is synchronized using a central NTP server, making the solution in the warning message not applicable. My limited analysis pointed to the `serve_logs.py` file, and the flask request object that's passed to it, but couldn't find the root cause. ### What you think should happen instead It should fetch and show remote celery worker logs on the webserver UI correctly, as it did in previous versions. ### How to reproduce Use airflow version 2.4.0 Use CeleryExecutor with RabbitMQ Use a separate Celery worker machine Run a dag/task on the remote worker Try to display task log on the web UI ### Operating System Red Hat Enterprise Linux 8.6 (Ootpa) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-celery==3.0.0 apache-airflow-providers-common-sql==1.1.0 apache-airflow-providers-ftp==3.0.0 apache-airflow-providers-hashicorp==3.0.0 apache-airflow-providers-http==3.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-microsoft-mssql==3.0.0 apache-airflow-providers-mysql==3.0.0 apache-airflow-providers-odbc==3.0.0 apache-airflow-providers-sftp==3.0.0 apache-airflow-providers-sqlite==3.0.0 apache-airflow-providers-ssh==3.0.0 ``` ### Deployment Virtualenv installation ### Deployment details Using CeleryExecutor / rabbitmq with 2 servers ### Anything else All remote task executions has the same problem. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md) </div>

https://github.com/apache/airflow/issues/26492

https://github.com/apache/airflow/pull/26493

b9c4e98d8f8bcc129cbb4079548bd521cd3981b9

52560b87c991c9739791ca8419219b0d86debacd

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg"]

get_dags does not fetch more than 100 dags.

Hi, The function does not return more than 100 dags even setting the limit to more than 100. So `get_dags(limit=500)` will only return max of 100 dags. I have to do the hack to mitigate this problem. ``` def _get_dags(self, max_dags: int = 500): i = 0 responses = [] while i <= max_dags: response = self._api.get_dags(offset=i) responses += response['dags'] i = i + 100 return [dag['dag_id'] for dag in responses] ``` Versions I am using are: ``` apache-airflow==2.3.2 apache-airflow-client==2.3.0 ``` and ``` apache-airflow==2.2.2 apache-airflow-client==2.1.0 ``` Best, Hamid

https://github.com/apache/airflow/issues/27425

https://github.com/apache/airflow/pull/29773

a0e13370053452e992d45e7956ff33290563b3a0

228d79c1b3e11ecfbff5a27c900f9d49a84ad365

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/main.js", "airflow/www/utils.py"]

Can not get task which status is null

### Apache Airflow version Other Airflow 2 version ### What happened with List Task Instance airflow webUI,when we search the task which state is null,the result is:no records found. ### What you think should happen instead should list the task which status is null ### How to reproduce use airflow webui List Task Instance add filter state equal to null ### Operating System oracle linux ### Versions of Apache Airflow Providers 2.2.3 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26427

https://github.com/apache/airflow/pull/26584

64622929a043436b235b9fb61fb076c5d2e02124

8e2e80a0ce0e1819874e183fb1662e879cdd8a08

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/extensions/init_views.py", "tests/api_connexion/endpoints/test_task_instance_endpoint.py"]

`POST /taskInstances/list` with wildcards returns unhelpful error

### Apache Airflow version 2.3.4 ### What happened https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/get_task_instances_batch fails with an error with wildcards while https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/get_task_instances succeeds with wildcards Error: ``` 400 "None is not of type 'object'" ``` ### What you think should happen instead _No response_ ### How to reproduce 1) `astro dev init` 2) `astro dev start` 3) `test1.py` and `python test1.py` ``` import requests host = "http://localhost:8080/api/v1" kwargs = { 'auth': ('admin', 'admin'), 'headers': {'content-type': 'application/json'} } r = requests.post(f'{host}/dags/~/dagRuns/~/taskInstances/list', **kwargs, timeout=10) print(r.url, r.text) ``` output ``` http://localhost:8080/api/v1/dags/~/dagRuns/~/taskInstances/list { "detail": "None is not of type 'object'", "status": 400, "title": "Bad Request", "type": "http://apache-airflow-docs.s3-website.eu-central-1.amazonaws.com/docs/apache-airflow/latest/stable-rest-api-ref.html#section/Errors/BadRequest" } ``` 3) `test2.py` and `python test2.py` ``` import requests host = "http://localhost:8080/api/v1" kwargs = { 'auth': ('admin', 'admin'), 'headers': {'content-type': 'application/json'} } r = requests.get(f'{host}/dags/~/dagRuns/~/taskInstances', **kwargs, timeout=10) # change here print(r.url, r.text) ``` ``` <correct output> ``` ### Operating System Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26424

https://github.com/apache/airflow/pull/30596

c2679c57aa0281dd455c6a01aba0e8cfbb6a0e1c

e89a7eeea6a7a5a5a30a3f3cf86dfabf7c343412

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/kubernetes_engine.py", "tests/providers/google/cloud/hooks/test_kubernetes_engine.py"]

GKEHook.create_cluster is not wait_for_operation using the input project_id parameter

### Apache Airflow version main (development) ### What happened In the GKEHook, the `create_cluster` method is creating a GKE cluster in the project_id specified by the input, but in `wait_for_operation`, it's waiting for the operation to appear in the default project_id (because no project_id explicitly provided) https://github.com/apache/airflow/blob/f6c579c1c0efb8cdd2eaf905909cda7bc7314f88/airflow/providers/google/cloud/hooks/kubernetes_engine.py#L231-L237 this throws a bug when we are trying to create clusters under different project_id (compared to the default project_id) ### What you think should happen instead instead it should be ```python resource = self.wait_for_operation(resource, project_id) ``` so that we won't get errors when trying to create a cluster under a different project_id ### How to reproduce ```python create_cluster = GKECreateClusterOperator( task_id="create_cluster", project_id=GCP_PROJECT_ID, location=GCP_LOCATION, body=CLUSTER, ) ``` and make sure the GCP_PROJECT_ID is not the same as the default project_id used by the default google service account ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26399

https://github.com/apache/airflow/pull/26418

0bca962cd2c9671adbe68923e17ebecf66a0c6be

e31590039634ff722ad005fe9f1fc02e5a669699

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/datasets/__init__.py", "tests/datasets/test_dataset.py", "tests/models/test_dataset.py"]

UI doesn't handle whitespace/empty dataset URI's well

### Apache Airflow version main (development) ### What happened Here are some poor choices for dataset URI's: ```python3 empty = Dataset("") colons = Dataset("::::::") whitespace = Dataset("\t\n") emoji = Dataset("😊") long = Dataset(5000 * "x") injection = Dataset("105'; DROP TABLE 'dag") ``` And a dag file which replicates the problems mentioned below: https://gist.github.com/MatrixManAtYrService/a32bba5d382cd9a925da72571772b060 (full tracebacks included as comments) Here's how they did: |dataset|behavior| |:-:|:--| |empty| dag triggered with no trouble, not selectable in the datasets UI| |emoji| `airflow dags reserialize`: `UnicodeEncodeError: 'ascii' codec can't encode character '\U0001f60a' in position 0: ordinal not in range(128)`| |colons| no trouble| |whitespace| dag triggered with no trouble, selectable in the datasets UI, but shows no history| |long|sqlalchemy error during serialization| |injection| no trouble| Finally, here's a screenshot: <img width="1431" alt="Screen Shot 2022-09-13 at 11 29 02 PM" src="https://user-images.githubusercontent.com/5834582/190069341-dc17c66a-f941-424d-a455-cd531580543a.png"> Notice that there are two empty rows in the datasets list, one for `empty`, the other for `whitespace`. Only `whitespace` is selectable, both look weird. ### What you think should happen instead I propose that we add a uri sanity check during serialization and just reject dataset URI's that are: - only whitespace - empty - long enough that they're going to cause a database problem The `emoji` case failed in a nice way. Ideally `whitespace`, `long` and `empty` can fail in the same way. If implemented, this would prevent any of the weird cases above from making it to the UI in the first place. ### How to reproduce Unpause the above dags ### Operating System Docker/debian ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astro dev start` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26380

https://github.com/apache/airflow/pull/26389

af39faafb7fdd53adbe37964ba88a3814f431cd8

bd181daced707680ed22f5fd74e1e13094f6b164

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/extensions/init_views.py", "airflow/www/templates/airflow/error.html", "airflow/www/views.py", "tests/api_connexion/test_error_handling.py"]

Airflow Webserver returns incorrect HTTP Error Response for custom REST API endpoints

### Apache Airflow version Other Airflow 2 version ### What happened We are using Airflow 2.3.1 Version. Apart from Airflow provided REST endpoints, we are also using the airflow webserver to host our own application REST API endpoints. We are doing this by loading our own blueprints and registering Flask Blueprint routes within the airflow plugin. Issue: Our Custom REST API endpoints are returning incorrect HTTP Error response code of 404 when 405 is expected (Invoke the REST API endpoint with an incorrect HTTP method, say POST instead of PUT) . This was working in airflow 1.x but is giving an issue with airflow 2.x Here is a sample airflow plugin code . If the '/sample-app/v1' API below is invoked with POST method, I would expect a 405 response. However, it returns a 404. I tried registering a blueprint error handler for 405 inside the plugin, but that did not work. ``` test_bp = flask.Blueprint('test_plugin', __name__) @test_bp.route( '/sample-app/v1/tags/<tag>', methods=['PUT']) def initialize_deployment(tag): """ Initialize the deployment of the metadata tag :rtype: flask.Response """ return 'Hello, World' class TestPlugin(plugins_manager.AirflowPlugin): name = 'test_plugin' flask_blueprints = [test_bp] ``` ### What you think should happen instead Correct HTTP Error response code should be returned. ### How to reproduce Issue the following curl request after loading the plugin - curl -X POST "http://localhost:8080/sample-app/v1/tags/abcd" -d '' The response will be 404 instead of 405. ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26375

https://github.com/apache/airflow/pull/26880

ea55626d79fdbd96b6d5f371883ac1df2a6313ec

8efb678e771c8b7e351220a1eb7eb246ae8ed97f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/bigquery.py", "docs/apache-airflow-providers-google/operators/cloud/bigquery.rst", "tests/system/providers/google/cloud/bigquery/example_bigquery_queries.py"]

Add SQLColumnCheck and SQLTableCheck Operators for BigQuery

### Description New operators under the Google provider for table and column data quality checking that is integrated with OpenLineage. ### Use case/motivation Allow OpenLineage support for BigQuery when using column and table check operators. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26367

https://github.com/apache/airflow/pull/26368

3cd4df16d4f383c27f7fc6bd932bca1f83ab9977

c4256ca1a029240299b83841bdd034385665cdda

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/serialization/serialized_objects.py", "tests/serialization/test_dag_serialization.py"]

dynamic dataset ref breaks when viewed in UI or when triggered (dagbag.py:_add_dag_from_db)

### Apache Airflow version 2.4.0b1 ### What happened Here's a file which defines three dags. "source" uses `Operator.partial` to reference either "sink". I'm not sure if it's supported to do so, but airlflow should at least fail more gracefully than it does. ```python3 from datetime import datetime, timedelta from time import sleep from airflow import Dataset from airflow.decorators import dag from airflow.operators.dummy import DummyOperator from airflow.operators.python import PythonOperator ps = Dataset("partial_static") p1 = Dataset("partial_dynamic_1") p2 = Dataset("partial_dynamic_2") p3 = Dataset("partial_dynamic_3") def sleep_n(n): sleep(n) @dag(start_date=datetime(1970, 1, 1), schedule=timedelta(days=365 * 30)) def two_kinds_dynamic_source(): # dataset ref is not dynamic PythonOperator.partial( task_id="partial_static", python_callable=sleep_n, outlets=[ps] ).expand(op_args=[[1], [20], [40]]) # dataset ref is dynamic PythonOperator.partial( task_id="partial_dynamic", python_callable=sleep_n ).expand_kwargs( [ {"op_args": [1], "outlets": [p1]}, {"op_args": [20], "outlets": [p2]}, {"op_args": [40], "outlets": [p3]}, ] ) two_kinds_dynamic_source() @dag(schedule=[ps], start_date=datetime(1970, 1, 1)) def two_kinds_static_sink(): DummyOperator(task_id="dummy") two_kinds_static_sink() @dag(schedule=[p1, p2, p3], start_date=datetime(1970, 1, 1)) def two_kinds_dynamic_sink(): DummyOperator(task_id="dummy") two_kinds_dynamic_sink() ``` Tried to trigger the dag in the browser, saw this traceback instead: ``` Python version: 3.9.13 Airflow version: 2.4.0.dev1640+astro.1 Node: airflow-webserver-6b969cbd87-4q5kh ------------------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/usr/local/lib/python3.9/site-packages/airflow/www/auth.py", line 46, in decorated return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/www/decorators.py", line 117, in view_func return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/www/decorators.py", line 80, in wrapper return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/www/views.py", line 2532, in grid dag = get_airflow_app().dag_bag.get_dag(dag_id, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 176, in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 251, in _add_dag_from_db dag = row.dag File "/usr/local/lib/python3.9/site-packages/airflow/models/serialized_dag.py", line 223, in dag dag = SerializedDAG.from_dict(self.data) # type: Any File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1220, in from_dict return cls.deserialize_dag(serialized_obj['dag']) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1197, in deserialize_dag setattr(task, k, kwargs_ref.deref(dag)) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 224, in deref value = {k: v.deref(dag) if isinstance(v, _XComRef) else v for k, v in self.value.items()} AttributeError: 'list' object has no attribute 'items' ``` I can also summon a similar traceback by just trying to view the dag in the grid view, or when running `airflow dags trigger` ### What you think should happen instead If there's something invalid about this dag, it should fail to parse--rather than successfully parsing and then breaking the UI. I'm a bit uncertain about what should happen in the dag dependency graph when the source dag runs. The dynamic outlets are not known until runtime, so it's reasonable that they don't show up in the graph. But what about after the dag runs? - do they still trigger the "sink" dag even though we didn't know about the dependency up front? - do we update the dependency graph now that we know about the dynamic dependency? Because of this error, we don't get far enough to find out. ### How to reproduce Include the dag above, try to display it in the grid view. ### Operating System kubernetes-in-docker on MacOS via helm ### Versions of Apache Airflow Providers n/a ### Deployment Other 3rd-party Helm chart ### Deployment details Deployed using [the astronomer helm chart ](https://github.com/astronomer/airflow-chart)and these values: ```yaml airflow: airflowHome: /usr/local/airflow airflowVersion: $VERSION defaultAirflowRepository: img defaultAirflowTag: $TAG executor: KubernetesExecutor gid: 50000 images: airflow: repository: img logs: persistence: enabled: true size: 2Gi ``` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26360

https://github.com/apache/airflow/pull/26369

5e9589c685bcec769041e0a1692035778869f718

b816a6b243d16da87ca00e443619c75e9f6f5816

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", "tests/providers/google/cloud/transfers/test_gcs_to_bigquery.py"]

GCSToBigQueryOperator max_id_key Not Written to XCOM

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.3.0 ### Apache Airflow version 2.3.4 ### Operating System OSX ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened `max_id` is not returned through XCOM when `max_id_key` is set. ### What you think should happen instead When `max_id_key` is set, the `max_id` value should be returned as the default XCOM value. This is based off of the parameter description: ``` The results will be returned by the execute() command, which in turn gets stored in XCom for future operators to use. ``` ### How to reproduce Execute the `GCSToBigQueryOperator` operator with a `max_id_key` parameter set. No XCOM value is set. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26283

https://github.com/apache/airflow/pull/26285

b4f8a069f07b18ce98c9b1286da5a5fcde2bff9f

07fe356de0743ca64d936738b78704f7c05774d1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", "tests/providers/google/cloud/transfers/test_gcs_to_bigquery.py"]

GCSToBigQueryOperator `max_id_key` Feature Throws Error

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers 8.3.0 ### Apache Airflow version 2.3.4 ### Operating System OSX ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened When using the `max_id_key` feature of `GCSToBigQueryOperator` it fails with the error: ``` Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 312, in execute row = list(bq_hook.get_job(job_id).result()) File "/usr/local/lib/python3.9/site-packages/airflow/providers/google/common/hooks/base_google.py", line 444, in inner_wrapper raise AirflowException( airflow.exceptions.AirflowException: You must use keyword arguments in this methods rather than positional ``` ### What you think should happen instead The max id value for the key should be returned. ### How to reproduce Any use of this column fails, since the error is related to retrieving the job result. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26279

https://github.com/apache/airflow/pull/26285

b4f8a069f07b18ce98c9b1286da5a5fcde2bff9f

07fe356de0743ca64d936738b78704f7c05774d1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/sql_to_gcs.py"]

SQLToGCSOperators Add Support for Dumping JSON

### Description If your output format for a SQLToGCSOperator is `json`, then any "dict" type object returned from a database, for example a Postgres JSON column, is not dumped to a string and is kept as a nested JSON object. Add option to dump `dict` objects to string in JSON exporter. ### Use case/motivation Currently JSON type columns are hard to ingest into BQ since a JSON field in a source database does not enforce a schema, and we can't reliably generate a `RECORD` schema for the column. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26273

https://github.com/apache/airflow/pull/26277

706a618014a6f94d5ead0476f26f79d9714bf93d

b4f8a069f07b18ce98c9b1286da5a5fcde2bff9f

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/helm-chart/manage-dags-files.rst"]

helm chart doc Manage DAGs files recommended Bake DAGs in Docker image need improvement.

### What do you see as an issue? https://airflow.apache.org/docs/helm-chart/1.6.0/manage-dags-files.html#bake-dags-in-docker-image > The recommended way to update your DAGs with this chart is to build a new Docker image with the latest DAG code: In this doc , recommended user manage dags way is build in image. But , ref this issue: https://github.com/airflow-helm/charts/issues/211#issuecomment-859678503 > but having the scheduler being restarted and not scheduling any task each time you do a change that is not even scheduler related (just to deploy a new DAG!!) > Helm Chart should be used to deploy "application" not to deploy another version of DAGs. So, I think bake dags in docker image should not be the most recommended way. At least. We should say this way weaknesses (restart all components when jsut deploy a new DAG!) in docs. ### Solving the problem _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26262

https://github.com/apache/airflow/pull/26401

2382c12cc3aa5d819fd089c73e62f8849a567a0a

11f8be879ba2dd091adc46867814bcabe5451540

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/models/dagrun.py", "airflow/www/views.py", "tests/models/test_dag.py"]

should we limit max queued dag runs for dataset-triggered dags

if a dataset-triggered dag is running, and upstreams are updated multiple times, many dag runs will be queued up because the scheduler checks frequently for new dag runs needed. you can easily limit max active dag runs but cannot easily limit max queued dag runs. in the dataset case this represents a meaningful difference in behavior and seems undesirable. i think it may make sense to limit max queued dag runs (for datasets) to 1. cc @ash @jedcunningham @uranusjr @blag @norm the graph below illustrates what happens in this scenario. you can reproduce with the example datasets dag file. change consumes 1 to be `sleep 60` , produces 1 to be `sleep 1`, then trigger producer repeatedly. 

https://github.com/apache/airflow/issues/26259

https://github.com/apache/airflow/pull/26348

9444d9789bc88e1063d81d28e219446b2251c0e1

b99d1cd5d32aea5721c512d6052b6b7b3e0dfefb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/datasets/manager.py", "airflow/jobs/scheduler_job.py", "tests/models/test_taskinstance.py"]

"triggered runs" dataset counter doesn't update until *next* run and never goes above 1

### Apache Airflow version 2.4.0b1 ### What happened I have [this test dag](https://gist.github.com/MatrixManAtYrService/2cf0ebbd85faa2aac682d9c441796c58) which I created to report [this issue](https://github.com/apache/airflow/issues/25210). The idea is that if you unpause "sink" and all of the "sources" then the sources will wait until the clock is like \*:\*:00 and they'll terminate at the same time. Since each source triggers the sink with a dataset called "counter", the "sink" dag will run just once, and it will have output like: `INFO - [(16, 1)]`, that's 16 sources and 1 sink that ran. At this point, you can look at the dataset history for "counter" and you'll see this: <img width="524" alt="Screen Shot 2022-09-08 at 6 07 44 PM" src="https://user-images.githubusercontent.com/5834582/189248999-d31141a4-2d0b-4ec2-9ea5-c4c3536b3a28.png"> So we've got a timestamp, but the "triggered runs" count is empty. That's weird. One run was triggered (and it finished by the time the screenshot was taken), so why doesn't it say `1`? So I redeploy and try it again, except this time I wait several seconds between each "unpause" click, the idea being that maybe some of them fire at 07:16:00 and the others fire at 07:17:00. I end up with this: <img width="699" alt="Screen Shot 2022-09-08 at 6 19 12 PM" src="https://user-images.githubusercontent.com/5834582/189252116-69067189-751d-40e7-89c5-8d1da1720237.png"> So fifteen of them finished at once and caused the dataset to update, and then just one straggler (number 9) is waiting for an additional minute. I wait for the straggler to complete and go back to the dataset view: <img width="496" alt="Screen Shot 2022-09-08 at 6 20 41 PM" src="https://user-images.githubusercontent.com/5834582/189253874-87bb3eb3-2237-42a1-bc3f-9fc210419f1a.png"> Now it's the straggler that is blank, but the rest of them are populated. Continuing to manually run these, I find that whichever one I have run most recently is blank, and all of the others are 1, even if this is the second or third time I've run them ### What you think should happen instead - The triggered runs counter should increment beyond 1 - It should increment immediately after the dag was triggered, not wait until after the *next* dag gets triggered. ### How to reproduce See dags in in this gist: https://gist.github.com/MatrixManAtYrService/2cf0ebbd85faa2aac682d9c441796c58 1. unpause "sink" 2. unpause half of sources 3. wait one minute 4. unpause the other half of the sources 5. wait for "sink" to run a second time 6. view the dataset history for "counter" 7. ask why only half of the counts are populated 8. manually trigger some sources, wait for them to trigger sink 9. view the dataset history again 10. ask why none of them show more than 1 dagrun triggered ### Operating System Kubernetes in Docker, deployed via helm ### Versions of Apache Airflow Providers n/a ### Deployment Other 3rd-party Helm chart ### Deployment details see "deploy.sh" in the gist: https://gist.github.com/MatrixManAtYrService/2cf0ebbd85faa2aac682d9c441796c58 It's just a fresh install into a k8s cluster ### Anything else n/a ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26256

https://github.com/apache/airflow/pull/26276

eb03959e437e11891b8c3696b76f664a991a37a4

954349a952d929dc82087e4bb20d19736f84d381

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/__init__.py"]

[BUG] 2.4.0b1 - google-provider - AttributeError: 'str' object has no attribute 'version'

### Apache Airflow version main (development) ### What happened when I start airflow 2.4.0b1 with the `apache-airflow-providers-google==8.3.0` the webserver log give : ```log [2022-09-08 14:39:53,158] {webserver_command.py:251} ERROR - [0 / 0] Some workers seem to have died and gunicorn did not restart them as expected [2022-09-08 14:39:53,275] {providers_manager.py:228} WARNING - Exception when importing 'airflow.providers.google.common.hooks.base_google.GoogleBaseHook' from 'apache-airflow-providers-google' package 2022-09-08T14:39:53.276959961Z Traceback (most recent call last): 2022-09-08T14:39:53.276965441Z File "/usr/local/lib/python3.8/site-packages/airflow/providers_manager.py", line 260, in _sanity_check 2022-09-08T14:39:53.276969533Z imported_class = import_string(class_name) 2022-09-08T14:39:53.276973476Z File "/usr/local/lib/python3.8/site-packages/airflow/utils/module_loading.py", line 32, in import_string 2022-09-08T14:39:53.276977496Z module = import_module(module_path) 2022-09-08T14:39:53.276981203Z File "/usr/local/lib/python3.8/importlib/__init__.py", line 127, in import_module 2022-09-08T14:39:53.276985012Z return _bootstrap._gcd_import(name[level:], package, level) 2022-09-08T14:39:53.277005418Z File "<frozen importlib._bootstrap>", line 1014, in _gcd_import 2022-09-08T14:39:53.277011581Z File "<frozen importlib._bootstrap>", line 991, in _find_and_load 2022-09-08T14:39:53.277016414Z File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked 2022-09-08T14:39:53.277020883Z File "<frozen importlib._bootstrap>", line 671, in _load_unlocked 2022-09-08T14:39:53.277025840Z File "<frozen importlib._bootstrap_external>", line 843, in exec_module 2022-09-08T14:39:53.277029603Z File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed 2022-09-08T14:39:53.277032868Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/common/hooks/base_google.py", line 49, in <module> 2022-09-08T14:39:53.277036076Z from airflow.providers.google.cloud.utils.credentials_provider import ( 2022-09-08T14:39:53.277038762Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/utils/credentials_provider.py", line 36, in <module> 2022-09-08T14:39:53.277041651Z from airflow.providers.google.cloud._internal_client.secret_manager_client import _SecretManagerClient 2022-09-08T14:39:53.277044383Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/_internal_client/secret_manager_client.py", line 26, in <module> 2022-09-08T14:39:53.277047248Z from airflow.providers.google.common.consts import CLIENT_INFO 2022-09-08T14:39:53.277050101Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/common/consts.py", line 23, in <module> 2022-09-08T14:39:53.277052974Z CLIENT_INFO = ClientInfo(client_library_version='airflow_v' + version.version) 2022-09-08T14:39:53.277055720Z AttributeError: 'str' object has no attribute 'version' [2022-09-08 14:39:53,299] {providers_manager.py:228} WARNING - Exception when importing 'airflow.providers.google.cloud.hooks.cloud_sql.CloudSQLHook' from 'apache-airflow-providers-google' package 2022-09-08T14:39:53.300816697Z Traceback (most recent call last): 2022-09-08T14:39:53.300822358Z File "/usr/local/lib/python3.8/site-packages/airflow/providers_manager.py", line 260, in _sanity_check 2022-09-08T14:39:53.300827098Z imported_class = import_string(class_name) 2022-09-08T14:39:53.300831757Z File "/usr/local/lib/python3.8/site-packages/airflow/utils/module_loading.py", line 32, in import_string 2022-09-08T14:39:53.300836033Z module = import_module(module_path) 2022-09-08T14:39:53.300840058Z File "/usr/local/lib/python3.8/importlib/__init__.py", line 127, in import_module 2022-09-08T14:39:53.300844580Z return _bootstrap._gcd_import(name[level:], package, level) 2022-09-08T14:39:53.300862499Z File "<frozen importlib._bootstrap>", line 1014, in _gcd_import 2022-09-08T14:39:53.300867522Z File "<frozen importlib._bootstrap>", line 991, in _find_and_load 2022-09-08T14:39:53.300871975Z File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked 2022-09-08T14:39:53.300876819Z File "<frozen importlib._bootstrap>", line 671, in _load_unlocked 2022-09-08T14:39:53.300880682Z File "<frozen importlib._bootstrap_external>", line 843, in exec_module 2022-09-08T14:39:53.300885112Z File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed 2022-09-08T14:39:53.300889697Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/cloud_sql.py", line 51, in <module> 2022-09-08T14:39:53.300893842Z from airflow.providers.google.common.hooks.base_google import GoogleBaseHook 2022-09-08T14:39:53.300898141Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/common/hooks/base_google.py", line 49, in <module> 2022-09-08T14:39:53.300903254Z from airflow.providers.google.cloud.utils.credentials_provider import ( 2022-09-08T14:39:53.300906904Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/utils/credentials_provider.py", line 36, in <module> 2022-09-08T14:39:53.300911707Z from airflow.providers.google.cloud._internal_client.secret_manager_client import _SecretManagerClient 2022-09-08T14:39:53.300916818Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/_internal_client/secret_manager_client.py", line 26, in <module> 2022-09-08T14:39:53.300920595Z from airflow.providers.google.common.consts import CLIENT_INFO 2022-09-08T14:39:53.300926003Z File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/common/consts.py", line 23, in <module> 2022-09-08T14:39:53.300931078Z CLIENT_INFO = ClientInfo(client_library_version='airflow_v' + version.version) 2022-09-08T14:39:53.300934596Z AttributeError: 'str' object has no attribute 'version' .... ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System ubuntu 22.04.1 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26238

https://github.com/apache/airflow/pull/26239

a45ab47d7afa97ba6b03471b1dd8816a48cb9689

b7a603cf89728e02a187409c83983d58cc554457

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_params_ui_tutorial.py", "airflow/www/static/js/trigger.js"]

