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apache/airflow

https://github.com/apache/airflow

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

"Operation not permitted" error when chmod on log folder

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.5.1 ### Kubernetes Version 1.24.6 ### Helm Chart configuration executor: "KubernetesExecutor" # however same issue happens with LocalExecutor logs: persistence: enabled: true size: 50Gi storageClassName: azurefile-csi ### Docker Image customizations Using airflow-2.5.1-python3.10 as a base image. Copy custom shared libraries into folder under /opt/airflow/company Copy DAGs /opt/airflow/dags ### What happened ```console After migrating from airflow 2.4.3 to 2.5.1 start getting error below. No other changes to custom image. No task is running because of this error: Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/cli/commands/task_command.py", line 384, in task_run ti.init_run_context(raw=args.raw) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 2414, in init_run_context self._set_context(self) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/logging_mixin.py", line 77, in _set_context set_context(self.log, context) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/logging_mixin.py", line 213, in set_context flag = cast(FileTaskHandler, handler).set_context(value) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 71, in set_context local_loc = self._init_file(ti) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 382, in _init_file self._prepare_log_folder(Path(full_path).parent) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 358, in _prepare_log_folder directory.chmod(mode) File "/usr/local/lib/python3.10/pathlib.py", line 1191, in chmod self._accessor.chmod(self, mode, follow_symlinks=follow_symlinks) PermissionError: [Errno 1] Operation not permitted: '/opt/airflow/logs/dag_id=***/run_id=manual__2023-01-22T02:59:43.752407+00:00/task_id=***' ``` ### What you think should happen instead Seem like airflow attempts to set change log folder permissions and not permissioned to do it. Getting same error when executing command manually (confirmed folder path exists): chmod 511 '/opt/airflow/logs/dag_id=***/run_id=manual__2023-01-22T02:59:43.752407+00:00/task_id=***' chmod: changing permissions of '/opt/airflow/logs/dag_id=***/run_id=scheduled__2023-01-23T15:30:00+00:00/task_id=***': Operation not permitted ### How to reproduce My understanding is that this error happens before any custom code is executed. ### Anything else Error happens every time, unable to start any DAG while using airflow 2.5.1. Exactly same configuration works with 2.5.0 and 2.4.3. Same image and configuration works fine while running locally using docker-composer. ### 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/29112

https://github.com/apache/airflow/pull/30123

f5ed6ae67d0788ea2a737d781b27fbcae1e8e8af

b87cbc388bae281e553da699212ebfc6bb723eea

python

closed

apache/airflow

https://github.com/apache/airflow

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

graph disappears during run time when using branch_task and a dynamic classic operator

### Apache Airflow version 2.5.1 ### What happened when using a dynamically generated task that gets the expand data from xcom after a branch_task the graph doesn't render. It reappears once the dag run is finished. tried with BashOperator and a KubernetesPodOperator. the developer console in the browser shows the error: `Uncaught TypeError: Cannot read properties of undefined (reading 'length') at z (graph.1c0596dfced26c638bfe.js:2:17499) at graph.1c0596dfced26c638bfe.js:2:17654 at Array.map (<anonymous>) at z (graph.1c0596dfced26c638bfe.js:2:17646) at graph.1c0596dfced26c638bfe.js:2:26602 at graph.1c0596dfced26c638bfe.js:2:26655 at graph.1c0596dfced26c638bfe.js:2:26661 at graph.1c0596dfced26c638bfe.js:2:222 at graph.1c0596dfced26c638bfe.js:2:227 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 ` grid view renders fine. ### What you think should happen instead graph should be rendered. ### How to reproduce ```@dag('branch_dynamic', schedule_interval=None, default_args=default_args, catchup=False) def branch_dynamic_flow(): @branch_task def choose_path(): return 'b' @task def a(): print('a') @task def get_args(): return ['echo 1', 'echo 2'] b = BashOperator.partial(task_id="b").expand(bash_command=get_args()) path = choose_path() path >> a() path >> b ``` ### Operating System red hat ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes | 5.1.1 | Kubernetes ### 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/29105

https://github.com/apache/airflow/pull/29042

b2825e11852890cf0b0f4d0bcaae592311781cdf

33ba242d7eb8661bf936a9b99a8cad4a74b29827

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/Dag.tsx", "airflow/www/static/js/dag/details/dagRun/index.tsx", "airflow/www/static/js/dag/details/taskInstance/Logs/LogBlock.tsx", "airflow/www/static/js/dag/details/taskInstance/index.tsx", "airflow/www/static/js/dag/grid/index.tsx", "airflow/www/static/js/utils/useOffsetHeight.tsx"]

Unnecessary scrollbars in grid view

### Apache Airflow version 2.5.0 ### What happened Compare the same DAG grid view in 2.4.3: (everything is scrolled using the "main" scrollbar of the window)  and in 2.5.0 (and 2.5.1) (left and right side of the grid have their own scrollbars):  It was much more ergonomic previously when only the main scrollbar was used. I think the relevant change was in #27560, where `maxHeight={offsetHeight}` was added to some places. Is this the intended way the grid view should look like or did happen as an accident? I tried to look around in the developer tools and it seems like removing the `max-height` from this element restores the old look: `div#react-container div div.c-1rr4qq7 div.c-k008qs div.c-19srwsc div.c-scptso div.c-l7cpmp`. Well it does for the left side of the grid view. Similar change has to be done for some other divs also.  ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Linux ### 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/29100

https://github.com/apache/airflow/pull/29367

1b18a501fe818079e535838fa4f232b03365fc75

643d736ebb32c488005b3832c2c3f226a77900b2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/ftp/operators/ftp.py", "airflow/providers/sftp/operators/sftp.py", "tests/providers/ftp/operators/test_ftp.py"]

FTP operator has logic in __init__

### Body Similarly to SFTP (fixed in https://github.com/apache/airflow/pull/29068) the logic from __init__ should be moved to execute. The #29068 provides a blueprint for that. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/29070

https://github.com/apache/airflow/pull/29073

8eb348911f2603feba98787d79b88bbd84bd17be

2b7071c60022b3c483406839d3c0ef734db5daad

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "tests/models/test_cleartasks.py"]

Recursively cleared external task sensors using reschedule mode instantly time out if previous run is older than sensor timeout

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Using Airflow 2.3.3, when recursively clearing downstream tasks any cleared external task sensors in other DAGs which are using reschedule mode will instantly fail with an `AirflowSensorTimeout` exception if the previous run is older than the sensor's timeout. ### What you think should happen instead The recursively cleared external task sensors should run normally, waiting for the cleared upstream task to complete, retrying up to the configured number of times and within the configured sensor timeout counting from the point in time when the sensor was cleared. ### How to reproduce 1. Load the following DAGs: ```python from datetime import datetime, timedelta, timezone from time import sleep from airflow.decorators import task from airflow.models import DAG from airflow.sensors.external_task import ExternalTaskMarker, ExternalTaskSensor from airflow.utils import timezone default_args = { 'start_date': datetime.now(timezone.utc).replace(second=0, microsecond=0), 'retries': 2, 'retry_delay': timedelta(seconds=10), } with DAG('parent_dag', schedule_interval='* * * * *', catchup=False, default_args=default_args) as parent_dag: @task(task_id='parent_task') def parent_sleep(): sleep(10) parent_task = parent_sleep() child_dag__wait_for_parent_task = ExternalTaskMarker( task_id='child_dag__wait_for_parent_task', external_dag_id='child_dag', external_task_id='wait_for_parent_task', ) parent_task >> child_dag__wait_for_parent_task with DAG('child_dag', schedule_interval='* * * * *', catchup=False, default_args=default_args) as child_dag: wait_for_parent_task = ExternalTaskSensor( task_id='wait_for_parent_task', external_dag_id='parent_dag', external_task_id='parent_task', mode='reschedule', poke_interval=15, timeout=60, ) @task(task_id='child_task') def child_sleep(): sleep(10) child_task = child_sleep() wait_for_parent_task >> child_task ``` 2. Enable the `parent_dag` and `child_dag` DAGs and wait for them to automatically run (they're scheduled to run every minute). 3. Wait for at least one additional minute (because the sensor timeout is configured to be one minute). 4. Clear the earliest `parent_dag.parent_task` task instance with the "Downstream" and "Recursive" options enabled. 5. When the cleared `child_dag.wait_for_parent_task` task tries to run it will immediately fail with an `AirflowSensorTimeout` exception. ### Operating System Debian 10.13 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else This appears to be due to a bug in `airflow.models.taskinstance.clear_task_instances()` where [it only increments the task instance's `max_tries` property if the task is found in the DAG passed in](https://github.com/apache/airflow/blob/2.3.3/airflow/models/taskinstance.py#L219-L223), but when recursively clearing tasks that won't work properly for tasks in downstream DAGs, because all task instances to be recursively cleared are passed to `clear_task_instances()` with [the DAG of the initial task being cleared](https://github.com/apache/airflow/blob/2.3.3/airflow/models/dag.py#L1905). When a cleared task instance for a sensor using reschedule mode doesn't have its `max_tries` property incremented that causes the [logic in `BaseSensorOperator.execute()`](https://github.com/apache/airflow/blob/2.3.3/airflow/sensors/base.py#L247-L264) to incorrectly choose an older `first_try_number` value, calculate the sensor run duration as the total time passed since that previous run, and fail with an `AirflowSensorTimeout` exception if that inflated run duration exceeds the sensor timeout. While I tested this in Airflow 2.3.3 because that's what my company is running, I also looked at the current `main` branch code and this appears to still be a problem in the latest version. IMO the best solution would be to change `airflow.models.taskinstance.clear_task_instances()` to make an effort to get the associated DAGs for all the task instances being cleared so their associated tasks can be read and their `max_tries` property can be incremented correctly. ### 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/29049

https://github.com/apache/airflow/pull/29065

7074167d71c93b69361d24c1121adc7419367f2a

0d2e6dce709acebdb46288faef17d322196f29a2

python

closed

apache/airflow

https://github.com/apache/airflow

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

Top level code imports in AWS transfer

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.3.3 ### Operating System MacOs/Linux ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened sql_to_s3.py transfer has top level python code imports considered as Bad Practices: https://github.com/apache/airflow/blob/be31214dcf14db39b7a5f422ca272cdc13e08268/airflow/providers/amazon/aws/transfers/sql_to_s3.py#L26 According to the [official docs](https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code): ```python import numpy as np # <-- THIS IS A VERY BAD IDEA! DON'T DO THAT! ``` All imports that are not related to DAG structure and creation should be moved to callable functions, such as the `execute` method. This causes timeout errors while filling the `DagBag`: ``` File "/opt/airflow/dags/mydag.py", line 6, in <module> from airflow.providers.amazon.aws.transfers.sql_to_s3 import SqlToS3Operator File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/transfers/sql_to_s3.py", line 25, in <module> import pandas as pd File "/home/airflow/.local/lib/python3.7/site-packages/pandas/__init__.py", line 50, in <module> from pandas.core.api import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/api.py", line 48, in <module> from pandas.core.groupby import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/__init__.py", line 1, in <module> from pandas.core.groupby.generic import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 73, in <module> from pandas.core.frame import DataFrame File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/frame.py", line 193, in <module> from pandas.core.series import Series File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/series.py", line 141, in <module> import pandas.plotting File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 724, in exec_module File "<frozen importlib._bootstrap_external>", line 859, in get_code File "<frozen importlib._bootstrap_external>", line 917, in get_data File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/timeout.py", line 68, in handle_timeout raise AirflowTaskTimeout(self.error_message) airflow.exceptions.AirflowTaskTimeout: DagBag import timeout for /opt/airflow/dags/mydag.py after 30.0s. Please take a look at these docs to improve your DAG import time: * https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code * https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#reducing-dag-complexity, PID: 7 ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29036

https://github.com/apache/airflow/pull/29045

af0bbe62a5fc26bac189acd9039f5bbc83c2d429

62825678b3100b0e0ea3b4e14419d259a36ba074

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py"]

Metrics dagrun.duration.failed.<dag_id> not updated when the dag run failed due to timeout

### Apache Airflow version 2.5.0 ### What happened When the dag was set with `dagrun_timeout` [parameter](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG) and the dag run failed due to time out reason, the metrics `dagrun.duration.failed.<dag_id>` was not triggered. ### What you think should happen instead According to the [doc](https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html#timers), the metrics `dagrun.duration.failed.<dag_id>` should capture `Milliseconds taken for a DagRun to reach failed state`. Then it should capture all kinds of dag failure including the failure caused by dag level time out. ### How to reproduce set `dagrun_timeout` [parameter](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG) (e.g. `dagrun_timeout=timedelta(seconds=5)`), then set up a BashOperator task run longer than dagrun_timeout. (e.g., `bash_command='sleep 120'`,). Then check the metrics, dagrun.duration.failed.<dag_id> can not capture this failed dag run due to timeout reason. ### Operating System Ubuntu 22.04.1 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==7.1.0 apache-airflow-providers-common-sql==1.3.3 apache-airflow-providers-ftp==3.3.0 apache-airflow-providers-http==4.1.1 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-sqlite==3.3.1 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else According to the [doc](https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html#timers), the metrics `dagrun.duration.failed.<dag_id>` should capture `Milliseconds taken for a DagRun to reach failed state`. However, if the dag run was failed due to the dag run level timeout, the metric can not capture the failed dag run. I deep dive to the airflow code and figured out the reason. The timer `dagrun.duration.failed.{self.dag_id}` was triggered in the method _emit_duration_stats_for_finished_state. [code](https://github.com/apache/airflow/blob/2.5.0/airflow/models/dagrun.py#L880-L894) ``` def _emit_duration_stats_for_finished_state(self): if self.state == State.RUNNING: return if self.start_date is None: self.log.warning("Failed to record duration of %s: start_date is not set.", self) return if self.end_date is None: self.log.warning("Failed to record duration of %s: end_date is not set.", self) return duration = self.end_date - self.start_date if self.state == State.SUCCESS: Stats.timing(f"dagrun.duration.success.{self.dag_id}", duration) elif self.state == State.FAILED: Stats.timing(f"dagrun.duration.failed.{self.dag_id}", duration) ``` The function `_emit_duration_stats_for_finished_state` was only called in the update_state() method for class DagRun(). [code](https://github.com/apache/airflow/blob/2.5.0/airflow/models/dagrun.py#L650-L677) If the update_state() method was not call, then `_emit_duration_stats_for_finished_state` will not used. ``` if self._state == DagRunState.FAILED or self._state == DagRunState.SUCCESS: msg = ( "DagRun Finished: dag_id=%s, execution_date=%s, run_id=%s, " "run_start_date=%s, run_end_date=%s, run_duration=%s, " "state=%s, external_trigger=%s, run_type=%s, " "data_interval_start=%s, data_interval_end=%s, dag_hash=%s" ) self.log.info( msg, self.dag_id, self.execution_date, self.run_id, self.start_date, self.end_date, (self.end_date - self.start_date).total_seconds() if self.start_date and self.end_date else None, self._state, self.external_trigger, self.run_type, self.data_interval_start, self.data_interval_end, self.dag_hash, ) session.flush() self._emit_true_scheduling_delay_stats_for_finished_state(finished_tis) self._emit_duration_stats_for_finished_state() ``` When a dag run was timed out, in the scheduler job, it will only call set_state(). [code](https://github.com/apache/airflow/blob/2.5.0/airflow/jobs/scheduler_job.py#L1280-L1312) ``` if ( dag_run.start_date and dag.dagrun_timeout and dag_run.start_date < timezone.utcnow() - dag.dagrun_timeout ): dag_run.set_state(DagRunState.FAILED) unfinished_task_instances = ( session.query(TI) .filter(TI.dag_id == dag_run.dag_id) .filter(TI.run_id == dag_run.run_id) .filter(TI.state.in_(State.unfinished)) ) for task_instance in unfinished_task_instances: task_instance.state = TaskInstanceState.SKIPPED session.merge(task_instance) session.flush() self.log.info("Run %s of %s has timed-out", dag_run.run_id, dag_run.dag_id) active_runs = dag.get_num_active_runs(only_running=False, session=session) # Work out if we should allow creating a new DagRun now? if self._should_update_dag_next_dagruns(dag, dag_model, active_runs): dag_model.calculate_dagrun_date_fields(dag, dag.get_run_data_interval(dag_run)) callback_to_execute = DagCallbackRequest( full_filepath=dag.fileloc, dag_id=dag.dag_id, run_id=dag_run.run_id, is_failure_callback=True, processor_subdir=dag_model.processor_subdir, msg="timed_out", ) dag_run.notify_dagrun_state_changed() return callback_to_execute ``` From the above code, we can see that when the DAG run was timed out, it will call the set_state() method only. Here update_state() method was not called and that is why the metrics dagrun.duration.failed.{self.dag_id} was not set up accordingly. Please fix this bug to let the timer `dagrun.duration.failed.<dag_id>` can capture the failed dag run due to dag level timed out. ### 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/29013

https://github.com/apache/airflow/pull/29076

9dedf81fa18e57755aa7d317f08f0ea8b6c7b287

ca9a59b3e8c08286c8efd5ca23a509f9178a3cc9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/xcom_arg.py", "tests/models/test_taskinstance.py"]

Dynamic Task Mapping skips tasks before upstream has started

### Apache Airflow version 2.5.0 ### What happened In some cases we are seeing dynamic mapped task being skipped before upstream tasks have started & the dynamic count for the task can be calculated. We see this both locally in a with the `LocalExecutor` & on our cluster with the `KubernetesExecutor`. To trigger the issue we need multiple dynamic tasks merging into a upstream task, see the images below for example. If there is no merging the tasks run as expected. The tasks also need to not know the number of dynamic tasks that will be created on DAG start, for example by chaining in an other dynamic task output.   If the DAG, task, or upstream tasks are cleared the skipped task runs as expected. The issue exists both on airflow 2.4.x & 2.5.0. Happy to help debug this further & answer any questions! ### What you think should happen instead The tasks should run after upstream tasks are done. ### How to reproduce The following code is able to reproduce the issue on our side: ```python from datetime import datetime from airflow import DAG from airflow.decorators import task from airflow.utils.task_group import TaskGroup from airflow.operators.empty import EmptyOperator # Only one chained tasks results in only 1 of the `skipped_tasks` skipping. # Add in extra tasks results in both `skipped_tasks` skipping, but # no earlier tasks are ever skipped. CHAIN_TASKS = 1 @task() def add(x, y): return x, y with DAG( dag_id="test_skip", schedule=None, start_date=datetime(2023, 1, 13), ) as dag: init = EmptyOperator(task_id="init_task") final = EmptyOperator(task_id="final") for i in range(2): with TaskGroup(f"task_group_{i}") as tg: chain_task = [i] for j in range(CHAIN_TASKS): chain_task = add.partial(x=j).expand(y=chain_task) skipped_task = ( add.override(task_id="skipped").partial(x=i).expand(y=chain_task) ) # Task isn't skipped if final (merging task) is removed. init >> tg >> final ``` ### Operating System MacOS ### Versions of Apache Airflow Providers This can be reproduced without any extra providers installed. ### 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/28973

https://github.com/apache/airflow/pull/30641

8cfc0f6332c45ca750bc2317ea1e283aaf2ac5bd

5f2628d36cb8481ee21bd79ac184fd8fdce3e47d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/docker/operators/docker.py", "tests/providers/docker/decorators/test_docker.py", "tests/providers/docker/operators/test_docker.py"]

Add a way to skip Docker Operator task

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow 2.3.3 Raising the `AirflowSkipException` in the source code, using the `DockerOperator`, is supposed to mark the task as skipped, according to the [docs](https://airflow.apache.org/docs/apache-airflow/stable/concepts/tasks.html#special-exceptions). However, what happens is that the task is marked as failed with the logs showing `ERROR - Task failed with exception`. ### What you think should happen instead Tasks should be marked as skipped, not failed. ### How to reproduce Raise the `AirflowSkipException` in the python source code, while using the `DockerOperator`. ### Operating System Ubuntu 20.04.5 LTS (GNU/Linux 5.4.0-125-generic x86_64) ### 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/28951

https://github.com/apache/airflow/pull/28996

bc5cecc0db27cb8684c238b36ad12c7217d0c3ca

3a7bfce6017207218889b66976dbee1ed84292dc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/decorators/kubernetes.py", "airflow/providers/cncf/kubernetes/python_kubernetes_script.jinja2", "tests/providers/cncf/kubernetes/decorators/test_kubernetes.py"]

@task.kubernetes TaskFlow decorator fails with IndexError and is unable to receive input

### Apache Airflow Provider(s) cncf-kubernetes ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes 5.0.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When passing arguments (either args or kwargs) to a @task.kubernetes decorated function, the following exception occurs: Task Logs: ``` [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:621} INFO - Building pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 with labels: {'dag_id': 'test_k8s_input_1673647477', 'task_id': 'k8s_with_input', 'run_id': 'backfill__2023-01-01T0000000000-c16e0472d', 'kubernetes_pod_operator': 'True', 'try_number': '1'} [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:404} INFO - Found matching pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 with labels {'airflow_kpo_in_cluster': 'True', 'airflow_version': '2.5.0', 'dag_id': 'test_k8s_input_1673647477', 'kubernetes_pod_operator': 'True', 'run_id': 'backfill__2023-01-01T0000000000-c16e0472d', 'task_id': 'k8s_with_input', 'try_number': '1'} [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:405} INFO - `try_number` of task_instance: 1 [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:406} INFO - `try_number` of pod: 1 [2023-01-13, 22:05:40 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:41 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:42 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:43 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - + python -c 'import base64, os;x = os.environ["__PYTHON_SCRIPT"];f = open("/tmp/script.py", "w"); f.write(x); f.close()' [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - + python /tmp/script.py [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - Traceback (most recent call last): [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - File "/tmp/script.py", line 14, in <module> [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - with open(sys.argv[1], "rb") as file: [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - IndexError: list index out of range [2023-01-13, 22:05:44 UTC] {kubernetes_pod.py:499} INFO - Deleting pod: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:44 UTC] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/decorators/kubernetes.py", line 104, in execute return super().execute(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/decorators/base.py", line 217, in execute return_value = super().execute(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 465, in execute self.cleanup( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 489, in cleanup raise AirflowException( airflow.exceptions.AirflowException: Pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 returned a failure: ``` ### What you think should happen instead K8's decorator should properly receive input. The [python command invoked here](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/cncf/kubernetes/decorators/kubernetes.py#L75) does not pass input. Contrast this with the [docker version of the decorator](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/docker/decorators/docker.py#L105) which does properly pass pickled input. ### How to reproduce Create a dag: ```py import os from airflow import DAG from airflow.decorators import task DEFAULT_TASK_ARGS = { "owner": "gcp-data-platform", "start_date": "2022-12-16", "retries": 0, } @task.kubernetes( image="python:3.8-slim-buster", namespace=os.getenv("AIRFLOW__KUBERNETES_EXECUTOR__NAMESPACE"), in_cluster=False, ) def k8s_with_input(val: str) -> str: import datetime print(f"Got val: {val}") return val with DAG( schedule_interval="@daily", max_active_runs=1, max_active_tasks=5, catchup=False, dag_id="test_oom_dag", default_args=DEFAULT_TASK_ARGS, ) as dag: output = k8s_with_input.override(task_id="k8s_with_input")("a") ``` Run and observe failure: <img width="907" alt="image" src="https://user-images.githubusercontent.com/9200263/212427952-15466317-4e61-4b71-9971-2cdedba4f7ba.png"> Task logs above. ### 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/28933

https://github.com/apache/airflow/pull/28942

73c8e7df0be8b254e3727890b51ca0f76308e6b5

9a5c3e0ac0b682d7f2c51727a56e06d68bc9f6be

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/auth/backend/kerberos_auth.py", "docs/apache-airflow/administration-and-deployment/security/api.rst"]

