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closed

apache/airflow

https://github.com/apache/airflow

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

GCSToBigQueryOperator and BigQueryToGCSOperator do not respect their project_id arguments

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We experienced this issue Airflow 2.6.1, but the problem exists in the Google provider rather than core Airflow, and were introduced with [these changes](https://github.com/apache/airflow/pull/30053/files). We are using version 10.0.0 of the provider. The [issue](https://github.com/apache/airflow/issues/29958) that resulted in these changes seems to be based on an incorrect understanding of how projects interact in BigQuery -- namely that the project used for storage and the project used for compute can be separate. The user reporting the issue appears to mistake an error about compute (`User does not have bigquery.jobs.create permission in project {project-A}.` for an error about storage, and this incorrect diagnosis resulted in a fix that inappropriately defaults the compute project to the project named in destination/source (depending on the operator) table. The change attempts to allow users to override this (imo incorrect) default, but unfortunately this does not currently work because `self.project_id` gets overridden with the named table's project [here](https://github.com/apache/airflow/pull/30053/files#diff-875bf3d1bfbba7067dc754732c0e416b8ebe7a5b722bc9ac428b98934f04a16fR512) and [here](https://github.com/apache/airflow/pull/30053/files#diff-875bf3d1bfbba7067dc754732c0e416b8ebe7a5b722bc9ac428b98934f04a16fR587). ### What you think should happen instead I think that the easiest fix would be to revert the change, and return to defaulting the compute project to the one specified in the default google cloud connection. However, since I can understand the desire to override the `project_id`, I think handling it correctly, and clearly distinguishing between the concepts of storage and compute w/r/t projects would also work. ### How to reproduce Attempt to use any other project for running the job, besides the one named in the source/destination table ### Operating System debian ### Versions of Apache Airflow Providers apache-airflow-providers-google==10.0.0 ### 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/32106

https://github.com/apache/airflow/pull/32232

b3db4de4985eccb859a30a07a2350499370c6a9a

2d690de110825ba09b9445967b47c44edd8f151c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/triggerer_job_runner.py", "tests/jobs/test_triggerer_job.py"]

Triggerer intermittent failure when running many triggerers

### Apache Airflow version 2.6.2 ### What happened We are running a dag with many deferrable tasks using a custom trigger that waits for an Azure Batch task to complete. When many tasks have been deferred, we can an intermittent error in the Triggerer. The logged error message is the following: ``` Exception in thread Thread-2: Traceback (most recent call last): File "/usr/local/lib/python3.9/threading.py", line 980, in _bootstrap_inner self.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 457, in run asyncio.run(self.arun()) File "/usr/local/lib/python3.9/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/usr/local/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete return future.result() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 470, in arun await self.create_triggers() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/triggerer_job_runner.py", line 492, in create_triggers dag_id = task_instance.dag_id AttributeError: 'NoneType' object has no attribute 'dag_id' ``` After this error occurs, the trigger still reports as healthy, but no events are triggered. Restarting the triggerer fixes the problem. ### What you think should happen instead The specific error in the trigger should be addressed to prevent the triggerer async thread from crashing. The triggerer should not perform heartbeat updates when the async triggerer thread has crashed. ### How to reproduce This occurs intermittently, and seems to be the results of running more than one triggerer. Running many deferred tasks eventually ends up with this error occurring. ### Operating System linux (standard airflow slim images extended with custom code running on kubernetes) ### Versions of Apache Airflow Providers postgres,celery,redis,ssh,statsd,papermill,pandas,github_enterprise ### Deployment Official Apache Airflow Helm Chart ### Deployment details Azure Kubernetes and helm chart 1.9.0. 2 replicas of both triggerer and scheduler. ### Anything else It seems that as triggers fire, the link between the trigger row and the associated task_instance for the trigger is removed before the trigger row is removed. This leaves a small amount of time where the trigger exists without an associated task_instance. The database updates are performed in a synchronous loop inside the triggerer, so with one triggerer, this is not a problem. However, it can be a problem with more than one triggerer. Also, once the triggerer async loop (that handles the trigger code) fails, the triggers no longer fire. However, the heartbeat is handled by the synchronous loop so the job still reports as healthy. I have included an associated PR to resolve these issues. ### 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/32091

https://github.com/apache/airflow/pull/32092

14785bc84c984b8747fa062b84e800d22ddc0477

e585b588fc49b1b1c73a8952e9b257d7a9e13314

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/dataproc.py", "tests/providers/google/cloud/hooks/test_dataproc.py"]

AioRpcError in DataprocCreateBatchOperator

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow version: 2.3.4 (Composer 2.1.12) I've been using the DataprocCreateBatchOperator with the deferrable=True option. It worked well for the past few months, but an error started appearing on June 21, 2023, at 16:51 UTC. The error message is as follows: ``` grpc.aio._call.AioRpcError: <AioRpcError of RPC that terminated with: status = StatusCode.INVALID_ARGUMENT details = "Request contains an invalid argument." debug_error_string = "UNKNOWN:Error received from peer ipv4:74.125.69.95:443 {grpc_message:"Request contains an invalid argument.", grpc_status:3, created_time:"2023-06-21T16:51:22.992951359+00:00"}" ``` ### What you think should happen instead The name argument in the [hook code](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/hooks/dataproc.py#L1746) follows the format "projects/PROJECT_ID/regions/DATAPROC_REGION/batches/BATCH_ID". However, according to the [Google Cloud DataProc API Reference](https://cloud.google.com/dataproc-serverless/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.GetBatchRequest), it should be in the format "projects/PROJECT_ID/locations/DATAPROC_REGION/batches/BATCH_ID . ### How to reproduce just run a dataproc operator like this ``` create_batch = DataprocCreateBatchOperator( task_id="create_batch", batch_id="batch_test", deferrable=True, ) ``` ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Google Cloud Composer ### Deployment details Composer 2.1.12 ### 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/32069

https://github.com/apache/airflow/pull/32070

479719297ff4efa8373dc7b6909bfc59a5444c3a

59d64d8f2ed3c0e7b93d3c07041d47883cabb908

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/celery_executor_utils.py", "tests/integration/executors/test_celery_executor.py"]

Celery Executor cannot connect to the database to get information, resulting in a scheduler exit abnormally

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We use Celery Executor where using RabbitMQ as a broker and postgresql as a result backend Airflow Version: 2.2.3 Celery Version: 5.2.3 apache-airflow-providers-celery==2.1.0 Below is the error message: ``` _The above exception was the direct cause of the following exception: Traceback (most recent call last): File"/app/airflow2.2.3/airflow/airflow/jobs/schedulerjob.py”, line 672, in _execute self._run_scheduler_loop() File"/app/airflow2.2.3/airflow/airflow/jobs/scheduler_job.py", line 754, in _run_scheduler_loop self.executor.heartbeat() File"/app/airflow2.2.3/airflow/airflow/executors/base_executor.py”, line 168, in heartbeat self.sync() File"/app/airflow2.2.3/airflow/airflow/executors/celery_executorpy”, line 330, in sync self.update_all_task_states() File"/app/airflow223/airflow/airflow/executors/celery_executor.py”,line 442,in update_all_task_states state_and_info_by_celery_task_id=self.bulk_state_fetcher.get_many(self.tasks. values()) File"/app/airflow2.2.3/airflow/airflow/executors/celery_executorpy”,line 598, in get_many result = self._get many_from db backend(async_results) File"/app/airflow2.2.3/airflow/airflow/executors/celery_executor.py”,line 618, in get_many_from_db_backend tasks-session.query(task_cls).filter(task_cls.task_id.in(task_ids)).all() File“/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/query.py”, line 3373, in all return list(self) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/query.py”, line 3535, in iter return self._execute_and_instances(context) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/query.py”,line 3556, in _execute_and_instances conn =self._get bind args( File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/salalchemy/orm/query.py”, line 3571, in _get_bind_args return fn( File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/query.py”,line 3550, in _connection_from_session conn=self.session.connection(**kw) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/session.py”, line 1142, in connection return self._connection_for_bind( File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/orm/session.py”,line 1150, in _connection_for_bind return self.transaction.connection_for bind( File“/app/airflow2.2.3/airflow2_env/Iib/python3.8/site-packages/sqlalchemy/orm/session.py”, line 433, in _connection_for_bind conn=bind._contextual_connect() File“/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/salalchemy/engine/base.py”,line 2302, in _contextual_connect self._wrap_pool_connect(self.pool.connect,None), File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/engine/base.py”, line 2339, in _wrap_pool_connect Tracking Connection.handle dbapi_exception_noconnection( File "/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/engine/base.py”， line 1583,in handle_dbapi_exception_noconnection util.raise ( File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/util/compat.py”, line 182, in raise ents raise exception File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/salalchemy/engine/base.py”, line 2336, in _wrap_pool_connect return fn() 2023-06-05 16:39:05.069 ERROR -Exception when executing SchedulerJob. run scheduler loop Traceback (most recent call last): File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/engine/base.py”, line 2336,in _wrap_pool_connect return fno File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/pool/base.py”, line 364, in connect returnConnectionFairy.checkout(self) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/pool/base.py”, line 778, in _checkout fairy=ConnectionRecordcheckout(pool) File"/app/airflow2.2.3/airflow2_env/lib/python3.8/site-packages/sqlalchemy/pool/base.py”, line 495, in checkout rec=pool. do_get() File“/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/pool/impl.py”, line 241, in _do_get return self._createconnection() File"/app/airflow2.2.3/airflow2_env/lib/python3.8/site-packages/salalchemy/pool/base.py”, line 309, in _create_connection return _ConnectionRecord(self) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/sitepackages/sqlalchemy/pool/base.py”, line 440, in init self. connect(firstconnectcheck=True) File"/app/airflow2.2.3/airflow2_env/lib/python3.8/site-packages/sqlalchemy/pool/base.py”, line 661, in connect pool.logger.debug"Error onconnect(:%s"，e) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/util/langhelpers.py”, line 68, in exit compat.raise( File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/salalchemy/util/compat.py", line 182, in raise raise exception File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/pool/base.py”, line 656, in _connect connection =pool.invoke_creator(sel f) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/engine/strategies.py”, line 114, in connect return dialect.connect(*cargs, **cparans) File"/app/airflow2.2.3/airflow2_env/1ib/python3.8/site-packages/sqlalchemy/engine/default.py”,line 508, in connect return self.dbapi.connect(*cargs, **cparams) File"/app/airflow2.2.3/airflow2_env/lib/python3.8/site-packages/psycopg2/init.py”, line 126, in connect conn=connect(dsn，connection_factory=connection_factory, **kwasync) psycopg2.0perationalError: could not connect to server: Connection timed out Is the server running on host"xxxxxxxxxx”and accepting TCP/IP connections on port 5432? ``` ### What you think should happen instead I think it may be caused by network jitter issues, add retries to solve it ### How to reproduce celeryExecutor fails to create a PG connection while retrieving metadata information, and it can be reproduced ### Operating System NAME="RedFlag Asianux" VERSION="7 (Lotus)" ### 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/32045

https://github.com/apache/airflow/pull/31998

de585f521b5898ba7687072a7717fd3b67fa8c5c

c3df47efc2911706897bf577af8a475178de4b1b

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow tasks run -m cli command giving 504 response

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Hello , we are facing "504 Gateway Time-out" error when running "tasks run -m" CLI command. We are trying to create Complex DAG and run tasks from cli command. When we are trying to run "tasks run -m" then we received gateway timeout error. We also observed high resources spike in web server when we tried to execute this cli command. After looking into further, when we run "task run -m" Airflow CLI command, what it does is it parses the list of DAGs and the parses through the task list. Because this , we observed high resources of webserver and received gateway timeout error. ### What you think should happen instead We are expecting that when to execute "tasks run" cli command, it should only parse DAG name and task name provided in the command and not parse DAG lists followed by task list. ### How to reproduce please follow below steps to reproduce this issue. 1. we have 900 DAG in airflow environment. 2. we have created web login token to access web server. 3. after that we tried to run "tasks run" using python script ### Operating System Amazon linux ### Versions of Apache Airflow Providers Airflow version 2.2.2 ### Deployment Amazon (AWS) MWAA ### 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/32020

https://github.com/apache/airflow/pull/32038

d49fa999a94a2269dd6661fe5eebbb4c768c7848

05a67efe32af248ca191ea59815b3b202f893f46

python

closed

apache/airflow

https://github.com/apache/airflow

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

ZeroDivisionError while poking ExternalTaskSensor

### Apache Airflow version 2.6.2 ### What happened After upgrading to version 2.6.2, we started getting a `ZeroDivisionError` the first time some _ExternalTaskSensor_ were poked. ### What you think should happen instead Sensor should exit with return code 0, as it did when cleared after the first fail: ``` [2023-06-19, 10:03:57 UTC] {external_task.py:240} INFO - Poking for task_group 'lote2db_etl' in dag 'malha_batch' on 2023-06-16T08:30:00+00:00 ... [2023-06-19, 10:03:57 UTC] {base.py:255} INFO - Success criteria met. Exiting. [2023-06-19, 10:03:57 UTC] {taskinstance.py:1345} INFO - Marking task as SUCCESS. dag_id=rotinas_risco, task_id=ets_malha_lote2db_etl, execution_date=20230616T080000, start_date=20230619T100357, end_date=20230619T100357 [2023-06-19, 10:03:57 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 0 ``` ### How to reproduce We have a DAG, called *malha_batch*, whose `schedule` parameter equals to `"30 8 * * 1-5"`. We then have another one, called *rotinas_risco*, whose `schedule` parameter equals to `"0 8 * * 1-5"`, with four _ExternalTaskSensor_ pointing to *malha_batch*. Below are their definitions: <details><summary>Excerpt from rotinas_risco.py</summary> ``` python ets_malha_bbg_post_processing = ExternalTaskSensor( task_id="ets_malha_bbg_post_processing", external_dag_id="malha_batch", external_task_group_id="bloomberg.post_processing", allowed_states=[State.SUCCESS, State.SKIPPED], failed_states=[State.FAILED], execution_delta=timedelta(minutes=-30), poke_interval=300, mode="reschedule", ) ets_malha_bbg_refreshes = ExternalTaskSensor( task_id="ets_malha_bbg_refreshes", external_dag_id="malha_batch", external_task_group_id="bloomberg.refreshes", allowed_states=[State.SUCCESS, State.SKIPPED], failed_states=[State.FAILED], execution_delta=timedelta(minutes=-30), poke_interval=300, mode="reschedule", ) ets_malha_bbg_conversion_factor_to_base = ExternalTaskSensor( task_id="ets_malha_bbg_conversion_factor_to_base", external_dag_id="malha_batch", external_task_id="prices.conversion_factor_to_base", allowed_states=[State.SUCCESS, State.SKIPPED], failed_states=[State.FAILED], execution_delta=timedelta(minutes=-30), poke_interval=300, mode="reschedule", ) ets_malha_lote2db_etl = ExternalTaskSensor( task_id="ets_malha_lote2db_etl", external_dag_id="malha_batch", external_task_group_id="lote2db_etl", allowed_states=[State.SUCCESS, State.SKIPPED], failed_states=[State.FAILED], execution_delta=timedelta(minutes=-30), poke_interval=300, mode="reschedule", ) ``` </details> Out of those four _ExternalTaskSensor_, just one behave as expected, while the three others failed upon the first poking attempt with the following traceback: ``` [2023-06-19, 08:00:02 UTC] {external_task.py:240} INFO - Poking for task_group 'lote2db_etl' in dag 'malha_batch' on 2023-06-16T08:30:00+00:00 ... [2023-06-19, 08:00:02 UTC] {taskinstance.py:1824} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/conda/envs/airflow/lib/python3.9/site-packages/airflow/sensors/base.py", line 225, in execute raise e File "/opt/conda/envs/airflow/lib/python3.9/site-packages/airflow/sensors/base.py", line 212, in execute poke_return = self.poke(context) File "/opt/conda/envs/airflow/lib/python3.9/site-packages/airflow/utils/session.py", line 76, in wrapper return func(*args, session=session, **kwargs) File "/opt/conda/envs/airflow/lib/python3.9/site-packages/airflow/sensors/external_task.py", line 260, in poke count_failed = self.get_count(dttm_filter, session, self.failed_states) File "/opt/conda/envs/airflow/lib/python3.9/site-packages/airflow/sensors/external_task.py", line 369, in get_count count = ( ZeroDivisionError: division by zero ``` The successful _ExternalTaskSensor_ logged as follows: ``` [2023-06-19, 08:00:02 UTC] {external_task.py:232} INFO - Poking for tasks ['prices.conversion_factor_to_base'] in dag malha_batch on 2023-06-16T08:30:00+00:00 ... [2023-06-19, 08:00:02 UTC] {taskinstance.py:1784} INFO - Rescheduling task, marking task as UP_FOR_RESCHEDULE [2023-06-19, 08:00:02 UTC] {local_task_job_runner.py:225} INFO - Task exited with return code 0 [2023-06-19, 08:00:02 UTC] {taskinstance.py:2653} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` I was not able to reproduce the error with a smaller example, but the mere fact that, out of four similarly-defined sensors, three failed and one succeeded, to me, suggests we are facing a bug. Additionally, the problem did not arise with version 2.6.1. ### Operating System Ubuntu 20.04.6 LTS (Focal Fossa) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==8.1.0 apache-airflow-providers-celery==3.2.0 apache-airflow-providers-common-sql==1.5.1 apache-airflow-providers-ftp==3.4.1 apache-airflow-providers-http==4.4.1 apache-airflow-providers-imap==3.2.1 apache-airflow-providers-postgres==5.5.0 apache-airflow-providers-redis==3.2.0 apache-airflow-providers-sqlite==3.4.1 ``` ### Deployment Virtualenv installation ### Deployment details Just a vanilla setup following https://airflow.apache.org/docs/apache-airflow/stable/installation/installing-from-pypi.html. ### Anything else Please let me know whether additional log files from the scheduler or executor (Celery) should be provided. ### 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/32007

https://github.com/apache/airflow/pull/32009

c508b8e5310447b302128d8fbcc5c297a3e6e244

14eb1d3116ecef15be7be9a8f9d08757e74f981c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

webserver - MAPPED tasks - rendered-templates page FAIL

### Apache Airflow version 2.6.2 ### What happened If I want see the common information of the N mapped task . I edit manually the URL cause there is no button in the current airflow console ```url http://localhost:8090/task?dag_id=kubernetes_dag&task_id=task-one&execution_date=2023-06-18T00%3A00%3A00%2B00%3A00&map_index=0 ``` by removing the `&map_index=0` if I click on `rendered-templates` it fail ```log {app.py:1744} ERROR - Exception on /rendered-templates [GET] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.11/site-packages/flask/app.py", line 2529, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/flask/app.py", line 1825, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/flask/app.py", line 1823, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/flask/app.py", line 1799, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/decorators.py", line 125, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/utils/session.py", line 76, in wrapper return func(*args, session=session, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/airflow/.local/lib/python3.11/site-packages/airflow/www/views.py", line 1354, in rendered_templates ti.refresh_from_task(raw_task) ^^^^^^^^^^^^^^^^^^^^ AttributeError: 'NoneType' object has no attribute 'refresh_from_task' ``` ### What you think should happen instead _No response_ ### How to reproduce [Screencast from 19-06-2023 15:07:02.webm](https://github.com/apache/airflow/assets/10202690/10a3546f-f2ef-4c2d-a704-7eb43573ad83) ### Operating System ubuntu 22.04 ### 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/32005

https://github.com/apache/airflow/pull/32011

2c645d59d505a99c8e7507ef05d6f3ecf430d578

62a534dbc7fa8ddb4c249ade85c558b64d1630dd

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg", "setup.py"]

log url breaks on login redirect

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened on 2.5.3: log url is https://airflow.hostname.de/log?execution_date=2023-06-19T04%3A00%3A00%2B00%3A00&task_id=task_name&dag_id=dag_name&map_index=-1 this url works when I am logged in. If I am logged out, the login screen will redirect me to https://airflow.hostname.de/log?execution_date=2023-06-19T04%3A00%3A00+00%3A00&task_id=task_name&dag_id=dag_name&map_index=-1 which shows me an empty log. the redirect seems to convert the `%2B` back to a `+` in the timezone component of the execution_date, while leaving all other escaped characters untouched. ### What you think should happen instead log url works correctly after login redirect ### How to reproduce https://airflow.hostname.de/log?execution_date=2023-06-19T04%3A00%3A00%2B00%3A00&task_id=task_name&dag_id=dag_name&map_index=-1 have a log url with a execution date using a timezone with a positive utc offset ### 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/32002

https://github.com/apache/airflow/pull/32054

e39362130b8659942672a728a233887f0b02dc8b

92497fa727a23ef65478ef56572c7d71427c4a40

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py", "tests/providers/google/cloud/operators/test_vertex_ai.py"]