Trigger DAG UI Extension w/ Flexible User Form Concept

### Description Proposal for Contribution for an extensible Trigger UI feature in Airflow. ## Design proposal (Feedback welcome) ### Part 1) Specifying Trigger UI on DAG Level We propose to extend the DAG class with an additional attribute so that UI(s) (one or multiple per DAG) can be specified in the DAG. * Attribute name proposal: `trigger_ui` * Type proposal: `Union[TriggerUIBase, List[TriggerUIBase]` (One or a list of UI definition inherited from an abstract UI class which implements the trigger UI) * Default value proposal: `[TriggerNoUI(), TriggerJsonUI()]` (Means the current/today's state, user can pick to trigger with or without parameters) With this extension the current behavior is continued and users can specify if a specific or multiple UIs are offered for the Trigger DAG option. ### Part 2) UI Changes for Trigger Button The function of the trigger DAG button in DAG overview landing ("Home" / `templates/airflow/dags.html`) as well as DAG detail pages (grid, graph, ... view / `templates/airflow/dag.html`) is adjusted so that: 1) If there is a single Trigger UI specified for the DAG, the button directly opens the form on click 2) If a list of Trigger UIs is defined for the DAG, then a list of UI's is presented, similar like today's drop-down with the today's two options (with and without parameters). Menu names for (2) and URLs are determined by the UI class members linked to the DAG. ### Part 3) Standard implementations for TriggerNoUI, TriggerJsonUI Two implementations for triggering w/o UI and parameters and the current JSON entry form will be migrated to the new UI structure, so that users can define that one, the other or both can be used for DAGs. Name proposals: 0) TriggerUIBase: Base class for any Trigger UI, defines the base parameters and defaults which every Trigger UI is expected to provide: * `url_template`: URL template (into which the DAG name is inserted to direct the user to) * `name`: Name of the trigger UI to display in the drop-down * `description`: Optional descriptive test to supply as hover-over/tool-tip) 1) TriggerNoUI (inherits TriggerUIBase): Skips a user confirmation and entry form and upon call runs the DAG w/o parameters (`DagRun.conf = {}`) 2) TriggerJsonUI (inherits TriggerUIBase): Same like the current UI to enter a JSON into a text box and trigger the DAG. Any valid JSON accepted. ### Part 4) Standard Implementation for Simple Forms (Actually core new feature) Implement/Contribute a user-definable key/value entry form named `TriggerFormUI` (inherits TriggerUIBase) which allows the user to easily enter parameters for triggering a DAG. Form could look like: ``` Parameter 1: <HTML input box for entering a value> (Optional Description and hints) Parameter 2: <HTML Select box of options> (Optional Description and hints) Parameter 3: <HTML Checkbox on/off> (Optional Description and hints) <Trigger DAG Button> ``` The resulting JSON would use the parameter keys and values and render the following `DagRun.conf` and trigger the DAG: ``` { "parameter_1": "user input", "parameter_2": "user selection", "parameter_3": true/false value } ``` The number of form values, parameter names, parameter types, options, order and descriptions should be freely configurable in the DAG definition. The trigger form should provide the following general parameters (at least): * `name`: The name of the form to be used in pick lists and in the headline * `description`: Descriptive test which is printed in hover over of menus and which will be rendered as description between headline and form start * (Implicitly the DAG to which the form is linked to which will be triggered) The trigger form elements (list of elements can be picked freely): * General options of each form element (Base class `TriggerFormUIElement`: * `name` (str): Name of the parameter, used as technical key in the JSON, must be unique per form (e.g. "param1") * `display` (str): Label which is displayed on left side of entry field (e.g. "Parameter 1") * `help` (Optional[str]=Null): Descriptive help text which is optionally rendered below the form element, might contain HTML formatting code * `required` (Optional[bool]=False): Flag if the user is required to enter/pick a value before submission is possible * `default` (Optional[str]=Null): Default value to present when the user opens the form * Element types provided in the base implementation * `TriggerFormUIString` (inherits `TriggerFormUIElement`): Provides a simple HTML string input box. * `TriggerFormUISelect` (inherits `TriggerFormUIElement`): Provides a HTML select box with a list of pre-defined string options. Options are provided static as array of strings. * `TriggerFormUIArray` (inherits `TriggerFormUIElement`): Provides a simple HTML text area allowing to enter multiple lines of text. Each line entered will be converted to a string and the strings will be used as value array. * `TriggerFormUICheckbox` (inherits `TriggerFormUIElement`): Provides a HTML Checkbox to select on/off, will be converted to true/false as value * Other element types (optionally, might be added later?) for making futher cool features - depending on how much energy is left * `TriggerFormUIHelp` (inherits `TriggerFormUIElement`): Provides no actual parameter value but allows to add a HTML block of help * `TriggerFormUIBreak` (inherits `TriggerFormUIElement`): Provides no actual parameter value but adds a horizontal splitter * Adding the options to validate string values e.g. with a RegEx * Allowing to provide int values (besides just strings) * Allowing to have an "advanced" section for more options which the user might not need in all cases * Allowing to view the generated `DagRun.conf` so that a user can copy/paste as well * Allowing to user extend the form elements... ### Part 5) (Optional) Extended for Templated Form based on the Simple form but uses fields to run a template through Jinja Implement (optionally, might be future extension as well?) a `TriggerTemplateFormUI` (inherits TriggerFormUI) which adds a Jinja2 JSON template which will be templated with the collected form fields so that more complex `DagRun.conf` parameter structures can be created on top of just key/value ### Part 6) Examples Provide 1-2 example DAGs which show how the trigger forms can be used. Adjust existing examples as needed. ### Part 7) Documentation Provide needed documentation to describe the feature and options. This would include an description how to add custom forms above the standards via Airflow Plugins and custom Python code. ### Use case/motivation As user of Airflow for our custom workflows we often use `DagRun.conf` attributes to control content and flow. Current UI allows (only) to launch via REST API with given parameters or using a JSON structure in the UI to trigger with parameters. This is technically feasible but not user friendly. A user needs to model, check and understand the JSON and enter parameters manually without the option to validate before trigger. Similar like Jenkins or Github/Azure pipelines we desire an UI option to trigger with a UI and specifying parameters. We'd like to have a similar capability in Airflow. Current workarounds used in multiple places are: 1) Implementing a custom (additional) Web UI which implements the required forms outside/on top of Airflow. This UI accepts user input and in the back-end triggers Airflow via REST API. This is flexible but replicates the efforts for operation, deployment, release as well and redundantly need to implement access control, logging etc. 2) Implementing an custom Airflow Plugin which hosts additional launch/trigger UIs inside Airflow. We are using this but it is actually a bit redundant to other trigger options and is only 50% user friendly I/we propose this as a feature and would like to contribute this with a following PR - would this be supported if we contribute this feature to be merged? ### Related issues Note: This proposal is similar and/or related to #11054 but a bit more detailed and concrete. Might be also related to #22408 and contribute to AIP-38 (https://github.com/apache/airflow/projects/9)? ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26215

https://github.com/apache/airflow/pull/29376

7ee1a5624497fc457af239e93e4c1af94972bbe6

9c6f83bb6f3e3b57ae0abbe9eb0582fcde265702

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/taskInstance/Logs/index.test.tsx", "airflow/www/static/js/dag/details/taskInstance/Logs/index.tsx"]

Extra entry for logs generated with 0 try number when clearing any task instances

### Apache Airflow version main (development) ### What happened When clearing any task instances an extra logs entry generated with Zero try number. <img width="1344" alt="Screenshot 2022-09-07 at 1 06 54 PM" src="https://user-images.githubusercontent.com/88504849/188819289-13dd4936-cd03-48b6-8406-02ee5fbf293f.png"> ### What you think should happen instead It should not create a entry with zero try number ### How to reproduce Clear a task instance by hitting clear button on UI and then observe the entry for logs in logs tab ### Operating System mac os ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26194

https://github.com/apache/airflow/pull/26556

6f1ab37d2091e26e67717d4921044029a01d6a22

6a69ad033fdc224aee14b8c83fdc1b672d17ac20

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_bigquery.py"]

GCSToBigQueryOperator Schema in Alternate GCS Bucket

### Description Currently the `GCSToBigQueryOperator` requires that a Schema object located in GCS be located in the same bucket as the Source Object(s). I'd like an option to have it located in a different bucket. ### Use case/motivation I have a GCS bucket where I store files with a 90 day auto-expiration on the whole bucket. I want to be able to store a fixed schema in GCS, but since this bucket has an auto-expiration of 90 days the schema is auto deleted at that time. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26189

https://github.com/apache/airflow/pull/26190

63562d7023a9d56783f493b7ea13accb2081121a

8cac96918becf19a4a04eef1e5bcf175f815f204

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py", "tests/core/test_configuration.py"]

Webserver fails to pull secrets from Hashicorp Vault on start up

### Apache Airflow version 2.3.4 ### What happened Since upgrading to Airflow 2.3.4 our webserver fails on start up to pull secrets from our Vault instance. Setting AIRFLOW__WEBSERVER_WORKERS = 1 allowed the webserver to start up successfully, but reverting the change added here [https://github.com/apache/airflow/pull/25556](url) was the only way we found to fix the issue without adjusting the webserver's worker count. ### What you think should happen instead The airflow webserver should be able to successfully read from Vault with AIRFLOW__WEBSERVERS__WORKERS > 1. ### How to reproduce Star a Webserver instance set to authenticate with Vault using the approle method and AIRFLOW__DATABASE__SQL_ALCHEMY_CONN_SECRET and AIRFLOW__WEBSERVER__SECRET_KEY_SECRET set. The webserver should fail to initialize all of the gunicorn workers and exit. ### Operating System Fedora 29 ### Versions of Apache Airflow Providers apache-airflow-providers-hashicorp==3.1.0 ### Deployment Docker-Compose ### Deployment details Python 3.9.13 Vault 1.9.4 ### Anything else None ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26185

https://github.com/apache/airflow/pull/26223

ebef9ed3fa4a9a1e69b4405945e7cd939f499ee5

c63834cb24c6179c031ce0d95385f3fa150f442e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/xcom_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/www/static/js/types/api-generated.ts", "tests/api_connexion/endpoints/test_xcom_endpoint.py"]

API Endpoints - /xcomEntries/{xcom_key} cannot deserialize customized xcom backend

### Description We use S3 as our xcom backend database and write serialize/deserialize method for xcoms. However, when we want to access xcom through REST API, it returns the s3 file url instead of the deserialized value. Could you please add the feature to support customized xcom backend for REST API access? ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26174

https://github.com/apache/airflow/pull/26343

3c9c0f940b67c25285259541478ebb413b94a73a

ffee6bceb32eba159a7a25a4613d573884a6a58d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "airflow/cli/commands/role_command.py", "tests/cli/commands/test_role_command.py"]

Add CLI to add/remove permissions from existed role

### Body Followup on https://github.com/apache/airflow/pull/25854 [Roles CLI](https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#roles) currently support create, delete, export, import, list It can be useful to have the ability to add/remove permissions from existed role. This has also been asked in https://github.com/apache/airflow/issues/15318#issuecomment-872496184 cc @chenglongyan ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/26155

https://github.com/apache/airflow/pull/26338

e31590039634ff722ad005fe9f1fc02e5a669699

94691659bd73381540508c3c7c8489d60efb2367

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile.ci", "airflow/serialization/serialized_objects.py", "setup.cfg"]

Remove `cattrs` from project

Cattrs is currently only used in two places: Serialization for operator extra links, and for Lineage. However cattrs is not a well maintained project and doesn't support many features that attrs itself does; in short, it's not worth the brain cycles to keep cattrs.

https://github.com/apache/airflow/issues/26130

https://github.com/apache/airflow/pull/34672

0c8e30e43b70e9d033e1686b327eb00aab82479c

e5238c23b30dfe3556fb458fa66f28e621e160ae

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/sqlalchemy.py", "tests/utils/test_sqlalchemy.py"]

Kubernetes Invalid executor_config, pod_override filled with Encoding.VAR

### Apache Airflow version 2.3.4 ### What happened Trying to start Kubernetes tasks using a `pod_override` results in pods not starting after upgrading from 2.3.2 to 2.3.4 The pod_override look very odd, filled with many Encoding.VAR objects, see following scheduler log: ``` {kubernetes_executor.py:550} INFO - Add task TaskInstanceKey(dag_id='commit_check', task_id='sync_and_build', run_id='5776-2-1662037155', try_number=1, map_index=-1) with command ['airflow', 'tasks', 'run', 'commit_check', 'sync_and_build', '5776-2-1662037155', '--local', '--subdir', 'DAGS_FOLDER/dag_on_commit.py'] with executor_config {'pod_override': {'Encoding.VAR': {'Encoding.VAR': {'Encoding.VAR': {'metadata': {'Encoding.VAR': {'annotations': {'Encoding.VAR': {}, 'Encoding.TYPE': 'dict'}}, 'Encoding.TYPE': 'dict'}, 'spec': {'Encoding.VAR': {'containers': REDACTED 'Encoding.TYPE': 'k8s.V1Pod'}, 'Encoding.TYPE': 'dict'}} {kubernetes_executor.py:554} ERROR - Invalid executor_config for TaskInstanceKey(dag_id='commit_check', task_id='sync_and_build', run_id='5776-2-1662037155', try_number=1, map_index=-1) ``` Looking in the UI, the task get stuck in scheduled state forever. By clicking instance details, it shows similar state of the pod_override with many Encoding.VAR. This appears like a recent addition, in 2.3.4 via https://github.com/apache/airflow/pull/24356. @dstandish do you understand if this is connected? ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.3.0 apache-airflow-providers-common-sql==1.1.0 apache-airflow-providers-docker==3.1.0 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-postgres==5.2.0 apache-airflow-providers-sqlite==3.2.0 kubernetes==23.6.0 ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26101

https://github.com/apache/airflow/pull/26191

af3a07427023d7089f3bc74a708723d13ce3cf73

87108d7b62a5c79ab184a50d733420c0930fdd93

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/baseoperator.py", "airflow/ti_deps/deps/trigger_rule_dep.py", "airflow/utils/trigger_rule.py", "docs/apache-airflow/concepts/dags.rst", "tests/ti_deps/deps/test_trigger_rule_dep.py", "tests/utils/test_trigger_rule.py"]

Add one_done trigger rule

### Body Action: trigger as soon as 1 upstream task is in success or failuire This has been requested in https://stackoverflow.com/questions/73501232/how-to-implement-the-one-done-trigger-rule-for-airflow I think this can be useful for the community. **The Task:** Add support for new trigger rule `one_done` You can use as reference previous PRs that added other trigger rules for example: https://github.com/apache/airflow/pull/21662 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/26099

https://github.com/apache/airflow/pull/26146

55d11464c047d2e74f34cdde75d90b633a231df2

baaea097123ed22f62c781c261a1d9c416570565

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/operators/container_instances.py"]

Add the parameter `network_profile` in `AzureContainerInstancesOperator`

### Description [apache-airflow-providers-microsoft-azure](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/index.html) uses `azure-mgmt-containerinstance==>=1.5.0,<2.0`. In `azure-mgmt-containerinstance==1.5.0`, [ContainerGroup](https://github.com/Azure/azure-sdk-for-python/blob/azure-mgmt-containerinstance_1.5.0/sdk/containerinstance/azure-mgmt-containerinstance/azure/mgmt/containerinstance/models/container_group_py3.py) accepts a parameter called `network_profile`, which is expecting a [ContainerGroupNetworkProfile](https://github.com/Azure/azure-sdk-for-python/blob/azure-mgmt-containerinstance_1.5.0/sdk/containerinstance/azure-mgmt-containerinstance/azure/mgmt/containerinstance/models/container_group_network_profile_py3.py). ### Use case/motivation I received the following error when I provide value to `IpAddress` in the `AzureContainerInstancesOperator`. ``` msrestazure.azure_exceptions.CloudError: Azure Error: PrivateIPAddressNotSupported Message: IP Address type in container group 'data-quality-test' is invalid. Private IP address is only supported when network profile is defined. ``` I would like to pass a ContainerGroupNetworkProfile object through so AzureContainerInstancesOperator can use in the [Container Group instantiation](https://github.com/apache/airflow/blob/main/airflow/providers/microsoft/azure/operators/container_instances.py#L243-L254). ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26097

https://github.com/apache/airflow/pull/26117

dd6b2e4e6cb89d9eea2f3db790cb003a2e89aeff

5060785988f69d01ee2513b1e3bba73fbbc0f310

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/bigquery.py", "tests/providers/google/cloud/hooks/test_bigquery.py"]

Creative use of BigQuery Hook Leads to Exception

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers 8.3.0 ### Apache Airflow version 2.3.4 ### Operating System Debian ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened When executing a query through a BigQuery Hook Cursor that does not have a schema, an exception is thrown. ### What you think should happen instead If a cursor does not contain a schema, revert to a `self.description` of None, like before the update. ### How to reproduce Execute an `UPDATE` sql statement using a cursor. ``` conn = bigquery_hook.get_conn() cursor = conn.cursor() cursor.execute(sql) ``` ### Anything else I'll be the first to admit that my users are slightly abusing cursors in BigQuery by running all statement types through them, but BigQuery doesn't care and lets you. Ref: https://github.com/apache/airflow/issues/22328 ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26095

https://github.com/apache/airflow/pull/26096

b7969d4a404f8b441efda39ce5c2ade3e8e109dc

12cbc0f1ddd9e8a66c5debe7f97b55a2c8001502

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_branch_day_of_week_operator.py", "airflow/operators/weekday.py", "airflow/sensors/weekday.py"]

BranchDayOfWeekOperator documentation don't mention how to use parameter use_taks_execution_day or how to use WeekDay

### What do you see as an issue? The constructor snippet shows clearly that there's a keyword parameter `use_task_exection_day=False`, but the doc does not explain how to use it. It also has `{WeekDay.TUESDAY}, {WeekDay.SATURDAY, WeekDay.SUNDAY}` as options for `week_day` but does not clarify how to import WeekDay. The tutorial is also very basic and only shows one usecase. The sensor has the same issues. ### Solving the problem I think docs should be added for `use_taks_execution_day` and there should be mentions of how one uses `WeekDay` class and where to import it from. The tutorial is also incomplete there. I would like to see examples for, say, multiple different workdays branches and/or some graph for resulting dags ### Anything else I feel like BranchDayOfWeekOperator is tragically underrepresented and hard to find, and I hope that improving docs would help make its use more common ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26071

https://github.com/apache/airflow/pull/26098

4b26c8c541a720044fa96475620fc70f3ac6ccab

dd6b2e4e6cb89d9eea2f3db790cb003a2e89aeff

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py", "tests/jobs/test_scheduler_job.py"]

Include external_executor_id in zombie detection method

### Description Adjust the SimpleTaskInstance to include the external_executor_id so that it shows up when the zombie detection method prints the SimpleTaskInstance to logs. ### Use case/motivation Since the zombie detection message originates in the dag file processor, further troubleshooting of the zombie task requires figuring out which worker was actually responsible for the task. Printing the external_executor_id makes it easier to find the task in a log aggregator like Kibana or Splunk than it is when using the combination of dag_id, task_id, logical_date, and map_index, at least for executors like Celery. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26067

https://github.com/apache/airflow/pull/26141

b6ba11ebece2c3aaf418738cb157174491a1547c

ef0b97914a6d917ca596200c19faed2f48dca88a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "tests/models/test_dag.py"]

[Graph view] After clearing the task (and its downstream tasks) in a task group the task group becomes disconnected from the dag

### Apache Airflow version 2.3.4 ### What happened n the graph view of the dag, after clearing the task (and its downstream tasks) in a task group and refreshing the page the browser the task group becomes disconnected from the dag. See attached gif.  The issue is not persistent and consistent. The graph view becomes disconnected from time to time as you can see on the attached video. ### What you think should happen instead The graph should be rendered properly and consistently. ### How to reproduce 1. Add the following dag to the dag folder: ``` import logging import time from typing import List import pendulum from airflow import DAG from airflow.operators.python import PythonOperator from airflow.utils.task_group import TaskGroup def log_function(message: str, **kwargs): logging.info(message) time.sleep(3) def create_file_handling_task_group(supplier): with TaskGroup(group_id=f"file_handlig_task_group_{supplier}", ui_color='#666666') as file_handlig_task_group: entry = PythonOperator( task_id='entry', python_callable=log_function, op_kwargs={'message': 'create_file_handlig_task_group-Entry-task'} ) with TaskGroup(group_id=f"file_handling_task_sub_group-{supplier}", ui_color='#666666') as file_handlig_task_sub_group: sub_group_submit = PythonOperator( task_id='sub_group_submit', python_callable=log_function, op_kwargs={'message': 'create_file_handlig_sub_group_submit'} ) sub_group_monitor = PythonOperator( task_id='sub_group_monitor', python_callable=log_function, op_kwargs={'message': 'create_file_handlig_sub_group_monitor'} ) sub_group_submit >> sub_group_monitor entry >> file_handlig_task_sub_group return file_handlig_task_group def get_stage_1_taskgroups(supplierlist: List) -> List[TaskGroup]: return [create_file_handling_task_group(supplier) for supplier in supplierlist] def connect_stage1_to_stage2(self, stage1_tasks: List[TaskGroup], stage2_tasks: List[TaskGroup]) -> None: if stage2_tasks: for stage1_task in stage1_tasks: supplier_code: str = self.get_supplier_code(stage1_task) stage2_task = self.get_suppliers_tasks(supplier_code, stage2_tasks) stage1_task >> stage2_task def get_stage_2_taskgroup(taskgroup_id: str): with TaskGroup(group_id=taskgroup_id, ui_color='#666666') as stage_2_taskgroup: sub_group_submit = PythonOperator( task_id='sub_group_submit', python_callable=log_function, op_kwargs={'message': 'create_file_handlig_sub_group_submit'} ) sub_group_monitor = PythonOperator( task_id='sub_group_monitor', python_callable=log_function, op_kwargs={'message': 'create_file_handlig_sub_group_monitor'} ) sub_group_submit >> sub_group_monitor return stage_2_taskgroup def create_dag(): with DAG( dag_id="horizon-task-group-bug", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, description="description" ) as dag: start = PythonOperator( task_id='start_main', python_callable=log_function, op_kwargs={'message': 'Entry-task'} ) end = PythonOperator( task_id='end_main', python_callable=log_function, op_kwargs={'message': 'End-task'} ) with TaskGroup(group_id=f"main_file_task_group", ui_color='#666666') as main_file_task_group: end_main_file_task_stage_1 = PythonOperator( task_id='end_main_file_task_stage_1', python_callable=log_function, op_kwargs={'message': 'end_main_file_task_stage_1'} ) first_stage = get_stage_1_taskgroups(['9001', '9002']) first_stage >> get_stage_2_taskgroup("stage_2_1_taskgroup") first_stage >> get_stage_2_taskgroup("stage_2_2_taskgroup") first_stage >> end_main_file_task_stage_1 start >> main_file_task_group >> end return dag dag = create_dag() ``` 2. Go to de graph view of the dag. 3. Run the dag. 4. After the dag run has finished. Clear the "sub_group_submit" task within the "stage_2_1_taskgroup" with downstream tasks. 5. Refresh the page multiple times and notice how from time to time the "stage_2_1_taskgroup" becomes disconnected from the dag. 6. Clear the "sub_group_submit" task within the "stage_2_2_taskgroup" with downstream tasks. 7. Refresh the page multiple times and notice how from time to time the "stage_2_2_taskgroup" becomes disconnected from the dag. ### Operating System Mac OS, Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Custom docker image based on apache/airflow:2.3.4-python3.10 ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26059

https://github.com/apache/airflow/pull/30129

4dde8ececf125abcded5910817caad92fcc82166

76a884c552a78bfb273fe8b65def58125fc7961a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/operators/sql.py", "tests/providers/common/sql/operators/test_sql.py"]

`isinstance()` check in `_hook()` breaking provider hook usage

### Apache Airflow Provider(s) common-sql ### Versions of Apache Airflow Providers Using `apache-airflow-providers-common-sql==1.1.0` ### Apache Airflow version 2.3.2 ### Operating System Debian GNU/Linux 11 bullseye ### Deployment Astronomer ### Deployment details astro-runtime:5.0.5 ### What happened The `isinstance()` method to check that the hook is a `DbApiHook` is breaking when a snowflake connection is passed to an operator's `conn_id` parameter, as the check finds an instance of `SnowflakeHook` and not `DbApiHook`. ### What you think should happen instead There should not be an error when subclasses of `DbApiHook` are used. This can be fixed by replacing `isinstance()` with something that checks the inheritance hierarchy. ### How to reproduce Run an operator from the common-sql provider with a Snowflake connection passed to `conn_id`. ### Anything else Occurs every time. Log: ``` [2022-08-29, 19:10:42 UTC] {manager.py:49} ERROR - Failed to extract metadata The connection type is not supported by SQLColumnCheckOperator. The associated hook should be a subclass of `DbApiHook`. Got SnowflakeHook task_type=SQLColumnCheckOperator airflow_dag_id=complex_snowflake_transform task_id=quality_check_group_forestfire.forestfire_column_checks airflow_run_id=manual__2022-08-29T19:04:54.998289+00:00 Traceback (most recent call last): File "/usr/local/airflow/include/openlineage/airflow/extractors/manager.py", line 38, in extract_metadata task_metadata = extractor.extract_on_complete(task_instance) File "/usr/local/airflow/include/openlineage/airflow/extractors/sql_check_extractors.py", line 26, in extract_on_complete return super().extract() File "/usr/local/airflow/include/openlineage/airflow/extractors/sql_extractor.py", line 50, in extract authority=self._get_authority(), File "/usr/local/airflow/include/openlineage/airflow/extractors/snowflake_extractor.py", line 57, in _get_authority return self.conn.extra_dejson.get( File "/usr/local/airflow/include/openlineage/airflow/extractors/sql_extractor.py", line 102, in conn self._conn = get_connection(self._conn_id()) File "/usr/local/airflow/include/openlineage/airflow/extractors/sql_extractor.py", line 91, in _conn_id return getattr(self.hook, self.hook.conn_name_attr) File "/usr/local/airflow/include/openlineage/airflow/extractors/sql_extractor.py", line 96, in hook self._hook = self._get_hook() File "/usr/local/airflow/include/openlineage/airflow/extractors/snowflake_extractor.py", line 63, in _get_hook return self.operator.get_db_hook() File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 112, in get_db_hook return self._hook File "/usr/local/lib/python3.9/functools.py", line 969, in __get__ val = self.func(instance) File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 95, in _hook raise AirflowException( airflow.exceptions.AirflowException: The connection type is not supported by SQLColumnCheckOperator. The associated hook should be a subclass of `DbApiHook`. Got SnowflakeHook ``` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26046

https://github.com/apache/airflow/pull/26051

d356560baa5a41d4bda87e4010ea6d90855d25f3

27e2101f6ee5567b2843cbccf1dca0b0e7c96186

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/backfill_job.py", "tests/jobs/test_backfill_job.py"]