Airflow API kerberos authentication error

### Apache Airflow version 2.5.0 ### What happened Configured AUTH_DB authentication for web server and Kerberos authentication for API. Web server works well. Try to get any API endpoint and get an error 500. I see Kerberos authentication step is done, but authorization step fails. 'User' object (now it is just a string) doesn't have such parameter. Request error ``` янв 13 13:54:14 nginx-test airflow[238738]: [2023-01-13 13:54:14,923] {app.py:1741} ERROR - Exception on /api/v1/dags [GET] янв 13 13:54:14 nginx-test airflow[238738]: Traceback (most recent call last): янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 2525, in wsgi_app янв 13 13:54:14 nginx-test airflow[238738]: response = self.full_dispatch_request() янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1822, in full_dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: rv = self.handle_user_exception(e) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1820, in full_dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: rv = self.dispatch_request() янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1796, in dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/decorator.py", line 68, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/uri_parsing.py", line 149, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/validation.py", line 399, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: return function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/response.py", line 112, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/parameter.py", line 120, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: return function(**kwargs) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/api_connexion/security.py", line 50, in decorated янв 13 13:54:14 nginx-test airflow[238738]: if appbuilder.sm.check_authorization(permissions, kwargs.get("dag_id")): янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/www/security.py", line 715, in check_authorization янв 13 13:54:14 nginx-test airflow[238738]: can_access_all_dags = self.has_access(*perm) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/www/security.py", line 419, in has_access янв 13 13:54:14 nginx-test airflow[238738]: if (action_name, resource_name) in user.perms: янв 13 13:54:14 nginx-test airflow[238738]: AttributeError: 'str' object has no attribute 'perms' янв 13 13:54:14 nginx-test airflow[238738]: 127.0.0.1 - - [13/Jan/2023:13:54:14 +0300] "GET /api/v1/dags HTTP/1.1" 500 1561 "-" "curl/7.68.0" ``` Starting airflow-webserver log (no errors) ``` янв 13 13:38:51 nginx-test airflow[238502]: ____________ _____________ янв 13 13:38:51 nginx-test airflow[238502]: ____ |__( )_________ __/__ /________ __ янв 13 13:38:51 nginx-test airflow[238502]: ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / янв 13 13:38:51 nginx-test airflow[238502]: ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / янв 13 13:38:51 nginx-test airflow[238502]: _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ янв 13 13:38:51 nginx-test airflow[238502]: Running the Gunicorn Server with: янв 13 13:38:51 nginx-test airflow[238502]: Workers: 4 sync янв 13 13:38:51 nginx-test airflow[238502]: Host: 0.0.0.0:10000 янв 13 13:38:51 nginx-test airflow[238502]: Timeout: 120 янв 13 13:38:51 nginx-test airflow[238502]: Logfiles: - - янв 13 13:38:51 nginx-test airflow[238502]: Access Logformat: янв 13 13:38:51 nginx-test airflow[238502]: ================================================================= янв 13 13:38:51 nginx-test airflow[238502]: [2023-01-13 13:38:51,209] {webserver_command.py:431} INFO - Received signal: 15. Closing gunicorn. янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238525 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238523 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238526 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238524 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [INFO] Shutting down: Master янв 13 13:38:52 nginx-test systemd[1]: airflow-webserver.service: Succeeded. янв 13 13:38:52 nginx-test systemd[1]: Stopped Airflow webserver daemon. янв 13 13:38:52 nginx-test systemd[1]: Started Airflow webserver daemon. янв 13 13:38:54 nginx-test airflow[238732]: /usr/local/lib/python3.8/dist-packages/airflow/api/auth/backend/kerberos_auth.py:50 DeprecationWarning: '_request_ctx_stack' is dep> янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,393] {kerberos_auth.py:78} INFO - Kerberos: hostname nginx-test.mycompany янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,393] {kerberos_auth.py:88} INFO - Kerberos init: airflow nginx-test.mycompany янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,394] {kerberos_auth.py:93} INFO - Kerberos API: server is airflow/nginx-test.mycompany@MYCOMPANY> янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Starting gunicorn 20.1.0 янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Listening at: http://0.0.0.0:10000 (238732) янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Using worker: sync янв 13 13:38:56 nginx-test airflow[238735]: [2023-01-13 13:38:56 +0300] [238735] [INFO] Booting worker with pid: 238735 янв 13 13:38:57 nginx-test airflow[238736]: [2023-01-13 13:38:57 +0300] [238736] [INFO] Booting worker with pid: 238736 янв 13 13:38:57 nginx-test airflow[238737]: [2023-01-13 13:38:57 +0300] [238737] [INFO] Booting worker with pid: 238737 янв 13 13:38:57 nginx-test airflow[238738]: [2023-01-13 13:38:57 +0300] [238738] [INFO] Booting worker with pid: 238738 ``` I tried to skip rights check, commenting problem lines and returning True from has_access function and if I remember it right in one more function from security.py. And I got it working. But it has been just a hack to check where is the problem. ### What you think should happen instead It should return right json answer with code 200. ### How to reproduce 1. webserver_config.py: default 2. airflow.cfg changed lines: ``` [core] security = kerberos [api] auth_backends = airflow.api.auth.backend.kerberos_auth,airflow.api.auth.backend.session [kerberos] ccache = /tmp/airflow_krb5_ccache principal = airflow/nginx-test.mycompany reinit_frequency = 3600 kinit_path = kinit keytab = /root/airflow/airflow2.keytab forwardable = True include_ip = True [webserver] base_url = http://localhost:10000 web_server_port = 10000 ``` 3. Create keytab file with airflow principal 4. Log in as domain user, make request (for example): curl --verbose --negotiate -u : http://nginx-test.mycompany:10000/api/v1/dags ### Operating System Ubuntu. VERSION="20.04.5 LTS (Focal Fossa)" ### 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/28919

https://github.com/apache/airflow/pull/29054

80dbfbc7ad8f63db8565baefa282bc01146803fe

135aef30be3f9b8b36556f3ff5e0d184b0f74f22

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/start.rst"]

quick start fails: DagRun for example_bash_operator with run_id or execution_date of '2015-01-01' not found

### Apache Airflow version 2.5.0 ### What happened I follow the [quick start guide](https://airflow.apache.org/docs/apache-airflow/stable/start.html) When I execute `airflow tasks run example_bash_operator runme_0 2015-01-01` I got the following error: ``` [2023-01-13 15:50:42,493] {dagbag.py:538} INFO - Filling up the DagBag from /root/airflow/dags [2023-01-13 15:50:42,761] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): prepare_email>, send_email already registered for DAG: example_dag_decorator [2023-01-13 15:50:42,761] {taskmixin.py:205} WARNING - Dependency <Task(EmailOperator): send_email>, prepare_email already registered for DAG: example_dag_decorator [2023-01-13 15:50:42,830] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_entry_group>, delete_entry_group already registered for DAG: example_complex [2023-01-13 15:50:42,830] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_entry_group>, create_entry_group already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_entry_gcs>, delete_entry already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_entry>, create_entry_gcs already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_tag>, delete_tag already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_tag>, create_tag already registered for DAG: example_complex [2023-01-13 15:50:42,852] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,852] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,853] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,853] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,854] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,854] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {example_python_operator.py:90} WARNING - The virtalenv_python example task requires virtualenv, please install it. [2023-01-13 15:50:43,608] {tutorial_taskflow_api_virtualenv.py:29} WARNING - The tutorial_taskflow_api_virtualenv example DAG requires virtualenv, please install it. /root/miniconda3/lib/python3.7/site-packages/airflow/models/dag.py:3524 RemovedInAirflow3Warning: Param `schedule_interval` is deprecated and will be removed in a future release. Please use `schedule` instead. Traceback (most recent call last): File "/root/miniconda3/bin/airflow", line 8, in <module> sys.exit(main()) File "/root/miniconda3/lib/python3.7/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 384, in task_run ti, _ = _get_ti(task, args.map_index, exec_date_or_run_id=args.execution_date_or_run_id, pool=args.pool) File "/root/miniconda3/lib/python3.7/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 163, in _get_ti session=session, File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 118, in _get_dag_run ) from None airflow.exceptions.DagRunNotFound: DagRun for example_bash_operator with run_id or execution_date of '2023-11-01' not found ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 18.04.3 LTS ### 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/28912

https://github.com/apache/airflow/pull/28949

c57c23dce39992eafcf86dc08a1938d7d407803f

a4f6f3d6fe614457ff95ac803fd15e9f0bd38d27

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/ecs.py"]

Misnamed param in EcsRunTaskOperator

### What do you see as an issue? In the `EcsRunTaskOperator`, one of the params in the docstring is `region_name`, but it should be `region`: https://github.com/apache/airflow/blob/2.5.0/airflow/providers/amazon/aws/operators/ecs.py#L281 ### Solving the problem _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28910

https://github.com/apache/airflow/pull/29562

eb46eeb33d58436aa5860f2f0031fad3dea3ce3b

cadab59e8df90588b07cf8d9ee3ce13f9a79f656

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/pgbouncer/pgbouncer-deployment.yaml", "chart/values.schema.json", "chart/values.yaml"]

Pgbouncer metrics exporter restarts

### Official Helm Chart version 1.6.0 ### Apache Airflow version 2.4.2 ### Kubernetes Version 1.21 ### Helm Chart configuration Nothing really specific ### Docker Image customizations _No response_ ### What happened From time to time we have pg_bouncer metrics exporter that fails its healthcheck. When it fails its healtchecks three times in a row, pgbouncer stop being reachable and drops all the ongoing connection. Is it possible to make the pgbouncer healtcheck configurable at least the timeout parameter of one second that seems really short? ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28891

https://github.com/apache/airflow/pull/29752

d0fba865aed1fc21d82f0a61cddb1fa0bd4b7d0a

44f89c6db115d91aba91955fde42475d1a276628

python

closed

apache/airflow

https://github.com/apache/airflow

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

`webserver.instance_name` shows markup text in `<title>` tag

### Apache Airflow version 2.5.0 ### What happened https://github.com/apache/airflow/pull/20888 enables the use of markup to style the `webserver.instance_name`. However, if the instance name has HTML code, this will also be reflected in the `<title>` tag, as shown in the screenshot below.  This is not a pretty behaviour. ### What you think should happen instead Ideally, if `webserver. instance_name_has_markup = True`, then the text inside the `<title>` should be stripped of HTML code. For example: - Set `webserver.instance_name` to some text with markup, like `<b style="color: red">title</b>` - Set `webserver.Instance_name_has_markup` to `true` This is how the `<title>` tag should look like: ```html <title>DAGs - title</title> ``` Instead of: ``` <title>DAGs - &lt;b style=&#34;color: red&#34;&gt;title&lt;b&gt;</title> ``` ### How to reproduce - Airflow version 2.3+, which is [when this change has been introduced](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#instance-name-has-markup) - Set `webserver.instance_name` to some text with markup, like `<b style="color: red">title</b>` - Set `webserver.Instance_name_has_markup` to `true` ### Operating System Doesn't matter ### 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/28888

https://github.com/apache/airflow/pull/28894

696b91fafe4a557f179098e0609eb9d9dcb73f72

971e3226dc3ca43900f0b79c42afffb14c59d691

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/hooks/wasb.py", "tests/providers/microsoft/azure/hooks/test_wasb.py"]

Azure Blob storage exposes crendentials in UI

### Apache Airflow version Other Airflow 2 version (please specify below) 2.3.3 ### What happened Azure Blob Storage exposes credentials in the UI <img width="1249" alt="Screenshot 2023-01-12 at 14 00 05" src="https://user-images.githubusercontent.com/35199552/212072943-adca75c4-2226-4251-9446-e8f18fb22081.png"> ### What you think should happen instead _No response_ ### How to reproduce Create an Azure Blob storage connection. then click on the edit button on the connection. ### Operating System debain ### 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/28884

https://github.com/apache/airflow/pull/28914

6f4544cfbdfa3cabb3faaeea60a651206cd84e67

3decb189f786781bb0dfb3420a508a4a2a22bd8b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/callModal.js", "airflow/www/templates/airflow/dag.html", "airflow/www/views.py"]

Graph UI: Add Filter Downstream & Filter DownStream & Upstream

### Description Currently Airflow has a `Filter Upstream` View/option inside the graph view. (As documented [here](https://docs.astronomer.io/learn/airflow-ui#graph-view) under `Filter Upstream`) <img width="682" alt="image" src="https://user-images.githubusercontent.com/9246654/211711759-670a1180-7f90-4ecd-84b0-2f3b290ff477.png"> It would be great if there were also the options 1. `Filter Downstream` & 2. `Filter Downstream & Upstream` ### Use case/motivation Sometimes it is useful to view downstream tasks & down & upstream tasks when reviewing dags. This feature would make it as easy to view those as it is to view upstream today. ### Related issues I found nothing with a quick search ### 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/28847

https://github.com/apache/airflow/pull/29226

624520db47f736af820b4bc834a5080111adfc96

a8b2de9205dd805ee42cf6b0e15e7e2805752abb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py", "airflow/providers/amazon/aws/waiters/README.md", "airflow/providers/amazon/aws/waiters/dynamodb.json", "docs/apache-airflow-providers-amazon/transfer/dynamodb_to_s3.rst", "tests/providers/amazon/aws/transfers/test_dynamodb_to_s3.py", "tests/providers/amazon/aws/waiters/test_custom_waiters.py", "tests/system/providers/amazon/aws/example_dynamodb_to_s3.py"]

Export DynamoDB table to S3 with PITR

### Description Airflow provides the Amazon DynamoDB to Amazon S3 below. https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/transfer/dynamodb_to_s3.html Most of Data Engineer build their "export DDB data to s3" pipeline using "within the point in time recovery window". https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb.html#DynamoDB.Client.export_table_to_point_in_time I appreciate if airflow has this function as a native function. ### Use case/motivation My daily batch job exports its data with pitr option. All of tasks is written by apache-airflow-providers-amazon except "export_table_to_point_in_time" task. "export_table_to_point_in_time" task only used the python operator. I expect I can unify the task as apache-airflow-providers-amazon library. ### 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/28830

https://github.com/apache/airflow/pull/31142

71c26276bcd3ddd5377d620e6b8baef30b72eaa0

cd3fa33e82922e01888d609ed9c24b9c2dadfa27

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/dag_run_endpoint.py", "airflow/api_connexion/schemas/dag_run_schema.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py"]

Bad request when triggering dag run with `note` in payload

### Apache Airflow version 2.5.0 ### What happened Specifying a `note` in the payload (as mentioned [in the doc](https://airflow.apache.org/docs/apache-airflow/2.5.0/stable-rest-api-ref.html#operation/post_dag_run)) when triggering a new dag run yield a 400 bad request (Git Version: .release:2.5.0+fa2bec042995004f45b914dd1d66b466ccced410) ### What you think should happen instead As far as I understand the documentation, I should be able to set a note for this dag run, and it is not the case. ### How to reproduce This is a local airflow, using default credentials and default setup when following [this guide](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#) DAG: <details> ``` import airflow from airflow import DAG import logging from airflow.operators.python import PythonOperator from airflow.operators.dummy import DummyOperator from datetime import timedelta logger = logging.getLogger("airflow.task") default_args = { "owner": "airflow", "depends_on_past": False, "retries": 0, "retry_delay": timedelta(minutes=5), } def log_body(**context): logger.info(f"Body: {context['dag_run'].conf}") with DAG( "my-validator", default_args=default_args, schedule_interval=None, start_date=airflow.utils.dates.days_ago(0), catchup=False ) as dag: ( PythonOperator( task_id="abcde", python_callable=log_body, provide_context=True ) >> DummyOperator( task_id="todo" ) ) ``` </details> Request: <details> ``` curl --location --request POST '0.0.0.0:8080/api/v1/dags/my-validator/dagRuns' \ --header 'Authorization: Basic YWlyZmxvdzphaXJmbG93' \ --header 'Content-Type: application/json' \ --data-raw '{ "conf": { "key":"value" }, "note": "test" }' ``` </details> Response: <details> ``` { "detail": "{'note': ['Unknown field.']}", "status": 400, "title": "Bad Request", "type": "https://airflow.apache.org/docs/apache-airflow/2.5.0/stable-rest-api-ref.html#section/Errors/BadRequest" } ``` </details> Removing the `note` key, returns 200... with a null `note`! <details> ``` { "conf": { "key": "value" }, "dag_id": "my-validator", "dag_run_id": "manual__2023-01-10T10:45:26.102802+00:00", "data_interval_end": "2023-01-10T10:45:26.102802+00:00", "data_interval_start": "2023-01-10T10:45:26.102802+00:00", "end_date": null, "execution_date": "2023-01-10T10:45:26.102802+00:00", "external_trigger": true, "last_scheduling_decision": null, "logical_date": "2023-01-10T10:45:26.102802+00:00", "note": null, "run_type": "manual", "start_date": null, "state": "queued" } ``` </details> ### Operating System Ubuntu 20.04.5 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else Everytime. ### 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/28825

https://github.com/apache/airflow/pull/29228

e626131563efb536f325a35c78585b74d4482ea3

b94f36bf563f5c8372086cec63b74eadef638ef8

python

closed

apache/airflow

https://github.com/apache/airflow

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

DatabricksSubmitRunOperator Get failed for Multi Task Databricks Job Run

### Apache Airflow Provider(s) databricks ### Versions of Apache Airflow Providers As we are running DatabricksSubmitRunOperator to run multi task databricks job as I am using airflow providers with mostly all flavours of versions, but when the databricks job get failed, DatabricksSubmitRunOperator gives below error its because this operator running get-output API, hence taking job run id instead of taking task run id Error ```console File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 355, in _do_api_call for attempt in self._get_retry_object(): File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/tenacity/__init__.py", line 382, in __iter__ do = self.iter(retry_state=retry_state) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/tenacity/__init__.py", line 349, in iter return fut.result() File "/usr/lib/python3.8/concurrent/futures/_base.py", line 437, in result return self.__get_result() File "/usr/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 365, in _do_api_call response.raise_for_status() File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/requests/models.py", line 960, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/operators/databricks.py", line 375, in execute _handle_databricks_operator_execution(self, hook, self.log, context) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/operators/databricks.py", line 90, in _handle_databricks_operator_execution run_output = hook.get_run_output(operator.run_id) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks.py", line 280, in get_run_output run_output = self._do_api_call(OUTPUT_RUNS_JOB_ENDPOINT, json) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 371, in _do_api_call raise AirflowException( airflow.exceptions.AirflowException: Response: b'{"error_code":"INVALID_PARAMETER_VALUE","message":"Retrieving the output of runs with multiple tasks is not supported. Please retrieve the output of each individual task run instead."}', Status Code: 400 [2023-01-10, 05:15:12 IST] {taskinstance.py} INFO - Marking task as FAILED. dag_id=experiment_metrics_store_experiment_4, task_id=, execution_date=20230109T180804, start_date=20230109T180810, end_date=20230109T181512 [2023-01-10, 05:15:13 IST] {warnings.py} WARNING - /home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/utils/email.py:119: PendingDeprecationWarning: Fetching SMTP credentials from configuration variables will be deprecated in a future release. Please set credentials using a connection instead. send_mime_email(e_from=mail_from, e_to=recipients, mime_msg=msg, conn_id=conn_id, dryrun=dryrun) ``` ### Apache Airflow version 2.3.2 ### Operating System macos ### Deployment Other ### Deployment details _No response_ ### What happened _No response_ ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28812

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

87a0bd969b5bdb06c6e93236432eff6d28747e59

679a85325a73fac814c805c8c34d752ae7a94312

python

closed

apache/airflow

https://github.com/apache/airflow

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

BaseSQLToGCSOperator no longer returns at least one file even if empty

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.6.0 ### Apache Airflow version 2.5.0 ### Operating System Debian GNU/Linux ### Deployment Astronomer ### Deployment details _No response_ ### What happened PR `Expose SQL to GCS Metadata (https://github.com/apache/airflow/pull/24382)` made a breaking change [here](https://github.com/apache/airflow/blob/3eee33ac8cb74cfbb08bce9090e9c601cf98da44/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L286) that results in no files being returned when there are no data rows (empty table) rather than a single empty file as in the past. ### What you think should happen instead I would like to preserve the original behavior of having at least one file returned even if it is empty. Or to make that behavior optional via a new parameter. The original behavior can be implemented with the following code change: FROM: ``` if file_to_upload["file_row_count"] > 0: yield file_to_upload ``` TO: ``` if file_no == 0 or file_to_upload["file_row_count"] > 0: yield file_to_upload ``` ### How to reproduce Create a DAG that uses BaseSQLToGCSOperator with a SQL command that references an empty SQL table or returns no rows. The `execute` method will not write any files. ### 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/28806

https://github.com/apache/airflow/pull/28959

7f2b065ccd01071cff8f298b944d81f3ff3384b5

5350be2194250366536db7f78b88dc8e49c9620e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/datasets/manager.py", "airflow/jobs/scheduler_job.py", "docs/apache-airflow/administration-and-deployment/logging-monitoring/metrics.rst"]

statsd metric for dataset count

### Description A count of datasets that are currently registered/declared in an Airflow deployment. ### Use case/motivation Would be nice to see how deployments are adopting datasets. ### 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/28803

https://github.com/apache/airflow/pull/28907

5d84b59554c93fd22e92b46a1061b40b899a8dec

7689592c244111b24bc52e7428c5a3bb80a4c2d6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "setup.cfg"]

Add colors in help outputs of Airfow CLI commands

### Body Folowing up after https://github.com/apache/airflow/pull/22613#issuecomment-1374530689 - seems that there is a new [rich-argparse](https://github.com/hamdanal/rich-argparse) project that might give us the option without rewriting Airflow's argument parsing to click (click has a number of possible performance issues that might impact airlfow's speed of CLI command parsing) Seems this might be rather easy thing to do (just adding the formatter class for argparse). Would be nice if someone implements it and tests (also for performance of CLI). ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28789

https://github.com/apache/airflow/pull/29116

c310fb9255ba458b2842315f65f59758b76df9d5

fdac67b3a5350ab4af79fd98612592511ca5f3fc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/manager.py"]

AIP-44 Migrate DagFileProcessorManager.clear_nonexistent_import_errors to Internal API

https://github.com/apache/airflow/blob/main/airflow/dag_processing/manager.py#L773

https://github.com/apache/airflow/issues/28785

https://github.com/apache/airflow/pull/28976

ca9a59b3e8c08286c8efd5ca23a509f9178a3cc9

09b3a29972430e5749d772359692fe4a9d528e48

python

closed

apache/airflow

https://github.com/apache/airflow

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

DAG Run List UI Breaks when a non-JSON serializable value is added to dag_run.conf

