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apache/airflow
https://github.com/apache/airflow
34,066
["airflow/www/static/js/dag/details/gantt/Row.tsx", "airflow/www/static/js/dag/details/graph/utils.ts", "airflow/www/static/js/dag/grid/TaskName.test.tsx", "airflow/www/static/js/dag/grid/TaskName.tsx", "airflow/www/static/js/dag/grid/ToggleGroups.tsx", "airflow/www/static/js/dag/grid/index.test.tsx", "airflow/www/static/js/dag/grid/renderTaskRows.tsx", "airflow/www/static/js/utils/graph.ts"]
Toggling TaskGroup toggles all TaskGroups with the same label on Graph/Grid
### Apache Airflow version main (development) ### What happened When you have 2 TaskGroups with the same `group_id` (nested under different parents), toggling either of them on the UI (graph or grid) toggles both. <img src="https://cdn-std.droplr.net/files/acc_1153680/9Z1Nvs" alt="image" width="50%"> ### What you think should happen instead Only the clicked TaskGroup should be toggled. They should be distinguishable since they have the parent's group_id as prefix. ### How to reproduce ``` from datetime import datetime from airflow.models import DAG from airflow.operators.empty import EmptyOperator from airflow.utils.task_group import TaskGroup with DAG( "my_dag", start_date=datetime(2023, 9, 4), ): with TaskGroup(group_id="a"): with TaskGroup(group_id="inner"): EmptyOperator(task_id="dummy") with TaskGroup(group_id="b"): with TaskGroup(group_id="inner"): EmptyOperator(task_id="dummy") ``` ### Operating System - ### Versions of Apache Airflow Providers _No response_ ### 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/34066
https://github.com/apache/airflow/pull/34072
e403c74524a980030ba120c3602de0c3dc867d86
b9acffa81bf61dcf0c5553942c52629c7f75ebe2
"2023-09-04T07:13:16Z"
python
"2023-09-06T10:23:20Z"
closed
apache/airflow
https://github.com/apache/airflow
34,058
["airflow/www/views.py"]
UI Grid error when DAG has been removed
**Reproduce on main:** - Run a DAG - Rename the DAG - Wait for dag file processor to remove the deleted DAG and add the new one. (Old Dag should not appear on the home page anymore) - Go to the Browse DAGRUN page. - Click on a DagRun of the deleted DAG, as if you want to see the 'details/grid' of that old dag run More info and screenshots here: https://github.com/notifications#discussioncomment-6898570 ![image](https://github.com/apache/airflow/assets/14861206/7af81643-6278-4d90-a20f-6aa7b259ce59)
https://github.com/apache/airflow/issues/34058
https://github.com/apache/airflow/pull/36028
fd0988369b3a94be01a994e46b7993e2d97b2028
549fac30eeefaa449df9bfdf58eb40a008e9fe75
"2023-09-03T19:36:42Z"
python
"2023-12-03T01:11:15Z"
closed
apache/airflow
https://github.com/apache/airflow
34,023
["airflow/ti_deps/deps/trigger_rule_dep.py", "tests/models/test_mappedoperator.py", "tests/ti_deps/deps/test_trigger_rule_dep.py"]
Trigger Rule ONE_FAILED does not work in task group with mapped tasks
### Apache Airflow version 2.7.0 ### What happened I have the following DAG: ```python from __future__ import annotations from datetime import datetime from airflow.decorators import dag, task, task_group from airflow.utils.trigger_rule import TriggerRule @task def get_records() -> list[str]: return ["a", "b", "c"] @task def submit_job(record: str) -> None: pass @task def fake_sensor(record: str) -> bool: raise RuntimeError("boo") @task def deliver_record(record: str) -> None: pass @task(trigger_rule=TriggerRule.ONE_FAILED) def handle_failed_delivery(record: str) -> None: pass @task_group(group_id="deliver_records") def deliver_record_task_group(record: str): ( submit_job(record=record) >> fake_sensor(record=record) >> deliver_record(record=record) >> handle_failed_delivery(record=record) ) @dag( dag_id="demo_trigger_one_failed", schedule=None, start_date=datetime(2023, 1, 1), ) def demo_trigger_one_failed() -> None: records = get_records() deliver_record_task_group.expand(record=records) demo_trigger_one_failed() ``` - `fake_sensor` is simulating a task that raises an exception. (It could be a `@task.sensor` raising a `AirflowSensorTimeout`; it doesn't matter, the behavior is the same.) - `handle_failed_delivery`'s `TriggerRule.ONE_FAILED` means **it is supposed to run whenever any task upstream fails.** So when `fake_sensor` fails, `handle_failed_delivery` should run. But this does not work. `handle_failed_delivery` is skipped, and (based on the UI) it's skipped very early, before it can know if the upstream tasks have completed successfully or errored. Here's what I see, progressively (see `How to reproduce` below for how I got this): | started ... | skipped too early ... | fake sensor about to fail... | ... done, didn't run | |--------|--------|--------|--------| | <img width="312" alt="Screenshot 2023-09-01 at 3 26 49 PM" src="https://github.com/apache/airflow/assets/354655/2a9bb897-dd02-4c03-a381-2deb774d1072"> | <img width="310" alt="Screenshot 2023-09-01 at 3 26 50 PM" src="https://github.com/apache/airflow/assets/354655/11d0f8c5-c7c0-400f-95dd-4ed3992701d0"> | <img width="308" alt="Screenshot 2023-09-01 at 3 26 53 PM" src="https://github.com/apache/airflow/assets/354655/dd81e42e-ca24-45fa-a18d-df2b435c3d82"> | <img width="309" alt="Screenshot 2023-09-01 at 3 26 56 PM" src="https://github.com/apache/airflow/assets/354655/d3a3303c-91d9-498a-88c3-f1aa1e8580b6"> | If I remove the task group and instead do, ```python @dag( dag_id="demo_trigger_one_failed", schedule=None, start_date=datetime(2023, 1, 1), ) def demo_trigger_one_failed() -> None: records = get_records() ( submit_job(record=records) >> fake_sensor.expand(record=records) >> deliver_record.expand(record=records) >> handle_failed_delivery.expand(record=records) ) ``` then it does the right thing: | started ... | waiting ... | ... done, triggered correctly | |--------|--------|--------| | <img width="301" alt="Screenshot 2023-09-01 at 3 46 48 PM" src="https://github.com/apache/airflow/assets/354655/7e52979b-0161-4469-b284-3411a0b1b1c4"> | <img width="306" alt="Screenshot 2023-09-01 at 3 46 50 PM" src="https://github.com/apache/airflow/assets/354655/733654f3-8cb0-4181-b6b7-bad02994469d"> | <img width="304" alt="Screenshot 2023-09-01 at 3 46 53 PM" src="https://github.com/apache/airflow/assets/354655/13ffb46f-d5ca-4e7a-8d60-caad2e4a7827"> | ### What you think should happen instead The behavior with the task group should be the same as without the task group: the `handle_failed_delivery` task with `trigger_rule=TriggerRule.ONE_FAILED` should be run when the upstream `fake_sensor` task fails. ### How to reproduce 1. Put the above DAG at a local path, `/tmp/dags/demo_trigger_one_failed.py`. 2. `docker run -it --rm --mount type=bind,source="/tmp/dags",target=/opt/airflow/dags -p 8080:8080 apache/airflow:2.7.0-python3.10 bash` 3. In the container: ``` airflow db init airflow users create --role Admin --username airflow --email airflow --firstname airflow --lastname airflow --password airflow airflow scheduler --daemon airflow webserver ``` 4. Open `http://localhost:8080` on the host. Login with `airflow` / `airflow`. Run the DAG. I tested this with: - `apache/airflow:2.6.2-python3.10` - `apache/airflow:2.6.3-python3.10` - `apache/airflow:2.7.0-python3.10` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers n/a ### Deployment Other Docker-based deployment ### Deployment details This can be reproduced using standalone Docker images, see Repro steps above. ### Anything else I wonder if this is related to (or fixed by?) https://github.com/apache/airflow/issues/33446 -> https://github.com/apache/airflow/pull/33732 ? (The latter was "added to the `Airflow 2.7.1` milestone 3 days ago." I can try to install that pre-release code in the container and see if it's fixed.) _edit_: nope, [not fixed](https://github.com/apache/airflow/issues/34023#issuecomment-1703298280) ### 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/34023
https://github.com/apache/airflow/pull/34337
c2046245c07fdd6eb05b996cc67c203c5ac456b6
69938fd163045d750b8c218500d79bc89858f9c1
"2023-09-01T19:58:24Z"
python
"2023-11-01T20:37:15Z"
closed
apache/airflow
https://github.com/apache/airflow
34,019
["airflow/www/static/css/main.css"]
please disable pinwheel animation as it violates ada guidelines
### Description _No response_ ### 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/34019
https://github.com/apache/airflow/pull/34020
7bf933192d845f85abce56483e3f395247e60b68
f8a5c8bf2b23b8a5a69b00e21ff37b58559c9dd6
"2023-09-01T17:13:38Z"
python
"2023-09-02T07:40:44Z"
closed
apache/airflow
https://github.com/apache/airflow
34,010
["airflow/providers/common/sql/operators/sql.py", "airflow/providers/google/cloud/operators/bigquery.py", "tests/providers/google/cloud/operators/test_bigquery.py"]
`BigQueryValueCheckOperator` doesn't respect `pass_value` in deferrable mode
### Apache Airflow version 2.7.0 ### What happened When running `BigQueryValueCheckOperator` in deferrable mode, the operator always reports a successful status even if the `pass_value` has not been met. ### What you think should happen instead If the value returned by the SQL given to the operator does not equal the `pass_value` then the operator should fail. This occurs when `deferrable=False` but not when it is `True`. ### How to reproduce The following DAG code should replicate the issue. Both tasks provide some SQL that just returns `false` and with a `pass_value` of `True`. The only difference is the fact that the first task is running in deferrable mode. ``` from datetime import datetime from airflow import models from airflow.providers.google.cloud.operators.bigquery import BigQueryValueCheckOperator with models.DAG( dag_id='bq_value_check', start_date=datetime(2023, 8, 31), catchup=False, schedule='0 0 * * *', ) as dag: test1 = BigQueryValueCheckOperator( task_id=f'test1', sql=f'SELECT false;', pass_value=True, retries=0, deferrable=False, ) test2 = BigQueryValueCheckOperator( task_id=f'test2', sql=f'SELECT false;', pass_value=True, retries=0, deferrable=True, ) ``` <img width="275" alt="Screenshot 2023-09-01 at 14 42 28" src="https://github.com/apache/airflow/assets/967119/8ac7539e-dee0-45d6-b1da-b5a9f32986b4"> Some log extracts: **test1** ``` [2023-09-01, 13:34:53 UTC] {bigquery.py:1596} INFO - Inserting job airflow_1693575293576477_43573f106bd19562ebd18f0679e80536 [2023-09-01, 13:34:54 UTC] {taskinstance.py:1824} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/airflow/providers/google/cloud/operators/bigquery.py", line 425, in execute super().execute(context=context) File "/usr/local/lib/python3.10/site-packages/airflow/providers/common/sql/operators/sql.py", line 857, in execute self._raise_exception(error_msg) File "/usr/local/lib/python3.10/site-packages/airflow/providers/common/sql/operators/sql.py", line 187, in _raise_exception raise AirflowException(exception_string) airflow.exceptions.AirflowException: Test failed. Pass value:True Tolerance:None Query: SELECT false; Results: [False] ``` **test2** ``` [2023-09-01, 13:34:53 UTC] {bigquery.py:1596} INFO - Inserting job airflow_1693575293256612_92474edc414865ab2efb14bd8b18e24d [2023-09-01, 13:34:53 UTC] {bigquery.py:446} INFO - Current state of job airflow_1693575293256612_92474edc414865ab2efb14bd8b18e24d is DONE [2023-09-01, 13:34:53 UTC] {taskinstance.py:1345} INFO - Marking task as SUCCESS. dag_id=bq_value_check, task_id=test2, execution_date=20230831T000000, start_date=20230901T133452, end_date=20230901T133453 [2023-09-01, 13:34:53 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 0 ``` ### Operating System n/a ### Versions of Apache Airflow Providers apache-airflow-providers-google==10.7.0 ### 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/34010
https://github.com/apache/airflow/pull/34018
6ef80e8be178e0ab8d119270a28b23d0bf47ed62
d757f6a3af24c3ec0d48c8c983d6ba5d6ed2202e
"2023-09-01T13:48:27Z"
python
"2023-09-03T21:18:50Z"
closed
apache/airflow
https://github.com/apache/airflow
34,005
["docs/conf.py"]
`version_added` field in configuration option doesn't work correctly in providers documentation
### Apache Airflow version 2.7.0 ### What happened Initial finding: https://github.com/apache/airflow/pull/33960#discussion_r1312748153 Since [Airflow 2.7.0](https://github.com/apache/airflow/pull/32629) we have an ability to store configuration options in providers, everything works fine, except field `version_added`. The logic around this field expect Airflow version and not Provider version. Any attempt to add in this field any value greater than current version of Airflow (2.7.0 at that moment) will result that configuration option won't rendered in documentation, seem like it not prevented to add this configuration at least `airflow config get-value` command return expected option. ### What you think should happen instead Various, depend on final solution and decision. _Option 1_: In case if we would not like use this field for providers we might ignore this field in providers configurations. For Community Providers we could always set it to `~` _Option 2_: Dynamically resolve depend on what a source of this configuration, Core/Provider _Option 3_: Add `provider_version_added` and use for show in which version of provider this configuration added We could keep `version_added` if configuration option in provider related to Airflow Version _Option 4_: Suggest you own 😺 ### How to reproduce Create configuration option with `version_added` greater than current version of Airflow, for stable it is 2.7.0 for dev 2.8.0 ```yaml config: aws: description: This section contains settings for Amazon Web Services (AWS) integration. options: session_factory: description: | Full import path to the class which implements a custom session factory for ``boto3.session.Session``. For more details please have a look at :ref:`howto/connection:aws:session-factory`. default: ~ example: my_company.aws.MyCustomSessionFactory type: string version_added: 3.1.1 ``` ### Operating System n/a ### Versions of Apache Airflow Providers n/a ### 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/34005
https://github.com/apache/airflow/pull/34011
04e9b0bd784e7c0045e029c6ed4ec0ac4ad6066f
559507558b1dff591f549dc8b24092d900ffb0fa
"2023-09-01T11:52:09Z"
python
"2023-09-01T14:28:04Z"
closed
apache/airflow
https://github.com/apache/airflow
33,958
["dev/breeze/src/airflow_breeze/commands/setup_commands.py"]
Add verification if short options in breeze are repeated
### Body When you have two option with the same short flag - in the same command, only one can be used - for example before https://github.com/apache/airflow/pull/33957 `static-checks -t` yielded `--image-tag` not `--static-checks`. We should check if any of the commands of breeze has such flag duplicated. This should be done in the place where we calculate hash from the context dictionary in setup_commands.py: ``` def get_command_hash_export() -> str: import rich_click hashes = [] with Context(main) as ctx: the_context_dict = ctx.to_info_dict() ``` This is used in `breeze setup regenerate-command-images` and the `the_context_dict` contains information about all commands and their flags, so we should be able to easily check if some flags are duplicated in all commands. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/33958
https://github.com/apache/airflow/pull/34269
6703f720cc4d49e223de2f7c542beda5a6164212
3a147ee1e64cd6363ae8f033a9c5215414551ce7
"2023-08-31T13:20:37Z"
python
"2023-09-12T22:22:35Z"
closed
apache/airflow
https://github.com/apache/airflow
33,949
["airflow/jobs/scheduler_job_runner.py", "tests/jobs/test_scheduler_job.py"]
Manual DAG triggers with Logical Date in the Past trigger a second run when schedule is timedelta
### Apache Airflow version 2.7.0 ### What happened Relates to investigation of #30327 When you define a DAG with a `schedule=datetime.timedelta(days=1)` and `catchup=False` and you manually trigger a run in the past (logical date in the past, e.g. a few days ago) then upon completion of the run another scheduled run is triggered - irrespective of a previous scheduled run was made just before (not a day ago) ### What you think should happen instead A manual run should not trigger another run as the scheduling in general should follow the delta time definition of the DAG. ### How to reproduce Use the following example DAG code and enable scheduling: ``` with DAG( dag_id="after_workday_delta_regression", start_date=pendulum.datetime(2023, 8, 1, tz="UTC"), catchup=False, schedule=datetime.timedelta(days=1), params={"test": 123} ): @task def test(ti: TaskInstance=None): print(ti.execution_date) test() ``` Wait a moment and see that one schedule is run automatically. Then trigger a manual DAG run and set the execution date/logical date a few days into the past. After execution you see a third run of the DAG just at the time of completion of the manual trigger as execution date. But the first execution was just a few moments before, not respecting the desired delta of 1 day. ### Operating System Ubuntu 20.04 / Breeze Dev setup in Py 3.8 Container ### Versions of Apache Airflow Providers not relevant ### Deployment Other ### Deployment details Started and tested with latest main branch and breeze. ### Anything else I tried to find the root cause but was not able to locate it. Suspect it is rooted in scheduler_job_runner.py. I'd supply a fix but was not able to fund the root cause. Expert knowledge is needed probably. ### 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/33949
https://github.com/apache/airflow/pull/34027
920d641b8ddbcdcbc5d9b889027521470c93a155
20d81428699db240b65f72a92183255c24e8c19b
"2023-08-31T09:27:57Z"
python
"2023-09-05T13:01:33Z"
closed
apache/airflow
https://github.com/apache/airflow
33,895
["setup.cfg", "setup.py"]
Search Function Not Working in Airflow UI
### Apache Airflow version 2.7.0 ### What happened Actual Behavior: Upon entering a keyword in the search bar, no search results are shown, and the UI remains unchanged. The search function seems to be non-responsive. You can see in the address bar, `undefined` appears instead of valid value. <img width="1427" alt="step-1" src="https://github.com/apache/airflow/assets/17428690/10da4758-a614-48c8-a926-6bc69f8595f1"> <img width="1433" alt="step-2" src="https://github.com/apache/airflow/assets/17428690/be8912f2-e49e-40ca-86f7-1803b516482e"> ### What you think should happen instead Expected Behavior: When I use the search function in the Airflow UI, I expect to see a list of results that match the entered keyword. This should help me quickly locate specific DAGs or tasks within the UI. ### How to reproduce Steps to Reproduce: 1. Log in to the Airflow UI. 2. Navigate to the "Connections" list view by clicking on the "Connections" link in the navigation menu or by directly visiting the URL: <your_airflow_ui_url>/connection/list/. 3. Attempt to use the search function by entering a keyword. 4. Observe that no search results are displayed, and the search appears to do nothing. ### Operating System MacOS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Standalone Airflow via official Quickstart documentation ### Anything else I encounter this issue since `Airflow 2.6.0`. It was fine on `Airflow 2.5.3`. ### 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/33895
https://github.com/apache/airflow/pull/33931
3b868421208f171dd44733c6a3376037b388bcef
ba261923d4de90d0609344843554bf7dfdab11c6
"2023-08-29T16:59:40Z"
python
"2023-08-31T06:03:06Z"
closed
apache/airflow
https://github.com/apache/airflow
33,887
["setup.cfg"]
Airflow db migrate AttributeError: 'Session' object has no attribute 'scalars'
### Apache Airflow version 2.7.0 ### What happened I try to execute airflow db migrate ```console /opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/configuration.py:751 UserWarning: Config scheduler.max_tis_per_query (value: 512) should NOT be greater than core.parallelism (value: 32). Will now use core.parallelism as the max task instances per query instead of specified value. /opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/configuration.py:857 FutureWarning: The 'log_id_template' setting in [elasticsearch] has the old default value of '{dag_id}-{task_id}-{execution_date}-{try_number}'. This value has been changed to '{dag_id}-{task_id}-{run_id}-{map_index}-{try_number}' in the running config, but please update your config before Apache Airflow 3.0. /opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/cli/cli_config.py:974 DeprecationWarning: The namespace option in [kubernetes] has been moved to the namespace option in [kubernetes_executor] - the old setting has been used, but please update your config. DB: postgresql+psycopg2://airflow:***@localhost/airflow Performing upgrade to the metadata database postgresql+psycopg2://airflow:***@localhost/airflow Traceback (most recent call last): File "/opt/miniconda3/envs/pytorch/bin/airflow", line 8, in <module> sys.exit(main()) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/__main__.py", line 60, in main args.func(args) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/cli/cli_config.py", line 49, in command return func(*args, **kwargs) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/cli.py", line 113, in wrapper return f(*args, **kwargs) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/providers_configuration_loader.py", line 56, in wrapped_function return func(*args, **kwargs) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/cli/commands/db_command.py", line 104, in migratedb db.upgradedb( File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/session.py", line 77, in wrapper return func(*args, session=session, **kwargs) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/db.py", line 1616, in upgradedb for err in _check_migration_errors(session=session): File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/db.py", line 1499, in _check_migration_errors yield from check_fn(session=session) File "/opt/miniconda3/envs/pytorch/lib/python3.8/site-packages/airflow/utils/db.py", line 979, in check_conn_id_duplicates dups = session.scalars( AttributeError: 'Session' object has no attribute 'scalars' ``` ### What you think should happen instead _No response_ ### How to reproduce Execute airflow db migrate with PostgresSql existing database ### Operating System Ubuntu ### 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/33887
https://github.com/apache/airflow/pull/33892
fe27031382e2034b59a23db1c6b9bdbfef259137
bfab7daffedd189a85214165cfc34944e2bf11c1
"2023-08-29T12:56:49Z"
python
"2023-08-29T17:07:49Z"
closed
apache/airflow
https://github.com/apache/airflow
33,882
["airflow/providers/databricks/hooks/databricks.py", "tests/providers/databricks/hooks/test_databricks.py"]
Providers - Databricks : Add support for Databricks "Queued" state for job run
### Apache Airflow version 2.7.0 ### What happened When using the Deferrable operator DatabricksRunNowOperator, if a task is in a queue state in Databricks because of a maximum concurrency set directly on databricks, the operator raises an exception after at least 1 defferal as the "QUEUED" state is not in the list of supported states on the Databricks Hook RunState is_terminal property. Error Snippet : ``` [2023-08-28, 19:39:22 UTC] {task_command.py:410} INFO - Running <TaskInstance: trigger_products_categorization_training_databricks.training_tasks.trigger_databricks_training_job manual__2023-08-28T17:28:04.408402+00:00 map_index=4 [running]> on host 10.4.114.101 [2023-08-28, 19:39:22 UTC] {logging_mixin.py:150} WARNING - /home/airflow/.local/lib/python3.8/site-packages/airflow/models/mappedoperator.py:615 AirflowProviderDeprecationWarning: `DatabricksRunNowDeferrableOperator` has been deprecated. Please use `airflow.providers.databricks.operators.DatabricksRunNowOperator` with `deferrable=True` instead. [2023-08-28, 19:39:22 UTC] {pod_generator.py:529} WARNING - Model file /opt/airflow/pod_templates/pod_template_file.yaml does not exist [2023-08-28, 19:39:23 UTC] {taskinstance.py:1598} ERROR - Trigger failed: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/triggerer_job_runner.py", line 537, in cleanup_finished_triggers result = details["task"].result() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/triggerer_job_runner.py", line 615, in run_trigger async for event in trigger.run(): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/databricks/triggers/databricks.py", line 84, in run if run_state.is_terminal: File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks.py", line 77, in is_terminal raise AirflowException( airflow.exceptions.AirflowException: Unexpected life cycle state: QUEUED: If the state has been introduced recently, please check the Databricks user guide for troubleshooting information ``` ### What you think should happen instead As the "QUEUED" state of a job run on Databricks is not a terminal state, the pod should be deffered using the usual logic. ### How to reproduce ```python import pendulum, os from itertools import chain from airflow import DAG from airflow.decorators import task from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator DAG_NAME = "myDag" default_args = { "owner": "airflow", "start_date": pendulum.today("UTC").add(days=-7), "retries": 0, "retry_exponential_backoff": False, } with DAG( DAG_NAME, default_args=default_args, catchup=False, schedule=None, ) as dag: myop = DatabricksRunNowOperator( task_id='trigger_run_job_op', dag=dag, databricks_conn_id='databricks_conn', job_id=<myJobId>, python_params=python_params, polling_period_seconds=300, deferrable=True, dag = dag, ) myop ``` ### Operating System Mac OS Ventura ### Versions of Apache Airflow Providers apache-airflow-providers-databricks==4.4.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else Code to rework appears to be here : https://github.com/apache/airflow/blob/ffc9854a81ce3195b2f3e8cdeb4ea90462e112f0/airflow/providers/databricks/hooks/databricks.py#L82 ### 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/33882
https://github.com/apache/airflow/pull/33886
a35c0d42cce17fd0647d58f247a8bb9b8b8fab60
f7a005db8c5b47fe86196374e3e857b40e9ea5ac
"2023-08-29T12:18:44Z"
python
"2023-08-30T16:19:24Z"
closed
apache/airflow
https://github.com/apache/airflow
33,876
["BREEZE.rst", "dev/README_RELEASE_PROVIDER_PACKAGES.md", "dev/breeze/src/airflow_breeze/commands/developer_commands.py", "dev/breeze/src/airflow_breeze/params/doc_build_params.py", "docs/README.rst", "docs/build_docs.py", "images/breeze/output-commands-hash.txt", "images/breeze/output_build-docs.svg"]
Replace `--package-filter` usage for docs breeze command with short package names
### Body The `--package-filter` while nice in theory to specify which packages to build, has quite bad UX (lots of repetitions when specifying multiple packages, long package names. We practically (except `--package-filter apache-airflow-providers-*` never use the functionality of the filter with glob patterns. It's much more practical to use "short" package names ("apache.hdfs" rather that `--package-filter apache-airflow-providers-apache-hdfs` and we already use it in a few places in Breeze. We should likely replace all the places when we use `--package-filter` with those short names, add a special alias for `all-providers` and this should help our users who build documentation and release manager to do their work faster and nicer. This would also allow to remove the separate ./dev/provider_packages/publish_provider_documentation.sh bash script that is aimed to do somethign similar in a "hacky way". ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/33876
https://github.com/apache/airflow/pull/34004
3d27504a6232cacb12a9e3dc5837513e558bd52b
e4b3c9e54481d2a6e2de75f73130a321e1ba426c
"2023-08-29T10:23:24Z"
python
"2023-09-04T19:23:48Z"
closed
apache/airflow
https://github.com/apache/airflow
33,871
["airflow/api_connexion/endpoints/variable_endpoint.py", "tests/api_connexion/endpoints/test_variable_endpoint.py"]
Airflow API for PATCH a variable key which doesn't exist, sends Internal Server Error