Backfill dagrun mistakenly evaluated as deadlocked

### Apache Airflow version Other Airflow 2 version ### What happened I used a bash operator to run a backfill command. The dagrun was marked as failed and was alerted for a deadlock even though the task instances themselves were still ran normally. This happens occasionally. ``` [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {dagrun.py:585} ERROR - Deadlock; marking run <DagRun load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental @ 2022-08-15 08:00:00+00:00: backfill__2022-08-15T08:00:00+00:00, externally triggered: False> failed ... [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 4 | running: 0 | failed: 0 | skipped: 5 | deadlocked: 0 | not ready: 0 [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {local_executor.py:390} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {backfill_job.py:879} INFO - Backfill done. Exiting. ``` Here is full backfill log. ``` [2022-08-23, 10:54:00 UTC] {subprocess.py:74} INFO - Running command: ['bash', '-c', 'cd $AIRFLOW_HOME && airflow dags backfill -s "2022-08-15 00:00:00" -e "2022-08-16 00:00:00" -c \'{"start_val":"1","end_val":"4"}\' --rerun-failed-tasks --reset-dagruns --yes load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental'] ... [2022-08-23, 10:54:21 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:21 UTC] {task_command.py:371} INFO - Running <TaskInstance: load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental.source.extract_withdrawals_venmo_withdrawal_aud_incremental_load backfill__2022-08-15T08:00:00+00:00 [queued]> on host a8870cb5a3e0 [2022-08-23, 10:54:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:19 UTC] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'source.extract_withdrawals_venmo_withdrawal_aud_incremental_load', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmp92x61y3k'] [2022-08-23, 10:54:24 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:24 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:29 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:29 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:34 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:34 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:39 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:39 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:44 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:44 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:49 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:49 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:54 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:54 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 8 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {dagrun.py:585} ERROR - Deadlock; marking run <DagRun load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental @ 2022-08-15 08:00:00+00:00: backfill__2022-08-15T08:00:00+00:00, externally triggered: False> failed [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {dagrun.py:609} INFO - DagRun Finished: dag_id=load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental, execution_date=2022-08-15 08:00:00+00:00, run_id=backfill__2022-08-15T08:00:00+00:00, run_start_date=None, run_end_date=2022-08-23 10:54:59.121952+00:00, run_duration=None, state=failed, external_trigger=False, run_type=backfill, data_interval_start=2022-08-15 08:00:00+00:00, data_interval_end=2022-08-16 08:00:00+00:00, dag_hash=None [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {dagrun.py:795} WARNING - Failed to record duration of <DagRun load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental @ 2022-08-15 08:00:00+00:00: backfill__2022-08-15T08:00:00+00:00, externally triggered: False>: start_date is not set. [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 8 | succeeded: 1 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 8 [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'destination.post_marker_staging_withdrawals_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmpd1nq6xe2'] [2022-08-23, 10:54:59 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:54:59 UTC] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'destination.post_marker_fdg_pii_fact_aw_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmps6ah6zww'] [2022-08-23, 10:55:04 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:04 UTC] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'destination.post_marker_fdg_pii_fact_aw_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmps6ah6zww'] [2022-08-23, 10:55:04 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:04 UTC] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'destination.post_marker_staging_withdrawals_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmpd1nq6xe2'] [2022-08-23, 10:55:04 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:04 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 3 | succeeded: 1 | running: 2 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 3 [2022-08-23, 10:55:06 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:06 UTC] {task_command.py:371} INFO - Running <TaskInstance: load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental.destination.post_marker_fdg_pii_fact_aw_venmo_withdrawal_aud backfill__2022-08-15T08:00:00+00:00 [queued]> on host a8870cb5a3e0 [2022-08-23, 10:55:06 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:06 UTC] {task_command.py:371} INFO - Running <TaskInstance: load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental.destination.post_marker_staging_withdrawals_venmo_withdrawal_aud backfill__2022-08-15T08:00:00+00:00 [queued]> on host a8870cb5a3e0 [2022-08-23, 10:55:09 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:09 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 1 | succeeded: 3 | running: 0 | failed: 0 | skipped: 5 | deadlocked: 0 | not ready: 1 [2022-08-23, 10:55:09 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:09 UTC] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'post_execution.rotate_checkpoint_withdrawals_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmpkve4mv_q'] [2022-08-23, 10:55:14 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:14 UTC] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental', 'post_execution.rotate_checkpoint_withdrawals_venmo_withdrawal_aud', 'backfill__2022-08-15T08:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/withdrawals_venmo_withdrawal_aud_jdbc_to_redshift_incremental_load.py', '--cfg-path', '/tmp/tmpkve4mv_q'] [2022-08-23, 10:55:14 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:14 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 3 | running: 1 | failed: 0 | skipped: 5 | deadlocked: 0 | not ready: 0 [2022-08-23, 10:55:15 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:15 UTC] {task_command.py:371} INFO - Running <TaskInstance: load_withdrawals_venmo_withdrawal_aud_to_redshift_withdrawals_venmo_withdrawal_aud_incremental.post_execution.rotate_checkpoint_withdrawals_venmo_withdrawal_aud backfill__2022-08-15T08:00:00+00:00 [queued]> on host a8870cb5a3e0 [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 4 | running: 0 | failed: 0 | skipped: 5 | deadlocked: 0 | not ready: 0 [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {local_executor.py:390} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. [2022-08-23, 10:55:19 UTC] {subprocess.py:92} INFO - [2022-08-23, 10:55:19 UTC] {backfill_job.py:879} INFO - Backfill done. Exiting. ``` ### What you think should happen instead The DAG is not deadlocked but still somehow was still [evaluated as deadlocked](https://github.com/apache/airflow/blob/main/airflow/models/dagrun.py#L581-L589). ### How to reproduce _No response_ ### Operating System N/A ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26044

https://github.com/apache/airflow/pull/26161

5b216e9480e965c7c1919cb241668beca53ab521

6931fbf8f7c0e3dfe96ce51ef03f2b1502baef07

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/commands/release_management_commands.py", "dev/breeze/src/airflow_breeze/utils/docker_command_utils.py", "images/breeze/output_release-management_generate-constraints.svg", "scripts/in_container/_in_container_script_init.sh", "scripts/in_container/_in_container_utils.sh", "scripts/in_container/in_container_utils.py", "scripts/in_container/install_airflow_and_providers.py", "scripts/in_container/run_generate_constraints.py", "scripts/in_container/run_generate_constraints.sh", "scripts/in_container/run_system_tests.sh"]

Rewrite the in-container scripts in Python

We have a number of "in_container" scripts written in Bash, They are doing a number of houseekeeping stuff but since we already have Python 3.7+ inside the CI image, we could modularise them more and make them run from external and simplify entrypoint_ci (for example separate script for tests).

https://github.com/apache/airflow/issues/26019

https://github.com/apache/airflow/pull/36158

36010f6d0e3231081dbae095baff5a5b5c5b34eb

f39cdcceff4fa64debcaaef6e30f345b7b21696e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "tests/models/test_dag.py"]

schedule_interval is not respecting the value assigned to that either it's one day or none

### Apache Airflow version main (development) ### What happened Schedule_interval is `none` even if `timedelta(days=365, hours=6)` also its 1 day for `schedule_interval=None` and `schedule_interval=timedelta(days=3)` ### What you think should happen instead It should respect the value assigned to it. ### How to reproduce Create a dag with `schedule_interval=None` or `schedule_interval=timedelta(days=5)` and observe the behaviour.  **DAG-** ``` with DAG( dag_id="branch_python_operator", start_date=days_ago(1), schedule_interval="* * * * *", doc_md=docs, tags=['core'] ) as dag: ``` **DB Results-** ``` postgres=# select schedule_interval from dag where dag_id='branch_python_operator'; schedule_interval ------------------------------------------------------------------------------ {"type": "timedelta", "attrs": {"days": 1, "seconds": 0, "microseconds": 0}} (1 row) ``` ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26013

https://github.com/apache/airflow/pull/26082

d4db9aecc3e534630c76e59c54d90329ed20a6ab

c982080ca1c824dd26c452bcb420df0f3da1afa8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/backfill_job.py", "tests/jobs/test_backfill_job.py"]

`start_date` for an existing dagrun is not set when ran with backfill flags ` --reset-dagruns --yes`

### Apache Airflow version 2.3.4 ### What happened When the dagrun already exists and is backfilled with the flags `--reset-dagruns --yes`, the dag run will not have a start date. This is because reset_dagruns calls [clear_task_instances](https://github.com/apache/airflow/blob/main/airflow/models/dag.py#L2020) which [sets the dagrun start date to None](https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L286-L291). Since the dagrun goes into running via BackfillJob rather than the SchedulerJob, the start date is not set. This doesn't happen to a new dagrun created by a BackfillJob because the [start date is determined at creation](https://github.com/apache/airflow/blob/main/airflow/jobs/backfill_job.py#L310-L320). Here is a recreation of the behaviour. First run of the backfill dagrun. No odd warnings and start date exists for Airflow to calculate the duration. ``` astro@75512ab5e882:/usr/local/airflow$ airflow dags backfill -s 2021-12-01 -e 2021-12-01 test_module /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528: DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. option = self._get_environment_variables(deprecated_key, deprecated_section, key, section) /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. /usr/local/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py:57 PendingDeprecationWarning: --ignore-first-depends-on-past is deprecated as the value is always set to True [2022-08-25 21:29:55,574] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dags /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. Nothing to clear. [2022-08-25 21:29:55,650] {executor_loader.py:105} INFO - Loaded executor: LocalExecutor [2022-08-25 21:29:55,896] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'test_module', 'run_python', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp_nuoic9m'] [2022-08-25 21:30:00,665] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'test_module', 'run_python', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp_nuoic9m'] [2022-08-25 21:30:00,679] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 1 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 1 [2022-08-25 21:30:00,695] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dags/test_module.py [2022-08-25 21:30:00,759] {task_command.py:371} INFO - Running <TaskInstance: test_module.run_python backfill__2021-12-01T00:00:00+00:00 [queued]> on host 75512ab5e882 [2022-08-25 21:30:05,686] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 1 | succeeded: 1 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 1 [2022-08-25 21:30:05,709] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'test_module', 'test', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp3w9pm1jj'] [2022-08-25 21:30:10,659] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'test_module', 'test', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp3w9pm1jj'] [2022-08-25 21:30:10,668] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 0 | succeeded: 1 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 0 [2022-08-25 21:30:10,693] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dags/test_module.py [2022-08-25 21:30:10,765] {task_command.py:371} INFO - Running <TaskInstance: test_module.test backfill__2021-12-01T00:00:00+00:00 [queued]> on host 75512ab5e882 [2022-08-25 21:30:15,678] {dagrun.py:564} INFO - Marking run <DagRun test_module @ 2021-12-01T00:00:00+00:00: backfill__2021-12-01T00:00:00+00:00, externally triggered: False> successful [2022-08-25 21:30:15,679] {dagrun.py:609} INFO - DagRun Finished: dag_id=test_module, execution_date=2021-12-01T00:00:00+00:00, run_id=backfill__2021-12-01T00:00:00+00:00, run_start_date=2022-08-25 21:29:55.815199+00:00, run_end_date=2022-08-25 21:30:15.679256+00:00, run_duration=19.864057, state=success, external_trigger=False, run_type=backfill, data_interval_start=2021-12-01T00:00:00+00:00, data_interval_end=2021-12-02T00:00:00+00:00, dag_hash=None [2022-08-25 21:30:15,680] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 2 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 0 [2022-08-25 21:30:15,684] {local_executor.py:390} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. [2022-08-25 21:30:15,829] {backfill_job.py:879} INFO - Backfill done. Exiting. ``` Second run of the backfill dagrun with the flags `--reset-dagruns --yes`. There is a warning about start_date is not set. ``` astro@75512ab5e882:/usr/local/airflow$ airflow dags backfill -s 2021-12-01 -e 2021-12-01 --reset-dagruns --yes test_module /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528: DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. option = self._get_environment_variables(deprecated_key, deprecated_section, key, section) /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. /usr/local/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py:57 PendingDeprecationWarning: --ignore-first-depends-on-past is deprecated as the value is always set to True [2022-08-25 21:30:46,895] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dag /usr/local/lib/python3.9/site-packages/airflow/configuration.py:528 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. [2022-08-25 21:30:46,996] {executor_loader.py:105} INFO - Loaded executor: LocalExecutor [2022-08-25 21:30:47,275] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'test_module', 'run_python', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp3s_3bn80'] [2022-08-25 21:30:52,010] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'test_module', 'run_python', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmp3s_3bn80'] [2022-08-25 21:30:52,029] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 1 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 1 [2022-08-25 21:30:52,045] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dags/test_module.py [2022-08-25 21:30:52,140] {task_command.py:371} INFO - Running <TaskInstance: test_module.run_python backfill__2021-12-01T00:00:00+00:00 [queued]> on host 75512ab5e882 [2022-08-25 21:30:57,028] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 1 | succeeded: 1 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 1 [2022-08-25 21:30:57,048] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'test_module', 'test', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmprxg7g5o8'] [2022-08-25 21:31:02,024] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'test_module', 'test', 'backfill__2021-12-01T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/test_module.py', '--cfg-path', '/tmp/tmprxg7g5o8'] [2022-08-25 21:31:02,032] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 0 | succeeded: 1 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 0 [2022-08-25 21:31:02,085] {dagbag.py:508} INFO - Filling up the DagBag from /usr/local/airflow/dags/test_module.py [2022-08-25 21:31:02,178] {task_command.py:371} INFO - Running <TaskInstance: test_module.test backfill__2021-12-01T00:00:00+00:00 [queued]> on host 75512ab5e882 [2022-08-25 21:31:07,039] {dagrun.py:564} INFO - Marking run <DagRun test_module @ 2021-12-01 00:00:00+00:00: backfill__2021-12-01T00:00:00+00:00, externally triggered: False> successful [2022-08-25 21:31:07,039] {dagrun.py:609} INFO - DagRun Finished: dag_id=test_module, execution_date=2021-12-01 00:00:00+00:00, run_id=backfill__2021-12-01T00:00:00+00:00, run_start_date=None, run_end_date=2022-08-25 21:31:07.039737+00:00, run_duration=None, state=success, external_trigger=False, run_type=backfill, data_interval_start=2021-12-01 00:00:00+00:00, data_interval_end=2021-12-02 00:00:00+00:00, dag_hash=None [2022-08-25 21:31:07,040] {dagrun.py:795} WARNING - Failed to record duration of <DagRun test_module @ 2021-12-01 00:00:00+00:00: backfill__2021-12-01T00:00:00+00:00, externally triggered: False>: start_date is not set. [2022-08-25 21:31:07,040] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 2 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 0 [2022-08-25 21:31:07,043] {local_executor.py:390} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. [2022-08-25 21:31:07,177] {backfill_job.py:879} INFO - Backfill done. Exiting. ``` ### What you think should happen instead When the BackfillJob fetches the dagrun, it will also need to set the start date. It can be done right after setting the run variable. ([source](https://github.com/apache/airflow/blob/main/airflow/jobs/backfill_job.py#L310-L320)) ### How to reproduce Run the backfill command first without `--reset-dagruns --yes` flags. ``` airflow dags backfill -s 2021-12-01 -e 2021-12-01 test_module ``` Run the backfill command with the `--reset-dagruns --yes` flags. ``` airflow dags backfill -s 2021-12-01 -e 2021-12-01 --reset-dagruns --yes test_module ``` ### Operating System N/A ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/26000

https://github.com/apache/airflow/pull/26135

4644a504f2b64754efb40f4c61f8d050f3e7b1b7

2d031ee47bc7af347040069a3162273de308aef6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/schemas/pool_schema.py", "airflow/models/pool.py", "airflow/www/views.py", "tests/api_connexion/endpoints/test_pool_endpoint.py", "tests/api_connexion/schemas/test_pool_schemas.py", "tests/api_connexion/test_auth.py", "tests/www/views/test_views_pool.py"]

Include "Scheduled slots" column in Pools view

### Description It would be nice to have a "Scheduled slots" column to see how many slots want to enter each pool. Currently we are only displaying the running and queued slots. ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25976

https://github.com/apache/airflow/pull/26006

1c73304bdf26b19d573902bcdfefc8ca5160511c

bcdc25dd3fbda568b5ff2c04701623d6bf11a61f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py", "tests/core/test_configuration.py"]

Unable to configure Google Secrets Manager in 2.3.4

### Apache Airflow version 2.3.4 ### What happened I am attempting to configure a Google Secrets Manager secrets backend using the `gcp_keyfile_dict` param in a `.env` file with the following ENV Vars: ``` AIRFLOW__SECRETS__BACKEND=airflow.providers.google.cloud.secrets.secret_manager.CloudSecretManagerBackend AIRFLOW__SECRETS__BACKEND_KWARGS='{"connections_prefix": "airflow-connections", "variables_prefix": "airflow-variables", "gcp_keyfile_dict": <json-keyfile>}' ``` In previous versions including 2.3.3 this worked without issue After upgrading to Astro Runtime 5.0.8 I get the following error taken from the scheduler container logs. The scheduler, webserver, and triggerer are continually restarting ``` Traceback (most recent call last): File "/usr/local/bin/airflow", line 5, in <module> from airflow.__main__ import main File "/usr/local/lib/python3.9/site-packages/airflow/__init__.py", line 35, in <module> from airflow import settings File "/usr/local/lib/python3.9/site-packages/airflow/settings.py", line 35, in <module> from airflow.configuration import AIRFLOW_HOME, WEBSERVER_CONFIG, conf # NOQA F401 File "/usr/local/lib/python3.9/site-packages/airflow/configuration.py", line 1618, in <module> secrets_backend_list = initialize_secrets_backends() File "/usr/local/lib/python3.9/site-packages/airflow/configuration.py", line 1540, in initialize_secrets_backends custom_secret_backend = get_custom_secret_backend() File "/usr/local/lib/python3.9/site-packages/airflow/configuration.py", line 1523, in get_custom_secret_backend return _custom_secrets_backend(secrets_backend_cls, **alternative_secrets_config_dict) TypeError: unhashable type: 'dict' ``` ### What you think should happen instead Containers should remain healthy and the secrets backend should successfully be added ### How to reproduce `astro dev init` a fresh project Dockerfile: `FROM quay.io/astronomer/astro-runtime:5.0.8` `.env` file: ``` AIRFLOW__SECRETS__BACKEND=airflow.providers.google.cloud.secrets.secret_manager.CloudSecretManagerBackend AIRFLOW__SECRETS__BACKEND_KWARGS='{"connections_prefix": "airflow-connections", "variables_prefix": "airflow-variables", "gcp_keyfile_dict": <service-acct-json-keyfile>}' ``` `astro dev start` ### Operating System macOS 11.6.8 ### Versions of Apache Airflow Providers [apache-airflow-providers-google](https://airflow.apache.org/docs/apache-airflow-providers-google/8.1.0/) 8.1.0 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25968

https://github.com/apache/airflow/pull/25970

876536ea3c45d5f15fcfbe81eda3ee01a101faa3

aa877637f40ddbf3b74f99847606b52eb26a92d9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/ecs.py", "airflow/providers/amazon/aws/sensors/ecs.py", "tests/providers/amazon/aws/operators/test_ecs.py"]

Invalid arguments were passed to EcsRunTaskOperator (aws_conn_id)

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==5.0.0 ### Apache Airflow version 2.4.4 ### Operating System linux ### Deployment Docker-Compose ### Deployment details Custom built docker image based on the official one. ### What happened When I was migrating legacy EcsOperator to EcsRunTaskOperator I received this error: ``` airflow.exceptions.AirflowException: Invalid arguments were passed to EcsRunTaskOperator. Invalid arguments were: **kwargs: {'aws_conn_id': 'aws_connection'} ``` From the source code and source code documentation it appears that `aws_conn_id` is a valid argument, but nevertheless the error gets thrown. ### What you think should happen instead EcsRunTaskOperator should work with provided `aws_conn_id` argument. ### How to reproduce Create an instance of EcsRunTaskOperator and provide valid `aws_conn_id` argument. ### Anything else During my investigation I compared current version of ecs module and previous one. From that investigation it's clear that `aws_conn_id` argument was removed from keyword arguments before it was passed to parent classes in the legacy version, but now it's not getting removed. In the end this error is caused by Airflow's BaseOperator receiving unknown argument `aws_conn_id`. [ecs.py L49](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/operators/ecs.py#L49) ``` class EcsBaseOperator(BaseOperator): """This is the base operator for all Elastic Container Service operators.""" def __init__(self, **kwargs): self.aws_conn_id = kwargs.get('aws_conn_id', DEFAULT_CONN_ID) self.region = kwargs.get('region') super().__init__(**kwargs) ``` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25963

https://github.com/apache/airflow/pull/25989

c9c89e5c3be37dd2475abf4214d5efdd2ad48c2a

dbfa6487b820e6c94770404b3ba29ab11ae2a05e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/rds.py", "airflow/providers/amazon/aws/sensors/rds.py", "docs/apache-airflow-providers-amazon/operators/rds.rst", "tests/providers/amazon/aws/operators/test_rds.py", "tests/system/providers/amazon/aws/example_rds_instance.py"]

Add RDS operators/sensors

### Description I think adding the following operators/sensors would benefit companies that need to start/stop RDS instances programmatically. Name | Description | PR :- | :- | :- `RdsStartDbOperator` | Start an instance, and optionally wait for it enter "available" state | #27076 `RdsStopDbOperator` | Start an instance, and optionally wait for it to enter "stopped" state | #27076 `RdsDbSensor` | Wait for the requested status (eg. available, stopped) | #26003 Is this something that would be accepted into the codebase? Please let me know. ### Use case/motivation #### 1. Saving money RDS is expensive. To save money, a company keeps test/dev environment relational databases shutdown until it needs to use them. With Airflow, they can start a database instance before running a workload, then turn it off after the workload finishes (or errors). #### 2. Force RDS to stay shutdown RDS automatically starts a database after 1 week of downtime. A company does not need this feature. They can create a DAG to continuously run the shutdown command on a list of databases instance ids stored in a `Variable`. The alternative is to create a shell script or login to the console and manually shutdown each database every week. #### 3. Making sure a database is running before scheduling workload A company programmatically starts/stops its RDS instances. Before they run a workload, they want to make sure it's running. They can use a sensor to make sure a database is available before attempting to run any jobs that require access. Also, during maintenance windows, RDS instances may be taken offline. Rather than tuning each DAG schedule to run outside of this window, a company can use a sensor to wait until the instance is available. (Yes, the availability check could also take place immediately before the maintenance window.) ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25952

https://github.com/apache/airflow/pull/27076

d4bfccb3c90d889863bb1d1500ad3158fc833aae

a2413cf6ca8b93e491a48af11d769cd13bce8884

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/api/useGridData.test.js", "airflow/www/static/js/api/useGridData.ts"]

Auto-refresh is broken in 2.3.4

### Apache Airflow version 2.3.4 ### What happened In PR #25042 a bug was introduced that prevents auto-refresh from working when tasks of type `scheduled` are running. ### What you think should happen instead Auto-refresh should work for any running or queued task, rather than only manually-scheduled tasks. ### How to reproduce _No response_ ### Operating System linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25949

https://github.com/apache/airflow/pull/25950

e996a88c7b19a1d30c529f5dd126d0a8871f5ce0

37ec752c818d4c42cba6e7fdb2e11cddc198e810

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/hooks/sql.py", "airflow/providers/common/sql/provider.yaml", "airflow/providers/presto/hooks/presto.py", "airflow/providers/presto/provider.yaml", "airflow/providers/sqlite/hooks/sqlite.py", "airflow/providers/sqlite/provider.yaml", "airflow/providers/trino/hooks/trino.py", "airflow/providers/trino/provider.yaml", "generated/provider_dependencies.json"]

TrinoHook uses wrong parameter representation when inserting rows

### Apache Airflow Provider(s) trino ### Versions of Apache Airflow Providers apache-airflow-providers-trino==4.0.0 ### Apache Airflow version 2.3.3 ### Operating System macOS 12.5.1 (21G83) ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened `TrinoHook.insert_rows()` throws a syntax error due to the underlying prepared statement using "%s" as representation for parameters, instead of "?" [which Trino uses](https://trino.io/docs/current/sql/prepare.html#description). ### What you think should happen instead `TrinoHook.insert_rows()` should insert rows using Trino-compatible SQL statements. The following exception is raised currently: `trino.exceptions.TrinoUserError: TrinoUserError(type=USER_ERROR, name=SYNTAX_ERROR, message="line 1:88: mismatched input '%'. Expecting: ')', <expression>, <query>", query_id=xxx)` ### How to reproduce Instantiate an `airflow.providers.trino.hooks.trino.TrinoHook` instance and use it's `insert_rows()` method. Operators using this method internally are also broken: e.g. `airflow.providers.trino.transfers.gcs_to_trino.GCSToTrinoOperator` ### Anything else The issue seems to come from `TrinoHook.insert_rows()` relying on `DbApiHook.insert_rows()`, which uses "%s" to represent query parameters. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25937

https://github.com/apache/airflow/pull/25939

4c3fb1ff2b789320cc2f19bd921ac0335fc8fdf1

a74d9349919b340638f0db01bc3abb86f71c6093

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-docker/decorators/docker.rst", "docs/apache-airflow-providers-docker/index.rst"]

How to guide for @task.docker decorator

### Body Hi. [The documentation for apache-airflow-providers-docker](https://airflow.apache.org/docs/apache-airflow-providers-docker/stable/index.html) does not provide information on how to use the `@task.dockker `decorator. We have this decorator described only in the API reference for this provider and documentation for the apache airflow package. Best regards, Kamil ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/25926

https://github.com/apache/airflow/pull/28251

fd5846d256b6d269b160deb8df67cd3d914188e0

74b69030efbb87e44c411b3563989d722fa20336

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/hooks/sql.py", "tests/providers/common/sql/hooks/test_sqlparse.py", "tests/providers/databricks/hooks/test_databricks_sql.py", "tests/providers/oracle/hooks/test_oracle.py"]

PL/SQL statement stop working after upgrade common-sql to 1.1.0

### Apache Airflow Provider(s) common-sql, oracle ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.1.0 apache-airflow-providers-oracle==3.3.0 ### Apache Airflow version 2.3.3 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened After upgrade provider common-sql==1.0.0 to 1.1.0 version, SQL with DECLARE stop working. Using OracleProvider 3.2.0 with common-sql 1.0.0: ``` [2022-08-19, 13:16:46 -04] {oracle.py:66} INFO - Executing: DECLARE v_sql LONG; BEGIN v_sql := ' create table usr_bi_cgj.dim_tarefa ( id_tarefa NUMBER(22) not null primary key, ds_tarefa VARCHAR2(4000) not NULL ); '; EXECUTE IMMEDIATE v_sql; COMMIT; EXCEPTION WHEN OTHERS THEN EXECUTE IMMEDIATE 'TRUNCATE TABLE usr_bi_cgj.dim_tarefa'; COMMIT; END; [2022-08-19, 13:16:46 -04] {base.py:68} INFO - Using connection ID 'bitjro' for task execution. [2022-08-19, 13:16:46 -04] {sql.py:255} INFO - Running statement: DECLARE v_sql LONG; BEGIN v_sql := ' create table usr_bi_cgj.dim_tarefa ( id_tarefa NUMBER(22) not null primary key, ds_tarefa VARCHAR2(4000) not NULL ); '; EXECUTE IMMEDIATE v_sql; COMMIT; EXCEPTION WHEN OTHERS THEN EXECUTE IMMEDIATE 'TRUNCATE TABLE usr_bi_cgj.dim_tarefa'; COMMIT; END;, parameters: None [2022-08-19, 13:16:46 -04] {sql.py:264} INFO - Rows affected: 0 [2022-08-19, 13:16:46 -04] {taskinstance.py:1420} INFO - Marking task as SUCCESS. dag_id=caixa_tarefa_pje, task_id=cria_temp_dim_tarefa, execution_date=20220819T080000, start_date=20220819T171646, end_date=20220819T171646 [2022-08-19, 13:16:46 -04] {local_task_job.py:156} INFO - Task exited with return code 0 ```  After upgrade OracleProvider to 3.3.0 with common-sql to 1.1.0 version, same statement now throws an exception: ``` [2022-08-20, 14:58:14 ] {sql.py:315} INFO - Running statement: DECLARE v_sql LONG; BEGIN v_sql := ' create table usr_bi_cgj.dim_tarefa ( id_tarefa NUMBER(22) not null primary key, ds_tarefa VARCHAR2(4000) not NULL ); '; EXECUTE IMMEDIATE v_sql; COMMIT; EXCEPTION WHEN OTHERS THEN EXECUTE IMMEDIATE 'TRUNCATE TABLE usr_bi_cgj.dim_tarefa'; COMMIT; END, parameters: None [2022-08-20, 14:58:14 ] {taskinstance.py:1909} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/oracle/operators/oracle.py", line 69, in execute hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/common/sql/hooks/sql.py", line 295, in run self._run_command(cur, sql_statement, parameters) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/common/sql/hooks/sql.py", line 320, in _run_command cur.execute(sql_statement) File "/home/airflow/.local/lib/python3.7/site-packages/oracledb/cursor.py", line 378, in execute impl.execute(self) File "src/oracledb/impl/thin/cursor.pyx", line 121, in oracledb.thin_impl.ThinCursorImpl.execute File "src/oracledb/impl/thin/protocol.pyx", line 375, in oracledb.thin_impl.Protocol._process_single_message File "src/oracledb/impl/thin/protocol.pyx", line 376, in oracledb.thin_impl.Protocol._process_single_message File "src/oracledb/impl/thin/protocol.pyx", line 369, in oracledb.thin_impl.Protocol._process_message oracledb.exceptions.DatabaseError: ORA-06550: linha 17, coluna 3: PLS-00103: Encontrado o símbolo "end-of-file" quando um dos seguintes símbolos era esperado: ; <um identificador> <um identificador delimitado por aspas duplas> O símbolo ";" foi substituído por "end-of-file" para continuar. ```  ### What you think should happen instead I think stripping `;` from statement is causing this error ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25851

https://github.com/apache/airflow/pull/25855

ccdd73ec50ab9fb9d18d1cce7a19a95fdedcf9b9

874a95cc17c3578a0d81c5e034cb6590a92ea310

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/client/api_client.py", "airflow/api/client/json_client.py", "airflow/api/client/local_client.py", "airflow/cli/cli_parser.py", "airflow/cli/commands/dag_command.py", "tests/cli/commands/test_dag_command.py"]

Support overriding `replace_microseconds` parameter for `airflow dags trigger` CLI command

### Description `airflow dags trigger` CLI command always defaults with `replace_microseconds=True` because of the default value in the API. It would be very nice to be able to control this flag from the CLI. ### Use case/motivation We use AWS MWAA. The exposed interface is Airflow CLI (yes, we could also ask to get a different interface from AWS MWAA, but I think this is something that was just overlooked for the CLI?), which does not support overriding `replace_microseconds` parameter when calling `airflow dags trigger` CLI command. For the most part, our dag runs for a given dag do not happen remotely at the same time. However, based on user behavior, they are sometimes triggered within the same second (albeit not microsecond). The first dag run is successfully triggered, but the second dag run fails the `replace_microseconds` parameter is wiping out the microseconds that we pass. Thus, DagRun.find_duplicates returns True for the second dag run that we're trying to trigger, and this raises the `DagRunAlreadyExists` exception. ### Related issues Not quite - they all seem to be around the experimental api and not directly related to the CLI. ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25836

https://github.com/apache/airflow/pull/27640

c30c0b5714e4ee217735649b9405f0f79af63059

b6013c0b8f1064c523af2d905c3f32ff1cbec421

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/s3.py", "tests/providers/amazon/aws/hooks/test_s3.py"]

Airflow Amazon provider S3Hook().download_file() fail when needs encryption arguments (SSECustomerKey etc..)