### Apache Airflow version 2.5.0 ### What happened When accessing `dag_run.conf` via a task's context, I was able to add a value that is non-JSON serializable. When I tried to access the Dag Run List UI (`/dagrun/list/`) or the Dag's Grid View, I was met with these error messages respectively: **Dag Run List UI** ``` Ooops! Something bad has happened. Airflow is used by many users, and it is very likely that others had similar problems and you can easily find a solution to your problem. Consider following these steps: * gather the relevant information (detailed logs with errors, reproduction steps, details of your deployment) * find similar issues using: * [GitHub Discussions](https://github.com/apache/airflow/discussions) * [GitHub Issues](https://github.com/apache/airflow/issues) * [Stack Overflow](https://stackoverflow.com/questions/tagged/airflow) * the usual search engine you use on a daily basis * if you run Airflow on a Managed Service, consider opening an issue using the service support channels * if you tried and have difficulty with diagnosing and fixing the problem yourself, consider creating a [bug report](https://github.com/apache/airflow/issues/new/choose). Make sure however, to include all relevant details and results of your investigation so far. ``` **Grid View** ``` Auto-refresh Error <!DOCTYPE html> <html lang="en"> <head> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> </head> <body> <div class="container"> <h1> Ooops! </h1> <div> <pre> 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 ``` I was able to push the same value to XCom with `AIRFLOW__CORE__ENABLE_XCOM_PICKLING=True`, and the XCom List UI (`/xcom/list/`) did **not** throw an error. In the postgres instance I am using for the Airflow DB, both `dag_run.conf` & `xcom.value` have `BYTEA` types. ### What you think should happen instead Since we are able to add (and commit) a non-JSON serializable value into a Dag Run's conf, the UI should not break when trying to load this value. We could also ensure that one DAG Run's conf does not break the List UI for all Dag Runs (across all DAGs), and the DAG's Grid View. ### How to reproduce - Set `AIRFLOW__CORE__ENABLE_XCOM_PICKLING=True` - Trigger this DAG: ``` import datetime from airflow.decorators import dag, task from airflow.models.xcom import XCom @dag( schedule_interval=None, start_date=datetime.datetime(2023, 1, 1), ) def ui_issue(): @task() def update_conf(**kwargs): dag_conf = kwargs["dag_run"].conf dag_conf["non_json_serializable_value"] = b"1234" print(dag_conf) @task() def push_to_xcom(**kwargs): dag_conf = kwargs["dag_run"].conf print(dag_conf) XCom.set(key="dag_conf", value=dag_conf, dag_id=kwargs["ti"].dag_id, task_id=kwargs["ti"].task_id, run_id=kwargs["ti"].run_id) return update_conf() >> push_to_xcom() dag = ui_issue() ``` - View both the Dag Runs and XCom lists in the UI. - The DAG Run List UI should break, and the XCom List UI should show a value of `{'non_json_serializable_value': b'1234'}` for `ui_issue.push_to_xcom`. ### Operating System Debian Bullseye ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else The XCom List UI was able to render this value. We could extend this capability to the DAG Run List UI. ### 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/28772

https://github.com/apache/airflow/pull/28777

82c5a5f343d2310822f7bb0d316efa0abe9d4a21

8069b500e8487675df0472b4a5df9081dcfa9d6c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/connection_command.py", "tests/cli/commands/test_connection_command.py"]

Cannot create connection without defining host using CLI

### Apache Airflow version 2.5.0 ### What happened In order to send logs to s3 bucket after finishing the task, I added a connection to airflow using cli. ```airflow connections add connection_id_1 --conn-uri aws://s3/?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` Then I got a logging warning saying: [2023-01-06T13:28:39.585+0000] {logging_mixin.py:137} WARNING - <string>:8 DeprecationWarning: Host s3 specified in the connection is not used. Please, set it on extra['endpoint_url'] instead Instead I was trying to remove the host from the `conn-uri` I provided but every attempt to create a connection failed (list of my attempts below): ```airflow connections add connection_id_1 --conn-uri aws://?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ```airflow connections add connection_id_1 --conn-uri aws:///?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ### What you think should happen instead I believe there are 2 options: 1. Allow to create connection without defining host or 2. Remove the warning log ### How to reproduce Create an S3 connection using CLI: ```airflow connections add connection_id_1 --conn-uri aws://s3/?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ### Operating System Linux - official airflow image from docker hub apache/airflow:slim-2.5.0 ### Versions of Apache Airflow Providers ``` apache-airflow-providers-cncf-kubernetes | 5.0.0 | Kubernetes apache-airflow-providers-common-sql | 1.3.1 | Common SQL Provider apache-airflow-providers-databricks | 4.0.0 | Databricks apache-airflow-providers-ftp | 3.2.0 | File Transfer Protocol (FTP) apache-airflow-providers-hashicorp | 3.2.0 | Hashicorp including Hashicorp Vault apache-airflow-providers-http | 4.1.0 | Hypertext Transfer Protocol (HTTP) apache-airflow-providers-imap | 3.1.0 | Internet Message Access Protocol (IMAP) apache-airflow-providers-postgres | 5.3.1 | PostgreSQL apache-airflow-providers-sqlite | 3.3.1 | SQLite ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else This log message is printed every second minute so it is pretty annoying. ### 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/28766

https://github.com/apache/airflow/pull/28922

c5ee4b8a3a2266ef98b379ee28ed68ff1b59ac5f

d8b84ce0e6d36850cd61b1ce37840c80aaec0116

python

closed

apache/airflow

https://github.com/apache/airflow

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

All Airflow Configurations set via Environment Variable are masked when `expose_config` is set as `non-sensitive-only`

### Apache Airflow version 2.5.0 ### What happened In [Airflow 2.4.0](https://github.com/apache/airflow/blob/main/RELEASE_NOTES.rst#airflow-240-2022-09-19), a new feature was added that added an option to mask sensitive data in UI configuration page ([PR](https://github.com/apache/airflow/pull/25346)). I have set `AIRFLOW__WEBSERVER__EXPOSE_CONFIG` as `NON-SENSITIVE-ONLY`. The feature is working partially as the `airflow.cfg` file display only has [sensitive configurations](https://github.com/apache/airflow/blob/2.5.0/airflow/configuration.py#L149-L160) marked as `< hidden >`. However, the `Running Configuration` table below the file display has all configuration set via environment variables marked as `< hidden >` which I believe is unintended. I did not change `airflow.cfg` so the value here is displaying the default value of `False` as expected.  The value for `expose_config` I expect to be shown as `NON-SENSITIVE-ONLY` but it shown as `< hidden >`.  ### What you think should happen instead As mentioned previously, the value for `expose_config` I expect to be shown as `NON-SENSITIVE-ONLY`. Only the [sensitive variables](https://github.com/apache/airflow/blob/2.5.0/airflow/configuration.py#L149-L160) should be set as `< hidden >`. ### How to reproduce Set an Airflow configuration through the environment variable and check on the Configuration page. ### 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/28756

https://github.com/apache/airflow/pull/28802

9a7f07491e603123182adfd5706fbae524e33c0d

0a8d0ab56689c341e65a36c0287c9d635bae1242

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/cloud_base.py", "tests/providers/google/cloud/operators/test_cloud_base.py"]

KubernetesExecutor leaves failed pods due to deepcopy issue with Google providers

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened With Airflow 2.3 and 2.4 there appears to be a bug in the KubernetesExecutor when used in conjunction with the Google airflow providers. This bug does not affect Airflow 2.2 due to the pip version requirements. The bug specifically presents itself when using nearly any Google provider operator. During the pod lifecycle, all is well until the executor in the pod starts to clean up following a successful run. Airflow itself still see's the task marked as a success, but in Kubernetes, while the task is finishing up after reporting status, it actually crashes and puts the pod into a Failed state silently: ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 103, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 382, in task_run _run_task_by_selected_method(args, dag, ti) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 189, in _run_task_by_selected_method _run_task_by_local_task_job(args, ti) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 247, in _run_task_by_local_task_job run_job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 247, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 137, in _execute self.handle_task_exit(return_code) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 168, in handle_task_exit self._run_mini_scheduler_on_child_tasks() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 253, in _run_mini_scheduler_on_child_tasks partial_dag = task.dag.partial_subset( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2188, in partial_subset dag.task_dict = { File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2189, in <dictcomp> t.task_id: _deepcopy_task(t) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2186, in _deepcopy_task return copy.deepcopy(t, memo) File "/usr/local/lib/python3.9/copy.py", line 153, in deepcopy y = copier(memo) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1163, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "/usr/local/lib/python3.9/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/usr/local/lib/python3.9/copy.py", line 264, in _reconstruct y = func(*args) File "/usr/local/lib/python3.9/enum.py", line 384, in __call__ return cls.__new__(cls, value) File "/usr/local/lib/python3.9/enum.py", line 702, in __new__ raise ve_exc ValueError: <object object at 0x7f570181a3c0> is not a valid _MethodDefault ``` Based on a quick look, it appears to be related to the default argument that Google is using in its operators which happens to be an Enum, and fails during a deepcopy at the end of the task. Example operator that is affected: https://github.com/apache/airflow/blob/403ed7163f3431deb7fc21108e1743385e139907/airflow/providers/google/cloud/hooks/dataproc.py#L753 Reference to the Google Python API core which has the Enum causing the problem: https://github.com/googleapis/python-api-core/blob/main/google/api_core/gapic_v1/method.py#L31 ### What you think should happen instead Kubernetes pods should succeed, be marked as `Completed`, and then be gracefully terminated. ### How to reproduce Use any `apache-airflow-providers-google` >= 7.0.0 which includes `google-api-core` >= 2.2.2. Run a DAG with a task which uses any of the Google operators which have `_MethodDefault` as a default argument. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.0.0 apache-airflow-providers-apache-hive==5.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.4.0 apache-airflow-providers-common-sql==1.3.1 apache-airflow-providers-docker==3.2.0 apache-airflow-providers-elasticsearch==4.2.1 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-google==8.4.0 apache-airflow-providers-grpc==3.0.0 apache-airflow-providers-hashicorp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-microsoft-azure==4.3.0 apache-airflow-providers-mysql==3.2.1 apache-airflow-providers-odbc==3.1.2 apache-airflow-providers-postgres==5.2.2 apache-airflow-providers-presto==4.2.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-sendgrid==3.0.0 apache-airflow-providers-sftp==4.1.0 apache-airflow-providers-slack==6.0.0 apache-airflow-providers-sqlite==3.2.1 apache-airflow-providers-ssh==3.2.0 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28751

https://github.com/apache/airflow/pull/29518

ec31648be4c2fc4d4a7ef2bd23be342ca1150956

5a632f78eb6e3dcd9dc808e73b74581806653a89

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/utils.py", "tests/test_utils/www.py", "tests/www/views/conftest.py", "tests/www/views/test_views_home.py"]

UIAlert returns AttributeError: 'NoneType' object has no attribute 'roles' when specifying AUTH_ROLE_PUBLIC

### Apache Airflow version 2.5.0 ### What happened When adding a [role-based UIAlert following these docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/customize-ui.html#add-custom-alert-messages-on-the-dashboard), I received the below stacktrace: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/views.py", line 780, in index dashboard_alerts = [ File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/views.py", line 781, in <listcomp> fm for fm in settings.DASHBOARD_UIALERTS if fm.should_show(get_airflow_app().appbuilder.sm) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/utils.py", line 820, in should_show user_roles = {r.name for r in securitymanager.current_user.roles} AttributeError: 'NoneType' object has no attribute 'roles' ``` On further inspection, I realized this is happening because my webserver_config.py has this specification: ```py # Uncomment and set to desired role to enable access without authentication AUTH_ROLE_PUBLIC = 'Viewer' ``` When we set AUTH_ROLE_PUBLIC to a role like Viewer, [this line](https://github.com/apache/airflow/blob/ad7f8e09f8e6e87df2665abdedb22b3e8a469b49/airflow/www/utils.py#L828) returns an exception because `securitymanager.current_user` is None. Relevant code snippet: ```py def should_show(self, securitymanager) -> bool:Open an interactive python shell in this frame """Determine if the user should see the message based on their role membership""" if self.roles: user_roles = {r.name for r in securitymanager.current_user.roles} if not user_roles.intersection(set(self.roles)): return False return True ``` ### What you think should happen instead If we detect that the securitymanager.current_user is None, we should not attempt to get its `roles` attribute. Instead, we can check to see if the AUTH_ROLE_PUBLIC is set in webserver_config.py which will tell us if a public role is being used. If it is, we can assume that because the current_user is None, the current_user's role is the public role. In code, this might look like this: ```py def should_show(self, securitymanager) -> bool: """Determine if the user should see the message based on their role membership""" if self.roles: user_roles = set() if hasattr(securitymanager.current_user, "roles"): user_roles = {r.name for r in securitymanager.current_user.roles} elif "AUTH_ROLE_PUBLIC" in securitymanager.appbuilder.get_app.config: # Give anonymous user public role user_roles = set([securitymanager.appbuilder.get_app.config["AUTH_ROLE_PUBLIC"]]) if not user_roles.intersection(set(self.roles)): return False return True ``` Expected result on the webpage: <img width="1440" alt="image" src="https://user-images.githubusercontent.com/9200263/210823778-4c619b75-40a3-4caa-9a2c-073651da7f0d.png"> ### How to reproduce Start breeze: ``` breeze --python 3.7 --backend postgres start-airflow ``` After the webserver, triggerer, and scheduler are started, modify webserver_config.py to uncomment AUTH_ROLE_PUBLIC and add airflow_local_settings.py: ```bash cd $AIRFLOW_HOME # Uncomment AUTH_ROLE_PUBLIC vi webserver_config.py mkdir -p config # Add sample airflow_local_settings.py below vi config/airflow_local_settings.py ``` ```py from airflow.www.utils import UIAlert DASHBOARD_UIALERTS = [ UIAlert("Role based alert", category="warning", roles=["Viewer"]), ] ``` Restart the webserver and navigate to airflow. You should see this page: <img width="1440" alt="image" src="https://user-images.githubusercontent.com/9200263/210820838-e74ffc23-7b6b-42dc-85f1-29ab8b0ee3d5.png"> ### Operating System Debian 11 ### Versions of Apache Airflow Providers 2.5.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Locally ### Anything else This problem only occurs if you add a role based UIAlert and are using AUTH_ROLE_PUBLIC ### 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/28746

https://github.com/apache/airflow/pull/28781

1e9c8e52fda95a0a30b3ae298d5d3adc1971ed45

f17e2ba48b59525655a92e04684db664a672918f

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/logs-persistent-volume-claim.yaml", "chart/values.schema.json", "chart/values.yaml"]

annotations in logs pvc

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.5.0 ### Kubernetes Version v1.22.8+d48376b ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened When creating the dags pvc, it is possible to inject annotations to the object. ### What you think should happen instead There should be the possibility to inject annotations to the logs pvc as well. ### How to reproduce _No response_ ### Anything else We are using annotations on pvc to disable the creation of backup snapshots provided by our company platform. (OpenShift) ### 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/28745

https://github.com/apache/airflow/pull/29270

6ef5ba9104f5a658b003f8ade274f19d7ec1b6a9

5835b08e8bc3e11f4f98745266d10bbae510b258

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/utils/waiter.py", "tests/providers/amazon/aws/utils/test_waiter.py"]

Fix custom waiter function in AWS provider package

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.5.0 ### Operating System MacOS ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened Discussed in #28294 ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28691

https://github.com/apache/airflow/pull/28753

2b92c3c74d3259ebac714f157c525836f0af50f0

ce188e509389737b3c0bdc282abea2425281c2b7

python

closed

apache/airflow

https://github.com/apache/airflow

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

Improve AWS Batch hook and operator

### Description AWS Batch hook and operator do not support the boto3 parameter shareIdentifier, which is required to submit jobs to specific types of queues. ### Use case/motivation I wish that AWS Batch hook and operator support the submit of jobs to queues that require shareIdentifier parameter. ### 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/28680

https://github.com/apache/airflow/pull/30829

bd542fdf51ad9550e5c4348f11e70b5a6c9adb48

612676b975a2ff26541bb2581fbdf2befc6c3de9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/telegram/CHANGELOG.rst", "airflow/providers/telegram/hooks/telegram.py", "airflow/providers/telegram/provider.yaml", "docs/spelling_wordlist.txt", "generated/provider_dependencies.json", "tests/providers/telegram/hooks/test_telegram.py"]

Support telegram-bot v20+

### Body Currently our telegram integration uses Telegram v13 telegram-bot library. On 1st of Jan 2023 a new, backwards incompatible version of Telegram-bot has been released : https://pypi.org/project/python-telegram-bot/20.0/#history and at least as reported by MyPy and our test suite test failures, Telegram 20 needs some changes to work: Here is a transition guide that might be helpful. Transition guide is here: https://github.com/python-telegram-bot/python-telegram-bot/wiki/Transition-guide-to-Version-20.0 In the meantime we limit telegram to < 20.0.0 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28670

https://github.com/apache/airflow/pull/28953

68412e166414cbf6228385e1e118ec0939857496

644cea14fff74d34f823b5c52c9dbf5bad33bd52

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/beam/operators/beam.py"]

BeamRunGoPipelineOperator: temp dir with Go file from GCS is removed before starting the pipeline

### Apache Airflow Provider(s) apache-beam ### Versions of Apache Airflow Providers apache-airflow-providers-apache-beam==4.1.0 apache-airflow-providers-google==8.6.0 ### Apache Airflow version 2.5.0 ### Operating System macOS 13.1 ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened When using the `BeamRunGoPipelineOperator` with a `go_file` on GCS, the object is downloaded to a temporary directory, however the directory with the file has already been removed by the time it is needed, i.e. when executing `go mod init` and starting the pipeline. ### What you think should happen instead The `BeamRunGoPipelineOperator.execute` method enters into a `tempfile.TemporaryDirectory` context manager using [with](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/apache/beam/operators/beam.py#L588) when downloading the `go_file` from GCS to the local filesystem. On completion of the context, this temporary directory is removed. `BeamHook.start_go_pipeline`, which uses the file, is called outside of the context however, which means the file no longer exists when `go mod init` is called. A suggested solution is to use the `enter_context` method of the existing `ExitStack` to also enter into the TemporaryDirectory context manager. This allows the go_file to still exist when it is time to initialize the go module and start the pipeline: ```python with ExitStack() as exit_stack: if self.go_file.lower().startswith("gs://"): gcs_hook = GCSHook(self.gcp_conn_id, self.delegate_to) tmp_dir = exit_stack.enter_context(tempfile.TemporaryDirectory(prefix="apache-beam-go")) tmp_gcs_file = exit_stack.enter_context( gcs_hook.provide_file(object_url=self.go_file, dir=tmp_dir) ) self.go_file = tmp_gcs_file.name self.should_init_go_module = True ``` ### How to reproduce The problem can be reproduced by creating a DAG which uses the `BeamRunGoPipelineOperator` and passing a `go_file` with a GS URI: ```python import pendulum from airflow import DAG from airflow.providers.apache.beam.operators.beam import BeamRunGoPipelineOperator with DAG( dag_id="beam_go_dag", start_date=pendulum.today("UTC"), ) as dag: BeamRunGoPipelineOperator( task_id="beam_go_pipeline", go_file="gs://my-bucket/main.go" ) ``` ### Anything else Relevant logs: ``` [2023-01-01T12:41:06.155+0100] {taskinstance.py:1303} INFO - Executing <Task(BeamRunGoPipelineOperator): beam_go_pipeline> on 2023-01-01 00:00:00+00:00 [2023-01-01T12:41:06.411+0100] {taskinstance.py:1510} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=airflow AIRFLOW_CTX_DAG_ID=beam_go_dag AIRFLOW_CTX_TASK_ID=beam_go_pipeline AIRFLOW_CTX_EXECUTION_DATE=2023-01-01T00:00:00+00:00 AIRFLOW_CTX_TRY_NUMBER=1 AIRFLOW_CTX_DAG_RUN_ID=backfill__2023-01-01T00:00:00+00:00 [2023-01-01T12:41:06.430+0100] {base.py:73} INFO - Using connection ID 'google_cloud_default' for task execution. [2023-01-01T12:41:06.441+0100] {credentials_provider.py:323} INFO - Getting connection using `google.auth.default()` since no key file is defined for hook. [2023-01-01T12:41:08.701+0100] {gcs.py:323} INFO - File downloaded to /var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4/tmp6j9g5090main.go [2023-01-01T12:41:08.704+0100] {process_utils.py:179} INFO - Executing cmd: go mod init main [2023-01-01T12:41:08.712+0100] {taskinstance.py:1782} ERROR - Task failed with exception Traceback (most recent call last): File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/cloud/hooks/gcs.py", line 402, in provide_file yield tmp_file File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/operators/beam.py", line 621, in execute self.beam_hook.start_go_pipeline( File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/hooks/beam.py", line 339, in start_go_pipeline init_module("main", working_directory) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/go_module_utils.py", line 37, in init_module execute_in_subprocess(go_mod_init_cmd, cwd=go_module_path) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/utils/process_utils.py", line 168, in execute_in_subprocess execute_in_subprocess_with_kwargs(cmd, cwd=cwd) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/utils/process_utils.py", line 180, in execute_in_subprocess_with_kwargs with subprocess.Popen( File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/subprocess.py", line 969, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/subprocess.py", line 1845, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/operators/beam.py", line 584, in execute with ExitStack() as exit_stack: File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 576, in __exit__ raise exc_details[1] File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 561, in __exit__ if cb(*exc_details): File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 153, in __exit__ self.gen.throw(typ, value, traceback) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/cloud/hooks/gcs.py", line 399, in provide_file with NamedTemporaryFile(suffix=file_name, dir=dir) as tmp_file: File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 502, in __exit__ self.close() File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 509, in close self._closer.close() File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 446, in close unlink(self.name) FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4/tmp6j9g5090main.go' [2023-01-01T12:41:08.829+0100] {taskinstance.py:1321} INFO - Marking task as FAILED. dag_id=beam_go_dag, task_id=beam_go_pipeline, execution_date=20230101T000000, start_date=20230101T114106, end_date=20230101T114108 [...] ``` ### 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/28662

https://github.com/apache/airflow/pull/28664

675af73ceb5bc8b03d46a7cd903a73f9b8faba6f

8da678ccd2e5a30f9c2d22c7526b7a238c185d2f

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/jobs/test_local_task_job.py"]

Fix Quarantine tests

### Body We have several tests marked in the code base with `@pytest.mark.quarantined` It means that the tests are flaky and if fail in CI it does not fail the build. The goal is to fix the tests and make them stable. This task is to gather all of them under the same issue instead of dedicated issue per test. - [x] [TestImpersonation](https://github.com/apache/airflow/blob/bfcae349b88fd959e32bfacd027a5be976fe2132/tests/core/test_impersonation_tests.py#L117) - [x] [TestImpersonationWithCustomPythonPath](https://github.com/apache/airflow/blob/bfcae349b88fd959e32bfacd027a5be976fe2132/tests/core/test_impersonation_tests.py#L181) - [x] [test_exception_propagation](https://github.com/apache/airflow/blob/76f81cd4a7433b7eeddb863b2ae6ee59176cf816/tests/jobs/test_local_task_job.py#L772) - [x] [test_localtaskjob_maintain_heart_rate](https://github.com/apache/airflow/blob/76f81cd4a7433b7eeddb863b2ae6ee59176cf816/tests/jobs/test_local_task_job.py#L402) - [x] [test_exception_propagation](https://github.com/apache/airflow/blob/4d0fd8ef6adc35f683c7561f05688a65fd7451f4/tests/executors/test_celery_executor.py#L103) - [x] [test_process_sigterm_works_with_retries](https://github.com/apache/airflow/blob/65010fda091242870a410c65478eae362899763b/tests/jobs/test_local_task_job.py#L770) ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28658

https://github.com/apache/airflow/pull/29087

90ce88bf34b2337f89eed67e41092f53bf24e9c1

a6e21bc6ce428eadf44f62b05aeea7bbd3447a7b

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/helm-chart/index.rst"]

version 2.4.1 migration job "run-airflow-migrations" run once only when deploy via helm or flux/kustomization