### Apache Airflow version 2.7.0 ### What happened ``` 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: * <b><a href="https://github.com/apache/airflow/discussions">GitHub Discussions</a></b> * <b><a href="https://github.com/apache/airflow/issues">GitHub Issues</a></b> * <b><a href="https://stackoverflow.com/questions/tagged/airflow">Stack Overflow</a></b> * 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 <b><a href="https://github.com/apache/airflow/issues/new/choose">bug report</a></b>. Make sure however, to include all relevant details and results of your investigation so far. Python version: 3.11.4 Airflow version: 2.7.0 Node: redact ------------------------------------------------------------------------------- Error! Please contact server admin. ``` The traceback received is: ``` [2023-08-29T08:26:46.947+0000] {app.py:1744} ERROR - Exception on /api/v1/variables/TEST_VAR [PATCH] Traceback (most recent call last): File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/flask/app.py", line 2529, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/flask/app.py", line 1825, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/flask/app.py", line 1823, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/flask/app.py", line 1799, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/decorator.py", line 68, in wrapper response = function(request) ^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/uri_parsing.py", line 149, in wrapper response = function(request) ^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/validation.py", line 196, in wrapper response = function(request) ^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/validation.py", line 399, in wrapper return function(request) ^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/response.py", line 112, in wrapper response = function(request) ^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/connexion/decorators/parameter.py", line 120, in wrapper return function(**kwargs) ^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/airflow/api_connexion/security.py", line 52, in decorated return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/airflow/utils/session.py", line 77, in wrapper return func(*args, session=session, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/airflow/www/decorators.py", line 127, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/Users/abhishekbhakat/Codes/Turbine/my_local_airflow/airflowenv/lib/python3.11/site-packages/airflow/api_connexion/endpoints/variable_endpoint.py", line 118, in patch_variable setattr(variable, key, val) AttributeError: 'NoneType' object has no attribute 'key' 127.0.0.1 - admin [29/Aug/2023:08:26:46 +0000] "PATCH /api/v1/variables/TEST_VAR HTTP/1.1" 500 1543 "-" "curl/8.1.2" ``` ### What you think should happen instead The API call should have thrown a different HTTP Response. Say, 400 BAD REQUEST and Variable does not exist. ### How to reproduce ``` curl -vvvv -X PATCH "http://localhost:8888/api/v1/variables/TEST_VAR" -H 'Content-Type: application/json' -H "Authorization: Basic YWRtaW46YWRtaW4=" -d '{"key":"TEST_VAR","value":"TRUE"}' ``` I used a basic auth with admin:admin as creds. ### Operating System macOS 13.5.1 22G90 arm64 ### Versions of Apache Airflow Providers Not relevant ### 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/33871
https://github.com/apache/airflow/pull/33885
d361761deeffe628f3c17ab0debd0e11515c22da
701c3b80107adb9f4c697f04331c1c7c4e315cd8
"2023-08-29T08:32:08Z"
python
"2023-08-30T07:06:08Z"
closed
apache/airflow
https://github.com/apache/airflow
33,854
[".pre-commit-config.yaml", "STATIC_CODE_CHECKS.rst", "dev/breeze/src/airflow_breeze/pre_commit_ids.py", "images/breeze/output-commands-hash.txt", "images/breeze/output_static-checks.svg", "pyproject.toml"]
`pyproject.toml` `[project]` section without `name` and `version` attributes is not pep 621 compliant
### Apache Airflow version 2.7.0 ### What happened https://peps.python.org/pep-0621/ https://github.com/apache/airflow/blob/83d09c0c423f3e8e3bbbfa6e0171d88893d1c18a/pyproject.toml#L31 Newer setuptools will complain and fail to install. When building the NixOS package for 2.7.0 we now get: ``` ValueError: invalid pyproject.toml config: `project`. configuration error: `project` must contain ['name'] properties ``` ### What you think should happen instead _No response_ ### How to reproduce - Read https://peps.python.org/pep-0621/#name - Read https://peps.python.org/pep-0621/#version ### Operating System All ### 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/33854
https://github.com/apache/airflow/pull/34014
6c649aefd2dccbc1765c077c5154c7edf384caeb
ba8ee909e4532318649df9c2d5a7ed70b357913d
"2023-08-28T20:52:38Z"
python
"2023-09-04T07:56:18Z"
closed
apache/airflow
https://github.com/apache/airflow
33,850
["airflow/providers/google/cloud/transfers/azure_fileshare_to_gcs.py", "airflow/providers/microsoft/azure/CHANGELOG.rst", "airflow/providers/microsoft/azure/hooks/fileshare.py", "airflow/providers/microsoft/azure/provider.yaml", "docs/apache-airflow-providers-microsoft-azure/connections/azure_fileshare.rst", "generated/provider_dependencies.json", "tests/providers/google/cloud/transfers/test_azure_fileshare_to_gcs.py", "tests/providers/microsoft/azure/hooks/test_azure_fileshare.py", "tests/test_utils/azure_system_helpers.py"]
Upgrade Azure File Share to v12
### Description In November 2019 the Azure File Share python package was "renamed from `azure-storage-file` to `azure-storage-file-share` along with renamed client modules": https://azure.github.io/azure-sdk/releases/2019-11/python.html Yet it is 2023 and we still have `azure-storage-file>=2.1.0` as a dependency for `apache-airflow-providers-microsoft-azure`. I am opening this issue to propose removing this over three year old deprecated package. I am aware of the challenges with earlier attempts to upgrade Azure Storage packages to v12 as discussed in https://github.com/apache/airflow/pull/8184. I hope those challenges are gone by now? Especially since `azure-storage-blob` already has been upgraded to v12 in this provider (https://github.com/apache/airflow/pull/12188). Also, I believe this is why `azure-storage-common>=2.1.0` is also still a dependency. Which is listed as deprecated on https://azure.github.io/azure-sdk/releases/deprecated/python.html: - I have not fully investigated but I believe it is possible once we upgrade to `azure-storage-file-share` v12 this provider will no longer need `azure-storage-common` as a dependency as as it just contains the common code shared by the old 2.x versions of "blob", "file" and "queue". We already upgraded "blob" to v12, and we don't have "queue" support, so "file" is the last remaining. - Also removing "azure-storage-common" will remove the warning: ``` ... site-packages/azure/storage/common/_connection.py:82 SyntaxWarning: "is" with a literal. Did you mean "=="? ``` (a fix was merged to "main" in 2020 however Microsoft no longer will release new versions of this package) I _used_ to be an active Azure Storage user (up until last year), but I am now mainly an AWS user, so I would appreciate it if someone else will create a PR for this, but if nobody does I suppose I could look into it. ### Use case/motivation Mainly to remove deprecated packages, and secondly to remove one SyntaxWarning ### Related issues This is the related issue to upgrade `azure-storage-blob` to v12: https://github.com/apache/airflow/issues/11968 ### 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/33850
https://github.com/apache/airflow/pull/33904
caf135f7c40ff07b31a9a026282695ac6202e6aa
b7f84e913b6aa4cee7fa63009082b0608b3a0bf1
"2023-08-28T20:08:20Z"
python
"2023-09-02T12:15:39Z"
closed
apache/airflow
https://github.com/apache/airflow
33,744
["airflow/providers/redis/provider.yaml", "generated/provider_dependencies.json"]
Celery Executor is not working with redis-py 5.0.0
### Apache Airflow version 2.7.0 ### What happened After upgrading to Airflow 2.7.0 in my local environment my Airflow DAGs won't run with Celery Executor using Redis even after changing `celery_app_name` configuration in `celery` section from `airflow.executors.celery_executor` to `airflow.providers.celery.executors.celery_executor`. I see the error actually is unrelated to the recent Airflow Celery provider changes, but is related to Celery's Redis support. What is happening is Airflow fails to send jobs to the worker as the Kombu module is not compatible with Redis 5.0.0 (released last week). It gives this error (I will update this to the full traceback once I can reproduce this error one more time): ``` AttributeError: module 'redis' has no attribute 'client' ``` Celery actually is limiting redis-py to 4.x in an upcoming version of Celery 5.3.x (it is merged to main on August 17, 2023 but it is not yet released: https://github.com/celery/celery/pull/8442 . The latest version is v5.3.1 released on June 18, 2023). Kombu is also going to match Celery and limit redis-py to 4.x in an upcoming version as well (the PR is draft, I am assuming they are waiting for the Celery change to be released: https://github.com/celery/kombu/pull/1776) For now there is not really a way to fix this unless there is a way we can do a redis constraint to avoid 5.x. Or maybe once the next Celery 5.3.x release includes limiting redis-py to 4.x we can possibly limit Celery provider to that version of Celery? ### What you think should happen instead Airflow should be able to send jobs to workers when using Celery Executor with Redis ### How to reproduce 1. Start Airflow 2.7.0 with Celery Executor with Redis 5.0.0 installed by default (at the time of this writing) 2. Run a DAG task 3. The scheduler fails to send the job to the worker Workaround: 1. Limit redis-py to 4.x the same way the upcoming release of Celery 5.3.x does, by using this in requirements.txt: `redis>=4.5.2,<5.0.0,!=4.5.5` 2. Start Airflow 2.7.0 with Celery Executor 3. Run a DAG task 4. The task runs successfully ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-celery==3.3.2 ### Deployment Docker-Compose ### Deployment details I am using `bitnami/airflow:2.7.0` image in Docker Compose when I first encountered this issue, but I will test with Breeze as well shortly and then update this issue. ### 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/33744
https://github.com/apache/airflow/pull/33773
42bc8fcb6bab2b02ef2ff62c3015b54a1ad2df62
3ba994d8f4c4b5ce3828bebcff28bbfc25170004
"2023-08-25T20:06:08Z"
python
"2023-08-26T16:02:08Z"
closed
apache/airflow
https://github.com/apache/airflow
33,711
["airflow/providers/amazon/CHANGELOG.rst", "airflow/providers/amazon/aws/operators/ecs.py", "tests/providers/amazon/aws/operators/test_ecs.py"]
EcsRunTaskOperator waiter default waiter_max_attempts too low - all Airflow tasks detach from ECS tasks at 10 minutes
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Running on MWAA v2.5.1 with `apache-airflow-providers-amazon` (EcsRunTaskOperator) upgraded to v8.3.0 All `EcsRunTaskOperator` tasks appear to 'detach' from the underlying ECS Task after 10 minutes. Running a command: ``` sleep 800 ``` results in: ``` [2023-08-25, 10:15:12 NZST] {{ecs.py:533}} INFO - EcsOperator overrides: {'containerOverrides': [{'name': 'meltano', 'command': ['sleep', '800']}]} ... [2023-08-25, 10:15:13 NZST] {{ecs.py:651}} INFO - ECS task ID is: b2681954f66148e8909d5e74c4b94c1a [2023-08-25, 10:15:13 NZST] {{ecs.py:565}} INFO - Starting ECS Task Log Fetcher [2023-08-25, 10:15:43 NZST] {{base_aws.py:554}} WARNING - Unable to find AWS Connection ID 'aws_ecs', switching to empty. [2023-08-25, 10:15:43 NZST] {{base_aws.py:160}} INFO - No connection ID provided. Fallback on boto3 credential strategy (region_name='ap-southeast-2'). See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html [2023-08-25, 10:25:13 NZST] {{taskinstance.py:1768}} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 570, in execute self._wait_for_task_ended() File "/usr/local/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 684, in _wait_for_task_ended waiter.wait( File "/usr/local/airflow/.local/lib/python3.10/site-packages/botocore/waiter.py", line 55, in wait Waiter.wait(self, **kwargs) File "/usr/local/airflow/.local/lib/python3.10/site-packages/botocore/waiter.py", line 388, in wait raise WaiterError( botocore.exceptions.WaiterError: Waiter TasksStopped failed: Max attempts exceeded ``` It appears to be caused by the addition of `waiter.wait` with different max_attempts (defaults to 100 instead of sys.maxsize - usually a very large number): ``` waiter.config.max_attempts = sys.maxsize # timeout is managed by airflow waiter.wait( cluster=self.cluster, tasks=[self.arn], WaiterConfig={ "Delay": self.waiter_delay, "MaxAttempts": self.waiter_max_attempts, }, ) ``` ### What you think should happen instead Set the default `waiter_max_attempts` in `EcsRunTaskOperator` to `sys.maxsize` to revert back to previous behaviour ### How to reproduce 1. You would need to set up ECS with a task definition, cluster, etc. 2. Assuming ECS is all setup - build a DAG with a EcsRunTaskOperator task 3. Run a task that should take more than 10 minutes, e.g. in `overrides` set `command` to `["sleep","800"]` 4. The Airflow task should fail while the ECS task should run for 800 seconds and complete successfully ### Operating System MWAA v2.5.1 Python 3.10 (Linux) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.3.0 ### Deployment Amazon (AWS) MWAA ### Deployment details n/a ### Anything else n/a ### 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/33711
https://github.com/apache/airflow/pull/33712
539797fdfb2e0b2aca82376095e74edaad775439
ea44ed9f54f6c0083aa6283b2f3f3712bc710a1f
"2023-08-24T23:36:43Z"
python
"2023-08-30T10:48:45Z"
closed
apache/airflow
https://github.com/apache/airflow
33,699
["airflow/www/static/js/dag/grid/index.tsx"]
Scrolling issues on DAG page
### Apache Airflow version 2.7.0 ### What happened When on a DAG page, there's an issue with scrolling behavior on the Grid and Gantt tabs: While my pointer is over the grid, the entire page should scroll once you get to the bottom of the grid, but instead I cannot scroll any further. This means that not only can't I get to the bottom of the page (where the Aiflow version, etc., is), but I can't even see the bottom of the grid if there are enough rows. Details, Graph, and Code tabs scroll fine. Important to note - this seems to only happen when there are enough DAG runs to require horizontal scrolling to be activated. ### What you think should happen instead Instead of stopping here: ![image](https://github.com/apache/airflow/assets/79997320/fcb2259f-f9fc-44f3-bc24-8bbfb5afb76c) I should be able to scroll all the way down to here: ![image](https://github.com/apache/airflow/assets/79997320/b6443c2d-d61b-457c-95bc-4713b9c38f9b) ### How to reproduce You should be able to see this behavior with any DAG that has enough DAG runs to cause the horizontal scroll bar to appear. I was able to replicate it with this DAG and triggering it 10 times. I have the vertical divider moved almost all the way to the left. ``` from airflow import DAG from airflow.operators.empty import EmptyOperator from datetime import datetime with DAG( dag_id='bad_scrolling', default_args={'start_date': datetime(2023, 8, 23, 14, 0, 0)}, ) as dag: t1 = EmptyOperator( task_id='fairly_long_name_here' ) ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ```apache-airflow-providers-amazon==8.5.1 apache-airflow-providers-celery==3.3.2 apache-airflow-providers-cncf-kubernetes==7.4.2 apache-airflow-providers-common-sql==1.7.0 apache-airflow-providers-datadog==3.3.1 apache-airflow-providers-elasticsearch==5.0.0 apache-airflow-providers-ftp==3.5.0 apache-airflow-providers-google==10.6.0 apache-airflow-providers-http==4.5.0 apache-airflow-providers-imap==3.3.0 apache-airflow-providers-microsoft-azure==6.2.4 apache-airflow-providers-postgres==5.6.0 apache-airflow-providers-redis==3.3.1 apache-airflow-providers-salesforce==5.4.1 apache-airflow-providers-slack==7.3.2 apache-airflow-providers-snowflake==4.4.2 apache-airflow-providers-sqlite==3.4.3 ``` ### 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/33699
https://github.com/apache/airflow/pull/35717
4f060a482c3233504e7905b3ab2d00fe56ea43cd
d37b91c102856e62322450606474aebd74ddf376
"2023-08-24T16:48:17Z"
python
"2023-11-28T21:37:00Z"
closed
apache/airflow
https://github.com/apache/airflow
33,698
["airflow/www/views.py"]
UI DAG counts including deleted DAGs
### Apache Airflow version 2.7.0 ### What happened On the DAGs page, the All, Active, and Paused counts include deleted DAGs. This is different from <= 2.6.1 (at least), where they were not included in the totals. Specifically this is for DAGs for which the DAG files have been removed, not DAGs that have been deleted via the UI. ### What you think should happen instead Including deleted DAGs in those counts is confusing, and this behavior should revert to the previous behavior. ### How to reproduce Create a DAG. Wait for totals to increment. Remove the DAG file. The totals will not change. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ```apache-airflow-providers-amazon==8.5.1 apache-airflow-providers-celery==3.3.2 apache-airflow-providers-cncf-kubernetes==7.4.2 apache-airflow-providers-common-sql==1.7.0 apache-airflow-providers-datadog==3.3.1 apache-airflow-providers-elasticsearch==5.0.0 apache-airflow-providers-ftp==3.5.0 apache-airflow-providers-google==10.6.0 apache-airflow-providers-http==4.5.0 apache-airflow-providers-imap==3.3.0 apache-airflow-providers-microsoft-azure==6.2.4 apache-airflow-providers-postgres==5.6.0 apache-airflow-providers-redis==3.3.1 apache-airflow-providers-salesforce==5.4.1 apache-airflow-providers-slack==7.3.2 apache-airflow-providers-snowflake==4.4.2 apache-airflow-providers-sqlite==3.4.3 ``` ### Deployment Astronomer ### Deployment details _No response_ ### Anything else I suspect the issue is with [DagModel.deactivate_deleted_dags](https://github.com/apache/airflow/blob/f971ba2f2f9703d0e1954e52aaded52a83c2f844/airflow/models/dag.py#L3564), but I'm unable to verify. ### 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/33698
https://github.com/apache/airflow/pull/33778
02af225e7b75552e074d7dfcfc1af5336c42b84d
64948fa7824d004e65089c2d159c5e6074727826
"2023-08-24T16:01:16Z"
python
"2023-08-27T17:02:14Z"
closed
apache/airflow
https://github.com/apache/airflow
33,697
["airflow/providers/cncf/kubernetes/operators/pod.py", "kubernetes_tests/test_kubernetes_pod_operator.py", "tests/providers/cncf/kubernetes/operators/test_pod.py"]
skip_on_exit_code parameter in KPO does not take effect
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened I am using following task to simulate the skipping of KPO ``` @task.kubernetes(image="python:3.8-slim-buster", namespace="dev", skip_on_exit_code=100 ) def print_pattern(): import sys some_condition = True if some_condition : sys.exit(100) ``` This task task results in the following logs - ``` 'container_statuses': [{'container_id': 'containerd://0e38f55c0d0b8ac21b2d0d4d4a58a0f', 'image': 'docker.io/library/python:3.8-slim-buster', 'image_id': 'docker.io/library/python@sha256:8799b0564103a9f36cfb8a8e1c562e11a9a6f2e3bb214e2adc23982b36a04511', 'last_state': {'running': None, 'terminated': None, 'waiting': None}, 'name': 'base', 'ready': False, 'restart_count': 0, 'started': False, 'state': {'running': None, 'terminated': {'container_id': 'containerd://0cd4eddd219dd25b658d240e675c59d0a0e38f55c0d0b8ac21b2d0d4d4a58a0f', 'exit_code': 100, 'finished_at': datetime.datetime(2023, 8, 23, 9, 38, 9, tzinfo=tzlocal()), 'message': None, 'reason': 'Error', 'signal': None, 'started_at': datetime.datetime(2023, 8, 23, 9, 38, 8, tzinfo=tzlocal())}, 'waiting': None}}], ``` The state in airflow upon execution of task is failed. ### What you think should happen instead I would expect the task to be skipped based on "skip_on_exit_code" paramter. ### How to reproduce Run the task in airflow installed using latest helm chart version 1.10.0 and airflow 2.6.2 ### Operating System "Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon | 8.1.0 apache-airflow-providers-celery | 3.2.0 apache-airflow-providers-cncf-kubernetes | 7.0.0 apache-airflow-providers-common-sql | 1.5.1 apache-airflow-providers-docker | 3.7.0 apache-airflow-providers-elasticsearch | 4.5.0 apache-airflow-providers-ftp | 3.4.1 apache-airflow-providers-google | 10.1.1 apache-airflow-providers-grpc | 3.2.0 apache-airflow-providers-hashicorp | 3.4.0 apache-airflow-providers-http | 4.4.1 apache-airflow-providers-imap | 3.2.1 apache-airflow-providers-microsoft-azure | 6.1.1 apache-airflow-providers-microsoft-mssql | 3.2.0 apache-airflow-providers-mysql | 5.1.0 apache-airflow-providers-odbc | 3.3.0 apache-airflow-providers-postgres | 5.5.0 apache-airflow-providers-redis | 3.2.0 apache-airflow-providers-sendgrid | 3.2.0 apache-airflow-providers-sftp | 4.3.0 apache-airflow-providers-slack | 7.3.0 apache-airflow-providers-snowflake | 4.1.0 apache-airflow-providers-sqlite | 3.4.1 apache-airflow-providers-ssh | 3.7.0 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details k8s version v1.24.16 ### Anything else The airflow code base uses `laststate.terminated.exit_code ` for matching the exit code as described [here](https://github.com/apache/airflow/blob/b5a4d36383c4143f46e168b8b7a4ba2dc7c54076/airflow/providers/cncf/kubernetes/operators/pod.py#L718C16-L718C16). However correct code should `state.terminated.exit_code`. ### 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/33697
https://github.com/apache/airflow/pull/33702
f971ba2f2f9703d0e1954e52aaded52a83c2f844
c47703103982ec4730ea28c8a5eda12ed2ce008a
"2023-08-24T14:40:50Z"
python
"2023-08-24T18:22:16Z"
closed
apache/airflow
https://github.com/apache/airflow
33,694
["airflow/template/templater.py", "airflow/utils/template.py", "docs/apache-airflow/core-concepts/operators.rst", "tests/models/test_baseoperator.py", "tests/template/test_templater.py"]
airflow jinja template render error
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened version 2.6.2 An error occurs when *.json is included in the parameters of BigQueryInsertJobOperator. ``` py to_gcs_task = BigQueryInsertJobOperator( dag=dag, task_id='to_gcs', gcp_conn_id='xxxx', configuration={ "extract": { # The error occurred at this location. "destinationUris": ['gs://xxx/yyy/*.json'], "sourceTable": { "projectId": "abc", "datasetId": "def", "tableId": "ghi" }, "destinationFormat": "NEWLINE_DELIMITED_JSON" } } ) ``` error log ``` jinja2.exceptions.TemplateNotFound: gs://xxx/yyy/*.json ``` ### What you think should happen instead According to the airflow.template.templater source : https://github.com/apache/airflow/blob/main/airflow/template/templater.py#L152 ```py if isinstance(value, str): if any(value.endswith(ext) for ext in self.template_ext): # A filepath. template = jinja_env.get_template(value) else: template = jinja_env.from_string(value) return self._render(template, context) ``` In the Jinja template source, if the value ends with .json or .sql, an attempt is made to read the resource file by calling jinja_env.get_template. ### How to reproduce just call BigQueryInsertJobOperator with configuration what i added ### Operating System m2 mac ### 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/33694
https://github.com/apache/airflow/pull/35017
69cea850cb37217675ccfef28917a9bd9679387d
46c0f85ba6dd654501fc429ddd831461ebfefd3c
"2023-08-24T13:54:27Z"
python
"2023-11-17T08:58:49Z"
closed
apache/airflow
https://github.com/apache/airflow
33,693
["airflow/www/static/js/dag/details/graph/Node.tsx"]
Long custom operator name overflows in graph view
### Apache Airflow version main (development) ### What happened 1. There was support added to configure UI elements in graph view in https://github.com/apache/airflow/issues/31949 2. Long custom operator names overflow out of the box. Meanwhile long task id are truncated with ellipsis. I guess same could be done by removing width attribute that has "fit-content" and "noOfLines" should be added. Originally wrapped before commit : ![Screenshot 2023-08-24 at 18-05-06 gh32757 - Grid - Airflow](https://github.com/apache/airflow/assets/3972343/2118975d-8467-4039-879f-13a87d9bcd79) main : ![Screenshot 2023-08-24 at 18-17-31 gh32757 - Grid - Airflow](https://github.com/apache/airflow/assets/3972343/7d0b86e1-39c3-4fb4-928c-1ea54697128e) ### What you think should happen instead _No response_ ### How to reproduce Sample dag to reproduce the issue in UI ```python from datetime import datetime from airflow.decorators import dag, task from airflow.models.baseoperator import BaseOperator from airflow.operators.bash import BashOperator class HelloOperator(BashOperator): custom_operator_name = "SampleLongNameOfOperator123456789" @dag(dag_id="gh32757", start_date=datetime(2023, 1, 1), catchup=False) def mydag(): bash = BashOperator(task_id="t1", bash_command="echo hello") hello = HelloOperator(task_id="SampleLongTaskId1234567891234890", bash_command="echo test") bash2 = BashOperator(task_id="t3", bash_command="echo bye") bash >> hello >> bash2 mydag() ``` ### Operating System Ubuntu ### 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/33693
https://github.com/apache/airflow/pull/35382
aaed909344b12aa4691a9e23ea9f9c98d641d853
4d872b87efac9950f125aff676b30f0a637b471e
"2023-08-24T13:00:06Z"
python
"2023-11-17T20:32:47Z"
closed
apache/airflow
https://github.com/apache/airflow
33,679
["airflow/providers/snowflake/operators/snowflake.py"]
SnowflakeCheckOperator connection id template issue
### Apache Airflow version 2.7.0 ### What happened When upgrading to apache-airflow-providers-snowflake==4.4.2, our SnowflakeCheckOperators are all failing with similar messages. The affected code seems to be from [this PR](https://github.com/apache/airflow/pull/30784). Code: ``` check_order_load = SnowflakeCheckOperator( task_id="check_row_count", sql='check_orders_load.sql', snowflake_conn_id=SF_CONNECTION_ID, ) ``` Errors: ``` [2023-08-23, 20:58:23 UTC] {taskinstance.py:1943} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.11/site-packages/airflow/models/abstractoperator.py", line 664, in _do_render_template_fields value = getattr(parent, attr_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'SnowflakeCheckOperator' object has no attribute 'snowflake_conn_id' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.11/site-packages/airflow/models/taskinstance.py", line 1518, in _run_raw_task self._execute_task_with_callbacks(context, test_mode, session=session) File "/usr/local/lib/python3.11/site-packages/airflow/models/taskinstance.py", line 1646, in _execute_task_with_callbacks task_orig = self.render_templates(context=context) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/airflow/models/taskinstance.py", line 2291, in render_templates original_task.render_template_fields(context) File "/usr/local/lib/python3.11/site-packages/airflow/models/baseoperator.py", line 1244, in render_template_fields self._do_render_template_fields(self, self.template_fields, context, jinja_env, set()) File "/usr/local/lib/python3.11/site-packages/airflow/utils/session.py", line 77, in wrapper return func(*args, session=session, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/airflow/models/abstractoperator.py", line 666, in _do_render_template_fields raise AttributeError( AttributeError: 'snowflake_conn_id' is configured as a template field but SnowflakeCheckOperator does not have this attribute.``` ``` ### What you think should happen instead This works fine in apache-airflow-providers-snowflake==4.4.1 - no errors. ### How to reproduce With `apache-airflow-providers-snowflake==4.4.2` Try running this code: ``` from airflow.providers.snowflake.operators.snowflake import SnowflakeCheckOperator check_task = SnowflakeCheckOperator( task_id='check_gmv_yoy', sql='select 1', snowflake_conn_id='NAME_OF_CONNECTION_ID', ) ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-snowflake==4.4.2 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else This happens every time with 4.4.2, never with <= 4.4.1. ### 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/33679
https://github.com/apache/airflow/pull/33681
2dbb9633240777d658031d32217255849150684b
d06c14f52757321f2049bb54212421f68bf3ed06
"2023-08-23T22:02:11Z"
python
"2023-08-24T07:22:04Z"
closed
apache/airflow
https://github.com/apache/airflow
33,667
["airflow/providers/google/cloud/operators/dataproc.py", "tests/providers/google/cloud/operators/test_dataproc.py"]
Google Cloud Dataproc cluster creation should eagerly delete ERROR state clusters.