Unable to find project ID causing authentication failure in AutoML task

### Apache Airflow version Other Airflow 2 version (please specify below) : 2.6.1 ### What happened I encountered an issue while running an AutoML task. The task failed with an authentication error due to the inability to find the project ID. Here are the details of the error: ``` [2023-06-17T18:42:48.916+0530] {[taskinstance.py:1308](http://taskinstance.py:1308/)} INFO - Starting attempt 1 of 1 [2023-06-17T18:42:48.931+0530] {[taskinstance.py:1327](http://taskinstance.py:1327/)} INFO - Executing <Task(CreateAutoMLTabularTrainingJobOperator): auto_ml_tabular_task> on 2023-06-17 13:12:33+00:00 [2023-06-17T18:42:48.964+0530] {[standard_task_runner.py:57](http://standard_task_runner.py:57/)} INFO - Started process 12974 to run task [2023-06-17T18:42:48.971+0530] {[standard_task_runner.py:84](http://standard_task_runner.py:84/)} INFO - Running: ['airflow', 'tasks', 'run', 'vi_create_auto_ml_tabular_training_job_dag', 'auto_ml_tabular_task', 'manual__2023-06-17T13:12:33+00:00', '--job-id', '175', '--raw', '--subdir', 'DAGS_FOLDER/vi_create_model_train.py', '--cfg-path', '/tmp/tmprijpfzql'] [2023-06-17T18:42:48.974+0530] {[standard_task_runner.py:85](http://standard_task_runner.py:85/)} INFO - Job 175: Subtask auto_ml_tabular_task [2023-06-17T18:42:49.043+0530] {[logging_mixin.py:149](http://logging_mixin.py:149/)} INFO - Changing /mnt/d/projects/airflow/logs/dag_id=vi_create_auto_ml_tabular_training_job_dag/run_id=manual__2023-06-17T13:12:33+00:00/task_id=auto_ml_tabular_task permission to 509 [2023-06-17T18:42:49.044+0530] {[task_command.py:410](http://task_command.py:410/)} INFO - Running <TaskInstance: vi_create_auto_ml_tabular_training_job_dag.auto_ml_tabular_task manual__2023-06-17T13:12:33+00:00 [running]> on host DESKTOP-EIFUHU2.localdomain [2023-06-17T18:42:49.115+0530] {[taskinstance.py:1545](http://taskinstance.py:1545/)} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='airflow' AIRFLOW_CTX_DAG_ID='vi_create_auto_ml_tabular_training_job_dag' AIRFLOW_CTX_TASK_ID='auto_ml_tabular_task' AIRFLOW_CTX_EXECUTION_DATE='2023-06-17T13:12:33+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='manual__2023-06-17T13:12:33+00:00' [2023-06-17T18:42:49.120+0530] {[base.py:73](http://base.py:73/)} INFO - Using connection ID 'gcp_conn' for task execution. [2023-06-17T18:42:52.123+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 1 of 3. Reason: timed out [2023-06-17T18:42:55.125+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 2 of 3. Reason: timed out [2023-06-17T18:42:58.128+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 3 of 3. Reason: timed out [2023-06-17T18:42:58.129+0530] {[_default.py:340](http://_default.py:340/)} WARNING - Authentication failed using Compute Engine authentication due to unavailable metadata server. [2023-06-17T18:42:58.131+0530] {[taskinstance.py:1824](http://taskinstance.py:1824/)} ERROR - Task failed with exception Traceback (most recent call last): File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 244, in project self._set_project_as_env_var_or_google_auth_default() File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 81, in _set_project_as_env_var_or_google_auth_default credentials, project = google.auth.default() File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/auth/_default.py", line 692, in default raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS) google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see [https://cloud.google.com/docs/authentication/external/set-up-adc](https://cloud.google.com/docs/authentication/external/set-up-adc?authuser=0) for more information. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py", line 359, in execute dataset=datasets.TabularDataset(dataset_name=self.dataset_id), File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/datasets/dataset.py", line 77, in __init__ super().__init__( File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/base.py", line 925, in __init__ VertexAiResourceNoun.__init__( File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/base.py", line 507, in __init__ self.project = project or initializer.global_config.project File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 247, in project raise GoogleAuthError(project_not_found_exception_str) from exc google.auth.exceptions.GoogleAuthError: Unable to find your project. Please provide a project ID by: - Passing a constructor argument - Using aiplatform.init() - Setting project using 'gcloud config set project my-project' - Setting a GCP environment variable [2023-06-17T18:42:58.139+0530] {[taskinstance.py:1345](http://taskinstance.py:1345/)} INFO - Marking task as FAILED. dag_id=vi_create_auto_ml_tabular_training_job_dag, task_id=auto_ml_tabular_task, execution_date=20230617T131233, start_date=20230617T131248, end_date=20230617T131258 [2023-06-17T18:42:58.152+0530] {[standard_task_runner.py:104](http://standard_task_runner.py:104/)} ERROR - Failed to execute job 175 for task auto_ml_tabular_task (Unable to find your project. Please provide a project ID by: - Passing a constructor argument - Using aiplatform.init() - Setting project using 'gcloud config set project my-project' - Setting a GCP environment variable; 12974) [2023-06-17T18:42:58.166+0530] {[local_task_job_runner.py:225](http://local_task_job_runner.py:225/)} INFO - Task exited with return code 1 [2023-06-17T18:42:58.183+0530] {[taskinstance.py:2651](http://taskinstance.py:2651/)} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you think should happen instead Expected Behavior: The AutoML task should execute successfully, using the appropriate project ID and credentials for authentication given as per the gcs_con_id provided in the dag. Actual Behavior: The task fails with an authentication error due to the inability to find the project ID and default credentials. ### How to reproduce To reproduce the issue and execute the CreateAutoMLTabularTrainingJobOperator task in Apache Airflow, follow these steps: Ensure that Apache Airflow is installed. If not, run the following command to install it: ``` pip install apache-airflow ``` Create an instance of the CreateAutoMLTabularTrainingJobOperator within the DAG context: ``` create_auto_ml_tabular_training_job = CreateAutoMLTabularTrainingJobOperator( gcp_conn_id='gcp_conn', task_id="auto_ml_tabular_task", display_name=TABULAR_DISPLAY_NAME, optimization_prediction_type="regression", optimization_objective="minimize-rmse", #column_transformations=COLUMN_TRANSFORMATIONS, dataset_id=tabular_dataset_id, # Get this // target_column="mean_temp", training_fraction_split=0.8, validation_fraction_split=0.1, test_fraction_split=0.1, model_display_name='your-model-display-name', disable_early_stopping=False, region=REGION, project_id=PROJECT_ID ) ``` Start the Apache Airflow scheduler and webserver. Open a terminal or command prompt and run the following commands: ``` # Start the scheduler airflow scheduler # Start the webserver airflow webserver ``` Access the Apache Airflow web UI by opening a web browser and navigating to http://localhost:8080. Ensure that the scheduler and webserver are running without any errors. Navigate to the DAGs page in the Airflow UI and locate the vi_create_auto_ml_tabular_training_job_dag DAG. Trigger the DAG manually, either by clicking the "Trigger DAG" button or using the Airflow CLI command. Monitor the DAG execution status and check if the auto_ml_tabular_task completes successfully or encounters any errors. ### Operating System DISTRIB_ID=Ubuntu DISTRIB_RELEASE=20.04 DISTRIB_CODENAME=focal DISTRIB_DESCRIPTION="Ubuntu 20.04 LTS" ### Versions of Apache Airflow Providers $ pip freeze aiofiles==23.1.0 aiohttp==3.8.4 aiosignal==1.3.1 alembic==1.11.1 anyio==3.7.0 apache-airflow==2.6.1 apache-airflow-providers-common-sql==1.5.1 apache-airflow-providers-ftp==3.4.1 apache-airflow-providers-google==10.1.1 apache-airflow-providers-http==4.4.1 apache-airflow-providers-imap==3.2.1 apache-airflow-providers-sqlite==3.4.1 apispec==5.2.2 argcomplete==3.1.1 asgiref==3.7.2 async-timeout==4.0.2 attrs==23.1.0 Babel==2.12.1 backoff==2.2.1 blinker==1.6.2 cachelib==0.9.0 cachetools==5.3.1 cattrs==23.1.2 certifi==2023.5.7 cffi==1.15.1 chardet==5.1.0 charset-normalizer==3.1.0 click==8.1.3 clickclick==20.10.2 colorama==0.4.6 colorlog==4.8.0 ConfigUpdater==3.1.1 connexion==2.14.2 cron-descriptor==1.4.0 croniter==1.4.1 cryptography==41.0.1 db-dtypes==1.1.1 Deprecated==1.2.14 dill==0.3.6 dnspython==2.3.0 docutils==0.20.1 email-validator==1.3.1 exceptiongroup==1.1.1 Flask==2.2.5 Flask-AppBuilder==4.3.0 Flask-Babel==2.0.0 Flask-Caching==2.0.2 Flask-JWT-Extended==4.5.2 Flask-Limiter==3.3.1 Flask-Login==0.6.2 flask-session==0.5.0 Flask-SQLAlchemy==2.5.1 Flask-WTF==1.1.1 frozenlist==1.3.3 future==0.18.3 gcloud-aio-auth==4.2.1 gcloud-aio-bigquery==6.3.0 gcloud-aio-storage==8.2.0 google-ads==21.2.0 google-api-core==2.11.1 google-api-python-client==2.89.0 google-auth==2.20.0 google-auth-httplib2==0.1.0 google-auth-oauthlib==1.0.0 google-cloud-aiplatform==1.26.0 google-cloud-appengine-logging==1.3.0 google-cloud-audit-log==0.2.5 google-cloud-automl==2.11.1 google-cloud-bigquery==3.11.1 google-cloud-bigquery-datatransfer==3.11.1 google-cloud-bigquery-storage==2.20.0 google-cloud-bigtable==2.19.0 google-cloud-build==3.16.0 google-cloud-compute==1.11.0 google-cloud-container==2.24.0 google-cloud-core==2.3.2 google-cloud-datacatalog==3.13.0 google-cloud-dataflow-client==0.8.3 google-cloud-dataform==0.5.1 google-cloud-dataplex==1.5.0 google-cloud-dataproc==5.4.1 google-cloud-dataproc-metastore==1.11.0 google-cloud-dlp==3.12.1 google-cloud-kms==2.17.0 google-cloud-language==2.10.0 google-cloud-logging==3.5.0 google-cloud-memcache==1.7.1 google-cloud-monitoring==2.15.0 google-cloud-orchestration-airflow==1.9.0 google-cloud-os-login==2.9.1 google-cloud-pubsub==2.17.1 google-cloud-redis==2.13.0 google-cloud-resource-manager==1.10.1 google-cloud-secret-manager==2.16.1 google-cloud-spanner==3.36.0 google-cloud-speech==2.20.0 google-cloud-storage==2.9.0 google-cloud-tasks==2.13.1 google-cloud-texttospeech==2.14.1 google-cloud-translate==3.11.1 google-cloud-videointelligence==2.11.2 google-cloud-vision==3.4.2 google-cloud-workflows==1.10.1 google-crc32c==1.5.0 google-resumable-media==2.5.0 googleapis-common-protos==1.59.1 graphviz==0.20.1 greenlet==2.0.2 grpc-google-iam-v1==0.12.6 grpcio==1.54.2 grpcio-gcp==0.2.2 grpcio-status==1.54.2 gunicorn==20.1.0 h11==0.14.0 httpcore==0.17.2 httplib2==0.22.0 httpx==0.24.1 idna==3.4 importlib-metadata==4.13.0 importlib-resources==5.12.0 inflection==0.5.1 itsdangerous==2.1.2 Jinja2==3.1.2 json-merge-patch==0.2 jsonschema==4.17.3 lazy-object-proxy==1.9.0 limits==3.5.0 linkify-it-py==2.0.2 lockfile==0.12.2 looker-sdk==23.10.0 Mako==1.2.4 Markdown==3.4.3 markdown-it-py==3.0.0 MarkupSafe==2.1.3 marshmallow==3.19.0 marshmallow-enum==1.5.1 marshmallow-oneofschema==3.0.1 marshmallow-sqlalchemy==0.26.1 mdit-py-plugins==0.4.0 mdurl==0.1.2 multidict==6.0.4 numpy==1.24.3 oauthlib==3.2.2 ordered-set==4.1.0 packaging==23.1 pandas==2.0.2 pandas-gbq==0.19.2 pathspec==0.9.0 pendulum==2.1.2 pkgutil-resolve-name==1.3.10 pluggy==1.0.0 prison==0.2.1 proto-plus==1.22.2 protobuf==4.23.3 psutil==5.9.5 pyarrow==12.0.1 pyasn1==0.4.8 pyasn1-modules==0.2.8 pycparser==2.21 pydantic==1.10.9 pydata-google-auth==1.8.0 Pygments==2.15.1 PyJWT==2.7.0 pyOpenSSL==23.2.0 pyparsing==3.0.9 pyrsistent==0.19.3 python-daemon==3.0.1 python-dateutil==2.8.2 python-nvd3==0.15.0 python-slugify==8.0.1 pytz==2023.3 pytzdata==2020.1 PyYAML==6.0 requests==2.31.0 requests-oauthlib==1.3.1 requests-toolbelt==1.0.0 rfc3339-validator==0.1.4 rich==13.4.2 rich-argparse==1.1.1 rsa==4.9 setproctitle==1.3.2 Shapely==1.8.5.post1 six==1.16.0 sniffio==1.3.0 SQLAlchemy==1.4.48 sqlalchemy-bigquery==1.6.1 SQLAlchemy-JSONField==1.0.1.post0 SQLAlchemy-Utils==0.41.1 sqlparse==0.4.4 tabulate==0.9.0 tenacity==8.2.2 termcolor==2.3.0 text-unidecode==1.3 typing-extensions==4.6.3 tzdata==2023.3 uc-micro-py==1.0.2 unicodecsv==0.14.1 uritemplate==4.1.1 urllib3==2.0.3 Werkzeug==2.3.6 wrapt==1.15.0 WTForms==3.0.1 yarl==1.9.2 zipp==3.15.0 ### Deployment Virtualenv installation ### Deployment details $ airflow info Apache Airflow version | 2.6.1 executor | SequentialExecutor task_logging_handler | airflow.utils.log.file_task_handler.FileTaskHandler sql_alchemy_conn | sqlite:////home/test1/airflow/airflow.db dags_folder | /mnt/d/projects/airflow/dags plugins_folder | /home/test1/airflow/plugins base_log_folder | /mnt/d/projects/airflow/logs remote_base_log_folder | System info OS | Linux architecture | x86_64 uname | uname_result(system='Linux', node='DESKTOP-EIFUHU2', release='4.4.0-19041-Microsoft', version='#1237-Microsoft Sat Sep 11 14:32:00 PST | 2021', machine='x86_64', processor='x86_64') locale | ('en_US', 'UTF-8') python_version | 3.8.10 (default, May 26 2023, 14:05:08) [GCC 9.4.0] python_location | /mnt/d/projects/tvenv/bin/python3 Tools info git | git version 2.25.1 ssh | OpenSSH_8.2p1 Ubuntu-4, OpenSSL 1.1.1f 31 Mar 2020 kubectl | NOT AVAILABLE gcloud | NOT AVAILABLE cloud_sql_proxy | NOT AVAILABLE mysql | NOT AVAILABLE sqlite3 | NOT AVAILABLE psql | NOT AVAILABLE Paths info airflow_home | /home/test1/airflow system_path | /mnt/d/projects/tvenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/mnt/c/Program Files | (x86)/Microsoft SDKs/Azure/CLI2/wbin:/mnt/c/Program Files/Python39/Scripts/:/mnt/c/Program | Files/Python39/:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/W | indows/System32/OpenSSH/:/mnt/c/Users/ibrez/AppData/Roaming/nvm:/mnt/c/Program Files/nodejs:/mnt/c/Program | Files/dotnet/:/mnt/c/Windows/system32/config/systemprofile/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/test1/AppData/Local/Microsoft/Wi | ndowsApps:/snap/bin python_path | /mnt/d/projects/tvenv/bin:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/mnt/d/projects/tvenv/lib/python3.8/site-p | ackages:/mnt/d/projects/airflow/dags:/home/test1/airflow/config:/home/test1/airflow/plugins airflow_on_path | True Providers info apache-airflow-providers-common-sql | 1.5.1 apache-airflow-providers-ftp | 3.4.1 apache-airflow-providers-google | 10.1.1 apache-airflow-providers-http | 4.4.1 apache-airflow-providers-imap | 3.2.1 apache-airflow-providers-sqlite | 3.4.1 ### Anything else To me it seems like the issue is at the line ``` dataset=datasets.TabularDataset(dataset_name=self.dataset_id), ``` [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py) The details like project id and credentials are not being passed to the TabularDataset class which causes issues down the like for ``` GoogleAuthError(project_not_found_exception_str) ``` ### 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/31986

https://github.com/apache/airflow/pull/31991

10aa704e3d87ce951cb79f28492eed916bc18fe3

f2ebc292fe63d2ddd0686d90c3acc0630f017a07

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/executors/kubernetes_executor.py", "docs/apache-airflow/administration-and-deployment/logging-monitoring/metrics.rst"]

Airflow Observability Improvement Request

### Description We have a scheduler house keeping work (adopt_or_reset_orphaned_tasks, check_trigger_timeouts, _emit_pool_metrics, _find_zombies, clear_not_launched_queued_tasks and _check_worker_pods_pending_timeout) runs on certain frequency. Right now, we don't have any latency metrics on these house keeping work. These will impact the scheduler heartbeat. Its good idea to capture these latency metrics to identify and tune the airflow configuration ### Use case/motivation As we run the airflow at a large scale, we have found that the adopt_or_reset_orphaned_tasks and clear_not_launched_queued_tasks functions might take time in a few minutes (> 5 minutes). These will delay the heartbeat of the scheduler and leads to the scheduler instance restarting/killed. In order to detect these latency issues, we need better metrics to capture these latencies. ### 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/31957

https://github.com/apache/airflow/pull/35579

5a6dcfd8655c9622f3838a0e66948dc3091afccb

cd296d2068b005ebeb5cdc4509e670901bf5b9f3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/graph/Node.tsx", "airflow/www/static/js/types/index.ts", "airflow/www/static/js/utils/graph.ts"]

Support Operator User Interface Elements in new graph view

### Description The new graph UI looks good but currently doesn't support the color options mentioned here https://airflow.apache.org/docs/apache-airflow/stable/howto/custom-operator.html#user-interface ### Use case/motivation It would be great for these features to be supported in the new grid view as they are in the old one ### Related issues [slack](https://apache-airflow.slack.com/archives/CCPRP7943/p1686866630874739?thread_ts=1686865767.351809&cid=CCPRP7943) ### 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/31949

https://github.com/apache/airflow/pull/32822

12a760f6df831c1d53d035e4d169a69887e8bb26

3bb63f1087176b24e9dc8f4cc51cf44ce9986d34

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/commands/testing_commands.py", "dev/breeze/src/airflow_breeze/commands/testing_commands_config.py", "images/breeze/output-commands-hash.txt", "images/breeze/output-commands.svg", "images/breeze/output_testing.svg", "images/breeze/output_testing_tests.svg"]

Add `--use-airflow-version` option to `breeze testing tests` command

### Description The option `--use-airflow-version` is available under the command `start-airflow` in `Breeze`. As an example, this is used when testing a release candidate as specified in [documentation](https://github.com/apache/airflow/blob/main/dev/README_RELEASE_AIRFLOW.md#verify-release-candidates-by-contributors): `breeze start-airflow --use-airflow-version <VERSION>rc<X> --python 3.8 --backend postgres`. The idea I have is to add that option as well under the command `testing tests` in `Breeze`. ### Use case/motivation Having the option `--use-airflow-version` available under the command `testing tests` in `Breeze` would make it possible to run system tests against a specific version of Airflow and provider. This could be helpful when releasing new version of Airflow and Airflow providers. As such, providers could run all system tests of their provider package on demand and share these results (somehow, a dashboard?, another way?) to the community/release manager. This would not replace the manual testing already in place that is needed when releasing such new version but would give more information/pointers to the release manager. Before submitting a PR, I wanted to have first some feedbacks about this idea. This might not be possible? This might not be a good idea? This might not be useful at all? ### 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/31907

https://github.com/apache/airflow/pull/31914

518b93c24fda6e7a1df0acf0f4dd1921967dc8f6

b07a26523fad4f17ceb4e3a2f88e043dcaff5e53

python

closed

apache/airflow

https://github.com/apache/airflow

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

MappedOperator doesn't allow `operator_extra_links` instance property

### Apache Airflow version 2.6.1 ### What happened When doing `.partial()` and `.expand()` on any operator which has an instance property (e.g. `@property`) of `operator_extra_links` the `MappedOperator` does not handle it properly, causing the dag to fail to import. The `BatchOperator` from `airflow.providers.amazon.aws.operators.batch` is one example of an operator which defines `operator_extra_links` on a per-instance basis. ### What you think should happen instead The dag should not fail to import (especially when using the AWS `BatchOperator`!) Either: - If per-instance `operator_extra_links` is deemed disallowed behaviour - `MappedOperator` should detect it's a property and give a more helpful error message - `BatchOperator` from the AWS provider should be changed. If I need to open another ticket elsewhere for that please let me know - If per-instance `operator_extra_links` is allowed - `MappedOperator` needs to be adjusted to account for that ### How to reproduce ``` import pendulum from airflow.models.baseoperator import BaseOperator from airflow.decorators import dag, task class BadOperator(BaseOperator): def __init__(self, *args, some_argument: str, **kwargs): super().__init__(*args, some_argument, **kwargs) @property def operator_extra_links(self): # !PROBLEMATIC FUNCTION! # ... Some code to create a collection of `BaseOperatorLink`s dynamically return tuple() @dag( schedule=None, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, tags=["example"] ) def airflow_is_bad(): """ Example to demonstrate issue with airflow API """ @task def create_arguments(): return [1,2,3,4] bad_operator_test_group = BadOperator.partial( task_id="bad_operator_test_group", ).expand(some_argument=create_arguments()) dag = airflow_is_bad() ``` Put this in your dags folder, Airflow will fail to import the dag with error ``` Broken DAG: [<USER>/airflow/dags/airflow_is_bad_minimal_example.py] Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 825, in _serialize_node serialize_op["_operator_extra_links"] = cls._serialize_operator_extra_links( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 1165, in _serialize_operator_extra_links for operator_extra_link in operator_extra_links: TypeError: 'property' object is not iterable ``` Commenting out the `operator_extra_links` from the `BadOperator` in the example will allow the dag to be imported fine ### Operating System macOS Ventura 13.4 ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.0.0 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-sqlite==3.3.2 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else I found adjusting `operator_extra_links` on `BatchOperator` to be `operator_extra_links = (BatchJobDetailsLink(), BatchJobDefinitionLink(), BatchJobQueueLink(), CloudWatchEventsLink())` solved my issue and made it run fine, however I've no idea if that's safe or generalises because I'm not sure what `operator_extra_links` is actually for internally. ### 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/31902

https://github.com/apache/airflow/pull/31904

225e3041d269698d0456e09586924c1898d09434

3318212482c6e11ac5c2e2828f7e467bca5b7245

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-google/api-auth-backend/google-openid.rst"]

Incorrect audience argument in Google OpenID authentication doc

### What do you see as an issue? I followed the [Google OpenID authentication doc](https://airflow.apache.org/docs/apache-airflow-providers-google/stable/api-auth-backend/google-openid.html) and got this error: ``` $ ID_TOKEN="$(gcloud auth print-identity-token "--audience=${AUDIENCE}")" ERROR: (gcloud.auth.print-identity-token) unrecognized arguments: --audience=759115288429-c1v16874eprg4455kt1di902b3vkjho2.apps.googleusercontent.com (did you mean '--audiences'?) To search the help text of gcloud commands, run: gcloud help -- SEARCH_TERMS ``` Perhaps the gcloud CLI parameter changed since this doc was written. ### Solving the problem Update the CLI argument in the doc. ### 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/31891

https://github.com/apache/airflow/pull/31893

ca13c7b77ea0e7d37bfe893871bab565d26884d0

fa07812d1013f964a4736eade3ba3e1a60f12692

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/databricks/operators/databricks.py"]

DatabricksSubmitRunOperator libraries parameter has incorrect type

### Apache Airflow version 2.6.1 ### What happened The type of the libraries field in the DatabricksSubmitRunOperator is incorrect. According to the Databricks docs, the values should look more like: ```python [ {"pypi": {"package": "simplejson"}}, {"pypi": {"package": "Faker"}}, ] ``` as opposed to what the type hint implies: ```python [ {"pypi": "simplejson"}, {"pypi": "Faker"}, ] ``` https://github.com/apache/airflow/blob/providers-databricks/4.2.0/airflow/providers/databricks/operators/databricks.py#L306 ### What you think should happen instead _No response_ ### How to reproduce n/a ### Operating System macOS ### 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/31877

https://github.com/apache/airflow/pull/31888

f2ebc292fe63d2ddd0686d90c3acc0630f017a07

69bc90b82403b705b3c30176cc3d64b767f2252e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/variable.py", "tests/models/test_variable.py"]

KubernetesPodOperator doesn't mask variables in Rendered Template that are used as arguments

### Apache Airflow version 2.6.1 ### What happened I am pulling a variable from Google Secret Manager and I'm using it as an argument in a KubernetesPodOperator task. I've also tried it with the KubernetesPodOperatorAsync operator and I'm getting the same behaviour. The variable value is not masked on Rendered Template page. If I use the exact same variable in a different operator, like the HttpSensorAsync, it is properly masked. That is quite critical and I can't deploy the DAG to production. ### What you think should happen instead The variable in the KubernetesPodOperator should be masked and only '***' should be shown in the Rendered Template page ### How to reproduce Here's the example of code where I use the exact same variable in two different Operators. It's in the arguments of the Kubernetes Operator and then used in a different operator next. ``` my_changeset = KubernetesPodOperator( task_id='my_load', namespace=kubernetes_namespace, service_account_name=service_account_name, image='my-feed:latest', name='changeset_load', in_cluster=in_cluster, cluster_context='docker-desktop', # is ignored when in_cluster is set to True is_delete_operator_pod=True, get_logs=True, image_pull_policy=image_pull_policy, arguments=[ '-k{{ var.json.faros_api_key.faros_api_key }}', ], container_resources=k8s.V1ResourceRequirements(requests=requests, limits=limits), volumes=volumes, volume_mounts=volume_mounts, log_events_on_failure=True, startup_timeout_seconds=60 * 5, ) test_var = HttpSensorAsync( task_id=f'wait_for_my_file', http_conn_id='my_paymentreports_http', endpoint='{{ var.json.my_paymentreports_http.client_id }}/report', headers={'user-agent': 'King'}, request_params={ 'access_token': '{{ var.json.faros_api_key.faros_api_key }}', }, response_check=lambda response: True if response.status_code == 200 else False, extra_options={'check_response': False}, timeout=60 * 60 * 8, ) ``` The same {{ var.json.faros_api_key.faros_api_key }} is used in both operators, but only masked in the HttpSensorAsync operator. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow==2.6.1+astro.3 apache-airflow-providers-amazon==8.1.0 apache-airflow-providers-celery==3.2.0 apache-airflow-providers-cncf-kubernetes==7.0.0 apache-airflow-providers-common-sql==1.5.1 apache-airflow-providers-datadog==3.3.0 apache-airflow-providers-elasticsearch==4.5.0 apache-airflow-providers-ftp==3.4.1 apache-airflow-providers-github==2.3.0 apache-airflow-providers-google==10.0.0 apache-airflow-providers-hashicorp==3.4.0 apache-airflow-providers-http==4.4.1 apache-airflow-providers-imap==3.2.1 apache-airflow-providers-microsoft-azure==6.1.1 apache-airflow-providers-mysql==5.1.0 apache-airflow-providers-postgres==5.5.0 apache-airflow-providers-redis==3.2.0 apache-airflow-providers-samba==4.2.0 apache-airflow-providers-sendgrid==3.2.0 apache-airflow-providers-sftp==4.3.0 apache-airflow-providers-slack==7.3.0 apache-airflow-providers-sqlite==3.4.1 apache-airflow-providers-ssh==3.7.0 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/31873

https://github.com/apache/airflow/pull/31964

fc0e5a4d42ee882ca5bc20ea65be38b2c739644d

e22ce9baed19ddf771db59b7da1d25e240430625

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_config.py", "airflow/cli/commands/connection_command.py", "airflow/cli/commands/variable_command.py", "airflow/cli/utils.py"]