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.4.1 ### Kubernetes Version v4.5.4 ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened manually copied from [the Q & A 27992 migration job](https://github.com/apache/airflow/discussions/27992) (the button create issue from discussion did not work) I found my migration job would not restart for the 2nd time (the 1st time run was when the default airflow is deployed onto Kubernetes and it had no issues), and then i started to apply changes to the values.yaml file such as **make the database to be azure postgresql**; but then it would not take the values into effect, see screen shots; of course my debug skills on kubernetes are not high, so i would need extra help if extra info is needed.    ``` database: sql_alchemy_conn_secret: airflow-postgres-redis sql_alchemy_connect_args: { "keepalives": 1, "keepalives_idle": 30, "keepalives_interval": 5, "keepalives_count": 5, } postgresql: enabled: false pgbouncer: enabled: false # Airflow database & redis config data: metadataSecretName: airflow-postgres-redis ``` check again the pod for waiting for the migration:  and below was the 1st success at the initial installation (which did not use external db) ``` kubectl describe job airflow-airflow-run-airflow-migrations Name: airflow-airflow-run-airflow-migrations Namespace: airflow Selector: controller-uid=efdc3c7b-5172-4841-abcf-17e055fa6e2e Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=run-airflow-migrations helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Tue, 27 Dec 2022 14:21:50 +0100 Completed At: Tue, 27 Dec 2022 14:22:29 +0100 Duration: 39s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=run-airflow-migrations controller-uid=efdc3c7b-5172-4841-abcf-17e055fa6e2e job-name=airflow-airflow-run-airflow-migrations release=airflow-airflow tier=airflow Service Account: airflow-airflow-migrate-database-job Containers: run-airflow-migrations: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow db upgrade Environment: PYTHONUNBUFFERED: 1 AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else my further experiment/try tells me the jobs were only run once. more independent tests could be done with a bit help, such as what kind of changes will trigger migration job to run. See below helm release history: the 1st installation worked; and i could not make the 3rd release to succeed even though the values are 100% correct; so **the bug/issue short description is: helmRelease in combination with `flux` have issues with db migration jobs (only run once <can be successful>) which makes it a stopper for further upgrade** ``` REVISION UPDATED STATUS CHART APP VERSION DESCRIPTION 1 Wed Dec 28 02:22:42 2022 superseded airflow-1.7.0 2.4.1 Install complete 2 Wed Dec 28 02:43:25 2022 deployed airflow-1.7.0 2.4.1 Upgrade complete ``` see below equivalent values , even tried to disable the db migration did not make flux to work with it. ``` createUserJob: useHelmHooks: false migrateDatabaseJob: useHelmHooks: false config: webserver: expose_config: 'non-sensitive-only' postgresql: enabled: false pgbouncer: enabled: true # The maximum number of connections to PgBouncer maxClientConn: 100 # The maximum number of server connections to the metadata database from PgBouncer metadataPoolSize: 10 # The maximum number of server connections to the result backend database from PgBouncer resultBackendPoolSize: 5 # Airflow database & redis config data: metadataSecretName: airflow-postgres-redis # to generate strong secret: python3 -c 'import secrets; print(secrets.token_hex(16))' webserverSecretKeySecretName: airflow-webserver-secret ``` and see below 2 jobs ``` $ kubectl describe job -n airflow Name: airflow-airflow-create-user Namespace: airflow Selector: controller-uid=8b09e28b-ba3a-4cee-b20f-693a3aa15363 Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=create-user-job helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Wed, 28 Dec 2022 03:22:46 +0100 Completed At: Wed, 28 Dec 2022 03:24:32 +0100 Duration: 106s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=create-user-job controller-uid=8b09e28b-ba3a-4cee-b20f-693a3aa15363 job-name=airflow-airflow-create-user release=airflow-airflow tier=airflow Service Account: airflow-airflow-create-user-job Containers: create-user: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow users create "$@" -- -r Admin -u admin -e [email protected] -f admin -l user -p admin Environment: AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> Name: airflow-airflow-run-airflow-migrations Namespace: airflow Selector: controller-uid=5da8c81f-7920-4eaf-9d7a-58a48c740bdc Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=run-airflow-migrations helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Wed, 28 Dec 2022 03:22:46 +0100 Completed At: Wed, 28 Dec 2022 03:23:07 +0100 Duration: 21s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=run-airflow-migrations controller-uid=5da8c81f-7920-4eaf-9d7a-58a48c740bdc job-name=airflow-airflow-run-airflow-migrations release=airflow-airflow tier=airflow Service Account: airflow-airflow-migrate-database-job Containers: run-airflow-migrations: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow db upgrade Environment: PYTHONUNBUFFERED: 1 AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> ``` ### 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/28637

https://github.com/apache/airflow/pull/29078

30ad26e705f50442f05dd579990372196323fc86

6c479437b1aedf74d029463bda56b42950278287

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/processor.py", "airflow/models/dagbag.py", "tests/models/test_dagbag.py"]

AIP-44 Migrate Dagbag.sync_to_db to internal API.

This method is used in DagFileProcessor.process_file - it may be easier to migrate all it's internal calls instead of the whole method.

https://github.com/apache/airflow/issues/28615

https://github.com/apache/airflow/pull/29188

05242e95bbfbaf153e4ae971fc0d0a5314d5bdb8

5c15b23023be59a87355c41ab23a46315cca21a5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/api_internal/internal_api_call.py", "airflow/models/dag.py", "tests/api_internal/test_internal_api_call.py"]

AIP-44 Migrate DagModel.get_paused_dag_ids to Internal API

https://github.com/apache/airflow/issues/28614

https://github.com/apache/airflow/pull/28693

f114c67c03a9b4257cc98bb8a970c6aed8d0c673

ad738198545431c1d10619f8e924d082bf6a3c75

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/trigger.py"]

AIP-44 Migrate Trigger class to Internal API

https://github.com/apache/airflow/issues/28613

https://github.com/apache/airflow/pull/29099

69babdcf7449c95fea7fe3b9055c677b92a74298

ee0a56a2caef0ccfb42406afe57b9d2169c13a01

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/xcom.py"]

AIP-44 Migrate XCom get*/clear* to Internal API

https://github.com/apache/airflow/issues/28612

https://github.com/apache/airflow/pull/29083

9bc48747ddbd609c2bd3baa54a5d0472e9fdcbe4

a1ffb26e5bcf4547e3b9e494cf7ccd24af30c2e6

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "STATIC_CODE_CHECKS.rst", "airflow/cli/commands/info_command.py", "scripts/ci/pre_commit/pre_commit_check_provider_yaml_files.py", "scripts/in_container/run_provider_yaml_files_check.py"]

Add pre-commit/test to verify extra links refer to existed classes

### Body We had an issue where extra link class (`AIPlatformConsoleLink`) was removed in [PR](https://github.com/apache/airflow/pull/26836) without removing the class from the `provider.yaml` extra links this resulted in web server exception as shown in https://github.com/apache/airflow/pull/28449 **The Task:** Add validation that classes of extra-links in provider.yaml are importable ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28510

https://github.com/apache/airflow/pull/28516

7ccbe4e7eaa529641052779a89e34d54c5a20f72

e47c472e632effbfe3ddc784788a956c4ca44122

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/css/main.css"]

Issues with Custom Menu Items on Smaller Windows

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We take advantage of the custom menu items with flask appbuilder offer a variety of dropdown menus with custom DAG filters. We've notice two things: 1. When you have too many dropdown menu items in a single category, several menu items are unreachable when using the Airflow UI on a small screen: <img width="335" alt="Screenshot 2022-12-19 at 6 34 24 PM" src="https://user-images.githubusercontent.com/40223998/208548419-f9d1ff57-6cad-4a40-bc58-dbf20148a92a.png"> 2. When you have too many menu categories, multiple rows of dropdown menus are displayed, but cover some other components. <img width="1077" alt="Screenshot 2022-12-19 at 6 32 05 PM" src="https://user-images.githubusercontent.com/40223998/208548222-44e50717-9040-4899-be06-d503a8c0f69a.png"> ### What you think should happen instead 1. When you have too many dropdown menu items in a single category, there should be a scrollbar. 2. When you have too many menu categories, multiple rows of dropdown menus are displayed, the menu shouldn't cover the dag import errors or any part of the UI ### How to reproduce 1. Add a bunch of menu items under the same category in a custom plugin and resize your window smaller 2. Add a large number of menu item categories in a custom plugin and resize your window smaller. ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers 2.4.3 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else I'm happy to make a PR for this. I just don't have the frontend context. If someone can point me in the right direction that'd be great ### 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/28483

https://github.com/apache/airflow/pull/28561

ea3be1a602b3e109169c6e90e555a418e2649f9a

2aa52f4ce78e1be7f34b0995d40be996b4826f26

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/jenkins/hooks/jenkins.py", "docs/apache-airflow-providers-jenkins/connections.rst", "tests/providers/jenkins/hooks/test_jenkins.py"]

Airflow 2.2.4 Jenkins Connection - unable to set as the hook expects to be

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Hello team, I am trying to use the `JenkinsJobTriggerOperator` version v3.1.0 on an Airflow instance version 2.2.4 Checking the documentation regards how to set up the connection and the hook in order to use `https` instead of the default `http`, I see https://airflow.apache.org/docs/apache-airflow-providers-jenkins/3.1.0/connections.html ``` Extras (optional) Specify whether you want to use http or https scheme by entering true to use https or false for http in extras. Default is http. ``` Unfortunately from the Airflow UI when trying to specify the connection and especially the `Extras` options it accepts a JSON-like object, so whatever you put differently to a dictionary the code fails to update the extra options for that connection. Checking in more details what the [Jenkins hook](https://airflow.apache.org/docs/apache-airflow-providers-jenkins/3.1.0/_modules/airflow/providers/jenkins/hooks/jenkins.html#JenkinsHook.conn_name_attr) does: ``` self.connection = connection connection_prefix = "http" # connection.extra contains info about using https (true) or http (false) if to_boolean(connection.extra): connection_prefix = "https" url = f"{connection_prefix}://{connection.host}:{connection.port}" ``` where the `connection.extra` cannot be a simple true/false string! ### What you think should happen instead Either we should get the `http` or `https` from the `Schema` Or we should update the [JenkinsHook](https://airflow.apache.org/docs/apache-airflow-providers-jenkins/stable/_modules/airflow/providers/jenkins/hooks/jenkins.html#JenkinsHook.default_conn_name) to read the provided dictionary for http value: `if to_boolean(connection.extra.https)` ### How to reproduce _No response_ ### Operating System macos Monterey 12.6.2 ### Versions of Apache Airflow Providers ``` pip freeze | grep apache-airflow-providers apache-airflow-providers-celery==2.1.0 apache-airflow-providers-common-sql==1.3.1 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.3 apache-airflow-providers-imap==2.2.0 apache-airflow-providers-jenkins==3.1.0 apache-airflow-providers-sqlite==2.1.0 ``` ### 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/28465

https://github.com/apache/airflow/pull/30301

f7d5b165fcb8983bd82a852dcc5088b4b7d26a91

1f8bf783b89d440ecb3e6db536c63ff324d9fc62

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/docker/operators/docker_swarm.py", "tests/providers/docker/operators/test_docker_swarm.py"]

TaskInstances do not succeed when using enable_logging=True option in DockerSwarmOperator

### Apache Airflow Provider(s) docker ### Versions of Apache Airflow Providers apache-airflow-providers-celery==3.1.0 apache-airflow-providers-docker==3.3.0 ### Apache Airflow version 2.5.0 ### Operating System centos 7 ### Deployment Other Docker-based deployment ### Deployment details Running an a docker-swarm cluster deployed locally. ### What happened Same issue as https://github.com/apache/airflow/issues/13675 With logging_enabled=True the DAG never completes and stays in running. When using DockerSwarmOperator together with the default enable_logging=True option, tasks do not succeed and stay in state running. When checking the docker service logs I can clearly see that the container ran and ended successfully. Airflow however does not recognize that the container finished and keeps the tasks in state running. ### What you think should happen instead DAG should complete. ### How to reproduce Docker-compose deployment: ```console curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.5.0/docker-compose.yaml' docker compose up airflow-init docker compose up -d ``` DAG code: ```python from airflow import DAG from docker.types import Mount, SecretReference from airflow.providers.docker.operators.docker_swarm import DockerSwarmOperator from datetime import timedelta from airflow.utils.dates import days_ago from airflow.models import Variable # Setup default args for the job default_args = { 'owner': 'airflow', 'start_date': days_ago(2), 'retries': 0 } # Create the DAG dag = DAG( 'test_dag', # DAG ID default_args=default_args, schedule_interval='0 0 * * *', catchup=False ) # # Create the DAG object with dag as dag: docker_swarm_task = DockerSwarmOperator( task_id="job_run", image="<any image>", execution_timeout=timedelta(minutes=5), command="<specific code>", api_version='auto', tty=True, enable_logging=True ) ``` ### 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/28452

https://github.com/apache/airflow/pull/35677

3bb5978e63f3be21a5bb7ae89e7e3ce9d06a4ab8

882108862dcaf08e7f5da519b3d186048d4ec7f9

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSToBigQueryOperator fails when schema_object is specified without schema_fields

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow 2.5.0 apache-airflow-providers-apache-beam 4.1.0 apache-airflow-providers-cncf-kubernetes 5.0.0 apache-airflow-providers-google 8.6.0 apache-airflow-providers-grpc 3.1.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details KubernetesExecutor ### What happened GCSToBigQueryOperator allows multiple ways to specify schema of the BigQuery table: 1. Setting autodetect == True 1. Setting schema_fields directly with autodetect == False 1. Setting a schema_object and optionally a schema_object_bucket with autodetect == False This third method seems to be broken in the latest provider version (8.6.0) and will always result in this error: ``` [2022-12-16, 21:06:18 UTC] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 395, in execute self.configuration = self._check_schema_fields(self.configuration) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 524, in _check_schema_fields raise RuntimeError( RuntimeError: Table schema was not found. Set autodetect=True to automatically set schema fields from source objects or pass schema_fields explicitly ``` The reason for this is because [this block](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L318-L320) where `if self.schema_object and self.source_format != "DATASTORE_BACKUP":`. fails to set self.schema_fields. It only sets the local variable, schema_fields. When self._check_schema_fields is subsequently called [here](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L395), we enter the [first block](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L523-L528) because autodetect is false and schema_fields is not set. ### What you think should happen instead No error should be raised if autodetect is set to False and a valid schema_object is provided ### How to reproduce 1. Create a simple BigQuery table with a single column col1: ```sql CREATE TABLE `my-project.my_dataset.test_gcs_to_bigquery` (col1 INT); ``` 2. Upload a json blob for this object to a bucket (e.g., data/schemas/table.json) 3. Upload a simple CSV for the source file to load to a bucket (e.g., data/source/file.csv) 4. Run the following command: ```py gcs_to_biquery = GCSToBigQueryOperator( task_id="gcs_to_bigquery", destination_project_dataset_table="my-project.my_dataset.test_gcs_to_bigquery", bucket="my_bucket_name", create_disposition="CREATE_IF_NEEDED", write_disposition="WRITE_TRUNCATE", source_objects=["data/source/file.csv"], source_format="CSV", autodetect=False, schema_object="data/schemas/table.json", ) ``` ### 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/28441

https://github.com/apache/airflow/pull/28444

032a542feeb617d1f92580b97fa0ad3cdca09d63

9eacf607be109eb6ab80f7e27d234a17fb128ae0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/provider.yaml"]

Webserver reports "ImportError: Module "airflow.providers.google.cloud.operators.mlengine" does not define a "AIPlatformConsoleLink" attribute/class"

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow 2.5.0 apache-airflow-providers-apache-beam 4.1.0 apache-airflow-providers-cncf-kubernetes 5.0.0 apache-airflow-providers-google 8.6.0 apache-airflow-providers-grpc 3.1.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details KubernetesExecutor ### What happened We are seeing this stacktrace on our webserver when a task is clicked: ``` 10.253.8.251 - - [15/Dec/2022:18:32:58 +0000] "GET /object/next_run_datasets/recs_ranking_purchase_ranker_dag HTTP/1.1" 200 2 "https://web.airflow.etsy-syseng-gke-prod.etsycloud.com/dags/recs_ranking_purchase_ranker_dag/code" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36" raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute/class') File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 38, in import_string imported_class = import_string(class_name) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers_manager.py", line 275, in _sanity_check Traceback (most recent call last): During handling of the above exception, another exception occurred: AttributeError: module 'airflow.providers.google.cloud.operators.mlengine' has no attribute 'AIPlatformConsoleLink' return getattr(module, class_name) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 36, in import_string Traceback (most recent call last): [2022-12-15 18:32:58,068] {providers_manager.py:243} WARNING - Exception when importing 'airflow.providers.google.cloud.operators.mlengine.AIPlatformConsoleLink' from 'apache-airflow-providers-google' package ImportError: Module "airflow.providers.google.cloud.operators.mlengine" does not define a "AIPlatformConsoleLink" attribute/class ``` ### What you think should happen instead These errors should now appear. ### How to reproduce Start webserver anew, navigate to a dag, click on a task, and tail webserver logs ### Anything else [This YAML file](https://github.com/apache/airflow/blob/providers-google/8.6.0/airflow/providers/google/provider.yaml#L968) is being utilized as config which then results in the import error here: https://github.com/apache/airflow/blob/providers-google/8.6.0/airflow/providers_manager.py#L885-L891 ``` extra-links: - airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleLink - airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleIndexableLink - airflow.providers.google.cloud.operators.mlengine.AIPlatformConsoleLink ``` We should remove this from extra-links as it was removed as of apache-airflow-providers-google 8.5.0 ### 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/28393

https://github.com/apache/airflow/pull/28449

b213f4fd2627bb2a2a4c96fe2845471db430aa5d

7950fb9711384f8ac4609fc19f319edb17e296ef

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "airflow/executors/kubernetes_executor.py", "airflow/www/views.py"]

Manual task trigger fails for kubernetes executor with psycopg2 InvalidTextRepresentation error

### Apache Airflow version main (development) ### What happened Manual task trigger fails for kubernetes executor with the following error. Manual trigger of dag works without any issue. ``` [2022-12-15 20:05:38,442] {app.py:1741} ERROR - Exception on /run [POST] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1900, 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) psycopg2.errors.InvalidTextRepresentation: invalid input syntax for integer: "manual" LINE 3: ...ate = 'queued' AND task_instance.queued_by_job_id = 'manual' ^ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/decorators.py", line 125, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/views.py", line 1896, in run executor.start() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 586, in start self.clear_not_launched_queued_tasks() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 510, in clear_not_launched_queued_tasks queued_tis: list[TaskInstance] = query.all() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2773, in all return self._iter().all() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2916, in _iter result = self.session.execute( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1714, in execute result = conn._execute_20(statement, params or {}, execution_options) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1705, 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 1572, in _execute_clauseelement ret = self._execute_context( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1943, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 2124, 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 1900, 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) sqlalchemy.exc.DataError: (psycopg2.errors.InvalidTextRepresentation) invalid input syntax for integer: "manual" LINE 3: ...ate = 'queued' AND task_instance.queued_by_job_id = 'manual' ``` ^ ### What you think should happen instead should be able to trigger the task manually from the UI ### How to reproduce deploy the main branch with kubernetes executor and postgres db. ### Operating System ubuntu 20 ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details Python version: 3.10.9 Airflow version: 2.6.0.dev0 helm.sh/chart=postgresql-10.5.3 ### Anything else the issue is caused due to this check: https://github.com/apache/airflow/blob/b263dbcb0f84fd9029591d1447a7c843cb970f15/airflow/executors/kubernetes_executor.py#L505-L507 in `celery_executor` there is a similar check, but i believe it is not called at the ti executor time. and also since it is in a try/catch the exception is not visible. https://github.com/apache/airflow/blob/b263dbcb0f84fd9029591d1447a7c843cb970f15/airflow/executors/celery_executor.py#L394-L412 ### 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/28391

https://github.com/apache/airflow/pull/28394

be0e35321f0bbd7d21c75096cad45dbe20c2359a

9510043546d1ac8ac56b67bafa537e4b940d68a4

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile.ci", "airflow/www/extensions/init_views.py", "airflow/www/package.json", "airflow/www/templates/swagger-ui/index.j2", "airflow/www/webpack.config.js", "airflow/www/yarn.lock", "setup.cfg"]

CVE-2019-17495 for swagger-ui

### Apache Airflow version 2.5.0 ### What happened this issue https://github.com/apache/airflow/issues/18383 still isn't closed. It seems like the underlying swagger-ui bundle has been abandoned by its maintainer, and we should instead point swagger UI bundle to this version which is kept up-to-date https://github.com/bartsanchez/swagger_ui_bundle edit : it seems like this might not be coming from the swagger_ui_bundle any more but instead perhaps from connexion. I'm not familiar with python dependencies, so forgive me if I'm mis-reporting this. There are CVE scanner tools that notifies https://github.com/advisories/GHSA-c427-hjc3-wrfw using the apache/airflow:2.1.4 The python deps include swagger-ui-2.2.10 and swagger-ui-3.30.0 as part of the bundle. It is already included at ~/.local/lib/python3.6/site-packages/swagger_ui_bundle swagger-ui-2.2.10 swagger-ui-3.30.0 ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System any ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28381

https://github.com/apache/airflow/pull/28788

35a8ffc55af220b16ea345d770f80f698dcae3fb

35ad16dc0f6b764322b1eb289709e493fbbb0ae0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/default_webserver_config.py"]

CSRF token should be expire with session

### Apache Airflow version 2.5.0 ### What happened In the default configuration, the CSRF token [expires in one hour](https://pythonhosted.org/Flask-WTF/config.html#forms-and-csrf). This setting leads to frequent errors in the UI – for no good reason. ### What you think should happen instead A short expiration date for the CSRF token is not the right value in my view and I [agree with this answer](https://security.stackexchange.com/a/56520/22108) that the CSRF token should basically never expire, instead pegging itself to the current session. That is, the CSRF token should last as long as the current session. The easiest way to accomplish this is by generating the CSRF token from the session id. ### How to reproduce _No response_ ### Operating System Linux ### 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/28356

https://github.com/apache/airflow/pull/28730

04306f18b0643dfed3ed97863bbcf24dc50a8973

543e9a592e6b9dc81467c55169725e192fe95e89

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/kubernetes_executor.py"]

Scheduler pod hang when K8s API call fail

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow version: `2.3.4` I have deployed airflow with the official Helm in K8s with `KubernetesExecutor`. Sometimes the scheduler hang when calling K8s API. The log: ``` bash ERROR - Exception when executing Executor.end Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 752, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 842, in _run_scheduler_loop self.executor.heartbeat() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/base_executor.py", line 171, in heartbeat self.sync() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 649, in sync next_event = self.event_scheduler.run(blocking=False) File "/usr/local/lib/python3.8/sched.py", line 151, in run action(*argument, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/event_scheduler.py", line 36, in repeat action(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 673, in _check_worker_pods_pending_timeout for pod in pending_pods().items: File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 15697, in list_namespaced_pod return self.list_namespaced_pod_with_http_info(namespace, **kwargs) # noqa: E501 File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 15812, in list_namespaced_pod_with_http_info return self.api_client.call_api( File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 348, in call_api return self.__call_api(resource_path, method, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 180, in __call_api response_data = self.request( File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 373, in request return self.rest_client.GET(url, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/rest.py", line 240, in GET return self.request("GET", url, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/rest.py", line 213, in request r = self.pool_manager.request(method, url, File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/request.py", line 74, in request return self.request_encode_url( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/request.py", line 96, in request_encode_url return self.urlopen(method, url, **extra_kw) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/poolmanager.py", line 376, in urlopen response = conn.urlopen(method, u.request_uri, **kw) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 815, in urlopen return self.urlopen( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 182, in _exit_gracefully sys.exit(os.EX_OK) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 773, in _execute self.executor.end() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 823, in end self._flush_task_queue() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 776, in _flush_task_queue self.log.debug('Executor shutting down, task_queue approximate size=%d', self.task_queue.qsize()) File "<string>", line 2, in qsize File "/usr/local/lib/python3.8/multiprocessing/managers.py", line 835, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) ConnectionResetError: [Errno 104] Connection reset by peer ``` Then the executor process was killed and the pod was still running. But the scheduler does not work. After restarting, the scheduler worked usually. ### What you think should happen instead When the error occurs, the executor needs to auto restart or the scheduler should be killed. ### How to reproduce _No response_ ### 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/28328

https://github.com/apache/airflow/pull/28685

57a889de357b269ae104b721e2a4bb78b929cea9

a3de721e2f084913e853aff39d04adc00f0b82ea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ti_deps/deps/prev_dagrun_dep.py", "tests/models/test_dagrun.py"]