### Description Google Cloud Dataproc cluster creation should eagerly delete ERROR state clusters. It is possible for Google Cloud Dataproc clusters to create in the ERROR state. The current operator (DataprocCreateClusterOperator) will require three total task attempts (original + two retries) in order to create the cluster, assuming underlying GCE infrastructure resolves itself between task attempts. This can be reduced to two total attempts by eagerly deleting a cluster in ERROR state before failing the current task attempt. Clusters in the ERROR state are not useable to submit Dataproc based jobs via the Dataproc API. ### Use case/motivation Reducing the number of task attempts can reduce GCP based cost as delays between retry attempts could be minutes. There's no reason to keep a running, costly cluster in the ERROR state if it can be detected in the initial create task. ### 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/33667
https://github.com/apache/airflow/pull/33668
075afe5a2add74d9e4e9fd57768b8354489cdb2b
d361761deeffe628f3c17ab0debd0e11515c22da
"2023-08-23T18:21:03Z"
python
"2023-08-30T05:29:20Z"
closed
apache/airflow
https://github.com/apache/airflow
33,661
["airflow/jobs/scheduler_job_runner.py", "airflow/utils/state.py", "tests/jobs/test_scheduler_job.py"]
Zombie tasks in RESTARTING state are not cleaned
### Apache Airflow version 2.7.0 Also reproduced on 2.5.0 ### What happened Recently we added some automation to restarting Airflow tasks with "clear" command so we use this feature a lot. We often clear tasks in RUNNING state, which means that they go into RESTARTING state. We noticed that a lot of those tasks get stuck in RESTARTING state. Our Airflow infrastructure runs in an environment where any process can get suddenly killed without graceful shutdown. We run Airflow on GKE but I managed to reproduce this behaviour on local environment with SequentialExecutor. See **"How to reproduce"** below for details. ### What you think should happen instead Tasks should get cleaned after scheduler restart and eventually get scheduled and executed. ### How to reproduce After some code investigation, I reproduced this kind of behaviour on local environment and it seems that RESTARTING tasks are only properly handled if the original restarting task is gracefully shut down so it can mark task as UP_FOR_RETRY or at least there is a healthy scheduler to do it if they fail for any other reason. The problem is with the following scenario: 1. Task is initially in RUNNING state. 2. Scheduler process dies suddenly. 3. The task process also dies suddenly. 4. "clear" command is executed on the task so the state is changed to RESTARTING state by webserver process. 5. From now on, even if we restart scheduler, the task will never get scheduled or change its state. It needs to have its state manually fixed, e.g. by clearing it again. A recording of steps to reproduce on local environment: https://vimeo.com/857192666?share=copy ### Operating System MacOS Ventura 13.4.1 ### Versions of Apache Airflow Providers N/A ### Deployment Official Apache Airflow Helm Chart ### Deployment details N/A ### 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/33661
https://github.com/apache/airflow/pull/33706
3f984edd0009ad4e3177a3c95351c563a6ac00da
5c35786ca29aa53ec08232502fc8a16fb1ef847a
"2023-08-23T15:41:09Z"
python
"2023-08-24T23:55:18Z"
closed
apache/airflow
https://github.com/apache/airflow
33,634
["airflow/providers/amazon/aws/log/cloudwatch_task_handler.py", "tests/providers/amazon/aws/log/test_cloudwatch_task_handler.py"]
Unable to fetch CloudWatch Logs of previous run attempts
### Apache Airflow version 2.7.0 ### What happened After upgrading to `apache-airflow-providers-amazon==8.5.1`, I am no longer able to view logs from previous run attempts. Airflow is able to find the log stream successfully, but there's no content viewable (even though there are logs in the actual streams): ``` REDACTED.us-west-2.compute.internal *** Reading remote log from Cloudwatch log_group: REDACTED log_stream: dag_id=REDACTED/run_id=REDACTED/task_id=REDACTED/attempt=1.log. ``` I believe this issue occurred from #33231 - Looking at the [CloudWatch Logs code](https://github.com/apache/airflow/blob/providers-amazon/8.5.1/airflow/providers/amazon/aws/log/cloudwatch_task_handler.py#L109-L133), I think `task_instance.start_date` and `task_instance.end_date` somehow refer to its __latest__ run, and so the log contents are getting filtered out on previous attempts. ### What you think should happen instead _No response_ ### How to reproduce 1. Configure environment with remote CloudWatch logging 2. Run task 3. Clear task and re-run 4. The logs for the first attempt now no longer show ### Operating System Debian 11 ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.5.1 ### 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/33634
https://github.com/apache/airflow/pull/33673
b1a3b4288022c67db22cbc7d24b0c4b2b122453b
53a89739528cda26b8b53670fc51769850eb263e
"2023-08-22T22:37:46Z"
python
"2023-08-24T05:03:46Z"
closed
apache/airflow
https://github.com/apache/airflow
33,606
["airflow/utils/db_cleanup.py", "tests/utils/test_db_cleanup.py"]
'airflow db clean' with --skip-archive flag fails
### Apache Airflow version 2.7.0 ### What happened Running `airflow db clean -y -v --skip-archive --clean-before-timestamp '2023-05-24 00:00:00'` fails. Running the same command without the `--skip-archive` flag passes successfully. ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/__main__.py", line 60, in main args.func(args) File "/home/airflow/.local/lib/python3.11/site-packages/airflow/cli/cli_config.py", line 49, in command return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/cli.py", line 113, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/providers_configuration_loader.py", line 56, in wrapped_function return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/cli/commands/db_command.py", line 241, in cleanup_tables run_cleanup( File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/session.py", line 77, in wrapper return func(*args, session=session, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/db_cleanup.py", line 437, in run_cleanup _cleanup_table( File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/db_cleanup.py", line 302, in _cleanup_table _do_delete(query=query, orm_model=orm_model, skip_archive=skip_archive, session=session) File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/db_cleanup.py", line 197, in _do_delete target_table.drop() File "/home/airflow/.local/lib/python3.11/site-packages/sqlalchemy/sql/schema.py", line 978, in drop bind = _bind_or_error(self) ^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/sqlalchemy/sql/base.py", line 1659, in _bind_or_error raise exc.UnboundExecutionError(msg) sqlalchemy.exc.UnboundExecutionError: Table object '_airflow_deleted__dag_run__20230822091212' is not bound to an Engine or Connection. Execution can not proceed without a database to execute against. ``` ### What you think should happen instead db clean command with --skip-archive should pass ### How to reproduce `airflow db clean -y -v --skip-archive --clean-before-timestamp '2023-05-24 00:00:00'` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.5.1 apache-airflow-providers-celery==3.3.2 apache-airflow-providers-cncf-kubernetes==7.4.2 apache-airflow-providers-common-sql==1.7.0 apache-airflow-providers-daskexecutor==1.0.0 apache-airflow-providers-docker==3.7.3 apache-airflow-providers-elasticsearch==5.0.0 apache-airflow-providers-ftp==3.5.0 apache-airflow-providers-google==8.3.0 apache-airflow-providers-grpc==3.2.1 apache-airflow-providers-hashicorp==3.4.2 apache-airflow-providers-http==4.5.0 apache-airflow-providers-imap==3.3.0 apache-airflow-providers-jenkins==3.3.1 apache-airflow-providers-microsoft-azure==6.2.4 apache-airflow-providers-mysql==5.2.1 apache-airflow-providers-odbc==4.0.0 apache-airflow-providers-openlineage==1.0.1 apache-airflow-providers-postgres==5.6.0 apache-airflow-providers-redis==3.3.1 apache-airflow-providers-salesforce==5.4.1 apache-airflow-providers-sendgrid==3.2.1 apache-airflow-providers-sftp==4.5.0 apache-airflow-providers-slack==7.3.2 apache-airflow-providers-snowflake==4.4.2 apache-airflow-providers-sqlite==3.4.3 apache-airflow-providers-ssh==3.7.1 apache-airflow-providers-tableau==4.2.1 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Helm version 1.9.0 Deployed on AWS EKS cluster MetaDB is a AWS Postgres RDS connected using a PGBouncer ### 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/33606
https://github.com/apache/airflow/pull/33622
0ca5f700ab5e153ff8eea2c27b0629f2f44c8cb3
911cf466218bcd548519a50c9a32c9df58ec8b2e
"2023-08-22T09:15:57Z"
python
"2023-08-23T09:38:52Z"
closed
apache/airflow
https://github.com/apache/airflow
33,596
["airflow/providers/apache/pinot/hooks/pinot.py", "airflow/providers/apache/pinot/provider.yaml"]
Apache Pinot provider.yaml references missing PinotHook class
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened When starting Airflow (the problem seems to be in both 2.6.3 and in 2.7.0, see "How to reproduce" below) I am getting the following warning: ``` {providers_manager.py:253} WARNING - Exception when importing 'airflow.providers.apache.pinot.hooks.pinot.PinotHook' from 'apache-airflow-providers-apache-pinot' package Traceback (most recent call last): File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 39, in import_string return getattr(module, class_name) AttributeError: module 'airflow.providers.apache.pinot.hooks.pinot' has no attribute 'PinotHook' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/providers_manager.py", line 285, in _sanity_check imported_class = import_string(class_name) File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 41, in import_string raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute/class') ImportError: Module "airflow.providers.apache.pinot.hooks.pinot" does not define a "PinotHook" attribute/class ``` I looked into the issue and it appears the problem in the Apache Pinot provider. The `airflow/providers/apache/pinot/provider.yaml` (which is loaded by the `_sanity_check` in `providers_manager`) is referencing a `PinotHook` class that does not exist: https://github.com/apache/airflow/blob/487b174073c01e03ae64760405a8d88f6a488ca6/airflow/providers/apache/pinot/provider.yaml#L57-L59 The module `airflow.providers.apache.pinot.hooks.pinot` contains `PinotAdminHook` and `PinotDbApiHook`, but not `PinotHook` (and the classes have been separate since before the Apache classes were split into the Apache provider). I am willing to fix this, but I am not sure which is a better fix: 1. I could list both classes in `connection-types` of `provider.yaml`, but keep both as `connection-type: pinot`, but there will be two connection types with the same name (which may not be possible?): ```yaml connection-types: - hook-class-name: airflow.providers.apache.pinot.hooks.pinot.PinotAdminHook connection-type: pinot - hook-class-name: airflow.providers.apache.pinot.hooks.pinot.PinotDbApiHook connection-type: pinot ``` - Note: `create_default_connections` in `airflow/utils/db.py` is currently including both connection with the same `conn_type="pinot"`: https://github.com/apache/airflow/blob/487b174073c01e03ae64760405a8d88f6a488ca6/airflow/utils/db.py#L474-L492 2. or we change one (or both) of the connection types to a different name. `PinotAdminHook` already uses a default connection name of `pinot_admin_default`, and `PinotDbApiHook` already uses a default connection name of `pinot_broker_default`, so it might make sense to name these connection types `pinot_admin` and `pinot_broker`: ```yaml connection-types: - hook-class-name: airflow.providers.apache.pinot.hooks.pinot.PinotAdminHook connection-type: pinot_admin - hook-class-name: airflow.providers.apache.pinot.hooks.pinot.PinotDbApiHook connection-type: pinot_broker ``` - I think we will need to change `create_default_connections` in `airflow/utils/db.py` (as shown above) if we end up changing the connection types. Possibly other places, but I have not seen any other references of `conn_type="pinot"` beside the default connections. Thoughts on which approach is better / less disruptive to users? ### What you think should happen instead When starting Airflow the `_sanity_check` in `providers_manager` should not trigger a warning. Both connection types should be useable. ### How to reproduce I am seeing this every time I start Airflow with the Docker image `bitnami/airflow:2.6.3`, but I will also test using Breeze and other ways to see if I see the same warning in 2.7.0. But since the problem line of code is unchanged in the `main`branch I am sure this is still an issue. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-apache-pinot==4.1.1 ### Deployment Docker-Compose ### Deployment details Docker image `bitnami/airflow:2.6.3` in Docker Compose (this is the latest Airflow version for Bitnami's image, they have not yet pushed up a 2.7.0 version) ### Anything else The issue https://github.com/apache/airflow/issues/28790 is related as it mentions that both hooks of the Apache Pinot provider is missing `conn_type`. ### 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/33596
https://github.com/apache/airflow/pull/33601
b1cdab39f85879f2b0189685c0b7f7dcdc8d62f0
5dfbbbbf5adf70a9814121de8706a7c36f241836
"2023-08-21T19:56:30Z"
python
"2023-08-23T18:39:49Z"
closed
apache/airflow
https://github.com/apache/airflow
33,586
["airflow/www/templates/appbuilder/navbar_right.html"]
Airflow 2.7 Webserver unreacheable with new authentication manager
### Apache Airflow version 2.7.0 ### What happened When connecting to the Airflow UI, we get the following message: ``` > Python version: 3.11.4 > Airflow version: 2.7.0 > Node: redact > ------------------------------------------------------------------------------- > Error! Please contact server admin. ``` If we investigate further and we look at the Kubernetes pod logs, we see that following error message is thrown: > File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/views.py", line 989, in index > return self.render_template( > ^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/views.py", line 694, in render_template > return super().render_template( > ^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/flask_appbuilder/baseviews.py", line 339, in render_template > return render_template( > ^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/flask/templating.py", line 147, in render_template > return _render(app, template, context) > ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/flask/templating.py", line 130, in _render > rv = template.render(context) > ^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/jinja2/environment.py", line 1301, in render > self.environment.handle_exception() > File "/home/airflow/.local/lib/python3.11/site-packages/jinja2/environment.py", line 936, in handle_exception > raise rewrite_traceback_stack(source=source) > File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/templates/airflow/dags.html", line 44, in top-level template code > {% elif curr_ordering_direction == 'asc' and request.args.get('sorting_key') == attribute_name %} > ^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/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/python3.11/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/python3.11/site-packages/flask_appbuilder/templates/appbuilder/init.html", line 42, in top-level template code > {% block body %} > File "/home/airflow/.local/lib/python3.11/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 8, in block 'body' > {% block navbar %} > File "/home/airflow/.local/lib/python3.11/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 10, in block 'navbar' > {% include 'appbuilder/navbar.html' %} > ^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/templates/appbuilder/navbar.html", line 53, in top-level template code > {% include 'appbuilder/navbar_right.html' %} > ^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/templates/appbuilder/navbar_right.html", line 71, in top-level template code > <span>{% for name in user_names %}{{ name[0].upper() }}{% endfor %}</span> > ^^^^^^^^^^^^^^^^^^^^^^^^^ > File "/home/airflow/.local/lib/python3.11/site-packages/jinja2/environment.py", line 485, in getattr > return getattr(obj, attribute) > ^^^^^^^^^^^^^^^^^^^^^^^ > jinja2.exceptions.UndefinedError: str object has no element 0 > ### What you think should happen instead Show the Airflow UI ### How to reproduce The deployment is done using the official Airflow helm chart with Azure AD authentication on the webserver. As soon as we did the upgrade to Airflow 2.7, the webserver became unreacheable when trying to access it with the error shown above. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==7.4.2 apache-airflow-providers-docker==3.6.0 apache-airflow-providers-microsoft-azure==4.3.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Webserver config: AUTH_TYPE = AUTH_OAUTH AUTH_ROLE_ADMIN = 'Admin' AUTH_USER_REGISTRATION = True AUTH_USER_REGISTRATION_ROLE = "Admin" OAUTH_PROVIDERS = [ { "name": "azure", "icon": "fa-microsoft", "token_key": "access_token", "remote_app": { "client_id": "${airflow_client_id}", "client_secret": "${airflow_client_secret}", "api_base_url": "https://login.microsoftonline.com/${airflow_tenant_id}/oauth2", "client_kwargs": { "scope": "User.read name preferred_username email profile", "resource": "${airflow_client_id}", }, "request_token_url": None, "access_token_url": "https://login.microsoftonline.com/${airflow_tenant_id}/oauth2/token", "authorize_url": "https://login.microsoftonline.com/${airflow_tenant_id}/oauth2/authorize", }, }, ] ### 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/33586
https://github.com/apache/airflow/pull/33617
41d9be072abacc47393f700aa8fb98bc2b9a3713
62b917a6ac61fd6882c377e3b04f72d908f52a58
"2023-08-21T15:08:38Z"
python
"2023-08-22T16:51:17Z"
closed
apache/airflow
https://github.com/apache/airflow
33,577
["airflow/providers/celery/provider.yaml", "dev/breeze/src/airflow_breeze/utils/path_utils.py", "generated/provider_dependencies.json", "setup.cfg", "setup.py"]
Airflow 2.7 is incompatible with SodaCore versions 3.0.24 and beyond
### Apache Airflow version 2.7.0 ### What happened When trying to install SodaCore on Airflow 2.7, the following error is received due to a conflict with `opentelemetry-api`. ``` ERROR: Cannot install apache-airflow==2.7.0 and soda-core==3.0.48 because these package versions have conflicting dependencies. The conflict is caused by: apache-airflow 2.7.0 depends on opentelemetry-api==1.15.0 soda-core 3.0.48 depends on opentelemetry-api~=1.16.0 ``` SodaCore has depended on `opentelemetry-api~=1.16.0` ever since v[3.0.24](https://github.com/sodadata/soda-core/releases/tag/v3.0.24). ### What you think should happen instead Airflow needs to support versions of `opentelemetry-api` 1.16.x. ### How to reproduce Simply running the following commands to install the two packages should reproduce the error. ``` $ python3 -m venv /tmp/soda $ /tmp/soda/bin/pip install apache-airflow==2.7.0 soda-core-bigquery==3.0.48 ``` ### Operating System n/a ### 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/33577
https://github.com/apache/airflow/pull/33579
73a37333918abe0612120d95169b9e377274810b
ae25a52ae342c9e0bc3afdb21d613447c3687f6c
"2023-08-21T12:10:44Z"
python
"2023-08-21T15:49:17Z"
closed
apache/airflow
https://github.com/apache/airflow
33,498
["airflow/providers/cncf/kubernetes/utils/pod_manager.py", "tests/providers/cncf/kubernetes/utils/test_pod_manager.py"]
KubernetesPodOperator duplicating logs when they are being interrupted
### Apache Airflow version main (development) ### What happened This is a random issue. The KubernetesPodOperator duplicates logs when they are interrupted. ### What you think should happen instead When the logs are interrupted, the KubernetesPodOperator should continue logging since the last captured timestamp, and not re-print logs that were already printed. ### How to reproduce If you run the following dag mulltiple times, you might get duplicated logs. Note that the issue is random and not easily reproducible: ``` import datetime from airflow import models from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator YESTERDAY = datetime.datetime.now() - datetime.timedelta(days=1) with models.DAG( dag_id="composer_sample_kubernetes_pod", schedule_interval=datetime.timedelta(days=1), start_date=YESTERDAY, ) as dag: timeout = 240 iterations = 1000 arguments = \ 'for i in {1..%(iterations)s}; do echo "$i of %(iterations)s"; done' % {'iterations': iterations} kubernetes_min_pod_0 = KubernetesPodOperator( task_id="pod-ex-minimum-0", name="pod-ex-minimum-0", cmds=["/bin/bash", "-c"], arguments=[arguments], namespace="default", image="gcr.io/gcp-runtimes/ubuntu_18_0_4", startup_timeout_seconds=timeout ) ``` Here is a sample output of duplicated logs: [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 994 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 995 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 996 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 997 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 998 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 999 of 1000 [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] 1000 of 1000 [2023-08-18, 13:32:54 UTC] {pod_manager.py:450} ERROR - Error parsing timestamp (no timestamp in message ''). Will continue execution but won't update timestamp [2023-08-18, 13:32:54 UTC] {kublogduplication.py:81} INFO - [base] [2023-08-18, 13:32:54 UTC] {kublogduplication.py:116} WARNING - Pod pod-ex-minimum-0-b3bf545v log read interrupted but container base still running [2023-08-18, 13:32:55 UTC] {before.py:35} INFO - Starting call to 'unusual_prefix_01add01418c7cdaf7afbb9c57d62a05595a85aa7_kublogduplication.CustomPodManager.fetch_container_logs.<locals>.consume_logs', this is the 1st time calling it. [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 1 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 2 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 3 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 4 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 5 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 6 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 7 of 1000 [2023-08-18, 13:32:55 UTC] {kublogduplication.py:81} INFO - [base] 8 of 1000 ### Operating System Ubuntu ### Versions of Apache Airflow Providers main ### Deployment Google Cloud 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/33498
https://github.com/apache/airflow/pull/33500
79b8cfc0fa77f11491fc1de4d5f009e176aa7c3a
6130993d781695bbd87e09d3665d8f0991bc32d0
"2023-08-18T16:53:45Z"
python
"2023-08-24T09:05:15Z"
closed
apache/airflow
https://github.com/apache/airflow
33,497
["airflow/www/jest-setup.js", "airflow/www/static/js/cluster-activity/live-metrics/Health.tsx", "airflow/www/static/js/index.d.ts", "airflow/www/templates/airflow/cluster_activity.html", "airflow/www/views.py"]
DAG Processor should not be visible in the Cluster Activity Page if there is no stand alone processor
### Apache Airflow version 2.7.0rc2 ### What happened In the Airflow UI, currently, the DAG Processor is visible in the Cluster Activity page even if there is no stand-alone dag processor. ### What you think should happen instead It should be hidden if there is no stand-alone dag processor. ### How to reproduce Run Airflow 2.7 ### Operating System Mac OS ### Versions of Apache Airflow Providers Airflow > 2.7 ### 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/33497
https://github.com/apache/airflow/pull/33611
b6318ffabce8cc3fdb02c30842726476b7e1fcca
c055e1da0b50e98820ffff8f8d10d0882f753384
"2023-08-18T15:59:33Z"
python
"2023-09-02T13:56:11Z"
closed
apache/airflow
https://github.com/apache/airflow
33,485
["airflow/utils/sqlalchemy.py"]
SQL_ALCHEMY_CONN_CMD causes triggerers to fail liveness probes on peak
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow version: **2.5.3** Related to this comment from @vchiapaikeo: https://github.com/apache/airflow/pull/33172#issuecomment-1677501450 A couple of mins after midnight UTC - when 100s of DAGs are kicked off - we noticed our triggerer replicas failing liveness probe checks and restarting systematically. Further profiling led to the discovery that the triggerer’s sync loop hangs for several minutes when there are 1000s of triggers running simultaneously, specifically while [bulk fetching triggers](https://github.com/apache/airflow/blob/v2-5-test/airflow/jobs/triggerer_job.py#L398), which causes the triggerer to miss heartbeats and eventually get restarted by k8s. With profiling still enabled, we observed that while the trigger is hanging and we profile the execution, we get this stack trace: ``` ncalls tottime percall cumtime percall filename:lineno(function) [506/45463] 1 0.000 0.000 29.928 29.928 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py:2757(all) 1 0.000 0.000 29.923 29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1468(all) 1 0.000 0.000 29.923 29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:395(_allrows) 1 0.000 0.000 29.923 29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1388(_fetchall_impl) 1 0.000 0.000 29.923 29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1808(_fetchall_impl) 2 0.000 0.000 29.922 14.961 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py:135(chunks) 1 0.000 0.000 29.921 29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:390(_raw_all_rows) 1 0.001 0.001 29.921 29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:393(<listcomp>) 125 0.000 0.000 29.919 0.239 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/sql/type_api.py:1711(process) 125 0.002 0.000 29.915 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:146(process_result_value) 125 0.001 0.000 29.909 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:122(db_supports_json) 125 0.001 0.000 29.908 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:562(get) 125 0.000 0.000 29.907 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:732(_get_environment_variables) 125 0.002 0.000 29.907 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:478(_get_env_var_option) 125 0.002 0.000 29.902 0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:103(run_command) 125 0.001 0.000 29.786 0.238 /usr/local/lib/python3.10/subprocess.py:1110(communicate) 125 0.006 0.000 29.785 0.238 /usr/local/lib/python3.10/subprocess.py:1952(_communicate) 250 0.003 0.000 29.762 0.119 /usr/local/lib/python3.10/selectors.py:403(select) 250 29.758 0.119 29.758 0.119 {method 'poll' of 'select.poll' objects} 125 0.002 0.000 0.100 0.001 /usr/local/lib/python3.10/subprocess.py:758(__init__) 125 0.004 0.000 0.094 0.001 /usr/local/lib/python3.10/subprocess.py:1687(_execute_child) ``` Which indicates that airflow is running a subprocess for each fetched row and that takes the vast majority of the execution time. We found that during the unmarshaling of the resulting rows into the Trigger model, the [kwargs column](https://github.com/apache/airflow/blob/v2-5-test/airflow/models/trigger.py#L57) (ExtendedJSON) runs [process_returned_value](https://github.com/apache/airflow/blob/v2-5-test/airflow/utils/sqlalchemy.py#L146), on each row, and reads the `SQL_ALCHEMY_CONN` configuration to determine whether the engine supports json or not and parse kwargs accordingly. However, in our case we define `SQL_ALCHEMY_CONN_CMD` as opposed to `SQL_ALCHEMY_CONN`, which causes the sync loop to spawn a new subprocess for every row ([here](https://github.com/apache/airflow/blob/v2-5-test/airflow/configuration.py#L485-L488)). We workaround it by using `SQL_ALCHEMY_CONN` instead of `SQL_ALCHEMY_CONN_CMD`, as it involves reading an environment variable instead of spawning a new subprocess. ### What you think should happen instead The triggerer model caches caches either the `SQL_ALCHEMY_CONN` or the [db_supports_json](https://github.com/apache/airflow/blob/v2-5-stable/airflow/utils/sqlalchemy.py#L122) property. ### How to reproduce Simultaneously kick off 100s of DAGs with at least a few deferrable operators each and use `SQL_ALCHEMY_CONN_CMD` instead of `SQL_ALCHEMY_CONN` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-airbyte==3.2.0 apache-airflow-providers-alibaba==2.2.0 apache-airflow-providers-amazon==7.3.0 apache-airflow-providers-apache-beam==4.3.0 apache-airflow-providers-apache-cassandra==3.1.1 apache-airflow-providers-apache-drill==2.3.1 apache-airflow-providers-apache-druid==3.3.1 apache-airflow-providers-apache-hdfs==3.2.0 apache-airflow-providers-apache-hive==5.1.3 apache-airflow-providers-apache-kylin==3.1.0 apache-airflow-providers-apache-livy==3.3.0 apache-airflow-providers-apache-pig==4.0.0 apache-airflow-providers-apache-pinot==4.0.1 apache-airflow-providers-apache-spark==4.0.0 apache-airflow-providers-apache-sqoop==3.1.1 apache-airflow-providers-arangodb==2.1.1 apache-airflow-providers-asana==2.1.0 apache-airflow-providers-atlassian-jira==2.0.1 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cloudant==3.1.0 apache-airflow-providers-cncf-kubernetes==5.2.2 apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-databricks==4.0.0 apache-airflow-providers-datadog==3.1.0 apache-airflow-providers-dbt-cloud==3.1.0 apache-airflow-providers-dingding==3.1.0 apache-airflow-providers-discord==3.1.0 apache-airflow-providers-docker==3.5.1 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-exasol==4.1.3 apache-airflow-providers-facebook==3.1.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-github==2.2.1 apache-airflow-providers-google==8.11.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.0 apache-airflow-providers-http==4.2.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-influxdb==2.1.0 apache-airflow-providers-jdbc==3.3.0 apache-airflow-providers-jenkins==3.2.0 apache-airflow-providers-microsoft-azure==5.2.1 apache-airflow-providers-microsoft-mssql==3.3.2 apache-airflow-providers-microsoft-psrp==2.2.0 apache-airflow-providers-microsoft-winrm==3.1.1 apache-airflow-providers-mongo==3.1.1 apache-airflow-providers-mysql==4.0.2 apache-airflow-providers-neo4j==3.2.1 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-openfaas==3.1.0 apache-airflow-providers-opsgenie==5.0.0 apache-airflow-providers-oracle==3.6.0 apache-airflow-providers-pagerduty==3.1.0 apache-airflow-providers-papermill==3.1.1 apache-airflow-providers-plexus==3.1.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-presto==4.2.2 apache-airflow-providers-qubole==3.3.1 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-salesforce==5.3.0 apache-airflow-providers-samba==4.1.0 apache-airflow-providers-segment==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-singularity==3.1.0 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.4 apache-airflow-providers-sqlite==3.3.1 apache-airflow-providers-ssh==3.5.0 apache-airflow-providers-tableau==4.1.0 apache-airflow-providers-tabular==1.1.0 apache-airflow-providers-telegram==4.0.0 apache-airflow-providers-trino==4.3.2 apache-airflow-providers-vertica==3.3.1 apache-airflow-providers-yandex==3.3.0 apache-airflow-providers-zendesk==4.2.0 ### Deployment Other 3rd-party Helm chart ### Deployment details Chart based on the official helm chart. Airflow running on Google Kubernetes Engine (GKE) using `KubernetesExecutor`. ### 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/33485
https://github.com/apache/airflow/pull/33503
d1e6a5c48d03322dda090113134f745d1f9c34d4
46aa4294e453d800ef6d327addf72a004be3765f
"2023-08-17T21:15:48Z"
python
"2023-08-18T19:40:52Z"
closed
apache/airflow
https://github.com/apache/airflow
33,482
["airflow/api_connexion/endpoints/dag_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/dag_schema.py", "airflow/models/dag.py", "airflow/www/static/js/types/api-generated.ts", "tests/api_connexion/endpoints/test_dag_endpoint.py", "tests/api_connexion/schemas/test_dag_schema.py"]
The `/dags/{dag_id}/details` endpoint returns less data than is documented
### What do you see as an issue? The [/dags/{dag_id}/details](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/post_set_task_instances_state) endpoint of the REST API does not return all of the keys that are listed in the documentation. If I run `curl -X GET localhost:8080/api/v1/dags/{my_dag}/details`, then compare the results with the results in the documentation, you can see the following missing keys: ```python >>> for key in docs.keys(): ... if not key in actual.keys(): ... print(key) ... root_dag_id last_parsed_time last_pickled last_expired scheduler_lock timetable_description has_task_concurrency_limits has_import_errors next_dagrun next_dagrun_data_interval_start next_dagrun_data_interval_end next_dagrun_create_after template_search_path ``` ### Solving the problem Either remove these keys from the documentation or fix the API endpoint ### 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/33482
https://github.com/apache/airflow/pull/34947
0e157b38a3e44b5a6fc084c581a025434a97a4c0
e8f62e8ee56519459d8282dadb1d8c198ea5b9f5
"2023-08-17T19:15:44Z"
python
"2023-11-24T09:47:33Z"
closed
apache/airflow
https://github.com/apache/airflow
33,478
["airflow/www/views.py"]
Rendered template malfunction when `execution_date` parameter is malformed
null
https://github.com/apache/airflow/issues/33478
https://github.com/apache/airflow/pull/33516
533afb5128383958889bc653226f46947c642351
d9814eb3a2fc1dbbb885a0a2c1b7a23ce1cfa148
"2023-08-17T16:46:41Z"
python
"2023-08-19T16:03:39Z"
closed
apache/airflow
https://github.com/apache/airflow
33,461
["airflow/providers/amazon/aws/waiters/appflow.json"]
AppflowHook with wait_for_completion = True does not finish executing the task although the appflow flow does.
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened I'm using airflow 2.6.2 with apache-airflow-providers-amazon 8.5.1 When I use AppflowHook with the wait_for_completion parameter set to True the task execution never finishes. I have checked in Appflow and the flow executes correctly and finishes in a couple of seconds, however, AppflowHook does not finish responding. If I change wait_for_completion to False, everything works correctly. The logs show a "403 FORBIDDEN" error and marking the task as success or failed fixes the logs. **Logs during task execution:** ```console 470b2412b735 *** Found local files: *** * /opt/airflow/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe/attempt=1.log *** !!!! Please make sure that all your Airflow components (e.g. schedulers, webservers, workers and triggerer) have the same 'secret_key' configured in 'webserver' section and time is synchronized on all your machines (for example with ntpd) See more at https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#secret-key *** Could not read served logs: Client error '403 FORBIDDEN' for url 'http://470b2412b735:8793/log/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe/attempt=1.log' For more information check: https://httpstatuses.com/403 [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [queued]> [2023-08-16, 19:04:44 CST] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [queued]> [2023-08-16, 19:04:44 CST] {taskinstance.py:1308} INFO - Starting attempt 1 of 1 [2023-08-16, 19:04:44 CST] {taskinstance.py:1327} INFO - Executing <Task(_PythonDecoratedOperator): extract_from_stripe> on 2023-08-17 01:04:41.723261+00:00 [2023-08-16, 19:04:44 CST] {standard_task_runner.py:57} INFO - Started process 796 to run task [2023-08-16, 19:04:44 CST] {standard_task_runner.py:84} INFO - Running: ['***', 'tasks', 'run', 'stripe_ingest_flow', 'extract_from_stripe', 'manual__2023-08-17T01:04:41.723261+00:00', '--job-id', '903', '--raw', '--subdir', 'DAGS_FOLDER/stripe_ingest_flow_to_lakehouse/dag.py', '--cfg-path', '/tmp/tmpqz8uvben'] [2023-08-16, 19:04:44 CST] {standard_task_runner.py:85} INFO - Job 903: Subtask extract_from_stripe [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {task_command.py:410} INFO - Running <TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [running]> on host 470b2412b735 [2023-08-16, 19:04:44 CST] {taskinstance.py:1545} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='dhernandez' AIRFLOW_CTX_DAG_ID='stripe_ingest_flow' AIRFLOW_CTX_TASK_ID='extract_from_stripe' AIRFLOW_CTX_EXECUTION_DATE='2023-08-17T01:04:41.723261+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='manual__2023-08-17T01:04:41.723261+00:00' [2023-08-16, 19:04:44 CST] {crypto.py:83} WARNING - empty cryptography key - values will not be stored encrypted. [2023-08-16, 19:04:44 CST] {base.py:73} INFO - Using connection ID 'siclo_***_lakehouse_conn' for task execution. [2023-08-16, 19:04:44 CST] {connection_wrapper.py:340} INFO - AWS Connection (conn_id='siclo_***_lakehouse_conn', conn_type='aws') credentials retrieved from login and password. [2023-08-16, 19:04:45 CST] {appflow.py:63} INFO - executionId: 58ad6275-0a70-48d9-8414-f0215924c876 ``` **Logs when marking the task as success or failed** ```console 470b2412b735 *** Found local files: *** * /opt/airflow/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe/attempt=1.log [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [queued]> [2023-08-16, 19:04:44 CST] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [queued]> [2023-08-16, 19:04:44 CST] {taskinstance.py:1308} INFO - Starting attempt 1 of 1 [2023-08-16, 19:04:44 CST] {taskinstance.py:1327} INFO - Executing <Task(_PythonDecoratedOperator): extract_from_stripe> on 2023-08-17 01:04:41.723261+00:00 [2023-08-16, 19:04:44 CST] {standard_task_runner.py:57} INFO - Started process 796 to run task [2023-08-16, 19:04:44 CST] {standard_task_runner.py:84} INFO - Running: ['***', 'tasks', 'run', 'stripe_ingest_flow', 'extract_from_stripe', 'manual__2023-08-17T01:04:41.723261+00:00', '--job-id', '903', '--raw', '--subdir', 'DAGS_FOLDER/stripe_ingest_flow_to_lakehouse/dag.py', '--cfg-path', '/tmp/tmpqz8uvben'] [2023-08-16, 19:04:44 CST] {standard_task_runner.py:85} INFO - Job 903: Subtask extract_from_stripe [2023-08-16, 19:04:44 CST] {logging_mixin.py:149} INFO - Changing /opt/***/logs/dag_id=stripe_ingest_flow/run_id=manual__2023-08-17T01:04:41.723261+00:00/task_id=extract_from_stripe permission to 509 [2023-08-16, 19:04:44 CST] {task_command.py:410} INFO - Running <TaskInstance: stripe_ingest_flow.extract_from_stripe manual__2023-08-17T01:04:41.723261+00:00 [running]> on host 470b2412b735 [2023-08-16, 19:04:44 CST] {taskinstance.py:1545} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='dhernandez' AIRFLOW_CTX_DAG_ID='stripe_ingest_flow' AIRFLOW_CTX_TASK_ID='extract_from_stripe' AIRFLOW_CTX_EXECUTION_DATE='2023-08-17T01:04:41.723261+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='manual__2023-08-17T01:04:41.723261+00:00' [2023-08-16, 19:04:44 CST] {crypto.py:83} WARNING - empty cryptography key - values will not be stored encrypted. [2023-08-16, 19:04:44 CST] {base.py:73} INFO - Using connection ID 'siclo_***_lakehouse_conn' for task execution. [2023-08-16, 19:04:44 CST] {connection_wrapper.py:340} INFO - AWS Connection (conn_id='siclo_***_lakehouse_conn', conn_type='aws') credentials retrieved from login and password. [2023-08-16, 19:04:45 CST] {appflow.py:63} INFO - executionId: 58ad6275-0a70-48d9-8414-f0215924c876 [2023-08-16, 19:05:24 CST] {local_task_job_runner.py:291} WARNING - State of this instance has been externally set to failed. Terminating instance. [2023-08-16, 19:05:24 CST] {process_utils.py:131} INFO - Sending 15 to group 796. PIDs of all processes in the group: [796] [2023-08-16, 19:05:24 CST] {process_utils.py:86} INFO - Sending the signal 15 to group 796 [2023-08-16, 19:05:24 CST] {taskinstance.py:1517} ERROR - Received SIGTERM. Terminating subprocesses. ``` ### What you think should happen instead That having wait_for_completion set to True, the task finishes successfully and retrieves the execution id from appflow. ### How to reproduce With a dag that has the following task ```python @task def extract(): appflow = AppflowHook( aws_conn_id='conn_id' ) execution_id = appflow.run_flow( flow_name='flow_name', wait_for_completion=True # with wait_for_completion=False if it works ) return execution_id ``` The aws connection has the following permissions - "appflow:DescribeFlow", - "appflow:StartFlow", - "appflow:RunFlow", - "appflow:ListFlows", - "appflow:DescribeFlowExecutionRecords" ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ``` apache-airflow==2.6.2 apache-airflow-providers-amazon==8.5.1 apache-airflow-providers-common-sql==1.5.1 apache-airflow-providers-http==4.4.1 boto3==1.26.76 asgiref==3.7.2 watchtower==2.0.1 jsonpath-ng==1.5.3 redshift-connector==2.0.911 sqlalchemy-redshift==0.8.14 mypy-boto3-appflow==1.28.16 mypy-boto3-rds==1.26.144 mypy-boto3-redshift-data==1.26.109 mypy-boto3-s3==1.26.153 celery==5.3.0 ``` ### Deployment Docker-Compose ### Deployment details Docker 4.10.1 (82475) Airflow image apache/airflow:2.6.2-python3.11 ### 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/33461
https://github.com/apache/airflow/pull/33613
2363fb562db1abaa5bc3bc93b67c96e018c1d78a
41d9be072abacc47393f700aa8fb98bc2b9a3713
"2023-08-17T01:47:56Z"
python
"2023-08-22T15:31:02Z"
closed
apache/airflow
https://github.com/apache/airflow
33,446
["airflow/utils/task_group.py", "tests/decorators/test_task_group.py"]
Task group gets marked as upstream_failed when dynamically mapped with expand_kwargs even though all upstream tasks were skipped or successfully finished.