### Apache Airflow version 2.3.3 ### What happened Bug when trying to use the S3Hook to download a file from S3 with extra parameters for security like an SSECustomerKey. The function [download_file](https://github.com/apache/airflow/blob/dd72e67524c99e34ba4c62bfb554e4caf877d5ec/airflow/providers/amazon/aws/hooks/s3.py#L854) fetches the `extra_args` from `self` where we can specify the security parameters about encryption as a `dict`. But [download_file](https://github.com/apache/airflow/blob/dd72e67524c99e34ba4c62bfb554e4caf877d5ec/airflow/providers/amazon/aws/hooks/s3.py#L854) is calling [get_key()](https://github.com/apache/airflow/blob/dd72e67524c99e34ba4c62bfb554e4caf877d5ec/airflow/providers/amazon/aws/hooks/s3.py#L870) which does not use these `extra_args` when calling the [load() method here](https://github.com/apache/airflow/blob/dd72e67524c99e34ba4c62bfb554e4caf877d5ec/airflow/providers/amazon/aws/hooks/s3.py#L472), this results in a `botocore.exceptions.ClientError: An error occurred (400) when calling the HeadObject operation: Bad Request.` error. This could be fixed like this: load as says [boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Object.load) is calling [S3.Client.head_object()](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Client.head_object) which can handle **kwargs and can have all the arguments below: ``` response = client.head_object( Bucket='string', IfMatch='string', IfModifiedSince=datetime(2015, 1, 1), IfNoneMatch='string', IfUnmodifiedSince=datetime(2015, 1, 1), Key='string', Range='string', VersionId='string', SSECustomerAlgorithm='string', SSECustomerKey='string', RequestPayer='requester', PartNumber=123, ExpectedBucketOwner='string', ChecksumMode='ENABLED' ) ``` An easy fix would be to give the `extra_args` to `get_key` then to `load(**self.extra_args) ` ### What you think should happen instead the extra_args should be used in get_key() and therefore obj.load() ### How to reproduce Try to use the S3Hook as below to download an encrypted file: ``` from airflow.providers.amazon.aws.hooks.s3 import S3Hook extra_args={ 'SSECustomerAlgorithm': 'YOUR_ALGO', 'SSECustomerKey': YOUR_SSE_C_KEY } hook = S3Hook(aws_conn_id=YOUR_S3_CONNECTION, extra_args=extra_args) hook.download_file( key=key, bucket_name=bucket_name, local_path=local_path ) ``` ### Operating System any ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25833

https://github.com/apache/airflow/pull/35037

36c5c111ec00075db30fab7c67ac1b6900e144dc

95980a9bc50c1accd34166ba608bbe2b4ebd6d52

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/operators/sql.py", "tests/providers/common/sql/operators/test_sql.py"]

SQLTableCheckOperator fails for Postgres

### Apache Airflow version 2.3.3 ### What happened `SQLTableCheckOperator` fails when used with Postgres. ### What you think should happen instead From the logs: ``` [2022-08-19, 09:28:14 UTC] {taskinstance.py:1910} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 296, in execute records = hook.get_first(self.sql) File "/usr/local/lib/python3.9/site-packages/airflow/hooks/dbapi.py", line 178, in get_first cur.execute(sql) psycopg2.errors.SyntaxError: subquery in FROM must have an alias LINE 1: SELECT MIN(row_count_check) FROM (SELECT CASE WHEN COUNT(*) ... ^ HINT: For example, FROM (SELECT ...) [AS] foo. ``` ### How to reproduce ```python import pendulum from datetime import timedelta from airflow import DAG from airflow.decorators import task from airflow.providers.common.sql.operators.sql import SQLTableCheckOperator from airflow.providers.postgres.operators.postgres import PostgresOperator _POSTGRES_CONN = "postgresdb" _TABLE_NAME = "employees" default_args = { "owner": "cs", "retries": 3, "retry_delay": timedelta(seconds=15), } with DAG( dag_id="sql_data_quality", start_date=pendulum.datetime(2022, 8, 1, tz="UTC"), schedule_interval=None, ) as dag: create_table = PostgresOperator( task_id="create_table", postgres_conn_id=_POSTGRES_CONN, sql=f""" CREATE TABLE IF NOT EXISTS {_TABLE_NAME} ( employee_name VARCHAR NOT NULL, employment_year INT NOT NULL ); """ ) populate_data = PostgresOperator( task_id="populate_data", postgres_conn_id=_POSTGRES_CONN, sql=f""" INSERT INTO {_TABLE_NAME} VALUES ('Adam', 2021); INSERT INTO {_TABLE_NAME} VALUES ('Chris', 2021); INSERT INTO {_TABLE_NAME} VALUES ('Frank', 2021); INSERT INTO {_TABLE_NAME} VALUES ('Fritz', 2021); INSERT INTO {_TABLE_NAME} VALUES ('Magda', 2022); INSERT INTO {_TABLE_NAME} VALUES ('Phil', 2021); """, ) check_row_count = SQLTableCheckOperator( task_id="check_row_count", conn_id=_POSTGRES_CONN, table=_TABLE_NAME, checks={ "row_count_check": {"check_statement": "COUNT(*) >= 3"} }, ) drop_table = PostgresOperator( task_id="drop_table", trigger_rule="all_done", postgres_conn_id=_POSTGRES_CONN, sql=f"DROP TABLE {_TABLE_NAME};", ) create_table >> populate_data >> check_row_count >> drop_table ``` ### Operating System macOS ### Versions of Apache Airflow Providers `apache-airflow-providers-common-sql==1.0.0` ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25815

https://github.com/apache/airflow/pull/25821

b535262837994ef3faf3993da8f246cce6cfd3d2

dd72e67524c99e34ba4c62bfb554e4caf877d5ec

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/bigquery.py", "tests/providers/google/cloud/operators/test_bigquery.py"]

BigQueryGetDataOperator does not support passing project_id

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.3.0 ### Apache Airflow version 2.3.2 ### Operating System MacOS ### Deployment Other ### Deployment details _No response_ ### What happened Can not actively pass project_id as an argument when using `BigQueryGetDataOperator`. This operator internally fallbacks into `default` project id. ### What you think should happen instead Should let developers pass project_id when needed ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25781

https://github.com/apache/airflow/pull/25782

98a7701942c683f3126f9c4f450c352b510a2734

fc6dfa338a76d02a426e2b7f0325d37ea5e95ac3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/abstractoperator.py", "airflow/models/taskmixin.py", "tests/models/test_baseoperator.py"]

XComs from another task group fail to populate dynamic task mapping metadata

### Apache Airflow version 2.3.3 ### What happened When a task returns a mappable Xcom within a task group, the dynamic task mapping feature (via `.expand`) causes the Airflow Scheduler to infinitely loop with a runtime error: ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 751, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 839, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 921, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1163, in _schedule_dag_run schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 524, in update_state info = self.task_instance_scheduling_decisions(session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 654, in task_instance_scheduling_decisions schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 710, in _get_ready_tis expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 614, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 600, in _resolve_map_lengths raise RuntimeError(f"Failed to populate all mapping metadata; missing: {keys}") RuntimeError: Failed to populate all mapping metadata; missing: 'x' ``` ### What you think should happen instead Xcoms from different task groups should be mappable within other group scopes. ### How to reproduce ``` from airflow import DAG from airflow.decorators import task from airflow.utils.task_group import TaskGroup import pendulum @task def enumerate(x): return [i for i in range(x)] @task def addOne(x): return x+1 with DAG( dag_id="TaskGroupMappingBug", schedule_interval=None, start_date=pendulum.now().subtract(days=1), ) as dag: with TaskGroup(group_id="enumerateNine"): y = enumerate(9) with TaskGroup(group_id="add"): # airflow scheduler throws error here so this is never reached z = addOne.expand(x=y) ``` ### Operating System linux/amd64 via Docker (apache/airflow:2.3.3-python3.9) ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details docker-compose version 1.29.2, build 5becea4c Docker Engine v20.10.14 ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25775

https://github.com/apache/airflow/pull/25793

6e66dd7776707936345927f8fccee3ddb7f23a2b

5c48ed19bd3b554f9c3e881a4d9eb61eeba4295b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py"]

Deadlock in Scheduler Loop when Updating Dag Run

### Apache Airflow version 2.3.3 ### What happened We have been getting occasional deadlock errors in our main scheduler loop that is causing the scheduler to error out of the main scheduler loop and terminate. The full stack trace of the error is below: ``` Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: [2022-08-13 00:01:17,377] {{scheduler_job.py:768}} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: Traceback (most recent call last): Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1800, in _execute_context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: cursor, statement, parameters, context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/dialects/mysql/mysqldb.py", line 193, in do_executemany Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: rowcount = cursor.executemany(statement, parameters) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 239, in executemany Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.rowcount = sum(self.execute(query, arg) for arg in args) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 239, in <genexpr> Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.rowcount = sum(self.execute(query, arg) for arg in args) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 206, in execute Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: res = self._query(query) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 319, in _query Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: db.query(q) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/connections.py", line 259, in query Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: _mysql.connection.query(self, query) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: MySQLdb._exceptions.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction') Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: The above exception was the direct cause of the following exception: Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: Traceback (most recent call last): Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 751, in _execute Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self._run_scheduler_loop() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 839, in _run_scheduler_loop Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: num_queued_tis = self._do_scheduling(session) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 924, in _do_scheduling Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: guard.commit() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/airflow/utils/sqlalchemy.py", line 296, in commit Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.session.commit() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 1451, in commit Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self._transaction.commit(_to_root=self.future) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 829, in commit Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self._prepare_impl() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.session.flush() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3383, in flush Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self._flush(objects) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3523, in _flush Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: transaction.rollback(_capture_exception=True) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/util/langhelpers.py", line 72, in __exit__ Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: with_traceback=exc_tb, Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 208, in raise_ Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: raise exception Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3483, in _flush Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: flush_context.execute() Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: rec.execute(self) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 633, in execute Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: uow, Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 242, in save_obj Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: update, Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 1002, in _emit_update_statements Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: statement, multiparams, execution_options=execution_options Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1631, in _execute_20 Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: return meth(self, args_10style, kwargs_10style, execution_options) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/sql/elements.py", line 333, in _execute_on_connection Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self, multiparams, params, execution_options Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1508, in _execute_clauseelement Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: cache_hit=cache_hit, Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1863, in _execute_context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: e, statement, parameters, cursor, context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 2044, in _handle_dbapi_exception Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: sqlalchemy_exception, with_traceback=exc_info[2], from_=e Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 208, in raise_ Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: raise exception Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1800, in _execute_context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: cursor, statement, parameters, context Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/sqlalchemy/dialects/mysql/mysqldb.py", line 193, in do_executemany Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: rowcount = cursor.executemany(statement, parameters) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 239, in executemany Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.rowcount = sum(self.execute(query, arg) for arg in args) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 239, in <genexpr> Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: self.rowcount = sum(self.execute(query, arg) for arg in args) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 206, in execute Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: res = self._query(query) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/cursors.py", line 319, in _query Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: db.query(q) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: File "/home/ubuntu/.virtualenvs/ycharts/lib/python3.7/site-packages/MySQLdb/connections.py", line 259, in query Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: _mysql.connection.query(self, query) Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: [SQL: UPDATE dag_run SET last_scheduling_decision=%s WHERE dag_run.id = %s] Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: [parameters: ((datetime.datetime(2022, 8, 13, 0, 1, 17, 280720), 9), (datetime.datetime(2022, 8, 13, 0, 1, 17, 213661), 11), (datetime.datetime(2022, 8, 13, 0, 1, 17, 40686), 12))] Aug 13 00:01:17 ip-10-0-2-218 bash[26063]: (Background on this error at: https://sqlalche.me/e/14/e3q8) ``` It appears the issue occurs when attempting to update the `last_scheduling_decision` field of the `dag_run` table, but we are unsure why this would cause a deadlock. This issue has only been occurring when we upgrade to version 2.3.3, this was not an issue with version 2.2.4. ### What you think should happen instead The scheduler loop should not have any deadlocks that cause it to exit out of its main loop and terminate. I would expect the scheduler loop to always be running constantly, which is not the case if a deadlock occurs in this loop. ### How to reproduce This is occurring for us when we run a `LocalExecutor` with smart sensors enabled (2 shards). We only have 3 other daily DAGs which run at different times, and the error seems to occur right when the start time comes for one DAG to start running. After we restart the scheduler after that first deadlock, it seems to run fine the rest of the day, but the next day when it comes time to start the DAG again, another deadlock occurs. ### Operating System Ubuntu 18.04.6 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==4.1.0 apache-airflow-providers-common-sql==1.0.0 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-mysql==3.1.0 apache-airflow-providers-sftp==4.0.0 apache-airflow-providers-sqlite==3.1.0 apache-airflow-providers-ssh==3.1.0 ### Deployment Other ### Deployment details We deploy airflow to 2 different ec2 instances. The scheduler lives on one ec2 instances and the webserver lives on a separate ec2 instance. We only run a single scheduler. ### Anything else This issue occurs once a day when the first of our daily DAGs gets triggered. When we restart the scheduler after the deadlock, it works fine for the rest of the day typically. We use a `LocalExecutor` with a `PARALLELISM` of 32, smart sensors enabled using 2 shards. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25765

https://github.com/apache/airflow/pull/26347

f977804255ca123bfea24774328ba0a9ca63688b

0da49935000476b1d1941b63d0d66d3c58d64fea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/airflow_local_settings.py"]

DeprecationWarning: Passing filename_template to FileTaskHandler is deprecated and has no effect

### Apache Airflow version 2.3.3 ### What happened After upgrading or installing airflow 2.3.3 the remote_logging in airflow.cfg cant be set to true without creating depreciation warning. I'm using remote logging to an s3 bucket. It doesn't matter which version of **apache-airflow-providers-amazon** i have installed. When using systemd units to start the airflow components, the webserver will spam the depreciation warning every second. Tested with Python 3.10 and 3.7.3 ### What you think should happen instead When using the remote logging It should not execute an action every second in the background which seems to be deprecated. ### How to reproduce You could quickly install an Python virtual Environment on a machine of you choice. After that install airflow and apache-airflow-providers-amazon over pip Then change the logging part in the airflow.cfg: **[logging] remote_logging = True** create a testdag.py containing at least: **from airflow import DAG** run it with Python to see the errors: python testdag.py hint: some more deprecationWarnings will appear because the standard airflow.cfg which get created when installing airflow is not the current state. The Deprication warning you should see when turning remote_logging to true is: `.../lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py:52 DeprecationWarning: Passing filename_template to FileTaskHandler is deprecated and has no effect` ### Operating System Debian GNU/Linux 10 (buster) and also tested Fedora release 36 (Thirty Six) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon 4.0.0 ### Deployment Virtualenv installation ### Deployment details Running a small setup. 2 Virtual Machines. Airflow installed over pip inside a Python virtual environment. ### Anything else The Problem occurs every dag run and it gets logged every second inside the journal produced by the webserver systemd unit. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25743

https://github.com/apache/airflow/pull/25764

0267a47e5abd104891e0ec6c741b5bed208eef1e

da616a1421c71c8ec228fefe78a0a90263991423

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/bigquery_dts.py"]

Incorrect config name generated for BigQueryDeleteDataTransferConfigOperator

### Apache Airflow version 2.3.3 ### What happened When we try to delete a big query transfer config using BigQueryDeleteDataTransferConfigOperator, we are unable to find the config, as the generated transfer config name is erroneous. As a result, although a transfer config id (that exists) is passed to the operator, we get an error saying that the transfer config doesn't exist. ### What you think should happen instead On further analysis, it was revealed that, in the bigquery_dts hook, the project name is incorrectly created as follows on the line 171: `project = f"/{project}/locations/{self.location}"` That is there's an extra / prefixed to the project. Removing the extra / shall fix this bug. ### How to reproduce 1. Create a transfer config in the BQ data transfers/or use the operator BigQueryCreateDataTransferOperator (in a project located in Europe). 2. Try to delete the transfer config using the BigQueryDeleteDataTransferConfigOperator by passing the location of the project along with the transfer config id. This step will throw the error. ### Operating System Windows 11 ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25718

https://github.com/apache/airflow/pull/25719

c6e9cdb4d013fec330deb79810dbb735d2c01482

fa0cb363b860b553af2ef9530ea2de706bd16e5d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/postgres/provider.yaml", "generated/provider_dependencies.json"]

postgres provider: use non-binary psycopg2 (recommended for production use)

### Apache Airflow Provider(s) postgres ### Versions of Apache Airflow Providers apache-airflow-providers-postgres==5.0.0 ### Apache Airflow version 2.3.3 ### Operating System Debian 11 (airflow docker image) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened psycopg2-binary package is installed. ### What you think should happen instead psycopg (non-binary) package is installed. According to the [psycopg2 docs](https://www.psycopg.org/docs/install.html#psycopg-vs-psycopg-binary), (emphasis theirs) "**For production use you are advised to use the source distribution.**". ### How to reproduce Either ``` docker run -it apache/airflow:2.3.3-python3.10 pip freeze |grep -E '(postgres|psycopg2)' ``` Or ``` docker run -it apache/airflow:slim-2.3.3-python3.10 curl -O curl https://raw.githubusercontent.com/apache/airflow/constraints-2.3.3/constraints-3.10.txt pip install -c constraints-3.10.txt apache-airflow-providers-postgres pip freeze |grep -E '(postgres|psycopg2)' ``` Either way, the output is: ``` apache-airflow-providers-postgres==5.0.0 psycopg2-binary==2.9.3 ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25712

https://github.com/apache/airflow/pull/25710

28165eef2ac26c66525849e7bebb55553ea5a451

14d56a5a9e78580c53cf85db504464daccffe21c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/mappedoperator.py", "tests/jobs/test_backfill_job.py", "tests/models/test_dagrun.py", "tests/models/test_taskinstance.py"]

Backfill mode with mapped tasks: "Failed to populate all mapping metadata"

### Apache Airflow version 2.3.3 ### What happened I was backfilling some DAGs that use dynamic tasks when I got an exception like the following: ``` Traceback (most recent call last): File "/opt/conda/envs/production/bin/airflow", line 11, in <module> sys.exit(main()) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py", line 107, in dag_backfill dag.run( File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/dag.py", line 2288, in run job.run() File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/backfill_job.py", line 847, in _execute self._execute_dagruns( File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/backfill_job.py", line 737, in _execute_dagruns processed_dag_run_dates = self._process_backfill_task_instances( File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/backfill_job.py", line 612, in _process_backfill_task_instances for node, run_id, new_mapped_tis, max_map_index in self._manage_executor_state( File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/jobs/backfill_job.py", line 270, in _manage_executor_state new_tis, num_mapped_tis = node.expand_mapped_task(ti.run_id, session=session) File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 614, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 600, in _resolve_map_lengths raise RuntimeError(f"Failed to populate all mapping metadata; missing: {keys}") RuntimeError: Failed to populate all mapping metadata; missing: 'x' ``` Digging further, it appears this always happens if the task used as input to an `.expand` raises an Exception. Airflow doesn't handle this exception gracefully like it does with exceptions in "normal" tasks, which can lead to other errors from deeper within Airflow. This also means that since this is not a "typical" failure case, things like `--rerun-failed-tasks` do not work as expected. ### What you think should happen instead Airflow should fail gracefully if exceptions are raised in dynamic task generators. ### How to reproduce ``` #!/usr/bin/env python3 import datetime import logging from airflow.decorators import dag, task logger = logging.getLogger(__name__) @dag( schedule_interval='@daily', start_date=datetime.datetime(2022, 8, 12), default_args={ 'retries': 5, 'retry_delay': 5.0, }, ) def test_backfill(): @task def get_tasks(ti=None): logger.info(f'{ti.try_number=}') if ti.try_number < 3: raise RuntimeError('') return ['a', 'b', 'c'] @task def do_stuff(x=None, ti=None): logger.info(f'do_stuff: {x=}, {ti.try_number=}') if ti.try_number < 3: raise RuntimeError('') do_stuff.expand(x=do_stuff.expand(x=get_tasks())) do_stuff() >> do_stuff() # this works as expected dag = test_backfill() if __name__ == '__main__': dag.cli() ``` ``` airflow dags backfill test_backfill -s 2022-08-05 -e 2022-08-07 --rerun-failed-tasks ``` You can repeat the `backfill` command multiple times to slowly make progress through the DAG. Things will eventually succeed (assuming the exception that triggers this bug stops being raised), but obviously this is a pain when trying to backfill a non-trivial number of DAG Runs. ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers None ### Deployment Other ### Deployment details Standalone ### Anything else I was able to reproduce this both with SQLite + `SequentialExecutor` as well as with Postgres + `LocalExecutor`. I haven't yet been able to reproduce this outside of `backfill` mode. Possibly related since they mention the same exception text: * #23533 * #23642 ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25698

https://github.com/apache/airflow/pull/25757

d51957165b2836fe0006d318c299c149fb5d35b0

728a3ce5c2f5abdd7aa01864a861ca18b1f27c1b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagrun.py", "tests/models/test_dagrun.py"]