Dynamic task mapping does not correctly handle depends_on_past

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Using Airflow 2.4.2. I've got a task that retrieves some filenames, which then creates dynamically mapped tasks to move the files, one per task. I'm using a similar task across multiple DAGs. However, task mapping fails on some DAG runs: it inconsistently happens per DAG run, and some DAGs do not seem to be affected at all. These seem to be the DAGs where no task was ever mapped, so that the mapped task instance ended up in a Skipped state. What happens is that multiple files will be found, but only a single dynamically mapped task will be created. This task never starts and has map_index of -1. It can be found under the "List instances, all runs" menu, but says "No Data found." under the "Mapped Tasks" tab. When I press the "Run" button when the mapped task is selected, the following error appears: ``` Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped! ``` The previous task *has* run however. No errors appeared in my Airflow logs. ### What you think should happen instead The appropriate amount of task instances should be created, they should correctly resolve the ```depends_on_past``` check and then proceed to run correctly. ### How to reproduce This DAG reliably reproduces the error for me. The first set of mapped tasks succeeds, the subsequent ones do not. ```python from airflow import DAG from airflow.decorators import task import datetime as dt from airflow.operators.python import PythonOperator @task def get_filenames_kwargs(): return [ {"file_name": i} for i in range(10) ] def print_filename(file_name): print(file_name) with DAG( dag_id="dtm_test", start_date=dt.datetime(2022, 12, 10), default_args={ "owner": "airflow", "depends_on_past": True, }, schedule="@daily", ) as dag: get_filenames_task = get_filenames_kwargs.override(task_id="get_filenames_task")() print_filename_task = PythonOperator.partial( task_id="print_filename_task", python_callable=print_filename, ).expand(op_kwargs=get_filenames_task) # Perhaps redundant get_filenames_task >> print_filename_task ``` ### Operating System Amazon Linux 2 ### 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/28296

https://github.com/apache/airflow/pull/28379

a62840806c37ef87e4112c0138d2cdfd980f1681

8aac56656d29009dbca24a5948c2a2097043f4f3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/sensors/s3.py", "tests/providers/amazon/aws/sensors/test_s3_key.py"]

S3KeySensor 'bucket_key' instantiates as a nested list when rendered as a templated_field

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.2.0 ### Apache Airflow version 2.5.0 ### Operating System Red Hat Enterprise Linux Server 7.6 (Maipo) ### Deployment Virtualenv installation ### Deployment details Simple virtualenv deployment ### What happened bucket_key is a template_field in S3KeySensor, which means that is expected to be rendered as a template field. The supported types for the attribute are both 'str' and 'list'. There is also a [conditional operation in the __init__ function](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L89) of the class that relies on the type of the input data, that converts the attribute to a list of strings. If a list of str is passed in through Jinja template, **self.bucket_key** is available as a _**doubly-nested list of strings**_, rather than a list of strings. This is because the input value of **bucket_key** can only be a string type that represents the template-string when used as a template_field. These template_fields are then converted to their corresponding values when instantiated as a task_instance. Example log from __init__ function: ` scheduler | DEBUG | type: <class 'list'> | val: ["{{ ti.xcom_pull(task_ids='t1') }}"]` Example log from poke function: `poke | DEBUG | type: <class 'list'> | val: [["s3://test_bucket/test_key1", "s3://test_bucket/test_key2"]]` This leads to the poke function throwing an [exception](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/s3.py#L172) as each individual key needs to be a string value to parse the url, but is being passed as a list (since self.bucket_key is a nested list). ### What you think should happen instead Instead of putting the input value of **bucket_key** in a list, we should store the value as-is upon initialization of the class, and just conditionally check the type of the attribute within the poke function. [def \_\_init\_\_](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L89) `self.bucket_key = bucket_key` (which willstore the input values correctly as a str or a list when the task instance is created and the template fields are rendered) [def poke](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L127) ``` def poke(self, context: Context): if isinstance(self.bucket_key, str): return self._check_key(key) else: return all(self._check_key(key) for key in self.bucket_key) ``` ### How to reproduce 1. Use a template field as the bucket_key attribute in S3KeySensor 2. Pass a list of strings as the rendered template input value for the bucket_key attribute in the S3KeySensor task. (e.g. as an XCOM or Variable pulled value) Example: ``` with DAG( ... render_template_as_native_obj=True, ) as dag: @task(task_id="get_list_of_str", do_xcom_push=True) def get_list_of_str(): return ["s3://test_bucket/test_key1", "s3://test_bucket/test_key1"] t = get_list_of_str() op = S3KeySensor(task_id="s3_key_sensor", bucket_key="{{ ti.xcom_pull(task_ids='get_list_of_str') }}") t >> op ``` ### 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/28272

https://github.com/apache/airflow/pull/28340

9d9b15989a02042a9041ff86bc7e304bb06caa15

381160c0f63a15957a631da9db875f98bb8e9d64

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/variable.py"]

AIP-44 Migrate Variable to Internal API

Link: https://github.com/apache/airflow/blob/main/airflow/models/variable.py Methods to migrate: - val - set - delete - update Note that get_variable_from_secrets shouls still be executed locally. It may be better to first close https://github.com/apache/airflow/issues/28267

https://github.com/apache/airflow/issues/28271

https://github.com/apache/airflow/pull/28795

9c3cd3803f0c4c83b1f8220525e1ac42dd676549

bea49094be3e9d84243383017ca7d21dda62f329

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/manager.py", "tests/api_internal/endpoints/test_rpc_api_endpoint.py", "tests/api_internal/test_internal_api_call.py", "tests/dag_processing/test_manager.py"]

AIP-44 Migrate DagFileProcessorManager._deactivate_stale_dags to Internal API

https://github.com/apache/airflow/issues/28270

https://github.com/apache/airflow/pull/28476

c18dbe963ad87c03d49e95dfe189b765cc18fbec

29a26a810ee8250c30f8ba0d6a72bc796872359c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/processor.py", "airflow/utils/log/logging_mixin.py", "tests/dag_processing/test_processor.py"]

AIP-44 Migrate DagFileProcessor.manage_slas to Internal API

https://github.com/apache/airflow/issues/28268

https://github.com/apache/airflow/pull/28502

7e2493e3c8b2dbeb378dba4e40110ab1e4ad24da

0359a42a3975d0d7891a39abe4395bdd6f210718

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/internal_api_call.py", "airflow/cli/commands/internal_api_command.py", "airflow/cli/commands/scheduler_command.py", "airflow/www/app.py", "tests/api_internal/test_internal_api_call.py"]

AIP-44 Provide information to internal_api_call decorator about the running component

Scheduler/Webserver should never use Internal API, so calling any method decorated with internal_api_call should still execute them locally

https://github.com/apache/airflow/issues/28267

https://github.com/apache/airflow/pull/28783

50b30e5b92808e91ad9b6b05189f560d58dd8152

6046aef56b12331b2bb39221d1935b2932f44e93

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "airflow/cli/cli_parser.py", "airflow/cli/commands/internal_api_command.py", "airflow/www/extensions/init_views.py", "tests/cli/commands/test_internal_api_command.py"]

AIP-44 Implement standalone internal-api component

https://github.com/apache/airflow/pull/27892 added Internal API as part of Webserver. We need to introduce `airlfow internal-api` CLI command that starts Internal API as a independent component.

https://github.com/apache/airflow/issues/28266

https://github.com/apache/airflow/pull/28425

760c52949ac41ffa7a2357aa1af0cdca163ddac8

367e8f135c2354310b67b3469317f15cec68dafa

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/role_command.py", "airflow/www/extensions/init_appbuilder.py"]

Airflow CLI to list roles is slow

### Apache Airflow version 2.5.0 ### What happened We're currently running a suboptimal setup where database connectivity is laggy, 125ms roundtrip. This has interesting consequences. For example, `airflow roles list` is really slow. Turns out that it's doing a lot of individual queries. ### What you think should happen instead Ideally, listing roles should be a single (perhaps complex) query. ### How to reproduce We're using py-spy to sample program execution: ```bash $ py-spy record -o spy.svg -i --rate 250 --nonblocking airflow roles list ``` Now, to see the bad behavior, the database should incur significant latency. ### Operating System Linux ### 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/28242

https://github.com/apache/airflow/pull/28244

2f5c77b0baa0ab26d2c51fa010850653ded80a46

e24733662e95ad082e786d4855066cd4d36015c9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler error: 'V1PodSpec' object has no attribute '_ephemeral_containers'

### Apache Airflow version 2.5.0 ### What happened After upgrade 2.2.5 -> 2.5.0 scheduler failing with error: ``` AttributeError: 'V1PodSpec' object has no attribute '_ephemeral_containers' ``` tried with no luck: ``` airflow dags reserialize ``` Full Traceback: ```verilog Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 73, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 43, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 247, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 759, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 889, in _run_scheduler_loop num_finished_events = self._process_executor_events(session=session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 705, in _process_executor_events self.executor.send_callback(request) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/celery_kubernetes_executor.py", line 213, in send_callback self.callback_sink.send(request) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/callbacks/database_callback_sink.py", line 34, in send db_callback = DbCallbackRequest(callback=callback, priority_weight=10) File "<string>", line 4, in __init__ File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/state.py", line 480, in _initialize_instance manager.dispatch.init_failure(self, args, kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ raise exception File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/state.py", line 477, in _initialize_instance return manager.original_init(*mixed[1:], **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/db_callback_request.py", line 46, in __init__ self.callback_data = callback.to_json() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/callbacks/callback_requests.py", line 91, in to_json val = BaseSerialization.serialize(self.__dict__, strict=True) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 450, in serialize return cls._encode(cls.serialize(var.__dict__, strict=strict), type_=DAT.SIMPLE_TASK_INSTANCE) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 412, in serialize json_pod = PodGenerator.serialize_pod(var) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/kubernetes/pod_generator.py", line 411, in serialize_pod return api_client.sanitize_for_serialization(pod) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 241, in sanitize_for_serialization return {key: self.sanitize_for_serialization(val) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 241, in <dictcomp> return {key: self.sanitize_for_serialization(val) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 237, in sanitize_for_serialization obj_dict = {obj.attribute_map[attr]: getattr(obj, attr) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 239, in <dictcomp> if getattr(obj, attr) is not None} File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 397, in ephemeral_containers return self._ephemeral_containers AttributeError: 'V1PodSpec' object has no attribute '_ephemeral_containers' ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details AWS EKS ### 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/28227

https://github.com/apache/airflow/pull/28454

dc06bb0e26a0af7f861187e84ce27dbe973b731c

27f07b0bf5ed088c4186296668a36dc89da25617

python

closed

apache/airflow

https://github.com/apache/airflow

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

SQLTableCheckOperator doesn't correctly handle templated partition clause

### Apache Airflow Provider(s) common-sql ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.3.1 ### Apache Airflow version 2.5.0 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Other Docker-based deployment ### Deployment details Docker image based on the official image (couple of tools added) deployed on AWS ECS Fargate ### What happened I have a task which uses the table check operator to run some table stakes data validation. But I don't want it failing every time it runs in our test environments which do not get records every day. So there's a templated switch in the sql: ```python test_data_changed_yesterday = SQLTableCheckOperator( task_id="test_data_changed_yesterday", table="reporting.events", conn_id="pg_conn", checks={"changed_record_count": {"check_statement": "count(*) > 0"}}, partition_clause=""" {% if var.value.get('is_test_env', False) %} modifieddate >= '2015-12-01T01:01:01.000Z' {% else %} modifieddate >= '{{ data_interval_start.isoformat() }}' and modifieddate < '{{ data_interval_end.isoformat() }}' {% endif %} """, ) ``` This shows correctly in the rendered field for the task. Not pretty, but it works: ``` ('\n' ' \n' " modifieddate >= '2015-12-01T01:01:01.000Z'\n" ' \n' ' ') ``` However, in the logs I see it's running this query: ``` [2022-12-07, 11:49:34 UTC] {sql.py:364} INFO - Running statement: SELECT check_name, check_result FROM ( SELECT 'changed_record_count' AS check_name, MIN(changed_record_count) AS check_result FROM (SELECT CASE WHEN count(*) > 0 THEN 1 ELSE 0 END AS changed_record_count FROM reporting.events WHERE {% if var.value.get('is_test_env', False) %} modifieddate >= '2015-12-01T01:01:01.000Z' {% else %} modifieddate >= '{{ data_interval_start.isoformat() }}' and modifieddate < '{{ data_interval_end.isoformat() }}' {% endif %} ) AS sq ) AS check_table, parameters: None ``` Which unsurprisingly the DB rejects as invalid sql! ### What you think should happen instead The rendered code should be used in the sql which is run! I think this error comes about through this line: https://github.com/apache/airflow/blob/main/airflow/providers/common/sql/operators/sql.py#L576 which is run in the `__init__` method of the operator. i.e. before templating is applied in the build up to calling `execute(context)`. ### How to reproduce Use the operator with a templated `partition_clause` ### Anything else Happens every time ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28195

https://github.com/apache/airflow/pull/28202

aace30b50cab3c03479fd0c889d145b7435f26a9

a6cda7cd230ef22f7fe042d6d5e9f78c660c4a75

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/models/abstractoperator.py", "airflow/models/taskinstance.py", "newsfragments/28172.misc.rst"]

Invalid retry date crashes scheduler "OverflowError: date value out of range"

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Our scheduler started failing with this trace: File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1187, in get_failed_dep_statuses for dep_status in dep.get_dep_statuses(self, session, dep_context): File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/base_ti_dep.py", line 95, in get_dep_statuses yield from self._get_dep_statuses(ti, session, dep_context) File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/not_in_retry_period_dep.py", line 47, in _get_dep_statuses next_task_retry_date = ti.next_retry_datetime() File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1243, in next_retry_datetime return self.end_date + delay OverflowError: date value out of range We found a dag with a large # of retries and exponential backoff will trigger this date error and take down the entire scheduler. The workaround is to force a max_delay setting. The bug is here: https://github.com/apache/airflow/blob/2.3.3/airflow/models/taskinstance.py#L1243 The current version seems to use the same code: https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1147 ### What you think should happen instead There are a few solutions. Exponential backoff should probably require a max delay value. At the very least, it shouldn't kill the scheduler. ### How to reproduce Create dag with exponential delay and force it to retry until it overflows. ### Operating System linux ### 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/28171

https://github.com/apache/airflow/pull/28172

e948b55a087f98d25a6a4730bf58f61689cdb116

2cbe5960476b1f444e940d11177145e5ffadf613

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/.babelrc", "airflow/www/babel.config.js", "airflow/www/jest.config.js", "airflow/www/package.json", "airflow/www/static/js/components/ReactMarkdown.tsx", "airflow/www/static/js/dag/details/NotesAccordion.tsx", "airflow/www/yarn.lock"]

Allow Markdown in Task comments

### Description Implement the support for Markdown in Task notes inside Airflow. ### Use case/motivation It would be helpful to use markdown syntax in Task notes/comments for the following usecases: - Formatting headers, lists, and tables to allow more complex note-taking. - Parsing a URL to reference a ticket in an Issue ticketing system (Jira, Pagerduty, etc.) ### 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/28167

https://github.com/apache/airflow/pull/28245

78b72f4fa07cac009ddd6d43d54627381e3e9c21

74e82af7eefe1d0d5aa6ea1637d096e4728dea1f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Links to dag graph some times display incorrect dagrun

### Apache Airflow version 2.5.0 ### What happened Open url `dags/gate/graph?dag_run_id=8256-8-1670328803&execution_date=2022-12-06T12%3A13%3A23.174592+00%3A00` The graph is displaying a completely different dagrun.  If you are not careful to review all the content, you might continue looking at the wrong results, or worse cancel a run with Mark failed. I got the link from one of our users, so not 100% sure if it was the original url. I believe there could be something wrong with the url-encoding of the last `+` character. In any case, if there are any inconsistencies in the URL parameters vs the found dagruns, it should not display another dagrun, rather redirect to grid-view or error message. ### What you think should happen instead * dag_run_id should be only required parameter, or have precedence over execution_date * Provided dag_run_id should always be the same run-id that is displayed in graph * Inconsistencies in any parameters should display error or redirect to grid view. ### How to reproduce _No response_ ### Operating System Ubuntu 22.04 ### 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/28155

https://github.com/apache/airflow/pull/29066

48cab7cfebf2c7510d9fdbffad5bd06d8f4751e2

9dedf81fa18e57755aa7d317f08f0ea8b6c7b287

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/xcom.py", "tests/models/test_taskinstance.py"]

Dynamic task context fails to be pickled

### Apache Airflow version 2.5.0 ### What happened When I upgrade to 2.5.0, run dynamic task test failed. ```py from airflow.decorators import task, dag import pendulum as pl @dag( dag_id='test-dynamic-tasks', schedule=None, start_date=pl.today().add(days=-3), tags=['example']) def test_dynamic_tasks(): @task.virtualenv(requirements=[]) def sum_it(values): print(values) @task.virtualenv(requirements=[]) def add_one(value): return value + 1 added_values = add_one.expand(value = [1,2]) sum_it(added_values) dag = test_dynamic_tasks() ``` ```log *** Reading local file: /home/andi/airflow/logs/dag_id=test-dynamic-tasks/run_id=manual__2022-12-06T10:07:41.355423+00:00/task_id=sum_it/attempt=1.log [2022-12-06, 18:07:53 CST] {taskinstance.py:1087} INFO - Dependencies all met for <TaskInstance: test-dynamic-tasks.sum_it manual__2022-12-06T10:07:41.355423+00:00 [queued]> [2022-12-06, 18:07:53 CST] {taskinstance.py:1087} INFO - Dependencies all met for <TaskInstance: test-dynamic-tasks.sum_it manual__2022-12-06T10:07:41.355423+00:00 [queued]> [2022-12-06, 18:07:53 CST] {taskinstance.py:1283} INFO - -------------------------------------------------------------------------------- [2022-12-06, 18:07:53 CST] {taskinstance.py:1284} INFO - Starting attempt 1 of 1 [2022-12-06, 18:07:53 CST] {taskinstance.py:1285} INFO - -------------------------------------------------------------------------------- [2022-12-06, 18:07:53 CST] {taskinstance.py:1304} INFO - Executing <Task(_PythonVirtualenvDecoratedOperator): sum_it> on 2022-12-06 10:07:41.355423+00:00 [2022-12-06, 18:07:53 CST] {standard_task_runner.py:55} INFO - Started process 25873 to run task [2022-12-06, 18:07:53 CST] {standard_task_runner.py:82} INFO - Running: ['airflow', 'tasks', 'run', 'test-dynamic-tasks', 'sum_it', 'manual__2022-12-06T10:07:41.355423+00:00', '--job-id', '41164', '--raw', '--subdir', 'DAGS_FOLDER/andi/test-dynamic-task.py', '--cfg-path', '/tmp/tmphudvake2'] [2022-12-06, 18:07:53 CST] {standard_task_runner.py:83} INFO - Job 41164: Subtask sum_it [2022-12-06, 18:07:53 CST] {task_command.py:389} INFO - Running <TaskInstance: test-dynamic-tasks.sum_it manual__2022-12-06T10:07:41.355423+00:00 [running]> on host sh-dataops-airflow.jinde.local [2022-12-06, 18:07:53 CST] {taskinstance.py:1511} INFO - Exporting the following env vars: [email protected] AIRFLOW_CTX_DAG_OWNER=andi AIRFLOW_CTX_DAG_ID=test-dynamic-tasks AIRFLOW_CTX_TASK_ID=sum_it AIRFLOW_CTX_EXECUTION_DATE=2022-12-06T10:07:41.355423+00:00 AIRFLOW_CTX_TRY_NUMBER=1 AIRFLOW_CTX_DAG_RUN_ID=manual__2022-12-06T10:07:41.355423+00:00 [2022-12-06, 18:07:53 CST] {process_utils.py:179} INFO - Executing cmd: /home/andi/airflow/venv38/bin/python -m virtualenv /tmp/venv7lc4m6na --system-site-packages [2022-12-06, 18:07:53 CST] {process_utils.py:183} INFO - Output: [2022-12-06, 18:07:54 CST] {process_utils.py:187} INFO - created virtual environment CPython3.8.0.final.0-64 in 220ms [2022-12-06, 18:07:54 CST] {process_utils.py:187} INFO - creator CPython3Posix(dest=/tmp/venv7lc4m6na, clear=False, no_vcs_ignore=False, global=True) [2022-12-06, 18:07:54 CST] {process_utils.py:187} INFO - seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/home/andi/.local/share/virtualenv) [2022-12-06, 18:07:54 CST] {process_utils.py:187} INFO - added seed packages: pip==22.2.1, setuptools==63.2.0, wheel==0.37.1 [2022-12-06, 18:07:54 CST] {process_utils.py:187} INFO - activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator [2022-12-06, 18:07:54 CST] {process_utils.py:179} INFO - Executing cmd: /tmp/venv7lc4m6na/bin/pip install -r /tmp/venv7lc4m6na/requirements.txt [2022-12-06, 18:07:54 CST] {process_utils.py:183} INFO - Output: [2022-12-06, 18:07:55 CST] {process_utils.py:187} INFO - Looking in indexes: http://pypi:8081 [2022-12-06, 18:08:00 CST] {process_utils.py:187} INFO - [2022-12-06, 18:08:00 CST] {process_utils.py:187} INFO - [notice] A new release of pip available: 22.2.1 -> 22.3.1 [2022-12-06, 18:08:00 CST] {process_utils.py:187} INFO - [notice] To update, run: python -m pip install --upgrade pip [2022-12-06, 18:08:00 CST] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/decorators/base.py", line 217, in execute return_value = super().execute(context) File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/operators/python.py", line 356, in execute return super().execute(context=serializable_context) File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/operators/python.py", line 175, in execute return_value = self.execute_callable() File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/operators/python.py", line 553, in execute_callable return self._execute_python_callable_in_subprocess(python_path, tmp_path) File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/operators/python.py", line 397, in _execute_python_callable_in_subprocess self._write_args(input_path) File "/home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/operators/python.py", line 367, in _write_args file.write_bytes(self.pickling_library.dumps({"args": self.op_args, "kwargs": self.op_kwargs})) _pickle.PicklingError: Can't pickle <class 'sqlalchemy.orm.session.Session'>: it's not the same object as sqlalchemy.orm.session.Session [2022-12-06, 18:08:00 CST] {taskinstance.py:1322} INFO - Marking task as FAILED. dag_id=test-dynamic-tasks, task_id=sum_it, execution_date=20221206T100741, start_date=20221206T100753, end_date=20221206T100800 [2022-12-06, 18:08:00 CST] {warnings.py:109} WARNING - /home/andi/airflow/venv38/lib/python3.8/site-packages/airflow/utils/email.py:120: RemovedInAirflow3Warning: Fetching SMTP credentials from configuration variables will be deprecated in a future release. Please set credentials using a connection instead. send_mime_email(e_from=mail_from, e_to=recipients, mime_msg=msg, conn_id=conn_id, dryrun=dryrun) [2022-12-06, 18:08:00 CST] {configuration.py:635} WARNING - section/key [smtp/smtp_user] not found in config [2022-12-06, 18:08:00 CST] {email.py:229} INFO - Email alerting: attempt 1 [2022-12-06, 18:08:01 CST] {email.py:241} INFO - Sent an alert email to ['[email protected]'] [2022-12-06, 18:08:01 CST] {standard_task_runner.py:100} ERROR - Failed to execute job 41164 for task sum_it (Can't pickle <class 'sqlalchemy.orm.session.Session'>: it's not the same object as sqlalchemy.orm.session.Session; 25873) [2022-12-06, 18:08:01 CST] {local_task_job.py:159} INFO - Task exited with return code 1 [2022-12-06, 18:08:01 CST] {taskinstance.py:2582} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you think should happen instead I expect this sample run passed. ### How to reproduce _No response_ ### Operating System centos 7.9 3.10.0-1160.el7.x86_64 ### Versions of Apache Airflow Providers ``` airflow-code-editor==5.2.2 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-microsoft-mssql==3.1.0 apache-airflow-providers-microsoft-psrp==2.0.0 apache-airflow-providers-microsoft-winrm==3.0.0 apache-airflow-providers-mysql==3.0.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-samba==4.0.0 apache-airflow-providers-sftp==3.0.0 autopep8==1.6.0 brotlipy==0.7.0 chardet==3.0.4 pip-chill==1.0.1 pyopenssl==19.1.0 pysocks==1.7.1 python-ldap==3.4.2 requests-credssp==2.0.0 swagger-ui-bundle==0.0.9 tqdm==4.51.0 virtualenv==20.16.2 yapf==0.32.0 ``` ### 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/28146

https://github.com/apache/airflow/pull/28191

84a5faff0de2a56f898b8a02aca578b235cb12ba

e981dfab4e0f4faf1fb932ac6993c3ecbd5318b2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/api/useTaskLog.ts", "airflow/www/static/js/dag/details/taskInstance/Logs/LogBlock.tsx", "airflow/www/static/js/dag/details/taskInstance/Logs/index.tsx"]