Allow variables to be printed to STDOUT

### Description Currently, the `airflow variables export` command requires an explicit file path and does not support output to stdout. However connections can be printed to stdout using `airflow connections export -`. This inconsistency between the two export commands can lead to confusion and limits the flexibility of the variables export command. ### Use case/motivation To bring some consistency, similar to connections, variables should also be printed to STDOUT, using `-` instead of filename. ### 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/31851

https://github.com/apache/airflow/pull/33279

bfa09da1380f0f1e0727dbbc9f1878bd44eb848d

09d478ec671f8017294d4e15d75db1f40b8cc404

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/log/wasb_task_handler.py", "airflow/providers/redis/log/__init__.py", "airflow/providers/redis/log/redis_task_handler.py", "airflow/providers/redis/provider.yaml", "airflow/utils/log/file_task_handler.py", "docs/apache-airflow-providers-redis/index.rst", "docs/apache-airflow-providers-redis/logging/index.rst", "tests/providers/redis/log/__init__.py", "tests/providers/redis/log/test_redis_task_handler.py"]

Redis task handler for logs

### Discussed in https://github.com/apache/airflow/discussions/31832 <div type='discussions-op-text'> ^{Originally posted by **michalc** June 10, 2023} Should something like the below be in the codebase? It's a simple handler for storing Airflow task logs in Redis, enforcing a max number of entries per try, and an expiry time for the logs Happy to raise a PR (and I guessed a lot at how things should be... so suspect can be improved upon...) ```python class RedisHandler(logging.Handler): def __init__(self, client, key): super().__init__() self.client = client self.key = key def emit(self, record): p = self.client.pipeline() p.rpush(self.key, self.format(record)) p.ltrim(self.key, start=-10000, end=-1) p.expire(self.key, time=60 * 60 * 24 * 28) p.execute() class RedisTaskHandler(FileTaskHandler, LoggingMixin): """ RedisTaskHandler is a python log handler that handles and reads task instance logs. It extends airflow FileTaskHandler and uploads to and reads from Redis. """ trigger_should_wrap = True def __init__(self, base_log_folder: str, redis_url): super().__init__(base_log_folder) self.handler = None self.client = redis.Redis.from_url(redis_url) def _read( self, ti, try_number, metadata=None, ): log_str = b"\n".join( self.client.lrange(self._render_filename(ti, try_number), start=0, end=-1) ).decode("utf-8") return log_str, {"end_of_log": True} def set_context(self, ti): super().set_context(ti) self.handler = RedisHandler( self.client, self._render_filename(ti, ti.try_number) ) self.handler.setFormatter(self.formatter) ```</div>

https://github.com/apache/airflow/issues/31834

https://github.com/apache/airflow/pull/31855

6362ba5ab45a38008814616df4e17717cc3726c3

42b4b43c4c2ccf0b6e7eaa105c982df495768d01

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/authoring-and-scheduling/deferring.rst"]

Improve the docs around deferrable mode for Sensors

### Body With Operators the use case of deferrable operators is pretty clear. However with Sensor new questions are raised. All Sensors inherit from `BaseSensorOperator` which adds [mode](https://github.com/apache/airflow/blob/a98621f4facabc207b4d6b6968e6863845e1f90f/airflow/sensors/base.py#L93) parameter a question comes to mind what is the difference between: `SomeSensor(..., mode='reschedule')` to: `SomeSensor(..., deferrable=true)` Thus unlike Operators, when working with Sensors and assuming the sensor has defer implemented the users have a choice of what to use and both can be explained as "something is not ready, lets wait without consuming resources". The docs should clarify the difference and compare between the two options that might look the same but are different. We should explain it in two fronts: 1. What happens in Airflow for each one of the options (task state in `defer` mode vs `up_for_reschedule`) etc... 2. What is the motivation/justification for each one. pros and cons. 3. Do we have some kind of general recommendation as "always prefer X over Y" or "In executor X better to use one of the options" etc... also wondering what `SomeSensor(..., , mode='reschedule', deferrable=true)` means and if we are protected against such usage. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/31819

https://github.com/apache/airflow/pull/31840

371833e076d033be84f109cce980a6275032833c

0db0ff14da449dc3dbfe9577ccdb12db946b9647

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_config.py", "airflow/cli/commands/db_command.py", "tests/cli/commands/test_db_command.py"]

Add retry + timeout to Airflow db check

### Description In my company usage of Airflow, developmental instances of Airflow run on containerized PostgreSQL that are spawned at the same time the Airflow container is spawned. Before the Airflow container runs its initialization scripts, it needs to make sure that the PostgreSQL instance can be reached, for which `airflow db check` is a great option. However, there is a non-deterministic race condition between the PSQL container and Airflow containers (not sure which will each readiness first and by how much), calling the `airflow db check` command once is not sufficient, and implementing a retry-timeout in shell script is feasible but unpleasant. It would be great if the `airflow db check` command can take two additional optional arguments: `--retry` and `--retry-delay` (just like with `curl`) so that the database connection can be checked repeatedly for up to a specified number of times. This command should exit with `0` exit code if any of the retries succeeds, and `1` if all of the retries failed. ### 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/31818

https://github.com/apache/airflow/pull/31836

a81ac70b33a589c58b59864df931d3293fada382

1b35a077221481e9bf4aeea07d1264973e7f3bf6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/hooks/wasb.py", "docs/apache-airflow-providers-microsoft-azure/connections/wasb.rst", "tests/providers/microsoft/azure/hooks/test_wasb.py"]

Airflow Connection Type Azure Blob Storage - Shared Access Key field

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow Version 2.5.2 Issue: Create a connection of type azure blob storage using the method #3 described in [OSS docs](https://airflow.apache.org/docs/apache-airflow-providers-microsoft-azure/stable/connections/wasb.html). When we use the storage account name and the access key as in the below screenshot, the connection works just fine. <img width="637" alt="connection-blob-storage-access-key" src="https://github.com/apache/airflow/assets/75730393/6cf76b44-f65f-40c0-8279-32c58b6d57ba"> Then what is the purpose of the extra field called `Blob Storage Shared Access Key(Optional)`? When I tried to put the access key in this field, then connection fails with the below error on testing: ``` invalid url http:// ``` Code reference: https://github.com/apache/airflow/blob/main/airflow/providers/microsoft/azure/hooks/wasb.py#L190-L192 ### What you think should happen instead _No response_ ### How to reproduce - Create a Azure Storage account - Go to Access Control (IAM), copy the key - Spin up new local airflow environment using Astro CLI with runtime version 7.4.1 - Go to Airflow UI -> Admin -> Connections -> create a new connection of type Azure blob Storage - Enter the name of the storage account in Blob Storage Login and the key copied from Azure to Blob Shared Access Key (Optional) and click on Test connection. ### Operating System Astro runtime 7.4.1 image on MacOS ### 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/31811

https://github.com/apache/airflow/pull/32082

0bc689ee6d4b6967d7ae99a202031aac14d181a2

46ee1c2c8d3d0e5793f42fd10bcd80150caa538b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/kafka/triggers/await_message.py"]

AwaitMessageTriggerFunctionSensor not firing all eligble messages

### Apache Airflow version 2.6.1 ### What happened The AwaitMessageTriggerFunctionSensor is showing some buggy behaviour. When consuming from a topic, it is correctly applying the apply_function in order to yield a TriggerEvent. However, it is consuming multiple messages at a time and not yielding a trigger for the correct amount of messages that would be eligble (return a value in the apply_function). The observed behaviour is as follows: - Sensor is deferred and messages start getting consumed - Multiple eligble messages trigger a single TriggerEvent instead of multiple TriggerEvents. - The sensor returns to a deferred state , repeating the cycle. The event_triggered_function is being called correctly. However, due to the issue in consuming and correctly generating the appropriate TriggerEvents some of them are missed. ### What you think should happen instead Each eligble message should create an individual TriggerEvent to be consumed by the event_triggered_function. ### How to reproduce - Use a producer DAG to produce a set amount of messages on your kafka topic - Use a listener DAG to consume this topic, screening for eligble messages (apply_function) and use the event_trigger_function to monitor the amount of events that are being consumed. ### Operating System Kubernetes cluster - Linux ### Versions of Apache Airflow Providers apache-airflow-providers-apache-kafka==1.1.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details helm chart version 1.9.0 ### Anything else Every time (independent of topic, message content, apply_function and event_triggered_function) ### 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/31795

https://github.com/apache/airflow/pull/31803

ead2530d3500dd27df54383a0802b6c94828c359

1b599c9fbfb6151a41a588edaa786745f50eec38

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/serialization/serde.py", "tests/serialization/test_serde.py"]

Deserialization of old xcom data fails after upgrade to 2.6.1 from 2.5.2 when calling /xcom/list/ [GET]

### Apache Airflow version 2.6.1 ### What happened After upgrading from airflow 2.5.2 to 2.6.1 calling the endpoint `xcom/list/` we get the following exception: ``` [2023-06-07T12:16:50.050+0000] {app.py:1744} ERROR - Exception on /xcom/list/ [GET] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 2529, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1825, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1823, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1799, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/security/decorators.py", line 139, in wraps return f(self, *args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/views.py", line 554, in list widgets = self._list() File "/home/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/baseviews.py", line 1177, in _list widgets = self._get_list_widget( File "/home/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/baseviews.py", line 1076, in _get_list_widget count, lst = self.datamodel.query( File "/home/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/models/sqla/interface.py", line 500, in query query_results = 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/engine/result.py", line 1476, in all return self._allrows() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py", line 401, in _allrows rows = self._fetchall_impl() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py", line 1389, in _fetchall_impl return self._real_result._fetchall_impl() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py", line 1813, in _fetchall_impl return list(self.iterator) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py", line 151, in chunks rows = [proc(row) for row in fetch] File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py", line 151, in <listcomp> rows = [proc(row) for row in fetch] File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py", line 984, in _instance state.manager.dispatch.load(state, context) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/event/attr.py", line 334, in __call__ fn(*args, **kw) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/mapper.py", line 3702, in _event_on_load instrumenting_mapper._reconstructor(state.obj()) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 128, in init_on_load self.value = self.orm_deserialize_value() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 677, in orm_deserialize_value return BaseXCom._deserialize_value(self, True) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 659, in _deserialize_value return json.loads(result.value.decode("UTF-8"), cls=XComDecoder, object_hook=object_hook) File "/usr/local/lib/python3.10/json/__init__.py", line 359, in loads return cls(**kw).decode(s) File "/usr/local/lib/python3.10/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/usr/local/lib/python3.10/json/decoder.py", line 353, in raw_decode obj, end = self.scan_once(s, idx) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/json.py", line 126, in orm_object_hook return deserialize(dct, False) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 209, in deserialize o = _convert(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 273, in _convert return {CLASSNAME: old[OLD_TYPE], VERSION: DEFAULT_VERSION, DATA: old[OLD_DATA][OLD_DATA]} KeyError: '__var' ``` Some xcom entries from previous airflow versions seem to be incompatible with the new refactored serialization from https://github.com/apache/airflow/pull/28067 ### What you think should happen instead xcom entries should be displayed ### How to reproduce Add an entry to your the xcom table where value contains: ` [{"__classname__": "airflow.datasets.Dataset", "__version__": 1, "__data__": {"__var": {"uri": "bq://google_cloud_default@?table=table_name&schema=schema_name", "extra": null}, "__type": "dict"}}]` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-apache-kafka==1.1.0 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==6.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-docker==3.6.0 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.1.1 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==6.0.0 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.5 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.6.0 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else The double `old[OLD_DATA][OLD_DATA]` looks suspicious to me in https://github.com/apache/airflow/blob/58fca5eb3c3521e3fa1b3beeb066acb15629deeb/airflow/serialization/serde.py#L273 ### 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/31769

https://github.com/apache/airflow/pull/31866

779226706c1d64e0fe1e19c5f077ead9c9b4914a

bd32467ede1a5a197e09456803f7cebaee9f9b77

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst"]

Update the troubleshoot section in breeze for pip running for long time.

### What do you see as an issue? Add an update on the troubleshooting section of BREEZE.rst if the pip is taking a significant amount of time with the following error: ``` ip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out. ``` ### Solving the problem If the pip is taking a significant amount of time and your internet connection is causing pip to be unable to download the libraries within the default timeout, it is advisable to modify the default timeout as follows and run the breeze again. ``` export PIP_DEFAULT_TIMEOUT=1000 ``` ### 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/31761

https://github.com/apache/airflow/pull/31760

07ea574fed5d56ca9405ee9e47828841289e3a3c

b9efbf513d8390b66d01ee380ccc43cd60d3c88b

python

closed

apache/airflow

https://github.com/apache/airflow

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

AttributeError exception when returning result to XCom

### Apache Airflow version 2.6.1 ### What happened When i use _do_xcom_push=True_ in **DatabricksSqlOperator** the an exception with following stack trace is thrown: ``` [2023-06-06, 08:52:24 UTC] {sql.py:375} INFO - Running statement: SELECT cast(max(id) as STRING) FROM prod.unified.sessions, parameters: None [2023-06-06, 08:52:25 UTC] {taskinstance.py:1824} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 2354, in xcom_push XCom.set( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 237, in set value = cls.serialize_value( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 632, in serialize_value return json.dumps(value, cls=XComEncoder).encode("UTF-8") File "/usr/local/lib/python3.10/json/__init__.py", line 238, in dumps **kw).encode(obj) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/json.py", line 102, in encode o = self.default(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/json.py", line 91, in default return serialize(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 144, in serialize return encode(classname, version, serialize(data, depth + 1)) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in serialize return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in <listcomp> return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 132, in serialize qn = qualname(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/module_loading.py", line 47, in qualname return f"{o.__module__}.{o.__name__}" File "/home/airflow/.local/lib/python3.10/site-packages/databricks/sql/types.py", line 161, in __getattr__ raise AttributeError(item) AttributeError: __name__. Did you mean: '__ne__'? ``` ### What you think should happen instead In _process_output() if self._output_path is False a list of tuples is returned: ``` def _process_output(self, results: list[Any], descriptions: list[Sequence[Sequence] | None]) -> list[Any]: if not self._output_path: return list(zip(descriptions, results)) ``` I suspect this breaks the serialization somehow which might be related to my own meta database(postgres). Replacing the Databricks SQL Operator with simple **PythonOperator** and **DatabricksSqlHook** works just fine: ``` def get_max_id(ti): hook = DatabricksSqlHook(databricks_conn_id=databricks_sql_conn_id, sql_endpoint_name='sql_endpoint') sql = "SELECT cast(max(id) as STRING) FROM prod.unified.sessions" return str(hook.get_first(sql)[0]) ``` ### How to reproduce ``` get_max_id_task = DatabricksSqlOperator( databricks_conn_id=databricks_sql_conn_id, sql_endpoint_name='sql_endpoint', task_id='get_max_id', sql="SELECT cast(max(id) as STRING) FROM prod.unified.sessions", do_xcom_push=True ) ``` ### Operating System Debian GNU/Linux 11 (bullseye) docker image, python 3.10 ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.5.1 databricks-sql-connector==2.5.2 apache-airflow-providers-databricks==4.2.0 ### Deployment Docker-Compose ### Deployment details Using extended Airflow image, LocalExecutor, Postgres 13 meta db as container in the same stack. docker-compose version 1.29.2, build 5becea4c Docker version 23.0.5, build bc4487a ### 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/31753

https://github.com/apache/airflow/pull/31780

1aa9e803c26b8e86ab053cfe760153fc286e177c

049c6184b730a7ede41db9406654f054ddc8cc5f

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 creates row group for each rows during parquet generation

### Apache Airflow version Other Airflow 2 version (please specify below) Airflow 2.4.2 ### What happened BaseSQLToGCSOperator creates row group for each rows during parquet generation, which cause compression not work and increase file size.  ### What you think should happen instead _No response_ ### How to reproduce OracleToGCSOperator( task_id='oracle_to_gcs_parquet_test', gcp_conn_id=GCP_CONNECTION, oracle_conn_id=ORACLE_CONNECTION, sql='', bucket=GCS_BUCKET_NAME, filename='', export_format='parquet', ) ### Operating System CentOS Linux 7 ### Versions of Apache Airflow Providers apache-airflow-providers-apache-hive 2.1.0 apache-airflow-providers-apache-sqoop 2.0.2 apache-airflow-providers-celery 3.0.0 apache-airflow-providers-common-sql 1.2.0 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-mysql 3.0.0 apache-airflow-providers-oracle 2.1.0 apache-airflow-providers-salesforce 5.3.0 apache-airflow-providers-sqlite 3.2.1 ### 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/31750

https://github.com/apache/airflow/pull/31831

ee83a2fbd1a65e6a5c7d550a39e1deee49856270

b502e665d633262f3ce52d9c002c0a25e6e4ec9d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/pod.py", "airflow/providers/cncf/kubernetes/utils/pod_manager.py", "kubernetes_tests/test_kubernetes_pod_operator.py", "tests/providers/cncf/kubernetes/utils/test_pod_manager.py"]

Add a process_line callback to KubernetesPodOperator

### Description Add a process_line callback to KubernetesPodOperator Like https://github.com/apache/airflow/blob/main/airflow/providers/apache/beam/operators/beam.py#LL304C36-L304C57 the `BeamRunPythonPipelineOperator`, which allows the user to add stateful plugins based on the logging from docker job ### Use case/motivation I can add a plugin based on the logging and also allow cleanup on_kill based on the job creation log. ### 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/31745

https://github.com/apache/airflow/pull/34153

d800a0de5194bb1ef3cfad44c874abafcc78efd6

b5057e0e1fc6b7a47e38037a97cac862706747f0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "airflow/www/extensions/init_wsgi_middlewares.py", "tests/www/test_app.py"]

redirect to same url after set base_url

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened ``` $ curl localhost:8080/airflow/ <!doctype html> <html lang=en> <title>Redirecting...</title> <h1>Redirecting...</h1> <p>You should be redirected automatically to the target URL: <a href="http://localhost:8080/airflow/">http://localhost:8080/airflow/</a>. If not, click the link. ``` ### What you think should happen instead At least not circular redirect. ### How to reproduce generate yaml: ``` helm template --name-template=airflow ~/downloads/airflow > airflow.yaml ``` add base_url in webserver section and remove health and ready check in webserver(to keep pod alive): ``` [webserver] enable_proxy_fix = True rbac = True base_url = http://my.domain.com/airflow/ ``` ### Operating System Ubuntu 22.04.2 LTS ### Versions of Apache Airflow Providers apache-airflow==2.5.3 apache-airflow-providers-amazon==7.3.0 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==5.2.2 apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-docker==3.5.1 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==8.11.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.0 apache-airflow-providers-http==4.2.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==5.2.1 apache-airflow-providers-mysql==4.0.2 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.4 apache-airflow-providers-sqlite==3.3.1 apache-airflow-providers-ssh==3.5.0 ### 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/31726

https://github.com/apache/airflow/pull/31833

69bc90b82403b705b3c30176cc3d64b767f2252e

fe4a6c843acd97c776d5890116bfa85356a54eee

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/triggerer_job_runner.py", "tests/jobs/test_triggerer_job.py"]

Add a log message when a trigger is canceled for timeout

### Body The _trigger_ log doesn't show that a trigger timed out when it is canceled due to timeout. We should try to see if we can add a log message that would show up in the right place. If we emit it from the trigger process, it might show up out of order. But then again, if we ultimately don't need to go back to the task, that would not be a problem. Additionally if we ultimately can "log from anywhere" then again, this would provide a clean solution. This came up in PR discussion here: https://github.com/apache/airflow/pull/30853#discussion_r1187018026 The relevant trigger code is here: https://github.com/apache/airflow/blob/main/airflow/jobs/triggerer_job_runner.py#L598-L619 I think we could add logic so that when we receive a cancelled error (which could be for a few different reasons) then we can log the reason for the cancellation. I think we could just add an `except CancelledError` and then log the reason. We might need also to update the code in the location where we actually _initiate_ the cancellation to add sufficient information for the log message. cc @syedahsn @phanikumv @jedcunningham @pankajastro ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/31720

https://github.com/apache/airflow/pull/31757

6becb7031618867bc253aefc9e3e216629575d2d

a60429eadfffb5fb0f867c220a6cecf628692dcf

python

closed

apache/airflow

https://github.com/apache/airflow

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

Schedule "@daily" is wrongly declared in the "DAG/Core Concepts"

### What do you see as an issue? I found a small bug in the DAG Core Concepts documentation regarding the `@daily`schedule: https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#running-dags DAGs do not require a schedule, but it’s very common to define one. You define it via the `schedule` argument, like this: ```python with DAG("my_daily_dag", schedule="@daily"): ... ``` The `schedule` argument takes any value that is a valid [Crontab](https://en.wikipedia.org/wiki/Cron) schedule value, so you could also do: ```python with DAG("my_daily_dag", schedule="0 * * * *"): ... ``` If I'm not mistaken, the daily crontab notation should be `0 0 * * *` instead of `0 * * * *`, otherwise the DAG would run every hour. The second `0`of course needs to be replaced with the hour, at which the DAG should run daily. ### Solving the problem I would change the documentation at the marked location: The `schedule` argument takes any value that is a valid [Crontab](https://en.wikipedia.org/wiki/Cron) schedule value, so for a daily run at 00:00, you could also do: ```python with DAG("my_daily_dag", schedule="0 0 * * *"): ... ``` ### 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/31668

https://github.com/apache/airflow/pull/31666

4ebf1c814c6e382169db00493a897b11c680e72b

6a69fbb10c08f30c0cb22e2ba68f56f3a5d465aa

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/base.py", "tests/decorators/test_setup_teardown.py"]

Param on_failure_fail_dagrun should be overridable through `task.override`

Currently when you define a teardown e.g. ``` @teardown(on_failure_fail_dagrun=True) def my_teardown(): ... ``` You can not change this when you instantiate the task e.g. with ``` my_teardown.override(on_failure_fail_dagrun=True)() ``` I don't think this is good because if you define a reusable taskflow function then it might depend on the context.

https://github.com/apache/airflow/issues/31656

https://github.com/apache/airflow/pull/31665

29d2a31dc04471fc92cbfb2943ca419d5d8a6ab0

8dd194493d6853c2de80faee60d124b5d54ec3a6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/ecs.py", "airflow/providers/amazon/aws/triggers/ecs.py", "airflow/providers/amazon/aws/utils/task_log_fetcher.py", "airflow/providers/amazon/provider.yaml", "tests/providers/amazon/aws/operators/test_ecs.py", "tests/providers/amazon/aws/triggers/test_ecs.py", "tests/providers/amazon/aws/utils/test_task_log_fetcher.py"]

Add deferrable mode to EcsRunTaskOperator

### Description I would greatly appreciate it if the `EcsRunTaskOperator` could incorporate the `deferrable` mode. Currently, this operator significantly affects the performance of my workers, and running multiple instances simultaneously proves to be inefficient. I have noticed that the `KubernetesPodOperator` already supports this functionality, so having a similar feature available for ECS would be a valuable addition. Note: This feature request relates to the `amazon` provider. ### Use case/motivation Reduce resource utilisation of my worker when running multiple ecs run task operator in paralllel. ### 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/31636

https://github.com/apache/airflow/pull/31881

e4ca68818eec0f29ef04a1a5bfec3241ea03bf8c

415e0767616121854b6a29b3e44387f708cdf81e

python

closed

apache/airflow

https://github.com/apache/airflow

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

[airflow 2.4.3] presto queries returning none following upgrade to common.sql provider

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened After upgrading apache-airflow-providers-common-sql from 1.2.0 to anything above 1.3.0 presto queries using the get_records() and or get_first() function returns none. using the same query -- `select 1`: 1.2.0: `Done. Returned value was: [[1]]` 1.3.0 and above: ``` Running statement: select 1, parameters: None [2023-05-30, 11:57:37 UTC] {{python.py:177}} INFO - Done. Returned value was: None ``` ### What you think should happen instead i would expect that running the query `select 1` on presto would provide the same result when the environment is running apache-airflow-providers-common-sql 1.2.0 or apache-airflow-providers-common-sql 1.5.1. ### How to reproduce run the following query: `PrestoHook(conn_id).get_records(`select 1`)` ensure that the requirements are as labelled below. ### Operating System NAME="Amazon Linux" VERSION="2" ID="amzn" ID_LIKE="centos rhel fedora" ### Versions of Apache Airflow Providers apache-airflow==2.4.3 apache-airflow-providers-amazon==6.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-common-sql==1.5.1 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-jenkins==3.0.0 apache-airflow-providers-mysql==3.2.1 apache-airflow-providers-postgres==5.2.2 apache-airflow-providers-presto==5.1.0 apache-airflow-providers-sendgrid==3.0.0 apache-airflow-providers-slack==6.0.0 apache-airflow-providers-snowflake==3.3.0 apache-airflow-providers-sqlite==3.2.1 apache-airflow-providers-trino==4.1.0 ### 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/31612

https://github.com/apache/airflow/pull/35132

789222cb1378079e2afd24c70c1a6783b57e27e6

8ef2a9997d8b6633ba04dd9f752f504a2ce93e25

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/task_group.py", "tests/decorators/test_task_group.py", "tests/utils/test_task_group.py"]