Scheduler enters crash loop in certain cases with dynamic task mapping

### Apache Airflow version 2.3.3 ### What happened The scheduler crashed when attempting to queue a dynamically mapped task which is directly downstream and only dependent on another dynamically mapped task. <details><summary>scheduler.log</summary> ``` scheduler | ____________ _____________ scheduler | ____ |__( )_________ __/__ /________ __ scheduler | ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / scheduler | ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / scheduler | _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ scheduler | [2022-08-11 08:41:10,922] {scheduler_job.py:708} INFO - Starting the scheduler scheduler | [2022-08-11 08:41:10,923] {scheduler_job.py:713} INFO - Processing each file at most -1 times scheduler | [2022-08-11 08:41:10,926] {executor_loader.py:105} INFO - Loaded executor: SequentialExecutor scheduler | [2022-08-11 08:41:10,929] {manager.py:160} INFO - Launched DagFileProcessorManager with pid: 52386 scheduler | [2022-08-11 08:41:10,932] {scheduler_job.py:1233} INFO - Resetting orphaned tasks for active dag runs scheduler | [2022-08-11 08:41:11 -0600] [52385] [INFO] Starting gunicorn 20.1.0 scheduler | [2022-08-11 08:41:11 -0600] [52385] [INFO] Listening at: http://0.0.0.0:8793 (52385) scheduler | [2022-08-11 08:41:11 -0600] [52385] [INFO] Using worker: sync scheduler | [2022-08-11 08:41:11 -0600] [52387] [INFO] Booting worker with pid: 52387 scheduler | [2022-08-11 08:41:11,656] {settings.py:55} INFO - Configured default timezone Timezone('UTC') scheduler | [2022-08-11 08:41:11,659] {manager.py:406} WARNING - Because we cannot use more than 1 thread (parsing_processes = 2) when using sqlite. So we set parallelism to 1. scheduler | [2022-08-11 08:41:11 -0600] [52388] [INFO] Booting worker with pid: 52388 scheduler | [2022-08-11 08:41:28,118] {dag.py:2968} INFO - Setting next_dagrun for bug_test to 2022-08-11T14:00:00+00:00, run_after=2022-08-11T15:00:00+00:00 scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:353} INFO - 20 tasks up for execution: scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=0 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=1 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=2 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=3 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=4 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=5 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=6 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=7 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=8 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=9 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=10 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=11 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=12 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=13 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=14 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=15 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [scheduled]> scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 0/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 1/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 2/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 3/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 4/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 5/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 6/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 7/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 8/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 9/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 10/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 11/16 running and queued tasks scheduler | [2022-08-11 08:41:28,161] {scheduler_job.py:418} INFO - DAG bug_test has 12/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 13/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 14/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 15/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 16/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:425} INFO - Not executing <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [scheduled]> since the number of tasks running or queued from DAG bug_test is >= to the DAG's max_active_tasks limit of 16 scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 16/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:425} INFO - Not executing <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [scheduled]> since the number of tasks running or queued from DAG bug_test is >= to the DAG's max_active_tasks limit of 16 scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 16/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:425} INFO - Not executing <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [scheduled]> since the number of tasks running or queued from DAG bug_test is >= to the DAG's max_active_tasks limit of 16 scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:418} INFO - DAG bug_test has 16/16 running and queued tasks scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:425} INFO - Not executing <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [scheduled]> since the number of tasks running or queued from DAG bug_test is >= to the DAG's max_active_tasks limit of 16 scheduler | [2022-08-11 08:41:28,162] {scheduler_job.py:504} INFO - Setting the following tasks to queued state: scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=0 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=1 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=2 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=3 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=4 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=5 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=6 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=7 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=8 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=9 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=10 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=11 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=12 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=13 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=14 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=15 [scheduled]> scheduler | [2022-08-11 08:41:28,164] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=0) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,165] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '0'] scheduler | [2022-08-11 08:41:28,165] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=1) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,165] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '1'] scheduler | [2022-08-11 08:41:28,165] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=2) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,165] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '2'] scheduler | [2022-08-11 08:41:28,165] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=3) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,165] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '3'] scheduler | [2022-08-11 08:41:28,165] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=4) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,166] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '4'] scheduler | [2022-08-11 08:41:28,166] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=5) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,166] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '5'] scheduler | [2022-08-11 08:41:28,166] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=6) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,166] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '6'] scheduler | [2022-08-11 08:41:28,166] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=7) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,166] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '7'] scheduler | [2022-08-11 08:41:28,167] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=8) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,167] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '8'] scheduler | [2022-08-11 08:41:28,167] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=9) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,167] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '9'] scheduler | [2022-08-11 08:41:28,167] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=10) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,167] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '10'] scheduler | [2022-08-11 08:41:28,167] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=11) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,167] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '11'] scheduler | [2022-08-11 08:41:28,167] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=12) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,167] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '12'] scheduler | [2022-08-11 08:41:28,168] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=13) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,168] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '13'] scheduler | [2022-08-11 08:41:28,168] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=14) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,168] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '14'] scheduler | [2022-08-11 08:41:28,168] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=15) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:28,168] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '15'] scheduler | [2022-08-11 08:41:28,170] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '0'] scheduler | [2022-08-11 08:41:29,131] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:29,227] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=0 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:29,584] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '1'] scheduler | [2022-08-11 08:41:30,492] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:30,593] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=1 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:30,969] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '2'] scheduler | [2022-08-11 08:41:31,852] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:31,940] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=2 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:32,308] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '3'] scheduler | [2022-08-11 08:41:33,199] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:33,289] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=3 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:33,656] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '4'] scheduler | [2022-08-11 08:41:34,535] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:34,631] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=4 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:35,013] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '5'] scheduler | [2022-08-11 08:41:35,928] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:36,024] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=5 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:36,393] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '6'] scheduler | [2022-08-11 08:41:37,296] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:37,384] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=6 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:37,758] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '7'] scheduler | [2022-08-11 08:41:38,642] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:38,732] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=7 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:39,113] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '8'] scheduler | [2022-08-11 08:41:39,993] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:40,086] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=8 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:40,461] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '9'] scheduler | [2022-08-11 08:41:41,383] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:41,473] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=9 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:41,865] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '10'] scheduler | [2022-08-11 08:41:42,761] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:42,858] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=10 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:43,236] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '11'] scheduler | [2022-08-11 08:41:44,124] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:44,222] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=11 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:44,654] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '12'] scheduler | [2022-08-11 08:41:45,545] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:45,635] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=12 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:45,998] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '13'] scheduler | [2022-08-11 08:41:46,867] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:46,955] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=13 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:47,386] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '14'] scheduler | [2022-08-11 08:41:48,270] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:48,362] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=14 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:48,718] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '15'] scheduler | [2022-08-11 08:41:49,569] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:49,669] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=15 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:50,022] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,022] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,022] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,022] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,023] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:50,036] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=0, run_start_date=2022-08-11 14:41:29.255370+00:00, run_end_date=2022-08-11 14:41:29.390095+00:00, run_duration=0.134725, state=success, executor_state=success, try_number=1, max_tries=0, job_id=5, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52421 scheduler | [2022-08-11 08:41:50,036] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=1, run_start_date=2022-08-11 14:41:30.628702+00:00, run_end_date=2022-08-11 14:41:30.768539+00:00, run_duration=0.139837, state=success, executor_state=success, try_number=1, max_tries=0, job_id=6, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52423 scheduler | [2022-08-11 08:41:50,036] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=2, run_start_date=2022-08-11 14:41:31.968933+00:00, run_end_date=2022-08-11 14:41:32.112968+00:00, run_duration=0.144035, state=success, executor_state=success, try_number=1, max_tries=0, job_id=7, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52425 scheduler | [2022-08-11 08:41:50,036] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=3, run_start_date=2022-08-11 14:41:33.318972+00:00, run_end_date=2022-08-11 14:41:33.458203+00:00, run_duration=0.139231, state=success, executor_state=success, try_number=1, max_tries=0, job_id=8, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52429 scheduler | [2022-08-11 08:41:50,036] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=4, run_start_date=2022-08-11 14:41:34.663829+00:00, run_end_date=2022-08-11 14:41:34.811273+00:00, run_duration=0.147444, state=success, executor_state=success, try_number=1, max_tries=0, job_id=9, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52437 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=5, run_start_date=2022-08-11 14:41:36.056658+00:00, run_end_date=2022-08-11 14:41:36.203243+00:00, run_duration=0.146585, state=success, executor_state=success, try_number=1, max_tries=0, job_id=10, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52440 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=6, run_start_date=2022-08-11 14:41:37.412705+00:00, run_end_date=2022-08-11 14:41:37.550794+00:00, run_duration=0.138089, state=success, executor_state=success, try_number=1, max_tries=0, job_id=11, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52442 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=7, run_start_date=2022-08-11 14:41:38.761691+00:00, run_end_date=2022-08-11 14:41:38.897424+00:00, run_duration=0.135733, state=success, executor_state=success, try_number=1, max_tries=0, job_id=12, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52446 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=8, run_start_date=2022-08-11 14:41:40.119057+00:00, run_end_date=2022-08-11 14:41:40.262712+00:00, run_duration=0.143655, state=success, executor_state=success, try_number=1, max_tries=0, job_id=13, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52450 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=9, run_start_date=2022-08-11 14:41:41.502857+00:00, run_end_date=2022-08-11 14:41:41.641680+00:00, run_duration=0.138823, state=success, executor_state=success, try_number=1, max_tries=0, job_id=14, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52452 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=10, run_start_date=2022-08-11 14:41:42.889206+00:00, run_end_date=2022-08-11 14:41:43.030804+00:00, run_duration=0.141598, state=success, executor_state=success, try_number=1, max_tries=0, job_id=15, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52454 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=11, run_start_date=2022-08-11 14:41:44.255197+00:00, run_end_date=2022-08-11 14:41:44.413457+00:00, run_duration=0.15826, state=success, executor_state=success, try_number=1, max_tries=0, job_id=16, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52461 scheduler | [2022-08-11 08:41:50,037] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=12, run_start_date=2022-08-11 14:41:45.665373+00:00, run_end_date=2022-08-11 14:41:45.803094+00:00, run_duration=0.137721, state=success, executor_state=success, try_number=1, max_tries=0, job_id=17, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52463 scheduler | [2022-08-11 08:41:50,038] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=13, run_start_date=2022-08-11 14:41:46.988348+00:00, run_end_date=2022-08-11 14:41:47.159584+00:00, run_duration=0.171236, state=success, executor_state=success, try_number=1, max_tries=0, job_id=18, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52465 scheduler | [2022-08-11 08:41:50,038] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=14, run_start_date=2022-08-11 14:41:48.393004+00:00, run_end_date=2022-08-11 14:41:48.533408+00:00, run_duration=0.140404, state=success, executor_state=success, try_number=1, max_tries=0, job_id=19, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52472 scheduler | [2022-08-11 08:41:50,038] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=15, run_start_date=2022-08-11 14:41:49.699253+00:00, run_end_date=2022-08-11 14:41:49.833084+00:00, run_duration=0.133831, state=success, executor_state=success, try_number=1, max_tries=0, job_id=20, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:28.163024+00:00, queued_by_job_id=4, pid=52476 scheduler | [2022-08-11 08:41:51,632] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=0 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=1 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=2 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=3 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=4 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=5 [success]>' scheduler | [2022-08-11 08:41:51,633] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=6 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=7 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=8 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=9 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=10 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=11 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=12 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=13 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=14 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=15 [success]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [scheduled]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [scheduled]>' scheduler | [2022-08-11 08:41:51,634] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [scheduled]>' scheduler | [2022-08-11 08:41:51,635] {dagrun.py:912} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [scheduled]>' scheduler | [2022-08-11 08:41:51,636] {dagrun.py:937} INFO - Restoring mapped task '<TaskInstance: bug_test.do_something_else scheduled__2022-08-11T13:00:00+00:00 [None]>' scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:353} INFO - 4 tasks up for execution: scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [scheduled]> scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:418} INFO - DAG bug_test has 0/16 running and queued tasks scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:418} INFO - DAG bug_test has 1/16 running and queued tasks scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:418} INFO - DAG bug_test has 2/16 running and queued tasks scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:418} INFO - DAG bug_test has 3/16 running and queued tasks scheduler | [2022-08-11 08:41:51,688] {scheduler_job.py:504} INFO - Setting the following tasks to queued state: scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [scheduled]> scheduler | <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [scheduled]> scheduler | [2022-08-11 08:41:51,690] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=16) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:51,690] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '16'] scheduler | [2022-08-11 08:41:51,690] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=17) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:51,690] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '17'] scheduler | [2022-08-11 08:41:51,690] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=18) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:51,690] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '18'] scheduler | [2022-08-11 08:41:51,690] {scheduler_job.py:546} INFO - Sending TaskInstanceKey(dag_id='bug_test', task_id='do_something', run_id='scheduled__2022-08-11T13:00:00+00:00', try_number=1, map_index=19) to executor with priority 2 and queue default scheduler | [2022-08-11 08:41:51,690] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '19'] scheduler | [2022-08-11 08:41:51,692] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '16'] scheduler | [2022-08-11 08:41:52,532] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:52,620] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=16 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:53,037] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '17'] scheduler | [2022-08-11 08:41:53,907] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:53,996] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=17 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:54,427] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '18'] scheduler | [2022-08-11 08:41:55,305] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:55,397] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=18 [queued]> on host somehost.com scheduler | [2022-08-11 08:41:55,816] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '19'] scheduler | [2022-08-11 08:41:56,726] {dagbag.py:508} INFO - Filling up the DagBag from /path/to/test/dir/bug_test/dags/bug_test.py scheduler | [2022-08-11 08:41:56,824] {task_command.py:371} INFO - Running <TaskInstance: bug_test.do_something scheduled__2022-08-11T13:00:00+00:00 map_index=19 [queued]> on host somehost.com scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | cursor.execute(statement, parameters) scheduler | sqlite3.IntegrityError: UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | scheduler | The above exception was the direct cause of the following exception: scheduler | scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/bin/airflow", line 8, in <module> scheduler | sys.exit(main()) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main scheduler | args.func(args) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper scheduler | return f(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 377, in task_run scheduler | _run_task_by_selected_method(args, dag, ti) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 183, in _run_task_by_selected_method scheduler | _run_task_by_local_task_job(args, ti) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 241, in _run_task_by_local_task_job scheduler | run_job.run() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run scheduler | self._execute() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 133, in _execute scheduler | self.handle_task_exit(return_code) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 171, in handle_task_exit scheduler | self._run_mini_scheduler_on_child_tasks() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper scheduler | return func(*args, session=session, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 261, in _run_mini_scheduler_on_child_tasks scheduler | info = dag_run.task_instance_scheduling_decisions(session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 654, in task_instance_scheduling_decisions scheduler | schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 710, in _get_ready_tis scheduler | expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 683, in expand_mapped_task scheduler | session.flush() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3345, in flush scheduler | self._flush(objects) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3485, in _flush scheduler | transaction.rollback(_capture_exception=True) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ scheduler | compat.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3445, in _flush scheduler | flush_context.execute() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute scheduler | rec.execute(self) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute scheduler | util.preloaded.orm_persistence.save_obj( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 236, in save_obj scheduler | _emit_update_statements( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 1000, in _emit_update_statements scheduler | c = connection._execute_20( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1614, in _execute_20 scheduler | return meth(self, args_10style, kwargs_10style, execution_options) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/sql/elements.py", line 325, in _execute_on_connection scheduler | return connection._execute_clauseelement( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1481, in _execute_clauseelement scheduler | ret = self._execute_context( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1845, in _execute_context scheduler | self._handle_dbapi_exception( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 2026, in _handle_dbapi_exception scheduler | util.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | cursor.execute(statement, parameters) scheduler | sqlalchemy.exc.IntegrityError: (sqlite3.IntegrityError) UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | [SQL: UPDATE task_instance SET map_index=? WHERE task_instance.task_id = ? AND task_instance.dag_id = ? AND task_instance.run_id = ? AND task_instance.map_index = ?] scheduler | [parameters: (0, 'do_something_else', 'bug_test', 'scheduled__2022-08-11T13:00:00+00:00', -1)] scheduler | (Background on this error at: https://sqlalche.me/e/14/gkpj) scheduler | [2022-08-11 08:41:57,311] {sequential_executor.py:66} ERROR - Failed to execute task Command '['airflow', 'tasks', 'run', 'bug_test', 'do_something', 'scheduled__2022-08-11T13:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/bug_test.py', '--map-index', '19']' returned non-zero exit status 1.. scheduler | [2022-08-11 08:41:57,313] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:57,313] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:57,313] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status success for try_number 1 scheduler | [2022-08-11 08:41:57,313] {scheduler_job.py:599} INFO - Executor reports execution of bug_test.do_something run_id=scheduled__2022-08-11T13:00:00+00:00 exited with status failed for try_number 1 scheduler | [2022-08-11 08:41:57,321] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=16, run_start_date=2022-08-11 14:41:52.649415+00:00, run_end_date=2022-08-11 14:41:52.787286+00:00, run_duration=0.137871, state=success, executor_state=success, try_number=1, max_tries=0, job_id=21, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:51.688924+00:00, queued_by_job_id=4, pid=52479 scheduler | [2022-08-11 08:41:57,321] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=17, run_start_date=2022-08-11 14:41:54.027712+00:00, run_end_date=2022-08-11 14:41:54.170371+00:00, run_duration=0.142659, state=success, executor_state=success, try_number=1, max_tries=0, job_id=22, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:51.688924+00:00, queued_by_job_id=4, pid=52484 scheduler | [2022-08-11 08:41:57,321] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=18, run_start_date=2022-08-11 14:41:55.426712+00:00, run_end_date=2022-08-11 14:41:55.566833+00:00, run_duration=0.140121, state=success, executor_state=success, try_number=1, max_tries=0, job_id=23, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:51.688924+00:00, queued_by_job_id=4, pid=52488 scheduler | [2022-08-11 08:41:57,321] {scheduler_job.py:642} INFO - TaskInstance Finished: dag_id=bug_test, task_id=do_something, run_id=scheduled__2022-08-11T13:00:00+00:00, map_index=19, run_start_date=2022-08-11 14:41:56.859387+00:00, run_end_date=2022-08-11 14:41:57.018604+00:00, run_duration=0.159217, state=success, executor_state=failed, try_number=1, max_tries=0, job_id=24, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-08-11 14:41:51.688924+00:00, queued_by_job_id=4, pid=52490 scheduler | [2022-08-11 08:41:57,403] {scheduler_job.py:768} ERROR - Exception when executing SchedulerJob._run_scheduler_loop scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | cursor.execute(statement, parameters) scheduler | sqlite3.IntegrityError: UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | scheduler | The above exception was the direct cause of the following exception: scheduler | scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 751, in _execute scheduler | self._run_scheduler_loop() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 839, in _run_scheduler_loop scheduler | num_queued_tis = self._do_scheduling(session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 921, in _do_scheduling scheduler | callback_to_run = self._schedule_dag_run(dag_run, session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1163, in _schedule_dag_run scheduler | schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 524, in update_state scheduler | info = self.task_instance_scheduling_decisions(session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 654, in task_instance_scheduling_decisions scheduler | schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 710, in _get_ready_tis scheduler | expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 683, in expand_mapped_task scheduler | session.flush() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3345, in flush scheduler | self._flush(objects) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3485, in _flush scheduler | transaction.rollback(_capture_exception=True) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ scheduler | compat.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3445, in _flush scheduler | flush_context.execute() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute scheduler | rec.execute(self) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute scheduler | util.preloaded.orm_persistence.save_obj( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 236, in save_obj scheduler | _emit_update_statements( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 1000, in _emit_update_statements scheduler | c = connection._execute_20( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1614, in _execute_20 scheduler | return meth(self, args_10style, kwargs_10style, execution_options) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/sql/elements.py", line 325, in _execute_on_connection scheduler | return connection._execute_clauseelement( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1481, in _execute_clauseelement scheduler | ret = self._execute_context( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1845, in _execute_context scheduler | self._handle_dbapi_exception( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 2026, in _handle_dbapi_exception scheduler | util.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | cursor.execute(statement, parameters) scheduler | sqlalchemy.exc.IntegrityError: (sqlite3.IntegrityError) UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | [SQL: UPDATE task_instance SET map_index=? WHERE task_instance.task_id = ? AND task_instance.dag_id = ? AND task_instance.run_id = ? AND task_instance.map_index = ?] scheduler | [parameters: (0, 'do_something_else', 'bug_test', 'scheduled__2022-08-11T13:00:00+00:00', -1)] scheduler | (Background on this error at: https://sqlalche.me/e/14/gkpj) scheduler | [2022-08-11 08:41:58,421] {process_utils.py:125} INFO - Sending Signals.SIGTERM to group 52386. PIDs of all processes in the group: [52386] scheduler | [2022-08-11 08:41:58,421] {process_utils.py:80} INFO - Sending the signal Signals.SIGTERM to group 52386 scheduler | [2022-08-11 08:41:58,609] {process_utils.py:75} INFO - Process psutil.Process(pid=52386, status='terminated', exitcode=0, started='08:41:10') (52386) terminated with exit code 0 scheduler | [2022-08-11 08:41:58,609] {scheduler_job.py:780} INFO - Exited execute loop scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | [2022-08-11 08:41:58 -0600] [52385] [INFO] Handling signal: term scheduler | cursor.execute(statement, parameters) scheduler | sqlite3.IntegrityError: UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | scheduler | The above exception was the direct cause of the following exception: scheduler | scheduler | Traceback (most recent call last): scheduler | File "/path/to/test/dir/bug_test/.env/bin/airflow", line 8, in <module> scheduler | sys.exit(main()) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main scheduler | args.func(args) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper scheduler | return f(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler scheduler | _run_scheduler_job(args=args) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job scheduler | job.run() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run scheduler | self._execute() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 751, in _execute scheduler | self._run_scheduler_loop() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 839, in _run_scheduler_loop scheduler | num_queued_tis = self._do_scheduling(session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 921, in _do_scheduling scheduler | [2022-08-11 08:41:58 -0600] [52387] [INFO] Worker exiting (pid: 52387) scheduler | [2022-08-11 08:41:58 -0600] [52388] [INFO] Worker exiting (pid: 52388) scheduler | callback_to_run = self._schedule_dag_run(dag_run, session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1163, in _schedule_dag_run scheduler | schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 524, in update_state scheduler | info = self.task_instance_scheduling_decisions(session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper scheduler | return func(*args, **kwargs) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 654, in task_instance_scheduling_decisions scheduler | schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/dagrun.py", line 710, in _get_ready_tis scheduler | expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 683, in expand_mapped_task scheduler | session.flush() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3345, in flush scheduler | self._flush(objects) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3485, in _flush scheduler | transaction.rollback(_capture_exception=True) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ scheduler | compat.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 3445, in _flush scheduler | flush_context.execute() scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute scheduler | rec.execute(self) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute scheduler | util.preloaded.orm_persistence.save_obj( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 236, in save_obj scheduler | _emit_update_statements( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/orm/persistence.py", line 1000, in _emit_update_statements scheduler | c = connection._execute_20( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1614, in _execute_20 scheduler | return meth(self, args_10style, kwargs_10style, execution_options) scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/sql/elements.py", line 325, in _execute_on_connection scheduler | return connection._execute_clauseelement( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1481, in _execute_clauseelement scheduler | ret = self._execute_context( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1845, in _execute_context scheduler | self._handle_dbapi_exception( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 2026, in _handle_dbapi_exception scheduler | util.raise_( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ scheduler | raise exception scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1802, in _execute_context scheduler | self.dialect.do_execute( scheduler | File "/path/to/test/dir/bug_test/.env/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute scheduler | cursor.execute(statement, parameters) scheduler | sqlalchemy.exc.IntegrityError: (sqlite3.IntegrityError) UNIQUE constraint failed: task_instance.dag_id, task_instance.task_id, task_instance.run_id, task_instance.map_index scheduler | [SQL: UPDATE task_instance SET map_index=? WHERE task_instance.task_id = ? AND task_instance.dag_id = ? AND task_instance.run_id = ? AND task_instance.map_index = ?] scheduler | [parameters: (0, 'do_something_else', 'bug_test', 'scheduled__2022-08-11T13:00:00+00:00', -1)] scheduler | (Background on this error at: https://sqlalche.me/e/14/gkpj) scheduler | [2022-08-11 08:41:58 -0600] [52385] [INFO] Shutting down: Master ``` </details> ### What you think should happen instead The scheduler does not crash and the dynamically mapped task executes normally ### How to reproduce ### Setup - one DAG with two tasks, one directly downstream of the other - the DAG has a schedule (e.g. @hourly) - both tasks use task expansion - the second task uses the output of the first task as its expansion parameter - the scheduler's pool size is smaller than the number of map indices in each task ### Steps to reproduce 1. enable the DAG and let it run ### Operating System MacOS and Dockerized Linux on MacOS ### Versions of Apache Airflow Providers None ### Deployment Other ### Deployment details I have tested and confirmed this bug is present in three separate deployments: 1. `airflow standalone` 2. DaskExecutor using docker compose 3. KubernetesExecutor using Docker Desktop's builtin Kubernetes cluster All three of these deployments were executed locally on a Macbook Pro. ### 1. `airflow standalone` I created a new Python 3.9 virtual environment, installed Airflow 2.3.3, configured a few environment variables, and executed `airflow standalone`. Here is a bash script that completes all of these tasks: <details><summary>airflow_standalone.sh</summary> ```bash #!/bin/bash # ensure working dir is correct DIR=$(cd $(dirname $BASH_SOURCE[0]) && pwd) cd $DIR set -x # set version parameters AIRFLOW_VERSION="2.3.3" PYTHON_VERSION="3.9" # configure Python environment if [ ~ -d "$DIR/.env" ] then python3 -m venv "$DIR/.env" fi source "$DIR/.env/bin/activate" pip install --upgrade pip # install Airflow CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt" pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}" # configure Airflow export AIRFLOW_HOME="$DIR/.airflow" export AIRFLOW__CORE__DAGS_FOLDER="$DIR/dags" export AIRFLOW__CORE__LOAD_EXAMPLES="False" export AIRFLOW__DATABASE__LOAD_DEFAULT_CONNECTIONS="False" # start Airflow exec "$DIR/.env/bin/airflow" standalone ``` </details> Here is the DAG code that can be placed in a `dags` directory in the same location as the above script. Note that this DAG code triggers the bug in all environments I tested. <details><summary>bug_test.py</summary> ```python import pendulum from airflow.decorators import dag, task @dag( 'bug_test', schedule_interval='@hourly', start_date=pendulum.now().add(hours=-2) ) def test_scheduler_bug(): @task def do_something(i): return i + 10 @task def do_something_else(i): import logging log = logging.getLogger('airflow.task') log.info("I'll never run") nums = do_something.expand(i=[i+1 for i in range(20)]) do_something_else.expand(i=nums) TEST_DAG = test_scheduler_bug() ``` </details> Once set up, simply activating the DAG will demonstrate the bug. ### 2. DaskExecutor on docker compose with Postgres 12 I cannot provide a full replication of this setup as it is rather in depth. The Docker image is starts from `python:3.9-slim` then installs Airflow with appropriate constraints. It has a lot of additional packages installed, both system and python. It also has a custom entrypoint that can run the Dask scheduler in addition to regular Airflow commands. Here are the applicable Airflow configuration values: <details><summary>airflow.cfg</summary> ```conf [core] donot_pickle = False executor = DaskExecutor load_examples = False max_active_tasks_per_dag = 16 parallelism = 4 [scheduler] dag_dir_list_interval = 0 catchup_by_default = False parsing_processes = 3 scheduler_health_check_threshold = 90 ``` </details> Here is a docker-compose file that is nearly identical to the one I use (I just removed unrelated bits): <details><summary>docker-compose.yml</summary> ```yml version: '3.7' services: metastore: image: postgres:12-alpine ports: - 5432:5432 container_name: airflow-metastore volumes: - ${AIRFLOW_HOME_DIR}/pgdata:/var/lib/postgresql/data environment: POSTGRES_USER: airflow POSTGRES_PASSWORD: ${AIRFLOW_DB_PASSWORD} PGDATA: /var/lib/postgresql/data/pgdata airflow-webserver: image: 'my_custom_image:tag' ports: - '8080:8080' depends_on: - metastore container_name: airflow-webserver environment: AIRFLOW_HOME: /opt/airflow AIRFLOW__WEBSERVER__SECRET_KEY: ${AIRFLOW_SECRET_KEY} AIRFLOW__CORE__FERNET_KEY: ${FERNET_KEY} AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:${AIRFLOW_DB_PASSWORD}@metastore:5432/${AIRFLOW_DB_DATABASE} env_file: container_vars.env command: - webserver - --daemon - --access-logfile - /var/log/airflow/webserver-access.log - --error-logfile - /var/log/airflow/webserver-errors.log - --log-file - /var/log/airflow/webserver.log volumes: - ${AIRFLOW_HOME_DIR}/logs:/var/log/airflow airflow-scheduler: image: 'my_custom_image:tag' depends_on: - metastore - dask-scheduler container_name: airflow-scheduler environment: AIRFLOW_HOME: /opt/airflow AIRFLOW__CORE__FERNET_KEY: ${FERNET_KEY} AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:${AIRFLOW_DB_PASSWORD}@metastore:5432/${AIRFLOW_DB_DATABASE} SCHEDULER_RESTART_INTERVAL: ${SCHEDULER_RESTART_INTERVAL} env_file: container_vars.env restart: unless-stopped command: - scheduler - --daemon - --log-file - /var/log/airflow/scheduler.log volumes: - ${AIRFLOW_HOME_DIR}/logs:/var/log/airflow dask-scheduler: image: 'my_custom_image:tag' ports: - 8787:8787 container_name: airflow-dask-scheduler command: - dask-scheduler dask-worker: image: 'my_custom_image:tag' depends_on: - dask-scheduler - metastore container_name: airflow-worker environment: AIRFLOW_HOME: /opt/airflow AIRFLOW__CORE__FERNET_KEY: ${FERNET_KEY} AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:${AIRFLOW_DB_PASSWORD}@metastore:5432/${AIRFLOW_DB_DATABASE} env_file: container_vars.env command: - dask-worker - dask-scheduler:8786 - --nprocs - '8' - --nthreads - '1' volumes: - ${AIRFLOW_HOME_DIR}/logs:/var/log/airflow ``` </details> I also had to manually change the default pool size to 15 in the UI in order to trigger the bug. With the default pool set to 128 the bug did not trigger. ### 3. KubernetesExecutor on Docker Desktop builtin Kubernetes cluster with Postgres 11 This uses the official [Airflow Helm Chart](https://airflow.apache.org/docs/helm-chart/stable/index.html) with the following values overrides: <details><summary>values.yaml</summary> ```yml defaultAirflowRepository: my_custom_image defaultAirflowTag: "my_image_tag" airflowVersion: "2.3.3" executor: "KubernetesExecutor" webserverSecretKeySecretName: airflow-webserver-secret-key fernetKeySecretName: airflow-fernet-key config: webserver: expose_config: 'True' base_url: http://localhost:8080 scheduler: catchup_by_default: 'False' api: auth_backends: airflow.api.auth.backend.default triggerer: enabled: false statsd: enabled: false redis: enabled: false cleanup: enabled: false logs: persistence: enabled: true workers: extraVolumes: - name: airflow-dags hostPath: path: /local/path/to/dags type: Directory extraVolumeMounts: - name: airflow-dags mountPath: /opt/airflow/dags readOnly: true scheduler: extraVolumes: - name: airflow-dags hostPath: path: /local/path/to/dags type: Directory extraVolumeMounts: - name: airflow-dags mountPath: /opt/airflow/dags readOnly: true ``` </details> The docker image is the official `airflow:2.3.3-python3.9` image with a single environment variable modified: ```conf PYTHONPATH="/opt/airflow/dags/repo/dags:${PYTHONPATH}" ``` ### Anything else This is my understanding of the timeline that produces the crash: 1. The scheduler queues some of the subtasks in the first task 1. Some subtasks run and yield their XCom results 1. The scheduler runs, queueing the remainder of the subtasks for the first task and creates some subtasks in the second task using the XCom results produced thus far 1. The remainder of the subtasks from the first task complete 1. The scheduler attempts to recreate all of the subtasks of the second task, including the ones already created, and a unique constraint in the database is violated and the scheduler crashes 1. When the scheduler restarts, it attempts the previous step again and crashes again, thus entering a crash loop It seems that if some but not all subtasks for the second task have been created when the scheduler attempts to queue the mapped task, then the scheduler tries to create all of the subtasks again which causes a unique constraint violation. **NOTES** - IF the scheduler can queue as many or more tasks as there are map indices for the task, then this won't happen. The provided test case succeeded on the DaskExecutor deployment when the default pool was 128, however when I reduced that pool to 15 this bug occurred. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25681

https://github.com/apache/airflow/pull/25788

29c33165a06b7a6233af3657ace4f2bdb4ec27e4

db818ae6665b37cd032aa6d2b0f97232462d41e1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "airflow/cli/commands/dag_command.py", "tests/cli/commands/test_dag_command.py"]

`airflow dags test` command with run confs

### Description Currently, the command [`airflow dags test`](https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#test) doesn't accept any configs to set run confs. We can do that in [`airflow dags trigger`](https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#trigger) command through `--conf` argument. The command `airflow dags test` is really useful when testing DAGs in local machines or CI/CD environment. Can we have that feature for the `airflow dags test` command? ### Use case/motivation We may put run confs same as `airflow dags trigger` command does. Example: ``` $ airflow dags test <DAG_ID> <EXECUTION_DATE> --conf '{"path": "some_path"}' ``` ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25671

https://github.com/apache/airflow/pull/25900

bcdc25dd3fbda568b5ff2c04701623d6bf11a61f

bcc2fe26f6e0b7204bdf73f57d25b4e6c7a69548

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/atlassian/jira/CHANGELOG.rst", "airflow/providers/atlassian/jira/hooks/jira.py", "airflow/providers/atlassian/jira/operators/jira.py", "airflow/providers/atlassian/jira/provider.yaml", "airflow/providers/atlassian/jira/sensors/jira.py", "generated/provider_dependencies.json", "tests/providers/atlassian/jira/hooks/test_jira.py", "tests/providers/atlassian/jira/operators/test_jira.py", "tests/providers/atlassian/jira/sensors/test_jira.py"]

change Jira sdk to official atlassian sdk

### Description Jira is a product of atlassian https://www.atlassian.com/ There are https://github.com/pycontribs/jira/issues and https://github.com/atlassian-api/atlassian-python-api ### Use case/motivation Motivation is that now Airflow use unoffical SDK which is limited only to jira and can't also add operators for the other productions. https://github.com/atlassian-api/atlassian-python-api is the official one and also contains more integrations to other atlassian products https://github.com/atlassian-api/atlassian-python-api/issues/1027 ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25669

https://github.com/apache/airflow/pull/27633

b5338b5825859355b017bed3586d5a42208f1391

f3c68d7e153b8d417edf4cc4a68d18dbc0f30e64

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/hooks/kubernetes.py", "tests/providers/cncf/kubernetes/operators/test_spark_kubernetes.py"]