Logs tab is automatically scrolling to the bottom while user is reading logs

### Apache Airflow version 2.5.0 ### What happened Open the logs tab for a task that is currently running. Scroll up to read things further up the log. Every 30 seconds or so the log automatically scrolls down to the bottom again. ### What you think should happen instead If the user has scrolled away from the bottom in the logs-panel, the live tailing of new logs should not scroll the view back to the bottom automatically. ### How to reproduce _No response_ ### Operating System Ubuntu 22.04 ### 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/28143

https://github.com/apache/airflow/pull/28386

5b54e8d21b1801d5e0ccd103592057f0b5a980b1

5c80d985a3102a46f198aec1c57a255e00784c51

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/sftp/sensors/sftp.py", "tests/providers/sftp/sensors/test_sftp.py"]

SFTP Sensor fails to locate file

### Apache Airflow version 2.5.0 ### What happened While creating SFTP sensor I have tried to find a file under directory. But I was getting error as Time Out, not found. So after debugging code found that there is a issue with [poke function](https://airflow.apache.org/docs/apache-airflow-providers-sftp/stable/_modules/airflow/providers/sftp/sensors/sftp.html#SFTPSensor.poke). As after getting matched file we are trying to find last modified time of the file using [self.hook.get_mod_time](https://airflow.apache.org/docs/apache-airflow-providers-sftp/stable/_modules/airflow/providers/sftp/hooks/sftp.html#SFTPHook.get_mod_time) which take full path (path + filename) and we are giving only filename as arguments. ### What you think should happen instead I have solved that issue by adding path with filename and then calling [self.hook.get_mod_time](https://airflow.apache.org/docs/apache-airflow-providers-sftp/stable/_modules/airflow/providers/sftp/hooks/sftp.html#SFTPHook.get_mod_time) function. Here is modified code, ``` def poke(self, context: Context) -> bool: self.hook = SFTPHook(self.sftp_conn_id) self.log.info("Poking for %s, with pattern %s", self.path, self.file_pattern) if self.file_pattern: file_from_pattern = self.hook.get_file_by_pattern(self.path, self.file_pattern) if file_from_pattern: '''actual_file_to_check = file_from_pattern''' actual_file_to_check = self.path + file_from_pattern else: return False else: actual_file_to_check = self.path try: mod_time = self.hook.get_mod_time(actual_file_to_check) self.log.info("Found File %s last modified: %s", str(actual_file_to_check), str(mod_time)) except OSError as e: if e.errno != SFTP_NO_SUCH_FILE: raise e return False self.hook.close_conn() if self.newer_than: _mod_time = convert_to_utc(datetime.strptime(mod_time, "%Y%m%d%H%M%S")) _newer_than = convert_to_utc(self.newer_than) return _newer_than <= _mod_time else: return True ``` ### How to reproduce You can get same issue by creating a DAG as mentioned ``` with DAG( dag_id='sftp_sensor_dag', max_active_runs=1, default_args=default_args, ) as dag: file_sensing_task = SFTPSensor( task_id='sensor_for_file', path= "Weekly/11/", file_pattern = "*pdf*, sftp_conn_id='sftp_hook_conn', poke_interval=30 ) ``` ### Operating System Microsoft Windows [Version 10.0.19044.2251] ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28121

https://github.com/apache/airflow/pull/29467

72c3817a44eea5005761ae3b621e8c39fde136ad

8e24387d6db177c662342245bb183bfd73fb9ee8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/base_job.py", "tests/jobs/test_base_job.py"]

Scheduler heartbeat warning message in Airflow UI displaying that scheduler is down sometimes incorrect

### Apache Airflow version main (development) ### What happened Steps to reproduce: 1. run 2 replicas of scheduler 2. initiate shut down of one of the schedulers 3. In Airflow UI observe message <img width="1162" alt="image" src="https://user-images.githubusercontent.com/1017130/205650336-fb1d8e39-2213-4aec-8530-abd1417db426.png"> 3rd step should be done immediately after 2nd (refreshing UI page few times). 2nd and 3rd steps might be repeated for couple of times in order to reproduce. ### What you think should happen instead Warning message shouldn't be displayed. The issue is that for this warning message recent (with latest heartbet) scheduler job is fetched https://github.com/apache/airflow/blob/f02a7e9a8292909b369daae6d573f58deed04440/airflow/jobs/base_job.py#L133. And this may point to job which is not running (state!="running") and that is why we see warning message. The warning message in this case is misleading as another replica of scheduler is running in parallel. ### How to reproduce _No response_ ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/28118

https://github.com/apache/airflow/pull/28119

3cd70ffee974c9f345aabb3a365dde4dbcdd84a4

56c0871dce2fb2b7ed2252e4b2d1d8d5d0c07c58

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagrun.py"]

IndexError in `airflow dags test` re: scheduling delay stats

### Apache Airflow version 2.5.0 ### What happened Very simple dag: ```python3 from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime, timedelta with DAG(dag_id="hello_world", schedule=timedelta(days=30 * 365), start_date=datetime(1970, 1, 1)) as dag: ( BashOperator(task_id="hello", bash_command="echo hello") >> BashOperator(task_id="world", bash_command="echo world") ) ``` Run it like `airflow dags test hello_world $(date +%Y-%m-%d)` End of output: ``` [2022-12-04 21:24:02,993] {dagrun.py:606} INFO - Marking run <DagRun hello_world @ 2022-12-04T00:00:00+00:00: manual__2022-12-04T00:00:00+00:00, state:running, queued_at: None. externally triggered: False> successful [2022-12-04 21:24:03,003] {dagrun.py:657} INFO - DagRun Finished: dag_id=hello_world, execution_date=2022-12-04T00:00:00+00:00, run_id=manual__2022-12-04T00:00:00+00:00, run_start_date=2022-12-04T00:00:00+00:00, run_end_date=2022-12-05 04:24:02.995279+00:00, run_duration=102242.995279, state=success, external_trigger=False, run_type=manual, data_interval_start=2022-12-04T00:00:00+00:00, data_interval_end=2052-11-26T00:00:00+00:00, dag_hash=None [2022-12-04 21:24:03,004] {dagrun.py:878} WARNING - Failed to record first_task_scheduling_delay metric: Traceback (most recent call last): File "/home/matt/2022/12/04/venv/lib/python3.9/site-packages/airflow/models/dagrun.py", line 866, in _emit_true_scheduling_delay_stats_for_finished_state first_start_date = ordered_tis_by_start_date[0].start_date IndexError: list index out of range ``` ### What you think should happen instead No warning (or, an explanation of what I can do address whatever it's warning about). ### How to reproduce _No response_ ### Operating System NixOS 22.11 (gnu/linux) ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28106

https://github.com/apache/airflow/pull/28138

7adf8a53ec8bd08a9c14418bf176574e149780c5

b3d7e17e72c05fd149a5514e3796d46a241ac4f7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py", "tests/providers/amazon/aws/transfers/test_dynamodb_to_s3.py"]

Type Error while using dynamodb_to_s3 operator

### Discussed in https://github.com/apache/airflow/discussions/28102 <div type='discussions-op-text'> ^{Originally posted by **p-madduri** December 1, 2022} ### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened https://github.com/apache/airflow/blob/430e930902792fc37cdd2c517783f7dd544fbebf/airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py#L39 if we use the below class at line 39: def _convert_item_to_json_bytes(item: dict[str, Any]) -> bytes: return (json.dumps(item) + "\n").encode("utf-8") its throwing below error TypeError: Object of type Decimal is not JSON serializable. can we use class DecimalEncoder(json.JSONEncoder): def encode(self, obj): if isinstance(obj, Mapping): return '{' + ', '.join(f'{self.encode(k)}: {self.encode(v)}' for (k, v) in obj.items()) + '}' elif isinstance(obj, Iterable) and (not isinstance(obj, str)): return '[' + ', '.join(map(self.encode, obj)) + ']' elif isinstance(obj, Decimal): return f'{obj.normalize():f}' # using normalize() gets rid of trailing 0s, using ':f' prevents scientific notation else: print(obj) return super().encode(obj) and need to update the code at line https://github.com/apache/airflow/blob/430e930902792fc37cdd2c517783f7dd544fbebf/airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py#L99 This solution is suggested in this article: https://randomwits.com/blog/export-dynamodb-s3 Airflow version of MWAA : 2.0.2 ### What you think should happen instead mentioned in what happened section ### How to reproduce mentioned in what happened section ### Operating System MAC ### Versions of Apache Airflow Providers from airflow.providers.amazon.aws.transfers.dynamodb_to_s3 import DynamoDBToS3Operator ### Deployment 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) </div>

https://github.com/apache/airflow/issues/28103

https://github.com/apache/airflow/pull/28158

39f501d4f4e87635c80d97bb599daf61096d23b8

0d90c62bac49de9aef6a31ee3e62d02e458b0d33

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/kubernetes_executor.py", "tests/executors/test_kubernetes_executor.py"]

Kubernetes logging errors - attempting to adopt taskinstance which was not specified by database

### Apache Airflow version 2.4.3 ### What happened Using following config ``` executor = CeleryKubernetesExecutor delete_worker_pods = False ``` 1. Start a few dags running in kubernetes, wait for them to complete. 2. Restart Scheduler. 3. Logs are flooded with hundreds of errors like` ERROR - attempting to adopt taskinstance which was not specified by database: TaskInstanceKey(dag_id='xxx', task_id='yyy', run_id='zzz', try_number=1, map_index=-1)` This is problematic because: * Our installation has thousands of dags and pods so this becomes very noisy and the adoption-process adds excessive startup-time to the scheduler, up to a minute some times. * It's hiding actual errors with resetting orphaned tasks, something that also happens for inexplicable reasons on scheduler restart with following log: `Reset the following 6 orphaned TaskInstances`. Making such much harder to debug. The cause of them can not be easily correlated with those that were not specified by database. The cause of these logs are the Kubernetes executor on startup loads all pods (`try_adopt_task_instances`), it then cross references them with all `RUNNING` TaskInstances loaded via `scheduler_job.adopt_or_reset_orphaned_tasks`. For all pods where a running TI can not be found, it logs the error above - But for TIs that were already completed this is not an error, and the pods should not have to be loaded at all. I have an idea of adding some code in the kubernetes_executor that patches in something like a `completion-acknowleged`-label whenever a pod is completed (unless `delete_worker_pods` is set). Then on startup, all pods having this label can be excluded. Is this a good idea or do you see other potential solutions? Another potential solution is to inside `try_adopt_task_instances` only fetch the exact pod-id specified in each task-instance, instead of listing all to later cross-reference them. ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 22.04 ### 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/28071

https://github.com/apache/airflow/pull/28899

f2bedcbd6722cd43772007eecf7f55333009dc1d

f64ac5978fb3dfa9e40a0e5190ef88e9f9615824

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/InstanceTooltip.test.tsx", "airflow/www/static/js/dag/InstanceTooltip.tsx", "airflow/www/static/js/dag/details/dagRun/index.tsx", "airflow/www/static/js/dag/details/taskInstance/Details.tsx", "airflow/www/yarn.lock"]

task duration in grid view is different when viewed at different times.

### Apache Airflow version 2.4.3 ### What happened I wrote this dag to test the celery executor's ability to tolerate OOMkills: ```python3 import numpy as np from airflow import DAG from airflow.decorators import task from datetime import datetime, timedelta from airflow.models.variable import Variable import subprocess import random def boom(): np.ones((1_000_000_000_000)) def maybe_boom(boom_hostname, boom_count, boom_modulus): """ call boom(), but only under certain conditions """ try: proc = subprocess.Popen("hostname", shell=True, stdout=subprocess.PIPE) hostname = proc.stdout.readline().decode().strip() # keep track of which hosts parsed the dag parsed = Variable.get("parsed", {}, deserialize_json=True) parsed.setdefault(hostname, 0) parsed[hostname] = parsed[hostname] + 1 Variable.set("parsed", parsed, serialize_json=True) # only blow up when the caller's condition is met print(parsed) try: count = parsed[boom_hostname] if hostname == boom_hostname and count % boom_modulus == boom_count: print("boom") boom() except (KeyError, TypeError): pass print("no boom") except: # key errors show up because of so much traffic on the variable # don't hold up parsing in those cases pass @task def do_stuff(): # tasks randomly OOMkill also if random.randint(1, 256) == 13: boom() run_size = 100 with DAG( dag_id="oom_on_parse", schedule=timedelta(seconds=30), start_date=datetime(1970, 1, 1), catchup=False, ): # OOM part-way through the second run # and every 3th run after that maybe_boom( boom_hostname="airflow-worker-0", boom_count=run_size + 50, boom_modulus=run_size * 3, ) [do_stuff() for _ in range(run_size)] ``` I'm not surprised that tasks are failing. The dag occasionally tries to allocate 1Tb of memory. That's a good reason to fail. What surprises me is that occasionally, the run durations are reported as 23:59:30 when I've only been running the test for 5 minutes. Also, this number changes if I view it later, behold:  23:55:09 -> 23:55:03 -> 23:55:09, they're decreasing. ### What you think should happen instead The duration should never be longer than I've had the deployment up, and whatever is reported, it should not change when viewed later on. ### How to reproduce Using the celery executor, unpause the dag above. Wait for failures to show up. View their duration in the grid view. This gist includes a script which shows all of the parameters I'm using (e.g. to helm and such): https://gist.github.com/MatrixManAtYrService/6e90a3b8c7c65b8d8b1deaccc8b6f042 ### Operating System k8s / helm / docker / macos ### Versions of Apache Airflow Providers n/a ### Deployment Official Apache Airflow Helm Chart ### Deployment details See script in this gist? https://gist.github.com/MatrixManAtYrService/6e90a3b8c7c65b8d8b1deaccc8b6f042 ### 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/28070

https://github.com/apache/airflow/pull/28395

4d0fa01f72ac4a947db2352e18f4721c2e2ec7a3

11f30a887c77f9636e88e31dffd969056132ae8c

python

closed

apache/airflow

https://github.com/apache/airflow

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

Queue up new tasks always returns an empty list

### Apache Airflow version main (development) ### What happened Currently when a new task is added to a dag and in the grid view, a user selects the top level of a dag run and then clicks on "Queue up new tasks", the list returned by the confirmation box is always empty. It appears that where the list of tasks is expected to be set, [here](https://github.com/apache/airflow/blob/ada91b686508218752fee176d29d63334364a7f2/airflow/api/common/mark_tasks.py#L516), `res` will always be an empty list. ### What you think should happen instead The UI should return a list of tasks that will be queued up once the confirmation button is pressed. ### How to reproduce Create a dag, trigger the dag, allow it to complete. Add a new task to the dag, click on "Queue up new tasks", the list will be empty. ### Operating System n/a ### Versions of Apache Airflow Providers 2.3.3 and upwards including main. I've not looked at earlier releases. ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else I have a PR prepared for this issue. ### 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/28065

https://github.com/apache/airflow/pull/28066

e29d33b89f7deea6eafb03006c37b60692781e61

af29ff0a8aa133f0476bf6662e6c06c67de21dd5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/config_templates/default_celery.py", "docs/apache-airflow/core-concepts/executor/celery.rst"]

Airflow does not pass through Celery's support for Redis Sentinel over SSL.

### Apache Airflow version 2.4.3 ### What happened When configuring Airflow/Celery to use Redis Sentinel as a broker, the following pops up: ``` airflow.exceptions.AirflowException: The broker you configured does not support SSL_ACTIVE to be True. Please use RabbitMQ or Redis if you would like to use SSL for broker. ``` ### What you think should happen instead Celery has supported TLS on Redis Sentinel [for a while](https://docs.celeryq.dev/en/latest/history/whatsnew-5.1.html#support-redis-sentinel-with-ssl) now. It looks like [this piece of code](https://github.com/apache/airflow/blob/main/airflow/config_templates/default_celery.py#L68-L88) explicitly prohibits from passing a valid Redis Sentinel TLS configuration through to Celery. (Since Sentinel broker URL's are prefixed with `sentinel://` instead of `redis://`.) ### How to reproduce This problem can be reproduced by deploying Airflow using Docker with the following environment variables: ``` AIRFLOW__CELERY__BROKER_URL=sentinel://sentinel1:26379;sentinel://sentinel2:26379;sentinel://sentinel3:26379 AIRFLOW__CELERY__SSL_ACTIVE=true AIRFLOW__CELERY_BROKER_TRANSPORT_OPTIONS__MASTER_NAME='some-master-name' AIRFLOW__CELERY_BROKER_TRANSPORT_OPTIONS__PASSWORD='some-password' AIRFLOW__LOGGING__LOGGING_LEVEL=DEBUG ``` Note that I'm not 100% certain of the syntax for the password environment var. I can't get to the point of testing this because without TLS connections to our internal brokers are denied (because they require TLS), and with TLS it doesn't attempt a connection because of the earlier linked code. I've verified with the reference `redis-cli` that the settings we use for `master-name` does result in a valid response and the Sentinel set-up works as expected. ### Operating System Docker (apache/airflow:2.4.3-python3.10) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Deployed using Nomad. ### Anything else This is my first issue with this open source project. Please let me know if there's more relevant information I can provide to follow through on this issue. I will try to make some time available soon to see if a simple code change in the earlier mentioned file would work, but as this is my first issue here I would still have to set-up a full development environment. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! If this is indeed a simple fix I'd be willing to look into making a PR. I would like some feedback on the problem first though if possible! ### 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/28010

https://github.com/apache/airflow/pull/30352

800ade7da6ae49c52b4fe412c1c5a60ceffb897c

2c270db714b7693a624ce70d178744ccc5f9e73e

python

closed

apache/airflow

https://github.com/apache/airflow

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

Clearing dag run via UI fails on main branch and 2.5.0rc2

### Apache Airflow version main (development) ### What happened Create a simple dag, allow it to completely run through. Next, when in grid view, on the left hand side click on the dag run at the top level. On the right hand side, then click on "Clear existing tasks". This will error with the following on the web server: ``` [2022-11-29 17:55:05,939] {app.py:1742} ERROR - Exception on /dagrun_clear [POST] Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.7/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.7/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.7/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/opt/airflow/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/opt/airflow/airflow/www/decorators.py", line 83, in wrapper return f(*args, **kwargs) File "/opt/airflow/airflow/www/views.py", line 2184, in dagrun_clear confirmed=confirmed, File "/opt/airflow/airflow/www/views.py", line 2046, in _clear_dag_tis session=session, File "/opt/airflow/airflow/utils/session.py", line 72, in wrapper return func(*args, **kwargs) File "/opt/airflow/airflow/models/dag.py", line 2030, in clear exclude_task_ids=exclude_task_ids, File "/opt/airflow/airflow/models/dag.py", line 1619, in _get_task_instances tis = session.query(TaskInstance) AttributeError: 'NoneType' object has no attribute 'query' ``` https://github.com/apache/airflow/blob/527fbce462429fc9836837378f801eed4e9d194f/airflow/models/dag.py#L1619 As per issue title, fails on main branch and `2.5.0rc2`. Works fine on `2.3.3` and `2.4.3`. ### What you think should happen instead Tasks within the dag should be cleared as expected. ### How to reproduce Run a dag, attempt to clear it within the UI at the top level of the dag. ### Operating System Ran via breeze ### Versions of Apache Airflow Providers N/A ### Deployment Other 3rd-party Helm chart ### Deployment details Tested via breeze. ### Anything else Happens every time. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28002

https://github.com/apache/airflow/pull/28003

527fbce462429fc9836837378f801eed4e9d194f

f43f50e3f11fa02a2025b4b68b8770d6456ba95d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/redshift_sql.py", "docs/apache-airflow-providers-amazon/connections/redshift.rst", "tests/providers/amazon/aws/hooks/test_redshift_sql.py"]

Add IAM authentication to Amazon Redshift Connection by AWS Connection

### Description Allow authenticating to Redshift Cluster in `airflow.providers.amazon.aws.hooks.redshift_sql.RedshiftSQLHook` with temporary IAM Credentials. This might be implemented by the same way as it already implemented into PostgreSQL Hook - manual obtain credentials by call [GetClusterCredentials](https://docs.aws.amazon.com/redshift/latest/APIReference/API_GetClusterCredentials.html) thought Redshift API. https://github.com/apache/airflow/blob/56b5f3f4eed6a48180e9d15ba9bb9664656077b1/airflow/providers/postgres/hooks/postgres.py#L221-L235 Or by passing obtained temporary credentials into [redshift-connector](https://github.com/aws/amazon-redshift-python-driver#example-using-iam-credentials) ### Use case/motivation This allows users connect to Redshift Cluster by re-use already existed [Amazon Web Services Connection](https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/connections/aws.html) ### 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/28000

https://github.com/apache/airflow/pull/28187

b7e5b47e2794fa0eb9ac2b22f2150d2fdd9ef2b1

2f247a2ba2fb7c9f1fe71567a80f0063e21a5f55

python

closed

apache/airflow

https://github.com/apache/airflow

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

References to the 'kubernetes' section cause parse errors

### Apache Airflow version main (development) ### What happened Here's a dag: ```python3 from airflow import DAG from airflow.decorators import task import airflow.configuration as conf from datetime import datetime @task def print_this(this): print(this) with DAG(dag_id="config_ref", schedule=None, start_date=datetime(1970, 1, 1)) as dag: namespace = conf.get("kubernetes", "NAMESPACE") print_this(namespace) ``` In 2.4.3 it parses without error, but in main (as of 2e7a4bcb550538283f28550208b01515d348fb51) the reference to the "kubernetes" section breaks. Likely because of this: https://github.com/apache/airflow/pull/26873 ``` ❯ airflow dags list-import-errors filepath | error ======================================+======================================================================================================== /Users/matt/2022/11/29/dags/config.py | Traceback (most recent call last): | File "/Users/matt/src/airflow/airflow/configuration.py", line 595, in get | return self._get_option_from_default_config(section, key, **kwargs) | File "/Users/matt/src/airflow/airflow/configuration.py", line 605, in _get_option_from_default_config | raise AirflowConfigException(f"section/key [{section}/{key}] not found in config") | airflow.exceptions.AirflowConfigException: section/key not found in config | ❯ python dags/config.py [2022-11-29 21:30:05,300] {configuration.py:603} WARNING - section/key [kubernetes/namespace] not found in config Traceback (most recent call last): File "/Users/matt/2022/11/29/dags/config.py", line 13, in <module> namespace = conf.get("kubernetes", "NAMESPACE") File "/Users/matt/src/airflow/airflow/configuration.py", line 1465, in get return conf.get(*args, **kwargs) File "/Users/matt/src/airflow/airflow/configuration.py", line 595, in get return self._get_option_from_default_config(section, key, **kwargs) File "/Users/matt/src/airflow/airflow/configuration.py", line 605, in _get_option_from_default_config raise AirflowConfigException(f"section/key [{section}/{key}] not found in config") airflow.exceptions.AirflowConfigException: section/key [kubernetes/namespace] not found in config ``` To quote @jedcunningham : > The backcompat layer only expects you to use the “new” section name. ### What you think should happen instead The recent section name change should be registered so that the old name still works. ### How to reproduce See above ### Operating System Mac OS / venv ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details `pip install -e ~/src/airflow` into a fresh venv ### Anything else Also, it's kind of weird that the important part of the error message (which section?) is missing from `list-import-errors`. I had to run the dag def like a script to realize that it was the kubernetes section that it was complaining about. ### 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/27999

https://github.com/apache/airflow/pull/28008

f1c4c27e4aed79eef01f2873fab3a66af2aa3fa0

3df03cc9331cb8984f39c5dbf0c9775ac362421e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/CHANGELOG.rst", "airflow/providers/snowflake/hooks/snowflake.py", "airflow/providers/snowflake/operators/snowflake.py", "tests/providers/snowflake/hooks/test_sql.py", "tests/providers/snowflake/operators/test_snowflake_sql.py"]