Override default_args between Nested TaskGroups

### What do you see as an issue? Hello! I don't know if this is intended, but `default_args` is not an override when using nested TaskGroups. ```python def callback_in_dag(context: Context): print("DAG!") def callback_in_task_group(context: Context): print("Parent TaskGroup!") with DAG( "some_dag_id", default_args={ "on_failure_callback": callback_in_dag }, schedule=None, start_date=datetime(2023, 1, 1) ) as dag: with TaskGroup( "parent_tg", default_args={ "on_failure_callback": callback_in_task_group } ) as parent_tg: with TaskGroup("child_tg") as child_tg: BashOperator(task_id="task_1", bash_command="nooooo_command") ``` I want the result to be "Parent TaskGroup!", but I get "DAG!". ``` [2023-05-30, 10:38:52 KST] {logging_mixin.py:137} INFO - DAG! ``` ### Solving the problem Add `_update_default_args` like [link](https://github.com/apache/airflow/blob/f6bb4746efbc6a94fa17b6c77b31d9fb17305ffc/airflow/models/baseoperator.py#L139) #### [airflow/utils/task_group.py](https://github.com/apache/airflow/blob/main/airflow/utils/task_group.py) ```python ... class TaskGroup(DAGNode): def __init__(...): ... self.default_args = copy.deepcopy(default_args or {}) # Call 'self._update_default_args' when exists parent_group if parent_group is not None: self._update_default_args(parent_group) ... ... # Update self.default_args def _update_default_args(parent_group: TaskGroup): if parent_group.default_args: self.default_args.update(parent_group.default_args) ... ``` ### 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/31604

https://github.com/apache/airflow/pull/31608

efe8473385426bf8c1e23a845f1ba26482843855

9e8627faa71e9d2047816b291061c28585809508

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQueryInsertJobOperator not exiting deferred state

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Using Apache Airflow 2.4.3 and apache airflow google provider 8.4 (also tried with 10.1.0). We have a query that in production should run for a long time, so we wanted to make the BigQueryInsertJobOperator deferrable. Making the operator deferrable runs the job, but the UI and triggerer process don't seem to be notified that the operator has finished. I have validated that the query is actually run as the data appears in the table, but the operator gets stuck in a deferred state. ### What you think should happen instead After the big query job is finished, the operator should exit it's deferred state. ### How to reproduce Skeleton of the code used ``` with DAG( dag_id="some_dag_id", schedule="@daily", catchup=False, start_date=pendulum.datetime(2023, 5, 8), ): extract_data = BigQueryInsertJobOperator( task_id="extract_data", impersonation_chain=GCP_ASTRO_TEAM_SA.get() params={"dst_table": _DST_TABLE, "lookback_days": _LOOKBACK_DAYS}, configuration={ "query": { "query": "{% include 'sql/sql_file.sql' %}", "useLegacySql": False, } }, outlets=DATASET, execution_timeout=timedelta(hours=2, minutes=30), deferrable=True, ) ``` ### Operating System Mac OS Ventura 13.3.1 ### Versions of Apache Airflow Providers apache airflow google provider 8.4 (also tried with 10.1.0). ### Deployment Official Apache Airflow Helm Chart ### Deployment details We're using astro and on local the Airflow environment is started using `astro dev start`. The issue appears when running the DAG locally. An entire other issue (may be unrelated) appears on Sandbox deployment. ### Anything else Every time the operator is marked as deferrable. I noticed this a few days ago (week started with Monday May 22nd 2023). ### 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/31584

https://github.com/apache/airflow/pull/31591

fcbbf47864c251046de108aafdad394d66e1df23

81b85ebcbd241e1909793d7480aabc81777b225c

python

closed

apache/airflow

https://github.com/apache/airflow

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

Vault AWS Login not working

### Apache Airflow version 2.6.1 ### What happened Trying to connect to Vault using `auth_type` = `aws_iam`. I am receiving the following error: `AttributeError: 'Client' has no attribute 'auth_aws_iam'`. Looking through [the code](https://github.com/apache/airflow/blob/main/airflow/providers/hashicorp/_internal_client/vault_client.py#L303), you are using `client.auth_aws_iam`, but the [HVAC docs](https://hvac.readthedocs.io/en/stable/usage/auth_methods/aws.html#caveats-for-non-default-aws-regions) use `client.auth.aws.iam_login`. You should also add support for `boto3.Session()` to use role based access. ### What you think should happen instead Airflow should authenticate to Vault using AWS auth. ### How to reproduce Setup a secrets backend of `airflow.providers.hashicorp.secrets.vault.VaultBackend` and set `auth_type` = `aws_iam`. It will error out saying that the client doesn't have an attribute named `auth_aws_iam`. ### Operating System Docker ### 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/31573

https://github.com/apache/airflow/pull/31593

ec18db170745a8b1df0bb75569cd22e69892b3e2

41ea700cbdce99cddd0f7b51b33b9fab51b993af

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/redshift_sql.py", "tests/providers/amazon/aws/hooks/test_redshift_sql.py"]

Redshift connection breaking change with IAM authentication

### Apache Airflow version 2.6.1 ### What happened This [PR](https://github.com/apache/airflow/pull/28187) introduced the get_iam_token method in `redshift_sql.py`. This is the breaking change as introduces the check for `iam` in extras, and it's set to False by default. Error log: ``` self = <airflow.providers.amazon.aws.hooks.redshift_sql.RedshiftSQLHook object at 0x7f29f7c208e0> conn = redshift_default def get_iam_token(self, conn: Connection) -> tuple[str, str, int]: """ Uses AWSHook to retrieve a temporary ***word to connect to Redshift. Port is required. If none is provided, default is used for each service """ port = conn.port or 5439 # Pull the custer-identifier from the beginning of the Redshift URL # ex. my-cluster.ccdre4hpd39h.us-east-1.redshift.amazonaws.com returns my-cluster > cluster_identifier = conn.extra_dejson.get("cluster_identifier", conn.host.split(".")[0]) E AttributeError: 'NoneType' object has no attribute 'split' .nox/test-3-8-airflow-2-6-0/lib/python3.8/site-packages/airflow/providers/amazon/aws/hooks/redshift_sql.py:107: AttributeError ``` ### What you think should happen instead It should have backward compatibility ### How to reproduce Run an example DAG for redshift with the AWS IAM profile given at hook initialization to retrieve a temporary password to connect to Amazon Redshift. ### Operating System mac-os ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/31551

https://github.com/apache/airflow/pull/31567

0f1cef27a5a19dd56e6b07ab0bf9868fb850421a

5b3382f63898e497d482870636ed156ce861afbc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Tag filter doesn't sort tags alphabetically

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow v: 2.6.0 This has been an issue since 2.4.0 for us at least. We recently did a refactor of many of our 160+ DAGs and part of that process was to remove some tags that we didn't want anymore. Unfortunately, the old tags were still left behind when we deployed our new image with the updated DAGs (been a consistent thing across several Airflow versions for us). There is also the issue that the tag filter doesn't sort our tags alphabetically. I tried to truncate the dag_tag table, and that did help to get rid of the old tags. However, the sorting issue remains. Example:  On one of our dev environments, we have just about 10 DAGs with a similar sorting problem, and the dag_tag table had 18 rows. I took a backup of it and truncated the dag_tag table, which was almost instantly refilled (I guess logs are DEBUG level on that, so I saw nothing). This did not initially fix the sorting problem, but after a couple of truncates, things got weird, and all the tags were sorted as expected, and the row count in the dag_tag table was now just 15, so 3 rows were removed in all. We also added a new DAG in there with a tag "arjun", which also got listed first - so all sorted on that environment. Summary: 1. Truncating of the dag_tag table got rid of the old tags that we no longer have in our DAGs. 2. The tags are still sorted incorrectly in the filter (see image). It seems that the logic here is contained in `www/static/js/dags.js.` I am willing to submit a PR if I can get some guidance :) ### What you think should happen instead _No response_ ### How to reproduce N/A ### Operating System debian ### 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/31547

https://github.com/apache/airflow/pull/31553

6f86b6cd070097dafca196841c82de91faa882f4

24e52f92bd9305bf534c411f9455460060515ea7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/skipmixin.py", "airflow/operators/python.py", "airflow/operators/subdag.py", "tests/operators/test_python.py", "tests/operators/test_subdag_operator.py"]

Short circuit task in expanded task group fails when it returns false

### Apache Airflow version 2.6.1 ### What happened I have a short circuit task which is in a task group that is expanded. The task work correctly when it returns true but the task fails when it returns false with the following error: ``` sqlalchemy.exc.IntegrityError: (psycopg2.errors.ForeignKeyViolation) insert or update on table "xcom" violates foreign key constraint "xcom_task_instance_fkey" DETAIL: Key (dag_id, task_id, run_id, map_index)=(pipeline_output_to_s3, transfer_output_file.already_in_manifest_short_circuit, manual__2023-05-24T20:21:35.420606+00:00, -1) is not present in table "task_instance". ``` It looks like it sets the map-index to -1 when false is returned which is causing the issue. If one task fails this way in the the task group, all other mapped tasks fail as well, even if the short circuit returns true. When this error occurs, all subsequent DAG runs will be stuck indefinitely in the running state unless the DAG is deleted. ### What you think should happen instead Returning false from the short circuit operator should skip downstream tasks without affecting other task groups / map indexes or subsequent DAG runs. ### How to reproduce include a short circuit operator in a mapped task group and have it return false. ### Operating System Red Hat Enterprise Linux 7 ### 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/31526

https://github.com/apache/airflow/pull/31541

c356e4fc22abc77f05aa136700094a882f2ca8c0

e2da3151d49dae636cb6901f3d3e124a49cbf514

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/airflow_health.py", "airflow/api_connexion/endpoints/health_endpoint.py", "airflow/www/views.py", "tests/api/__init__.py", "tests/api/common/__init__.py", "tests/api/common/test_airflow_health.py"]

`/health` endpoint missed when adding triggerer health status reporting

### Apache Airflow version main (development) ### What happened https://github.com/apache/airflow/pull/27755 added the triggerer to the rest api health endpoint, but not the main one served on `/health`. ### What you think should happen instead As documented [here](https://airflow.apache.org/docs/apache-airflow/2.6.1/administration-and-deployment/logging-monitoring/check-health.html#webserver-health-check-endpoint), the `/health` endpoint should include triggerer info like shown on `/api/v1/health`. ### How to reproduce Compare `/api/v1/health` and `/health` responses. ### 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/31522

https://github.com/apache/airflow/pull/31529

afa9ead4cea767dfc4b43e6f301e6204f7521e3f

f048aba47e079e0c81417170a5ac582ed00595c4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/user_command.py"]

Unable to delete user via CI

### Apache Airflow version 2.6.1 ### What happened I am unable to delete users via the "delete command". I am trying to create a new user delete the default admin user. so I tried running the command `airflow users delete -u admin`. Running this command gave the following error output: ``` Feature not implemented,tasks route disabled /usr/local/lib/python3.10/site-packages/flask_limiter/extension.py:293 UserWarning: Using the in-memory storage for tracking rate limits as no storage was explicitly specified. This is not recommended for production use. See: https://flask-limiter.readthedocs.io#configuring-a-storage-backend for documentation about configuring the storage backend. /usr/local/lib/python3.10/site-packages/astronomer/airflow/version_check/update_checks.py:440 UserWarning: The setup method 'app_context_processor' can no longer be called on the blueprint 'UpdateAvailableView'. It has already been registered at least once, any changes will not be applied consistently. Make sure all imports, decorators, functions, etc. needed to set up the blueprint are done before registering it. This warning will become an exception in Flask 2.3. /usr/local/lib/python3.10/site-packages/flask/blueprints.py:673 UserWarning: The setup method 'record_once' can no longer be called on the blueprint 'UpdateAvailableView'. It has already been registered at least once, any changes will not be applied consistently. Make sure all imports, decorators, functions, etc. needed to set up the blueprint are done before registering it. This warning will become an exception in Flask 2.3. /usr/local/lib/python3.10/site-packages/flask/blueprints.py:321 UserWarning: The setup method 'record' can no longer be called on the blueprint 'UpdateAvailableView'. It has already been registered at least once, any changes will not be applied consistently. Make sure all imports, decorators, functions, etc. needed to set up the blueprint are done before registering it. This warning will become an exception in Flask 2.3. /usr/local/lib/python3.10/site-packages/airflow/www/fab_security/sqla/manager.py:151 SAWarning: Object of type <Role> not in session, delete operation along 'User.roles' won't proceed [2023-05-24T12:34:19.438+0000] {manager.py:154} ERROR - Remove Register User Error: (psycopg2.errors.ForeignKeyViolation) update or delete on table "ab_user" violates foreign key constraint "ab_user_role_user_id_fkey" on table "ab_user_role" DETAIL: Key (id)=(4) is still referenced from table "ab_user_role". [SQL: DELETE FROM ab_user WHERE ab_user.id = %(id)s] [parameters: {'id': 4}] (Background on this error at: https://sqlalche.me/e/14/gkpj) Failed to delete user ``` Deleting via the UI works fine. The error also occurs for users that have a different role, such as Viewer. ### What you think should happen instead No error should occur and the specified user should be deleted. ### How to reproduce Run a Dag with a task that is a BashOperator that deletes the user e.g.: ```python remove_default_admin_user_task = BashOperator(task_id="remove_default_admin_user", bash_command="airflow users delete -u admin") ``` ### Operating System Docker containers run in Debian 10 ### Versions of Apache Airflow Providers Astronomer ### Deployment Astronomer ### Deployment details I use Astronomer 8.0.0 ### Anything else Always. It also occurs when ran inside the webserver docker container or when ran via the astro CLI with the command `astro dev run airflow users delete -u admin`. ### 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/31509

https://github.com/apache/airflow/pull/31539

0fd42ff015be02d1a6a6c2e1a080f8267194b3a5

3ec66bb7cc686d060ff728bb6bf4d4e70e387ae3

python

closed

apache/airflow

https://github.com/apache/airflow

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

XCom - Attribute Error when serializing output of `Merge Into` databricks sql command.

### Apache Airflow version 2.6.1 ### What happened After upgrading from Airflow 2.5.3 to 2.6.1 - the dag started to fail and it's related to XCom serialization. I noticed that something has changed in regards to serializing XCom:``` key | Value Version 2.5.3 | Value Version 2.6.1 | result -- | -- | -- | -- return_value | [[['Result', 'string', None, None, None, None, None]], []] | (['(num_affected_rows,bigint,None,None,None,None,None)', '(num_inserted_rows,bigint,None,None,None,None,None)'],[]) | ✅ return_value | [[['Result', 'string', None, None, None, None, None]], []] | (['(Result,string,None,None,None,None,None)'],[]) | ✅ return_value | [[['num_affected_rows', 'bigint', None, None, None, None, None], ['num_updated_rows', 'bigint', None, None, None, None, None], ['num_deleted_rows', 'bigint', None, None, None, None, None], ['num_inserted_rows', 'bigint', None, None, None, None, None]], [[1442, 605, 0, 837]]] | `AttributeError: __name__. Did you mean: '__ne__'?` | ❌ Query syntax that procuded an error: MERGE INTO https://docs.databricks.com/sql/language-manual/delta-merge-into.html Stacktrace included below: ``` [2023-05-24, 01:12:43 UTC] {taskinstance.py:1824} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 2354, in xcom_push XCom.set( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 237, in set value = cls.serialize_value( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/xcom.py", line 632, in serialize_value return json.dumps(value, cls=XComEncoder).encode("UTF-8") File "/usr/local/lib/python3.10/json/__init__.py", line 238, in dumps **kw).encode(obj) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/json.py", line 102, in encode o = self.default(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/json.py", line 91, in default return serialize(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 144, in serialize return encode(classname, version, serialize(data, depth + 1)) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in serialize return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in <listcomp> return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in serialize return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 123, in <listcomp> return [serialize(d, depth + 1) for d in o] File "/home/airflow/.local/lib/python3.10/site-packages/airflow/serialization/serde.py", line 132, in serialize qn = qualname(o) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/module_loading.py", line 47, in qualname return f"{o.__module__}.{o.__name__}" File "/home/airflow/.local/lib/python3.10/site-packages/databricks/sql/types.py", line 161, in __getattr__ raise AttributeError(item) AttributeError: __name__. Did you mean: '__ne__'? ``` ### What you think should happen instead Serialization should finish without an exception raised. ### How to reproduce 1. Dag file with declared operator: ``` task = DatabricksSqlOperator( task_id="task", databricks_conn_id="databricks_conn_id", sql_endpoint_name="name", sql="file.sql" ) ``` file.sql ``` MERGE INTO table_name ON condition WHEN MATCHED THEN UPDATE WHEN NOT MATCHED THEN INSERT ``` https://docs.databricks.com/sql/language-manual/delta-merge-into.html Query output is a table num_affected_rows | num_updated_rows | num_deleted_rows | num_inserted_rows -- | -- | -- | -- 0 | 0 | 0 | 0 EDIT: It also happens for a select command ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-cncf-kubernetes==6.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-databricks==4.1.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.0.0 apache-airflow-providers-hashicorp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.6.0 ``` ### 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/31499

https://github.com/apache/airflow/pull/31780

1aa9e803c26b8e86ab053cfe760153fc286e177c

049c6184b730a7ede41db9406654f054ddc8cc5f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/links/emr.py", "tests/providers/amazon/aws/links/test_links.py"]

Missing LogUri from emr describe-cluster API when executing EmrCreateJobFlowOperator

### Apache Airflow version main (development) ### What happened Encounter the following error when executing `EmrCreateJobFlowOperator` ``` Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/airflow/providers/amazon/aws/operators/emr.py", line 695, in execute log_uri=get_log_uri(emr_client=self._emr_hook.conn, job_flow_id=self._job_flow_id), File "/usr/local/lib/python3.10/site-packages/airflow/providers/amazon/aws/links/emr.py", line 61, in get_log_uri log_uri = S3Hook.parse_s3_url(response["Cluster"]["LogUri"]) KeyError: 'LogUri' ``` According to the [this document](https://docs.aws.amazon.com/cli/latest/reference/emr/describe-cluster.html), it seems we might not always be able to get `["Cluster"]["LogUri"]` and we encounter errors after the release in https://github.com/apache/airflow/issues/31322. ### What you think should happen instead The `EmrCreateJobFlowOperator` should finish execution without error. ### How to reproduce 1. `git clone github.com/apache/airflow/` 2. `cd airflow` 3. `git checkout c082aec089405ed0399cfee548011b0520be0011` (the main branch when I found this issue) 4. Add the following DAG to `files/dags/` and name it as `example_emr.py` ```python import os from datetime import datetime, timedelta from airflow import DAG from airflow.providers.amazon.aws.operators.emr import EmrCreateJobFlowOperator, EmrTerminateJobFlowOperator JOB_FLOW_OVERRIDES = { "Name": "example_emr_sensor_cluster", "ReleaseLabel": "emr-5.29.0", "Applications": [{"Name": "Spark"}], "Instances": { "InstanceGroups": [ { "Name": "Primary node", "Market": "ON_DEMAND", "InstanceRole": "MASTER", "InstanceType": "m4.large", "InstanceCount": 1, }, ], "KeepJobFlowAliveWhenNoSteps": False, "TerminationProtected": False, }, "JobFlowRole": "EMR_EC2_DefaultRole" "ServiceRole": "EMR_DefaultRole", } DEFAULT_ARGS = { "execution_timeout": timedelta(hours=6), "retries": 2, "retry_delay": 60, } with DAG( dag_id="example_emr_sensor", schedule=None, start_date=datetime(2022, 1, 1), default_args=DEFAULT_ARGS, catchup=False, ) as dag: create_job_flow = EmrCreateJobFlowOperator( task_id="create_job_flow", job_flow_overrides=JOB_FLOW_OVERRIDES, aws_conn_id="aws_default", ) remove_job_flow = EmrTerminateJobFlowOperator( task_id="remove_job_flow", job_flow_id=create_job_flow.output, aws_conn_id="aws_default", trigger_rule="all_done", ) create_job_flow >> remove_job_flow ``` 5. `breeze --python 3.8 --backend sqlite start-airflow` 6. Trigger the DAG from web UI ### Operating System mac OS 13.4 ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else https://github.com/apache/airflow/issues/31322#issuecomment-1556876252 ### 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/31480

https://github.com/apache/airflow/pull/31482

27001a23718d6b8b5118eb130be84713af9a4477

a8c45b088e088a5f1d9c924f9efb660c80c0ce12

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/kubernetes_command.py", "tests/cli/commands/test_kubernetes_command.py"]

cleanup-pod CLI command fails due to incorrect host

### Apache Airflow version 2.6.1 ### What happened When running `airflow kubernetes cleanup-pods`, the API call to delete a pod fails. A snippet of the log is below: ``` urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=80): Max retries exceeded with url: /api/v1/namespaces/airflow/pods/my-task-avd79fq1 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f52f9aebfd0>: Failed to establish a new connection: [Errno 111] Connection refused')) ``` [The Kubernetes client provisioned in _delete_pod](https://github.com/apache/airflow/blob/main/airflow/cli/commands/kubernetes_command.py#L151) incorrectly has the host as `http:localhost`. On the scheduler pod if I start a Python environment I can see that the configuration differs from the `get_kube_client()` configuration: ``` >>> get_kube_client().api_client.configuration.host 'https://172.20.0.1:443' >>> client.CoreV1Api().api_client.configuration.host 'http://localhost/' ``` On Airflow 2.5.3 these two clients have the same configuration. It's possible I have some mistake in my configuration but I'm not sure what it could be. The above fails on 2.6.0 also. ### What you think should happen instead Pods should clean up without error ### How to reproduce Run the following from a Kubernetes deployment of Airflow: ```python from airflow.kubernetes.kube_client import get_kube_client from kubernetes import client print(get_kube_client().api_client.configuration.host) print(client.CoreV1Api().api_client.configuration.host) ``` Alternatively run `airflow kubernetes cleanup-pods` with pods available for cleanup ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details Using `in_cluster` configuration for KubernetesExecutor ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/31476

https://github.com/apache/airflow/pull/31477

739e6b5d775412f987a3ff5fb71c51fbb7051a89

adf0cae48ad4e87612c00fe9facffca9b5728e7d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/connection.py", "tests/models/test_connection.py"]

Add capability in Airflow connections to validate host

### Apache Airflow version 2.6.1 ### What happened While creating connections in airflow, the form doesn't check for the correctness of the format of the host provided. For instance, we can proceed providing something like this which is not a valid url: `spark://k8s://100.68.0.1:443?deploy-mode=cluster`. It wont instantly fail but will return faulty hosts and other details if called later. Motivation: https://github.com/apache/airflow/pull/31376#discussion_r1200112106 ### What you think should happen instead The Connection form can have a validator which should check for these scenarios and report if there is an issue to save developers and users time later. ### How to reproduce 1. Go to airflow connections form 2. Fill in connection host as: `spark://k8s://100.68.0.1:443?deploy-mode=cluster`, other details can be anything 3. Create the connection 4. Run `airflow connections get <name>` 5. The host and schema will be wrong ### Operating System Macos ### 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/31460

https://github.com/apache/airflow/pull/31465

232771869030d708c57f840aea735b18bd4bffb2

0560881f0eaef9c583b11e937bf1f79d13e5ac7c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_params_ui_tutorial.py", "airflow/www/static/js/trigger.js", "airflow/www/templates/airflow/trigger.html", "docs/apache-airflow/core-concepts/params.rst"]