SparkKubernetesOperator application file attribute "name" is not mandatory

### Apache Airflow version 2.3.3 ### What happened Since commit https://github.com/apache/airflow/commit/3c5bc73579080248b0583d74152f57548aef53a2 the SparkKubernetesOperator application file is expected to have an attribute metadata:name and the operator execution fails with exception `KeyError: 'name'` if it not exists. Please find the example error stack below: ``` [2022-07-27, 12:58:07 UTC] {taskinstance.py:1909} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/bitnami/airflow/venv/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/operators/spark_kubernetes.py", line 69, in execute response = hook.create_custom_object( File "/opt/bitnami/airflow/venv/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/hooks/kubernetes.py", line 316, in create_custom_object name=body_dict["metadata"]["name"], KeyError: 'name' ``` ### What you think should happen instead The operator should start successfully, ignoring the field absence The attribute metadata:name in NOT mandatory, and a pair metadata:generateName can be user instead - please find proof here: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.22/#objectmeta-v1-meta, particularly in the following: ``` GenerateName is an optional prefix, used by the server, to generate a unique name ONLY IF the Name field has not been provided ``` ### How to reproduce Start a DAG with SparkKubernetesOperator with an application file like this in the beginning: ``` apiVersion: sparkoperator.k8s.io/v1beta2 kind: SparkApplication metadata: generateName: spark_app_name [...] ``` ### Operating System linux ### Versions of Apache Airflow Providers apache-airflow==2.3.3 apache-airflow-providers-cncf-kubernetes==4.2.0 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else Every time ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25668

https://github.com/apache/airflow/pull/25787

4dc9b1c592497686dada05e45147b1364ec338ea

2d2f0daad66416d565e874e35b6a487a21e5f7b1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/backfill_job.py", "tests/jobs/test_backfill_job.py"]

Deferrable Operators get stuck as "scheduled" during backfill

### Apache Airflow version 2.3.3 ### What happened If you try to backfill a DAG that uses any [deferrable operators](https://airflow.apache.org/docs/apache-airflow/stable/concepts/deferring.html), those tasks will get indefinitely stuck in a "scheduled" state. If I watch the Grid View, I can see the task state change: "scheduled" (or sometimes "queued") -> "deferred" -> "scheduled". I've tried leaving in this state for over an hour, but there are no further state changes. When the task is stuck like this, the log appears as empty in the web UI. The corresponding log file *does* exist on the worker, but it does not contain any errors or warnings that might point to the source of the problem. Ctrl-C-ing the backfill at this point seems to hang on "Shutting down LocalExecutor; waiting for running tasks to finish." **Force-killing and restarting the backfill will "unstick" the stuck tasks.** However, any deferrable operators downstream of the first will get back into that stuck state, requiring multiple restarts to get everything to complete successfully. ### What you think should happen instead Deferrable operators should work as normal when backfilling. ### How to reproduce ``` #!/usr/bin/env python3 import datetime import logging import pendulum from airflow.decorators import dag, task from airflow.sensors.time_sensor import TimeSensorAsync logger = logging.getLogger(__name__) @dag( schedule_interval='@daily', start_date=datetime.datetime(2022, 8, 10), ) def test_backfill(): time_sensor = TimeSensorAsync( task_id='time_sensor', target_time=datetime.time(0).replace(tzinfo=pendulum.UTC), # midnight - should succeed immediately when the trigger first runs ) @task def some_task(): logger.info('hello') time_sensor >> some_task() dag = test_backfill() if __name__ == '__main__': dag.cli() ``` `airflow dags backfill test_backfill -s 2022-08-01 -e 2022-08-04` ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers None ### Deployment Other ### Deployment details Self-hosted/standalone ### Anything else I was able to reproduce this with the following configurations: * `standalone` mode + SQLite backend + `SequentialExecutor` * `standalone` mode + Postgres backend + `LocalExecutor` * Production deployment (self-hosted) + Postgres backend + `CeleryExecutor` I have not yet found anything telling in any of the backend logs. Possibly related: * #23693 * #23145 * #13542 - A modified version of the workaround mentioned in [this comment](https://github.com/apache/airflow/issues/13542#issuecomment-1011598836) works to unstick the first stuck task. However if you run it multiple times to try to unstick any downstream tasks, it causes the backfill command to crash. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25653

https://github.com/apache/airflow/pull/26205

f01eed6490acd3bb3a58824e7388c4c3cd50ae29

3396d1f822caac7cbeb14e1e67679b8378a84a6c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/dag_audit_log.html", "airflow/www/views.py"]

Improve audit log

### Discussed in https://github.com/apache/airflow/discussions/25638 See the discussion. There are a couple of improvements that can be done: * add atribute to download the log rather than open it in-browser * add .log or similar (.txt?) extension * sort the output * possibly more <div type='discussions-op-text'> ^{Originally posted by **V0lantis** August 10, 2022} ### Apache Airflow version 2.3.3 ### What happened The audit log link crashes because there is too much data displayed. ### What you think should happen instead The windows shouldn't crash ### How to reproduce Display a dag audit log with thousand or millions lines should do the trick ### Operating System ``` NAME="Amazon Linux" VERSION="2" ID="amzn" ID_LIKE="centos rhel fedora" VERSION_ID="2" PRETTY_NAME="Amazon Linux 2" ANSI_COLOR="0;33" CPE_NAME="cpe:2.3:o:amazon:amazon_linux:2" HOME_URL="https://amazonlinux.com/" ``` ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==4.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.1.0 apache-airflow-providers-datadog==3.0.0 apache-airflow-providers-docker==3.0.0 apache-airflow-providers-ftp==3.0.0 apache-airflow-providers-github==2.0.0 apache-airflow-providers-google==8.1.0 apache-airflow-providers-grpc==3.0.0 apache-airflow-providers-hashicorp==3.0.0 apache-airflow-providers-http==3.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-jira==3.0.0 apache-airflow-providers-mysql==3.0.0 apache-airflow-providers-postgres==5.0.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-sftp==3.0.0 apache-airflow-providers-slack==5.0.0 apache-airflow-providers-sqlite==3.0.0 apache-airflow-providers-ssh==3.0.0 apache-airflow-providers-tableau==3.0.0 apache-airflow-providers-zendesk==4.0.0 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details k8s ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md) </div>

https://github.com/apache/airflow/issues/25641

https://github.com/apache/airflow/pull/25856

634b9c03330c8609949f070457e7b99a6e029f26

50016564fa6ab6c6b02bdb0c70fccdf9b75c2f10

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/hooks/batch.py", "airflow/providers/microsoft/azure/operators/batch.py"]

AzureBatchOperator not handling exit code correctly

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers [apache-airflow-providers-microsoft-azure 3.9.0](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/3.9.0/) ### Apache Airflow version v2.3.2 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### What happened I have a task in my Airflow DAG that uses AzureBatchOperator. As `batch_task_command_line` we pass something like `/bin/bash -c "some-script.sh"`. The Azure Batch task correctly executes this command, and runs `some-script.sh`. All good. When `some-script.sh` exits with a non-zero exit code, the Azure Batch task is correctly marked as failed (in Azure Portal), as is the job containing the task. However, in Airflow, the AzureBatchOperator task _always_ shows up as succeeded, ignoring the underlying Azure Batch job or task status. It even shows in the Airflow DAG logs. Below are the logs of a run with the shell script returning a non-zero exit code. Airflow still considers the task to be a SUCCESS. ```sh [2022-08-10, 10:01:27 UTC] {batch.py:362} INFO - Waiting for {hidden} to complete, currently on running state [2022-08-10, 10:01:42 UTC] {taskinstance.py:1395} INFO - Marking task as SUCCESS. dag_id={hidden}, task_id={hidden}, execution_date=20220809T141257, start_date=20220810T100024, end_date=20220810T100142 [2022-08-10, 10:01:42 UTC] {local_task_job.py:156} INFO - Task exited with return code 0 ``` The `some-script.sh` contains the following at the top, so that can't be the issue I think. ```bash #!/bin/bash set -euo pipefail ``` I tried passing `set -e` to the `batch_task_command_line`, so `set -e; /bin/bash -c "some-script.sh"` but that doesn't work, it gives me a `CommandProgramNotFound` exception. ### What you think should happen instead When using AzureBatchOperator, I want this task in Airflow to fail when the command line that is passed to AzureBatchOperator exits with a non-zero exit code. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25635

https://github.com/apache/airflow/pull/25844

810f3847c241453195fa2c27f447ecf7fe06bbfc

afb282aee4329042b273d501586ff27505c16b22

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py"]

MySQL Not Using Correct Index for Scheduler Critical Section Query

### Apache Airflow version Other Airflow 2 version ### What happened Airflow Version: 2.2.5 MySQL Version: 8.0.18 In the Scheduler, we are coming across instances where MySQL is inefficiently optimizing the [critical section task queuing query](https://github.com/apache/airflow/blob/2.2.5/airflow/jobs/scheduler_job.py#L294-L303). When a large number of task instances are scheduled, MySQL failing to use the `ti_state` index to filter the `task_instance` table, resulting in a full table scan (about 7.3 million rows). Normally, when running the critical section query the index on `task_instance.state` is used to filter scheduled `task_instances`. ```bash | -> Limit: 512 row(s) (actual time=5.290..5.413 rows=205 loops=1) -> Sort row IDs: <temporary>.tmp_field_0, <temporary>.execution_date, limit input to 512 row(s) per chunk (actual time=5.289..5.391 rows=205 loops=1) -> Table scan on <temporary> (actual time=0.003..0.113 rows=205 loops=1) -> Temporary table (actual time=5.107..5.236 rows=205 loops=1) -> Nested loop inner join (cost=20251.99 rows=1741) (actual time=0.100..4.242 rows=205 loops=1) -> Nested loop inner join (cost=161.70 rows=12) (actual time=0.071..2.436 rows=205 loops=1) -> Index lookup on task_instance using ti_state (state='scheduled') (cost=80.85 rows=231) (actual time=0.051..1.992 rows=222 loops=1) -> Filter: ((dag_run.run_type <> 'backfill') and (dag_run.state = 'running')) (cost=0.25 rows=0) (actual time=0.002..0.002 rows=1 loops=222) -> Single-row index lookup on dag_run using dag_run_dag_id_run_id_key (dag_id=task_instance.dag_id, run_id=task_instance.run_id) (cost=0.25 rows=1) (actual time=0.001..0.001 rows=1 loops=222) -> Filter: ((dag.is_paused = 0) and (task_instance.dag_id = dag.dag_id)) (cost=233.52 rows=151) (actual time=0.008..0.008 rows=1 loops=205) -> Index range scan on dag (re-planned for each iteration) (cost=233.52 rows=15072) (actual time=0.008..0.008 rows=1 loops=205) 1 row in set, 1 warning (0.03 sec) ``` When a large number of task_instances are in scheduled state at the same time, the index on `task_instance.state` is not being used to filter scheduled `task_instances`. ```bash | -> Limit: 512 row(s) (actual time=12110.251..12110.573 rows=512 loops=1) -> Sort row IDs: <temporary>.tmp_field_0, <temporary>.execution_date, limit input to 512 row(s) per chunk (actual time=12110.250..12110.526 rows=512 loops=1) -> Table scan on <temporary> (actual time=0.005..0.800 rows=1176 loops=1) -> Temporary table (actual time=12109.022..12109.940 rows=1176 loops=1) -> Nested loop inner join (cost=10807.83 rows=3) (actual time=1.328..12097.528 rows=1176 loops=1) -> Nested loop inner join (cost=10785.34 rows=64) (actual time=1.293..12084.371 rows=1193 loops=1) -> Filter: (dag.is_paused = 0) (cost=1371.40 rows=1285) (actual time=0.087..22.409 rows=13264 loops=1) -> Table scan on dag (cost=1371.40 rows=12854) (actual time=0.085..15.796 rows=13508 loops=1) -> Filter: ((task_instance.state = 'scheduled') and (task_instance.dag_id = dag.dag_id)) (cost=0.32 rows=0) (actual time=0.907..0.909 rows=0 loops=13264) -> Index lookup on task_instance using PRIMARY (dag_id=dag.dag_id) (cost=0.32 rows=70) (actual time=0.009..0.845 rows=553 loops=13264) -> Filter: ((dag_run.run_type <> 'backfill') and (dag_run.state = 'running')) (cost=0.25 rows=0) (actual time=0.010..0.011 rows=1 loops=1193) -> Single-row index lookup on dag_run using dag_run_dag_id_run_id_key (dag_id=task_instance.dag_id, run_id=task_instance.run_id) (cost=0.25 rows=1) (actual time=0.009..0.010 rows=1 loops=1193) 1 row in set, 1 warning (12.14 sec) ``` ### What you think should happen instead To resolve this, I added a patch on the `scheduler_job.py` file, adding a MySQL index hint to use the `ti_state` index. ```diff --- /usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py +++ /usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py @@ -293,6 +293,7 @@ class SchedulerJob(BaseJob): # and the dag is not paused query = ( session.query(TI) + .with_hint(TI, 'USE INDEX (ti_state)', dialect_name='mysql') .join(TI.dag_run) .filter(DR.run_type != DagRunType.BACKFILL_JOB, DR.state == DagRunState.RUNNING) .join(TI.dag_model) ``` I think it makes sense to add this index hint upstream. ### How to reproduce Schedule a large number of dag runs and tasks in a short period of time. ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details Airflow 2.2.5 on Kubernetes MySQL Version: 8.0.18 ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25627

https://github.com/apache/airflow/pull/25673

4d9aa3ae48bae124793b1a8ee394150eba0eee9b

134b5551db67f17b4268dce552e87a154aa1e794

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/mappedoperator.py", "tests/models/test_mappedoperator.py"]

Mapped KubernetesPodOperater not rendering nested templates

### Apache Airflow version 2.3.3 ### What happened Nested values, such as `env_vars` for the `KubernetesPodOperater` are not being rendered when used as a dynamically mapped operator. Assuming the following: ```python op = KubernetesPodOperater.partial( env_vars=[k8s.V1EnvVar(name='AWS_ACCESS_KEY_ID', value='{{ var.value.aws_access_key_id }}')], # Other arguments ).expand(arguments=[[1], [2]]) ``` The *Rendered Template* results for `env_vars` should be: ``` ("[{'name': 'AWS_ACCESS_KEY_ID', 'value': 'some-super-secret-value', 'value_from': None}]") ``` Instead the actual *Rendered Template* results for `env_vars` are un-rendered: ``` ("[{'name': 'AWS_ACCESS_KEY_ID', 'value': '{{ var.value.aws_access_key_id }}', 'value_from': None}]") ``` This is probably caused by the fact that `MappedOperator` is not calling [`KubernetesPodOperater._render_nested_template_fields`](https://github.com/apache/airflow/blob/main/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py#L286). ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 18.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25588

https://github.com/apache/airflow/pull/25599

762588dcf4a05c47aa253b864bda00726a5569dc

ed39703cd4f619104430b91d7ba67f261e5bfddb

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/ci.yml", "BREEZE.rst", "TESTING.rst", "dev/breeze/src/airflow_breeze/commands/testing_commands.py", "dev/breeze/src/airflow_breeze/commands/testing_commands_config.py", "images/breeze/output-commands-hash.txt", "images/breeze/output_testing.svg", "images/breeze/output_testing_helm-tests.svg", "images/breeze/output_testing_tests.svg", "scripts/in_container/check_environment.sh"]

Convet running Helm unit tests to use the new breeze

The unit tests of Helm (using `helm template` still use bash scripts not the new breeze - we should switch them).

https://github.com/apache/airflow/issues/25580

https://github.com/apache/airflow/pull/25581

0d34355ffa3f9f2ecf666d4518d36c4366a4c701

a562cc396212e4d21484088ac5f363ade9ac2b8d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py", "tests/core/test_configuration.py"]

Airflow doesn't re-use a secrets backend instance when loading configuration values

### Apache Airflow version main (development) ### What happened When airflow is loading its configuration, it creates a new secrets backend instance for each configuration backend it loads from secrets and then additionally creates a global secrets backend instance that is used in `ensure_secrets_loaded` which code outside of the configuration file uses. This can cause issues with the vault backend (and possibly others, not sure) since logging in to vault can be an expensive operation server-side and each instance of the vault secrets backend needs to re-login to use its internal client. ### What you think should happen instead Ideally, airflow would attempt to create a single secrets backend instance and re-use this. This can possibly be patched in the vault secrets backend, but instead I think updating the `configuration` module to cache the secrets backend would be preferable since it would then apply to any secrets backend. ### How to reproduce Use the hashicorp vault secrets backend and store some configuration in `X_secret` values. See that it logs in more than you'd expect. ### Operating System Ubuntu 18.04 ### Versions of Apache Airflow Providers ``` apache-airflow==2.3.0 apache-airflow-providers-hashicorp==2.2.0 hvac==0.11.2 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25555

https://github.com/apache/airflow/pull/25556

33fbe75dd5100539c697d705552b088e568d52e4

5863c42962404607013422a40118d8b9f4603f0b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/graph.js"]

Mapped, classic operator tasks within TaskGroups prepend `group_id` in Graph View

### Apache Airflow version main (development) ### What happened When mapped, classic operator tasks exist within TaskGroups, the `group_id` of the TaskGroup is prepended to the displayed `task_id` in the Graph View. In the below screenshot, all displayed task IDs only contain the direct `task_id` except for the "mapped_classic_task". This particular task is a mapped `BashOperator` task. The prepended `group_id` does not appear for unmapped, classic operator tasks, nor mapped and unmapped TaskFlow tasks. <img width="1440" alt="image" src="https://user-images.githubusercontent.com/48934154/182760586-975a7886-bcd6-477d-927b-25e82139b5b7.png"> ### What you think should happen instead The pattern of the displayed task names should be consistent for all task types (mapped/unmapped, classic operators/TaskFlow functions). Additionally, having the `group_id` prepended to the mapped, classic operator tasks is a little redundant and less readable. ### How to reproduce 1. Use an example DAG of the following: ```python from pendulum import datetime from airflow.decorators import dag, task, task_group from airflow.operators.bash import BashOperator @dag(start_date=datetime(2022, 1, 1), schedule_interval=None) def task_group_task_graph(): @task_group def my_task_group(): BashOperator(task_id="not_mapped_classic_task", bash_command="echo") BashOperator.partial(task_id="mapped_classic_task").expand( bash_command=["echo", "echo hello", "echo world"] ) @task def another_task(input=None): ... another_task.override(task_id="not_mapped_taskflow_task")() another_task.override(task_id="mapped_taskflow_task").expand(input=[1, 2, 3]) my_task_group() _ = task_group_task_graph() ``` 2. Navigate to the Graph view 3. Notice that the `task_id` for the "mapped_classic_task" prepends the TaskGroup `group_id` of "my_task_group" while the other tasks in the TaskGroup do not. ### Operating System Debian GNU/Linux ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Breeze ### Anything else Setting `prefix_group_id=False` for the TaskGroup does remove the prepended `group_id` from the tasks display name. ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25523

https://github.com/apache/airflow/pull/26108

5697e9fdfa9d5af2d48f7037c31972c2db1f4397

3b76e81bcc9010cfec4d41fe33f92a79020dbc5b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/batch_client.py", "airflow/providers/amazon/aws/operators/batch.py", "tests/providers/amazon/aws/hooks/test_batch_client.py", "tests/providers/amazon/aws/operators/test_batch.py"]

Support AWS Batch multinode job types

### Description Support [multinode job types](https://docs.aws.amazon.com/batch/latest/userguide/multi-node-parallel-jobs.html) in the [AWS Batch Operator](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/operators/batch.py). The [boto3 `submit_job` method](https://boto3.amazonaws.com/v1/documentation/api/1.9.88/reference/services/batch.html#Batch.Client.submit_job) supports container, multinode, and array batch jobs with the mutually exclusive `nodeOverrides` and `containerOverrides` (+ `arrayProperties`) parameters. But currently the AWS Batch Operator only supports submission of container jobs and array jobs by hardcoding the boto3 `submit_job` parameter `containerOverrides`: https://github.com/apache/airflow/blob/3c08cefdfd2e2636a714bb835902f0cb34225563/airflow/providers/amazon/aws/operators/batch.py#L200 & https://github.com/apache/airflow/blob/3c08cefdfd2e2636a714bb835902f0cb34225563/airflow/providers/amazon/aws/hooks/batch_client.py#L99 The [`get_job_awslogs_info`](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/batch_client.py#L419) method in the batch client hook is also hardcoded for the container type job: https://github.com/apache/airflow/blob/3c08cefdfd2e2636a714bb835902f0cb34225563/airflow/providers/amazon/aws/hooks/batch_client.py#L425 To support multinode jobs the `get_job_awslogs_info` method would need to access `nodeProperties` from the [`describe_jobs`](https://boto3.amazonaws.com/v1/documentation/api/1.9.88/reference/services/batch.html#Batch.Client.describe_jobs) response. ### Use case/motivation Multinode jobs are a supported job type of AWS Batch, are supported by the underlying boto3 library, and should be also be available to be managed by Airflow. I've extended the AWS Batch Operator for our own use cases, but would prefer to not maintain a separate operator. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25522

https://github.com/apache/airflow/pull/29522

f080e1e3985f24293979f2f0fc28f1ddf72ee342

2ce11300064ec821ffe745980012100fc32cb4b4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/grid/index.tsx"]

Vertical overlay scrollbar on Grid view blocks last DAG run column

### Apache Airflow version 2.3.3 (latest released) ### What happened The vertical overlay scrollbar in Grid view on the Web UI (#22134) covers up the final DAG run column and makes it impossible to click on the tasks for that DAG run:   ### What you think should happen instead Either pad the Grid view so the scrollbar does not appear on top of the content or force the scroll bar to take up its own space ### How to reproduce Have a DAG run with enough tasks to cause vertical overflow. Found on Linux + FF 102 ### Operating System Fedora 36 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25512

https://github.com/apache/airflow/pull/25554

5668888a7e1074a620b3d38f407ecf1aa055b623

fe9772949eba35c73101c3cd93a7c76b3e633e7e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/0118_2_5_0_add_updated_at_to_dagrun_and_ti.py", "airflow/models/dagrun.py", "airflow/models/taskinstance.py", "docs/apache-airflow/img/airflow_erd.sha256", "docs/apache-airflow/img/airflow_erd.svg", "docs/apache-airflow/migrations-ref.rst", "tests/models/test_taskinstance.py"]

add lastModified columns to DagRun and TaskInstance.

I wonder if we should add lastModified columns to DagRun and TaskInstance. It might help a lot of UI/API queries. _Originally posted by @ashb in https://github.com/apache/airflow/issues/23805#issuecomment-1143752368_

https://github.com/apache/airflow/issues/25508

https://github.com/apache/airflow/pull/26252

768865e10c811bc544590ec268f9f5c334da89b5

4930df45f5bae89c297dbcd5cafc582a61a0f323

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "tests/www/views/test_views_base.py", "tests/www/views/test_views_home.py"]

URL contains tag query parameter but Airflow UI does not correctly visualize the tags

### Apache Airflow version 2.3.3 (latest released) ### What happened An URL I saved in the past, `https://astronomer.astronomer.run/dx4o2568/home?tags=test`, has the tag field in the query parameter though I was not aware of this. When I clicked on the URL, I was confused because I did not see any DAGs when I should have a bunch. After closer inspection, I realized that the URL has the tag field in the query parameter but then noticed that the tag box in the Airflow UI wasn't properly populated.  ### What you think should happen instead When I clicked on the URL, the tag box should have been populated with the strings in the URL. ### How to reproduce Start an Airflow deployment with some DAGs and add the tag query parameter. More specifically, it has to be a tag that is not used by any DAG. ### Operating System N/A ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25493

https://github.com/apache/airflow/pull/25715

ea306c9462615d6b215d43f7f17d68f4c62951b1

485142ac233c4ac9627f523465b7727c2d089186

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/plugin_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/plugin_schema.py", "airflow/www/static/js/types/api-generated.ts"]

API server /plugin crashes

### Apache Airflow version 2.3.3 (latest released) ### What happened The `/plugins` endpoint returned a 500 http status code. ``` curl -X GET http://localhost:8080/api/v1/plugins\?limit\=1 \ -H 'Cache-Control: no-cache' \ --user "admin:admin" { "detail": "\"{'name': 'Test View', 'category': 'Test Plugin', 'view': 'test.appbuilder_views.TestAppBuilderBaseView'}\" is not of type 'object'\n\nFailed validating 'type' in schema['allOf'][0]['properties']['plugins']['items']['properties']['appbuilder_views']['items']:\n {'nullable': True, 'type': 'object'}\n\nOn instance['plugins'][0]['appbuilder_views'][0]:\n (\"{'name': 'Test View', 'category': 'Test Plugin', 'view': \"\n \"'test.appbuilder_views.TestAppBuilderBaseView'}\")", "status": 500, "title": "Response body does not conform to specification", "type": "http://apache-airflow-docs.s3-website.eu-central-1.amazonaws.com/docs/apache-airflow/latest/stable-rest-api-ref.html#section/Errors/Unknown" } ``` The error message in the webserver is as followed ``` [2022-08-03 17:07:57,705] {validation.py:244} ERROR - http://localhost:8080/api/v1/plugins?limit=1 validation error: "{'name': 'Test View', 'category': 'Test Plugin', 'view': 'test.appbuilder_views.TestAppBuilderBaseView'}" is not of type 'object' Failed validating 'type' in schema['allOf'][0]['properties']['plugins']['items']['properties']['appbuilder_views']['items']: {'nullable': True, 'type': 'object'} On instance['plugins'][0]['appbuilder_views'][0]: ("{'name': 'Test View', 'category': 'Test Plugin', 'view': " "'test.appbuilder_views.TestAppBuilderBaseView'}") 172.18.0.1 - admin [03/Aug/2022:17:10:17 +0000] "GET /api/v1/plugins?limit=1 HTTP/1.1" 500 733 "-" "curl/7.79.1" ``` ### What you think should happen instead The response should contain all the plugins integrated with Airflow. ### How to reproduce Create a simple plugin in the plugin directory. `appbuilder_views.py` ``` from flask_appbuilder import expose, BaseView as AppBuilderBaseView # Creating a flask appbuilder BaseView class TestAppBuilderBaseView(AppBuilderBaseView): @expose("/") def test(self): return self.render_template("test_plugin/test.html", content="Hello galaxy!") ``` `plugin.py` ``` from airflow.plugins_manager import AirflowPlugin from test.appbuilder_views import TestAppBuilderBaseView class TestPlugin(AirflowPlugin): name = "test" appbuilder_views = [ { "name": "Test View", "category": "Test Plugin", "view": TestAppBuilderBaseView() } ] ``` Call the `/plugin` endpoint. ``` curl -X GET http://localhost:8080/api/v1/plugins\?limit\=1 \ -H 'Cache-Control: no-cache' \ --user "admin:admin" ``` ### Operating System N/A ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25492

https://github.com/apache/airflow/pull/25524

7e3d2350dbb23b9c98bbadf73296425648e1e42d

5de11e1410b432d632e8c0d1d8ca0945811a56f0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/postgres_to_gcs.py"]

PostgresToGCSOperator parquet format mapping inconsistencies converts boolean data type to string

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==6.8.0 ### Apache Airflow version 2.3.2 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened When converting postgres native data type to bigquery data types, [this](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L288) function is responsible for converting from postgres types -> bigquery types -> parquet types. The [map](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/postgres_to_gcs.py#L80) in the PostgresToGCSOperator indicates that the postgres boolean type matches to the bigquery `BOOLEAN` data type. Then when converting from bigquery to parquet data types [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L288), the [map](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L289) does not have the `BOOLEAN` data type in its keys. Because the type defaults to string in the following [line](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L305), the BOOLEAN data type is converted into string, which then fails when converting the data into `pa.bool_()`. When converting the boolean data type into `pa.string()` pyarrow raises an error. ### What you think should happen instead I would expect the postgres boolean type to map to `pa.bool_()` data type. Changing the [map](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/postgres_to_gcs.py#L80) to include the `BOOL` key instead of `BOOLEAN` would correctly map the postgres type to the final parquet type. ### How to reproduce 1. Create a postgres connection on airflow with id `postgres_test_conn`. 2. Create a gcp connection on airflow with id `gcp_test_conn`. 3. In the database referenced by the `postgres_test_conn`, in the public schema create a table `test_table` that includes a boolean data type, and insert data into the table. 4. Create a bucket named `issue_PostgresToGCSOperator_bucket`, in the gcp account referenced by the `gcp_test_conn`. 5. Run the dag below that inserts the data from the postgres table into the cloud storage bucket. ```python import pendulum from airflow import DAG from airflow.providers.google.cloud.transfers.postgres_to_gcs import PostgresToGCSOperator with DAG( dag_id="issue_PostgresToGCSOperator", start_date=pendulum.parse("2022-01-01"), )as dag: task = PostgresToGCSOperator( task_id='extract_task', filename='uploading-{}.parquet', bucket="issue_PostgresToGCSOperator_bucket", export_format='parquet', sql="SELECT * FROM test_table", postgres_conn_id='postgres_test_conn', gcp_conn_id='gcp_test_conn', ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25474

https://github.com/apache/airflow/pull/25475

4da2b0c216c92795f19862a3ff6634e5a5936138

faf3c4fe474733965ab301465f695e3cc311169c

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/statsd/statsd-deployment.yaml", "chart/values.schema.json", "chart/values.yaml", "tests/charts/test_annotations.py"]