### Apache Airflow version 2.6.3 ### What happened I am writing a DAG that transfers data from MSSQL to BigQuery, The part of the ETL process that actually fetches the data from MSSQL and moves it to BQ needs to parallelized. I am trying to write it as a task group where the first task moves data from MSSQL to GCS, and the 2nd task loads the file into BQ. for some odd reason when I expand the task group it is automatically marked as upstream_failed , at the very first moment the DAG is triggered. I have tested this with a simple dag (provided below) as well and the bug was reproduced. I found a similar issue [here](https://github.com/apache/airflow/issues/27449) but the bug seems to persist even after configuring `AIRFLOW__SCHEDULER__SCHEDULE_AFTER_TASK_EXECUTION=False` ### What you think should happen instead The task group should be dynamically expanded **after all upstream tasks have finished** since `expand_kwargs` needs the previous task's output. ### How to reproduce ```from datetime import timedelta from airflow.decorators import dag, task, task_group from airflow.operators.bash import BashOperator from pendulum import datetime @dag( dag_id="example_task_group_expansion", schedule="@once", default_args={ "depends_on_past": False, "email": ["airflow@example.com"], "email_on_failure": True, "email_on_retry": True, "retries": 0, "retry_delay": timedelta(minutes=5), }, start_date=datetime(2023, 8, 1), catchup=False, ) def example_dag(): @task(task_id="TaskDistributer") def task_distributer(): step = 10_000 return [dict(interval_start=i, interval_end=i + step) for i in range(0, 100_000, step)] @task_group(group_id="tg1") def tg(interval_start, interval_end): task1 = BashOperator( task_id="task1", bash_command="echo $interval_start -- $interval_end", env={"interval_start": str(interval_start), "interval_end": str(interval_end)}, ) task2 = BashOperator( task_id="task2", bash_command="echo $interval_start -- $interval_end", env={"interval_start": str(interval_start), "interval_end": str(interval_end)}, ) task1 >> task2 return task2 tg.expand_kwargs(task_distributer()) example_dag() ``` ### Operating System MacOS 13.4.1 ### Versions of Apache Airflow Providers No providers needed to reproduce ### Deployment Docker-Compose ### Deployment details Docker-compose Airflow image: apache/airflow:2.6.3-python3.9 Executor: Celery Messaging queue: redis Metadata DB: MySQL 5.7 ### Anything else The problem occurs every time. Here are some of the scheduler logs that may be relevant. ``` docker logs 3d4e47791238 | grep example_task_group_expansion INFO [alembic.runtime.migration] Context impl PostgresqlImpl. INFO [alembic.runtime.migration] Will assume transactional DDL. /usr/local/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py:189 DeprecationWarning: The '[celery] stalled_task_timeout' config option is deprecated. Please update your config to use '[scheduler] task_queued_timeout' instead. [2023-08-16 14:09:33 +0000] [15] [INFO] Starting gunicorn 20.1.0 [2023-08-16 14:09:33 +0000] [15] [INFO] Listening at: http://[::]:8793 (15) [2023-08-16 14:09:33 +0000] [15] [INFO] Using worker: sync [2023-08-16 14:09:33 +0000] [16] [INFO] Booting worker with pid: 16 [2023-08-16 14:09:33 +0000] [17] [INFO] Booting worker with pid: 17 [2023-08-16T14:10:04.870+0000] {dag.py:3504} INFO - Setting next_dagrun for example_task_group_expansion to None, run_after=None [2023-08-16T14:10:04.881+0000] {scheduler_job_runner.py:1449} DEBUG - DAG example_task_group_expansion not changed structure, skipping dagrun.verify_integrity [2023-08-16T14:10:04.883+0000] {dagrun.py:711} DEBUG - number of tis tasks for <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False>: 3 task(s) [2023-08-16T14:10:04.883+0000] {dagrun.py:732} DEBUG - number of scheduleable tasks for <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False>: 3 task(s) [2023-08-16T14:10:04.883+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Not In Retry Period' PASSED: True, The task instance was not marked for retrying. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Previous Dagrun State' PASSED: True, The task did not have depends_on_past set. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Trigger Rule' PASSED: True, The task instance did not have any upstream tasks. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1103} DEBUG - Dependencies all met for dep_context=None ti=<TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [None]> [2023-08-16T14:10:04.884+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task1 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Not In Retry Period' PASSED: True, The task instance was not marked for retrying. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task1 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Previous Dagrun State' PASSED: True, The task did not have depends_on_past set. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task1 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Trigger Rule' PASSED: True, The task instance did not have any upstream tasks. [2023-08-16T14:10:04.884+0000] {taskinstance.py:1103} DEBUG - Dependencies all met for dep_context=None ti=<TaskInstance: example_task_group_expansion.tg1.task1 scheduled__2023-08-01T00:00:00+00:00 [None]> [2023-08-16T14:10:04.895+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Not In Retry Period' PASSED: True, The task instance was not marked for retrying. [2023-08-16T14:10:04.895+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Previous Dagrun State' PASSED: True, The task did not have depends_on_past set. [2023-08-16T14:10:04.897+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Trigger Rule' PASSED: False, Task's trigger rule 'all_success' requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_states=_UpstreamTIStates(success=0, skipped=0, failed=0, upstream_failed=0, removed=0, done=0), upstream_task_ids={'tg1.task1'} [2023-08-16T14:10:04.897+0000] {taskinstance.py:1093} DEBUG - Dependencies not met for <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]>, dependency 'Trigger Rule' FAILED: Task's trigger rule 'all_success' requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_states=_UpstreamTIStates(success=0, skipped=0, failed=0, upstream_failed=0, removed=0, done=0), upstream_task_ids={'tg1.task1'} [2023-08-16T14:10:04.902+0000] {scheduler_job_runner.py:1476} DEBUG - Skipping SLA check for <DAG: example_task_group_expansion> because no tasks in DAG have SLAs <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [scheduled]> [2023-08-16T14:10:04.910+0000] {scheduler_job_runner.py:476} INFO - DAG example_task_group_expansion has 0/16 running and queued tasks <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [scheduled]> [2023-08-16T14:10:04.911+0000] {scheduler_job_runner.py:625} INFO - Sending TaskInstanceKey(dag_id='example_task_group_expansion', task_id='TaskDistributer', run_id='scheduled__2023-08-01T00:00:00+00:00', try_number=1, map_index=-1) to executor with priority 1 and queue default [2023-08-16T14:10:04.911+0000] {base_executor.py:147} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'example_task_group_expansion', 'TaskDistributer', 'scheduled__2023-08-01T00:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/example.py'] [2023-08-16T14:10:04.915+0000] {local_executor.py:86} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'example_task_group_expansion', 'TaskDistributer', 'scheduled__2023-08-01T00:00:00+00:00', '--local', '--subdir', 'DAGS_FOLDER/example.py'] [2023-08-16T14:10:04.948+0000] {scheduler_job_runner.py:1449} DEBUG - DAG example_task_group_expansion not changed structure, skipping dagrun.verify_integrity [2023-08-16T14:10:04.954+0000] {dagrun.py:711} DEBUG - number of tis tasks for <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False>: 3 task(s) [2023-08-16T14:10:04.954+0000] {dagrun.py:732} DEBUG - number of scheduleable tasks for <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False>: 1 task(s) [2023-08-16T14:10:04.954+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Not In Retry Period' PASSED: True, The task instance was not marked for retrying. [2023-08-16T14:10:04.954+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> dependency 'Previous Dagrun State' PASSED: True, The task did not have depends_on_past set. [2023-08-16T14:10:04.958+0000] {taskinstance.py:899} DEBUG - Setting task state for <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [None]> to upstream_failed [2023-08-16T14:10:04.958+0000] {taskinstance.py:1112} DEBUG - <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [upstream_failed]> dependency 'Trigger Rule' PASSED: False, Task's trigger rule 'all_success' requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_states=_UpstreamTIStates(success=0, skipped=0, failed=0, upstream_failed=1, removed=0, done=1), upstream_task_ids={'tg1.task1'} [2023-08-16T14:10:04.958+0000] {taskinstance.py:1093} DEBUG - Dependencies not met for <TaskInstance: example_task_group_expansion.tg1.task2 scheduled__2023-08-01T00:00:00+00:00 [upstream_failed]>, dependency 'Trigger Rule' FAILED: Task's trigger rule 'all_success' requires all upstream tasks to have succeeded, but found 1 non-success(es). upstream_states=_UpstreamTIStates(success=0, skipped=0, failed=0, upstream_failed=1, removed=0, done=1), upstream_task_ids={'tg1.task1'} [2023-08-16T14:10:04.963+0000] {scheduler_job_runner.py:1476} DEBUG - Skipping SLA check for <DAG: example_task_group_expansion> because no tasks in DAG have SLAs [2023-08-16T14:10:05.236+0000] {dagbag.py:506} DEBUG - Loaded DAG <DAG: example_task_group_expansion> Changing /usr/local/airflow/logs/dag_id=example_task_group_expansion/run_id=scheduled__2023-08-01T00:00:00+00:00/task_id=TaskDistributer permission to 509 [2023-08-16T14:10:05.265+0000] {task_command.py:410} INFO - Running <TaskInstance: example_task_group_expansion.TaskDistributer scheduled__2023-08-01T00:00:00+00:00 [queued]> on host 3d4e47791238 [2023-08-16T14:10:05.453+0000] {listener.py:32} INFO - TaskInstance Details: dag_id=example_task_group_expansion, task_id=TaskDistributer, dagrun_id=scheduled__2023-08-01T00:00:00+00:00, map_index=-1, run_start_date=2023-08-16 14:10:05.346669+00:00, try_number=1, job_id=302, op_classpath=airflow.decorators.python._PythonDecoratedOperator, airflow.decorators.base.DecoratedOperator, airflow.operators.python.PythonOperator [2023-08-16T14:10:06.001+0000] {scheduler_job_runner.py:1449} DEBUG - DAG example_task_group_expansion not changed structure, skipping dagrun.verify_integrity [2023-08-16T14:10:06.002+0000] {dagrun.py:711} DEBUG - number of tis tasks for <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False>: 3 task(s) [2023-08-16T14:10:06.002+0000] {dagrun.py:609} ERROR - Marking run <DagRun example_task_group_expansion @ 2023-08-01 00:00:00+00:00: scheduled__2023-08-01T00:00:00+00:00, state:running, queued_at: 2023-08-16 14:10:04.858967+00:00. externally triggered: False> failed [2023-08-16T14:10:06.002+0000] {dagrun.py:681} INFO - DagRun Finished: dag_id=example_task_group_expansion, execution_date=2023-08-01 00:00:00+00:00, run_id=scheduled__2023-08-01T00:00:00+00:00, run_start_date=2023-08-16 14:10:04.875813+00:00, run_end_date=2023-08-16 14:10:06.002810+00:00, run_duration=1.126997, state=failed, external_trigger=False, run_type=scheduled, data_interval_start=2023-08-01 00:00:00+00:00, data_interval_end=2023-08-01 00:00:00+00:00, dag_hash=a89f91f4d5dab071c49b1d98a4bd5c13 [2023-08-16T14:10:06.004+0000] {dag.py:3504} INFO - Setting next_dagrun for example_task_group_expansion to None, run_after=None [2023-08-16T14:10:06.005+0000] {scheduler_job_runner.py:1476} DEBUG - Skipping SLA check for <DAG: example_task_group_expansion> because no tasks in DAG have SLAs [2023-08-16T14:10:06.010+0000] {base_executor.py:299} DEBUG - Changing state: TaskInstanceKey(dag_id='example_task_group_expansion', task_id='TaskDistributer', run_id='scheduled__2023-08-01T00:00:00+00:00', try_number=1, map_index=-1) [2023-08-16T14:10:06.011+0000] {scheduler_job_runner.py:677} INFO - Received executor event with state success for task instance TaskInstanceKey(dag_id='example_task_group_expansion', task_id='TaskDistributer', run_id='scheduled__2023-08-01T00:00:00+00:00', try_number=1, map_index=-1) [2023-08-16T14:10:06.012+0000] {scheduler_job_runner.py:713} INFO - TaskInstance Finished: dag_id=example_task_group_expansion, task_id=TaskDistributer, run_id=scheduled__2023-08-01T00:00:00+00:00, map_index=-1, run_start_date=2023-08-16 14:10:05.346669+00:00, run_end_date=2023-08-16 14:10:05.518275+00:00, run_duration=0.171606, state=success, executor_state=success, try_number=1, max_tries=0, job_id=302, pool=default_pool, queue=default, priority_weight=1, operator=_PythonDecoratedOperator, queued_dttm=2023-08-16 14:10:04.910449+00:00, queued_by_job_id=289, pid=232 ``` As can be seen from the logs, no upstream tasks are in `done` state yet the expanded task is set as `upstream_failed`. [slack discussion](https://apache-airflow.slack.com/archives/CCQ7EGB1P/p1692107385230939) ### 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/33446
https://github.com/apache/airflow/pull/33732
869f84e9c398dba453456e89357876ed8a11c547
fe27031382e2034b59a23db1c6b9bdbfef259137
"2023-08-16T15:21:54Z"
python
"2023-08-29T16:48:43Z"
closed
apache/airflow
https://github.com/apache/airflow
33,377
["docs/apache-airflow/administration-and-deployment/logging-monitoring/metrics.rst"]
Statsd metrics description is incorrect
### What do you see as an issue? <img width="963" alt="Screenshot 2023-08-14 at 9 55 11 AM" src="https://github.com/apache/airflow/assets/10162465/bb493eb2-1cfd-45bb-928a-a4e21e015251"> Here dagrun duration success and failure have description where success is stored in seconds while that of failure is shown as stored in milliseconds from the description. But when checked in code part, where this two metrics are recorded, it is the same duration time that gets stored varying depending on the dag_id run state. <img width="963" alt="Screenshot 2023-08-14 at 10 00 28 AM" src="https://github.com/apache/airflow/assets/10162465/53d5aaa8-4c57-4357-b9c2-8d64164fad7c"> ### Solving the problem It looks the documentation description for that statsd metrics is misleading ### 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/33377
https://github.com/apache/airflow/pull/34532
08729eddbd7414b932a654763bf62c6221a0e397
117e40490865f04aed38a18724fc88a8cf94aacc
"2023-08-14T04:31:46Z"
python
"2023-09-21T18:53:34Z"
closed
apache/airflow
https://github.com/apache/airflow
33,375
["airflow/models/taskinstance.py", "airflow/operators/python.py", "airflow/utils/context.py", "airflow/utils/context.pyi", "tests/operators/test_python.py"]
Ability to retrieve prev_end_date_success
### Discussed in https://github.com/apache/airflow/discussions/33345 <div type='discussions-op-text'> <sup>Originally posted by **vuphamcs** August 11, 2023</sup> ### Description Is there a variable similar to `prev_start_date_success` but for the previous DAG run’s completion date? The value I’m hoping to retrieve to use within the next DAG run is `2023-08-10 16:04:30` ![image](https://github.com/apache/airflow/assets/1600760/9e2af349-4c3e-47e1-8a1a-9d8827b56f57) ### Use case/motivation One particular use case is to help guarantee that the next DAG run only queries data that was inserted during the existing DAG run, and not the past DAG run. ```python prev_ts = context['prev_end_date_success'] sql = f"SELECT * FROM results WHERE created_at > {context['prev_end_date_success']}" ``` ### 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) </div>
https://github.com/apache/airflow/issues/33375
https://github.com/apache/airflow/pull/34528
2bcd450e84426fd678b3fa2e4a15757af234e98a
61a9ab7600a856bb2b1031419561823e227331da
"2023-08-13T23:52:10Z"
python
"2023-11-03T18:31:26Z"
closed
apache/airflow
https://github.com/apache/airflow
33,368
["BREEZE.rst"]
Preview feature broken for ./BREEZE.rst
### What do you see as an issue? The preview for [BREEZE.rst](https://github.com/apache/airflow/blob/main/BREEZE.rst) does not show up since #33318. This is most probably due to the tilde `~` character for marking sections that is used in this commit. [The markup specification for sections](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html#sections) allows for several characters including `~` but it seems that it breaks the GitHub preview feature. Screenshot of the preview being broken: <img width="814" alt="preview feature broken" src="https://github.com/apache/airflow/assets/9881262/3dca3de9-68c5-4ed9-861c-accf6d0abdf1"> ### Solving the problem The problem can be solved by reverting to a more consensual character like `-`. Screenshot of the preview feature restored after replacing `~` with `-`: <img width="802" alt="preview feature restored" src="https://github.com/apache/airflow/assets/9881262/d1e7c8f8-db77-4423-8a5f-c939d3d4cfce"> ### 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/33368
https://github.com/apache/airflow/pull/33369
0cb256411a02516dc9eca88b570abfb8c8a3c35b
42638549efb5fccce8b5a93e3c2d05716f4ec59c
"2023-08-13T17:32:10Z"
python
"2023-08-13T18:40:16Z"
closed
apache/airflow
https://github.com/apache/airflow
33,344
["airflow/config_templates/config.yml", "airflow/www/views.py", "newsfragments/33351.significant.rst"]
Not able to trigger DAG with config from UI if param is not defined in a DAG and dag_run.conf is used
### Apache Airflow version 2.7.0rc1 ### What happened As per https://github.com/apache/airflow/pull/31583, now we can only run DAG with config from UI if DAG has params, however, if a DAG is using dag_run.conf there is no way to run with config from UI and as dag_run.conf is not deprecated most of the users will be impacted by this @hussein-awala also mentioned it in his [voting](https://lists.apache.org/thread/zd9ppxw1xwxsl66w0tyw1wch9flzb03w) ### What you think should happen instead I think there should be a way to provide param values from UI when dag_run.conf is used in DAG ### How to reproduce Use below DAG in 2.7.0rc and you will notice there is no way to provide conf value from airflow UI DAG CODE ``` from airflow.models import DAG from airflow.operators.bash import BashOperator from airflow.operators.python import PythonOperator from airflow.utils.dates import days_ago dag = DAG( dag_id="trigger_target_dag", default_args={"start_date": days_ago(2), "owner": "Airflow"}, tags=["core"], schedule_interval=None, # This must be none so it's triggered by the controller is_paused_upon_creation=False, # This must be set so other workers can pick this dag up. mabye it's a bug idk ) def run_this_func(**context): print( f"Remotely received value of {context['dag_run'].conf['message']} for key=message " ) run_this = PythonOperator( task_id="run_this", python_callable=run_this_func, dag=dag, ) bash_task = BashOperator( task_id="bash_task", bash_command='echo "Here is the message: $message"', env={"message": '{{ dag_run.conf["message"] if dag_run else "" }}'}, dag=dag, ) ``` ### Operating System MAS os Monterey ### 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/33344
https://github.com/apache/airflow/pull/33351
45713446f37ee4b1ee972ab8b5aa1ac0b2482197
c0362923fd8250328eab6e60f0cf7e855bfd352e
"2023-08-12T09:34:55Z"
python
"2023-08-13T12:57:07Z"
closed
apache/airflow
https://github.com/apache/airflow
33,325
["airflow/www/views.py"]
providers view shows description with HTML element
### Body In Admin -> Providers view The description shows a `<br>` <img width="1286" alt="Screenshot 2023-08-11 at 21 54 25" src="https://github.com/apache/airflow/assets/45845474/2cdba81e-9cea-4ed4-8420-1e9ab4c2eee2"> ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/33325
https://github.com/apache/airflow/pull/33326
682176d57263aa2aab1aa8703723270ab3148af4
23d542462a1aaa5afcd36dedc3c2a12c840e1d2c
"2023-08-11T19:04:02Z"
python
"2023-08-11T22:58:12Z"
closed
apache/airflow
https://github.com/apache/airflow
33,323
["tests/jobs/test_triggerer_job.py"]
Flaky test `test_trigger_firing` with ' SQLite objects created in a thread can only be used in that same thread.'
### Body Observed in https://github.com/apache/airflow/actions/runs/5835505313/job/15827357798?pr=33309 ``` ___________________ ERROR at teardown of test_trigger_firing ___________________ self = <sqlalchemy.future.engine.Connection object at 0x7f92d3327910> def _rollback_impl(self): assert not self.__branch_from if self._has_events or self.engine._has_events: self.dispatch.rollback(self) if self._still_open_and_dbapi_connection_is_valid: if self._echo: if self._is_autocommit_isolation(): self._log_info( "ROLLBACK using DBAPI connection.rollback(), " "DBAPI should ignore due to autocommit mode" ) else: self._log_info("ROLLBACK") try: > self.engine.dialect.do_rollback(self.connection) /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1062: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <sqlalchemy.dialects.sqlite.pysqlite.SQLiteDialect_pysqlite object at 0x7f92d9698650> dbapi_connection = <sqlalchemy.pool.base._ConnectionFairy object at 0x7f92d33839d0> def do_rollback(self, dbapi_connection): > dbapi_connection.rollback() E sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 140269149157120 and this is thread id 140269532822400. /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/default.py:683: ProgrammingError The above exception was the direct cause of the following exception: @pytest.fixture(autouse=True, scope="function") def close_all_sqlalchemy_sessions(): from sqlalchemy.orm import close_all_sessions close_all_sessions() yield > close_all_sessions() tests/conftest.py:953: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/local/lib/python3.11/site-packages/sqlalchemy/orm/session.py:4315: in close_all_sessions sess.close() /usr/local/lib/python3.11/site-packages/sqlalchemy/orm/session.py:1816: in close self._close_impl(invalidate=False) /usr/local/lib/python3.11/site-packages/sqlalchemy/orm/session.py:1858: in _close_impl transaction.close(invalidate) /usr/local/lib/python3.11/site-packages/sqlalchemy/orm/session.py:926: in close transaction.close() /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2426: in close self._do_close() /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2649: in _do_close self._close_impl() /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2635: in _close_impl self._connection_rollback_impl() /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2627: in _connection_rollback_impl self.connection._rollback_impl() /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1064: in _rollback_impl self._handle_dbapi_exception(e, None, None, None, None) /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2134: in _handle_dbapi_exception util.raise_( /usr/local/lib/python3.11/site-packages/sqlalchemy/util/compat.py:211: in raise_ raise exception /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1062: in _rollback_impl self.engine.dialect.do_rollback(self.connection) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <sqlalchemy.dialects.sqlite.pysqlite.SQLiteDialect_pysqlite object at 0x7f92d9698650> dbapi_connection = <sqlalchemy.pool.base._ConnectionFairy object at 0x7f92d33839d0> def do_rollback(self, dbapi_connection): > dbapi_connection.rollback() E sqlalchemy.exc.ProgrammingError: (sqlite3.ProgrammingError) SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 140269149157120 and this is thread id 140269532822400. E (Background on this error at: https://sqlalche.me/e/14/f405) /usr/local/lib/python3.11/site-packages/sqlalchemy/engine/default.py:683: ProgrammingError ------------------------------ Captured log call ------------------------------- INFO airflow.jobs.triggerer_job_runner:triggerer_job_runner.py:171 Setting up TriggererHandlerWrapper with handler <FileTaskHandler (NOTSET)> INFO airflow.jobs.triggerer_job_runner:triggerer_job_runner.py:227 Setting up logging queue listener with handlers [<LocalQueueHandler (NOTSET)>, <TriggererHandlerWrapper (NOTSET)>] INFO airflow.jobs.triggerer_job_runner.TriggerRunner:triggerer_job_runner.py:596 trigger test_dag/test_run/test_ti/-1/1 (ID 1) starting INFO airflow.jobs.triggerer_job_runner.TriggerRunner:triggerer_job_runner.py:600 Trigger test_dag/test_run/test_ti/-1/1 (ID 1) fired: TriggerEvent<True> Level 100 airflow.triggers.testing.SuccessTrigger:triggerer_job_runner.py:633 trigger end INFO airflow.jobs.triggerer_job_runner.TriggerRunner:triggerer_job_runner.py:622 trigger test_dag/test_run/test_ti/-1/1 (ID 1) completed ``` ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/33323
https://github.com/apache/airflow/pull/34075
601b9cd33c5f1a92298eabb3934a78fb10ca9a98
47f79b9198f3350951dc21808c36f889bee0cd06
"2023-08-11T18:50:00Z"
python
"2023-09-04T14:40:24Z"
closed
apache/airflow
https://github.com/apache/airflow
33,319
[".github/workflows/release_dockerhub_image.yml"]
Documentation outdated on dockerhub
### What do you see as an issue? On: https://hub.docker.com/r/apache/airflow It says in several places that the last version is 2.3.3 like here: ![image](https://github.com/apache/airflow/assets/19591174/640b0e73-dfcb-4118-ae5f-c0376ebb98a3) ### Solving the problem Update the version and requirements to 2.6.3. ### 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/33319
https://github.com/apache/airflow/pull/33348
50765eb0883652c16b40d69d8a1ac78096646610
98fb7d6e009aaf4bd06ffe35e526af2718312607
"2023-08-11T17:06:15Z"
python
"2023-08-12T14:22:22Z"
closed
apache/airflow
https://github.com/apache/airflow
33,310
["airflow/dag_processing/manager.py", "airflow/models/dag.py", "airflow/models/dagcode.py", "airflow/models/serialized_dag.py", "tests/dag_processing/test_job_runner.py", "tests/models/test_dag.py"]
Multiple DAG processors with separate DAG directories keep deactivating each other's DAGs
### Apache Airflow version 2.6.3 ### What happened When running multiple standalone DAG processors with separate DAG directories using the `--subdir` argument the processors keep deactivating each other's DAGs (and reactivating their own). After stepping through the code with a debugger I think the issue is that the calls [here](https://github.com/apache/airflow/blob/2.6.3/airflow/dag_processing/manager.py#L794) and [here](https://github.com/apache/airflow/blob/2.6.3/airflow/dag_processing/manager.py#L798) have no awareness of the DAG directories. ### What you think should happen instead The DAG processors should not touch each other's DAGs in the metadata DB. ### How to reproduce Start two or more standalone DAG processors with separate DAG directories and observe (e.g. via the UI) how the list of active DAGs keeps changing constantly. ### Operating System Linux 94b223524983 6.1.32-0-virt #1-Alpine SMP PREEMPT_DYNAMIC Mon, 05 Jun 2023 09:39:09 +0000 x86_64 x86_64 x86_64 GNU/Linux ### Versions of Apache Airflow Providers _No response_ ### 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/33310
https://github.com/apache/airflow/pull/33357
3857d3399c2e5f4c3e0a838b7a76296c4aa19b3e
35b18306a4928152fd1834964fc8ce0033811817
"2023-08-11T08:28:44Z"
python
"2023-08-14T20:45:47Z"
closed
apache/airflow
https://github.com/apache/airflow
33,300
["airflow/providers/mysql/hooks/mysql.py", "tests/providers/mysql/hooks/test_mysql.py", "tests/providers/mysql/hooks/test_mysql_connector_python.py"]
MySqlHook add support for init_command
### Description There is currently no way to pass an `init_command` connection argument for a mysql connection when using either the `mysqlclient` or `mysql-connector-python` libraries with the MySql provider's `MySqlHook`. Documentation for connection arguments for `mysqlclient` library, listing `init_command`: https://mysqlclient.readthedocs.io/user_guide.html?highlight=init_command#functions-and-attributes Documentation for connection arguments for `mysql-connector-python` library, listing `init_command`: https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html There can be many uses for `init_command`, but also what comes to mind is why do we explicitly provide support to certain connection arguments and not others? ### Use case/motivation For my own use right now I am currently am subclassing the hook and then altering the connection arguments to pass in `init_command` to set the `time_zone` session variable to UTC at connection time, like so: ```python conn_config['init_command'] = r"""SET time_zone = '+00:00';""" ``` Note: This is just an example, there can be many other uses for `init_command` besides the example above. Also, I am aware there is a `time_zone` argument for connections via the `mysql-connector-python` library, however that argument is not supported by connections made with `mysqlclient` library. Both libraries do support the `init_command` argument. ### 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/33300
https://github.com/apache/airflow/pull/33359
ea8519c0554d16b13d330a686f8479fc10cc58f2
dce9796861e0a535952f79b0e2a7d5a012fcc01b
"2023-08-11T00:41:55Z"
python
"2023-08-18T05:58:51Z"
closed
apache/airflow
https://github.com/apache/airflow
33,256
["airflow/sensors/time_sensor.py", "tests/sensors/test_time_sensor.py"]
TimeSensorAsync does not use DAG timezone to convert naive time input
### Apache Airflow version 2.6.3 ### What happened TimeSensor and TimeSensorAsync convert timezones differently. TimeSensor converts a naive time into an tz-aware time with `self.dag.timezone`. TimeSensorAsync does not, and erronously converts it to UTC instead. ### What you think should happen instead TimeSensor and TimeSensorAsync should behave the same. ### How to reproduce Compare the logic of TimeSensor versus TimeSensorAsync, given a DAG with a UTC+2 (for example `Europe/Berlin`) timezone and the target_time input of `datetime.time(9, 0)`. ### Operating System Official container image, Debian GNU/Linux 11 (bullseye), Python 3.10.12 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details EKS + Kustomize stack with airflow-ui, airflow-scheduler, and airflow-triggerer. ### 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/33256
https://github.com/apache/airflow/pull/33406
84a3daed8691d5e129eaf3e02061efb8b6ca56cb
6c50ef59cc4f739f126e5b123775340a3351a3e8
"2023-08-09T11:52:35Z"
python
"2023-10-12T03:27:19Z"
closed
apache/airflow
https://github.com/apache/airflow
33,255
["airflow/providers/microsoft/azure/secrets/key_vault.py"]
Azure KeyVault Backend logging level