Multi-Select, Text Proposals and Value Labels forTrigger Forms

### Description After the release for Airflow 2.6.0 I was integrating some forms into our setup and was missing some options selections - and some nice features to make selections user friendly. I'd like do contribute some few features into the user forms: * A select box option where proposals are made but user is not limited to hard `enum` list (`enum` is restricting user input to only the options provided) * A multi-option pick list (because sometimes a single selection is just not enough * Labels so that technical values used to control the DAG are different from what is presented as option to user ### Use case/motivation After the initial release of UI trigger forms, add more features (incrementially) ### Related issues Relates or potentially has a conflict with #31299, so this should be merged before. ### 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/31440

https://github.com/apache/airflow/pull/31441

1ac35e710afc6cf5ea4466714b18efacdc44e1f7

c25251cde620481592392e5f82f9aa8a259a2f06

python

closed

apache/airflow

https://github.com/apache/airflow

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

`BigQueryGetDataOperator`'s query job is bugged in deferrable mode

### Apache Airflow version main (development) ### What happened 1. When not providing `project_id` to `BigQueryGetDataOperator` in deferrable mode (`project_id=None`), the query generated by `generate_query` method is bugged, i.e.,: ````sql from `None.DATASET.TABLE_ID` limit ... ```` 2. `as_dict` param does not work `BigQueryGetDataOperator`. ### What you think should happen instead 1. When `project_id` is `None` - it should be removed from the query along with the trailing dot, i.e.,: ````sql from `DATASET.TABLE_ID` limit ... ```` 2. `as_dict` should be added to the serialization method of `BigQueryGetDataTrigger`. ### How to reproduce 1. Create a DAG file with `BigQueryGetDataOperator` defined as follows: ```python BigQueryGetDataOperator( task_id="bq_get_data_op", # project_id="PROJECT_ID", <-- Not provided dataset_id="DATASET", table_id="TABLE", use_legacy_sql=False, deferrable=True ) ```` 2. 1. Create a DAG file with `BigQueryGetDataOperator` defined as follows: ```python BigQueryGetDataOperator( task_id="bq_get_data_op", project_id="PROJECT_ID", dataset_id="DATASET", table_id="TABLE", use_legacy_sql=False, deferrable=True, as_dict=True ) ```` ### Operating System Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else The `generate_query` method is not unit tested (which would have prevented it in the first place) - will be better to add one. ### 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/31432

https://github.com/apache/airflow/pull/31433

0e8bff9c4ec837d086dbe49b3d583a8d23f49e0e

0d6e626b050a860462224ad64dc5e9831fe8624d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/0125_2_6_2_add_onupdate_cascade_to_taskmap.py", "airflow/migrations/versions/0126_2_7_0_add_index_to_task_instance_table.py", "airflow/models/taskmap.py", "docs/apache-airflow/img/airflow_erd.sha256", "docs/apache-airflow/img/airflow_erd.svg", "docs/apache-airflow/migrations-ref.rst", "tests/models/test_taskinstance.py"]

Clearing a task flow function executed earlier with task changed to mapped task crashes scheduler

### Apache Airflow version main (development) ### What happened Clearing a task flow function executed earlier with task changed to mapped task crashes scheduler. It seems TaskMap stored has a foreign key reference by map_index which needs to be cleared before execution. ``` airflow scheduler /home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/cli/cli_config.py:1001 DeprecationWarning: The namespace option in [kubernetes] has been moved to the namespace option in [kubernetes_executor] - the old setting has been used, but please update your config. ____________ _____________ ____ |__( )_________ __/__ /________ __ ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ /home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py:196 DeprecationWarning: The '[celery] task_adoption_timeout' config option is deprecated. Please update your config to use '[scheduler] task_queued_timeout' instead. /home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py:201 DeprecationWarning: The worker_pods_pending_timeout option in [kubernetes] has been moved to the worker_pods_pending_timeout option in [kubernetes_executor] - the old setting has been used, but please update your config. /home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py:206 DeprecationWarning: The '[kubernetes_executor] worker_pods_pending_timeout' config option is deprecated. Please update your config to use '[scheduler] task_queued_timeout' instead. [2023-05-19T23:41:07.907+0530] {executor_loader.py:114} INFO - Loaded executor: SequentialExecutor [2023-05-19 23:41:07 +0530] [15527] [INFO] Starting gunicorn 20.1.0 [2023-05-19 23:41:07 +0530] [15527] [INFO] Listening at: http://[::]:8793 (15527) [2023-05-19 23:41:07 +0530] [15527] [INFO] Using worker: sync [2023-05-19 23:41:07 +0530] [15528] [INFO] Booting worker with pid: 15528 [2023-05-19T23:41:07.952+0530] {scheduler_job_runner.py:789} INFO - Starting the scheduler [2023-05-19T23:41:07.952+0530] {scheduler_job_runner.py:796} INFO - Processing each file at most -1 times [2023-05-19T23:41:07.954+0530] {scheduler_job_runner.py:1542} INFO - Resetting orphaned tasks for active dag runs [2023-05-19 23:41:07 +0530] [15529] [INFO] Booting worker with pid: 15529 [2023-05-19T23:41:08.567+0530] {scheduler_job_runner.py:853} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 836, in _execute self._run_scheduler_loop() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 970, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 1052, in _do_scheduling callback_tuples = self._schedule_all_dag_runs(guard, dag_runs, session) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/retries.py", line 90, in wrapped_function for attempt in run_with_db_retries(max_retries=retries, logger=logger, **retry_kwargs): File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __iter__ do = self.iter(retry_state=retry_state) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/tenacity/__init__.py", line 349, in iter return fut.result() File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/retries.py", line 99, in wrapped_function return func(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 1347, in _schedule_all_dag_runs callback_tuples = [(run, self._schedule_dag_run(run, session=session)) for run in dag_runs] File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2811, in __iter__ return self._iter().__iter__() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2818, in _iter result = self.session.execute( File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1669, in execute conn = self._connection_for_bind(bind, close_with_result=True) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1519, in _connection_for_bind return self._transaction._connection_for_bind( File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 721, in _connection_for_bind self._assert_active() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/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.ForeignKeyViolation) update or delete on table "task_instance" violates foreign key constraint "task_map_task_instance_fkey" on table "task_map" DETAIL: Key (dag_id, task_id, run_id, map_index)=(bash_simple, get_command, manual__2023-05-18T13:54:01.345016+00:00, -1) is still referenced from table "task_map". [SQL: UPDATE task_instance SET map_index=%(map_index)s, updated_at=%(updated_at)s WHERE task_instance.dag_id = %(task_instance_dag_id)s AND task_instance.task_id = %(task_instance_task_id)s AND task_instance.run_id = %(task_instance_run_id)s AND task_instance.map_index = %(task_instance_map_index)s] [parameters: {'map_index': 0, 'updated_at': datetime.datetime(2023, 5, 19, 18, 11, 8, 90512, tzinfo=Timezone('UTC')), 'task_instance_dag_id': 'bash_simple', 'task_instance_task_id': 'get_command', 'task_instance_run_id': 'manual__2023-05-18T13:54:01.345016+00:00', 'task_instance_map_index': -1}] (Background on this error at: http://sqlalche.me/e/14/gkpj) (Background on this error at: http://sqlalche.me/e/14/7s2a) [2023-05-19T23:41:08.572+0530] {scheduler_job_runner.py:865} INFO - Exited execute loop Traceback (most recent call last): File "/home/karthikeyan/stuff/python/airflow/.env/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/__main__.py", line 48, in main args.func(args) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/cli/cli_config.py", line 51, in command return func(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/cli.py", line 112, in wrapper return f(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/cli/commands/scheduler_command.py", line 77, in scheduler _run_scheduler_job(job_runner, skip_serve_logs=args.skip_serve_logs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/cli/commands/scheduler_command.py", line 42, in _run_scheduler_job run_job(job=job_runner.job, execute_callable=job_runner._execute) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/session.py", line 76, in wrapper return func(*args, session=session, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/job.py", line 284, in run_job return execute_job(job, execute_callable=execute_callable) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/job.py", line 313, in execute_job ret = execute_callable() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 836, in _execute self._run_scheduler_loop() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 970, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 1052, in _do_scheduling callback_tuples = self._schedule_all_dag_runs(guard, dag_runs, session) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/retries.py", line 90, in wrapped_function for attempt in run_with_db_retries(max_retries=retries, logger=logger, **retry_kwargs): File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __iter__ do = self.iter(retry_state=retry_state) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/tenacity/__init__.py", line 349, in iter return fut.result() File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/utils/retries.py", line 99, in wrapped_function return func(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/airflow/jobs/scheduler_job_runner.py", line 1347, in _schedule_all_dag_runs callback_tuples = [(run, self._schedule_dag_run(run, session=session)) for run in dag_runs] File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2811, in __iter__ return self._iter().__iter__() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2818, in _iter result = self.session.execute( File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1669, in execute conn = self._connection_for_bind(bind, close_with_result=True) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1519, in _connection_for_bind return self._transaction._connection_for_bind( File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 721, in _connection_for_bind self._assert_active() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/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.ForeignKeyViolation) update or delete on table "task_instance" violates foreign key constraint "task_map_task_instance_fkey" on table "task_map" DETAIL: Key (dag_id, task_id, run_id, map_index)=(bash_simple, get_command, manual__2023-05-18T13:54:01.345016+00:00, -1) is still referenced from table "task_map". [SQL: UPDATE task_instance SET map_index=%(map_index)s, updated_at=%(updated_at)s WHERE task_instance.dag_id = %(task_instance_dag_id)s AND task_instance.task_id = %(task_instance_task_id)s AND task_instance.run_id = %(task_instance_run_id)s AND task_instance.map_index = %(task_instance_map_index)s] [parameters: {'map_index': 0, 'updated_at': datetime.datetime(2023, 5, 19, 18, 11, 8, 90512, tzinfo=Timezone('UTC')), 'task_instance_dag_id': 'bash_simple', 'task_instance_task_id': 'get_command', 'task_instance_run_id': 'manual__2023-05-18T13:54:01.345016+00:00', 'task_instance_map_index': -1}] (Background on this error at: http://sqlalche.me/e/14/gkpj) (Background on this error at: http://sqlalche.me/e/14/7s2a) ``` ### What you think should happen instead _No response_ ### How to reproduce 1. Create the dag with `command = get_command(1, 1)` and trigger a dagrun waiting for it to complete 2. Now change this to `command = get_command.partial(arg1=[1]).expand(arg2=[1, 2, 3, 4])` so that the task is now mapped. 3. Clear the existing task that causes the scheduler to crash. ```python import datetime, time from airflow.operators.bash import BashOperator from airflow import DAG from airflow.decorators import task with DAG( dag_id="bash_simple", start_date=datetime.datetime(2022, 1, 1), schedule=None, catchup=False, ) as dag: @task def get_command(arg1, arg2): for i in range(10): time.sleep(1) print(i) return ["echo hello"] command = get_command(1, 1) # command = get_command.partial(arg1=[1]).expand(arg2=[1, 2, 3, 4]) t1 = BashOperator.partial(task_id="bash").expand(bash_command=command) if __name__ == "__main__": dag.test() ``` ### 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? - [ ] 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/31431

https://github.com/apache/airflow/pull/31445

adf0cae48ad4e87612c00fe9facffca9b5728e7d

f6bb4746efbc6a94fa17b6c77b31d9fb17305ffc

python

closed

apache/airflow

https://github.com/apache/airflow

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

allow other states than success/failed in tasks by REST API

### Apache Airflow version main (development) ### What happened see also: https://github.com/apache/airflow/issues/25463 From the conversation there, it sounds like it's intended to be possible to set a task to "skipped" via REST API, but it's not. Instead the next best thing we have is marking as success & adding a note. ### What you think should happen instead I see no reason for users to not just be able to set tasks to "skipped". I could imagine reasons to avoid "queued", and some other more "internal" states, but skipped makes perfect sense to me for - tasks that failed and got fixed externally - tasks that failed but are now irrelevant because of a newer run in case you still want to be able to see those in the future (actually, a custom state would be even better) ### How to reproduce ```python # task is just a dictionary with the right values for below r = requests.patch( f"{base_url}/api/v1/dags/{task['dag_id']}/dagRuns/{task['dag_run_id']}/taskInstances/{task['task_id']}", json={ "new_state": "skipped", }, headers={"Authorization": token}, ) ``` -> r.json() gives `{'detail': "'skipped' is not one of ['success', 'failed'] - 'new_state'", 'status': 400, 'title': 'Bad Request', 'type': 'http://apache-airflow-docs.s3-website.eu-central-1.amazonaws.com/docs/apache-airflow/latest/stable-rest-api-ref.html#section/Errors/BadRequest'}` ### Operating System / ### Versions of Apache Airflow Providers / ### Deployment Astronomer ### Deployment details / ### 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/31420

https://github.com/apache/airflow/pull/31421

233663046d5210359ce9f4db2fe3db4f5c38f6ee

fba6f86ed7e59c166d0cf7717f1734ae30ba4d9c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/base.py", "tests/sensors/test_base.py"]

ZeroDivisionError in BaseSensorOperator with `exponential_backoff=True` and `poke_interval=1`

### Apache Airflow version 2.6.1 ### What happened Sensor is fired with an exception ZeroDivisionError, if set up `mode="reschedule"`, `exponential_backoff=True` and `poke_interval=1` ``` ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/sensors/base.py", line 244, in execute next_poke_interval = self._get_next_poke_interval(started_at, run_duration, try_number) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/sensors/base.py", line 274, in _get_next_poke_interval modded_hash = min_backoff + run_hash % min_backoff ZeroDivisionError: integer division or modulo by zero ``` ### What you think should happen instead Throw an human-readable exception about corner values for `poke_interval` or allow to set up this value less then 2 (if it's `>=` 2 - rescheduling works fine.) ### How to reproduce ``` from airflow.sensors.base import BaseSensorOperator from airflow import DAG import datetime class TestSensor(BaseSensorOperator): def poke(self, context): return False with DAG("test_dag", start_date=datetime.datetime.today(), schedule=None): sensor = TestSensor(task_id="sensor", mode="reschedule", poke_interval=1, exponential_backoff=True, max_wait=5) ``` ### Operating System Debian GNU/Linux 10 (buster) ### 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/31409

https://github.com/apache/airflow/pull/31412

ba220b091c9fe9ba530533a71e88a9f5ca35d42d

a98621f4facabc207b4d6b6968e6863845e1f90f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job_runner.py"]

Future DagRun rarely triggered by Race Condition when max_active_runs has reached its upper limit

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened The scheduler rarely triggers a DagRun to be executed in the future. Here are the conditions as I understand them. - max_active_runs is set and upper limit is reached - The preceding DagRun completes very slightly earlier than the following DagRun Details in "Anything else". ### What you think should happen instead DagRun should wait until scheduled ### How to reproduce I have confirmed reproduction in Airflow 2.2.2 with the following code. I reproduced it in my environment after running it for about half a day. ``` python import copy import logging import time from datetime import datetime, timedelta import pendulum from airflow import DAG, AirflowException from airflow.sensors.python import PythonSensor from airflow.utils import timezone logger = logging.getLogger(__name__) # very small min_file_process_interval may help to reproduce more. e.g. min_file_process_interval=3 def create_dag(interval): with DAG( dag_id=f"example_reproduce_{interval:0>2}", schedule_interval=f"*/{interval} * * * *", start_date=datetime(2021, 1, 1), catchup=False, max_active_runs=2, tags=["example_race_condition"], ) as dag: target_s = 10 def raise_if_future(context): now = timezone.utcnow() - timedelta(seconds=30) if context["data_interval_start"] > now: raise AirflowException("DagRun supposed to be triggered in the future triggered") def wait_sync(): now_dt = pendulum.now() if now_dt.minute % (interval * 2) == 0: # wait until target time to synchronize end time with the preceding job target_dt = copy.copy(now_dt).replace(second=target_s + 2) wait_sec = (target_dt - now_dt).total_seconds() logger.info(f"sleep {now_dt} -> {target_dt} in {wait_sec} seconds") if wait_sec > 0: time.sleep(wait_sec) return True PythonSensor( task_id="t2", python_callable=wait_sync, # To avoid getting stuck in SequentialExecutor, try to re-poke after the next job starts poke_interval=interval * 60 + target_s, mode="reschedule", pre_execute=raise_if_future, ) return dag for i in [1, 2]: globals()[i] = create_dag(i) ``` ### Operating System Amazon Linux 2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details MWAA 2.2.2 ### Anything else The assumed flow and the associated actual query logs for the case max_active_runs=2 are shown below. **The assumed flow** 1. The first DagRun (DR1) starts 1. The subsequent DagRun (DR2) starts 1. DR2 completes; The scheduler set `next_dagrun_create_after=null` if max_active_runs is exceeded - https://github.com/apache/airflow/blob/2.2.2/airflow/jobs/scheduler_job.py#L931 1. DR1 completes; The scheduler calls dag_model.calculate_dagrun_date_fields() in SchedulerJobRunner._schedule_dag_run(). The session is NOT committed yet - note: the result of `calculate_dagrun_date_fields` is the old DR1-based value from `dag.get_run_data_interval(DR"2")`. - https://github.com/apache/airflow/blob/2.2.2/airflow/jobs/scheduler_job.py#L1017 1. DagFileProcessorProcess modifies next_dagrun_create_after - note: the dag record fetched in step 4 are not locked, so the `Processor` can select it and update it. - https://github.com/apache/airflow/blob/2.2.2/airflow/dag_processing/processor.py#L646 1. The scheduler reflects the calculation result of DR1 to DB by `guard.commit()` - note: Only the `next_dagrun_create_after` column set to null in step 2 is updated because sqlalchemy only updates the difference between the record retrieved in step 4 and the calculation result - https://github.com/apache/airflow/blob/2.2.2/airflow/jobs/scheduler_job.py#L795 1. The scheduler triggers a future DagRun because the current time satisfies next_dagrun_create_after updated in step 6 **The associated query log** ``` sql bb55c5b0bdce: /# grep "future_dagrun_00" /var/lib/postgresql/data/log/postgresql-2023-03-08_210056.log | grep "next_dagrun" 2023-03-08 22: 00: 01.678 UTC[57378] LOG: statement: UPDATE dag SET next_dagrun_create_after = NULL WHERE dag.dag_id = 'future_dagrun_0' # set in step 3 2023-03-08 22: 00: 08.162 UTC[57472] LOG: statement: UPDATE dag SET last_parsed_time = '2023-03-08T22:00:07.683786+00:00':: timestamptz, next_dagrun = '2023-03-08T22:00:00+00:00':: timestamptz, next_dagrun_data_interval_start = '2023-03-08T22:00:00+00:00':: timestamptz, next_dagrun_data_interval_end = '2023-03-08T23:00:00+00:00':: timestamptz, next_dagrun_create_after = '2023-03-08T23:00:00+00:00'::timestamptz WHERE dag.dag_id = 'future_dagrun_00' # set in step 5 2023-03-08 22: 00: 09.137 UTC[57475] LOG: statement: UPDATE dag SET next_dagrun_create_after = '2023-03-08T22:00:00+00:00'::timestamptz WHERE dag.dag_id = 'future_dagrun_00' # set in step 6 2023-03-08 22: 00: 10.418 UTC[57479] LOG: statement: UPDATE dag SET next_dagrun = '2023-03-08T23:00:00+00:00':: timestamptz, next_dagrun_data_interval_start = '2023-03-08T23:00:00+00:00':: timestamptz, next_dagrun_data_interval_end = '2023-03-09T00:00:00+00:00':: timestamptz, next_dagrun_create_after = '2023-03-09T00:00:00+00:00'::timestamptz WHERE dag.dag_id = 'future_dagrun_00' # set in step 7 ``` From what I've read of the relevant code in the latest v2.6.1, I believe the problem continues. ### 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/31407

https://github.com/apache/airflow/pull/31414

e43206eb2e055a78814fcff7e8c35c6fd9c11e85

b53e2aeefc1714d306f93e58d211ad9d52356470

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_params_ui_tutorial.py", "airflow/www/templates/airflow/trigger.html"]

Trigger UI Form Dropdowns with enums do not set default correct

### Apache Airflow version 2.6.1 ### What happened When playing around with the form features as interoduced in AIP-50 and using the select list option via `enum` I realized that the default value will not be correctly picked when the form is loaded. Instead the first value always will be pre-selected. ### What you think should happen instead Default value should be respected ### How to reproduce Modify the `airflow/example_dags/example_params_ui_tutorial.py:68` and change the default to some other value. Load the form and see that `value 1` is still displayed on form load. ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers not relevant ### Deployment Official Apache Airflow Helm Chart ### Deployment details not relevant ### Anything else Workaround: Make your default currently top of the list. ### 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/31399

https://github.com/apache/airflow/pull/31400

3bc0e3296abc9601dcaf7d77835e80e5fea43def

58aab1118a95ef63ba00784760fd13730dd46501

python

closed

apache/airflow

https://github.com/apache/airflow

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

GKEStartPodOperator cannot connect to Private IP after upgrade to 2.6.x

### Apache Airflow version 2.6.1 ### What happened After upgrading to 2.6.1, GKEStartPodOperator stopped creating pods. According with release notes we created a specific gcp connection. But connection defaults to GKE Public endpoint (in error message masked as XX.XX.XX.XX) instead of private IP which is best since our cluster do not have public internet access. [2023-05-17T07:02:33.834+0000] {connectionpool.py:812} WARNING - Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f0e47049ba0>, 'Connection to XX.XX.XX.XX timed out. (connect timeout=None)')': /api/v1/namespaces/airflow/pods?labelSelector=dag_id%3Dmytask%2Ckubernetes_pod_operator%3DTrue%2Crun_id%3Dscheduled__2023-05-16T0700000000-8fb0e9fa9%2Ctask_id%3Dmytask%2Calready_checked%21%3DTrue%2C%21airflow-sa Seems like with this change "use_private_ip" has been deprecated, what would be the workaround in this case then to connect using private endpoint? Also doc has not been updated to reflect this change in behaviour: https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/kubernetes_engine.html#using-with-private-cluster ### What you think should happen instead There should still be an option to connect using previous method with option "--private-ip" so API calls to Kubernetes call the private endpoint of GKE Cluster. ### How to reproduce 1. Create DAG file with GKEStartPodOperator. 2. Deploy said DAG in an environment with no access tu public internet. ### Operating System cos_coaintainerd ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==5.2.2 apache-airflow-providers-google==8.11.0 ### 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/31387

https://github.com/apache/airflow/pull/31391

45b6cfa138ae23e39802b493075bd5b7531ccdae

c082aec089405ed0399cfee548011b0520be0011

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/utils/run_utils.py"]

Breeze asset compilation causing OOM on dev-mode

### Apache Airflow version main (development) ### What happened asset compilation background thread is not killed when running `stop_airflow` or `breeze stop`. Webpack process takes a lot of memory, each `start-airflow` starts 4-5 of them. After a few breeze start, we end up with 15+ webpack background processes that takes more than 20G RAM ### What you think should happen instead `run_compile_www_assets` should stop when running `stop_airflow` from tmux. It looks like it spawn a `compile-www-assets-dev` pre-commit in a subprocess, that doesn't get killed when stopping breeze ### How to reproduce ``` breeze start-airflow --backend postgres --python 3.8 --dev-mode # Wait for tmux session to start breeze_stop breeze start-airflow --backend postgres --python 3.8 --dev-mode # Wait for tmux session to start breeze_stop # do a couple more if needed ``` Open tmux and monitor your memory, and specifically webpack processes. ### Operating System Ubuntu 20.04.6 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/31384

https://github.com/apache/airflow/pull/31403

c63b7774cdba29394ec746b381f45e816dcb0830

ac00547512f33b1222d699c7857108360d99b233

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/dags.html"]

The `Next Run` column name and tooltip is misleading

### Description > Expected date/time of the next DAG Run, or for dataset triggered DAGs, how many datasets have been updated since the last DAG Run "Expected date/time of the next DAG Run" to me sounds like Run After. Should the tooltip indicate something along the lines of "start interval of the next dagrun" or maybe the header Next Run is outdated? Something like "Next Data Interval"? On the same vein, "Last Run" is equally as confusing. The header can be "Last Data Interval" in additional to a tool tip that describe it is the data interval start of the last dagrun. ### Use case/motivation Users confused "Next Run" as when the next dagrun will be queued and ran and does not interpret it as the next dagrun's data interval start. ### 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/31365

https://github.com/apache/airflow/pull/31467

f1d484c46c18a83e0b8bc010044126dafe4467bc

7db42fe6655c28330e80b8a062ef3e07968d6e76

python

closed

apache/airflow

https://github.com/apache/airflow

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

Changing task from unmapped to mapped task with task instance note and task reschedule

### Apache Airflow version main (development) ### What happened Changing a non-mapped task with task instance note and task reschedule to a mapped task crashes scheduler when the task is cleared for rerun. Related commit where a similar fix was done. commit a770edfac493f3972c10a43e45bcd0e7cfaea65f Author: Ephraim Anierobi <[email protected]> Date: Mon Feb 20 20:45:25 2023 +0100 Fix Scheduler crash when clear a previous run of a normal task that is now a mapped task (#29645) The fix was to clear the db references of the taskinstances in XCom, RenderedTaskInstanceFields and TaskFail. That way, we are able to run the mapped tasks ### What you think should happen instead _No response_ ### How to reproduce 1. Create below dag file with BashOperator non-mapped. 2. Schedule a dag run and wait for it to finish. 3. Add a task instance note to bash operator. 4. Change t1 to ` t1 = BashOperator.partial(task_id="bash").expand(bash_command=command)` and return `["echo hello"]` from get_command. 5. Restart the scheduler and clear the task. 6. scheduler crashes on trying to use map_index though foreign key reference exists to task instance note and task reschedule. ```python import datetime from airflow.operators.bash import BashOperator from airflow import DAG from airflow.decorators import task with DAG(dag_id="bash_simple", start_date=datetime.datetime(2022, 1, 1), schedule=None, catchup=False) as dag: @task def get_command(arg1, arg2): return "echo hello" command = get_command(1, 1) t1 = BashOperator(task_id="bash", bash_command=command) if __name__ == '__main__': dag.test() ``` ### 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/31351

https://github.com/apache/airflow/pull/31352

b1ea3f32f9284c6f53bab343bdf79ab3081276a8

f82246abe9491a49701abdb647be001d95db7e9f

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSHook support for cacheControl