Helm Chart: Allow adding annotations to statsd deployment

### Description Helm Chart [does not allow adding annotations](https://github.com/apache/airflow/blob/40eefd84797f5085e6c3fef6cbd6f713ceb3c3d8/chart/templates/statsd/statsd-deployment.yaml#L60-L63) to StatsD deployment. We should add it. ### Use case/motivation In our Kubernetes cluster we need to set annotations on deployments that should be scraped by Prometheus. Having an exporter that does not get scraped defeats the purpose :slightly_smiling_face: ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25446

https://github.com/apache/airflow/pull/25732

fdecf12051308a4e064f5e4bf5464ffc9b183dad

951b7084619eca7229cdaadda99fd1191d4793e7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/provider.yaml", "airflow/providers/snowflake/transfers/copy_into_snowflake.py", "airflow/providers/snowflake/transfers/s3_to_snowflake.py", "scripts/in_container/verify_providers.py", "tests/providers/snowflake/transfers/test_copy_into_snowflake.py"]

GCSToSnowflakeOperator with feature parity to the S3ToSnowflakeOperator

### Description Require an operator similar to the S3ToSnowflakeOperator but for GCS to load data stored in GCS to a Snowflake table. ### Use case/motivation Same as the S3ToSnowflakeOperator. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25395

https://github.com/apache/airflow/pull/25541

2ee099655b1ca46935dbf3e37ae0ec1139f98287

5c52bbf32d81291b57d051ccbd1a2479ff706efc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/jdbc/operators/jdbc.py", "tests/providers/jdbc/operators/test_jdbc.py"]

apache-airflow-providers-jdbc fails with jaydebeapi.Error

### Apache Airflow Provider(s) jdbc ### Versions of Apache Airflow Providers I am using apache-airflow-providers-jdbc==3.0.0 for Airflow 2.3.3 as per constraint [file](https://raw.githubusercontent.com/apache/airflow/constraints-2.3.3/constraints-3.10.txt) ### Apache Airflow version 2.3.3 (latest released) ### Operating System K8 on Linux ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened I am using JdbcOperator to execute one ALTER sql statement but it returns the following error: File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/providers/jdbc/operators/jdbc.py", line 76, in execute return hook.run(self.sql, self.autocommit, parameters=self.parameters, handler=fetch_all_handler) File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/hooks/dbapi.py", line 213, in run result = handler(cur) File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/providers/jdbc/operators/jdbc.py", line 30, in fetch_all_handler return cursor.fetchall() File "/usr/local/airflow/.local/lib/python3.10/site-packages/jaydebeapi/__init__.py", line 593, in fetchall row = self.fetchone() File "/usr/local/airflow/.local/lib/python3.10/site-packages/jaydebeapi/__init__.py", line 558, in fetchone raise Error() jaydebeapi.Error ### What you think should happen instead The introduction of handler=fetch_all_handler in File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/providers/jdbc/operators/jdbc.py", line 76, in execute return hook.run(self.sql, self.autocommit, parameters=self.parameters, handler=fetch_all_handler) is breaking the script. With the previous version which did not have fetch_all_handler in jdbc.py, it was running perfectly. ### How to reproduce Try submitting ALTER statement in airflow jdbcOperator. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25388

https://github.com/apache/airflow/pull/25412

3dfa44566c948cb2db016e89f84d6fe37bd6d824

1708da9233c13c3821d76e56dbe0e383ff67b0fd

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/abstractoperator.py", "airflow/models/baseoperator.py", "airflow/operators/trigger_dagrun.py", "airflow/providers/qubole/operators/qubole.py", "airflow/www/static/js/dag.js", "airflow/www/static/js/dag/details/taskInstance/index.tsx", "docs/spelling_wordlist.txt"]

Extra Links do not works with mapped operators

### Apache Airflow version main (development) ### What happened I found that Extra Links do not work with dynamic tasks at all - links inaccessible, but same Extra Links works fine with not mapped operators. I think the nature of that extra links assign to parent task instance (i do not know how to correct name this TI) but not to actual mapped TIs. As result we only have `number extra links defined` in operator not `(number extra links defined in operator) x number of mapped TIs.` ### What you think should happen instead _No response_ ### How to reproduce ```python from pendulum import datetime from airflow.decorators import dag from airflow.sensors.external_task import ExternalTaskSensor from airflow.operators.empty import EmptyOperator EXTERNAL_DAG_IDS = [f"example_external_dag_{ix:02d}" for ix in range(3)] DAG_KWARGS = { "start_date": datetime(2022, 7, 1), "schedule_interval": "@daily", "catchup": False, "tags": ["mapped_extra_links", "AIP-42", "serialization"], } def external_dags(): EmptyOperator(task_id="dummy") @dag(**DAG_KWARGS) def external_regular_task_sensor(): for external_dag_id in EXTERNAL_DAG_IDS: ExternalTaskSensor( task_id=f'wait_for_{external_dag_id}', external_dag_id=external_dag_id, poke_interval=5, ) @dag(**DAG_KWARGS) def external_mapped_task_sensor(): ExternalTaskSensor.partial( task_id='wait', poke_interval=5, ).expand(external_dag_id=EXTERNAL_DAG_IDS) dag_external_regular_task_sensor = external_regular_task_sensor() dag_external_mapped_task_sensor = external_mapped_task_sensor() for dag_id in EXTERNAL_DAG_IDS: globals()[dag_id] = dag(dag_id=dag_id, **DAG_KWARGS)(external_dags)() ``` https://user-images.githubusercontent.com/3998685/180994213-847b3fd3-d351-4836-b246-b54056f34ad6.mp4 ### Operating System macOs 12.5 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25360

https://github.com/apache/airflow/pull/25500

4ecaa9e3f0834ca0ef08002a44edda3661f4e572

d9e924c058f5da9eba5bb5b85a04bfea6fb2471a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/backfill_job.py", "tests/jobs/test_backfill_job.py"]

Backfill stalls with certain combination of skipped tasks & trigger rules

### Apache Airflow version 2.3.0 ### What happened While trying to run a backfill for one of our DAGs, we noticed that the backfill stalled after completing all the tasks for a given DAG. The `max_active_runs` for this DAG was set to `1`, so the entire backfill stalled even though all the tasks in the last DAG it ran completed successfully. ### What you think should happen instead I would assume that once all the tasks are complete in a DAG (whether succeeded, skipped, or failed) during a backfill, the backfill should mark the DAG with the proper state and proceed on with the rest of the tasks. ### How to reproduce Here is simulacrum of our DAG with all the actual logic stripped out: ```python from datetime import datetime from airflow.decorators import dag from airflow.exceptions import AirflowSkipException from airflow.operators.python import PythonOperator from airflow.utils.task_group import TaskGroup from airflow.utils.trigger_rule import TriggerRule def skipme(): raise AirflowSkipException("Skip") def run(): return @dag( schedule_interval="@daily", start_date=datetime(2022, 7, 14), catchup=False, max_active_runs=1, ) def sample_dag_with_skip(): a = PythonOperator( task_id="first", python_callable=run, ) with TaskGroup(group_id="subgroup") as tg: b = PythonOperator( task_id="run_and_skip", trigger_rule=TriggerRule.NONE_SKIPPED, python_callable=skipme, ) c = PythonOperator( task_id="run_fine", trigger_rule=TriggerRule.NONE_SKIPPED, python_callable=skipme, ) d = PythonOperator( task_id="gather", python_callable=run, ) e = PythonOperator( task_id="always_succeed", trigger_rule=TriggerRule.ALL_DONE, python_callable=run, ) [b, c] >> d >> e f = PythonOperator( task_id="final", trigger_rule=TriggerRule.ALL_DONE, python_callable=run, ) a >> tg >> f skip_dag = sample_dag_with_skip() ``` Here's a screenshot of the DAG:  Note that the DAG is still shown as "running" even though the last task ended several minutes ago:  Here's the backfill command I ran for this DAG: `airflow dags backfill -s 2022-07-25 -e 2022-07-26 sample_dag_with_skip --reset-dagruns -y` And here are the logs from the backfill process: <details> <summary>Backfill logs</summary> ``` airflow@42a81ed08a3d:~$ airflow dags backfill -s 2022-07-25 -e 2022-07-26 sample_dag_with_skip --reset-dagruns -y /usr/local/airflow/.local/lib/python3.10/site-packages/airflow/cli/commands/dag_command.py:57 PendingDeprecationWarning: --ignore-first-depends-on-past is deprecated as the value is always set to True [2022-07-27 23:15:44,655] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/airflow/openverse_catalog/dags [2022-07-27 23:15:44,937] {urls.py:74} INFO - https://creativecommons.org/publicdomain/zero/1.0 was rewritten to https://creativecommons.org/publicdomain/zero/1.0/ [2022-07-27 23:15:44,948] {media.py:63} INFO - Initialized image MediaStore with provider brooklynmuseum [2022-07-27 23:15:44,948] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,948] {media.py:186} INFO - Output path: /var/workflow_output/brooklynmuseum_image_v001_20220727231544.tsv [2022-07-27 23:15:44,952] {media.py:63} INFO - Initialized image MediaStore with provider europeana [2022-07-27 23:15:44,952] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,952] {media.py:186} INFO - Output path: /var/workflow_output/europeana_image_v001_20220727231544.tsv [2022-07-27 23:15:44,953] {media.py:63} INFO - Initialized image MediaStore with provider finnishmuseums [2022-07-27 23:15:44,953] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,953] {media.py:186} INFO - Output path: /var/workflow_output/finnishmuseums_image_v001_20220727231544.tsv [2022-07-27 23:15:44,955] {media.py:63} INFO - Initialized image MediaStore with provider flickr [2022-07-27 23:15:44,955] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,955] {media.py:186} INFO - Output path: /var/workflow_output/flickr_image_v001_20220727231544.tsv [2022-07-27 23:15:44,957] {media.py:63} INFO - Initialized audio MediaStore with provider freesound [2022-07-27 23:15:44,957] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,957] {media.py:186} INFO - Output path: /var/workflow_output/freesound_audio_v001_20220727231544.tsv [2022-07-27 23:15:44,959] {media.py:63} INFO - Initialized audio MediaStore with provider jamendo [2022-07-27 23:15:44,959] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,959] {media.py:186} INFO - Output path: /var/workflow_output/jamendo_audio_v001_20220727231544.tsv [2022-07-27 23:15:44,961] {media.py:63} INFO - Initialized image MediaStore with provider met [2022-07-27 23:15:44,961] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,961] {media.py:186} INFO - Output path: /var/workflow_output/met_image_v001_20220727231544.tsv [2022-07-27 23:15:44,962] {media.py:63} INFO - Initialized image MediaStore with provider museumsvictoria [2022-07-27 23:15:44,962] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,962] {media.py:186} INFO - Output path: /var/workflow_output/museumsvictoria_image_v001_20220727231544.tsv [2022-07-27 23:15:44,964] {media.py:63} INFO - Initialized image MediaStore with provider nypl [2022-07-27 23:15:44,964] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,964] {media.py:186} INFO - Output path: /var/workflow_output/nypl_image_v001_20220727231544.tsv [2022-07-27 23:15:44,965] {media.py:63} INFO - Initialized image MediaStore with provider phylopic [2022-07-27 23:15:44,965] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,965] {media.py:186} INFO - Output path: /var/workflow_output/phylopic_image_v001_20220727231544.tsv [2022-07-27 23:15:44,967] {media.py:63} INFO - Initialized image MediaStore with provider rawpixel [2022-07-27 23:15:44,967] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,967] {media.py:186} INFO - Output path: /var/workflow_output/rawpixel_image_v001_20220727231544.tsv [2022-07-27 23:15:44,968] {media.py:63} INFO - Initialized image MediaStore with provider sciencemuseum [2022-07-27 23:15:44,968] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,968] {media.py:186} INFO - Output path: /var/workflow_output/sciencemuseum_image_v001_20220727231544.tsv [2022-07-27 23:15:44,970] {media.py:63} INFO - Initialized image MediaStore with provider smithsonian [2022-07-27 23:15:44,970] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,970] {media.py:186} INFO - Output path: /var/workflow_output/smithsonian_image_v001_20220727231544.tsv [2022-07-27 23:15:44,971] {media.py:63} INFO - Initialized image MediaStore with provider smk [2022-07-27 23:15:44,972] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,972] {media.py:186} INFO - Output path: /var/workflow_output/smk_image_v001_20220727231544.tsv [2022-07-27 23:15:44,974] {media.py:63} INFO - Initialized image MediaStore with provider waltersartmuseum [2022-07-27 23:15:44,974] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,974] {media.py:186} INFO - Output path: /var/workflow_output/waltersartmuseum_image_v001_20220727231544.tsv [2022-07-27 23:15:44,976] {media.py:63} INFO - Initialized audio MediaStore with provider wikimedia_audio [2022-07-27 23:15:44,976] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,976] {media.py:186} INFO - Output path: /var/workflow_output/wikimedia_audio_audio_v001_20220727231544.tsv [2022-07-27 23:15:44,976] {media.py:63} INFO - Initialized image MediaStore with provider wikimedia [2022-07-27 23:15:44,976] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,977] {media.py:186} INFO - Output path: /var/workflow_output/wikimedia_image_v001_20220727231544.tsv [2022-07-27 23:15:44,980] {media.py:63} INFO - Initialized image MediaStore with provider wordpress [2022-07-27 23:15:44,980] {media.py:168} INFO - No given output directory. Using OUTPUT_DIR from environment. [2022-07-27 23:15:44,980] {media.py:186} INFO - Output path: /var/workflow_output/wordpress_image_v001_20220727231544.tsv [2022-07-27 23:15:45,043] {executor_loader.py:106} INFO - Loaded executor: LocalExecutor [2022-07-27 23:15:45,176] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'first', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpfowbb78c'] [2022-07-27 23:15:50,050] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'first', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpfowbb78c'] [2022-07-27 23:15:50,060] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.skipme backfill__2022-07-25T00:00:00+00:00 [skipped]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:15:50,061] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.always_run backfill__2022-07-25T00:00:00+00:00 [success]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:15:50,063] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 2 | tasks waiting: 7 | succeeded: 0 | running: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 5 [2022-07-27 23:15:50,071] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/***/openverse_catalog/dags/simple_backfill_example.py [2022-07-27 23:15:50,089] {task_command.py:369} INFO - Running <TaskInstance: sample_dag_with_skip.first backfill__2022-07-25T00:00:00+00:00 [queued]> on host 42a81ed08a3d [2022-07-27 23:15:50,486] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:15:50,487] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:15:50,488] {base_aws.py:206} INFO - Credentials retrieved from login [2022-07-27 23:15:50,488] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-07-27 23:15:50,525] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:15:55,064] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.skipme backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:15:55,065] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.always_run backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:15:55,067] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 2 | tasks waiting: 7 | succeeded: 1 | running: 0 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 5 [2022-07-27 23:15:55,081] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.run_and_skip', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmp1uesref4'] [2022-07-27 23:15:55,170] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.run_fine', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpib__7p5u'] [2022-07-27 23:16:00,058] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.run_fine', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpib__7p5u'] [2022-07-27 23:16:00,058] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.run_and_skip', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmp1uesref4'] [2022-07-27 23:16:00,063] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.skipme backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:16:00,064] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.always_run backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:16:00,065] {backfill_job.py:367} INFO - [backfill progress] | finished run 0 of 2 | tasks waiting: 5 | succeeded: 1 | running: 2 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 3 [2022-07-27 23:16:00,077] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/***/openverse_catalog/dags/simple_backfill_example.py [2022-07-27 23:16:00,080] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/***/openverse_catalog/dags/simple_backfill_example.py [2022-07-27 23:16:00,096] {task_command.py:369} INFO - Running <TaskInstance: sample_dag_with_skip.subgroup.run_fine backfill__2022-07-25T00:00:00+00:00 [queued]> on host 42a81ed08a3d [2022-07-27 23:16:00,097] {task_command.py:369} INFO - Running <TaskInstance: sample_dag_with_skip.subgroup.run_and_skip backfill__2022-07-25T00:00:00+00:00 [queued]> on host 42a81ed08a3d [2022-07-27 23:16:00,504] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:00,505] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:00,506] {base_aws.py:206} INFO - Credentials retrieved from login [2022-07-27 23:16:00,506] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-07-27 23:16:00,514] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:00,515] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:00,516] {base_aws.py:206} INFO - Credentials retrieved from login [2022-07-27 23:16:00,516] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-07-27 23:16:00,541] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:00,559] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:05,071] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.skipme backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:16:05,072] {dagrun.py:647} WARNING - Failed to get task '<TaskInstance: sample_dag_with_skip.always_run backfill__2022-07-25T00:00:00+00:00 [removed]>' for dag 'sample_dag_with_skip'. Marking it as removed. [2022-07-27 23:16:05,073] {dagrun.py:583} ERROR - Deadlock; marking run <DagRun sample_dag_with_skip @ 2022-07-25 00:00:00+00:00: backfill__2022-07-25T00:00:00+00:00, externally triggered: False> failed [2022-07-27 23:16:05,073] {dagrun.py:607} INFO - DagRun Finished: dag_id=sample_dag_with_skip, execution_date=2022-07-25 00:00:00+00:00, run_id=backfill__2022-07-25T00:00:00+00:00, run_start_date=None, run_end_date=2022-07-27 23:16:05.073628+00:00, run_duration=None, state=failed, external_trigger=False, run_type=backfill, data_interval_start=2022-07-25 00:00:00+00:00, data_interval_end=2022-07-26 00:00:00+00:00, dag_hash=None [2022-07-27 23:16:05,075] {dagrun.py:799} WARNING - Failed to record duration of <DagRun sample_dag_with_skip @ 2022-07-25 00:00:00+00:00: backfill__2022-07-25T00:00:00+00:00, externally triggered: False>: start_date is not set. [2022-07-27 23:16:05,075] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 5 | succeeded: 1 | running: 0 | failed: 0 | skipped: 2 | deadlocked: 0 | not ready: 3 [2022-07-27 23:16:05,096] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.always_succeed', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpulo4p958'] [2022-07-27 23:16:10,065] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'subgroup.always_succeed', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmpulo4p958'] [2022-07-27 23:16:10,067] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 3 | succeeded: 1 | running: 1 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 1 [2022-07-27 23:16:10,084] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/***/openverse_catalog/dags/simple_backfill_example.py [2022-07-27 23:16:10,104] {task_command.py:369} INFO - Running <TaskInstance: sample_dag_with_skip.subgroup.always_succeed backfill__2022-07-25T00:00:00+00:00 [queued]> on host 42a81ed08a3d [2022-07-27 23:16:10,500] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:10,501] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:10,502] {base_aws.py:206} INFO - Credentials retrieved from login [2022-07-27 23:16:10,502] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-07-27 23:16:10,537] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:15,074] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 3 | succeeded: 2 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 1 [2022-07-27 23:16:15,091] {base_executor.py:91} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'final', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmp7ifr68s2'] [2022-07-27 23:16:20,073] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'sample_dag_with_skip', 'final', 'backfill__2022-07-25T00:00:00+00:00', '--ignore-depends-on-past', '--local', '--pool', 'default_pool', '--subdir', 'DAGS_FOLDER/simple_backfill_example.py', '--cfg-path', '/tmp/tmp7ifr68s2'] [2022-07-27 23:16:20,075] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 2 | running: 1 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:20,094] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/***/openverse_catalog/dags/simple_backfill_example.py [2022-07-27 23:16:20,114] {task_command.py:369} INFO - Running <TaskInstance: sample_dag_with_skip.final backfill__2022-07-25T00:00:00+00:00 [queued]> on host 42a81ed08a3d [2022-07-27 23:16:20,522] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:20,523] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:20,524] {base_aws.py:206} INFO - Credentials retrieved from login [2022-07-27 23:16:20,524] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-07-27 23:16:20,561] {base.py:68} INFO - Using connection ID 'aws_default' for task execution. [2022-07-27 23:16:25,082] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:30,083] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:35,089] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:40,093] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:45,099] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:50,105] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:16:55,112] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:00,117] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:05,122] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:10,128] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:15,133] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:20,137] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:25,141] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:30,145] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:35,149] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:40,153] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:45,157] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:50,161] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:17:55,166] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:18:00,169] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:18:05,174] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 [2022-07-27 23:18:10,177] {backfill_job.py:367} INFO - [backfill progress] | finished run 1 of 2 | tasks waiting: 2 | succeeded: 3 | running: 0 | failed: 0 | skipped: 3 | deadlocked: 0 | not ready: 0 ^C[2022-07-27 23:18:11,199] {backfill_job.py:870} WARNING - Backfill terminated by user. [2022-07-27 23:18:11,199] {local_executor.py:390} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. ``` </details> --- It's worth noting that I tried to replicate this with a DAG that only had a single skip task, and a DAG with skip -> run -> skip, and both succeeded with the backfill. So my guess would be that there's an odd interaction with the TaskGroup, skipped tasks, trigger rules, and possibly `max_active_runs`. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-sqlite==2.1.3 ``` ### Deployment Docker-Compose ### Deployment details This is a custom configured Docker image, but doesn't deviate too much from a standard deployment: https://github.com/WordPress/openverse-catalog/blob/main/docker/airflow/Dockerfile ### Anything else I'll try to see if I can continue to pare down the DAG to see if there are pieces I can throw out and still replicate the error. I don't think I'd be comfortable submitting a PR for this one because my gut says it's probably deep in the bowels of the codebase 😅 If it's something clear or straightforward though, I'd be happy to take a stab at it! I'd just need to be pointed in the right direction within the codebase 🙂 ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25353

https://github.com/apache/airflow/pull/26161

5b216e9480e965c7c1919cb241668beca53ab521

6931fbf8f7c0e3dfe96ce51ef03f2b1502baef07

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/base.py", "airflow/models/expandinput.py", "airflow/models/mappedoperator.py", "tests/models/test_taskinstance.py", "tests/models/test_xcom_arg_map.py"]

expand_kwargs.map(func) gives unhelpful error message if func returns list

### Apache Airflow version main (development) ### What happened Here's a DAG: ```python3 with DAG( dag_id="expand_list", doc_md="try to get kwargs from a list", schedule_interval=None, start_date=datetime(2001, 1, 1), ) as expand_list: @expand_list.task def do_this(): return [ ("echo hello $USER", "USER", "foo"), ("echo hello $USER", "USER", "bar"), ] def mapper(tuple): if tuple[2] == "bar": return [1, 2, 3] else: return {"bash_command": tuple[0], "env": {tuple[1]: tuple[2]}} BashOperator.partial(task_id="one_cmd").expand_kwargs(do_this().map(mapper)) ``` The `foo` task instance succeeds as expected, and the `bar` task fails as expected. But the error message that it gives isn't particularly helpful to a user who doesn't know what they did wrong: ``` ERROR - Failed to execute task: resolve() takes 3 positional arguments but 4 were given. Traceback (most recent call last): File "/home/matt/src/airflow/airflow/executors/debug_executor.py", line 78, in _run_task ti.run(job_id=ti.job_id, **params) File "/home/matt/src/airflow/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1782, in run self._run_raw_task( File "/home/matt/src/airflow/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1445, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1580, in _execute_task_with_callbacks task_orig = self.render_templates(context=context) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 2202, in render_templates rendered_task = self.task.render_template_fields(context) File "/home/matt/src/airflow/airflow/models/mappedoperator.py", line 751, in render_template_fields unmapped_task = self.unmap(mapped_kwargs) File "/home/matt/src/airflow/airflow/models/mappedoperator.py", line 591, in unmap kwargs = self._expand_mapped_kwargs(resolve) File "/home/matt/src/airflow/airflow/models/mappedoperator.py", line 546, in _expand_mapped_kwargs return expand_input.resolve(*resolve) TypeError: resolve() takes 3 positional arguments but 4 were given ``` ### What you think should happen instead Whatever checks the return value for mappability should do more to point the user to their error. Perhaps something like: > UnmappableDataError: Expected a dict with keys that BashOperator accepts, got `[1, 2, 3]` instead ### How to reproduce Run the dag above ### Operating System Linux 5.10.101 #1-NixOS SMP Wed Feb 16 11:54:31 UTC 2022 x86_64 GNU/Linux ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25352

https://github.com/apache/airflow/pull/25355

f6b48ac6dfaf931a5433ec16369302f68f038c65

4e786e31bcdf81427163918e14d191e55a4ab606

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/hashicorp/_internal_client/vault_client.py", "tests/providers/hashicorp/_internal_client/test_vault_client.py", "tests/providers/hashicorp/hooks/test_vault.py"]

Vault client for hashicorp provider prints a deprecation warning when using kubernetes login

### Apache Airflow Provider(s) hashicorp ### Versions of Apache Airflow Providers ``` apache-airflow==2.3.0 apache-airflow-providers-hashicorp==2.2.0 hvac==0.11.2 ``` ### Apache Airflow version 2.3.0 ### Operating System Ubuntu 18.04 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened Using the vault secrets backend prints a deprecation warning when using the kubernetes auth method: ``` /home/airflow/.local/lib/python3.8/site-packages/airflow/providers/hashicorp/_internal_client/vault_client.py:284 DeprecationWarning: Call to deprecated function 'auth_kubernetes'. This method will be removed in version '1.0.0' Please use the 'login' method on the 'hvac.api.auth_methods.kubernetes' class moving forward. ``` This code is still present in `main` at https://github.com/apache/airflow/blob/main/airflow/providers/hashicorp/_internal_client/vault_client.py#L258-L260. ### What you think should happen instead The new kubernetes authentication method should be used instead. This code: ```python if self.auth_mount_point: _client.auth_kubernetes(role=self.kubernetes_role, jwt=jwt, mount_point=self.auth_mount_point) else: _client.auth_kubernetes(role=self.kubernetes_role, jwt=jwt) ``` Should be able to be updated to: ```python from hvac.api.auth_methods import Kubernetes if self.auth_mount_point: Kubernetes(_client.adapter).login(role=self.kubernetes_role, jwt=jwt, mount_point=self.auth_mount_point) else: Kubernetes(_client.adapter).login(role=self.kubernetes_role, jwt=jwt) ``` ### How to reproduce Use the vault secrets backend with the kubernetes auth method and look at the logs. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25349

https://github.com/apache/airflow/pull/25351

f4b93cc097dab95437c9c4b37474f792f80fd14e

ad0a4965aaf0702f0e8408660b912e87d3c75c22

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/abstractoperator.py", "tests/models/test_baseoperator.py"]

Improve Airflow logging for operator Jinja template processing

### Description When an operator uses Jinja templating, debugging issues is difficult because the Airflow task log only displays a stack trace. ### Use case/motivation When there's a templating issue, I'd like to have some specific, actionable info to help understand the problem. At minimum: * Which operator or task had the issue? * Which field had the issue? * What was the Jinja template? Possibly also the Jinja context, although that can be very verbose. I have prototyped this in my local Airflow dev environment, and I propose something like the following. (Note the logging commands, which are not present in the Airflow repo.) Please let me know if this sounds reasonable, and I will be happy to create a PR. ``` def _do_render_template_fields( self, parent, template_fields, context, jinja_env, seen_oids, ) -> None: """Copied from Airflow 2.2.5 with added logging.""" logger.info(f"BaseOperator._do_render_template_fields(): Task {self.task_id}") for attr_name in template_fields: content = getattr(parent, attr_name) if content: logger.info(f"Rendering template for '{attr_name}' field: {content!r}") rendered_content = self.render_template(content, context, jinja_env, seen_oids) + setattr(parent, attr_name, rendered_content) ``` ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25344

https://github.com/apache/airflow/pull/25452

9c632684341fb3115d654aecb83aa951d80b19af

4da2b0c216c92795f19862a3ff6634e5a5936138

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/callbacks/callback_requests.py", "airflow/models/taskinstance.py", "tests/callbacks/test_callback_requests.py"]

Object of type datetime is not JSON serializable after detecting zombie jobs with CeleryExecutor and separated Scheduler and DAG-Processor