### Apache Airflow version 2.6.3 ### What happened I've set up Azure keyvaults as a [backend](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/secrets-backends/azure-key-vault.html) for fetching connections, and it works fine. However, there's just too much logging and it's causing issues for our users to read logs. For example: ``` [2023-08-09, 13:32:30 CEST] {_universal.py:513} INFO - Request URL: 'https://REDACTED.vault.azure.net/secrets/airflow-connections-ode-odbc-dev-dw/?api-version=REDACTED' Request method: 'GET' Request headers: 'Accept': 'application/json' 'x-ms-client-request-id': '6cdf2a74-36a8-11ee-8cac-6ac595ee5ea6' 'User-Agent': 'azsdk-python-keyvault-secrets/4.7.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' No body was attached to the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 401 Response headers: 'Cache-Control': 'no-cache' 'Pragma': 'no-cache' 'Content-Length': '97' 'Content-Type': 'application/json; charset=utf-8' 'Expires': '-1' 'WWW-Authenticate': 'Bearer authorization="https://login.microsoftonline.com/100b3c99-f3e2-4da0-9c8a-b9d345742c36", resource="https://vault.azure.net"' 'x-ms-keyvault-region': 'REDACTED' 'x-ms-client-request-id': '6cdf2a74-36a8-11ee-8cac-6ac595ee5ea6' 'x-ms-request-id': '563d7428-9df4-4d6a-9766-19626395056f' 'x-ms-keyvault-service-version': '1.9.908.1' 'x-ms-keyvault-network-info': 'conn_type=Ipv4;addr=20.76.1.64;act_addr_fam=InterNetwork;' 'X-Content-Type-Options': 'REDACTED' 'Strict-Transport-Security': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' [2023-08-09, 13:32:30 CEST] {_universal.py:513} INFO - Request URL: 'https://login.microsoftonline.com/100b3c99-f3e2-4da0-9c8a-b9d345742c36/v2.0/.well-known/openid-configuration' Request method: 'GET' Request headers: 'User-Agent': 'azsdk-python-identity/1.13.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' No body was attached to the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 200 Response headers: 'Cache-Control': 'max-age=86400, private' 'Content-Type': 'application/json; charset=utf-8' 'Strict-Transport-Security': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'Access-Control-Allow-Origin': 'REDACTED' 'Access-Control-Allow-Methods': 'REDACTED' 'P3P': 'REDACTED' 'x-ms-request-id': '80869b0e-4cde-47f7-8721-3f430a8c3600' 'x-ms-ests-server': 'REDACTED' 'X-XSS-Protection': 'REDACTED' 'Set-Cookie': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' 'Content-Length': '1753' [2023-08-09, 13:32:30 CEST] {_universal.py:513} INFO - Request URL: 'https://login.microsoftonline.com/common/discovery/instance?api-version=REDACTED&authorization_endpoint=REDACTED' Request method: 'GET' Request headers: 'Accept': 'application/json' 'User-Agent': 'azsdk-python-identity/1.13.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' No body was attached to the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 200 Response headers: 'Cache-Control': 'max-age=86400, private' 'Content-Type': 'application/json; charset=utf-8' 'Strict-Transport-Security': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'Access-Control-Allow-Origin': 'REDACTED' 'Access-Control-Allow-Methods': 'REDACTED' 'P3P': 'REDACTED' 'x-ms-request-id': '93b3dfad-72c7-4629-8625-d2b335363a00' 'x-ms-ests-server': 'REDACTED' 'X-XSS-Protection': 'REDACTED' 'Set-Cookie': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' 'Content-Length': '945' [2023-08-09, 13:32:30 CEST] {_universal.py:510} INFO - Request URL: 'https://login.microsoftonline.com/100b3c99-f3e2-4da0-9c8a-b9d345742c36/oauth2/v2.0/token' Request method: 'POST' Request headers: 'Accept': 'application/json' 'x-client-sku': 'REDACTED' 'x-client-ver': 'REDACTED' 'x-client-os': 'REDACTED' 'x-client-cpu': 'REDACTED' 'x-ms-lib-capability': 'REDACTED' 'client-request-id': 'REDACTED' 'x-client-current-telemetry': 'REDACTED' 'x-client-last-telemetry': 'REDACTED' 'User-Agent': 'azsdk-python-identity/1.13.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' A body is sent with the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 200 Response headers: 'Cache-Control': 'no-store, no-cache' 'Pragma': 'no-cache' 'Content-Type': 'application/json; charset=utf-8' 'Expires': '-1' 'Strict-Transport-Security': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'P3P': 'REDACTED' 'client-request-id': 'REDACTED' 'x-ms-request-id': '79cf595b-4f41-47c3-a370-f9321c533a00' 'x-ms-ests-server': 'REDACTED' 'x-ms-clitelem': 'REDACTED' 'X-XSS-Protection': 'REDACTED' 'Set-Cookie': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' 'Content-Length': '1313' [2023-08-09, 13:32:30 CEST] {chained.py:87} INFO - DefaultAzureCredential acquired a token from EnvironmentCredential [2023-08-09, 13:32:30 CEST] {_universal.py:513} INFO - Request URL: 'https://REDACTED.vault.azure.net/secrets/airflow-connections-ode-odbc-dev-dw/?api-version=REDACTED' Request method: 'GET' Request headers: 'Accept': 'application/json' 'x-ms-client-request-id': '6cdf2a74-36a8-11ee-8cac-6ac595ee5ea6' 'User-Agent': 'azsdk-python-keyvault-secrets/4.7.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' 'Authorization': 'REDACTED' No body was attached to the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 404 Response headers: 'Cache-Control': 'no-cache' 'Pragma': 'no-cache' 'Content-Length': '332' 'Content-Type': 'application/json; charset=utf-8' 'Expires': '-1' 'x-ms-keyvault-region': 'REDACTED' 'x-ms-client-request-id': '6cdf2a74-36a8-11ee-8cac-6ac595ee5ea6' 'x-ms-request-id': 'ac41c47c-30f0-46cf-9157-6e5dba031ffa' 'x-ms-keyvault-service-version': '1.9.908.1' 'x-ms-keyvault-network-info': 'conn_type=Ipv4;addr=20.76.1.64;act_addr_fam=InterNetwork;' 'x-ms-keyvault-rbac-assignment-id': 'REDACTED' 'x-ms-keyvault-rbac-cache': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'Strict-Transport-Security': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' [2023-08-09, 13:32:30 CEST] {base.py:73} INFO - Using connection ID 'ode-odbc-dev-dw' for task execution. [2023-08-09, 13:32:30 CEST] {_universal.py:513} INFO - Request URL: 'https://REDACTED.vault.azure.net/secrets/airflow-connections-ode-odbc-dev-dw/?api-version=REDACTED' Request method: 'GET' Request headers: 'Accept': 'application/json' 'x-ms-client-request-id': '6d2b797e-36a8-11ee-8cac-6ac595ee5ea6' 'User-Agent': 'azsdk-python-keyvault-secrets/4.7.0 Python/3.9.17 (Linux-5.4.0-1111-azure-x86_64-with-glibc2.31)' 'Authorization': 'REDACTED' No body was attached to the request [2023-08-09, 13:32:30 CEST] {_universal.py:549} INFO - Response status: 404 Response headers: 'Cache-Control': 'no-cache' 'Pragma': 'no-cache' 'Content-Length': '332' 'Content-Type': 'application/json; charset=utf-8' 'Expires': '-1' 'x-ms-keyvault-region': 'REDACTED' 'x-ms-client-request-id': '6d2b797e-36a8-11ee-8cac-6ac595ee5ea6' 'x-ms-request-id': 'ac37f859-14cc-48e3-8a88-6214b96ef75e' 'x-ms-keyvault-service-version': '1.9.908.1' 'x-ms-keyvault-network-info': 'conn_type=Ipv4;addr=20.76.1.64;act_addr_fam=InterNetwork;' 'x-ms-keyvault-rbac-assignment-id': 'REDACTED' 'x-ms-keyvault-rbac-cache': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'Strict-Transport-Security': 'REDACTED' 'Date': 'Wed, 09 Aug 2023 11:32:30 GMT' [2023-08-09, 13:32:31 CEST] {base.py:73} INFO - Using connection ID 'ode-odbc-dev-dw' for task execution. ``` Changing airflow logging_level to WARNING/ERROR is one way, but then the task logs don't have sufficient information. Is it possible to influence just the logging level on SecretClient? ### What you think should happen instead Ideally, it should be possible to set logging level specifically for the keyvault backend in the backend_kwargs: ``` backend_kwargs = {"connections_prefix": "airflow-connections", "variables_prefix": "airflow-variables", "vault_url": "https://example-akv-resource-name.vault.azure.net/", "logging_level": "WARNING"} ``` ### How to reproduce Set up KV backend as described [here](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/secrets-backends/azure-key-vault.html). ### Operating System Debian GNU/Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deployed with Helm chart on AKS. ### 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/33255
https://github.com/apache/airflow/pull/33314
dfb2403ec4b6d147ac31125631677cee9e12347e
4460356c03e5c1dedd72ce87a8ccfb9b19a33d76
"2023-08-09T11:31:35Z"
python
"2023-08-13T22:40:42Z"
closed
apache/airflow
https://github.com/apache/airflow
33,248
["airflow/providers/amazon/aws/hooks/glue.py", "airflow/providers/amazon/aws/operators/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py", "tests/providers/amazon/aws/operators/test_glue.py"]
GlueOperator: iam_role_arn as a parameter
### Description Hi, There is mandatory parameter iam_role_name parameter for GlueJobOperator/GlueJobHook. It adds additional step of translating it to the arn, which needs connectivity to the global iam AWS endpoint (no privatelink availabale). For private setups it needs opening connectivity + proxy configuration to make it working. It would be great to have also possibility to just pass directly iam_role_arn and avoid this additional step. ### Use case/motivation Role assignation does not need external connectivity, possibility of adding arn instead of the name. ### 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/33248
https://github.com/apache/airflow/pull/33408
cc360b73c904b7f24a229282458ee05112468f5d
60df70526a00fb9a3e245bb3ffb2a9faa23582e7
"2023-08-09T07:59:10Z"
python
"2023-08-15T21:20:58Z"
closed
apache/airflow
https://github.com/apache/airflow
33,217
["airflow/models/taskinstance.py", "tests/models/test_taskinstance.py"]
get_current_context not present in user_defined_macros
### Apache Airflow version 2.6.3 ### What happened get_current_context() fail in a user_defined_macros give ``` {abstractoperator.py:594} ERROR - Exception rendering Jinja template for task 'toot', field 'op_kwargs'. Template: {'arg': '{{ macro_run_id() }}'} Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/abstractoperator.py", line 586, in _do_render_template_fields rendered_content = self.render_template( File "/home/airflow/.local/lib/python3.8/site-packages/airflow/template/templater.py", line 168, in render_template return {k: self.render_template(v, context, jinja_env, oids) for k, v in value.items()} File "/home/airflow/.local/lib/python3.8/site-packages/airflow/template/templater.py", line 168, in <dictcomp> return {k: self.render_template(v, context, jinja_env, oids) for k, v in value.items()} File "/home/airflow/.local/lib/python3.8/site-packages/airflow/template/templater.py", line 156, in render_template return self._render(template, context) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/abstractoperator.py", line 540, in _render return super()._render(template, context, dag=dag) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/template/templater.py", line 113, in _render return render_template_to_string(template, context) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/helpers.py", line 288, in render_template_to_string return render_template(template, cast(MutableMapping[str, Any], context), native=False) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/helpers.py", line 283, in render_template return "".join(nodes) File "<template>", line 12, in root File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/sandbox.py", line 393, in call return __context.call(__obj, *args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/jinja2/runtime.py", line 298, in call return __obj(*args, **kwargs) File "/opt/airflow/dags/dags/exporter/finance_closing.py", line 7, in macro_run_id schedule_interval = get_current_context()["dag"].schedule_interval.replace("@", "") File "/home/airflow/.local/lib/python3.8/site-packages/airflow/operators/python.py", line 784, in get_current_context raise AirflowException( airflow.exceptions.AirflowException: Current context was requested but no context was found! Are you running within an airflow task? ``` ### What you think should happen instead User macros should be able to access to the current context ``` airflow.exceptions.AirflowException: Current context was requested but no context was found! Are you running within an airflow task? ``` ### How to reproduce ```python from airflow.models import DAG from airflow.operators.python import PythonOperator from airflow.utils.dates import days_ago def macro_run_id(): from airflow.operators.python import get_current_context a = get_current_context()["dag"].schedule_interval.replace("@", "") if a == "None": a = "manual" return a with DAG(dag_id="example2", start_date=days_ago(61), user_defined_macros={"macro_run_id": macro_run_id}, schedule_interval="@monthly"): def toto(arg): print(arg) PythonOperator(task_id="toot", python_callable=toto, op_kwargs={"arg": "{{ macro_run_id() }}"}) ``` ### Operating System ubuntu 22.04 ### 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/33217
https://github.com/apache/airflow/pull/33645
47682042a45501ab235d612580b8284a8957523e
9fa782f622ad9f6e568f0efcadf93595f67b8a20
"2023-08-08T17:17:47Z"
python
"2023-08-24T13:33:15Z"
closed
apache/airflow
https://github.com/apache/airflow
33,203
["airflow/providers/microsoft/azure/hooks/wasb.py", "tests/providers/microsoft/azure/hooks/test_wasb.py"]
Provider apache-airflow-providers-microsoft-azure no longer==6.2.3 expose `account_name`
### Apache Airflow version 2.6.3 ### What happened Till version apache-airflow-providers-microsoft-azure no longer==6.2.2 if you do `WasbHook(wasb_conn_id=self.conn_id).get_conn().account_name` you will get the `account_name` But in version `apache-airflow-providers-microsoft-azure==6.2.3` this is not longer working for below connection: ``` - conn_id: wasb_conn_with_access_key conn_type: wasb host: astrosdk.blob.core.windows.net description: null extra: shared_access_key: $AZURE_WASB_ACCESS_KEY ``` ### What you think should happen instead We should get the `account_name` for `apache-airflow-providers-microsoft-azure==6.2.3`. ### How to reproduce Try installing the version `apache-airflow-providers-microsoft-azure==6.2.3` and try running below code `WasbHook(wasb_conn_id=self.conn_id).get_conn().account_name` ### Operating System Mac ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### 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/33203
https://github.com/apache/airflow/pull/33457
8b7e0babe1c3e9bef6e934d1e362564bc73fda4d
bd608a56abd1a6c2a98987daf7f092d2dabea555
"2023-08-08T12:00:13Z"
python
"2023-08-17T07:55:58Z"
closed
apache/airflow
https://github.com/apache/airflow
33,178
["airflow/cli/commands/task_command.py", "airflow/models/mappedoperator.py", "airflow/models/taskinstance.py", "airflow/utils/task_instance_session.py", "tests/decorators/test_python.py", "tests/models/test_mappedoperator.py", "tests/models/test_renderedtifields.py", "tests/models/test_xcom_arg_map.py"]
Flaky `test_xcom_map_error_fails_task` test
### Body This flaky test appears recently in our jobs and it seems this is a real problem with our code - after few attempts of fixing it, it still appears in our builds: ``` tests/models/test_xcom_arg_map.py:174: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/utils/session.py:74: in wrapper return func(*args, **kwargs) airflow/models/taskinstance.py:1840: in run self._run_raw_task( airflow/utils/session.py:74: in wrapper return func(*args, **kwargs) airflow/models/taskinstance.py:1494: in _run_raw_task session.commit() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:1454: in commit self._transaction.commit(_to_root=self.future) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:832: in commit self._prepare_impl() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:811: in _prepare_impl self.session.flush() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3449: in flush self._flush(objects) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3589: in _flush transaction.rollback(_capture_exception=True) /usr/local/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70: in __exit__ compat.raise_( /usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py:211: in raise_ raise exception /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3549: in _flush flush_context.execute() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py:456: in execute rec.execute(self) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py:630: in execute util.preloaded.orm_persistence.save_obj( /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py:237: in save_obj _emit_update_statements( /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py:1001: in _emit_update_statements c = connection._execute_20( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1710: in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) /usr/local/lib/python3.8/site-packages/sqlalchemy/sql/elements.py:334: in _execute_on_connection return connection._execute_clauseelement( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1577: in _execute_clauseelement ret = self._execute_context( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1953: in _execute_context self._handle_dbapi_exception( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:2134: in _handle_dbapi_exception util.raise_( /usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py:211: in raise_ raise exception /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1910: in _execute_context self.dialect.do_execute( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py:736: in do_execute cursor.execute(statement, parameters) /usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py:174: in execute self._discard() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <MySQLdb.cursors.Cursor object at 0x7f52bc978a60> def _discard(self): self.description = None self.description_flags = None # Django uses some member after __exit__. # So we keep rowcount and lastrowid here. They are cleared in Cursor._query(). # self.rowcount = 0 # self.lastrowid = None self._rows = None self.rownumber = None if self._result: self._result.discard() self._result = None con = self.connection if con is None: return > while con.next_result() == 0: # -1 means no more data. E sqlalchemy.exc.ProgrammingError: (MySQLdb.ProgrammingError) (2014, "Commands out of sync; you can't run this command now") E [SQL: UPDATE task_instance SET pid=%s, updated_at=%s WHERE task_instance.dag_id = %s AND task_instance.task_id = %s AND task_instance.run_id = %s AND task_instance.map_index = %s] E [parameters: (90, datetime.datetime(2023, 8, 7, 14, 44, 7, 580365), 'test_dag', 'pull', 'test', 0)] E (Background on this error at: https://sqlalche.me/e/14/f405) ``` ``` E sqlalchemy.exc.ProgrammingError: (MySQLdb.ProgrammingError) (2014, "Commands out of sync; you can't run this command now") E [SQL: UPDATE task_instance SET pid=%s, updated_at=%s WHERE task_instance.dag_id = %s AND task_instance.task_id = %s AND task_instance.run_id = %s AND task_instance.map_index = %s] E [parameters: (90, datetime.datetime(2023, 8, 7, 14, 44, 7, 580365), 'test_dag', 'pull', 'test', 0)] E (Background on this error at: https://sqlalche.me/e/14/f405) ``` Example failures: * https://github.com/apache/airflow/actions/runs/5786336184/job/15681127372?pr=33144 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/33178
https://github.com/apache/airflow/pull/33309
20d81428699db240b65f72a92183255c24e8c19b
ef85c673d81cbeb60f29a978c5dc61787d61253e
"2023-08-07T15:43:04Z"
python
"2023-09-05T14:32:35Z"
closed
apache/airflow
https://github.com/apache/airflow
33,162
["Dockerfile", "scripts/docker/clean-logs.sh"]
Empty log folders are not removed when clean up
### Apache Airflow version main (development) ### What happened Empty log folders use up all Inodes and they are not removed by [clean-logs.sh](https://github.com/apache/airflow/blob/main/scripts/docker/clean-logs.sh) This is the diff after cleaning empty folders. (50GB disk used) ``` airflow@airflow-worker-2:/opt/airflow/logs$ df -i Filesystem Inodes IUsed IFree IUse% Mounted on /dev/nvme2n1 3276800 1311542 1965258 41% /opt/airflow/logs airflow@airflow-worker-2:/opt/airflow/logs$ find . -type d -empty -delete airflow@airflow-worker-2:/opt/airflow/logs$ df -i Filesystem Inodes IUsed IFree IUse% Mounted on /dev/nvme2n1 3276800 158708 3118092 5% /opt/airflow/logs ``` ### What you think should happen instead _No response_ ### How to reproduce Have lots of frequent DAGs. ### Operating System Debian 11 ### 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/33162
https://github.com/apache/airflow/pull/33252
bd11ea81e50f602d1c9f64c44c61b4e7294aafa9
93c3ccbdf2e60a7c3721ce308edae8b6591c9f23
"2023-08-07T02:28:16Z"
python
"2023-08-13T22:10:53Z"
closed
apache/airflow
https://github.com/apache/airflow
33,138
["airflow/providers/redis/sensors/redis_pub_sub.py", "tests/providers/redis/sensors/test_redis_pub_sub.py"]
Move redis subscribe to poke() method in Redis Sensor (#32984): @potiuk
The fix has a bug (subscribe happens too frequently)
https://github.com/apache/airflow/issues/33138
https://github.com/apache/airflow/pull/33139
76ca94d2f23de298bb46668998c227a86b4ecbd0
29a59de237ccd42a3a5c20b10fc4c92b82ff4475
"2023-08-05T09:05:21Z"
python
"2023-08-05T10:28:37Z"
closed
apache/airflow
https://github.com/apache/airflow
33,099
["chart/templates/_helpers.yaml", "chart/templates/configmaps/configmap.yaml", "chart/templates/scheduler/scheduler-deployment.yaml", "chart/templates/webserver/webserver-deployment.yaml", "chart/values.schema.json", "chart/values.yaml", "helm_tests/airflow_core/test_scheduler.py", "helm_tests/webserver/test_webserver.py"]
Add startupProbe to airflow helm charts
### Description Introducing a startupProbe onto the airflow services would be useful for slow starting container and most of all it doesn't have side effects. ### Use case/motivation We have an internal feature where we perform a copy of venv from airflow services to cloud storages which can sometimes take a few minutes. Copying of a venv is a metadata heavy load: https://learn.microsoft.com/en-us/troubleshoot/azure/azure-storage/files-troubleshoot-performance?tabs=linux#cause-2-metadata-or-namespace-heavy-workload. Introducing a startupProbe onto the airflow services would be useful for slow starting container and most of all it doesn't have side effects. ### 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/33099
https://github.com/apache/airflow/pull/33107
9736143468cfe034e65afb3df3031ab3626f0f6d
ca5acda1617a5cdb1d04f125568ffbd264209ec7
"2023-08-04T07:14:41Z"
python
"2023-08-07T20:03:38Z"
closed
apache/airflow
https://github.com/apache/airflow
33,061
["airflow/utils/log/secrets_masker.py", "tests/utils/log/test_secrets_masker.py"]
TriggerDagRunOperator DAG task log showing Warning: Unable to redact <DagRunState.SUCCESS: 'success'>
### Apache Airflow version main (development) ### What happened When TriggerDagRunOperator task log showing below warning `WARNING - Unable to redact <DagRunState.SUCCESS: 'success'>, please report this via <https://github.com/apache/airflow/issues>. Error was: TypeError: EnumMeta.__call__() missing 1 required positional argument: 'value'` <img width="1479" alt="image" src="https://github.com/apache/airflow/assets/43964496/0c183ffc-2440-49ee-b8d0-951ddc078c36"> ### What you think should happen instead There should not be any warning in logs ### How to reproduce Steps to Repo: 1. Launch airflow using Breeze with main 2. Trigger any TriggerDagRunOperator 3. Check logs DAG : ```python from airflow import DAG from airflow.operators.trigger_dagrun import TriggerDagRunOperator from airflow.operators.dummy import DummyOperator from airflow.utils.dates import days_ago """This example illustrates the use of the TriggerDagRunOperator. There are 2 entities at work in this scenario: 1. The Controller DAG - the DAG that conditionally executes the trigger 2. The Target DAG - DAG being triggered (in trigger_dagrun_target.py) """ dag = DAG( dag_id="trigger_controller_dag", default_args={"owner": "airflow", "start_date": days_ago(2)}, schedule_interval=None, tags=["core"], ) trigger = TriggerDagRunOperator( task_id="test_trigger_dagrun", trigger_dag_id="trigger_target_dag", reset_dag_run=True, wait_for_completion=True, conf={"message": "Hello World"}, dag=dag, ) ``` Note: create a DAG `trigger_target_dag` which maybe sleeps for sometime ### Operating System OS x ### 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/33061
https://github.com/apache/airflow/pull/33065
1ff33b800246fdbfa7aebe548055409d64307f46
b0f61be2f9791b75da3bca0bc30fdbb88e1e0a8a
"2023-08-03T05:57:24Z"
python
"2023-08-03T13:30:18Z"
closed
apache/airflow
https://github.com/apache/airflow
33,016
["airflow/providers/amazon/aws/operators/batch.py"]
Deferred AWS BatchOperator appears to re-trigger task and error out
### Apache Airflow version 2.6.3 ### What happened I started a long-running job with the BatchOperator in deferrable mode. Traceback is below: The strange behavior here is that just before erroring out, it looks like the BatchOperator resubmits the job. However, the Batch is not actually submitted (verified in the console) - however, this seems to break the trigger and it errors out immediately after. ```ip-172-20-18-105.ec2.internal *** Found local files: *** * /home/airflow/airflow/logs/dag_id=foxy_salesforce/run_id=scheduled__2023-08-01T19:30:00+00:00/task_id=foxy_salesforce_batch_job/attempt=2.log *** * /home/airflow/airflow/logs/dag_id=foxy_salesforce/run_id=scheduled__2023-08-01T19:30:00+00:00/task_id=foxy_salesforce_batch_job/attempt=2.log.trigger.17484.log [2023-08-01, 20:35:37 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [queued]> [2023-08-01, 20:35:37 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [queued]> [2023-08-01, 20:35:37 UTC] {taskinstance.py:1308} INFO - Starting attempt 2 of 2 [2023-08-01, 20:35:37 UTC] {taskinstance.py:1327} INFO - Executing <Task(BatchOperator): foxy_salesforce_batch_job> on 2023-08-01 19:30:00+00:00 [2023-08-01, 20:35:37 UTC] {standard_task_runner.py:57} INFO - Started process 8834 to run task [2023-08-01, 20:35:37 UTC] {standard_task_runner.py:84} INFO - Running: ['airflow', 'tasks', 'run', 'foxy_salesforce', 'foxy_salesforce_batch_job', 'scheduled__2023-08-01T19:30:00+00:00', '--job-id', '17555', '--raw', '--subdir', 'DAGS_FOLDER/foxy_salesforce.py', '--cfg-path', '/tmp/tmpr0isg35r'] [2023-08-01, 20:35:37 UTC] {standard_task_runner.py:85} INFO - Job 17555: Subtask foxy_salesforce_batch_job [2023-08-01, 20:35:37 UTC] {task_command.py:410} INFO - Running <TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [running]> on host ip-172-20-18-105.ec2.internal [2023-08-01, 20:35:37 UTC] {taskinstance.py:1545} INFO - Exporting env vars: AIRFLOW_CTX_DAG_EMAIL='data_engineering_alerts@intelycare.com' AIRFLOW_CTX_DAG_OWNER='nrobinson' AIRFLOW_CTX_DAG_ID='foxy_salesforce' AIRFLOW_CTX_TASK_ID='foxy_salesforce_batch_job' AIRFLOW_CTX_EXECUTION_DATE='2023-08-01T19:30:00+00:00' AIRFLOW_CTX_TRY_NUMBER='2' AIRFLOW_CTX_DAG_RUN_ID='scheduled__2023-08-01T19:30:00+00:00' [2023-08-01, 20:35:37 UTC] {batch.py:255} INFO - Running AWS Batch job - job definition: foxy_dev_batch_job:1 - on queue foxy-queue [2023-08-01, 20:35:37 UTC] {batch.py:262} INFO - AWS Batch job - container overrides: {'command': ['-tap', 'salesforce', '-ds', 'salesforce', '-to', 'data_engineering_alerts@intelycare.com']} [2023-08-01, 20:35:37 UTC] {base.py:73} INFO - Using connection ID 'aws_prod_batch' for task execution. [2023-08-01, 20:35:37 UTC] {credentials.py:1051} INFO - Found credentials from IAM Role: DSAirflowIAMStack-DSDaggerServerRoleBC9B4D69-BVJN2Q2JAYNS [2023-08-01, 20:35:37 UTC] {batch.py:292} INFO - AWS Batch job (0c52c6e5-6b77-4e48-88d6-e98478fdcae2) started: {'ResponseMetadata': {'RequestId': '4510c251-622d-4882-aec1-50eef30d2b7d', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Tue, 01 Aug 2023 20:35:37 GMT', 'content-type': 'application/json', 'content-length': '165', 'connection': 'keep-alive', 'x-amzn-requestid': '4510c251-622d-4882-aec1-50eef30d2b7d', 'access-control-allow-origin': '*', 'x-amz-apigw-id': 'I_3oCGPhoAMEa6Q=', 'access-control-expose-headers': 'X-amzn-errortype,X-amzn-requestid,X-amzn-errormessage,X-amzn-trace-id,X-amz-apigw-id,date', 'x-amzn-trace-id': 'Root=1-64c96c99-57cff0982ce9c97360d0fd02'}, 'RetryAttempts': 0}, 'jobArn': 'arn:aws:batch:us-east-1:806657589280:job/0c52c6e5-6b77-4e48-88d6-e98478fdcae2', 'jobName': 'foxy_salesforce', 'jobId': '0c52c6e5-6b77-4e48-88d6-e98478fdcae2'} [2023-08-01, 20:35:37 UTC] {taskinstance.py:1415} INFO - Pausing task as DEFERRED. dag_id=foxy_salesforce, task_id=foxy_salesforce_batch_job, execution_date=20230801T193000, start_date=20230801T203537 [2023-08-01, 20:35:37 UTC] {local_task_job_runner.py:222} INFO - Task exited with return code 100 (task deferral) [2023-08-01, 20:35:38 UTC] {base.py:73} INFO - Using connection ID 'aws_prod_batch' for task execution. [2023-08-01, 20:35:38 UTC] {credentials.py:1051} INFO - Found credentials from IAM Role: DSAirflowIAMStack-DSDaggerServerRoleBC9B4D69-BVJN2Q2JAYNS [2023-08-01, 20:35:39 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['STARTING'] [2023-08-01, 20:36:09 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['STARTING'] [2023-08-01, 20:36:39 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:37:09 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:37:39 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:38:09 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:38:39 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:39:09 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:39:39 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:40:09 UTC] {waiter_with_logging.py:129} INFO - Batch job 0c52c6e5-6b77-4e48-88d6-e98478fdcae2 not ready yet: ['RUNNING'] [2023-08-01, 20:40:12 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [queued]> [2023-08-01, 20:40:12 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [queued]> [2023-08-01, 20:40:12 UTC] {taskinstance.py:1306} INFO - Resuming after deferral [2023-08-01, 20:40:12 UTC] {taskinstance.py:1327} INFO - Executing <Task(BatchOperator): foxy_salesforce_batch_job> on 2023-08-01 19:30:00+00:00 [2023-08-01, 20:40:12 UTC] {standard_task_runner.py:57} INFO - Started process 21621 to run task [2023-08-01, 20:40:12 UTC] {standard_task_runner.py:84} INFO - Running: ['airflow', 'tasks', 'run', 'foxy_salesforce', 'foxy_salesforce_batch_job', 'scheduled__2023-08-01T19:30:00+00:00', '--job-id', '17561', '--raw', '--subdir', 'DAGS_FOLDER/foxy_salesforce.py', '--cfg-path', '/tmp/tmpyl2o5l2k'] [2023-08-01, 20:40:12 UTC] {standard_task_runner.py:85} INFO - Job 17561: Subtask foxy_salesforce_batch_job [2023-08-01, 20:40:12 UTC] {task_command.py:410} INFO - Running <TaskInstance: foxy_salesforce.foxy_salesforce_batch_job scheduled__2023-08-01T19:30:00+00:00 [running]> on host ip-172-20-18-105.ec2.internal [2023-08-01, 20:40:12 UTC] {taskinstance.py:1598} ERROR - Trigger failed: Traceback (most recent call last): File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/providers/amazon/aws/utils/waiter_with_logging.py", line 122, in async_wait await waiter.wait(**args, WaiterConfig={"MaxAttempts": 1}) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/waiter.py", line 49, in wait await AIOWaiter.wait(self, **kwargs) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/waiter.py", line 139, in wait raise WaiterError( botocore.exceptions.WaiterError: Waiter batch_job_complete failed: Max attempts exceeded During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 537, in cleanup_finished_triggers result = details["task"].result() File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 615, in run_trigger async for event in trigger.run(): File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/providers/amazon/aws/triggers/base.py", line 121, in run await async_wait( File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/providers/amazon/aws/utils/waiter_with_logging.py", line 131, in async_wait raise AirflowException("Waiter error: max attempts reached") airflow.exceptions.AirflowException: Waiter error: max attempts reached [2023-08-01, 20:40:12 UTC] {taskinstance.py:1824} ERROR - Task failed with exception airflow.exceptions.TaskDeferralError: Trigger failure [2023-08-01, 20:40:12 UTC] {taskinstance.py:1345} INFO - Marking task as FAILED. dag_id=foxy_salesforce, task_id=foxy_salesforce_batch_job, execution_date=20230801T193000, start_date=20230801T203537, end_date=20230801T204012 [2023-08-01, 20:40:12 UTC] {logging_mixin.py:150} WARNING - /home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/utils/email.py:153 RemovedInAirflow3Warning: Fetching SMTP credentials from configuration variables will be deprecated in a future release. Please set credentials using a connection instead. [2023-08-01, 20:40:12 UTC] {email.py:269} INFO - Email alerting: attempt 1 [2023-08-01, 20:40:12 UTC] {email.py:281} INFO - Sent an alert email to ['data_engineering_alerts@intelycare.com'] [2023-08-01, 20:40:12 UTC] {standard_task_runner.py:104} ERROR - Failed to execute job 17561 for task foxy_salesforce_batch_job (Trigger failure; 21621) [2023-08-01, 20:40:12 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 1 [2023-08-01, 20:40:12 UTC] {taskinstance.py:2653} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you think should happen instead _No response_ ### How to reproduce Run a long-running Batch Job using the BatchOperator with `deferrable=True` ### Operating System AmazonLinux ### Versions of Apache Airflow Providers Version 8.4.0 of the AWS provider ### 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/33016
https://github.com/apache/airflow/pull/33045
44234c2bf05f93a9772b7c9320a69a5c150c1d56
4e42edb203a0fa0958830ac3aa56a37b8eb678e8
"2023-08-01T21:08:19Z"
python
"2023-08-03T14:36:23Z"
closed
apache/airflow
https://github.com/apache/airflow
33,014
["airflow/www/views.py", "tests/www/views/test_views_tasks.py"]
Clearing task from List Task Instance page in UI does not also clear downstream tasks?