KeyError: 0 error with common-sql version 1.3.0

### Apache Airflow Provider(s) common-sql ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==6.0.0 apache-airflow-providers-apache-hive==4.0.1 apache-airflow-providers-apache-livy==3.1.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.4.0 apache-airflow-providers-common-sql==1.3.0 apache-airflow-providers-databricks==3.3.0 apache-airflow-providers-dbt-cloud==2.2.0 apache-airflow-providers-elasticsearch==4.2.1 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-google==8.4.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-microsoft-azure==4.3.0 apache-airflow-providers-postgres==5.2.2 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-sftp==4.1.0 apache-airflow-providers-snowflake==3.3.0 apache-airflow-providers-sqlite==3.2.1 apache-airflow-providers-ssh==3.2.0 ``` ### Apache Airflow version 2.4.3 ### Operating System Debian Bullseye ### Deployment Astronomer ### Deployment details _No response_ ### What happened With the latest version of common-sql provider, the `get_records` from hook is now a ordinary dictionary, causing this KeyError with SqlSensor: ``` [2022-11-29, 00:39:18 UTC] {taskinstance.py:1851} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/sensors/base.py", line 189, in execute poke_return = self.poke(context) File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/sensors/sql.py", line 98, in poke first_cell = records[0][0] KeyError: 0 ``` I have only tested with Snowflake, I haven't tested it with other databases. Reverting back to 1.2.0 solves the issue. ### What you think should happen instead It should return an iterable list as usual with the query. ### How to reproduce ``` from datetime import datetime from airflow import DAG from airflow.providers.common.sql.sensors.sql import SqlSensor with DAG( dag_id="sql_provider_snowflake_test", schedule=None, start_date=datetime(2022, 1, 1), catchup=False, ): t1 = SqlSensor( task_id="snowflake_test", conn_id="snowflake", sql="select 0", fail_on_empty=False, poke_interval=20, mode="poke", timeout=60 * 5, ) ``` ### 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/27978

https://github.com/apache/airflow/pull/28006

6c62985055e7f9a715c3ae47f6ff584ad8378e2a

d9cefcd0c50a1cce1c3c8e9ecb99cfacde5eafbf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/CHANGELOG.rst", "airflow/providers/snowflake/hooks/snowflake.py", "airflow/providers/snowflake/operators/snowflake.py", "tests/providers/snowflake/hooks/test_sql.py", "tests/providers/snowflake/operators/test_snowflake_sql.py"]

`SQLColumnCheckOperator` failures after upgrading to `common-sql==1.3.0`

### Apache Airflow Provider(s) common-sql ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.2.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-salesforce==5.0.0 apache-airflow-providers-slack==5.1.0 apache-airflow-providers-snowflake==3.2.0 Issue: apache-airflow-providers-common-sql==1.3.0 ### Apache Airflow version 2.4.3 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Astronomer ### Deployment details _No response_ ### What happened Problem occurred when upgrading from common-sql=1.2.0 to common-sql=1.3.0 Getting a `KEY_ERROR` when running a unique_check and null_check on a column. 1.3.0 log: <img width="1609" alt="Screen Shot 2022-11-28 at 2 01 20 PM" src="https://user-images.githubusercontent.com/15257610/204390144-97ae35b7-1a2c-4ee1-9c12-4f3940047cde.png"> 1.2.0 log: <img width="1501" alt="Screen Shot 2022-11-28 at 2 00 15 PM" src="https://user-images.githubusercontent.com/15257610/204389994-7e8eae17-a346-41ac-84c4-9de4be71af20.png"> ### What you think should happen instead Potential causes: - seems to be indexing based on the test query column `COL_NAME` instead of the table column `STRIPE_ID` - the `record` from the test changed types went from a tuple to a list of dictionaries. - no `tolerance` is specified for these tests, so `.get('tolerance')` looks like it will cause an error without a default specified like `.get('tolerance', None)` Expected behavior: - these tests continue to pass with the upgrade - `tolerance` is not a required key. ### How to reproduce ``` from datetime import datetime from airflow import DAG from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator from airflow.providers.common.sql.operators.sql import SQLColumnCheckOperator my_conn_id = "snowflake_default" default_args={"conn_id": my_conn_id} with DAG( dag_id="airflow_providers_example", schedule=None, start_date=datetime(2022, 11, 27), default_args=default_args, ) as dag: create_table = SnowflakeOperator( task_id="create_table", sql=""" CREATE OR REPLACE TABLE testing AS ( SELECT 1 AS row_num, 'not null' AS field UNION ALL SELECT 2 AS row_num, 'test' AS field UNION ALL SELECT 3 AS row_num, 'test 2' AS field )""", ) column_checks = SQLColumnCheckOperator( task_id="column_checks", table="testing", column_mapping={ "field": {"unique_check": {"equal_to": 0}, "null_check": {"equal_to": 0}} }, ) create_table >> column_checks ``` ### 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/27976

https://github.com/apache/airflow/pull/28006

6c62985055e7f9a715c3ae47f6ff584ad8378e2a

d9cefcd0c50a1cce1c3c8e9ecb99cfacde5eafbf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/mark_tasks.py", "airflow/models/taskinstance.py", "airflow/utils/log/file_task_handler.py", "airflow/utils/log/log_reader.py", "airflow/utils/state.py", "airflow/www/utils.py", "airflow/www/views.py", "tests/www/views/test_views_grid.py"]

Latest log not shown in grid view for deferred task

### Apache Airflow version 2.4.3 ### What happened In the grid view I do not see the logs for the latest try number if the task is in deferred state. I do see it in the "old" log view. Grid view:  "Old" view:  It could have something to do with the deferred task getting its try_number reduced by 1 - in my example try_number=1 and next_try_number=2. https://github.com/apache/airflow/blob/3e288abd0bc3e5788dcd7f6d9f6bef26ec4c7281/airflow/models/taskinstance.py#L1617-L1618 ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Red Hat ### 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/27955

https://github.com/apache/airflow/pull/26993

ad7f8e09f8e6e87df2665abdedb22b3e8a469b49

f110cb11bf6fdf6ca9d0deecef9bd51fe370660a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/external_task.py", "tests/sensors/test_external_task_sensor.py"]

can not use output of task decorator as input for external_task_ids of ExternalTaskSensor

### Apache Airflow version 2.4.3, 2.5.0 ### What happened when use output from task decorator as parameter (external_task_ids) in ExternalTaskSensor, it show up this log: ``` Broken DAG: [+++++/airflow/dags/TEST_NEW_PIPELINE.py] Traceback (most recent call last): File "+++++/env3.10.5/lib/python3.10/site-packages/airflow/models/baseoperator.py", line 408, in apply_defaults result = func(self, **kwargs, default_args=default_args) File "+++++/env3.10.5/lib/python3.10/site-packages/airflow/sensors/external_task.py", line 164, in __init__ if external_task_ids and len(external_task_ids) > len(set(external_task_ids)): TypeError: object of type 'PlainXComArg' has no len() ``` note: +++++ is just a mask for irrelevant information. ### What you think should happen instead this document https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html show that we can use it without any warning, note about it. found relative problem in [https://github.com/apache/airflow/issues/27328](url) should move all the check in __\_\_init\_\___ into __poke__ method. ### How to reproduce ``` from airflow.decorators import dag, task from airflow.operators.python import get_current_context from airflow.sensors.external_task import ExternalTaskSensor from datetime import datetime configure = {"dag_id": "test_new_skeleton", "schedule": None, "start_date": datetime(2022,1,1), } @task def preprocess_dependency() -> list: return ["random-task-name"] @dag(**configure) def pipeline(): t_preprocess = preprocess_dependency() task_dependency = ExternalTaskSensor(task_id=f"Check_Dependency", external_dag_id='random-dag-name-that-exist', external_task_ids=t_preprocess , poke_interval=60, mode="reschedule", timeout=172800, allowed_states=['success'], failed_states=['failed', 'skipped'], check_existence=True,) dag = pipeline() ``` ### Operating System REHL 7 ### 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/27952

https://github.com/apache/airflow/pull/28692

3d89797889e43bda89d4ceea37130bdfbc3db32c

7f18fa96e434c64288d801904caf1fcde18e2cbf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/components/Table/Cells.tsx"]

Datasets triggered run modal is not scrollable

### Apache Airflow version main (development) ### What happened Datasets modal which used to display triggered runs is not scrollable even if there are records  ### What you think should happen instead It should be scrollable if there are records to display ### How to reproduce 1. trigger a datasets dag with multiple triggered runs 2. click on datasets 3. click on uri which have multiple triggered runs DAG- ``` from airflow import Dataset, DAG from airflow.operators.python import PythonOperator from datetime import datetime fan_out = Dataset("fan_out") fan_in = Dataset("fan_in") # the leader with DAG( dag_id="momma_duck", start_date=datetime(1970, 1, 1), schedule_interval=None ) as leader: PythonOperator( task_id="has_outlet", python_callable=lambda: None, outlets=[fan_out] ) # the many for i in range(1, 40): with DAG( dag_id=f"duckling_{i}", start_date=datetime(1970, 1, 1), schedule=[fan_out] ) as duck: PythonOperator( task_id="has_outlet", python_callable=lambda: None, outlets=[fan_in] ) globals()[f"duck_{i}"] = duck # the straggler with DAG( dag_id="straggler_duck", start_date=datetime(1970, 1, 1), schedule=[fan_in] ) as straggler: PythonOperator(task_id="has_outlet", python_callable=lambda: None) ``` ### Operating System mac os ### 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/27936

https://github.com/apache/airflow/pull/27965

a158fbb6bde07cd20003680a4cf5e7811b9eda98

5e4f4a3556db5111c2ae36af1716719a8494efc7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/base_executor.py", "airflow/providers/celery/executors/celery_executor.py", "airflow/providers/cncf/kubernetes/executors/kubernetes_executor.py", "docs/apache-airflow-providers-celery/cli-ref.rst", "docs/apache-airflow-providers-celery/index.rst", "docs/apache-airflow-providers-cncf-kubernetes/cli-ref.rst", "docs/apache-airflow-providers-cncf-kubernetes/index.rst"]

AIP-51 - Executor Specific CLI Commands

### Overview Some Executors have their own first class CLI commands (now that’s hardcoding/coupling!) which setup or modify various components related to that Executor. ### Examples - **5a**) Celery Executor commands: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L1689-L1734 - **5b**) Kubernetes Executor commands: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L1754-L1771 - **5c**) Default CLI parser has hardcoded logic for Celery and Kubernetes Executors specifically: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L63-L99 ### Proposal Update the BaseExecutor interface with a pluggable mechanism to vend CLI `GroupCommands` and parsers. Executor subclasses would then implement these methods, if applicable, which would then be called to fetch commands and parsers from within Airflow Core cli parser code. We would then migrate the existing Executor CLI code from cli_parser to the respective Executor class. Pseudo-code example for vending `GroupCommand`s: ```python # Existing code in cli_parser.py ... airflow_commands: List[CLICommand] = [ GroupCommand( name='dags', help='Manage DAGs', subcommands=DAGS_COMMANDS, ), ... ] # New code to add groups vended by executor classes executor_cls, _ = ExecutorLoader.import_executor_cls(conf.get('core', 'EXECUTOR')) airflow_commands.append(executor_cls.get_cli_group_commands()) ... ```

https://github.com/apache/airflow/issues/27932

https://github.com/apache/airflow/pull/33081

bbc096890512ba2212f318558ca1e954ab399657

879fd34e97a5343e6d2bbf3d5373831b9641b5ad

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/base_executor.py", "airflow/executors/celery_kubernetes_executor.py", "airflow/executors/debug_executor.py", "airflow/executors/local_kubernetes_executor.py", "airflow/sensors/base.py", "tests/sensors/test_base.py"]

AIP-51 - Single Threaded Executors

### Overview Some Executors, currently a subset of the local Executors, run in a single threaded fashion and have certain limitations and requirements, many of which are hardcoded. To add a new single threaded Executor would require changes to core Airflow code. Note: This coupling often shows up with SQLite compatibility checks since it does not support multiple connections. ### Examples - **2a**) SQLite check done in configuration.py: https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/configuration.py#L412-L419 - **2b**) When running in standalone mode SQLite compatibility is checked: https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/cli/commands/standalone_command.py#L160-L165 - **2c**) Sensors in `poke` mode can block execution of DAGs when running with single process Executors, currently hardcoded to DebugExecutor (although should also include SequentialExecutor): https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/sensors/base.py#L243 ### Proposal A static method or attribute on the Executor class which can be checked by core code. There is a precedent already set with the `supports_ad_hoc_ti_run` attribute, see: https://github.com/apache/airflow/blob/fb741fd87254e235f99d7d67e558dafad601f253/airflow/executors/kubernetes_executor.py#L435 https://github.com/apache/airflow/blob/26f94c5370587f73ebd935cecf208c6a36bdf9b6/airflow/www/views.py#L1735-L1737

https://github.com/apache/airflow/issues/27929

https://github.com/apache/airflow/pull/28934

0359a42a3975d0d7891a39abe4395bdd6f210718

e5730364b4eb5a3b30e815ca965db0f0e710edb6

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add export_format to template_fields of BigQueryToGCSOperator

### Description There might be an use case where the export_format can be based on some dynamic values. So, adding export_format will help developers in future ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/27909

https://github.com/apache/airflow/pull/27910

3fef6a47834b89b99523db6d97d6aa530657a008

f0820e8d9e8a36325987278bcda2bd69bd53f3a5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/decorators.py"]

Password is not masked in audit logs for connections/variables

### Apache Airflow version main (development) ### What happened Password for connections and variables with secret in the name are not masked in audit logs. <img width="1337" alt="Screenshot 2022-11-25 at 12 58 59 PM" src="https://user-images.githubusercontent.com/88504849/203932123-c47fd66f-8e63-4bc6-9bf1-b9395cb26675.png"> <img width="1352" alt="Screenshot 2022-11-25 at 12 56 32 PM" src="https://user-images.githubusercontent.com/88504849/203932220-3f02984c-94b5-4773-8767-6f19cb0ceff0.png"> <img width="1328" alt="Screenshot 2022-11-25 at 1 43 40 PM" src="https://user-images.githubusercontent.com/88504849/203933183-e97b2358-9414-45c8-ab8f-d2f913117301.png"> ### What you think should happen instead Password/value should be masked ### How to reproduce 1. Create a connection or variable(with secret in the name i.e. test_secret) 2. Open audit logs 3. Observe the password ### Operating System mac os ### 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/27907

https://github.com/apache/airflow/pull/27923

5e45cb019995e8b80104b33da1c93eefae12d161

1e73b1cea2d507d6d09f5eac6a16b649f8b52522

python

closed

apache/airflow

https://github.com/apache/airflow

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

current_state method of TaskInstance fails for mapped task instance

### Apache Airflow version 2.4.3 ### What happened `current_state` method on TaskInstance doesn't filter by `map_index` so calling this method on mapped task instance fails. https://github.com/apache/airflow/blob/fb7c6afc8cb7f93909bd2e654ea185eb6abcc1ea/airflow/models/taskinstance.py#L708-L726 ### What you think should happen instead map_index should also be filtered in the query to return single TaskInstance object. ### How to reproduce ```python with create_session() as session: print(session.query(TaskInstance).filter(TaskInstance.dag_id == "divide_by_zero", TaskInstance.map_index == 1, TaskInstance.run_id == 'scheduled__2022-11-22T00:00:00+00:00') .scalar().current_state()) --------------------------------------------------------------------------- MultipleResultsFound Traceback (most recent call last) Input In [7], in <cell line: 1>() 1 with create_session() as session: ----> 2 print(session.query(TaskInstance).filter(TaskInstance.dag_id == "divide_by_zero", TaskInstance.map_index == 1, TaskInstance.run_id == 'scheduled__2022-11-22T00:00:00+00:00').scalar().current_state()) File ~/stuff/python/airflow/airflow/utils/session.py:75, in provide_session.<locals>.wrapper(*args, **kwargs) 73 else: 74 with create_session() as session: ---> 75 return func(*args, session=session, **kwargs) File ~/stuff/python/airflow/airflow/models/taskinstance.py:725, in TaskInstance.current_state(self, session) 708 @provide_session 709 def current_state(self, session: Session = NEW_SESSION) -> str: 710 """ 711 Get the very latest state from the database, if a session is passed, 712 we use and looking up the state becomes part of the session, otherwise (...) 715 :param session: SQLAlchemy ORM Session 716 """ 717 return ( 718 session.query(TaskInstance.state) 719 .filter( 720 TaskInstance.dag_id == self.dag_id, 721 TaskInstance.task_id == self.task_id, 722 TaskInstance.run_id == self.run_id, 723 724 ) --> 725 .scalar() 726 ) File ~/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py:2803, in Query.scalar(self) 2801 # TODO: not sure why we can't use result.scalar() here 2802 try: -> 2803 ret = self.one() 2804 if not isinstance(ret, collections_abc.Sequence): 2805 return ret File ~/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py:2780, in Query.one(self) 2762 def one(self): 2763 """Return exactly one result or raise an exception. 2764 2765 Raises ``sqlalchemy.orm.exc.NoResultFound`` if the query selects (...) 2778 2779 """ -> 2780 return self._iter().one() File ~/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1162, in Result.one(self) 1134 def one(self): 1135 # type: () -> Row 1136 """Return exactly one row or raise an exception. 1137 1138 Raises :class:`.NoResultFound` if the result returns no (...) 1160 1161 """ -> 1162 return self._only_one_row(True, True, False) File ~/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/engine/result.py:620, in ResultInternal._only_one_row(self, raise_for_second_row, raise_for_none, scalar) 618 if next_row is not _NO_ROW: 619 self._soft_close(hard=True) --> 620 raise exc.MultipleResultsFound( 621 "Multiple rows were found when exactly one was required" 622 if raise_for_none 623 else "Multiple rows were found when one or none " 624 "was required" 625 ) 626 else: 627 next_row = _NO_ROW MultipleResultsFound: Multiple rows were found when exactly one was required ``` ### 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/27864

https://github.com/apache/airflow/pull/27898

c931d888936a958ae40b69077d35215227bf1dff

51c70a5d6990a6af1188aab080ae2cbe7b935eb2

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSToBigQueryOperator no longer uses field_delimiter or time_partitioning

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers google=8.5.0 ### Apache Airflow version 2.4.3 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened The newest version of the google providers no longer provides the `field_delimiter` or `time_partitioning` fields to the bq job configuration for the GCStoBQ transfers. Looking at the code it seems like this behavior was removed during the change to use deferrable operations ### What you think should happen instead These fields should continue to be provided ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/27842

https://github.com/apache/airflow/pull/27961

5cdff505574822ad3d2a226056246500e4adea2f

2d663df0552542efcef6e59bc2bc1586f8d1c7f3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/operators/sql.py"]

apache-airflow-providers-common-sql==1.3.0 breaks BigQuery operators

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened **Airflow version**: 2.3.4 (Cloud Composer 2.0.32) **Issue**: `apache-airflow-providers-common-sql==1.3.0` breaks all BigQuery operators provided by the `apache-airflow-providers-google==8.4.0` package. The error is as follows: ```python Broken DAG: [/home/airflow/gcs/dags/test-dag.py] Traceback (most recent call last): File "/home/airflow/gcs/dags/test-dag.py", line 6, in <module> from airflow.providers.google.cloud.operators.bigquery import BigQueryExecuteQueryOperator File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/operators/bigquery.py", line 35, in <module> from airflow.providers.common.sql.operators.sql import ( ImportError: cannot import name '_get_failed_checks' from 'airflow.providers.common.sql.operators.sql' (/opt/python3.8/lib/python3.8/site-packages/airflow/providers/common/sql/operators/sql.py) ``` **Why this issue is tricky**: other providers such as `apache-airflow-providers-microsoft-mssql==3.3.0` and `apache-airflow-providers-oracle==3.5.0` have a dependency on `apache-airflow-providers-common-sql>=1.3.0` and will therefore install it when adding to the Composer environment **Current mitigation**: Downgrade provider packages such that `apache-airflow-providers-common-sql==1.2.0` is installed instead ### What you think should happen instead A minor version upgrade of `apache-airflow-providers-common-sql` (1.2.0 to 1.3.0) should not break other providers (e.g. apache-airflow-providers-google==8.4.0) ### How to reproduce - Deploy fresh deployment of Composer `composer-2.0.32-airflow-2.3.4` - Install `apache-airflow-providers-common-sql==1.3.0` via Pypi package install feature - Deploy a dag that uses one of the BigQuery operators, such as ```python import airflow from airflow import DAG from datetime import timedelta from airflow.providers.google.cloud.operators.bigquery import BigQueryExecuteQueryOperator default_args = { 'start_date': airflow.utils.dates.days_ago(0), 'retries': 1, 'retry_delay': timedelta(minutes=5) } dag = DAG( 'test-dag', default_args=default_args, schedule_interval=None, dagrun_timeout=timedelta(minutes=20)) t1 = BigQueryExecuteQueryOperator( ... ) ``` ### Operating System Ubuntu 18.04.6 LTS ### Versions of Apache Airflow Providers - apache-airflow-providers-apache-beam @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_apache_beam-4.0.0-py3-none-any.whl - apache-airflow-providers-cncf-kubernetes @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_cncf_kubernetes-4.4.0-py3-none-any.whl - apache-airflow-providers-common-sql @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_common_sql-1.3.0-py3-none-any.whl - apache-airflow-providers-dbt-cloud @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_dbt_cloud-2.2.0-py3-none-any.whl - apache-airflow-providers-ftp @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_ftp-3.1.0-py3-none-any.whl - apache-airflow-providers-google @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_google-8.4.0-py3-none-any.whl - apache-airflow-providers-hashicorp @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_hashicorp-3.1.0-py3-none-any.whl - apache-airflow-providers-http @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_http-4.0.0-py3-none-any.whl - apache-airflow-providers-imap @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_imap-3.0.0-py3-none-any.whl - apache-airflow-providers-mysql @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_mysql-3.2.1-py3-none-any.whl - apache-airflow-providers-postgres @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_postgres-5.2.2-py3-none-any.whl - apache-airflow-providers-sendgrid @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_sendgrid-3.0.0-py3-none-any.whl - apache-airflow-providers-sqlite @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_sqlite-3.2.1-py3-none-any.whl - apache-airflow-providers-ssh @ file:///usr/local/lib/airflow-pypi-dependencies-2.3.4/python3.8/apache_airflow_providers_ssh-3.2.0-py3-none-any.whl ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/27838

https://github.com/apache/airflow/pull/27843

0b0d2990fdb31749396305433b0f8cc54db7aee8

dbb4b59dcbc8b57243d1588d45a4d2717c3e7758

python

closed

apache/airflow

https://github.com/apache/airflow

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

Databricks - Run job by job name not working with DatabricksRunNowDeferrableOperator