### Description When a file is uploaded to GCS, [by default](https://cloud.google.com/storage/docs/metadata#cache-control), public files will get `Cache-Control: public, max-age=3600`. I've tried setting `cors` for the whole bucket (didn't work) and setting `Cache-Control` on individual file (disappears on file re-upload from airflow) Setting `metadata` for GCSHook is for different field (can't be used to set cache-control) ### Use case/motivation Allow GCSHook to set cache control rather than overriding the `upload` function ### 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/31337

https://github.com/apache/airflow/pull/31338

ba3665f76a2205bad4553ba00537026a1346e9ae

233663046d5210359ce9f4db2fe3db4f5c38f6ee

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/triggers/pod.py", "tests/providers/cncf/kubernetes/triggers/test_pod.py", "tests/providers/google/cloud/triggers/test_kubernetes_engine.py"]

KPO deferable "random" false fail

### Apache Airflow version 2.6.1 ### What happened With the KPO and only in deferrable I have "random" false fail the dag ```python from pendulum import today from airflow import DAG from airflow.providers.cncf.kubernetes.operators.pod import KubernetesPodOperator dag = DAG( dag_id="kubernetes_dag", schedule_interval="0 0 * * *", start_date=today("UTC").add(days=-1) ) with dag: cmd = "echo toto && sleep 22 && echo finish" KubernetesPodOperator.partial( task_id="task-one", namespace="default", kubernetes_conn_id="kubernetes_default", config_file="/opt/airflow/include/.kube/config", # bug of deferrable corrected in 6.2.0 name="airflow-test-pod", image="alpine:3.16.2", cmds=["sh", "-c", cmd], is_delete_operator_pod=True, deferrable=True, get_logs=True, ).expand(env_vars=[{"a": "a"} for _ in range(8)]) ```  the log of the task in error : [dag_id=kubernetes_dag_run_id=scheduled__2023-05-16T00_00_00+00_00_task_id=task-one_map_index=2_attempt=1.log](https://github.com/apache/airflow/files/11492973/dag_id.kubernetes_dag_run_id.scheduled__2023-05-16T00_00_00%2B00_00_task_id.task-one_map_index.2_attempt.1.log) ### What you think should happen instead KPO should not fail ( in deferable ) if the container succesfully run in K8S ### How to reproduce If I remove the **sleep 22** from the cmd then I do not see any more random task fails ### Operating System ubuntu 22.04 ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==6.1.0 ### 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/31335

https://github.com/apache/airflow/pull/31348

57b7ba16a3d860268f03cd2619e5d029c7994013

8f5de83ee68c28100efc085add40ae4702bc3de1

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/files/pod-template-file.kubernetes-helm-yaml", "tests/charts/airflow_aux/test_pod_template_file.py"]

Worker pod template file doesn't have option to add priorityClassName

### Apache Airflow version 2.6.0 ### What happened Worker pod template file doesn't have an option to add priorityClassName. ### What you think should happen instead Airflow workers deployment however has the option to add it via the override airflow.workers.priorityClassName . We should reuse this for the worker pod template file too. ### How to reproduce Trying to add a priorityClassName for airflow worker pod doesnt work. Unless we override the whole worker pod template with our own. But that is not preferrable as we will need to duplicate a lot of the existing template file. ### Operating System Rhel8 ### 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/31311

https://github.com/apache/airflow/pull/31328

fbb095605ab009869ef021535c16b62a3d18a562

2c9ce803d744949674e4ec9ac88f73ad0a361399

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/administration-and-deployment/logging-monitoring/logging-tasks.rst"]

Outdated 'airflow info' output in Logging for Tasks page

### What do you see as an issue? https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/logging-tasks.html#troubleshooting The referenced `airflow info` format is very outdated. ### Solving the problem Current output format is something like this: ``` Apache Airflow version | 2.7.0.dev0 executor | LocalExecutor task_logging_handler | airflow.utils.log.file_task_handler.FileTaskHandler sql_alchemy_conn | postgresql+psycopg2://postgres:airflow@postgres/airflow dags_folder | /files/dags plugins_folder | /root/airflow/plugins base_log_folder | /root/airflow/logs remote_base_log_folder | System info OS | Linux architecture | arm uname | uname_result(system='Linux', node='fe54afd888cd', release='5.15.68-0-virt', version='#1-Alpine SMP Fri, 16 Sep | 2022 06:29:31 +0000', machine='aarch64', processor='') locale | ('en_US', 'UTF-8') python_version | 3.7.16 (default, May 3 2023, 09:44:48) [GCC 10.2.1 20210110] python_location | /usr/local/bin/python Tools info git | git version 2.30.2 ssh | OpenSSH_8.4p1 Debian-5+deb11u1, OpenSSL 1.1.1n 15 Mar 2022 kubectl | NOT AVAILABLE gcloud | NOT AVAILABLE cloud_sql_proxy | NOT AVAILABLE mysql | mysql Ver 15.1 Distrib 10.5.19-MariaDB, for debian-linux-gnu (aarch64) using EditLine wrapper sqlite3 | 3.34.1 2021-01-20 14:10:07 10e20c0b43500cfb9bbc0eaa061c57514f715d87238f4d835880cd846b9ealt1 psql | psql (PostgreSQL) 15.2 (Debian 15.2-1.pgdg110+1) Paths info airflow_home | /root/airflow system_path | /files/bin/:/opt/airflow/scripts/in_container/bin/:/root/.local/bin:/usr/local/bin:/usr/local/sbin:/usr/local/bin: | /usr/sbin:/usr/bin:/sbin:/bin:/opt/airflow python_path | /usr/local/bin:/opt/airflow:/usr/local/lib/python37.zip:/usr/local/lib/python3.7:/usr/local/lib/python3.7/lib-dynl | oad:/usr/local/lib/python3.7/site-packages:/files/dags:/root/airflow/config:/root/airflow/plugins airflow_on_path | True Providers info [too long to include] ``` ### 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/31304

https://github.com/apache/airflow/pull/31336

6d184d3a589b988c306aa3614e0f83e514b3f526

fc4f37b105ca0f03de7cc49ab4f00751287ae145

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_config.py"]

airflow dags list-jobs missing state metavar and choices keyword arguments

### What do you see as an issue? The `--state` CLI flag on `airflow dags list-jobs` does not tell the user what arguments it can take. https://github.com/apache/airflow/blob/1bd538be8c5b134643a6c5eddd06f70e6f0db2e7/airflow/cli/cli_config.py#L280 It probably needs some keyword args similar to the following: ```python metavar="(table, json, yaml, plain)", choices=("table", "json", "yaml", "plain"), ``` ### Solving the problem The problem can be solved by adding those keyword arguments so that the user gets a suggestion for what state arguments can be passed in. ### Anything else Any suggestions on what can be a valid state would be much appreciated. Otherwise, I'll find some time to read through the code and/or docs and figure it out. ### 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/31303

https://github.com/apache/airflow/pull/31308

fc4f37b105ca0f03de7cc49ab4f00751287ae145

8e296a09fc5c49188a129356caca8c3ea5eee000

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/discord/notifications/__init__.py", "airflow/providers/discord/notifications/discord.py", "airflow/providers/discord/provider.yaml", "tests/providers/discord/notifications/__init__.py", "tests/providers/discord/notifications/test_discord.py"]

Discord notification

### Description The new [Slack notification](https://airflow.apache.org/docs/apache-airflow-providers-slack/stable/notifications/slack_notifier_howto_guide.html) feature allows users to send messages to a slack channel using the various [on_*_callbacks](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/callbacks.html) at both the DAG level and Task level. However, this solution needs a little more implementation ([Webhook](https://airflow.apache.org/docs/apache-airflow-providers-discord/stable/_api/airflow/providers/discord/hooks/discord_webhook/index.html)) to perform notifications on Discord. ### Use case/motivation Send Task/DAG status or other messages as notifications to a discord channel. ### 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/31238

https://github.com/apache/airflow/pull/31273

3689cee485215651bdb5ef434f24ab8774995a37

bdfebad5c9491234a78453856bd8c3baac98f75e

python

closed

apache/airflow

https://github.com/apache/airflow

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

The @task.branch inside the dags.html seems to be incorrect

### What do you see as an issue? In the documentation page [https://airflow.apache.org/docs/apache-airflow/2.6.0/core-concepts/dags.html#branching)](https://airflow.apache.org/docs/apache-airflow/2.6.0/core-concepts/dags.html#branching), there is an incorrect usage of the @task.branch decorator. ``` python @task.branch(task_id="branch_task") def branch_func(ti): xcom_value = int(ti.xcom_pull(task_ids="start_task")) if xcom_value >= 5: return "continue_task" elif xcom_value >= 3: return "stop_task" else: return None branch_op = branch_func() ``` This code snippet is incorrect as it attempts to initialize branch_func without providing the required parameter. ### Solving the problem The correct version maybe is like that ```python def branch_func(**kwargs): ti: TaskInstance = kwargs["ti"] ``` ### 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/31236

https://github.com/apache/airflow/pull/31265

d6051fd10a0949264098af23ce74c76129cfbcf4

d59b0533e18c7cf0ff17f8af50731d700a2e4b4d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/jobs/job.py", "airflow/jobs/scheduler_job_runner.py", "newsfragments/31277.significant.rst", "tests/jobs/test_base_job.py"]

Constant "The scheduler does not appear to be running" warning on the UI following 2.6.0 upgrade

### Apache Airflow version 2.6.0 ### What happened Ever since we upgraded to Airflow 2.6.0 from 2.5.2, we have seen that there is a warning stating "The scheduler does not appear to be running" intermittently. This warning goes away by simply refreshing the page. And this conforms with our findings that the scheduler has not been down at all, at any point. By calling the /health point constantly, we can get it to show an "unhealthy" status: These are just approx. 6 seconds apart: ``` {"metadatabase": {"status": "healthy"}, "scheduler": {"latest_scheduler_heartbeat": "2023-05-11T07:42:36.857007+00:00", "status": "healthy"}} {"metadatabase": {"status": "healthy"}, "scheduler": {"latest_scheduler_heartbeat": "2023-05-11T07:42:42.409344+00:00", "status": "unhealthy"}} ``` This causes no operational issues, but it is misleading for end-users. What could be causing this? ### What you think should happen instead The warning should not be shown unless the last heartbeat was at least 30 sec earlier (default config). ### How to reproduce There are no concrete steps to reproduce it, but the warning appears in the UI after a few seconds of browsing around, or simply refresh the /health endpoint constantly. ### Operating System Debian GNU/Linux 11 ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==6.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-docker==3.6.0 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.0.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==6.0.0 apache-airflow-providers-microsoft-mssql==3.3.2 apache-airflow-providers-microsoft-psrp==2.2.0 apache-airflow-providers-microsoft-winrm==3.0.0 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-oracle==3.0.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.5 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.6.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deployed on AKS with helm ### Anything else None more than in the description above. ### 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/31200

https://github.com/apache/airflow/pull/31277

3193857376bc2c8cd2eb133017be1e8cbcaa8405

f366d955cd3be551c96ad7f794e0b8525900d13d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/FilterTasks.tsx", "airflow/www/static/js/dag/details/dagRun/ClearRun.tsx", "airflow/www/static/js/dag/details/dagRun/MarkRunAs.tsx", "airflow/www/static/js/dag/details/index.tsx", "airflow/www/static/js/dag/details/taskInstance/taskActions/ClearInstance.tsx"]

Problems after redesign grid view

### Apache Airflow version 2.6.0 ### What happened The changes in #30373 have had some unintended consequences. - The clear task button can now go of screen if the dag / task name is long enough. This is rather unfortunate since it is by far the most important button to fix issues (hence the reason it is taking up a lot of real estate) - Above issue is exacerbated by the fact that the task name can also push the grid off screen as well. I now have dags where I can see grid or the clear state button, but not both. - Downstream and Recursive don't seem to be selected default anymore for the clear task button. For some reason recursive is only selected for the latest task (maybe this was already the case?). The first two are an annoyance, the last one is preventing us from updating to 2.6.0 ### What you think should happen instead - Downstream should be selected by default again. (and possibly Recursive) - The clear task button should *always* be visible, no matter how implausibly small the viewport is. - Ideally long taks names should no longer hide the grid. ### How to reproduce To reproduce just make a dag with a long name with some tasks with long names and open the grid view on a small screen. ### Operating System unix ### 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/31186

https://github.com/apache/airflow/pull/31232

d1fe67184da26fb0bca2416e26f321747fa4aa5d

03b04a3d54c0c2aff9873f88de116fad49f90600

python

closed

apache/airflow

https://github.com/apache/airflow

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

SparkKubernetesSensor: 'None' has no attribute 'metadata'

### Apache Airflow version 2.6.0 ### What happened After upgrading to 2.6.0 version, pipelines with SparkKubernetesOperator -> SparkKubernetesSensor stopped working correctly. [this PR](https://github.com/apache/airflow/pull/29977) introduces some enhancement into Spark Kubernetes logic, now SparkKubernetesOperator receives the log from spark pods (which is great), but it doesn't monitor the status of a pod, which means if spark application fails - a task in Airflow finishes successfully. On the other hand, using previous pipelines (Operator + Sensor) is impossible now, cause SparkKubernetesSensor fails with `jinja2.exceptions.UndefinedError: 'None' has no attribute 'metadata'` as SparkKubernetesOperator is no longer pushing info to xcom. ### What you think should happen instead Old pipelines should be compatible with Airflow 2.6.0, even though the log would be retrieved in two places - operator and sensor. OR remove the sensor completely and implement all the functionality in the operator (log + status) ### How to reproduce Create a DAG with two operators ``` t1 = SparkKubernetesOperator( kubernetes_conn_id='common/kubernetes_default', task_id=f"task-submit", namespace="namespace", application_file="spark-applications/app.yaml", do_xcom_push=True, dag=dag, ) t2 = SparkKubernetesSensor( task_id=f"task-sensor", namespace="namespace", application_name=f"{{{{ task_instance.xcom_pull(task_ids='task-submit')['metadata']['name'] }}}}", dag=dag, attach_log=True, ) ``` ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-apache-spark==4.0.1 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==6.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-docker==3.6.0 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.0.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==6.0.0 apache-airflow-providers-microsoft-mssql==3.3.2 apache-airflow-providers-microsoft-psrp==2.2.0 apache-airflow-providers-microsoft-winrm==3.1.1 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-oracle==3.6.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.5 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.6.0 apache-airflow-providers-telegram==4.0.0 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/31183

https://github.com/apache/airflow/pull/31798

771362af4784f3d913d6c3d3b44c78269280a96e

6693bdd72d70989f4400b5807e2945d814a83b85

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/administration-and-deployment/listeners.rst"]

Plugin for listeners - on_dag_run_running hook ignored

### Apache Airflow version 2.6.0 ### What happened I created a plugin for custom listeners, the task level listeners works fine, but the dag level listeners are not triggered. The [docs](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/listeners.html) states that listeners defined in `airflow/listeners/spec` should be supported. ``` @hookimpl def on_task_instance_failed(previous_state: TaskInstanceState, task_instance: TaskInstance, session): """ This method is called when task state changes to FAILED. Through callback, parameters like previous_task_state, task_instance object can be accessed. This will give more information about current task_instance that has failed its dag_run, task and dag information. """ print("This works fine") @hookimpl def on_dag_run_failed(dag_run: DagRun, msg: str): """ This method is called when dag run state changes to FAILED. """ print("This is not called!") ``` ### What you think should happen instead The dag specs defined `airflow/listeners/spec/dagrun.py` should be working ### How to reproduce Create a plugin and add the two hooks into a listeners. ### 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/31180

https://github.com/apache/airflow/pull/32269

bc3b2d16d3563d5b9bccd283db3f9e290d1d823d

ab2c861dd8a96f22b0fda692368ce9b103175322

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg", "setup.py"]

Searching task instances by state doesn't work

### Apache Airflow version 2.6.0 ### What happened After specifying a state such as "Equal to" "failed", the search doesn't return anything but resetting the whole page (the specified filter is gone) https://github.com/apache/airflow/assets/14293802/5fb7f550-c09f-4040-963f-76dc0a2c1a53 ### What you think should happen instead _No response_ ### How to reproduce Go to "Browse" tab -> click "Task Instances" -> "Add Filter" -> "State" -> "Use anything (equal to, contains, etc)" -> Click "Search" ### Operating System Debian GNU/Linux 10 (buster) ### 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/31156

https://github.com/apache/airflow/pull/31203

d59b0533e18c7cf0ff17f8af50731d700a2e4b4d

1133035f7912fb2d2612c7cee5017ebf01f8ec9d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add support for standard SQL in `BigQueryGetDataOperator`

### Description Currently, the BigQueryGetDataOperator always utilizes legacy SQL when submitting jobs (set as the default by the BQ API). This approach may cause problems when using standard SQL features, such as names for projects, datasets, or tables that include hyphens (which is very common nowadays). We would like to make it configurable, so users can set a flag in the operator to enable the use of standard SQL instead. ### Use case/motivation When implementing #30887 to address #24460, I encountered some unusual errors, which were later determined to be related to the usage of hyphens in the GCP project ID name. ### Related issues - #24460 - #28522 (PR) adds this parameter to `BigQueryCheckOperator` ### 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/31109

https://github.com/apache/airflow/pull/31190

24532312b694242ba74644fdd43a487e93122235

d1fe67184da26fb0bca2416e26f321747fa4aa5d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/emr.py", "tests/providers/amazon/aws/operators/test_emr_serverless.py"]

Can't cancel EMR Serverless task

### Apache Airflow version 2.6.0 ### What happened When marking an EMR Serverless job as failed, the job continues to run. ### What you think should happen instead The job should be cancelled. Looking at the [EMR Serverless Operator](https://github.com/apache/airflow/blob/a6be96d92828a86e982b53646a9e2eeca00a5463/airflow/providers/amazon/aws/operators/emr.py#L939), I don't see an `on_kill` method, so assuming we just need to add that. I'm not sure how to handle the `EmrServerlessCreateApplicationOperator` operator, though - if the workflow has a corresponding `EmrServerlessDeleteApplicationOperator`, we'd probably want to delete the application if the job is cancelled. ### How to reproduce - Start an EMR Serverless DAG - Mark the job as failed in the Airflow UI - See that the EMR Serverless job continues to run ### Operating System n/a ### Versions of Apache Airflow Providers `apache-airflow-providers-amazon==8.0.0` ### Deployment Amazon (AWS) MWAA ### 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/31099

https://github.com/apache/airflow/pull/31169

761c0da723799c3c37d9eb2cadaa9c4fa256d13a

d6051fd10a0949264098af23ce74c76129cfbcf4

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile.ci", "scripts/docker/entrypoint_ci.sh"]

Latest Botocore breaks SQS tests

### Body Our tests are broken in main due to latest botocore failing SQS tests. Example here: https://github.com/apache/airflow/actions/runs/4887737387/jobs/8724954226 ``` E botocore.exceptions.ClientError: An error occurred (400) when calling the SendMessage operation: <ErrorResponse xmlns="http://queue.amazonaws.com/doc/2012-11-05/"><Error><Type>Sender</Type> <Code>AWS.SimpleQueueService.NonExistentQueue</Code><Message>The specified queue does not exist for this wsdl version.</Message><Detail/></Error> <RequestId>ETDUP0OoJOXmn0WS6yWmB0dOhgYtpdVJCVwFWA28lYLKLmGJLAGu</RequestId></ErrorResponse> ``` The problem seems to come from botocore not recognizing just added queue: ``` QUEUE_NAME = "test-queue" QUEUE_URL = f"https://{QUEUE_NAME}" ``` Even if we replace it with the full queue name that gets returned by the "create_queue" API call to `moto`, it still does not work with latest botocore: ``` QUEUE_URL = f"https://sqs.us-east-1.amazonaws.com/123456789012/{QUEUE_NAME}" ``` Which indicates this likely a real botocore issue. ## How to reproduce: 1. Get working venv with `[amazon]` extra of Airlow (or breeze). Should be (when constraints from main are used): ``` root@b0c430d9a328:/opt/airflow# pip list | grep botocore aiobotocore 2.5.0 botocore 1.29.76 ``` 3. `pip uninstall aiobotocore` 4. `pip install --upgrade botocore` ``` root@b0c430d9a328:/opt/airflow# pip list | grep botocore botocore 1.29.127 ``` 6. `pytest tests/providers/amazon/aws/sensors/test_sqs.py` Result: ``` ===== 4 failed, 7 passed in 2.43s === ``` ---------------------------------- Comparing it to "success case": When you run it breeze (with the current constrained botocore): ``` root@b0c430d9a328:/opt/airflow# pip list | grep botocore aiobotocore 2.5.0 botocore 1.29.76 ``` 1, `pytest tests/providers/amazon/aws/sensors/test_sqs.py` Result: ``` ============ 11 passed in 4.57s ======= ``` ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/31087

https://github.com/apache/airflow/pull/31103

41c87464428d8d31ba81444b3adf457bc968e11d

49cc213919a7e2a5d4bdc9f952681fa4ef7bf923

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/docker-stack/build.rst", "docs/docker-stack/docker-examples/extending/add-airflow-configuration/Dockerfile"]

Changing configuration as part of the custom airflow docker image

### What do you see as an issue? https://airflow.apache.org/docs/docker-stack/build.html This docs doesn't share information on how we can edit the airflow.cfg file for the airflow installed via docker. Adding this to docs, would give better idea about editing the configuration file ### Solving the problem Add more details in build your docker image for editing the airflow.cfg file ### 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/31084

https://github.com/apache/airflow/pull/31842

7a786de96ed178ff99aef93761d82d100b29bdf3

9cc72bbaec0d7d6041ecd53541a524a2f1e523d0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/operators/sql.py", "airflow/providers/common/sql/provider.yaml", "airflow/providers/databricks/operators/databricks_sql.py", "airflow/providers/databricks/provider.yaml", "generated/provider_dependencies.json", "tests/providers/databricks/operators/test_databricks_sql.py"]