### Apache Airflow version 2.6.3 ### What happened Select tasks from List Task Instance page in UI and select clear Only those tasks are cleared and downsteam tasks are not also cleared as they are in the DAG graph view ### What you think should happen instead downstream tasks should also be cleared ### How to reproduce Select tasks from List Task Instance page in UI for which there are downstream tasks and select clear ### Operating System rocky ### 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/33014
https://github.com/apache/airflow/pull/34529
541c9addb6b2ee56244793503cbf5c218e80dec8
5b0ce3db4d36e2a7f20a78903daf538bbde5e38a
"2023-08-01T19:39:33Z"
python
"2023-09-22T17:54:58Z"
closed
apache/airflow
https://github.com/apache/airflow
32,996
["airflow/models/taskinstance.py"]
Task instance log_url is overwrites existing path in base_url
### Apache Airflow version 2.6.3 ### What happened A task instance's [log_url](https://github.com/apache/airflow/blob/2.6.3/airflow/models/taskinstance.py#L726) does not contain the full URL defined in [base_url](https://github.com/apache/airflow/blob/2.6.3/airflow/models/taskinstance.py#L729C9-L729C69). ### What you think should happen instead The base_url may contain paths that should be acknowledged when build the log_url. The log_url is built with [urljoin](https://docs.python.org/3/library/urllib.parse.html#urllib.parse.urljoin). Due to how urljoin builds URLs, any existing paths are ignored leading to a faulty URL. ### How to reproduce This snippet showcases how urljoin ignores existing paths when building the url. ``` >>> from urllib.parse import urljoin >>> >>> >>> urljoin( ... "https://my.astronomer.run/path", ... f"log?execution_date=test" ... f"&task_id=wow" ... f"&dag_id=super" ... f"&map_index=-1", ... ) 'https://eochgroup.astronomer.run/log?execution_date=test&task_id=wow&dag_id=super&map_index=-1' ``` ### Operating System n/a ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else This was introduced by #31833. A way to fix this can be to utilize [urlsplit](https://docs.python.org/3/library/urllib.parse.html#urllib.parse.urlsplit) and [urlunsplit](https://docs.python.org/3/library/urllib.parse.html#urllib.parse.urlunsplit) to account for existing paths. ``` from urllib.parse import urlsplit, urlunsplit parts = urlsplit("https://my.astronomer.run/paths") urlunsplit(( parts.scheme, parts.netloc, f"{parts.path}/log", f"execution_date=test" f"&task_id=wow" f"&dag_id=super" f"&map_index=-1", "" ) ) ``` Here is the fix in action. ``` >>> parts = urlsplit("https://my.astronomer.run/paths") >>> urlunsplit(( ... parts.scheme, ... parts.netloc, ... f"{parts.path}/log", ... f"execution_date=test" ... f"&task_id=wow" ... f"&dag_id=super" ... f"&map_index=-1", ... '')) 'https://my.astronomer.run/paths/log?execution_date=test&task_id=wow&dag_id=super&map_index=-1' >>> >>> parts = urlsplit("https://my.astronomer.run/paths/test") >>> urlunsplit(( ... parts.scheme, ... parts.netloc, ... f"{parts.path}/log", ... f"execution_date=test" ... f"&task_id=wow" ... f"&dag_id=super" ... f"&map_index=-1", ... '')) 'https://my.astronomer.run/paths/test/log?execution_date=test&task_id=wow&dag_id=super&map_index=-1' ``` ### 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/32996
https://github.com/apache/airflow/pull/33063
3bb63f1087176b24e9dc8f4cc51cf44ce9986d34
baa1bc0438baa05d358b236eec3c343438d8d53c
"2023-08-01T08:42:28Z"
python
"2023-08-03T09:19:21Z"
closed
apache/airflow
https://github.com/apache/airflow
32,993
["airflow/providers/vertica/hooks/vertica.py", "tests/providers/vertica/hooks/test_vertica.py"]
Error not detected in multi-statement vertica query
### Apache Airflow version 2.6.3 ### What happened Hello, There is a problem with multi-statement query and vertica, error will be detected only if it happens on the first statement of the sql. for example if I run the following sql with default SQLExecuteQueryOperator options: INSERT INTO MyTable (Key, Label) values (1, 'test 1'); INSERT INTO MyTable (Key, Label) values (1, 'test 2'); INSERT INTO MyTable (Key, Label) values (3, 'test 3'); the first insert will be commited, the nexts won't and no errors will be returned. the same sql runed on mysql will return an error and no row will be inserted. It seems to be linked to the way the vertica python client works (an issue has been opened on their git 4 years ago, [Duplicate key values error is not thrown as exception and is getting ignored](https://github.com/vertica/vertica-python/issues/255)) but since a workaround was provided I don't think it will be fixed in a near future. For the moment, to workaroud I use the split statement option with disabling auto-commit but I think it's dangerous to let this behaviour as is. ### What you think should happen instead _No response_ ### How to reproduce create a table MyTable with two columns Key and Lbl, declare Key as primary key. Run the following query with SQLExecuteQueryOperator INSERT INTO MyTable (Key, Label) values (1, 'test 1'); INSERT INTO MyTable (Key, Label) values (1, 'test 2'); INSERT INTO MyTable (Key, Label) values (3, 'test 3'); ### Operating System Debian GNU/Linux 11 (bullseye) ### 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? - [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/32993
https://github.com/apache/airflow/pull/34041
6b2a0cb3c84eeeaec013c96153c6b9538c6e74c4
5f47e60962b3123b1e6c8b42bef2c2643f54b601
"2023-08-01T08:06:25Z"
python
"2023-09-06T21:09:53Z"
closed
apache/airflow
https://github.com/apache/airflow
32,969
["airflow/providers/databricks/hooks/databricks_base.py", "docs/apache-airflow-providers-databricks/connections/databricks.rst", "tests/providers/databricks/hooks/test_databricks.py"]
Databricks support for Service Principal Oauth
### Description Authentication using OAuth for Databricks Service Principals is now in Public Preview. I would like to implement this into the Databricks Hook. By adding "service_principal_oauth" as a boolean value set to `true` in the extra configuration, the Client Id and Client Secret can be supplied as a username and password. https://docs.databricks.com/dev-tools/authentication-oauth.html ### Use case/motivation Before Authentication using Oauth the only way to use the Databricks Service Principals was by another user account performing a token request on-behalf of the Service Principal. This process is difficult to utilize in the real world, but this new way of collecting access tokens changes the process and should make a big difference. ### 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/32969
https://github.com/apache/airflow/pull/33005
a1b5bdb25a6f9565ac5934a9a458e9b079ccf3ae
8bf53dd5545ecda0e5bbffbc4cc803cbbde719a9
"2023-07-31T13:43:43Z"
python
"2023-08-14T10:16:33Z"
closed
apache/airflow
https://github.com/apache/airflow
32,926
["airflow/providers/apache/kafka/operators/consume.py", "tests/integration/providers/apache/kafka/operators/test_consume.py", "tests/providers/apache/kafka/operators/test_consume.py"]
Bug in Apache Kafka Provider Consumer Operator
### Apache Airflow version 2.5.3 ### What happened Ran the apache-airflow-providers-apache-kafka version 1.1.2 and got this error: ![image](https://github.com/apache/airflow/assets/118911990/bbb24811-8770-4210-b701-b4aa696c0fdf) ### What you think should happen instead I was not at the end of my topic and I did not set a max messages so this should have processed another 1000 messages ### How to reproduce Run the provider on a topic that has more than 1000 messages ### Operating System PRETTY_NAME="Debian GNU/Linux 11 (bullseye)" NAME="Debian GNU/Linux" VERSION_ID="11" VERSION="11 (bullseye)" VERSION_CODENAME=bullseye ID=debian HOME_URL="https://www.debian.org/" SUPPORT_URL="https://www.debian.org/support" BUG_REPORT_URL="https://bugs.debian.org/" ### Versions of Apache Airflow Providers apache-airflow-providers-apache-kafka version 1.1.2 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deploying onto Kubernetes ### Anything else This problem occurs every time I run it. Looks like the problem is in "consume.py" as I am using the ConsumeFromTopicOperator. Debugging a little, it is looking like the first 1000 messages it runs fine, but then the second time through for some reason it is setting batch_size wrong: ![image](https://github.com/apache/airflow/assets/118911990/1c571a44-d1be-4eeb-b87b-dc4db66d120b) I think this problems stems from 2 lines. This line: messages_left -= len(msgs) sets the message to -999, but then this line: batch_size = self.max_batch_size if messages_left > self.max_batch_size else messages_left will pick the messages_left which is set to -999. I believe the fix here would be to change one of the two lines, but from looking at the logic I think the correct change would either be to change to messages_left = len(msgs) or messages_left += len(msgs) ### 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/32926
https://github.com/apache/airflow/pull/33321
d0c94d6bee2a9494e44f29c2c242c956877e9619
c9d0fcd967cf21ea8373662c3686a5c8468eaae0
"2023-07-28T21:45:32Z"
python
"2023-08-12T09:53:14Z"
closed
apache/airflow
https://github.com/apache/airflow
32,920
["airflow/providers/amazon/aws/transfers/gcs_to_s3.py"]
GCSToS3Operator is providing an unexpected argument to GCSHook.list
### Apache Airflow version 2.6.3 ### What happened https://github.com/apache/airflow/blob/d800c1bc3967265280116a05d1855a4da0e1ba10/airflow/providers/amazon/aws/transfers/gcs_to_s3.py#L148-L150 This line in `GCSToS3Operator` is currently broken on Airflow 2.6.3: ``` Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/airflow/providers/amazon/aws/transfers/gcs_to_s3.py", line 148, in execute files = hook.list( TypeError: GCSHook.list() got an unexpected keyword argument 'match_glob' ``` The call signature for `GCSHook.list` does not have a `match_glob` argument on Airflow 2.6.3 https://github.com/apache/airflow/blob/eb24742d5300d2d87b17b4bcd67f639dbafd9818/airflow/providers/google/cloud/hooks/gcs.py#L699 However it does on the `main` branch: https://github.com/apache/airflow/blob/0924389a877c5461733ef8a048e860b951d81a56/airflow/providers/google/cloud/hooks/gcs.py#L702-L710 It appears that `GCSToS3Operator` jumped the gun on using the `match_glob` ### What you think should happen instead _No response_ ### How to reproduce Create a task that uses `airflow.providers.amazon.aws.transfers.gcs_to_s3.GCSToS3Operator`. Execute the task. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.3.1 apache-airflow-providers-celery==3.2.1 apache-airflow-providers-cncf-kubernetes==7.3.0 apache-airflow-providers-common-sql==1.6.0 apache-airflow-providers-datadog==3.3.1 apache-airflow-providers-dbt-cloud==3.2.2 apache-airflow-providers-elasticsearch==4.5.1 apache-airflow-providers-ftp==3.4.2 apache-airflow-providers-google==10.0.0 apache-airflow-providers-http==4.5.0 apache-airflow-providers-imap==3.2.2 apache-airflow-providers-microsoft-azure==6.2.1 apache-airflow-providers-postgres==5.5.2 apache-airflow-providers-redis==3.2.1 apache-airflow-providers-sftp==4.4.0 apache-airflow-providers-slack==7.3.1 apache-airflow-providers-sqlite==3.4.2 apache-airflow-providers-ssh==3.7.1 ### Deployment Astronomer ### Deployment details Using Astro Runtime 8.8.0 ### 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/32920
https://github.com/apache/airflow/pull/32925
cf7e0c5aa5ccc7b8a3963b14eadde0c8bc7c4eb7
519d99baee058dfa56f293f94222309c493ba3c4
"2023-07-28T15:16:28Z"
python
"2023-08-04T17:40:57Z"
closed
apache/airflow
https://github.com/apache/airflow
32,897
["airflow/providers/amazon/aws/hooks/logs.py", "airflow/providers/amazon/aws/log/cloudwatch_task_handler.py", "airflow/providers/amazon/aws/utils/__init__.py", "tests/providers/amazon/aws/hooks/test_logs.py", "tests/providers/amazon/aws/log/test_cloudwatch_task_handler.py"]
Enhance Airflow Logs API to fetch logs from Amazon Cloudwatch with time range
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened MWAA Version: 2.4.3 Airflow Version: 2.4.3 Airflow Logs currently do not fetch logs from Cloudwatch without time range, so when the cloudwatch logs are large and CloudWatch log streams are OLD, the airflow UI cannot display logs with error message: ``` *** Reading remote log from Cloudwatch log_group: airflow-cdp-airflow243-XXXX-Task log_stream: dag_id=<DAG_NAME>/run_id=scheduled__2023-07-27T07_25_00+00_00/task_id=<TASK_ID>/attempt=1.log. Could not read remote logs from log_group: airflow-cdp-airflow243-XXXXXX-Task log_group: airflow-cdp-airflow243-XXXX-Task log_stream: dag_id=<DAG_NAME>/run_id=scheduled__2023-07-27T07_25_00+00_00/task_id=<TASK_ID>/attempt=1.log ``` The Airflow API need to pass start and end timestamps to GetLogEvents API from Amazon CloudWatch to resolve this error and it also improves performance of fetching logs. This is critical issue for customers when they would like to fetch logs to investigate failed pipelines form few days to weeks old ### What you think should happen instead The Airflow API need to pass start and end timestamps to GetLogEvents API from Amazon CloudWatch to resolve this error. This should also improve performance of fetching logs. ### How to reproduce This issue is intermittent and happens mostly on FAILD tasks. 1. Log onto Amazon MWAA Service 2. Open Airflow UI 3. Select DAG 4. Select the Failed Tasks 5. Select Logs You should see error message like below in the logs: ``` *** Reading remote log from Cloudwatch log_group: airflow-cdp-airflow243-XXXX-Task log_stream: dag_id=<DAG_NAME>/run_id=scheduled__2023-07-27T07_25_00+00_00/task_id=<TASK_ID>/attempt=1.log. Could not read remote logs from log_group: airflow-cdp-airflow243-XXXXXX-Task log_group: airflow-cdp-airflow243-XXXX-Task log_stream: dag_id=<DAG_NAME>/run_id=scheduled__2023-07-27T07_25_00+00_00/task_id=<TASK_ID>/attempt=1.log ``` ### Operating System Running with Amazon MWAA ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.3.1 apache-airflow==2.4.3 ### Deployment Amazon (AWS) MWAA ### 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/32897
https://github.com/apache/airflow/pull/33231
5707103f447be818ad4ba0c34874b822ffeefc09
c14cb85f16b6c9befd35866327fecb4ab9bc0fc4
"2023-07-27T21:01:44Z"
python
"2023-08-10T17:30:23Z"
closed
apache/airflow
https://github.com/apache/airflow
32,890
["airflow/www/static/js/connection_form.js"]
Airflow UI ignoring extra connection field during test connection
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened In Airflow 2.6.1 I can no longer use the `extra` field in any `http` based connection when testing the connection. Inspecting the web request for testing the connection I see that the `extra` field is empty, even though I have data in there: ```json { "connection_id": "", "conn_type": "http", "extra": "{}" } ``` <img width="457" alt="image" src="https://github.com/apache/airflow/assets/6411855/d6bab951-5d03-4695-a397-8bf6989d93a7"> I saw [this issue](https://github.com/apache/airflow/issues/31330#issuecomment-1558315370) which seems related. It was closed because the opener worked around the issue by creating the connection in code instead of the Airflow UI. I couldn't find any other issues mentioning this problem. ### What you think should happen instead The `extra` field should be included in the test connection request. ### How to reproduce Create an `http` connection in the Airflow UI using at least version 2.6.1. Put any value in the `extra` field and test the connection while inspecting the network request. Notice that the `extra` field value is not supplied in the request. ### Operating System N/A ### Versions of Apache Airflow Providers N/A ### Deployment Astronomer ### Deployment details _No response_ ### Anything else If I had to guess, I think it might be related to [this PR](https://github.com/apache/airflow/pull/28583) where a json linter was added to the extra field. Saving the connection seems to work fine, just not testing it. ### 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/32890
https://github.com/apache/airflow/pull/35122
ef497bc3412273c3a45f43f40e69c9520c7cc74c
789222cb1378079e2afd24c70c1a6783b57e27e6
"2023-07-27T17:45:31Z"
python
"2023-10-23T15:18:00Z"
closed
apache/airflow
https://github.com/apache/airflow
32,877
["dev/README_RELEASE_AIRFLOW.md"]
Wrong version in Dockerfile
### Apache Airflow version 2.6.3 ### What happened I want to use `2.6.3` stable version of `Airflow`. I cloned the project and checkout on the `tags/2.6.3`. ```bash git checkout tags/2.6.3 -b my_custom_branch ``` After checkout I check the `Dockerfile` and there is what I see below: ```bash ARG AIRFLOW_VERSION="2.6.2" ``` Then I just download code as a `zip` [2.6.3 link](https://github.com/apache/airflow/releases/tag/2.6.3) and I see the same under `Dockerfile`. Does `AIRFLOW_VERSION` have a wrong value ? Thanks ! ### What you think should happen instead _No response_ ### How to reproduce I need confirmation that, version is definitely wrong under `Dockerfile`. ### 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/32877
https://github.com/apache/airflow/pull/32888
db8d737ad690b721270d0c2fd3a83f08d7ce5c3f
7ba7fb1173e55c24c94fe01f0742fd00cd9c0d82
"2023-07-27T07:47:07Z"
python
"2023-07-28T04:53:00Z"
closed
apache/airflow
https://github.com/apache/airflow
32,866
["airflow/providers/databricks/hooks/databricks.py", "airflow/providers/databricks/operators/databricks.py", "docs/apache-airflow-providers-databricks/operators/submit_run.rst", "tests/providers/databricks/operators/test_databricks.py"]
DatabricksSubmitRunOperator should accept a pipeline name for a pipeline_task
### Description It would be nice if we could give the DatabricksSubmitRunOperator a pipeline name instead of a pipeline_id for cases when you do not already know the pipeline_id but do know the name. I'm not sure if there's an easy way to fetch a pipeline_id. ### Use case/motivation Avoid hardcoding pipeline ID's, storing the ID's elsewhere, or fetching the pipeline list and filtering it manually if the pipeline name is known, but ID is not. ### 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/32866
https://github.com/apache/airflow/pull/32903
f7f3b675ecd40e32e458b71b5066864f866a60c8
c45617c4d5988555f2f52684e082b96b65ca6c17
"2023-07-26T15:33:05Z"
python
"2023-09-07T00:44:06Z"
closed
apache/airflow
https://github.com/apache/airflow
32,862
["airflow/jobs/triggerer_job_runner.py"]
Change log level of message for event loop block
### Description Currently, when the event loop is blocked for more than 0.2 seconds, an error message is logged to the Triggerer notifying the user that the async thread was blocked, likely due to a badly written trigger. The issue with this message is that there currently no support for async DB reads. So whenever a DB read is performed (for getting connection information etc.) the event loop is blocked for a short while (~0.3 - 0.8 seconds). This usually only happens once during a Trigger execution, and is not an issue at all in terms of performance. Based on our internal user testing, I noticed that this error message causes confusion for a lot of users who are new to Deferrable operators. As such, I am proposing that we change the log level of that message to `INFO` so that the message is retained, but does not cause confusion. Until a method is available that would allow us to read from the database asynchronously, there is nothing that can be done about the message. ### Use case/motivation ![image](https://github.com/apache/airflow/assets/103602455/201a41c7-ac76-4226-8d3a-7f83ccf7f146) I'd like the user to see this message as an INFO rather than an ERROR, because there it is not something that can be addressed at the moment, and it does not cause any noticeable impact to the user. ### 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/32862
https://github.com/apache/airflow/pull/32979
6ada88a407a91a3e1d42ab8a30769a4a6f55588b
9cbe494e231a5b2e92e6831a4be25802753f03e5
"2023-07-26T12:57:35Z"
python
"2023-08-02T10:23:08Z"
closed
apache/airflow
https://github.com/apache/airflow
32,840
["airflow/decorators/branch_python.py", "airflow/decorators/external_python.py", "airflow/decorators/python_virtualenv.py", "airflow/decorators/short_circuit.py"]
Usage of templates_dict in short_circuit decorated task
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened I believe that the `@task.short_circuit` operator is missing the code to handle the usage of `templates_dict`, as described in the [documentation](https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html#id4). The code snippet below demonstrates the issue. In it, I create two tasks. The first is a basic python task, and in the log, from the print statement, I see the contents of the `.sql` file. (So the templating worked correctly.) However, in the log for the second task, I see only the string itself, with no jinja templating performed. I think this contradicts the documentation. ```python from airflow.decorators import dag, task import datetime as dt @dag( dag_id='test_dag', schedule=None, start_date=dt.datetime(2023, 7, 25, 0, 0, 0, tzinfo=dt.timezone.utc), ) def test_dag(): @task(task_id='test_task', templates_dict={'query': 'sql/myquery.sql'}, templates_exts=['.sql']) def test_task(templates_dict=None): print(templates_dict['query']) @task.short_circuit(task_id='test_tasksc', templates_dict={'query': 'sql/myquery.sql'}, templates_exts=['.sql']) def test_tasksc(templates_dict=None): print(templates_dict['query']) return True test_task() >> test_tasksc() test_dag() ``` Output of first task: `SELECT * FROM ...`. Output of second task: `sql/myquery.sql`. As a guess, I think the problem could be in [this line](https://github.com/apache/airflow/blob/main/airflow/decorators/short_circuit.py#L41), where `templates_dict` and `templatex_ext` are not explicitly passed to the super-class's init function. I am happy to make an MR if it's that small of a change. ### What you think should happen instead Output of first task: `SELECT * FROM ...`. Output of second task: `SELECT * FROM ...`. ### How to reproduce See MRE above. ### Operating System RHEL 7.9 (Maipo) ### Versions of Apache Airflow Providers apache-airflow==2.5.0 No relevant providers. ### 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/32840
https://github.com/apache/airflow/pull/32845
2ab78ec441a748ae4d99e429fe336b80a601d7b1
8f12e7e4a9374e886965f3134aa801a5a267a36d
"2023-07-25T21:27:50Z"
python
"2023-07-31T20:15:24Z"
closed
apache/airflow
https://github.com/apache/airflow
32,839
["airflow/www/security.py", "docs/apache-airflow/security/access-control.rst", "tests/www/test_security.py"]
DAG-level permissions set in Web UI disappear from roles on DAG sync
### Apache Airflow version 2.6.3 ### What happened Versions: 2.6.2, 2.6.3, main PR [#30340](https://github.com/apache/airflow/pull/30340) introduced a bug that happens whenever a DAG gets updated or a new DAG is added **Potential fix:** Adding the code that was removed in PR [#30340](https://github.com/apache/airflow/pull/30340) back to `airflow/models/dagbag.py` fixes the issue. I've tried it on the current main branch using Breeze. ### What you think should happen instead Permissions set in Web UI stay whenever a DAG sync happens ### How to reproduce 1. Download `docker-compose.yaml`: ```bash curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.6.2/docker-compose.yaml' ``` 2. Create dirs and set the right Airflow user: ```bash mkdir -p ./dags ./logs ./plugins ./config && \ echo -e "AIRFLOW_UID=$(id -u)" > .env ``` 4. Add `test_dag.py` to ./dags: ```python import datetime import pendulum from airflow import DAG from airflow.operators.bash import BashOperator with DAG( dag_id="test", schedule="0 0 * * *", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, dagrun_timeout=datetime.timedelta(minutes=60), ) as dag: test = BashOperator( task_id="test", bash_command="echo 1", ) if __name__ == "__main__": dag.test() ``` 5. Run docker compose: `docker compose up` 6. Create role in Web UI: Security > List Roles > Add a new record: Name: test Permissions: `can read on DAG:test` 7. Update `test_dag.py`: change `bash_command="echo 1"` to `bash_command="echo 2"` 8. Check test role's permissions: `can read on DAG:test` will be removed Another option is to add a new dag instead of changing the existing one: 6. Add another dag to ./dags, code doesn't matter 7. Restart scheduler: `docker restart [scheduler container name]` 9. Check test role's permissions: `can read on DAG:test` will be removed ### Operating System Ubuntu 22.04.1 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details Docker 24.0.2 ### 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/32839
https://github.com/apache/airflow/pull/33632
83efcaa835c4316efe2f45fd9cfb619295b25a4f
370348a396b5ddfe670e78ad3ab87d01f6d0107f
"2023-07-25T19:13:12Z"
python
"2023-08-24T19:20:13Z"
closed
apache/airflow
https://github.com/apache/airflow
32,804
["airflow/utils/helpers.py"]
`DagRun.find()` fails when given `execution_date` obtained from `context.get('execution_date')` directly
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened #### Airflow version 2.5.3+composer (The latest available airflow version in Cloud Composer) #### What happened `DagRun.find()` (the `find` method of SQLAlchemy model `DagRun` ) fails with the error message `TypeError: 'DateTime' object is not iterable`, when passing `execution_date` that is directly obtained from context: ```py def delete_previous_dagrun_func(dag_to_delete, **context): execution_date = context.get('execution_date') dagruns_today = DagRun.find(dag_id=dag_to_delete.dag_id, execution_date=execution_date) ``` #### What is the cause Upon closer inspection, execution_date is of type `lazy_object_proxy.Proxy` and the `is_container()` function used in `DagRun.find()` determines if the variable is a "container" by the presence of the `__iter__` field. `lazy_object_proxy.Proxy` has `__iter__`, so the `execution_date` is determined to be a container, and as a result it is passed as an array element to SQLAlchemy, which caused the "not iterable" error. https://github.com/apache/airflow/blob/6313e5293280773aed7598e1befb8d371e8f5614/airflow/models/dagrun.py#L406-L409 https://github.com/apache/airflow/blob/6313e5293280773aed7598e1befb8d371e8f5614/airflow/utils/helpers.py#L117-L120 I think `is_container()` should have another conditional branch to deal with `lazy_object_proxy.Proxy`. #### workaround It works fine by unwrapping `lazy_object_proxy.Proxy` before passing it. ```py def delete_previous_dagrun_func(dag_to_delete, **context): execution_date = context.get('execution_date') dagruns_today = DagRun.find(dag_id=dag_to_delete.dag_id, execution_date=execution_date.__wrapped__) ``` https://github.com/ionelmc/python-lazy-object-proxy/blob/c56c68bda23b8957abbc2fef3d21f32dd44b7f93/src/lazy_object_proxy/simple.py#L76-L83 ### What you think should happen instead The variable fetched from `context.get('execution_date')` can be used directly as an argument of `DagRun.find()` ### How to reproduce ```py dag_to_delete = DAG('DAG_TO_DELETE') dag = DAG('DAG') def delete_previous_dagrun_func(dag_to_delete, **context): execution_date = context.get('execution_date') dagruns_today = DagRun.find(dag_id=dag_to_delete.dag_id, execution_date=execution_date) # do something for `dagruns_today` (Delete the dagruns for today) op = PythonOperator( task_id='Delete_Previous_DagRun', python_callable=delete_previous_dagrun_func, op_args=[dag_to_delete], provide_context=True, dag=dag) ``` This code produces the following error at `DagRun.find` line: ``` [2023-07-24, 19:09:06 JST] {taskinstance.py:1778} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/python3.8/lib/python3.8/site-packages/airflow/operators/python.py", line 210, in execute branch = super().execute(context) File "/opt/python3.8/lib/python3.8/site-packages/airflow/operators/python.py", line 175, in execute return_value = self.execute_callable() File "/opt/python3.8/lib/python3.8/site-packages/airflow/operators/python.py", line 192, in execute_callable return self.python_callable(*self.op_args, **self.op_kwargs) File "/home/airflow/gcs/dags/*******.py", line ***, in delete_previous_dagrun_func dagruns_today = DagRun.find(dag_id=dag_to_delete.dag_id, File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/dagrun.py", line 386, in find qry = qry.filter(cls.execution_date.in_(execution_date)) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 641, in in_ return self.operate(in_op, other) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/orm/attributes.py", line 317, in operate return op(self.comparator, *other, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 1423, in in_op return a.in_(b) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 641, in in_ return self.operate(in_op, other) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/orm/properties.py", line 426, in operate return op(self.__clause_element__(), *other, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 1423, in in_op return a.in_(b) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 641, in in_ return self.operate(in_op, other) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 870, in operate return op(self.comparator, *other, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 1423, in in_op return a.in_(b) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/operators.py", line 641, in in_ return self.operate(in_op, other) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/type_api.py", line 1373, in operate return super(TypeDecorator.Comparator, self).operate( File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/type_api.py", line 77, in operate return o[0](self.expr, op, *(other + o[1:]), **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/default_comparator.py", line 159, in _in_impl seq_or_selectable = coercions.expect( File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/coercions.py", line 193, in expect resolved = impl._literal_coercion( File "/opt/python3.8/lib/python3.8/site-packages/sqlalchemy/sql/coercions.py", line 573, in _literal_coercion element = list(element) TypeError: 'DateTime' object is not iterable ``` ### Operating System Google Cloud Composer (Ubuntu 20.04.6 LTS on Kubernetes) ### Versions of Apache Airflow Providers No relevant providers ### Deployment Google Cloud Composer ### Deployment details Google Cloud Composer image version: `composer-2.3.4-airflow-2.5.3` ### Anything else I have recently begun preparing to upgrade Airflow from 1.10 to Series 2.x. The code described in the reproduce section still works in Airflow 1.10 environment. I want to know it is intentional OR accidental incompatibility between 1.10 and 2.x. (If it is intentional, Adding more helpful error message should save time to resolve.) I am willing to submit a PR, if it is super easy (like changing 1 line of code). If it is not the case and I need to make a big change, I think I do not have enough time to make PR timely. ### 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/32804
https://github.com/apache/airflow/pull/32850
319045492d2559bd856a43a1fa810adf59358d7d
12228d16be13afb2918139ea3c5a285a23242bd0
"2023-07-24T12:35:00Z"
python
"2023-07-28T06:17:51Z"
closed
apache/airflow
https://github.com/apache/airflow
32,778
["airflow/jobs/backfill_job_runner.py", "tests/providers/daskexecutor/test_dask_executor.py"]
Flaky dask backfill test in quarantine