### Apache Airflow version 2.3.3 (latest released) ### What happened After running for a certain period (few minutes until several hours depending on the number of active DAGs in the environment) The scheduler crashes with the following error message: ``` [2022-07-26 15:07:24,362] {executor_loader.py:105} INFO - Loaded executor: CeleryExecutor [2022-07-26 15:07:24,363] {scheduler_job.py:1252} INFO - Resetting orphaned tasks for active dag runs [2022-07-26 15:07:25,585] {celery_executor.py:532} INFO - Adopted the following 1 tasks from a dead executor <TaskInstance: freewheel_uafl_data_scala.freewheel.delivery_data scheduled__2022-07-25T04:15:00+00:00 [running]> in state STARTED [2022-07-26 15:07:35,881] {scheduler_job.py:1381} WARNING - Failing (1) jobs without heartbeat after 2022-07-26 12:37:35.868798+00:00 [2022-07-26 15:07:35,881] {scheduler_job.py:1389} ERROR - Detected zombie job: {'full_filepath': '/data/dags/09_scala_apps/freewheel_uafl_data_scala.py', 'msg': 'Detected <TaskInstance: freewheel_uafl_data_scala.freewheel.delivery_data scheduled__2022-07-25T04:15:00+00:00 [running]> as zombie', 'simple_task_instance': <airflow.models.taskinstance.SimpleTaskInstance object at 0x7fb4a1105690>, 'is_failure_callback': True} [2022-07-26 15:07:35,883] {scheduler_job.py:769} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 752, in _execute self._run_scheduler_loop() File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 873, in _run_scheduler_loop next_event = timers.run(blocking=False) File "/usr/lib/python3.10/sched.py", line 151, in run action(*argument, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/utils/event_scheduler.py", line 36, in repeat action(*args, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 1390, in _find_zombies self.executor.send_callback(request) File "/usr/lib/python3.10/site-packages/airflow/executors/base_executor.py", line 363, in send_callback self.callback_sink.send(request) File "/usr/lib/python3.10/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/callbacks/database_callback_sink.py", line 34, in send db_callback = DbCallbackRequest(callback=callback, priority_weight=10) File "<string>", line 4, in __init__ File "/usr/lib/python3.10/site-packages/sqlalchemy/orm/state.py", line 481, in _initialize_instance with util.safe_reraise(): File "/usr/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/usr/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 208, in raise_ raise exception File "/usr/lib/python3.10/site-packages/sqlalchemy/orm/state.py", line 479, in _initialize_instance return manager.original_init(*mixed[1:], **kwargs) File "/usr/lib/python3.10/site-packages/airflow/models/db_callback_request.py", line 44, in __init__ self.callback_data = callback.to_json() File "/usr/lib/python3.10/site-packages/airflow/callbacks/callback_requests.py", line 79, in to_json return json.dumps(dict_obj) File "/usr/lib/python3.10/json/__init__.py", line 231, in dumps return _default_encoder.encode(obj) File "/usr/lib/python3.10/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/usr/lib/python3.10/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/usr/lib/python3.10/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type datetime is not JSON serializable [2022-07-26 15:07:36,100] {scheduler_job.py:781} INFO - Exited execute loop Traceback (most recent call last): File "/usr/bin/airflow", line 8, in <module> sys.exit(main()) File "/usr/lib/python3.10/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/usr/lib/python3.10/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/usr/lib/python3.10/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/usr/lib/python3.10/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 752, in _execute self._run_scheduler_loop() File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 873, in _run_scheduler_loop next_event = timers.run(blocking=False) File "/usr/lib/python3.10/sched.py", line 151, in run action(*argument, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/utils/event_scheduler.py", line 36, in repeat action(*args, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/jobs/scheduler_job.py", line 1390, in _find_zombies self.executor.send_callback(request) File "/usr/lib/python3.10/site-packages/airflow/executors/base_executor.py", line 363, in send_callback self.callback_sink.send(request) File "/usr/lib/python3.10/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/usr/lib/python3.10/site-packages/airflow/callbacks/database_callback_sink.py", line 34, in send db_callback = DbCallbackRequest(callback=callback, priority_weight=10) File "<string>", line 4, in __init__ File "/usr/lib/python3.10/site-packages/sqlalchemy/orm/state.py", line 481, in _initialize_instance with util.safe_reraise(): File "/usr/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/usr/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 208, in raise_ raise exception File "/usr/lib/python3.10/site-packages/sqlalchemy/orm/state.py", line 479, in _initialize_instance return manager.original_init(*mixed[1:], **kwargs) File "/usr/lib/python3.10/site-packages/airflow/models/db_callback_request.py", line 44, in __init__ self.callback_data = callback.to_json() File "/usr/lib/python3.10/site-packages/airflow/callbacks/callback_requests.py", line 79, in to_json return json.dumps(dict_obj) File "/usr/lib/python3.10/json/__init__.py", line 231, in dumps return _default_encoder.encode(obj) File "/usr/lib/python3.10/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/usr/lib/python3.10/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/usr/lib/python3.10/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type datetime is not JSON serializable ``` ### What you think should happen instead The scheduler should handle zombie jobs without crashing. ### How to reproduce The following conditions are necessary: - dag-processor and scheduler run in separated containers - AirFlow uses the CeleryExecutor - There are zombie jobs ### Operating System Alpine Linux 3.16.1 ### Versions of Apache Airflow Providers ``` apache-airflow-providers-apache-hdfs==3.0.1 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.2.0 apache-airflow-providers-common-sql==1.0.0 apache-airflow-providers-datadog==3.0.0 apache-airflow-providers-exasol==2.1.3 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-jenkins==3.0.0 apache-airflow-providers-microsoft-mssql==3.1.0 apache-airflow-providers-odbc==3.1.0 apache-airflow-providers-oracle==3.1.0 apache-airflow-providers-postgres==5.1.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-slack==5.1.0 apache-airflow-providers-sqlite==3.1.0 apache-airflow-providers-ssh==3.1.0 ``` ### Deployment Other 3rd-party Helm chart ### Deployment details One Pod on Kubernetes containing the following containers - 1 Container for the webserver service - 1 Container for the scheduler service - 1 Container for the dag-processor service - 1 Container for the flower service - 1 Container for the redis service - 2 or 3 containers for the celery workers services Due to a previous issue crashing the scheduler with the message `UNEXPECTED COMMIT - THIS WILL BREAK HA LOCKS`, we substitute `scheduler_job.py` with the file `https://raw.githubusercontent.com/tanelk/airflow/a4b22932e5ac9c2b6f37c8c58345eee0f63cae09/airflow/jobs/scheduler_job.py`. Sadly I don't remember which issue or MR exactly but it was related to scheduler and dag-processor running in separate containers. ### Anything else It looks like that only the **combination of CeleryExecutor and separated scheduler and dag-processor** services crashes the scheduler when handling zombie jobs. The KubernetesExecutor with separated scheduler and dag-processor doesn't crash the scheduler. It looks like the CeleryExecutor with scheduler and dag-processor in the same container doesn't crash the scheduler. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25343

https://github.com/apache/airflow/pull/25471

3421ecc21bafaf355be5b79ec4ed19768e53275a

d7e14ba0d612d8315238f9d0cba4ef8c44b6867c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/operators/bash.py"]

User defined `env` clobbers PATH, BashOperator can't find bash

### Apache Airflow version main (development) ### What happened NixOS is unconventional in some ways. For instance `which bash` prints `/run/current-system/sw/bin/bash`, which isn't a place that most people expect to go looking for bash. I can't be sure if this is the reason--or if it's some other peculiarity--but on NixOS, cases where `BashOperator` defines an `env` cause the task to fail with this error: ``` venv ❯ airflow dags test nopath "$(date +%Y-%m-%d)" [2022-07-26 21:54:09,704] {dagbag.py:508} INFO - Filling up the DagBag from /home/matt/today/dags [2022-07-26 21:54:10,129] {base_executor.py:91} INFO - Adding to queue: ['<TaskInstance: nopath.nopath backfill__2022-07-26T00:00:00+00:00 [queued]>'] [2022-07-26 21:54:15,148] {subprocess.py:62} INFO - Tmp dir root location: /tmp [2022-07-26 21:54:15,149] {subprocess.py:74} INFO - Running command: ['bash', '-c', 'echo hello world'] [2022-07-26 21:54:15,238] {debug_executor.py:84} ERROR - Failed to execute task: [Errno 2] No such file or directory: 'bash'. Traceback (most recent call last): File "/home/matt/src/airflow/airflow/executors/debug_executor.py", line 78, in _run_task ti.run(job_id=ti.job_id, **params) File "/home/matt/src/airflow/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1782, in run self._run_raw_task( File "/home/matt/src/airflow/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1445, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1623, in _execute_task_with_callbacks result = self._execute_task(context, task_orig) File "/home/matt/src/airflow/airflow/models/taskinstance.py", line 1694, in _execute_task result = execute_callable(context=context) File "/home/matt/src/airflow/airflow/operators/bash.py", line 183, in execute result = self.subprocess_hook.run_command( File "/home/matt/src/airflow/airflow/hooks/subprocess.py", line 76, in run_command self.sub_process = Popen( File "/nix/store/cgxc3jz7idrb1wnb2lard9rvcx6aw2si-python3-3.9.6/lib/python3.9/subprocess.py", line 951, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "/nix/store/cgxc3jz7idrb1wnb2lard9rvcx6aw2si-python3-3.9.6/lib/python3.9/subprocess.py", line 1821, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: 'bash' ``` On the other hand, tasks succeed if: - The author doesn't use the `env` kwarg - `env` is replaced with `append_env` - they use `env` to explicitly set `PATH` to a folder containing `bash` - or they are run on a more conventional system (like my MacBook) Here is a DAG which demonstrates this: ```python3 from airflow.models import DAG from airflow.operators.bash import BashOperator from datetime import datetime, timedelta with DAG( dag_id="withpath", start_date=datetime(1970, 1, 1), schedule_interval=None, ) as withpath: BashOperator( task_id="withpath", env={"PATH": "/run/current-system/sw/bin/", "WORLD": "world"}, bash_command="echo hello $WORLD", ) with DAG( dag_id="nopath", start_date=datetime(1970, 1, 1), schedule_interval=None, ) as nopath: BashOperator( task_id="nopath", env={"WORLD": "world"}, bash_command="echo hello $WORLD", ) ``` `withpath` succeeds, but `nopath` fails, showing the above error. ### What you think should happen instead Unless the user explicitly sets PATH via the `env` kwarg, airflow should populate it with whatever it finds in the enclosing environment. ### How to reproduce I can reproduce it reliably, but only on this machine. I'm willing to fix this myself--since I can test it right here--but I'm filing this issue because I need a hint. Where should I start? ### Operating System NixOS 21.11 (Porcupine) ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25330

https://github.com/apache/airflow/pull/25331

900c81b87a76a9df8a3a6435a0d42348e88c5bbb

c3adf3e65d32d8145e2341989a5336c3e5269e62

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-amazon/connections/aws.rst", "docs/apache-airflow-providers-amazon/img/aws-base-conn-airflow.png", "docs/apache-airflow-providers-amazon/logging/s3-task-handler.rst", "docs/spelling_wordlist.txt"]

Amazon S3 for logging using IAM role for service accounts(IRSA)

### What do you see as an issue? I am using the latest Helm Chart version (see the version below) to deploy Airflow on Amazon EKS and trying to configure S3 for logging. We have few docs that explain how to add logging variables through `values.yaml` but that isn't sufficient for configuring S3 logging with IRSA. I couldn't find any other logs that explains this configuration in detail hence i am adding solution below Here is the link that i am referring to.. Amazon S3 for Logging https://github.com/apache/airflow/blob/main/docs/apache-airflow-providers-amazon/logging/s3-task-handler.rst Airflow config ``` apiVersion: v2 name: airflow version: 1.6.0 appVersion: 2.3.3 ``` ### Solving the problem **I have managed to get S3 logging working with IAM role for service accounts(IRSA).** # Writing logs to Amazon S3 using AWS IRSA ## Step1: Create IAM role for service account (IRSA) Create IRSA using `eksctl or terraform`. This command uses eksctl to create IAM role and service account ```sh eksctl create iamserviceaccount --cluster="<EKS_CLUSTER_ID>" --name="<SERVICE_ACCOUNT_NAME>" --namespace=airflow --attach-policy-arn="<IAM_POLICY_ARN>" --approve # e.g., eksctl create iamserviceaccount --cluster=airflow-eks-cluster --name=airflow-sa --namespace=airflow --attach-policy-arn=arn:aws:iam::aws:policy/AmazonS3FullAccess --approve ``` ## Step2: Update Helm Chart `values.yaml` with Service Account Add the above Service Account (e.g., `airflow-sa`) to Helm Chart `values.yaml` under the following sections. We are using the existing `serviceAccount` hence `create: false` with existing name as `name: airflow-sa`. Annotations may not be required as this will be added by **Step1**. Adding this for readability ```yaml workers: serviceAccount: create: false name: airflow-sa # Annotations to add to worker Kubernetes service account. annotations: eks.amazonaws.com/role-arn: <ENTER_IAM_ROLE_ARN_CREATED_BY_EKSCTL_COMMAND> webserver: serviceAccount: create: false name: airflow-sa # Annotations to add to worker Kubernetes service account. annotations: eks.amazonaws.com/role-arn: <ENTER_IAM_ROLE_ARN_CREATED_BY_EKSCTL_COMMAND config: logging: remote_logging: 'True' logging_level: 'INFO' remote_base_log_folder: 's3://<ENTER_YOUR_BUCKET_NAME>/<FOLDER_PATH' remote_log_conn_id: 'aws_s3_conn' # notice this name is be used in Step3 delete_worker_pods: 'False' encrypt_s3_logs: 'True' ``` ## Step3: Create S3 connection in Airflow Web UI Now the final step to create connections under Airflow UI before executing the DAGs - Login to Airflow Web UI and Navigate to `Admin -> Connections` - Create connection for S3 and select the options as shown in the image <img width="861" alt="image (1)" src="https://user-images.githubusercontent.com/19464259/181126084-2a0ddf43-01a4-4abd-9031-b53fb4d8870f.png"> ## Step4: Verify the logs - Execute example DAGs - Verify the logs in S3 bucket - Verify the logs from Airflow UI from DAGs log ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25322

https://github.com/apache/airflow/pull/25931

3326a0d493c92b15eea8cd9a874729db7b7a255c

bd3d6d3ee71839ec3628fa47294e0b3b8a6a6b9f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/sql_to_gcs.py"]

BaseSQLToGCSOperator parquet export format not limiting file size bug

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==6.8.0 ### Apache Airflow version 2.3.2 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened When using the `PostgresToGCSOperator(..., export_format='parquet', approx_max_file_size_bytes=Y, ...)`, when a temporary file exceeds the size defined by Y, the current file is not yielded, and no new chunk is created. Meaning that only 1 chunk will be uploaded irregardless of the size specified Y. I believe [this](https://github.com/apache/airflow/blob/d876b4aa6d86f589b9957a2e69484c9e5365eba8/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L253) line of code which is responsible for verifying whether the temporary file has exceeded its size, to be the culprit, considering the call to `tmp_file_handle.tell()` is always returning 0 after a `parquet_writer.write_table(tbl)` call [[here]](https://github.com/apache/airflow/blob/d876b4aa6d86f589b9957a2e69484c9e5365eba8/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L240). Therefore, regardless of the size of the temporary file already being bigger than the defined approximate limit Y, no new file will be created and only a single chunk will be uploaded. ### What you think should happen instead This behaviour is erroneous as when the temporary file exceeds the size defined by Y, it should upload the current temporary file and then create a new file to upload after successfully uploading the current file to GCS. A possible fix could be to use the `import os` package to determine the size of the temporary file with `os.stat(tmp_file_handle).st_size`, instead of using `tmp_file_handle.tell()`. ### How to reproduce 1. Create a postgres connection on airflow with id `postgres_test_conn`. 2. Create a gcp connection on airflow with id `gcp_test_conn`. 3. In the database referenced by the `postgres_test_conn`, in the public schema create a table `large_table`, where the total amount of data In the table is big enough to exceed the 10MB limit defined in the `approx_max_file_size_bytes` parameter. 4. Create a bucket named `issue_BaseSQLToGCSOperator_bucket`, in the gcp account referenced by the `gcp_test_conn`. 5. Create the dag exemplified in the excerpt below, and manually trigger the dag to fetch all the data from `large_table`, to insert in the `issue_BaseSQLToGCSOperator_bucket`. We should expect multiple chunks to be created, but due to this bug, only 1 chunk will be uploaded with the whole data from `large_table`. ```python import pendulum from airflow import DAG from airflow.providers.google.cloud.transfers.postgres_to_gcs import PostgresToGCSOperator with DAG( dag_id="issue_BaseSQLToGCSOperator", start_date=pendulum.parse("2022-01-01"), )as dag: task = PostgresToGCSOperator( task_id='extract_task', filename='uploading-{}.parquet', bucket="issue_BaseSQLToGCSOperator_bucket", export_format='parquet', approx_max_file_size_bytes=10_485_760, sql="SELECT * FROM large_table", postgres_conn_id='postgres_test_conn', gcp_conn_id='gcp_test_conn', ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25313

https://github.com/apache/airflow/pull/25469

d0048414a6d3bdc282cc738af0185a9a1cd63ef8

803c0e252fc78a424a181a34a93e689fa9aaaa09

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/exceptions.py", "airflow/models/taskinstance.py", "tests/models/test_taskinstance.py"]

on_failure_callback is not called when task is terminated externally

### Apache Airflow version 2.2.5 ### What happened `on_failure_callback` is not called when task is terminated externally. A similar issue was reported in [#14422](https://github.com/apache/airflow/issues/14422) and fixed in [#15172](https://github.com/apache/airflow/pull/15172). However, the code that fixed this was changed in a later PR [#16301](https://github.com/apache/airflow/pull/16301), after which `task_instance._run_finished_callback` is no longer called when SIGTERM is received (https://github.com/apache/airflow/pull/16301/files#diff-d80fa918cc75c4d6aa582d5e29eeb812ba21371d6977fde45a4749668b79a515L85). ### What you think should happen instead `on_failure_callback` should be called when task fails regardless of how the task fails. ### How to reproduce DAG file: ``` import datetime import pendulum from airflow.models import DAG from airflow.operators.bash_operator import BashOperator DEFAULT_ARGS = { 'email': ['example@airflow.com'] } TZ = pendulum.timezone("America/Los_Angeles") test_dag = DAG( dag_id='test_callback_in_manually_terminated_dag', schedule_interval='*/10 * * * *', default_args=DEFAULT_ARGS, catchup=False, start_date=datetime.datetime(2022, 7, 14, 0, 0, tzinfo=TZ) ) with test_dag: BashOperator( task_id='manually_terminated_task', bash_command='echo start; sleep 60', on_failure_callback=lambda context: print('This on_failure_back should be called when task fails.') ) ``` While the task instance is running, either force quitting the scheduler or manually updating its state to None in the database will cause the task to get SIGTERM and terminate. In either case, a failure callback will not be called which does not match the behavior of previous versions of Airflow. The stack trace is attached below and `on_failure_callback` is not called. ``` [2022-07-15, 02:02:24 UTC] {process_utils.py:124} INFO - Sending Signals.SIGTERM to group 10571. PIDs of all processes in the group: [10573, 10575, 10571] [2022-07-15, 02:02:24 UTC] {process_utils.py:75} INFO - Sending the signal Signals.SIGTERM to group 10571 [2022-07-15, 02:02:24 UTC] {taskinstance.py:1431} ERROR - Received SIGTERM. Terminating subprocesses. [2022-07-15, 02:02:24 UTC] {subprocess.py:99} INFO - Sending SIGTERM signal to process group [2022-07-15, 02:02:24 UTC] {process_utils.py:70} INFO - Process psutil.Process(pid=10575, status='terminated', started='02:02:11') (10575) terminated with exit code None [2022-07-15, 02:02:24 UTC] {taskinstance.py:1776} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/python3.7/lib/python3.7/site-packages/airflow/operators/bash.py", line 182, in execute cwd=self.cwd, File "/opt/python3.7/lib/python3.7/site-packages/airflow/hooks/subprocess.py", line 87, in run_command for raw_line in iter(self.sub_process.stdout.readline, b''): File "/opt/python3.7/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1433, in signal_handler raise AirflowException("Task received SIGTERM signal") airflow.exceptions.AirflowException: Task received SIGTERM signal [2022-07-15, 02:02:24 UTC] {taskinstance.py:1289} INFO - Marking task as FAILED. dag_id=test_callback_in_manually_terminated_dag, task_id=manually_terminated_task, execution_date=20220715T015100, start_date=20220715T020211, end_date=20220715T020224 [2022-07-15, 02:02:24 UTC] {logging_mixin.py:109} WARNING - /opt/python3.7/lib/python3.7/site-packages/airflow/utils/email.py:108 PendingDeprecationWarning: Fetching SMTP credentials from configuration variables will be deprecated in a future release. Please set credentials using a connection instead. [2022-07-15, 02:02:24 UTC] {configuration.py:381} WARNING - section/key [smtp/smtp_user] not found in config [2022-07-15, 02:02:24 UTC] {email.py:214} INFO - Email alerting: attempt 1 [2022-07-15, 02:02:24 UTC] {configuration.py:381} WARNING - section/key [smtp/smtp_user] not found in config [2022-07-15, 02:02:24 UTC] {email.py:214} INFO - Email alerting: attempt 1 [2022-07-15, 02:02:24 UTC] {taskinstance.py:1827} ERROR - Failed to send email to: ['example@airflow.com'] ... OSError: [Errno 101] Network is unreachable [2022-07-15, 02:02:24 UTC] {standard_task_runner.py:98} ERROR - Failed to execute job 159 for task manually_terminated_task (Task received SIGTERM signal; 10571) [2022-07-15, 02:02:24 UTC] {process_utils.py:70} INFO - Process psutil.Process(pid=10571, status='terminated', exitcode=1, started='02:02:11') (10571) terminated with exit code 1 [2022-07-15, 02:02:24 UTC] {process_utils.py:70} INFO - Process psutil.Process(pid=10573, status='terminated', started='02:02:11') (10573) terminated with exit code None ``` ### Operating System CentOS Linux 7 ### Deployment Other Docker-based deployment ### Anything else This is an issue in 2.2.5. However, I notice that it appears to be fixed in the main branch by PR [#21877](https://github.com/apache/airflow/pull/21877/files#diff-62f7d8a52fefdb8e05d4f040c6d3459b4a56fe46976c24f68843dbaeb5a98487R1885-R1887) although it was not intended to fix this issue. Is there a timeline for getting that PR into a release? We are happy to test it out to see if it fixes the issue once it's released. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25297

https://github.com/apache/airflow/pull/29743

38b901ec3f07e6e65880b11cc432fb8ad6243629

671b88eb3423e86bb331eaf7829659080cbd184e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/param.py", "tests/models/test_param.py"]

ParamsDict represents the class object itself, not keys and values on Task Instance Details

### Apache Airflow version 2.3.3 (latest released) ### What happened ParamsDict's printable presentation shows the class object itself like `<airflow.models.param.ParamsDict object at 0x7fd0eba9bb80>` on the page of Task Instance Detail because it does not have `__repr__` method in its class. <img width="791" alt="image" src="https://user-images.githubusercontent.com/16971553/180902761-88b9dd9f-7102-4e49-b8b8-0282b31dda56.png"> It used to be `dict` object and what keys and values Params include are shown on UI before replacing Params with the advanced Params by #17100. ### What you think should happen instead It was originally shown below when it was `dict` object.  I think it can be fixed by adding `__repr__` method to the class like below. ```python class ParamsDict(dict): ... def __repr__(self): return f"{self.dump()}" ``` ### How to reproduce I guess it all happens on Airflow using 2.2.0+ ### Operating System Linux, but it's not depending on OS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25295

https://github.com/apache/airflow/pull/25305

285c23a2f90f4c765053aedbd3f92c9f58a84d28

df388a3d5364b748993e61b522d0b68ff8b8124a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/databricks/hooks/databricks.py", "airflow/providers/databricks/operators/databricks.py", "tests/providers/databricks/operators/test_databricks.py"]

Error description cannot be shown

### Apache Airflow version 2.3.3 (latest released) ### What happened Unfortunately, I cannot get further information about my actual error because of the following KeyError ``` Traceback (most recent call last): File "/usr/local/airflow/dags/common/databricks/operator.py", line 59, in execute _handle_databricks_operator_execution(self, hook, self.log, context) File "/usr/local/lib/python3.9/site-packages/airflow/providers/databricks/operators/databricks.py", line 64, in _handle_databricks_operator_execution notebook_error = run_output['error'] KeyError: 'error' ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System I Assume some Linux distribution ### Versions of Apache Airflow Providers Astronomer Certified:[ v2.3.3.post1 ](https://www.astronomer.io/downloads/ac/v2-3-3)based on Apache Airflow v2.3.3 Git Version: .release:2.3.3+astro.1+4446ad3e6781ad048c8342993f7c1418db225b25 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25286

https://github.com/apache/airflow/pull/25427

87a0bd969b5bdb06c6e93236432eff6d28747e59

679a85325a73fac814c805c8c34d752ae7a94312

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/hooks/sql.py", "tests/providers/common/sql/hooks/test_dbapi.py"]

Apache Airflow SqlSensor DbApiHook Error

### Apache Airflow version 2.3.3 (latest released) ### What happened I trying to make SqlSensor to work with Oracle database, I've installed all the required provider and successfully tested the connection. When I run SqlSensor I got this error message `ERROR - Failed to execute job 32 for task check_exec_date (The connection type is not supported by SqlSensor. The associated hook should be a subclass of `DbApiHook`. Got OracleHook; 419)` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 20.04.4 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.0.0 apache-airflow-providers-ftp==3.0.0 apache-airflow-providers-http==3.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-oracle==3.2.0 apache-airflow-providers-postgres==5.1.0 apache-airflow-providers-sqlite==3.0.0 ### Deployment Other ### Deployment details Run on Windows Subsystem for Linux ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25274

https://github.com/apache/airflow/pull/25293

7e295b7d992f4ed13911e593f15fd18e0d4c16f6

b0fd105f4ade9933476470f6e247dd5fa518ffc9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/plugins_manager.py", "airflow/utils/entry_points.py", "tests/plugins/test_plugins_manager.py", "tests/utils/test_entry_points.py", "tests/www/views/test_views.py"]

Version 2.3.3 breaks "Plugins as Python packages" feature

### Apache Airflow version 2.3.3 (latest released) ### What happened In 2.3.3 If I use https://airflow.apache.org/docs/apache-airflow/stable/plugins.html#plugins-as-python-packages feature, then I see these Error: short: `ValueError: The name 'airs' is already registered for this blueprint. Use 'name=' to provide a unique name.` long: > i'm trying to reproduce it... If I don't use it(workarounding by AIRFLOW__CORE__PLUGINS_FOLDER), errors doesn't occur. It didn't happend in 2.3.2 and earlier ### What you think should happen instead Looks like plugins are import multiple times if it is plugins-as-python-packages. Perhaps flask's major version change is the main cause. Presumably, in flask 1.0, duplicate registration of blueprint was quietly filtered out, but in 2.0 it seems to have been changed to generate an error. (I am trying to find out if this hypothesis is correct) Anyway, use the latest version of FAB is important. we will have to adapt to this change, so plugins will have to be imported once regardless how it defined. ### How to reproduce > It was reproduced in the environment used at work, but it is difficult to disclose or explain it. > I'm working to reproduce it with the breeze command, and I open the issue first with the belief that it's not just me. ### Operating System CentOS Linux release 7.9.2009 (Core) ### Versions of Apache Airflow Providers ```sh $ SHIV_INTERPRETER=1 airsflow -m pip freeze | grep apache- apache-airflow==2.3.3 apache-airflow-providers-apache-hive==3.1.0 apache-airflow-providers-apache-spark==2.1.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-common-sql==1.0.0 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-http==3.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-postgres==5.1.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-sqlite==3.1.0 ``` but I think these are irrelevant. ### Deployment Other 3rd-party Helm chart ### Deployment details docker image based on centos7, python 3.9.10 interpreter, self-written helm2 chart .... ... but I think these are irrelevant. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25271

https://github.com/apache/airflow/pull/25296

cd14f3f65ad5011058ab53f2119198d6c082e82c

c30dc5e64d7229cbf8e9fbe84cfa790dfef5fb8c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "tests/www/views/test_views_grid.py"]

Add has_dataset_outlets in /grid_data

Return `has_dataset_outlets` in /grid_data so we can know whether to check for downstream dataset events in grid view. Also: add `operator` Also be mindful of performance on those endpoints (e.g do things in a bulk query), and it should be part of the acceptance criteria.

https://github.com/apache/airflow/issues/25241

https://github.com/apache/airflow/pull/25323

e994f2b0201ca9dfa3397d22b5ac9d10a11a8931

d2df9fe7860d1e795040e40723828c192aca68be

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/forms.py", "tests/www/views/test_views_connection.py"]

Strip white spaces from values entered into fields in Airflow UI Connections form

### Apache Airflow version 2.3.3 (latest released) ### What happened I accidentally (and then intentionally) added leading and trailing white spaces while adding connection parameters in Airflow UI Connections form. What followed was an error message that was not so helpful in tracking down the input error by the user. ### What you think should happen instead Ideally, I expected that there should be a frontend or backend logic that strips off accidental leading or trailing white spaces when adding Connections parameters in Airflow. ### How to reproduce Intentionally add leading or trailing white spaces while adding Connections parameters. <img width="981" alt="Screenshot 2022-07-22 at 18 49 54" src="https://user-images.githubusercontent.com/9834450/180497315-0898d803-c104-4d93-b464-c0b33a466b4d.png"> ### Operating System Mac OS ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25240

https://github.com/apache/airflow/pull/32292

410d0c0f86aaec71e2c0050f5adbc53fb7b441e7

394cedb01abd6539f6334a40757bf186325eb1dd

python