SQLExecuteQueryOperator AttributeError exception when returning result to XCom

### Apache Airflow version 2.6.0 ### What happened I am using DatabricksSqlOperator which writes the result to a file. When the task finishes it writes all the data correctly to the file the throws the following exception: > [2023-05-05, 07:56:22 UTC] {taskinstance.py:1847} ERROR - Task failed with exception > Traceback (most recent call last): > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 73, in wrapper > return func(*args, **kwargs) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2377, in xcom_push > XCom.set( > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 73, in wrapper > return func(*args, **kwargs) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/xcom.py", line 237, in set > value = cls.serialize_value( > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/xcom.py", line 632, in serialize_value > return json.dumps(value, cls=XComEncoder).encode("UTF-8") > File "/usr/local/lib/python3.9/json/__init__.py", line 234, in dumps > return cls( > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/json.py", line 102, in encode > o = self.default(o) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/json.py", line 91, in default > return serialize(o) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 144, in serialize > return encode(classname, version, serialize(data, depth + 1)) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 123, in serialize > return [serialize(d, depth + 1) for d in o] > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 123, in <listcomp> > return [serialize(d, depth + 1) for d in o] > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 123, in serialize > return [serialize(d, depth + 1) for d in o] > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 123, in <listcomp> > return [serialize(d, depth + 1) for d in o] > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serde.py", line 132, in serialize > qn = qualname(o) > File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 47, in qualname > return f"{o.__module__}.{o.__name__}" > File "/home/airflow/.local/lib/python3.9/site-packages/databricks/sql/types.py", line 161, in __getattr__ > raise AttributeError(item) > AttributeError: __name__ I found that **SQLExecuteQueryOperator** always return the result(so pushing XCom) from its execute() method except when the parameter **do_xcom_push** is set to **False**. But if do_xcom_push is False then the method _process_output() is not executed and DatabricksSqlOperator wont write the results to a file. ### What you think should happen instead I am not sure if the problem should be fixed in DatabricksSqlOperator or in SQLExecuteQueryOperator. In any case setting do_xcom_push shouldn't automatically prevent the exevution of _process_output(): ``` if not self.do_xcom_push: return None if return_single_query_results(self.sql, self.return_last, self.split_statements): # For simplicity, we pass always list as input to _process_output, regardless if # single query results are going to be returned, and we return the first element # of the list in this case from the (always) list returned by _process_output return self._process_output([output], hook.descriptions)[-1] return self._process_output(output, hook.descriptions) ``` What happens now is - i have in the same time big result in a file AND in the XCom. ### How to reproduce I suspect that the actual Exception is related to writing the XCom to the meta database and it might not fail on other scenarios. ### Operating System Debian GNU/Linux 11 (bullseye) docker image ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==8.0.0 apache-airflow-providers-apache-spark==4.0.1 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==6.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-databricks==4.1.0 apache-airflow-providers-docker==3.6.0 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==10.0.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==6.0.0 apache-airflow-providers-microsoft-mssql==3.3.2 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-oracle==3.6.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-samba==4.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==7.2.0 apache-airflow-providers-snowflake==4.0.5 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.6.0 apache-airflow-providers-telegram==4.0.0 ### Deployment Docker-Compose ### Deployment details Using extended Airflow image, LocalExecutor, Postgres 13 meta db as container in the same stack. docker-compose version 1.29.2, build 5becea4c Docker version 23.0.5, build bc4487a ### 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/31080

https://github.com/apache/airflow/pull/31136

521dae534dd0b906e4dd9a7446c6bec3f9022ac3

edd7133a1336c9553d77ba13c83bc7f48d4c63f0

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

[BUG] apache.hive extra is referencing incorrect provider name

### Apache Airflow version 2.6.0 ### What happened When creating docker image with airflow 2.6.0 I receive the error: `ERROR: No matching distribution found for apache-airflow-providers-hive>=5.1.0; extra == "apache.hive"` After which, I see that the package name should be `apache-airflow-providers-apache-hive` and not `apache-airflow-providers-hive`. ### What you think should happen instead We should change this line to say `apache-airflow-providers-apache-hive` and not `apache-airflow-providers-hive`, this will reference a provider which exists. ### How to reproduce Build image for airflow 2.6.0 with the dependency `apache.hive`. Such as `pip3 install apache-airflow[apache.hive]==2.6.0 --constraint https://raw.githubusercontent.com/apache/airflow/constraints-2.6.0/constraints-3.8.txt`. ### Operating System ubuntu:22.04 ### Versions of Apache Airflow Providers Image does not build. ### 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/31067

https://github.com/apache/airflow/pull/31068

da61bc101eba0cdb17554f5b9ae44998bb0780d3

9e43d4aee3b86134b1b9a42f988fb9d3975dbaf7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/log/file_task_handler.py", "tests/providers/amazon/aws/log/test_s3_task_handler.py", "tests/utils/test_log_handlers.py"]

Logs no longer shown after task completed CeleryExecutor

### Apache Airflow version 2.6.0 ### What happened Stream logging works as long as the task is running. Once the task finishes, no logs are printed to the UI (only the hostname of the worker is printed) <img width="1657" alt="image" src="https://user-images.githubusercontent.com/16529101/236212701-aecf6cdc-4d87-4817-a685-0778b94d182b.png"> ### What you think should happen instead Expected to see the complete log of a task ### How to reproduce Start an airflow task. You should be able to see the logs coming in as a stream, once it finishes, the logs are gone ### Operating System CentOS 7 ### Versions of Apache Airflow Providers airflow-provider-great-expectations==0.1.5 apache-airflow==2.6.0 apache-airflow-providers-airbyte==3.1.0 apache-airflow-providers-apache-hive==4.0.1 apache-airflow-providers-apache-spark==3.0.0 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-common-sql==1.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-http==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-mysql==5.0.0 apache-airflow-providers-oracle==3.4.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-sqlite==3.3.2 ### Deployment Virtualenv installation ### Deployment details Celery with 4 workers nodes/VMs. Scheduler and Webserver on a different VM ### 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/31059

https://github.com/apache/airflow/pull/31101

10dda55e8b0fed72e725b369c17cb5dfb0d77409

672ee7f0e175dd7edb041218850d0cd556d62106

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/processor.py", "tests/dag_processing/test_processor.py"]

Packaged DAGs not getting loaded in Airflow 2.6.0 (ValueError: source code string cannot contain null bytes)

### Apache Airflow version 2.6.0 ### What happened I am trying to upgrade Airflow from version 2.3.1 to 2.6.0. I have a zip file with a few test DAGs which used to get loaded correctly in 2.3.1 but after the upgrade I get the following error in the scheduler logs. ``` Process ForkProcess-609: Traceback (most recent call last): File "/usr/local/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap self.run() File "/usr/local/lib/python3.7/multiprocessing/process.py", line 99, in run self._target(*self._args, **self._kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 259, in _run_processor_manager processor_manager.start() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 493, in start return self._run_parsing_loop() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 572, in _run_parsing_loop self.start_new_processes() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 1092, in start_new_processes processor.start() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/processor.py", line 196, in start for module in iter_airflow_imports(self.file_path): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/file.py", line 389, in iter_airflow_imports parsed = ast.parse(Path(file_path).read_bytes()) File "/usr/local/lib/python3.7/ast.py", line 35, in parse return compile(source, filename, mode, PyCF_ONLY_AST) ValueError: source code string cannot contain null bytes ``` Single .py files are getting loaded without issues. ### What you think should happen instead Packaged file should be parsed and the DAGs inside it should be available in the DagBag. ### How to reproduce - Setup Airflow 2.6.0 using the official Docker image and helm chart. - Create a folder and place the python file below inside it. - Create a packaged DAG using command `zip -r test_dags.zip ./*` from within the folder. - Place the `test_dags.zip` file in `/opt/airflow/dags` directory. ```python import time from datetime import timedelta from textwrap import dedent from airflow import DAG from airflow.operators.python_operator import PythonOperator from airflow.utils.dates import days_ago default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email': ['[email protected]'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=1), } def test_function(task_name): print(f"{task_name}: Test function invoked") print(f"{task_name}: Sleeping 10 seconds") time.sleep(10) print(f"{task_name}: Exiting") with DAG( 'airflow2_test_dag_1', default_args=default_args, description='DAG for testing airflow 2.0', schedule_interval=timedelta(days=1), start_date=days_ago(1) ) as dag: t1 = PythonOperator( task_id='first_task', python_callable=test_function, op_kwargs={"task_name": "first_task"}, dag=dag, ) t2 = PythonOperator( task_id='second_task', python_callable=test_function, op_kwargs={"task_name": "second_task"}, dag=dag, ) t3 = PythonOperator( task_id='third_task', python_callable=test_function, op_kwargs={"task_name": "third_task"}, dag=dag, queue='kubernetes' ) t1 >> t2 >> t3 ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==7.3.0 apache-airflow-providers-apache-hive==2.3.2 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==5.2.2 apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-docker==3.5.1 apache-airflow-providers-elasticsearch==4.4.0 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-google==6.8.0 apache-airflow-providers-grpc==3.1.0 apache-airflow-providers-hashicorp==2.2.0 apache-airflow-providers-http==4.2.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==5.2.1 apache-airflow-providers-mysql==4.0.2 apache-airflow-providers-odbc==3.2.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sendgrid==3.1.0 apache-airflow-providers-sftp==4.2.4 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-snowflake==4.0.4 apache-airflow-providers-sqlite==3.3.1 apache-airflow-providers-ssh==3.5.0 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else The issue only occurs with `2.6.0`. I used the `2.5.3` docker image with everything else remaining same and packaged DAGs loaded with no issues. ### 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/31039

https://github.com/apache/airflow/pull/31061

91e18bfc3e53002c191b33dbbfd017e152b23935

34b6230f3c7815b8ae7e99443e45a56921059d3f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/default_celery.py"]

Airflow doesn't recognize `rediss:...` url to point to a Redis broker

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow 2.5.3 Redis is attached using `rediss:...` url. While deploying the instance, it Airflow/Celery downgrades `rediss` to `redis` with the warning `[2023-05-02 18:38:30,377: WARNING/MainProcess] Secure redis scheme specified (rediss) with no ssl options, defaulting to insecure SSL behaviour.` Adding `AIRFLOW__CELERY__SSL_ACTIVE=True` as an environmental variable (the same as `ssl_active = true` in `airflow.cfg` file `[celery]` section) fails with the error `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.` <img width="1705" alt="Screenshot 2023-05-12 at 12 07 45 PM" src="https://github.com/apache/airflow/assets/94494788/b56cf054-d122-4baf-b6e9-75effe804731"> ### What you think should happen instead It seems that Airflow doesn't recognize `rediss:...` url to be related to Redis broker ### How to reproduce Airflow 2.5.3 Python 3.10.9 Redis 4.0.14 (url starts with `rediss:...`)  You need to add `AIRFLOW__CELERY__SSL_ACTIVE=True` as an environmental variable or `ssl_active = true` to `airflow.cfg` file `[celery]` section and deploy the instance  ### Operating System Ubuntu 22.04.2 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Heroku platform, heroku-22 stack, python 3.10.9 ### 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/31027

https://github.com/apache/airflow/pull/31028

d91861d3bdbde18c937978c878d137d6c758e2c6

471fdacd853a5bcb190e1ffc017a4e650097ed69

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/graph/Node.tsx", "airflow/www/static/js/dag/details/graph/utils.ts", "airflow/www/static/js/utils/graph.ts"]

New graph view renders incorrectly when prefix_group_id=false

### Apache Airflow version 2.6.0 ### What happened If a task_group in a dag has `prefix_group_id=false` in its config, the new graph won't render correctly. When the group is collapsed nothing is shown and there is an error in the console. When the group is expanded, the nodes will render but its edges become disconnected. As reported in https://github.com/apache/airflow/issues/29852#issuecomment-1531766479 This is because we use the prefix to determine where an edge is supposed to be rendered. We shouldn't make that assumption and actually iterate through the nodes to find where an edge belongs. ### What you think should happen instead It renders like any other task group ### How to reproduce Add `prefix_group_id=false` to a task group ### Operating System any ### 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/31025

https://github.com/apache/airflow/pull/32764

53c6305bd0a914738074821d5f5f233e3ed5bee5

3e467ba510d29e912d89115769726111b8bce891

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/trigger.js", "airflow/www/templates/airflow/trigger.html", "docs/apache-airflow/core-concepts/params.rst", "tests/www/views/test_views_trigger_dag.py"]

Exception when manually triggering dags via UI with `params` defined.

### Apache Airflow version 2.6.0 ### What happened When clicking the "Trigger DAG w/ config" in a DAG UI I receive a 500 "Oops" page when `params` are defined for the DAG. The Airflow webserver logs show this: ``` 2023-05-02T13:02:50 - [2023-05-02T12:02:50.249+0000] {app.py:1744} ERROR - Exception on /trigger [GET] 2023-05-02T13:02:50 - Traceback (most recent call last): 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 2529, in wsgi_app 2023-05-02T13:02:50 - response = self.full_dispatch_request() 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1825, in full_dispatch_request 2023-05-02T13:02:50 - rv = self.handle_user_exception(e) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1823, in full_dispatch_request 2023-05-02T13:02:50 - rv = self.dispatch_request() 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1799, in dispatch_request 2023-05-02T13:02:50 - return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/auth.py", line 47, in decorated 2023-05-02T13:02:50 - return func(*args, **kwargs) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/decorators.py", line 125, in wrapper 2023-05-02T13:02:50 - return f(*args, **kwargs) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 76, in wrapper 2023-05-02T13:02:50 - return func(*args, session=session, **kwargs) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/views.py", line 1967, in trigger 2023-05-02T13:02:50 - return self.render_template( 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/views.py", line 640, in render_template 2023-05-02T13:02:50 - return super().render_template( 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/baseviews.py", line 339, in render_template 2023-05-02T13:02:50 - return render_template( 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/templating.py", line 147, in render_template 2023-05-02T13:02:50 - return _render(app, template, context) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask/templating.py", line 130, in _render 2023-05-02T13:02:50 - rv = template.render(context) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/jinja2/environment.py", line 1301, in render 2023-05-02T13:02:50 - self.environment.handle_exception() 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/jinja2/environment.py", line 936, in handle_exception 2023-05-02T13:02:50 - raise rewrite_traceback_stack(source=source) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/templates/airflow/trigger.html", line 106, in top-level template code 2023-05-02T13:02:50 - <span class="help-block">{{ form_details.description }}</span> 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/templates/airflow/main.html", line 21, in top-level template code 2023-05-02T13:02:50 - {% from 'airflow/_messages.html' import show_message %} 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 2, in top-level template code 2023-05-02T13:02:50 - {% import 'appbuilder/baselib.html' as baselib %} 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/templates/appbuilder/init.html", line 42, in top-level template code 2023-05-02T13:02:50 - {% block body %} 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 19, in block 'body' 2023-05-02T13:02:50 - {% block content %} 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/templates/airflow/trigger.html", line 162, in block 'content' 2023-05-02T13:02:50 - {{ form_element(form_key, form_details) }} 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/jinja2/runtime.py", line 777, in _invoke 2023-05-02T13:02:50 - rv = self._func(*arguments) 2023-05-02T13:02:50 - File "/opt/airflow/.local/lib/python3.10/site-packages/airflow/www/templates/airflow/trigger.html", line 83, in template 2023-05-02T13:02:50 - {%- for txt in form_details.value -%} 2023-05-02T13:02:50 - TypeError: 'NoneType' object is not iterable ``` ### What you think should happen instead No error is shown (worked in 2.5.2) ### How to reproduce Create a DAG with the following config defined for parameters: ``` params={ "delete_actions": Param( False, description="Whether to delete actions after execution.", type="boolean", ), "dates": Param( None, description="An explicit list of date strings to run on.", type=["null", "array"], minItems=1, ), "start_date_inclusive": Param( None, description="An inclusive start-date used to generate a list of dates to run on.", type=["null", "string"], pattern="^[0-9]{4}[-/][0-9]{2}[-/][0-9]{2}$", ), "end_date_exclusive": Param( None, description="An exclusive end-date used to generate a list of dates to run on.", type=["null", "string"], pattern="^[0-9]{4}[-/][0-9]{2}[-/][0-9]{2}$", ), "actions_bucket_name": Param( None, description='An S3 bucket to read batch actions from. Set as "ACTIONS_BUCKET".', type=["null", "string"], ), "actions_path_prefix": Param( None, description='An S3 bucket to read batch actions from. Prefixes "ACTIONS_PATH".', type=["null", "string"], pattern="^.+/$", ), "sns_output_topic_name": Param( None, description='An SNS output topic ARN to set as "DATA_READY_TO_INDEX_OUTPUT_TOPIC."', type=["null", "string"], ), }, # required to convert params to their correct types render_template_as_native_obj=True, ``` Deploy the DAG, click the manual trigger button. ### Operating System Debian ### Versions of Apache Airflow Providers N/A ### Deployment Other Docker-based deployment ### Deployment details Amazon ECS Python version: 3.10.11 Airflow version: 2.6.0 (official docker image as base) ### Anything else Occurs every time. Does *NOT* occur when `params` are not defined on the DAG. ### 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/31014

https://github.com/apache/airflow/pull/31078

49cc213919a7e2a5d4bdc9f952681fa4ef7bf923

b8b18bd74b72edc4b40e91258fccc54cf3aff3c1

python

closed

apache/airflow

https://github.com/apache/airflow

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

Unable to remove DagRun and TaskInstance with note

### Apache Airflow version 2.6.0 ### What happened Hi, I'm unable to remove DagRun and TaskInstance when they have note attached. ### What you think should happen instead Should be able to remove DagRuns or TaskInstances with or without notes. Also note should be removed when parent entity is removed. ### How to reproduce 1. Create note in DagRun or TaskInstance 2. Try to remove the row that note has been added by clicking delete record icon. This will display alert in the UI `General Error <class 'AssertionError'>` 3. Select checkbox DagRun containing note, click `Actions` dropdown and select `Delete`. This won't display anything in the UI. ### Operating System OSX 12.6 ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==5.2.2 apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-http==4.2.0 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 Deployed using Postgresql 13.9 and sqlite 3 ### Anything else DagRun deletion Log ``` [2023-05-01T13:06:42.125+0700] {interface.py:790} ERROR - Delete record error: Dependency rule tried to blank-out primary key column 'dag_run_note.dag_run_id' on instance '<DagRunNote at 0x1125afa00>' Traceback (most recent call last): File "/opt/airflow/.venv/lib/python3.10/site-packages/flask_appbuilder/models/sqla/interface.py", line 775, in delete self.session.commit() File "<string>", line 2, in commit File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1451, in commit self._transaction.commit(_to_root=self.future) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 829, in commit self._prepare_impl() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl self.session.flush() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3446, in flush self._flush(objects) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3585, in _flush with util.safe_reraise(): File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3546, in _flush flush_context.execute() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute rec.execute(self) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 577, in execute self.dependency_processor.process_deletes(uow, states) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/dependency.py", line 552, in process_deletes self._synchronize( File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/dependency.py", line 610, in _synchronize sync.clear(dest, self.mapper, self.prop.synchronize_pairs) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/sync.py", line 86, in clear raise AssertionError( AssertionError: Dependency rule tried to blank-out primary key column 'dag_run_note.dag_run_id' on instance '<DagRunNote at 0x1125afa00>' ``` TaskInstance deletion Log ``` [2023-05-01T13:06:42.125+0700] {interface.py:790} ERROR - Delete record error: Dependency rule tried to blank-out primary key column 'task_instance_note.task_id' on instance '<TaskInstanceNote at 0x1126ba770>' Traceback (most recent call last): File "/opt/airflow/.venv/lib/python3.10/site-packages/flask_appbuilder/models/sqla/interface.py", line 775, in delete self.session.commit() File "<string>", line 2, in commit File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1451, in commit self._transaction.commit(_to_root=self.future) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 829, in commit self._prepare_impl() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl self.session.flush() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3446, in flush self._flush(objects) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3585, in _flush with util.safe_reraise(): File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 3546, in _flush flush_context.execute() File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute rec.execute(self) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/unitofwork.py", line 577, in execute self.dependency_processor.process_deletes(uow, states) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/dependency.py", line 552, in process_deletes self._synchronize( File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/dependency.py", line 610, in _synchronize sync.clear(dest, self.mapper, self.prop.synchronize_pairs) File "/opt/airflow/.venv/lib/python3.10/site-packages/sqlalchemy/orm/sync.py", line 86, in clear raise AssertionError( AssertionError: Dependency rule tried to blank-out primary key column 'task_instance_note.task_id' on instance '<TaskInstanceNote at 0x1126ba770>' ``` ### 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/30984

https://github.com/apache/airflow/pull/30987

ec7674f111177c41c02e5269ad336253ed9c28b4

0212b7c14c4ce6866d5da1ba9f25d3ecc5c2188f

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst"]

BREEZE: add troubleshooting section to cover ETIMEDOUT during start-airflow

### What do you see as an issue? BREEZE troubleshooting section does not have issue related to ETIMEOUT and potential fix when it happens: https://github.com/apache/airflow/blob/main/BREEZE.rst#troubleshooting ### Solving the problem BREEZE.rst can be improved by having ways to troubleshoot and fix the ETIMEOUT error when running `start-airflow`, which seemed to be one of the common problems when using BREEZE. ### 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/30947

https://github.com/apache/airflow/pull/30949

783aa9cecbf47b4d0e5509d1919f644b9689b6b3

bd542fdf51ad9550e5c4348f11e70b5a6c9adb48

python

closed

apache/airflow

https://github.com/apache/airflow

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

Tasks created using "dynamic task mapping" ignore the Task Group passed as argument

### Apache Airflow version main (development) ### What happened When creating a DAG with Task Groups and a Mapped Operator, if the Task Group is passed as argument to Mapped Operator's `partial` method it is ignored and the operator is not added to the group. ### What you think should happen instead The Mapped Operator should be added to the Task Group passed as an argument. ### How to reproduce Create a DAG with a source code like the following one ```python from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.empty import EmptyOperator from airflow.utils import timezone from airflow.utils.task_group import TaskGroup with DAG("dag", start_date=timezone.datetime(2016, 1, 1)) as dag: start = EmptyOperator(task_id="start") finish = EmptyOperator(task_id="finish") group = TaskGroup("test-group") commands = ["echo a", "echo b", "echo c"] mapped = BashOperator.partial(task_id="task_2", task_group=group).expand(bash_command=commands) start >> group >> finish # assert mapped.task_group == group ``` ### Operating System macOS 13.2.1 (22D68) ### 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/30932

https://github.com/apache/airflow/pull/30933

1d4b1410b027c667d4e2f51f488f98b166facf71

4ee2de1e38a85abb89f9f313a3424c7368e12d1a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/dag_endpoint.py", "tests/api_connexion/endpoints/test_dag_endpoint.py"]

REST API, order_by parameter in dags list is not taken into account

### Apache Airflow version 2.5.3 ### What happened It seems that the order_by parameters is not used when calling dags list with the rest api The following two commands returns the same results which should not be possible cause one is ascending and the other descending curl -X 'GET' 'http://<server_name>:<port>/api/v1/dags?limit=100&order_by=dag_id&only_active=true' -H 'accept: application/json' curl -X 'GET' 'http://<server_name>:<port>/api/v1/dags?limit=100&order_by=-dag_id&only_active=true' -H 'accept: application/json' by the way, giving an incorrect field name doesn't throw an error ### What you think should happen instead _No response_ ### How to reproduce The following two commands returns the same results curl -X 'GET' 'http://<server_name>:<port>/api/v1/dags?limit=100&order_by=-dag_id&only_active=true' -H 'accept: application/json' curl -X 'GET' 'http://<server_name>:<port>/api/v1/dags?limit=100&order_by=dag_id&only_active=true' -H 'accept: application/json' Same problem is visible with the swagger ui ### Operating System PRETTY_NAME="Debian GNU/Linux 11 (bullseye)" NAME="Debian GNU/Linux" VERSION_ID="11" VERSION="11 (bullseye)" VERSION_CODENAME=bullseye ID=debian HOME_URL="https://www.debian.org/" SUPPORT_URL="https://www.debian.org/support" BUG_REPORT_URL="https://bugs.debian.org/" ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-ftp==3.3.1 apache-airflow-providers-http==4.2.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-mssql==3.3.2 apache-airflow-providers-mysql==4.0.2 apache-airflow-providers-oracle==3.6.0 apache-airflow-providers-sqlite==3.3.1 apache-airflow-providers-vertica==3.3.1 ### 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/30900

https://github.com/apache/airflow/pull/30926

36fe6d0377d37b5f6be8ea5659dcabb44b4fc233

1d4b1410b027c667d4e2f51f488f98b166facf71

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/dag_processor_job_runner.py"]