### Body We have recently started to observe a very flaky `tests/executors/test_dask_executor.py::TestDaskExecutor::test_backfill_integration` test - especially Python 3.8 + postgres 3.11 combo seems to trigger it easily -but not always. Example of failure here: https://github.com/apache/airflow/actions/runs/5632434844/job/15260418883?pr=32776 Example errors: ``` E psycopg2.errors.DeadlockDetected: deadlock detected E DETAIL: Process 604 waits for ShareLock on transaction 7154; blocked by process 690. E Process 690 waits for ShareLock on transaction 7152; blocked by process 604. E HINT: See server log for query details. E CONTEXT: while updating tuple (2,204) in relation "dag_run" ``` Details: ``` self = <sqlalchemy.dialects.postgresql.psycopg2.PGDialect_psycopg2 object at 0x7fd29cc25880> cursor = <cursor object at 0x7fd29c8589a0; closed: -1> statement = 'UPDATE dag_run SET last_scheduling_decision=%(last_scheduling_decision)s, updated_at=%(updated_at)s WHERE dag_run.id = %(dag_run_id)s' parameters = {'dag_run_id': 23, 'last_scheduling_decision': None, 'updated_at': datetime.datetime(2023, 7, 22, 19, 58, 26, 211427, tzinfo=Timezone('UTC'))} context = <sqlalchemy.dialects.postgresql.psycopg2.PGExecutionContext_psycopg2 object at 0x7fd27524c9a0> ``` ``` airflow/jobs/backfill_job_runner.py:914: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/utils/session.py:74: in wrapper return func(*args, **kwargs) airflow/jobs/backfill_job_runner.py:802: in _execute_dagruns processed_dag_run_dates = self._process_backfill_task_instances( airflow/jobs/backfill_job_runner.py:645: in _process_backfill_task_instances session.commit() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:1454: in commit self._transaction.commit(_to_root=self.future) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:832: in commit self._prepare_impl() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:811: in _prepare_impl self.session.flush() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3449: in flush self._flush(objects) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3589: in _flush transaction.rollback(_capture_exception=True) /usr/local/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70: in __exit__ compat.raise_( /usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py:211: in raise_ raise exception /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py:3549: in _flush flush_context.execute() /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py:456: in execute rec.execute(self) /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py:630: in execute util.preloaded.orm_persistence.save_obj( /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py:237: in save_obj _emit_update_statements( /usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py:1001: in _emit_update_statements c = connection._execute_20( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1710: in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) /usr/local/lib/python3.8/site-packages/sqlalchemy/sql/elements.py:334: in _execute_on_connection return connection._execute_clauseelement( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1577: in _execute_clauseelement ret = self._execute_context( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1953: in _execute_context self._handle_dbapi_exception( /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:2134: in _handle_dbapi_exception util.raise_( /usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py:211: in raise_ raise exception /usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py:1910: in _execute_context self.dialect.do_execute( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ``` Eventually failing ``` E sqlalchemy.exc.PendingRollbackError: This Session's transaction has been rolled back due to a previous exception during flush. To begin a new transaction with this Session, first issue Session.rollback(). Original exception was: (psycopg2.errors.DeadlockDetected) deadlock detected E DETAIL: Process 604 waits for ShareLock on transaction 7154; blocked by process 690. E Process 690 waits for ShareLock on transaction 7152; blocked by process 604. E HINT: See server log for query details. E CONTEXT: while updating tuple (2,204) in relation "dag_run" E E [SQL: UPDATE dag_run SET last_scheduling_decision=%(last_scheduling_decision)s, updated_at=%(updated_at)s WHERE dag_run.id = %(dag_run_id)s] E [parameters: {'last_scheduling_decision': None, 'updated_at': datetime.datetime(2023, 7, 22, 19, 58, 26, 211427, tzinfo=Timezone('UTC')), 'dag_run_id': 23}] E (Background on this error at: https://sqlalche.me/e/14/e3q8) (Background on this error at: https://sqlalche.me/e/14/7s2a) ``` Would be great to track it down. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32778
https://github.com/apache/airflow/pull/32991
a4903ee950c674ec9e999a7a6de2ffd6d334c525
f616ee8dff8e6ba9b37cbce0d22235dc47d4fc93
"2023-07-22T20:26:27Z"
python
"2023-08-09T11:00:48Z"
closed
apache/airflow
https://github.com/apache/airflow
32,761
["airflow/models/abstractoperator.py", "tests/serialization/test_dag_serialization.py"]
Extra links order is not predictable causing shuffling in UI during webserver restarts
### Apache Airflow version main (development) ### What happened Currently `extra_links` is a cached property that returns a list without any order since set is used. Since we have 3 links per operator this order gets shuffled during webserver restarts as reported by users. It would be good to have this sorted so that the order is predictable. This is already done in extra_links Airflow API output https://github.com/apache/airflow/blob/d7899ecfafb20cc58f8fb43e287d1c6778b8fa9f/airflow/models/abstractoperator.py#L470-L472 https://github.com/apache/airflow/blob/d7899ecfafb20cc58f8fb43e287d1c6778b8fa9f/airflow/api_connexion/endpoints/extra_link_endpoint.py#L75-L78 ### What you think should happen instead extra link order should be predictable ### How to reproduce 1. Create an operator with 3 or more extra links. 2. Render the links in UI. 3. Restart the webserver and check the extra link order. ### Operating System Ubuntu ### 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/32761
https://github.com/apache/airflow/pull/32762
3e467ba510d29e912d89115769726111b8bce891
4c878798ef88a1fa45956163630d71b6fc4f401f
"2023-07-22T06:42:40Z"
python
"2023-07-22T10:24:03Z"
closed
apache/airflow
https://github.com/apache/airflow
32,747
["airflow/www/app.py", "docs/apache-airflow/howto/set-config.rst", "docs/apache-airflow/security/webserver.rst"]
The application context is not passed to webserver_config.py
### Apache Airflow version 2.6.3 ### What happened Hi, I would like to pass a custom [WSGI Middleware](https://medium.com/swlh/creating-middlewares-with-python-flask-166bd03f2fd4) to the underlying flask server. I could theoretically do so in *webserver_config.py* by accessing `flask.current_app`: ```python # webserver_config.py from flask import current_app from airflow.www.fab_security.manager import AUTH_REMOTE_USER class MyAuthMiddleware: def __init__(self, wsgi_app) -> None: self.wsgi_app = wsgi_app def __call__(self, environ: dict, start_response): print("--> Custom authenticating logic") environ["REMOTE_USER"] = "username" return self.wsgi_app(environ, start_response) current_app.wsgi_app = MyAuthMiddleware(current_app.wsgi_app) AUTH_TYPE = AUTH_REMOTE_USER ``` But for this to work [the application context](https://flask.palletsprojects.com/en/2.3.x/appcontext/) should be pushed while reading the webserver config. Thus https://github.com/apache/airflow/blob/fbeddc30178eec7bddbafc1d560ff1eb812ae37a/airflow/www/app.py#L84 should become ```python with flask_app.app_context(): flask_app.config.from_pyfile(settings.WEBSERVER_CONFIG, silent=True) ``` ### 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/32747
https://github.com/apache/airflow/pull/32759
5a0494f83e8ad0e5cbf0d3dcad3022a3ea89d789
7847b6ead3c039726bb82e0de3a39e5ef5eb00aa
"2023-07-21T14:51:00Z"
python
"2023-08-08T07:00:12Z"
closed
apache/airflow
https://github.com/apache/airflow
32,744
["airflow/providers/cncf/kubernetes/operators/spark_kubernetes.py"]
Getting 410 from spark_kubernetes_operator when a task runs for long
### Apache Airflow version 2.6.3 ### What happened A spark job which runs for >1 hours gets a 410 Expired error regardless of what the actual output of the spark application was. Logs - ``` [2023-07-21, 19:41:57 IST] {spark_kubernetes.py:126} INFO - 2023-07-21T14:11:57.424338279Z 23/07/21 19:41:57 INFO MetricsSystemImpl: s3a-file-system metrics system stopped. [2023-07-21, 19:41:57 IST] {spark_kubernetes.py:126} INFO - 2023-07-21T14:11:57.424355219Z 23/07/21 19:41:57 INFO MetricsSystemImpl: s3a-file-system metrics system shutdown complete. [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-1] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-1] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-1] is running [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-1] completed [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-2] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-2] is running [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-3] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-3] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-4] is pending [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-4] is running [2023-07-21, 19:41:58 IST] {spark_kubernetes.py:117} INFO - Executor [adhoc-d9e5ed897882bd27-exec-3] is running [2023-07-21, 19:41:58 IST] {taskinstance.py:1824} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/operators/spark_kubernetes.py", line 112, in execute for event in namespace_event_stream: File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/watch/watch.py", line 182, in stream raise client.rest.ApiException( kubernetes.client.exceptions.ApiException: (410) Reason: Expired: The resourceVersion for the provided watch is too old. [2023-07-21, 19:41:58 IST] {taskinstance.py:1345} INFO - Marking task as FAILED. dag_id=adhoc, task_id=submit_job, execution_date=20230721T125127, start_date=20230721T125217, end_date=20230721T141158 [2023-07-21, 19:41:58 IST] {standard_task_runner.py:104} ERROR - Failed to execute job 247326 for task submit_job ((410) Reason: Expired: The resourceVersion for the provided watch is too old. ; 15) [2023-07-21, 19:41:58 IST] {local_task_job_runner.py:225} INFO - Task exited with return code 1 [2023-07-21, 19:41:58 IST] {taskinstance.py:2653} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` In this case, I am able to see the logs of the task and only the final state is wrongly reported. But in case the task runs for even longer (>4 hours) then even the logs are not seen. ### What you think should happen instead In the first case, the tasks should have reported the correct state of the spark application. In the second case, logs should have been still visible and the correct state of the spark application should have been reported ### How to reproduce To reproduce it, have a long running (>1 hour) task submitted using the SparkKubernetesOperator. If the task gets completed before 4 hours then you should see a 410 Expired error regardless of what the actual output of the spark application was. If the task takes longer, you should see the task fail around the 4 hour mark due to 410 even when the spark application is still running. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.2.0 apache-airflow-providers-celery==3.2.1 apache-airflow-providers-cncf-kubernetes==7.3.0 apache-airflow-providers-common-sql==1.5.2 apache-airflow-providers-docker==3.7.1 apache-airflow-providers-elasticsearch==4.5.1 apache-airflow-providers-ftp==3.4.2 apache-airflow-providers-google==10.2.0 apache-airflow-providers-grpc==3.2.1 apache-airflow-providers-hashicorp==3.4.1 apache-airflow-providers-http==4.4.2 apache-airflow-providers-imap==3.2.2 apache-airflow-providers-microsoft-azure==6.1.2 apache-airflow-providers-mysql==5.1.1 apache-airflow-providers-odbc==4.0.0 apache-airflow-providers-postgres==5.5.1 apache-airflow-providers-redis==3.2.1 apache-airflow-providers-sendgrid==3.2.1 apache-airflow-providers-sftp==4.3.1 apache-airflow-providers-slack==7.3.1 apache-airflow-providers-snowflake==4.2.0 apache-airflow-providers-sqlite==3.4.2 apache-airflow-providers-ssh==3.7.1 ### Deployment Official Apache Airflow Helm Chart ### Deployment details k8s version - 1.23 hosted using EKS python 3.8 I have upgraded the apache-airflow-providers-cncf-kubernetes to ensure that the bug has not been fixed in the newer versions. ### Anything else I think this issue is because of the kubernetes 'Watch().stream''s APIException not being handled. According to its docs - ``` Note that watching an API resource can expire. The method tries to resume automatically once from the last result, but if that last result is too old as well, an `ApiException` exception will be thrown with ``code`` 410. In that case you have to recover yourself, probably by listing the API resource to obtain the latest state and then watching from that state on by setting ``resource_version`` to one returned from listing. ``` this error needs to be handled by Airflow and not by the kubernetes client. ### 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/32744
https://github.com/apache/airflow/pull/32768
4c878798ef88a1fa45956163630d71b6fc4f401f
fcc6f284c742bdc554edecc5a83d9eaa7d9d7ba4
"2023-07-21T14:19:09Z"
python
"2023-07-22T11:32:53Z"
closed
apache/airflow
https://github.com/apache/airflow
32,732
["airflow/providers/amazon/aws/hooks/base_aws.py", "tests/providers/amazon/aws/hooks/test_base_aws.py"]
airflow.providers.amazong.aws.hooks.base_aws.BaseSessionFactory feeds synchronous credentials to aiobotocore when using `assume_role`
### Apache Airflow version 2.6.3 ### What happened Hi all, I'm having a bit of a problem with aiobotocore and the deferrable AWS Batch Operator. When deferrable is off, everything works fine, but for some very long running batch jobs I wanted to try out the async option. Example DAG: ```python from airflow.decorators import dag from airflow.providers.amazon.aws.operators.batch import BatchOperator from datetime import datetime, timedelta default_args = { "owner": "rkulkarni", ... } @dag( default_args=default_args, catchup=False, schedule="0 1/8 * * *", ) def batch_job_to_do(): submit_batch_job = BatchOperator( task_id="submit_batch_job", job_name="job_name", job_queue="job_queue", job_definition="job_definition:1", overrides={}, aws_conn_id="aws_prod_batch", region_name="us-east-1", awslogs_enabled=True, awslogs_fetch_interval=timedelta(seconds=30), deferrable=True ) submit_batch_job # type: ignore batch_job_to_do() ``` And, for reference, this is running in an EC2 instance in one account that assumes a role in another account via STS to submit the job. Again, this all works fine when deferrable=False If deferrable=True, however, the DAG works properly until it wakes up the first time. I've identified the cause of this error: https://github.com/apache/airflow/blob/15d42b4320d535cf54743929f134e36f59c615bb/airflow/providers/amazon/aws/hooks/base_aws.py#L211 and a related error: https://github.com/apache/airflow/blob/15d42b4320d535cf54743929f134e36f59c615bb/airflow/providers/amazon/aws/hooks/base_aws.py#L204 These should be creating `aiobotocore.credentials.AioRefreshableCredentials` and `aiobotocore.credentials.AioDeferredRefreshableCredentials`, respectively. I can confirm that replacing `session._session._credentials` attribute with these fixes the above error. I'm happy to submit a PR to resolve this. ### What you think should happen instead _No response_ ### How to reproduce Attempt to use STS-based authentication with a deferrable AWS operator (any operator) and it will produce the below error: ``` Traceback (most recent call last): File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 537, in cleanup_finished_triggers result = details["task"].result() File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 615, in run_trigger async for event in trigger.run(): File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/providers/amazon/aws/triggers/base.py", line 121, in run await async_wait( File "/home/airflow/dagger/venv/lib/python3.9/site-packages/airflow/providers/amazon/aws/utils/waiter_with_logging.py", line 122, in async_wait await waiter.wait(**args, WaiterConfig={"MaxAttempts": 1}) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/waiter.py", line 49, in wait await AIOWaiter.wait(self, **kwargs) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/waiter.py", line 94, in wait response = await self._operation_method(**kwargs) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/waiter.py", line 77, in __call__ return await self._client_method(**kwargs) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/client.py", line 361, in _make_api_call http, parsed_response = await self._make_request( File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/client.py", line 386, in _make_request return await self._endpoint.make_request( File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/endpoint.py", line 96, in _send_request request = await self.create_request(request_dict, operation_model) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/endpoint.py", line 84, in create_request await self._event_emitter.emit( File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/hooks.py", line 66, in _emit response = await resolve_awaitable(handler(**kwargs)) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/_helpers.py", line 15, in resolve_awaitable return await obj File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/signers.py", line 24, in handler return await self.sign(operation_name, request) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/signers.py", line 73, in sign auth = await self.get_auth_instance(**kwargs) File "/home/airflow/dagger/venv/lib/python3.9/site-packages/aiobotocore/signers.py", line 147, in get_auth_instance await self._credentials.get_frozen_credentials() TypeError: object ReadOnlyCredentials can't be used in 'await' expression ``` ### Operating System AmazonLinux2 ### Versions of Apache Airflow Providers _No response_ ### 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/32732
https://github.com/apache/airflow/pull/32733
43a5b4750590bf43bb59cc7bd8377934737f63e8
57f203251b223550d6e7bb717910109af9aeed29
"2023-07-21T01:10:11Z"
python
"2023-07-22T17:28:30Z"
closed
apache/airflow
https://github.com/apache/airflow
32,712
["airflow/providers/google/cloud/operators/bigquery.py", "tests/providers/google/cloud/operators/test_bigquery.py"]
bigquery hook for source_format="PARQUET"
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow version: 2.5.3 Description: While using BigQueryCreateExternalTableOperator, when we to create external table on top of parquet files located in GCS, airflow shows below error: google.api_core.exceptions.BadRequest: 400 POST ... failed. CsvOptions can only be specified if storage format is CSV. No csvOptions are mentioned while passing the source_format as Parquet. There seems to be an issue with bigquery.py hook. ### What you think should happen instead _No response_ ### How to reproduce While using BigQueryCreateExternalTableOperator, try to create external table on top of parquet files located in GCS. Sample usage: `create_external_table = BigQueryCreateExternalTableOperator( task_id="create_external_table", destination_project_dataset_table="table_name", bucket=GCS_BUCKET_NAME, source_objects=[<list of URIs of parquet files>], source_format="PARQUET", autodetect=True, gcp_conn_id="gcp_conn_id", google_cloud_storage_conn_id="test_conn_id", )` ### Operating System - ### 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/32712
https://github.com/apache/airflow/pull/33540
da8004dac8e51a7485637b70a788aad5a0f53c60
46fa5a2743c0c864f5282abd6055c5418585955b
"2023-07-20T09:23:58Z"
python
"2023-08-20T22:13:28Z"
closed
apache/airflow
https://github.com/apache/airflow
32,708
["Dockerfile", "Dockerfile.ci", "docs/docker-stack/build-arg-ref.rst", "docs/docker-stack/changelog.rst", "scripts/docker/install_mysql.sh"]
MYSQL_OPT_RECONNECT is deprecated. When exec airflow db upgrade.
### Apache Airflow version 2.6.3 ### What happened When I install airflow can set backend database, I set mysql as my backend. And I exec `airflow db upgrade` It shows many warning info contains "WARNING: MYSQL_OPT_RECONNECT is deprecated and will be removed in a future version." ### What you think should happen instead _No response_ ### How to reproduce mysql_config --version 8.0.34 mysql --version mysql Ver 8.0.34 for Linux on x86_64 (MySQL Community Server - GPL) setup airflow backend and run `airflow db upgrade` ### Operating System CentOS 7 ### 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/32708
https://github.com/apache/airflow/pull/35070
dcb72b5a4661223c9de7beea40264a152298f24b
1f26ae13cf974a0b2af6d8bc94c601d65e2bd98a
"2023-07-20T07:13:49Z"
python
"2023-10-24T08:54:09Z"
closed
apache/airflow
https://github.com/apache/airflow
32,706
["airflow/cli/commands/scheduler_command.py", "tests/cli/commands/test_scheduler_command.py"]
Scheduler becomes zombie when run_job raises unhandled exception
### Apache Airflow version 2.6.3 ### What happened # Context When the backend database shuts down (for maintenance, for example), Airflow scheduler's main scheduler loop crashes, but the scheduler process does not exit. In my company's setup, the scheduler process is monitored by `supervisord`, but since the scheduler process does not exit, `supervisord` did not pick up on the scheduler failure, causing prolonged scheduler outage. # Root cause In the `airflow/cli/commands/scheduler_command.py`, the main function call of the `airflow scheduler` command is the `_run_scheduler_job` function. When the `_run_scheduler_job` function is called, depending on the configuration, two sub-processes `serve_logs` and/or `health_check` may be started. The life cycle of these two sub-processes are managed by a context manager, so that when the context exits, the two sub-processes are terminated by the context managers: ```python def _run_scheduler_job(job_runner: SchedulerJobRunner, *, skip_serve_logs: bool) -> None: InternalApiConfig.force_database_direct_access() enable_health_check = conf.getboolean("scheduler", "ENABLE_HEALTH_CHECK") with _serve_logs(skip_serve_logs), _serve_health_check(enable_health_check): run_job(job=job_runner.job, execute_callable=job_runner._execute) @contextmanager def _serve_logs(skip_serve_logs: bool = False): """Starts serve_logs sub-process.""" from airflow.utils.serve_logs import serve_logs sub_proc = None executor_class, _ = ExecutorLoader.import_default_executor_cls() if executor_class.serve_logs: if skip_serve_logs is False: sub_proc = Process(target=serve_logs) sub_proc.start() yield if sub_proc: sub_proc.terminate() @contextmanager def _serve_health_check(enable_health_check: bool = False): """Starts serve_health_check sub-process.""" sub_proc = None if enable_health_check: sub_proc = Process(target=serve_health_check) sub_proc.start() yield if sub_proc: sub_proc.terminate() ``` The mis-behavior happens when `run_job` raises unhandled exception. The exception takes over the control flow, and the context managers will not properly exit. When the main Python process tries to exit, the `multiprocessing` module tries to terminate all child processes (https://github.com/python/cpython/blob/1e1f4e91a905bab3103250a3ceadac0693b926d9/Lib/multiprocessing/util.py#L320C43-L320C43) by first calling `join()`. Because the sub-processes `serve_logs` and/or `health_check` are never terminated, calling `join()` on them will hang indefinitely, thus causing the zombie state. Note that this behavior was introduced since 2.5.0 (2.4.3 does not have this issue) when the two sub-processes are not managed with context manager, and the scheduler job is placed inside a try-catch-finally block. ### What you think should happen instead The scheduler process should never hang. If something went wrong, such as a database disconnect, the scheduler should simply crash, and let whoever manages the scheduler process handle respawn. As to how this should be achieved, I think the best way is to place `run_job` inside a try-catch block so that any exception can be caught and gracefully handled, although I am open to feedback. ### How to reproduce # To reproduce the scheduler zombie state Start an Airflow cluster with breeze: ``` breeze start-airflow --python 3.9 --backend postgres [with any version at or later than 2.5.0] ``` After the command opens the `tmux` windows, stop the `postgres` container `docker stop docker-compose-postgres-1` The webserver will not do anything. The triggerer should correctly crash and exit. The scheduler will crash but not exit. # To reproduce the context manager's failure to exit ```python from multiprocessing import Process from contextlib import contextmanager import time def busybox(): time.sleep(24 * 3600) # the entire day @contextmanager def some_resource(): subproc = Process(target=busybox) subproc.start() print(f"Sub-process {subproc} started") yield subproc.terminate() subproc.join() print(f"Sub-process {subproc} terminated") def main(): with some_resource(): raise Exception("Oops") if __name__ == "__main__": main() ``` ### Operating System MacOS with Docker Desktop ### 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? - [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/32706
https://github.com/apache/airflow/pull/32707
9570cb1482d25f288e607aaa1210b2457bc5ed12
f2108892e89085f695f8a3f52e076b39288497c6
"2023-07-19T23:58:15Z"
python
"2023-07-25T22:02:01Z"
closed
apache/airflow
https://github.com/apache/airflow
32,702
["airflow/providers/amazon/aws/operators/sagemaker.py", "docs/apache-airflow-providers-amazon/operators/sagemaker.rst", "tests/providers/amazon/aws/operators/test_sagemaker_notebook.py", "tests/system/providers/amazon/aws/example_sagemaker_notebook.py"]
Support for SageMaker Notebook Operators
### Description Today, Amazon provider package supports SageMaker operators for a few operations, like training, tuning, pipelines, but it lacks the support for SageMaker Notebook instances. Boto3 provides necessary APIs to [create notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_notebook_instance.html), [start notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/start_notebook_instance.html), [stop network instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/stop_notebook_instance.html) and [delete notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/delete_notebook_instance.html). Leveraging these APIs, we should add new operators to SageMaker set under Amazon provider. At the same time, having a sensor (synchronous as well as deferrable) for notebook instance execution that utilizes [describe notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_notebook_instance.html) and waits for Stopped/Failed status would help with observability of the execution. ### Use case/motivation Data Scientists are orchestrating ML use cases via Apache Airflow. A key component of ML use cases is running Jupyter Notebook on SageMaker. Having built-in operators and sensors would make it easy for Airflow users to run Notebook instances on SageMaker. ### 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/32702
https://github.com/apache/airflow/pull/33219
45d5f6412731f81002be7e9c86c11060394875cf
223b41d68f53e7aa76588ffb8ba1e37e780d9e3b
"2023-07-19T19:27:24Z"
python
"2023-08-16T16:53:33Z"
closed
apache/airflow
https://github.com/apache/airflow
32,657
["airflow/migrations/versions/0131_2_8_0_make_connection_login_password_text.py", "airflow/models/connection.py", "airflow/utils/db.py", "docs/apache-airflow/img/airflow_erd.sha256", "docs/apache-airflow/img/airflow_erd.svg", "docs/apache-airflow/migrations-ref.rst"]
Increase connections HTTP login length to 5000 characters
### Description The current length limit for the `login` parameter in an HTTP connection is 500 characters. It'd be nice if this was 5000 characters like the `password` parameter. ### Use case/motivation We've run into an issue with an API we need to integrate with. It uses basic HTTP authentication, and both username and password are about 900 characters long each. We don't have any control over this API, so we cannot change the authentication method, nor the length of these usernames and passwords. ### 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/32657
https://github.com/apache/airflow/pull/32815
a169cf2c2532a8423196c8d98eede86029a9de9a
8e38c5a4d74b86af25b018b19f7a7d90d3e7610f
"2023-07-17T17:20:44Z"
python
"2023-09-26T17:00:36Z"
closed
apache/airflow
https://github.com/apache/airflow
32,622
["airflow/decorators/base.py", "tests/decorators/test_python.py"]
When multiple-outputs gets None as return value it crashes
### Body Currently when you use multiple-outputs in decorator and it gets None value, it crashes. As explained in https://github.com/apache/airflow/issues/32553 and workaround for ShortCircuitOperator has been implemented here: https://github.com/apache/airflow/pull/32569 But a more complete fix for multiple-outputs handling None is needed. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32622
https://github.com/apache/airflow/pull/32625
ea0deaa993674ad0e4ef777d687dc13809b0ec5d
a5dd08a9302acca77c39e9552cde8ef501fd788f
"2023-07-15T07:25:42Z"
python
"2023-07-16T14:31:32Z"
closed
apache/airflow
https://github.com/apache/airflow
32,621
["docs/apache-airflow-providers-apache-beam/operators.rst"]
Apache beam operators that submits to Dataflow requires gcloud CLI
### What do you see as an issue? It's unclear in the [apache beam provider documentation](https://airflow.apache.org/docs/apache-airflow-providers-apache-beam/stable/index.html) about [Apache Beam Operators](https://airflow.apache.org/docs/apache-airflow-providers-apache-beam/stable/operators.html) that the operators require gcloud CLI. For example, `BeamRunPythonPipelineOperator` calls [provide_authorized_gcloud](https://github.com/apache/airflow/blob/providers-apache-beam/5.1.1/airflow/providers/apache/beam/operators/beam.py#L303C41-L303C66) which executes a [bash command that uses gcloud](https://github.com/apache/airflow/blob/main/airflow/providers/google/common/hooks/base_google.py#L545-L552). ### Solving the problem A callout box in the apache beam provider documentation would be very helpful. Something like this [callout](https://airflow.apache.org/docs/apache-airflow-providers-google/10.3.0/operators/cloud/dataflow.html) in the google provider documentation. ``` This operator requires gcloud command (Google Cloud SDK) must be installed on the Airflow worker <https://cloud.google.com/sdk/docs/install>`__ ``` ### 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/32621
https://github.com/apache/airflow/pull/32663
f6bff828af28a9f7f25ef35ec77da4ca26388258
52d932f659d881a0b17bc1c1ba7e7bfd87d45848
"2023-07-15T07:13:45Z"
python
"2023-07-18T11:22:58Z"
closed
apache/airflow
https://github.com/apache/airflow
32,590
["chart/templates/_helpers.yaml", "chart/templates/secrets/metadata-connection-secret.yaml", "chart/templates/workers/worker-kedaautoscaler.yaml", "chart/values.schema.json", "chart/values.yaml", "helm_tests/other/test_keda.py"]
When using KEDA and pgbouncer together, KEDA logs repeated prepared statement errors
### Official Helm Chart version 1.10.0 (latest released) ### Apache Airflow version 2.6.2 ### Kubernetes Version v1.26.5-gke.1200 ### Helm Chart configuration values.pgbouncer.enabled: true workers.keda.enabled: true And configure a postgres database of any sort. ### Docker Image customizations _No response_ ### What happened If KEDA is enabled in the helm chart, and pgbouncer is also enabled, KEDA will be configured to use the connection string from the worker pod to connect to the postgres database. That means it will connect to pgbouncer. Pgbouncer is configured in transaction pool mode according to the secret: [pgbouncer] pool_mode = transaction And it appears that KEDA uses prepared statements in it's queries to postgres, resulting in numerous repeated errors in the KEDA logs: ``` 2023-07-13T18:21:35Z ERROR postgresql_scaler could not query postgreSQL: ERROR: prepared statement "stmtcache_47605" does not exist (SQLSTATE 26000) {"type": "ScaledObject", "namespace": "airflow-sae-int", "name": "airflow-sae-int-worker", "error": "ERROR: prepared statement \"stmtcache_47605\" does not exist (SQLSTATE 26000)"} ``` Now KEDA still works, as it does the query again without the prepared statement, but this is not ideal and results in a ton of error logging. ### What you think should happen instead I suggest having the KEDA connection go direct to the upstream configured postgresql server, as it's only one connection, instead of using pgbouncer. ### How to reproduce Enabled KEDA for workers, and pgbouncer at the same time. ### 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/32590
https://github.com/apache/airflow/pull/32608
51052bbbce159340e962e9fe40b6cae6ce05ab0c
f7ad549f2d7119a6496e3e66c43f078fbcc98ec1
"2023-07-13T18:25:32Z"
python
"2023-07-15T20:52:38Z"
closed
apache/airflow
https://github.com/apache/airflow
32,585
["airflow/providers/apache/kafka/triggers/await_message.py"]
Commit failed: Local: No offset stored while using AwaitMessageTriggerFunctionSensor