### Apache Airflow Provider(s) databricks ### Versions of Apache Airflow Providers apache-airflow-providers-databricks==3.3.0 ### Apache Airflow version 2.4.2 ### Operating System Mac OS 13.0 ### Deployment Virtualenv installation ### Deployment details Virtualenv deployment with Python 3.10 ### What happened Submitting a Databricks job run by name (`job_name`) with the deferrable version (`DatabricksRunNowDeferrableOperator`) does not actually fill the `job_id` and the Databricks API responds with an HTTP 400 bad request - attempting to run a job (POST `https://<databricks-instance>/api/2.1/jobs/run-now`) without an ID specidied. Sample errors from the Airflow logs: ``` requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://[subdomain].azuredatabricks.net/api/2.1/jobs/run-now During handling of the above exception, another exception occurred: [...truncated message...] airflow.exceptions.AirflowException: Response: b'{"error_code":"INVALID_PARAMETER_VALUE","message":"Job 0 does not exist."}', Status Code: 400 ``` ### What you think should happen instead The deferrable version (`DatabricksRunNowDeferrableOperator`) should maintain the behavior of the parent class (`DatabricksRunNowOperator`) and use the `job_name` to find the `job_id`. The following logic is missing in the deferrable version: ``` # Sample from the DatabricksRunNowOperator#execute hook = self._hook if "job_name" in self.json: job_id = hook.find_job_id_by_name(self.json["job_name"]) if job_id is None: raise AirflowException(f"Job ID for job name {self.json['job_name']} can not be found") self.json["job_id"] = job_id del self.json["job_name"] ``` ### How to reproduce To reproduce, use a deferrable run now operator with the job name as an argument in an airflow task: ``` from airflow.providers.databricks.operators.databricks import DatabricksRunNowDeferrableOperator DatabricksRunNowDeferrableOperator( job_name='some-name', # Other args ) ``` ### Anything else The problem occurs at every call. ### 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/27837

https://github.com/apache/airflow/pull/32806

c4b6f06f6e2897b3f1ee06440fc66f191acee9a8

58e21c66fdcc8a416a697b4efa852473ad8bd6fc

python

closed

apache/airflow

https://github.com/apache/airflow

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

DAG Run fails when chaining multiple empty mapped tasks

### Apache Airflow version 2.4.3 ### What happened A significant fraction of the DAG Runs of a DAG that has 2+ consecutive mapped tasks which are are being passed an empty list are marked as failed when all tasks are either succeeding or being skipped. This was supposedly fixed with issue #25200 but the problem still persists.  ### What you think should happen instead The DAG Run should be marked success. ### How to reproduce The real world version of this DAG has several mapped tasks that all point to the same list, and that list is frequently empty. I have made a minimal reproducible example. ``` from datetime import datetime from airflow import DAG from airflow.decorators import task with DAG(dag_id="break_mapping", start_date=datetime(2022, 3, 4)) as dag: @task def add_one(x: int): return x + 1 @task def say_hi(): print("Hi") @task def say_bye(): print("Bye") added_values = add_one.expand(x=[]) added_more_values = add_one.expand(x=[]) added_more_more_values = add_one.expand(x=[]) say_hi() >> say_bye() >> added_values added_values >> added_more_values >> added_more_more_values ``` ### Operating System Debian Bullseye ### 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/27824

https://github.com/apache/airflow/pull/27964

b60006ae26c41e887ec0102bce8b726fce54007d

f89ca94c3e60bfae888dfac60c7472d207f60f22

python

closed

apache/airflow

https://github.com/apache/airflow

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

Triggering a DAG with the same run_id as a scheduled one causes the scheduler to crash

### Apache Airflow version 2.5.0 ### What happened A user with access to manually triggering DAGs can trigger a DAG. provide a run_id that matches the pattern used when creating scheduled runs and cause the scheduler to crash due to database unique key violation: ``` 2022-12-12 12:58:00,793] {scheduler_job.py:776} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 759, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 885, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 956, in _do_scheduling self._create_dagruns_for_dags(guard, session) File "/usr/local/lib/python3.8/site-packages/airflow/utils/retries.py", line 78, in wrapped_function for attempt in run_with_db_retries(max_retries=retries, logger=logger, **retry_kwargs): File "/usr/local/lib/python3.8/site-packages/tenacity/__init__.py", line 384, in __iter__ do = self.iter(retry_state=retry_state) File "/usr/local/lib/python3.8/site-packages/tenacity/__init__.py", line 351, in iter return fut.result() File "/usr/local/lib/python3.8/concurrent/futures/_base.py", line 437, in result return self.__get_result() File "/usr/local/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/usr/local/lib/python3.8/site-packages/airflow/utils/retries.py", line 87, in wrapped_function return func(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1018, in _create_dagruns_for_dags query, dataset_triggered_dag_info = DagModel.dags_needing_dagruns(session) File "/usr/local/lib/python3.8/site-packages/airflow/models/dag.py", line 3341, in dags_needing_dagruns for x in session.query( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2773, in all return self._iter().all() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 2916, in _iter result = self.session.execute( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1713, in execute conn = self._connection_for_bind(bind) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1552, in _connection_for_bind return self._transaction._connection_for_bind( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 721, in _connection_for_bind self._assert_active() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 601, in _assert_active raise sa_exc.PendingRollbackError( 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.UniqueViolation) duplicate key value violates unique constraint "dag_run_dag_id_run_id_key" DETAIL: Key (dag_id, run_id)=(example_branch_dop_operator_v3, scheduled__2022-12-12T12:57:00+00:00) already exists. [SQL: INSERT INTO dag_run (dag_id, queued_at, execution_date, start_date, end_date, state, run_id, creating_job_id, external_trigger, run_type, conf, data_interval_start, data_interval_end, last_scheduling_decision, dag_hash, log_template_id, updated_at) VALUES (%(dag_id)s, %(queued_at)s, %(execution_date)s, %(start_date)s, %(end_date)s, %(state)s, %(run_id)s, %(creating_job_id)s, %(external_trigger)s, %(run_type)s, %(conf)s, %(data_interval_start)s, %(data_interval_end)s, %(last_scheduling_decision)s, %(dag_hash)s, (SELECT max(log_template.id) AS max_1 FROM log_template), %(updated_at)s) RETURNING dag_run.id] [parameters: {'dag_id': 'example_branch_dop_operator_v3', 'queued_at': datetime.datetime(2022, 12, 12, 12, 58, 0, 435945, tzinfo=Timezone('UTC')), 'execution_date': DateTime(2022, 12, 12, 12, 57, 0, tzinfo=Timezone('UTC')), 'start_date': None, 'end_date': None, 'state': <DagRunState.QUEUED: 'queued'>, 'run_id': 'scheduled__2022-12-12T12:57:00+00:00', 'creating_job_id': 1, 'external_trigger': False, 'run_type': <DagRunType.SCHEDULED: 'scheduled'>, 'conf': <psycopg2.extensions.Binary object at 0x7f283a82af60>, 'data_interval_start': DateTime(2022, 12, 12, 12, 57, 0, tzinfo=Timezone('UTC')), 'data_interval_end': DateTime(2022, 12, 12, 12, 58, 0, tzinfo=Timezone('UTC')), 'last_scheduling_decision': None, 'dag_hash': '1653a588de69ed25c5b1dcfef928479c', 'updated_at': datetime.datetime(2022, 12, 12, 12, 58, 0, 436871, tzinfo=Timezone('UTC'))}] (Background on this error at: https://sqlalche.me/e/14/gkpj) (Background on this error at: https://sqlalche.me/e/14/7s2a) ``` Worse yet, the scheduler will keep crashing after a restart with the same exception. ### What you think should happen instead A user should not be able to crash the scheduler from the UI. I see 2 alternatives for solving this: 1. Reject custom run_id that would (or could) collide with a scheduled one, preventing this situation from happening. 2. Handle the database error and assign a different run_id to the scheduled run. ### How to reproduce 1. Find an unpaused DAG. 2. Trigger DAG w/ config, set the run id to something like scheduled__2022-11-21T12:00:00+00:00 (adjust the time to be in the future where there is no run yet). 3. Let the manual DAG run finish. 4. Wait for the scheduler to try to schedule another DAG run with the same run id. 5. :boom: 6. Attempt to restart the scheduler. 7. :boom: ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-postgres==5.3.1 ### Deployment Docker-Compose ### Deployment details I'm using a Postgres docker container as a metadata database that is linked via docker networking to the scheduler and the rest of the components. Scheduler, workers and webserver are all running in separate containers (using CeleryExecutor backed by a Redis container), though I do not think it is relevant in this case. ### 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/27818

https://github.com/apache/airflow/pull/28397

8fb7be2fb5c64cc2f31a05034087923328b1137a

7ccbe4e7eaa529641052779a89e34d54c5a20f72

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "airflow/utils/cli.py"]

First task in Quick Start fails

### Apache Airflow version 2.4.3 ### What happened When I ran `airflow tasks run example_bash_operator runme_0 2015-01-01` I got the error ``` (venv) myusername@MacBook-Air airflow % airflow tasks run example_bash_operator runme_0 2015-01-01 [2022-11-14 23:49:19,228] {dagbag.py:537} INFO - Filling up the DagBag from /Users/myusername/airflow/dags [2022-11-14 23:49:19,228] {cli.py:225} WARNING - Dag '\x1b[01mexample_bash_operator\x1b[22m' not found in path /Users/myusername/airflow/dags; trying path /Users/myusername/airflow/dags [2022-11-14 23:49:19,228] {dagbag.py:537} INFO - Filling up the DagBag from /Users/myusername/airflow/dags Traceback (most recent call last): File "/Users/myusername/airflow/venv/bin/airflow", line 8, in <module> sys.exit(main()) File "/Users/myusername/airflow/venv/lib/python3.10/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/Users/myusername/airflow/venv/lib/python3.10/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/Users/myusername/airflow/venv/lib/python3.10/site-packages/airflow/utils/cli.py", line 103, in wrapper return f(*args, **kwargs) File "/Users/myusername/airflow/venv/lib/python3.10/site-packages/airflow/cli/commands/task_command.py", line 366, in task_run dag = get_dag(args.subdir, args.dag_id, include_examples=False) File "/Users/myusername/airflow/venv/lib/python3.10/site-packages/airflow/utils/cli.py", line 228, in get_dag raise AirflowException( airflow.exceptions.AirflowException: Dag 'example_bash_operator' could not be found; either it does not exist or it failed to parse. ``` ### What you think should happen instead Successful completion of the task ### How to reproduce 1. Create a Python venv based on Python 3.10 2. Follow the [Quick Start instructions](https://airflow.apache.org/docs/apache-airflow/stable/start.html) through `airflow tasks run example_bash_operator runme_0 2015-01-01` 3. The error should appear ### Operating System MacOS 12.5.1 ### Versions of Apache Airflow Providers Not applicable ### Deployment Other ### Deployment details This is just on my local machine ### 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/27799

https://github.com/apache/airflow/pull/27813

d8dbdccef7cc14af7bacbfd4ebc48d8aabfaf7f0

b9729d9e469f7822212e0d6d76e10d95411e739a

python

closed

apache/airflow

https://github.com/apache/airflow

[".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"]

Add pre-commit rule to validate using `urlsplit` rather than `urlparse`

### Body Originally suggested in https://github.com/apache/airflow/pull/27389#issuecomment-1297252026 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/27715

https://github.com/apache/airflow/pull/27841

cd01650192b74573b49a20803e4437e611a4cf33

a99254ffd36f9de06feda6fe45773495632e3255

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/trigger.js", "airflow/www/templates/airflow/trigger.html", "airflow/www/utils.py", "airflow/www/views.py"]

Re-use recent DagRun JSON-configurations

### Description Allow users to re-use recent DagRun configurations upon running a DAG. This can be achieved by adding a dropdown that contains some information about recent configurations. When user selects an item, the relevant JSON configuration can be pasted to the "Configuration JSON" textbox. <img width="692" alt="Screen Shot 2022-11-16 at 16 22 30" src="https://user-images.githubusercontent.com/39705397/202209536-c709ec75-c768-48ab-97d4-82b02af60569.png"> <img width="627" alt="Screen Shot 2022-11-16 at 16 22 38" src="https://user-images.githubusercontent.com/39705397/202209553-08828521-dba2-4e83-8e2a-6dec850086de.png"> <img width="612" alt="Screen Shot 2022-11-16 at 16 38 40" src="https://user-images.githubusercontent.com/39705397/202209755-0946521a-e1a5-44cb-ae74-d43ca3735f31.png"> ### Use case/motivation Commonly, DAGs are triggered using repetitive configurations. Sometimes the same configuration is used for triggering a DAG, and sometimes, the configuration differs by just a few parameters. This interaction forces a user to store the templates he uses somewhere on his machine or to start searching for the configuration he needs in `dagrun/list/`, which does take extra time. It will be handy to offer a user an option to select one of the recent configurations upon running a DAG. ### 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/27714

https://github.com/apache/airflow/pull/27805

7f0332de2d1e57cde2e031f4bb7b4e6844c4b7c1

e2455d870056391eed13e32e2d0ed571cc7089b4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/kubernetes/pod_template_file_examples/git_sync_template.yaml", "chart/values.schema.json", "chart/values.yaml", "newsfragments/27698.significant.rst"]

Update git-sync with newer version

### Official Helm Chart version 1.7.0 (latest released) ### What happened The current git-sync image that is used is coming up on one year old. It is also using the deprecated `--wait` arg. ### What you think should happen instead In order to stay current, we should update git-sync from 3.4.0 to 3.6.1. ### 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/27698

https://github.com/apache/airflow/pull/27848

af9143eacdff62738f6064ae7556dd8f4ca8d96d

98221da0d96b102b009d422870faf7c5d3d931f4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/hive/hooks/hive.py", "tests/providers/apache/hive/hooks/test_hive.py"]

Improve filtering for invalid schemas in Hive hook

### Description #27647 has introduced filtering for invalid schemas in Hive hook based on the characters `;` and `!`. I'm wondering if a more generic filtering could be introduced, e.g. one that adheres to the regex `[^a-z0-9_]`, since Hive schemas (and table names) can only contain alphanumeric characters and the character `_`. Note: since the Hive metastore [stores schemas and tables in lowercase](https://stackoverflow.com/questions/57181316/how-to-keep-column-names-in-camel-case-in-hive/57183048#57183048), checking against `[^a-z0-9_]` is probably better than `[^a-zA-Z0-9_]`. ### Use case/motivation Ensure that Hive schemas used in `apache-airflow-providers-apache-hive` hooks contain no invalid characters. ### 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/27695

https://github.com/apache/airflow/pull/27808

017ed9ac662d50b6e2767f297f36cb01bf79d825

2d45f9d6c30aabebce3449eae9f152ba6d2306e2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Calendar view does not load when using CronTriggerTimeTable

### Apache Airflow version 2.4.2 ### What happened Create a DAG and set the schedule parameter using a CronTriggerTimeTable instance. Enable the DAG so that there is DAG run data. Try to access the Calendar View for the DAG. An ERR_EMPTY_RESPONSE error is displayed instead of the page. The Calendar View is accessible for other DAGs that are using the schedule_interval set to a cron string instead. ### What you think should happen instead The Calendar View should have been displayed. ### How to reproduce Create a DAG and set the schedule parameter to a CronTriggerTimeTable instance. Enable the DAG and allow some DAG runs to occur. Try to access the Calender View for the DAG. ### Operating System Red Hat Enterprise Linux 8.6 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details Airflow 2.4.2 installed via pip with Python3.9 to venv using constraints. ### 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/27645

https://github.com/apache/airflow/pull/28411

4b3eb77e65748b1a6a31116b0dd55f8295fe8a20

467a5e3ab287013db2a5381ef4a642e912f8b45b

python

closed

apache/airflow

https://github.com/apache/airflow

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

AirflowException Crashing the Scheduler During the scheduling loop (_verify_integrity_if_dag_changed)

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened ### Deployment * Airflow Version: 2.4.1 * Infrastructure: AWS ECS * Number of DAG: 162 ``` Version: [v2.4.1](https://pypi.python.org/pypi/apache-airflow/2.4.1) Git Version: .release:2.4.1+7b979def75923ba28dd64e31e613043d29f34fce ``` ### The issue We have seen this issue when the Scheduler is trying to schedule **too many DAG (140+)** around the same time ``` [2022-11-11T00:15:00.311+0000] {{dagbag.py:196}} WARNING - Serialized DAG mongodb-assistedbreakdown-jobs-processes no longer exists [2022-11-11T00:15:00.312+0000] {{scheduler_job.py:763}} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 746, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 866, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 948, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 1292, in _schedule_dag_run self._verify_integrity_if_dag_changed(dag_run=dag_run, session=session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 1321, in _verify_integrity_if_dag_changed dag_run.verify_integrity(session=session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 72, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagrun.py", line 874, in verify_integrity dag = self.get_dag() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagrun.py", line 484, in get_dag raise AirflowException(f"The DAG (.dag) for {self} needs to be set") airflow.exceptions.AirflowException: The DAG (.dag) for <DagRun mongodb-assistedbreakdown-jobs-processes @ 2022-11-10 00:10:00+00:00: scheduled__2022-11-10T00:10:00+00:00, state:running, queued_at: 2022-11-11 00:10:09.363852+00:00. externally triggered: False> needs to be set ``` Main Cause ``` raise AirflowException(f"The DAG (.dag) for {self} needs to be set") ``` [We believe this is happening here, airflow github](https://github.com/apache/airflow/blob/7b979def75923ba28dd64e31e613043d29f34fce/airflow/jobs/scheduler_job.py#L1318) We saw a large amount of Connection hitting our airflow Database, but CPU was around 60%. Is there any workaround or configuration that can help the scheduler not crash when this happen? ### What you think should happen instead Can the scheduler be safe, or when it come back to reschedule the dags that got stuck ### How to reproduce _No response_ ### Operating System Amazon Linux 2, Fargate deployment using the airflow Image ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details AWS ECS Fargate ### 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/27622

https://github.com/apache/airflow/pull/27720

a5d5bd0232b98c6b39e587dd144086f4b7d8664d

15e842da56d9b3a1c2f47f9dec7682a4230dbc41

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py"]

AWS GlueJobOperator is not updating job config if job exists

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.0.0 ### Apache Airflow version 2.2.5 ### Operating System Linux Ubuntu ### Deployment Virtualenv installation ### Deployment details Airflow deployed on ec2 instance ### What happened `GlueJobOperator` from airflow-amazon-provider is not updating job configuration (like its arguments or number of workers for example) if the job already exists and if there was a change in the configuration for example: ```python def get_or_create_glue_job(self) -> str: """ Creates(or just returns) and returns the Job name :return:Name of the Job """ glue_client = self.get_conn() try: get_job_response = glue_client.get_job(JobName=self.job_name) self.log.info("Job Already exist. Returning Name of the job") return get_job_response['Job']['Name'] except glue_client.exceptions.EntityNotFoundException: self.log.info("Job doesn't exist. Now creating and running AWS Glue Job") ... ``` Is there a particular reason to not doing it? Or it was just not done during the implementation of the operarot? ### What you think should happen instead _No response_ ### How to reproduce Create a `GlueJobOperator` with a simple configuration: ```python from airflow.providers.amazon.aws.operators.glue import GlueJobOperator submit_glue_job = GlueJobOperator( task_id='submit_glue_job', job_name='test_glue_job job_desc='test glue job', script_location='s3://bucket/path/to/the/script/file', script_args={}, s3_bucket='bucket', concurrent_run_limit=1, retry_limit=0, num_of_dpus=5, wait_for_completion=False ) ``` Then update one of the initial configuration like `num_of_dpus=10` and validate that the operator is not updating glue job configuration on AWS when it is run again. ### Anything else There is `GlueCrawlerOperator` which is similar to GlueJobOperator and is doing it: ```python def execute(self, context: Context): """ Executes AWS Glue Crawler from Airflow :return: the name of the current glue crawler. """ crawler_name = self.config['Name'] if self.hook.has_crawler(crawler_name): self.hook.update_crawler(**self.config) else: self.hook.create_crawler(**self.config) ... ``` This behavior could be reproduced in the AWSGlueJobOperator if we agree to do it. ### 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/27592

https://github.com/apache/airflow/pull/27893

4fdfef909e3b9a22461c95e4ee123a84c47186fd

b609ab9001102b67a047b3078dc0b67fbafcc1e1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/glue_crawler.py", "airflow/providers/amazon/aws/operators/glue_crawler.py", "tests/providers/amazon/aws/hooks/test_glue_crawler.py", "tests/providers/amazon/aws/operators/test_glue_crawler.py"]

Using GlueCrawlerOperator fails when using tags

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We are using tags on resource in AWS. When setting tags when using `GlueCrawlerOperator` it works the first time, when Airflow creates the crawler. However on subsequent runs in fails because `boto3.get_crawler()` does not return the Tags. Hence we get the error below. ``` [2022-11-08, 14:48:49 ] {taskinstance.py:1774} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/glue_crawler.py", line 80, in execute self.hook.update_crawler(**self.config) File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/glue_crawler.py", line 86, in update_crawler key: value for key, value in crawler_kwargs.items() if current_crawler[key] != crawler_kwargs[key] File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/glue_crawler.py", line 86, in <dictcomp> key: value for key, value in crawler_kwargs.items() if current_crawler[key] != crawler_kwargs[key] KeyError: 'Tags' ``` ### What you think should happen instead Ignore tags when checking if the crawler should be updated. ### How to reproduce Use `GlueCrawlerOperator` with Tags like below and trigger the task multiple times. It will fail the second time around. ``` GlueCrawlerOperator( dag=dag, task_id="the_task_id", config={ "Name": "name_of_the_crwaler", "Role": "some-role", "DatabaseName": "some_database", "Targets": {"S3Targets": [{"Path": "s3://..."}]}, "TablePrefix": "a_table_prefix", "RecrawlPolicy": { "RecrawlBehavior": "CRAWL_EVERYTHING" }, "SchemaChangePolicy": { "UpdateBehavior": "UPDATE_IN_DATABASE", "DeleteBehavior": "DELETE_FROM_DATABASE" }, "Tags": { "TheTag": "value-of-my-tag" } } ``` ### Operating System Ubuntu ### Versions of Apache Airflow Providers ``` apache-airflow-providers-cncf-kubernetes==3.0.0 apache-airflow-providers-google==6.7.0 apache-airflow-providers-amazon==3.2.0 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-http==2.1.2 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-ssh==2.4.3 apache-airflow-providers-jdbc==2.1.3 ``` ### Deployment Other 3rd-party Helm chart ### Deployment details Airflow v2.2.5 Self-hosted Airflow in Kubernetes. ### 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/27556

https://github.com/apache/airflow/pull/28005

b3d7e17e72c05fd149a5514e3796d46a241ac4f7

3ee5c404b7a0284fc1f3474519b3833975aaa644

python