### Apache Airflow version 2.6.3 ### What happened While trying to use AwaitMessageTriggerFunctionSensor i'm increasing count of dagruns. I've encountered an exception `cimpl.KafkaException: KafkaError{code=_NO_OFFSET,val=-168,str="Commit failed: Local: No offset stored"}`. I tried to set consumers count less, equal and more than partitions but every time the error happened. Here is a log: ```[2023-07-13, 14:37:07 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [queued]> [2023-07-13, 14:37:07 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [queued]> [2023-07-13, 14:37:07 UTC] {taskinstance.py:1308} INFO - Starting attempt 1 of 1 [2023-07-13, 14:37:07 UTC] {taskinstance.py:1327} INFO - Executing <Task(AwaitMessageTriggerFunctionSensor): await_message> on 2023-07-13 14:35:00+00:00 [2023-07-13, 14:37:07 UTC] {standard_task_runner.py:57} INFO - Started process 8918 to run task [2023-07-13, 14:37:07 UTC] {standard_task_runner.py:84} INFO - Running: ['airflow', 'tasks', 'run', 'kafka_test_dag', 'await_message', 'scheduled__2023-07-13T14:35:00+00:00', '--job-id', '629111', '--raw', '--subdir', 'DAGS_FOLDER/dags/kafka_consumers_dag.py', '--cfg-path', '/tmp/tmp3de57b65'] [2023-07-13, 14:37:07 UTC] {standard_task_runner.py:85} INFO - Job 629111: Subtask await_message [2023-07-13, 14:37:08 UTC] {task_command.py:410} INFO - Running <TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [running]> on host airflow-worker-1.airflow-worker.syn-airflow-dev.svc.opus.s.mesh [2023-07-13, 14:37:08 UTC] {taskinstance.py:1545} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='airflow' AIRFLOW_CTX_DAG_ID='kafka_test_dag' AIRFLOW_CTX_TASK_ID='await_message' AIRFLOW_CTX_EXECUTION_DATE='2023-07-13T14:35:00+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='scheduled__2023-07-13T14:35:00+00:00' [2023-07-13, 14:37:09 UTC] {taskinstance.py:1415} INFO - Pausing task as DEFERRED. dag_id=kafka_test_dag, task_id=await_message, execution_date=20230713T143500, start_date=20230713T143707 [2023-07-13, 14:37:09 UTC] {local_task_job_runner.py:222} INFO - Task exited with return code 100 (task deferral) [2023-07-13, 14:38:43 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=non-requeueable deps ti=<TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [queued]> [2023-07-13, 14:38:43 UTC] {taskinstance.py:1103} INFO - Dependencies all met for dep_context=requeueable deps ti=<TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [queued]> [2023-07-13, 14:38:43 UTC] {taskinstance.py:1306} INFO - Resuming after deferral [2023-07-13, 14:38:44 UTC] {taskinstance.py:1327} INFO - Executing <Task(AwaitMessageTriggerFunctionSensor): await_message> on 2023-07-13 14:35:00+00:00 [2023-07-13, 14:38:44 UTC] {standard_task_runner.py:57} INFO - Started process 9001 to run task [2023-07-13, 14:38:44 UTC] {standard_task_runner.py:84} INFO - Running: ['airflow', 'tasks', 'run', 'kafka_test_dag', 'await_message', 'scheduled__2023-07-13T14:35:00+00:00', '--job-id', '629114', '--raw', '--subdir', 'DAGS_FOLDER/dags/kafka_consumers_dag.py', '--cfg-path', '/tmp/tmpo6xz234q'] [2023-07-13, 14:38:44 UTC] {standard_task_runner.py:85} INFO - Job 629114: Subtask await_message [2023-07-13, 14:38:45 UTC] {task_command.py:410} INFO - Running <TaskInstance: kafka_test_dag.await_message scheduled__2023-07-13T14:35:00+00:00 [running]> on host airflow-worker-1.airflow-worker.airflow-dev.svc.opus.s.mesh [2023-07-13, 14:38:46 UTC] {taskinstance.py:1598} ERROR - Trigger failed: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.11/site-packages/airflow/jobs/triggerer_job_runner.py", line 537, in cleanup_finished_triggers result = details["task"].result() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/jobs/triggerer_job_runner.py", line 615, in run_trigger async for event in trigger.run(): File "/home/airflow/.local/lib/python3.11/site-packages/airflow/providers/apache/kafka/triggers/await_message.py", line 114, in run await async_commit(asynchronous=False) File "/home/airflow/.local/lib/python3.11/site-packages/asgiref/sync.py", line 479, in __call__ ret: _R = await loop.run_in_executor( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/asgiref/sync.py", line 538, in thread_handler return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ cimpl.KafkaException: KafkaError{code=_NO_OFFSET,val=-168,str="Commit failed: Local: No offset stored"} [2023-07-13, 14:38:47 UTC] {taskinstance.py:1824} ERROR - Task failed with exception airflow.exceptions.TaskDeferralError: Trigger failure [2023-07-13, 14:38:47 UTC] {taskinstance.py:1345} INFO - Marking task as FAILED. dag_id=kafka_test_dag, task_id=await_message, execution_date=20230713T143500, start_date=20230713T143707, end_date=20230713T143847 [2023-07-13, 14:38:48 UTC] {standard_task_runner.py:104} ERROR - Failed to execute job 629114 for task await_message (Trigger failure; 9001) [2023-07-13, 14:38:48 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 1 [2023-07-13, 14:38:48 UTC] {taskinstance.py:2653} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you think should happen instead Sensor should get a message without errors. Each message should be committed once. ### How to reproduce Example of a DAG: ``` from airflow.decorators import dag from airflow.models import Variable from airflow.operators.trigger_dagrun import TriggerDagRunOperator from airflow.utils.dates import days_ago from airflow.providers.apache.kafka.sensors.kafka import \ AwaitMessageTriggerFunctionSensor import uuid def check_message(message): if message: return True def trigger_dag(**context): TriggerDagRunOperator( trigger_dag_id='triggerer_test_dag', task_id=f"triggered_downstream_dag_{uuid.uuid4()}" ).execute(context) @dag( description="This DAG listens kafka topic and triggers DAGs " "based on received message.", schedule_interval='* * * * *', start_date=days_ago(2), max_active_runs=4, catchup=False ) def kafka_test_dag(): AwaitMessageTriggerFunctionSensor( task_id="await_message", topics=['my_test_topic'], apply_function="dags.kafka_consumers_dag.check_message", event_triggered_function=trigger_dag ) kafka_test_dag() ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-apache-kafka==1.1.2 ### Deployment Other 3rd-party 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/32585
https://github.com/apache/airflow/pull/36272
41096e0c266e3adb0ac3985d2609701f53aded00
148233a19ea68f424a7077d3bba6e6ca81679c10
"2023-07-13T14:47:38Z"
python
"2023-12-18T10:22:49Z"
closed
apache/airflow
https://github.com/apache/airflow
32,553
["airflow/operators/python.py", "tests/decorators/test_python.py", "tests/decorators/test_short_circuit.py", "tests/operators/test_python.py"]
ShortCircuitOperator returns None when condition is Falsy which errors with multiple_outputs=True
### Apache Airflow version 2.6.3 ### What happened When the condition in ```ShortCircuitOperator``` is truthy it is returned at https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/operators/python.py#L252 but when it is falsy, the function falls through without returning anything. If ```multiple_outputs``` is set ```true``` (with for example ```@task.short_circuit(multiple_outputs=True```) ```_handle_output``` of ```DecoratedOperator``` at https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/decorators/base.py#L242 does not test for None and raises at https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/decorators/base.py#L255 This makes it impossible to pass a falsy value (i.e. an empty dictionary) so ```ShortCircuitOperator``` is unusable with ```multiple_outputs=true``` ### What you think should happen instead Probably the ```xcom_push``` should not be attempted with ```None```, or possibly the condition should be returned by ```ShortCircuitOperator``` even if it is falsy ### How to reproduce ```python @task.short_circuit(multiple_outputs=True) def test(): return {} ``` ### Operating System Ubuntu 22.04.2 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/32553
https://github.com/apache/airflow/pull/32569
9b466bd13dd34d2a37b49687241f54f4d2df3b18
32a18d9e4373bd705087992d0066663833c65abd
"2023-07-12T11:46:53Z"
python
"2023-07-15T07:21:40Z"
closed
apache/airflow
https://github.com/apache/airflow
32,551
["airflow/providers/snowflake/operators/snowflake.py", "tests/providers/snowflake/operators/test_snowflake.py"]
SnowflakeValueCheckOperator - database, warehouse, schema parameters doesn't ovveride connection
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We are using Airflow 2.6.0 with Airflow Snowflake Provider 4.3.0. When we add database, schema and warehouse parameters to SnowflakeOperator all are overriding extra part of Snowflake connection definition. Same set of parameters in SnowflakeValueCheckOperator none of parameter is overriden. ### What you think should happen instead When we go through Snowflake Provider source code we found, that for SnowflakeOperator hooks_params are created before parent class init. it is looked like: ``` if any([warehouse, database, role, schema, authenticator, session_parameters]): hook_params = kwargs.pop("hook_params", {}) kwargs["hook_params"] = { "warehouse": warehouse, "database": database, "role": role, "schema": schema, "authenticator": authenticator, "session_parameters": session_parameters, **hook_params, } super().__init__(conn_id=snowflake_conn_id, **kwargs) ``` For SnowflakeValueCheckOperator parent class init is added before initialization of class arguments: ``` super().__init__(sql=sql, parameters=parameters, conn_id=snowflake_conn_id, **kwargs) self.snowflake_conn_id = snowflake_conn_id self.sql = sql self.autocommit = autocommit self.do_xcom_push = do_xcom_push self.parameters = parameters self.warehouse = warehouse self.database = database ``` Probably hook that is used in SnowflakeValueCheckOperator (and probably in the rest of classes) is initiated base on connection values and overriding is not working. ### How to reproduce We should create connection with different database and warehouse than TEST_DB and TEST_WH. Table dual should exist only in TEST_DB.TEST_SCHEMA and not exists in connection db/schema. ``` from pathlib import Path from datetime import timedelta, datetime from time import time, sleep from airflow import DAG from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator from airflow.providers.snowflake.operators.snowflake import SnowflakeValueCheckOperator warehouse = 'TEST_WH' database ='TEST_DB' schema = 'TEST_SCHEMA' args = { 'owner': 'airflow', 'depends_on_past': False, 'email_on_failure': True, 'email_on_retry': False, 'start_date': pendulum.now(tz='Europe/Warsaw').add(months=-1), 'retries': 0, 'concurrency': 10, 'dagrun_timeout': timedelta(hours=24) } with DAG( dag_id=dag_id, template_undefined=jinja2.Undefined, default_args=args, description='Sequence ' + sequence_id, schedule=schedule, max_active_runs=10, catchup=False, tags=tags ) as dag: value_check_task = SnowflakeValueCheckOperator( task_id='value_check_task', sql='select 1 from dual', snowflake_conn_id ='con_snowflake_zabka', warehouse=warehouse, database=database, schema=schema, pass_value=1 ) snowflake_export_data_task = SnowflakeOperator( task_id='snowflake_export_data_task', snowflake_conn_id='con_snowflake', sql=f"select 1 from dual", warehouse=warehouse, database=database, schema=schema ) ``` ### Operating System Ubuntu 20.04.5 LTS ### Versions of Apache Airflow Providers apache-airflow 2.6.0 apache-airflow-providers-celery 3.1.0 apache-airflow-providers-common-sql 1.4.0 apache-airflow-providers-ftp 3.3.1 apache-airflow-providers-http 4.3.0 apache-airflow-providers-imap 3.1.1 apache-airflow-providers-microsoft-azure 6.1.1 apache-airflow-providers-odbc 3.2.1 apache-airflow-providers-oracle 3.6.0 apache-airflow-providers-postgres 5.4.0 apache-airflow-providers-redis 3.1.0 apache-airflow-providers-snowflake 4.3.0 apache-airflow-providers-sqlite 3.3.2 apache-airflow-providers-ssh 3.6.0 ### Deployment Virtualenv installation ### Deployment details Python 3.9.5 ### 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/32551
https://github.com/apache/airflow/pull/32605
2b0d88e450f11af8e447864ca258142a6756126d
2ab78ec441a748ae4d99e429fe336b80a601d7b1
"2023-07-12T11:00:55Z"
python
"2023-07-31T19:21:00Z"
closed
apache/airflow
https://github.com/apache/airflow
32,503
["airflow/www/utils.py", "tests/www/views/test_views_tasks.py"]
execution date is missing from Task Instance tooltip
### Apache Airflow version main (development) ### What happened It seems [this](https://github.com/apache/airflow/commit/e16207409998b38b91c1f1697557d5c229ed32d1) commit has made the task instance execution date disappear from the task instance tooltip completely: ![image](https://github.com/apache/airflow/assets/18099224/62c9fec5-9e02-4319-93b9-197d25a8b027) Note the missing `Run: <execution date>` between Task_id and Run_id. I think there's a problem with the task instance execution date, because it's always `undefined`. In an older version of Airflow (2.4.3), I can see that the tooltip always shows the **current** datetime instead of the actual execution date, which is what the author of the commit identified in the first place I think. ### What you think should happen instead The tooltip should properly show the task instance's execution date, not the current datetime (or nothing). There's a deeper problem here that causes `ti.execution_date` to be `undefined`. ### How to reproduce Run the main branch of Airflow, with a simple DAG that finishes a run successfully. Go to the Graph view of a DAG and hover over any completed task with the mouse. ### Operating System Ubuntu 22.04 ### 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/32503
https://github.com/apache/airflow/pull/32527
58e21c66fdcc8a416a697b4efa852473ad8bd6fc
ed689f2be90cc8899438be66e3c75c3921e156cb
"2023-07-10T21:02:35Z"
python
"2023-07-25T06:53:10Z"
closed
apache/airflow
https://github.com/apache/airflow
32,499
["airflow/providers/google/cloud/hooks/dataproc.py", "airflow/providers/google/cloud/operators/dataproc.py", "tests/providers/google/cloud/hooks/test_dataproc.py", "tests/providers/google/cloud/operators/test_dataproc.py"]
Add filtering to DataprocListBatchesOperator
### Description The Python Google Cloud Dataproc API version has been updated in Airflow to support filtering on the Dataproc Batches API. The DataprocListBatchesOperator can be updated to make use of this filtering. Currently, DataprocListBatchesOperator returns, and populates xcom with every job run in the project. This almost surely will fail as the return object is large and xcom storage is low, especially with MySQL. Filtering on job prefix and create_time are much more useful capabilities. ### Use case/motivation The ability to filter lists of GCP Dataproc Batches jobs. ### Related issues None ### 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/32499
https://github.com/apache/airflow/pull/32500
3c14753b03872b259ce2248eda92f7fb6f4d751b
99b8a90346b8826756ac165b73464a701e2c33aa
"2023-07-10T19:47:11Z"
python
"2023-07-20T18:24:22Z"
closed
apache/airflow
https://github.com/apache/airflow
32,491
["BREEZE.rst", "dev/breeze/src/airflow_breeze/commands/release_management_commands.py", "dev/breeze/src/airflow_breeze/commands/release_management_commands_config.py", "dev/breeze/src/airflow_breeze/utils/add_back_references.py", "images/breeze/output-commands-hash.txt", "images/breeze/output_release-management.svg", "images/breeze/output_release-management_add-back-references.svg", "images/breeze/output_setup_check-all-params-in-groups.svg", "images/breeze/output_setup_regenerate-command-images.svg"]
Implement `breeze publish-docs` command
### Body We need a small improvement for our docs publishing process. We currently have those two scripts: * [x] docs/publish_docs.py https://github.com/apache/airflow/blob/main/docs/publish_docs.py in airflow repo * [ ] post-docs/ in airflow-site https://github.com/apache/airflow-site/blob/main/post-docs/add-back-references.py We have currently the steps that are describing how to publish the documentation in our release documentation: * https://github.com/apache/airflow/blob/main/dev/README_RELEASE_AIRFLOW.md * https://github.com/apache/airflow/blob/main/dev/README_RELEASE_PROVIDER_PACKAGES.md * https://github.com/apache/airflow/blob/main/dev/README_RELEASE_HELM_CHART.md This is the "Publish documentation" chapter They currently consists of few steps: 1) checking out the main in "airflow-sites" 2) setting the AIRFLOW_SITE_DIRECTORY env variable to the checked out repo 3) building docs (with `breeze build-docs`) 4) Running publish_docs.py scripts in docs that copies the generated docs to "AIRFLOW_SITE_DIRECTORY" 5) **I just added those** running post-docs post-processing for back references 6) Commiting the changes and pushing them to airflow-site (there are few variants of those depends what docs you are building). The problem with that is that it requires several venvs to setup independently (and they might sometimes miss stuff) and those commands are distributed across repositories. The goal of the change is to replace publish + post-docs with single, new breeze command - similarly as we have "build-docs" now. I imagine this command should be similar to: ``` breeze publish-docs --airflow-site-directory DIRECTORY --package-filter .... and the rest of other arguments that publish_docs.py has ``` This command should copy the files and run post-processing on back-references (depending which package documentation we publish). Then the process of publish docs should like: 1) checking out the main in "airflow-sites" 2) setting the AIRFLOW_SITE_DIRECTORY env variable to the checked out repo 3) building docs (with `breeze build-docs`) 4) publishing docs (with `breeze publish-docs`) 5) Commiting the changes and pushing them to airflow-site The benefits: * no separate venvs to manage (all done in breeze's env) - automatically manged * nicer integration in our dev/CI environment * all code for publishing docs in one place - in breeze ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32491
https://github.com/apache/airflow/pull/32594
1a1753c7246a2b35b993aad659f5551afd7e0215
945f48a1fdace8825f3949e5227bed0af2fd38ff
"2023-07-10T13:20:05Z"
python
"2023-07-14T16:36:14Z"
closed
apache/airflow
https://github.com/apache/airflow
32,458
["airflow/providers/cncf/kubernetes/operators/pod.py", "airflow/providers/cncf/kubernetes/triggers/pod.py", "tests/providers/cncf/kubernetes/operators/test_pod.py", "tests/providers/cncf/kubernetes/triggers/test_pod.py", "tests/providers/google/cloud/triggers/test_kubernetes_engine.py"]
Deferrable KPO - stuck with do_xcom_push=True
### Apache Airflow version 2.6.2 ### What happened The version 7.2.0 of the cncf-kubernetes is never setting the task in success if you use the KPO with do_xcom_push and deferrable at true the sidecar airflow-xcom-sidecar is in running state in K8S and the operator log ```log {taskinstance.py:1308} INFO - Starting attempt 1 of 1 {taskinstance.py:1327} INFO - Executing <Mapped(KubernetesPodOperator): task-one> on 2023-07-09 15:01:01.455462+00:00 {standard_task_runner.py:57} INFO - Started process 157 to run task {standard_task_runner.py:84} INFO - Running: ['airflow', 'tasks', 'run', 'kubernetes_dag', 'task-one', 'manual__2023-07-09T15:01:01.455462+00:00', '--job-id', '8', '--raw', '--subdir', 'DAGS_FOLDER/kubernetes_dag.py', '--cfg-path', '/tmp/tmpve9e4m0j', '--map-index', '0'] {standard_task_runner.py:85} INFO - Job 8: Subtask task-one {task_command.py:410} INFO - Running <TaskInstance: kubernetes_dag.task-one manual__2023-07-09T15:01:01.455462+00:00 map_index=0 [running]> on host efa1d3dea00b {logging_mixin.py:149} WARNING - /home/airflow/.local/lib/python3.11/site-packages/airflow/models/mappedoperator.py:615 AirflowProviderDeprecationWarning: `is_delete_operator_pod` parameter is deprecated, please use `on_finish_action` {taskinstance.py:1545} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='airflow' AIRFLOW_CTX_DAG_ID='kubernetes_dag' AIRFLOW_CTX_TASK_ID='task-one' AIRFLOW_CTX_EXECUTION_DATE='2023-07-09T15:01:01.455462+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='manual__2023-07-09T15:01:01.455462+00:00' {pod.py:878} INFO - Building pod airflow-test-pod-q3ez8146 with labels: {'dag_id': 'kubernetes_dag', 'task_id': 'task-one', 'run_id': 'manual__2023-07-09T150101.4554620000-198a0a929', 'kubernetes_pod_operator': 'True', 'map_index': '0', 'try_number': '1'} {base.py:73} INFO - Using connection ID 'kubernetes_default' for task execution. {taskinstance.py:1415} INFO - Pausing task as DEFERRED. dag_id=kubernetes_dag, task_id=task-one, execution_date=20230709T150101, start_date=20230709T150102 {local_task_job_runner.py:222} INFO - Task exited with return code 100 (task deferral) {pod.py:142} INFO - Checking pod 'airflow-test-pod-q3ez8146' in namespace 'default'. {base.py:73} INFO - Using connection ID 'kubernetes_default' for task execution. {pod.py:175} INFO - Container is not completed and still working. {pod.py:194} INFO - Sleeping for 2 seconds. {pod.py:175} INFO - Container is not completed and still working. {pod.py:194} INFO - Sleeping for 2 seconds. {pod.py:175} INFO - Container is not completed and still working. {pod.py:194} INFO - Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. {pod.py:158} INFO - Pod airflow-test-pod-q3ez8146 is still running. Sleeping for 2 seconds. ``` ### What you think should happen instead _No response_ ### How to reproduce ```python from pendulum import today from airflow import DAG from airflow.providers.cncf.kubernetes.operators.pod import KubernetesPodOperator dag = DAG( dag_id="kubernetes_dag", schedule_interval="0 0 * * *", start_date=today("UTC").add(days=-1) ) with dag: cmd = "echo toto && sleep 4 && echo finish" KubernetesPodOperator.partial( task_id="task-one", namespace="default", kubernetes_conn_id="kubernetes_default", name="airflow-test-pod", image="alpine:3.16.2", cmds=["sh", "-c", cmd], is_delete_operator_pod=True, deferrable=True, poll_interval=2, do_xcom_push=True, get_logs=True, ).expand(env_vars=[{"a": "a"} for _ in range(1)]) ``` ### Operating System Ubuntu 22.04 ### 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/32458
https://github.com/apache/airflow/pull/32467
04a6e850e7d4437fe93a60d713c1d82e8f23885f
b3ce1161926efb880c3f525ac0a031ab4812fb95
"2023-07-09T15:05:37Z"
python
"2023-07-12T10:05:03Z"
closed
apache/airflow
https://github.com/apache/airflow
32,442
["airflow/www/static/js/components/ViewTimeDelta.tsx", "airflow/www/static/js/dag/details/Dag.tsx"]
Dag run timeout with timedelta value is rendered as [object object] in UI
### Apache Airflow version main (development) ### What happened Dag run timeout with timedelta value is rendered as [object object] in UI. It seems the data is fetched and string is used to render the value. timedelta is handled for schedule_interval which should also be done here. ![image](https://github.com/apache/airflow/assets/3972343/0a1e2257-4e7c-49a2-bde8-e9a796146cc5) ### What you think should happen instead timedelta should be handled similar to how it's done in scheduleInterval value here. https://github.com/apache/airflow/blob/e70bee00cd12ecf1462485a747c0e3296ef7d48c/airflow/www/static/js/dag/details/Dag.tsx#L278C2-L293 ### How to reproduce 1. Create a dag with dag_run_timeout as timedelta value. 2. Visit the dag details tab in grid view to check dag_run_timeout ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### 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/32442
https://github.com/apache/airflow/pull/32565
41164dd663c003c6be80abdf3b2180ec930a82e4
f1fc6dc4b9bd496ddd25898eea63d83f12cb6ad0
"2023-07-08T16:19:47Z"
python
"2023-07-13T23:50:36Z"
closed
apache/airflow
https://github.com/apache/airflow
32,412
["setup.py"]
Click 8.1.4 breaks mypy typing checks
### Body The Click 8.1.4 released 06.06.2023 broke a number of mypy checks. Until the problem is fixed, we need to limit click to unbreak main. Example failing job: https://github.com/apache/airflow/actions/runs/5480089808/jobs/9983219862 Example failures: ``` dev/breeze/src/airflow_breeze/utils/common_options.py:78: error: Need type annotation for "option_verbose" [var-annotated] option_verbose = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:89: error: Need type annotation for "option_dry_run" [var-annotated] option_dry_run = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:100: error: Need type annotation for "option_answer" [var-annotated] option_answer = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:109: error: Need type annotation for "option_github_repository" [var-annotated] option_github_repository = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:118: error: Need type annotation for "option_python" [var-annotated] option_python = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:127: error: Need type annotation for "option_backend" [var-annotated] option_backend = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:136: error: Need type annotation for "option_integration" [var-annotated] option_integration = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:142: error: Need type annotation for "option_postgres_version" [var-annotated] option_postgres_version = click.option( ^ dev/breeze/src/airflow_breeze/utils/common_options.py:150: error: Need type annotation for "option_mysql_version" [var-annotated] option_mysql_version = click.option( ^ ``` ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32412
https://github.com/apache/airflow/pull/32634
7092cfdbbfcfd3c03909229daa741a5bcd7ccc64
7123dc162bb222fdee7e4c50ae8a448c43cdd7d3
"2023-07-06T21:54:23Z"
python
"2023-07-20T04:30:54Z"
closed
apache/airflow
https://github.com/apache/airflow
32,390
["airflow/providers/http/hooks/http.py", "tests/providers/http/hooks/test_http.py"]
Fix HttpAsyncHook headers
### Body The hook uses `_headers`: https://github.com/apache/airflow/blob/9276310a43d17a9e9e38c2cb83686a15656896b2/airflow/providers/http/hooks/http.py#L340-L341 but passes `headers` to the async function https://github.com/apache/airflow/blob/9276310a43d17a9e9e38c2cb83686a15656896b2/airflow/providers/http/hooks/http.py#L368-L375 The task: `headers=headers` needs to be `headers=_headers` https://github.com/apache/airflow/blob/9276310a43d17a9e9e38c2cb83686a15656896b2/airflow/providers/http/hooks/http.py#L372 There was attempt to address it in https://github.com/apache/airflow/pull/31010 but the PR become stale as no response from the author. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32390
https://github.com/apache/airflow/pull/32409
ee38382efa54565c4b389eaeb536f0d45e12d498
358e6e8fa18166084fc17b23e75c6c29a37f245f
"2023-07-06T06:42:10Z"
python
"2023-07-06T20:59:04Z"
closed
apache/airflow
https://github.com/apache/airflow
32,367
["airflow/api_connexion/endpoints/xcom_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/xcom_schema.py", "airflow/www/static/js/types/api-generated.ts", "tests/api_connexion/endpoints/test_xcom_endpoint.py", "tests/api_connexion/schemas/test_xcom_schema.py", "tests/conftest.py"]
Unable to get mapped task xcom value via REST API. Getting MultipleResultsFound error
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow 2.3.4 (but actual code seems to have same behaviour). I have mapped task with xcom value. I want to get xcom value of particular instance or xcom values of all task instances. I am using standard REST API method /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries/{xcom_key} And it throws an error ` File &#34;/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/query.py&#34;, line 2850, in one_or_none return self._iter().one_or_none() File &#34;/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/result.py&#34;, line 1510, in one_or_none return self._only_one_row( File &#34;/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/result.py&#34;, line 614, in _only_one_row raise exc.MultipleResultsFound( sqlalchemy.exc.MultipleResultsFound: Multiple rows were found when one or none was required ` Is it any way of getting xcom of mapped tasks via API? ### What you think should happen instead _No response_ ### How to reproduce Make dag with mapped task. Return xcom value in every task. Try to get xcom value via API. ### Operating System ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Standard ### 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/32367
https://github.com/apache/airflow/pull/32453
2aa3cfb6abd10779029b0c072493a1c1ed820b77
bc97646b262e7f338b4f3d4dce199e640e24e250
"2023-07-05T10:16:02Z"
python
"2023-07-10T08:34:21Z"
closed
apache/airflow
https://github.com/apache/airflow
32,330
["airflow/providers/amazon/aws/hooks/glue_crawler.py", "tests/providers/amazon/aws/hooks/test_glue_crawler.py"]
AWS GlueCrawlerOperator deletes existing tags on run
### Apache Airflow version 2.6.2 ### What happened We are currently on AWS Provider 6.0.0 and looking to upgrade to the latest version 8.2.0. However, there are some issues with the GlueCrawlerOperator making the upgrade challenging, namely that the operator attempts to update the crawler tags on every run. Because we manage our resource tagging through Terraform, we do not provide any tags to the operator, which results in all of the tags being deleted (as well as needing additional `glue:GetTags` and `glue:UntagResource` permissions needing to be added to relevant IAM roles to even run the crawler). It seems strange that the default behaviour of the operator has been changed to make modifications to infrastructure, especially as this differs from the GlueJobOperator, which only performs updates when certain parameters are set. Potentially something similar could be done here, where if no `Tags` key is present in the `config` dict they aren't modified at all. Not sure what the best approach is. ### What you think should happen instead The crawler should run without any alterations to the existing infrastructure ### How to reproduce Run a GlueCrawlerOperator without tags in config, against a crawler with tags present ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers Amazon 8.2.0 ### 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/32330
https://github.com/apache/airflow/pull/32331
9a0f41ba53185031bc2aa56ead2928ae4b20de99
7a3bc8d7c85448447abd39287ef6a3704b237a90
"2023-07-03T13:40:23Z"
python
"2023-07-06T11:09:48Z"
closed
apache/airflow
https://github.com/apache/airflow
32,311
["airflow/serialization/pydantic/dag_run.py", "setup.cfg"]
Pydantic 2.0.0 support for Airflow
### Body Currently Pydantic 2.0.0 released on 30th of June 2023 breaks Airflow CI - building providers and running Kubernetes tests. Therefore we limit Pydantic to < 2.0.0 in setup.cfg for now. This should be fixed, especially that 2.0.0 brings a number of speed improvements. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
https://github.com/apache/airflow/issues/32311
https://github.com/apache/airflow/pull/32366
723eb7d453e50fb82652a8cf1f6a538410be777f
9cb463e20e4557efb4d1a6320b196c65ae519c23
"2023-07-02T06:32:48Z"
python
"2023-07-07T20:37:49Z"
closed
apache/airflow
https://github.com/apache/airflow
32,301
["airflow/serialization/pydantic/dataset.py"]
= instead of : in type hints - failing Pydantic 2
### Apache Airflow version 2.6.2 ### What happened airflow doesn't work correct UPDATE: with Pydantic 2 released on 30th of June UPDATE:, raises `pydantic.errors.PydanticUserError: A non-annotated attribute was detected: `dag_id = <class 'str'>`. All model fields require a type annotation; if `dag_id` is not meant to be a field, you may be able to resolve this error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.` ### What you think should happen instead _No response_ ### How to reproduce just install apache-airflow and run `airflow db init` ### Operating System Ubuntu 22.04.2 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/32301
https://github.com/apache/airflow/pull/32307
df4c8837d022e66921bc0cf33f3249b235de6fdd
4d84e304b86c97d0437fddbc6b6757b5201eefcc
"2023-07-01T12:00:53Z"
python
"2023-07-01T21:41:59Z"
closed
apache/airflow
https://github.com/apache/airflow
32,294
["airflow/models/renderedtifields.py"]
K8 executor throws MySQL DB error 'Deadlock found when trying to get lock; try restarting transaction'
### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Apache Airflow version: 2.6.1 When multiple runs for a dag executing simultaneously, K8 executor fails with the following MySQL exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 1407, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 1534, in _execute_task_with_callbacks RenderedTaskInstanceFields.write(rtif) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper with create_session() as session: File "/usr/local/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 37, in create_session session.commit() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1454, in commit self._transaction.commit(_to_root=self.future) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 832, in commit self._prepare_impl() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 811, in _prepare_impl self.session.flush() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3449, in flush self._flush(objects) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3588, in _flush with util.safe_reraise(): File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3549, in _flush flush_context.execute() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute rec.execute(self) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute util.preloaded.orm_persistence.save_obj( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/persistence.py", line 237, in save_obj _emit_update_statements( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/persistence.py", line 1001, in _emit_update_statements c = connection._execute_20( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1710, in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/sql/elements.py", line 334, in _execute_on_connection return connection._execute_clauseelement( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1577, in _execute_clauseelement ret = self._execute_context( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1948, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 2129, in _handle_dbapi_exception util.raise_( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1905, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.10/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.10/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.10/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') SQL: UPDATE rendered_task_instance_fields SET k8s_pod_yaml=%s WHERE rendered_task_instance_fields.dag_id = %s AND rendered_task_instance_fields.task_id = %s AND rendered_task_instance_fields.run_id = %s AND rendered_task_instance_fields.map_index = %s ### What you think should happen instead K8 executor should complete processing successfully without error ### How to reproduce Trigger multiple runs of the same dag simultaneously so that tasks under the dag get executed around the same time ### Operating System Airflow docker image tag 2.6.1-python3.10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details User community airflow-helm chart https://github.com/airflow-helm ### Anything else This occurs consistently. If multiple runs for the dag are executed with a delay of few minutes, K8 executor completes successfully ### 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/32294
https://github.com/apache/airflow/pull/32341
e53320d62030a53c6ffe896434bcf0fc85803f31
c8a3c112a7bae345d37bb8b90d68c8d6ff2ef8fc
"2023-06-30T22:51:45Z"
python
"2023-07-05T11:28:16Z"
closed
apache/airflow
https://github.com/apache/airflow
32,290
["airflow/www/views.py", "tests/www/views/test_views_tasks.py"]
Try number is incorrect
### Apache Airflow version 2.6.2 ### What happened All tasks were run 1 time. The try number is 2 for all tasks ### What you think should happen instead Try number should be 1 if only tried 1 time ### How to reproduce Run a task and use the UI to look up try number ### Operating System centos ### 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/32290
https://github.com/apache/airflow/pull/32361
43f3e57bf162293b92154f16a8ce33e6922fbf4e
a8e4b8aee602e8c672ab879b7392a65b5c2bb34e
"2023-06-30T16:45:01Z"
python
"2023-07-05T08:30:51Z"