DagProcessor Performance Regression

### Apache Airflow version 2.5.3 ### What happened Upgrading from `2.4.3` to `2.5.3` caused a significant increase in dag processing time on standalone dag processor (~1-2s to 60s): ``` /opt/airflow/dags/ecco_airflow/dags/image_processing/product_image_load.py 0 -1 56.68s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/known_consumers/known_consumers.py 0 -1 56.64s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/monitoring/row_counts.py 0 -1 56.67s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/omnichannel/base.py 0 -1 56.66s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/omnichannel/oc_data.py 0 -1 56.67s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/omnichannel/oc_stream.py 0 -1 56.52s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/reporting/reporting_data_foundation.py 0 -1 56.63s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/retail_analysis/retail_analysis_dbt.py 0 -1 56.66s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/rfm_segments/rfm_segments.py 0 -1 56.02s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/utils/airflow.py 0 -1 56.65s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/bronze/aad_users_listing.py 1 0 55.51s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/bronze/funnel_io.py 1 0 56.13s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/bronze/iar_param.py 1 0 56.50s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/bronze/sfmc_copy.py 1 0 56.59s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/bronze/us_legacy_datawarehouse.py 1 0 55.15s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/cdp/ecco_cdp_auditing.py 1 0 56.54s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/cdp/ecco_cdp_budget_daily_phasing.py 1 0 56.63s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/cdp/ecco_cdp_gold_rm_tests.py 1 0 55.00s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/consumer_entity_matching/graph_entity_matching.py 1 0 56.67s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/data_backup/data_backup.py 1 0 56.69s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/hive/adhoc_entity_publish.py 1 0 55.33s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/image_regression/train.py 1 0 56.63s 2023-04-26T12:56:15 /opt/airflow/dags/ecco_airflow/dags/maintenance/db_maintenance.py 1 0 56.58s 2023-04-26T12:56:15 ``` Also seeing messages like these ``` [2023-04-26T12:56:15.322+0000] {manager.py:979} DEBUG - Processor for /opt/airflow/dags/ecco_airflow/dags/bronze/us_legacy_datawarehouse.py finished [2023-04-26T12:56:15.323+0000] {processor.py:296} DEBUG - Waiting for <ForkProcess name='DagFileProcessor68-Process' pid=116 parent=7 stopped exitcode=0> [2023-04-26T12:56:15.323+0000] {manager.py:979} DEBUG - Processor for /opt/airflow/dags/ecco_airflow/dags/cdp/ecco_cdp_gold_rm_tests.py finished [2023-04-26T12:56:15.323+0000] {processor.py:296} DEBUG - Waiting for <ForkProcess name='DagFileProcessor69-Process' pid=122 parent=7 stopped exitcode=0> [2023-04-26T12:56:15.324+0000] {manager.py:979} DEBUG - Processor for /opt/airflow/dags/ecco_airflow/dags/bronze/streaming/sap_inventory_feed.py finished [2023-04-26T12:56:15.324+0000] {processor.py:314} DEBUG - Waiting for <ForkProcess name='DagFileProcessor70-Process' pid=128 parent=7 stopped exitcode=-SIGKILL> [2023-04-26T12:56:15.324+0000] {manager.py:986} ERROR - Processor for /opt/airflow/dags/ecco_airflow/dags/bronze/streaming/sap_inventory_feed.py exited with return code -9. ``` In `2.4.3`: ``` /opt/airflow/dags/ecco_airflow/dags/image_regression/train.py 1 0 1.34s 2023-04-26T14:19:08 /opt/airflow/dags/ecco_airflow/dags/known_consumers/known_consumers.py 1 0 1.12s 2023-04-26T14:19:00 /opt/airflow/dags/ecco_airflow/dags/maintenance/db_maintenance.py 1 0 0.63s 2023-04-26T14:18:27 /opt/airflow/dags/ecco_airflow/dags/monitoring/row_counts.py 1 0 3.74s 2023-04-26T14:18:45 /opt/airflow/dags/ecco_airflow/dags/omnichannel/oc_data.py 1 0 1.21s 2023-04-26T14:18:47 /opt/airflow/dags/ecco_airflow/dags/omnichannel/oc_stream.py 1 0 1.22s 2023-04-26T14:18:30 /opt/airflow/dags/ecco_airflow/dags/reporting/reporting_data_foundation.py 1 0 1.39s 2023-04-26T14:19:08 /opt/airflow/dags/ecco_airflow/dags/retail_analysis/retail_analysis_dbt.py 1 0 1.32s 2023-04-26T14:18:51 /opt/airflow/dags/ecco_airflow/dags/rfm_segments/rfm_segments.py 1 0 1.20s 2023-04-26T14:18:34 ``` ### What you think should happen instead Dag processing time remains unchanged ### How to reproduce Provision Airflow with the following settings: ## Airflow 2.5.3 - K8s 1.25.6 - Kubernetes executor - Postgres backend (Postgres 11.0) - Deploy using Airflow Helm **v1.9.0** with image **2.5.3-python3.9** - pgbouncer enabled - standalone dag processort with 3500m cpu / 4000Mi memory, single replica - dags and logs mounted from RWM volume (Azure files) ## Airflow 2.4.3 - K8s 1.25.6 - Kubernetes executor - Postgres backend (Postgres 11.0) - Deploy using Airflow Helm **v1.7.0** with image **2.4.3-python3.9** - pgbouncer enabled - standalone dag processort with 2500m cpu / 2000Mi memory, single replica - dags and logs mounted from RWM volume (Azure files) ## Image modifications We use image built from `apache/airflow:2.4.3-python3.9`, with some dependencies added/reinstalled with different versions. ### Poetry dependency spec: For `2.5.3`: ``` [tool.poetry.dependencies] python = ">=3.9,<3.11" authlib = "~1.0.1" adapta = { version = "==2.2.3", extras = ["azure", "storage"] } numpy = "==1.23.3" db-dtypes = "~1.0.4" gevent = "^21.12.0" sqlalchemy = ">=1.4,<2.0" snowflake-sqlalchemy = ">=1.4,<2.0" esd-services-api-client = "~0.6.0" apache-airflow-providers-common-sql = "~1.3.1" apache-airflow-providers-databricks = "~3.1.0" apache-airflow-providers-google = "==8.4.0" apache-airflow-providers-microsoft-azure = "~5.2.1" apache-airflow-providers-datadog = "~3.0.0" apache-airflow-providers-snowflake = "~3.3.0" apache-airflow = "==2.5.3" dataclasses-json = ">=0.5.7,<0.6" ``` For `2.4.3`: ``` [tool.poetry.dependencies] python = ">=3.9,<3.11" authlib = "~1.0.1" adapta = { version = "==2.2.3", extras = ["azure", "storage"] } numpy = "==1.23.3" db-dtypes = "~1.0.4" gevent = "^21.12.0" sqlalchemy = ">=1.4,<2.0" snowflake-sqlalchemy = ">=1.4,<2.0" esd-services-api-client = "~0.6.0" apache-airflow-providers-common-sql = "~1.3.1" apache-airflow-providers-databricks = "~3.1.0" apache-airflow-providers-google = "==8.4.0" apache-airflow-providers-microsoft-azure = "~5.2.1" apache-airflow-providers-datadog = "~3.0.0" apache-airflow-providers-snowflake = "~3.3.0" apache-airflow = "==2.4.3" dataclasses-json = ">=0.5.7,<0.6" ``` ### Operating System Container OS: Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.4.0 apache-airflow-providers-common-sql==1.3.4 apache-airflow-providers-databricks==3.1.0 apache-airflow-providers-datadog==3.0.0 apache-airflow-providers-docker==3.2.0 apache-airflow-providers-elasticsearch==4.2.1 apache-airflow-providers-ftp==3.3.1 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==4.3.0 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==5.2.1 apache-airflow-providers-mysql==3.2.1 apache-airflow-providers-odbc==3.1.2 apache-airflow-providers-postgres==5.2.2 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-snowflake==3.3.0 apache-airflow-providers-sqlite==3.3.2 apache-airflow-providers-ssh==3.2.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details See How-to-reproduce section ### Anything else Occurs by upgrading the helm chart from 1.7.0/2.4.3 to 1.9.0/2.5.3 installation. ### 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/30884

https://github.com/apache/airflow/pull/30899

7ddad1a24b1664cef3827b06d9c71adbc558e9ef

00ab45ffb7dee92030782f0d1496d95b593fd4a7

python

closed

apache/airflow

https://github.com/apache/airflow

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

Sort Dag List by Last Run Date

### Description It would be helpful to me if I could see the most recently ran DAGs and their health in the Airflow UI. Right now many fields are sortable but not last run. The solution here would likely build off the previous work from this issue: https://github.com/apache/airflow/issues/8459 ### Use case/motivation When my team updates a docker image we want to confirm our DAGs are still running healthy. One way to do that would be to pop open the Airflow UI and look at our teams DAGs (using the label tag) and confirm the most recently ran jobs are still healthy. ### Related issues I think it would build off of https://github.com/apache/airflow/issues/8459 ### 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/30838

https://github.com/apache/airflow/pull/31234

7ebda3898db2eee72d043a9565a674dea72cd8fa

3363004450355582712272924fac551dc1f7bd56

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/serialization/serde.py", "tests/utils/test_json.py"]

Deserialization of nested dict failing

### Apache Airflow version 2.6.0b1 ### What happened When returning nested dictionary data from Task A and passing the returned value in Task B the deserialization fails if the data is nested dictionary with nonprimitive or iterable type. ``` Traceback (most recent call last): File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/models/abstractoperator.py", line 570, in _do_render_template_fields rendered_content = self.render_template( File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/template/templater.py", line 162, in render_template return tuple(self.render_template(element, context, jinja_env, oids) for element in value) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/template/templater.py", line 162, in <genexpr> return tuple(self.render_template(element, context, jinja_env, oids) for element in value) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/template/templater.py", line 158, in render_template return value.resolve(context) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/utils/session.py", line 76, in wrapper return func(*args, session=session, **kwargs) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/models/xcom_arg.py", line 342, in resolve result = ti.xcom_pull( File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/utils/session.py", line 73, in wrapper return func(*args, **kwargs) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 2454, in xcom_pull return XCom.deserialize_value(first) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/models/xcom.py", line 666, in deserialize_value return BaseXCom._deserialize_value(result, False) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/models/xcom.py", line 659, in _deserialize_value return json.loads(result.value.decode("UTF-8"), cls=XComDecoder, object_hook=object_hook) File "/opt/homebrew/Cellar/[email protected]/3.10.10_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/__init__.py", line 359, in loads return cls(**kw).decode(s) File "/opt/homebrew/Cellar/[email protected]/3.10.10_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/opt/homebrew/Cellar/[email protected]/3.10.10_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/json/decoder.py", line 353, in raw_decode obj, end = self.scan_once(s, idx) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/utils/json.py", line 122, in object_hook val = deserialize(dct) File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/serialization/serde.py", line 212, in deserialize return {str(k): deserialize(v, full) for k, v in o.items()} File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/serialization/serde.py", line 212, in <dictcomp> return {str(k): deserialize(v, full) for k, v in o.items()} File "/Users/utkarsharma/sandbox/astronomer/apache-airflow-provider-transfers/.nox/dev/lib/python3.10/site-packages/airflow/serialization/serde.py", line 206, in deserialize raise TypeError() ``` The way we are deserializing is by adding a [custom encoder](https://docs.python.org/3/library/json.html#encoders-and-decoders) for JSON and we are overriding the `object_hook` as shown below. https://github.com/apache/airflow/blob/ebe2f2f626ffee4b9d0f038fe5b89c322125a49b/airflow/utils/json.py#L107-L126 But if you try to run below code: ``` import json def object_hook(dct: dict) -> dict: print("dct : ", dct) return dct if __name__ == "__main__": val = json.dumps({"a": {"a-1": 1, "a-2": {"a-2-1": 1, "a-2-2": 2}}, "b": {"b-1": 1, "b-2": 2}, "c": {"c-1": 1, "c-2": 2}}) print("val : ", val, "\n\n") return_val = json.loads(val, object_hook=object_hook) ``` Output: ``` val : {"a": {"a-1": 1, "a-2": {"a-2-1": 1, "a-2-2": 2}}, "b": {"b-1": 1, "b-2": 2}, "c": {"c-1": 1, "c-2": 2}} dct : {'a-2-1': 1, 'a-2-2': 2} dct : {'a-1': 1, 'a-2': {'a-2-1': 1, 'a-2-2': 2}} dct : {'b-1': 1, 'b-2': 2} dct : {'c-1': 1, 'c-2': 2} dct : {'a': {'a-1': 1, 'a-2': {'a-2-1': 1, 'a-2-2': 2}}, 'b': {'b-1': 1, 'b-2': 2}, 'c': {'c-1': 1, 'c-2': 2}} ``` `object_hook` is called with every decoded value. Because of this `deserialize` is getting called even with the deserialized data causing this issue. deserialize function code: https://github.com/apache/airflow/blob/ebe2f2f626ffee4b9d0f038fe5b89c322125a49b/airflow/serialization/serde.py#L174 ### What you think should happen instead Airflow should be able to Serialization/Deserialization without any issue ### How to reproduce Refer - https://github.com/apache/airflow/pull/30798 Run below code: ``` import pandas as pd from airflow import DAG from astro import sql as aql from airflow.utils import timezone with DAG("random-string", start_date=timezone.datetime(2016, 1, 1), catchup=False): @task def taskA(): return {"foo": 1, "bar": 2, "baz": pd.DataFrame({"numbers": [1, 2, 3], "Colors": ["red", "white", "blue"]})} @task def taskB(x): print(x) v = taskA() taskB(v) ``` ### Operating System Mac - ventura ### 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/30797

https://github.com/apache/airflow/pull/30819

cbaea573b3658dd941335e21c5f29118b31cb6d8

58e26d9df42f10e4e2b46cd26c6832547945789b

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/authoring-and-scheduling/plugins.rst"]

Tasks forked by the Local Executor are loading stale modules when the modules are also referenced by plugins

### Apache Airflow version 2.5.3 ### What happened After upgrading from Airflow 2.4.3 to 2.5.3, tasks forked by the `Local Executor` can run with outdated module imports if those modules are also imported by plugins. It seems as though tasks will reuse imports that were first loaded when the scheduler boots, and any subsequent updates to those shared modules do not get reflected in new tasks. I verified this issue occurs for all patch versions of 2.5. ### What you think should happen instead Given that the plugin documentation states: > if you make any changes to plugins and you want the webserver or scheduler to use that new code you will need to restart those processes. this behavior may be attended. But it's not clear that this affects the code for forked tasks as well. So if this is not actually a bug then perhaps the documentation can be updated. ### How to reproduce Given a plugin file like: ```python from airflow.models.baseoperator import BaseOperatorLink from src.custom_operator import CustomOperator class CustomerOperatorLink(BaseOperatorLink): operators = [CustomOperator] ``` And a dag file like ``` from src.custom_operator import CustomOperator ... ``` Any updates to the `CustomOperator` will not be reflected in new running tasks after the scheduler boots. ### Operating System Debian bullseye ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else Workarounds - Set `execute_tasks_new_python_interpreter` to `False` - In my case of using Operator Links, I can alternatively set the Operator Link in my custom operator using `operator_extra_links`, which wouldn't require importing the operator from the plugin file. ### 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/30796

https://github.com/apache/airflow/pull/31781

ab8c9ec2545caefb232d8e979b18b4c8c8ad3563

18f2b35c8fe09aaa8d2b28065846d7cf1e85cae2

python

closed

apache/airflow

https://github.com/apache/airflow

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

ExternalTaskSensor waits forever for TaskGroup containing mapped tasks

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened If you have an `ExternalTaskSensor` that uses `external_task_group_id` to wait on a `TaskGroup`, and if that `TaskGroup` contains any [mapped tasks](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html), the sensor will be stuck waiting forever even after the task group is successful. ### What you think should happen instead `ExternalTaskSensor` should be able to wait on `TaskGroup`s, regardless of whether or not that group contains mapped tasks. ### How to reproduce ``` #!/usr/bin/env python3 import datetime import logging from airflow.decorators import dag, task from airflow.operators.empty import EmptyOperator from airflow.sensors.external_task import ExternalTaskSensor from airflow.utils.task_group import TaskGroup logger = logging.getLogger(__name__) @dag( schedule_interval='@daily', start_date=datetime.datetime(2023, 4, 17), ) def task_groups(): with TaskGroup(group_id='group'): EmptyOperator(task_id='operator1') >> EmptyOperator(task_id='operator2') with TaskGroup(group_id='mapped_tasks'): @task def get_tasks(): return [1, 2, 3] @task def process(x): print(x) process.expand(x=get_tasks()) ExternalTaskSensor( task_id='wait_for_normal_task_group', external_dag_id='task_groups', external_task_group_id='group', poke_interval=3, check_existence=True, ) ExternalTaskSensor( task_id='wait_for_mapped_task_group', external_dag_id='task_groups', external_task_group_id='mapped_tasks', poke_interval=3, check_existence=True, ) task_groups() ``` ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Standalone ### Anything else I think the bug is [here](https://github.com/apache/airflow/blob/731ef3d692fc7472e245f39f3e3e42c2360cb769/airflow/sensors/external_task.py#L364): ``` elif self.external_task_group_id: external_task_group_task_ids = self.get_external_task_group_task_ids(session) count = ( self._count_query(TI, session, states, dttm_filter) .filter(TI.task_id.in_(external_task_group_task_ids)) .scalar() ) / len(external_task_group_task_ids) ``` If the group contains mapped tasks, `external_task_group_ids` only contains a list of task names (not expanded to include mapped task indices), but the `count` will count all mapped instances. This returns a larger value than the calling function expects to receive when it checks for `count_allowed == len(dttm_filter)`, so `poke` always returns `False`. ### 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/30689

https://github.com/apache/airflow/pull/30742

ae3a61775a79a3000df0a8bdf50807033f4e3cdc

3c30e54de3b8a6fe793b0ff1ed8225562779d96c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/openlineage/utils/utils.py"]

Open-Lineage type-ignore in OpenLineageRedactor likely hides some problem

### Body The new `attrs` package 23.1 released on 16th of April (11 hours ago) added typing information to "attrs.asdict" method and it mypy tests started to fail with ``` airflow/providers/openlineage/utils/utils.py:345: error: Argument 1 to "asdict" has incompatible type "Type[AttrsInstance]"; expected "AttrsInstance" [arg-type] ... for dict_key, subval in attrs.asdict(item, recurse=False).... ^ airflow/providers/openlineage/utils/utils.py:345: note: ClassVar protocol member AttrsInstance.__attrs_attrs__ can never be matched by a class object ``` The nature of this error (receiving Type where expecting instance indicates that there is somewhat serious issue here. Especially that there were a `type: ignore` one line above that would indicate that something is quite wrong here (when we ignore typing issue, we usually comment why and ignore very specific error (`type: ignore[attr-undefined]` for example) when we have good reason to ignore it. Since open-lineage is not yet released/functional and partially in progress, this is not an issue to be solved immediately, but soon (cc: @mobuchowski). For now I am workaroudning this by adding another `type: ignore` to stop the static checks from failing (they fail only for PRs that are updating dependencies) and allow to upgrade to attrs 23.1. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/30673

https://github.com/apache/airflow/pull/30677

2557c07aa5852d415a647679180d4dbf81a5d670

6a6455ad1c2d76eaf9c60814c2b0a0141ad29da0

python

closed

apache/airflow

https://github.com/apache/airflow

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

`BigQueryGetDataOperator` does not respect project_id parameter

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.11.0 google-cloud-bigquery==2.34.4 ### Apache Airflow version 2.5.2+astro.2 ### Operating System OSX ### Deployment Astronomer ### Deployment details _No response_ ### What happened When setting a `project_id` parameter for `BigQueryGetDataOperator` the default project from env is not overwritten. Maybe something broke after it was added in? https://github.com/apache/airflow/pull/25782 ### What you think should happen instead Passing in as parameter should take precedence over reading in from environment ### How to reproduce Part1 ```py from airflow.providers.google.cloud.operators.bigquery import BigQueryGetDataOperator bq = BigQueryGetDataOperator( task_id=f"my_test_query_task_id", gcp_conn_id="bigquery", table_id="mytable", dataset_id="mydataset", project_id="my_non_default_project", ) f2 = bq.execute(None) ``` in env i have set ```py AIRFLOW_CONN_BIGQUERY=gcpbigquery:// GOOGLE_CLOUD_PROJECT=my_primary_project GOOGLE_APPLICATION_CREDENTIALS=/usr/local/airflow/gcloud/application_default_credentials.json ``` The credentials json file doesn't have project Part2 Unsetting GOOGLE_CLOUD_PROJECT and rerunning results in ```sh Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.9/site-packages/airflow/providers/google/cloud/operators/bigquery.py", line 886, in execute schema: dict[str, list] = hook.get_schema( File "/usr/local/lib/python3.9/site-packages/airflow/providers/google/common/hooks/base_google.py", line 463, in inner_wrapper raise AirflowException( airflow.exceptions.AirflowException: The project id must be passed either as keyword project_id parameter or as project_id extra in Google Cloud connection definition. Both are not set! ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/30635

https://github.com/apache/airflow/pull/30651

d3aeb4db0c539f2151ef395300cb2b5efc6dce08

4eab616e9f0a89c1a6268d5b5eaba526bfa9be6d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/base_aws.py", "tests/providers/amazon/aws/hooks/test_dynamodb.py"]

DynamoDBHook - not able to registering a custom waiter

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon=7.4.1 ### Apache Airflow version airflow=2.5.3 ### Operating System Mac ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened We can register a custom waiter by adding a JSON file to the path - `airflow/airflow/providers/amazon/aws/waiters/`. The should be named `<client_type>.json` in this case - `dynamodb.json`. Once registered we can use the custom waiter. content of the file - `airflow/airflow/providers/amazon/aws/waiters/dynamodb.json`: ``` { "version": 2, "waiters": { "export_table": { "operation": "ExportTableToPointInTime", "delay": 30, "maxAttempts": 60, "acceptors": [ { "matcher": "path", "expected": "COMPLETED", "argument": "ExportDescription.ExportStatus", "state": "success" }, { "matcher": "path", "expected": "FAILED", "argument": "ExportDescription.ExportStatus", "state": "failure" }, { "matcher": "path", "expected": "IN_PROGRESS", "argument": "ExportDescription.ExportStatus", "state": "retry" } ] } } } ``` Getting below error post running test case: ``` class TestCustomDynamoDBServiceWaiters: """Test waiters from ``amazon/aws/waiters/dynamodb.json``.""" STATUS_COMPLETED = "COMPLETED" STATUS_FAILED = "FAILED" STATUS_IN_PROGRESS = "IN_PROGRESS" @pytest.fixture(autouse=True) def setup_test_cases(self, monkeypatch): self.client = boto3.client("dynamodb", region_name="eu-west-3") monkeypatch.setattr(DynamoDBHook, "conn", self.client) @pytest.fixture def mock_export_table_to_point_in_time(self): """Mock ``DynamoDBHook.Client.export_table_to_point_in_time`` method.""" with mock.patch.object(self.client, "export_table_to_point_in_time") as m: yield m def test_service_waiters(self): assert os.path.exists('/Users/utkarsharma/sandbox/airflow-sandbox/airflow/airflow/providers/amazon/aws/waiters/dynamodb.json') hook_waiters = DynamoDBHook(aws_conn_id=None).list_waiters() assert "export_table" in hook_waiters ``` ## Error tests/providers/amazon/aws/waiters/test_custom_waiters.py:273 (TestCustomDynamoDBServiceWaiters.test_service_waiters) 'export_table' != ['table_exists', 'table_not_exists'] Expected :['table_exists', 'table_not_exists'] Actual :'export_table' <Click to see difference> self = <tests.providers.amazon.aws.waiters.test_custom_waiters.TestCustomDynamoDBServiceWaiters object at 0x117f085e0> def test_service_waiters(self): assert os.path.exists('/Users/utkarsharma/sandbox/airflow-sandbox/airflow/airflow/providers/amazon/aws/waiters/dynamodb.json') hook_waiters = DynamoDBHook(aws_conn_id=None).list_waiters() > assert "export_table" in hook_waiters E AssertionError: assert 'export_table' in ['table_exists', 'table_not_exists'] test_custom_waiters.py:277: AssertionError ### What you think should happen instead It should register the custom waiter and test case should pass.the ### How to reproduce Add the file mentioned above to Airflow's code base and try running the test case provided. ### 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/30613

https://github.com/apache/airflow/pull/30595

cb5a2c56b99685305eecdd3222b982a1ef668019

7c2d3617bf1be0781e828d3758ee6d9c6490d0f0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/manager.py", "airflow/models/dag.py", "tests/dag_processing/test_job_runner.py"]

DAGs deleted from zips aren't deactivated

### Apache Airflow version 2.5.3 ### What happened When a DAG is removed from a zip in the DAGs directory, but the zip file remains, it is not marked correctly as inactive. It is still visible in the UI, and attempting to open the DAG results in an `DAG "mydag" seems to be missing from DagBag.` error in the UI. The DAG is removed from the SerializedDag table, resulting in the scheduler repeatedly erroring with `[2023-04-12T12:43:51.165+0000] {scheduler_job.py:1063} ERROR - DAG 'mydag' not found in serialized_dag table`. I have done some minor investigating and it appears that [this piece of code](https://github.com/apache/airflow/blob/2.5.3/airflow/dag_processing/manager.py#L748-L772) may be the cause. `dag_filelocs` provides the path to a specific python file within a zip, so `SerializedDagModel.remove_deleted_dags` is able to remove the missing DAG. However, `self._file_paths` only contains the top-level zip name, so `DagModel.deactivate_deleted_dags` will only deactivate DAGs where the zip they are contained in is deleted, regardless of whether the DAG is still inside the zip. I can see there are [other methods that handle DAG deactivation](https://github.com/apache/airflow/blob/2.5.3/airflow/models/dag.py#L2945-L2968) and I'm not sure how these all interact but this does seem to cause this specific issue. ### What you think should happen instead DAGS that are no longer in the DagBag are marked as inactive ### How to reproduce Running airflow locally with docker-compose: - Create a zipfile with 2 DAG py files in in ./dags - Wait for the DAGs to be parsed by the scheduler and appear in the UI - Overwrite the existing DAG zip, with a new zip containing only 1 of the original DAG py files - Wait for scheduler loop to parse the new zip - Attempt to open the removed DAG in the UI, you will see an error ### 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 If I replace the docker image in the docker compose with an image built from this Dockerfile: ``` FROM apache/airflow:2.5.3 RUN sed -i '772s/self._file_paths/dag_filelocs/' /home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py RUN sed -i '3351s/correct_maybe_zipped(dag_model.fileloc)/dag_model.fileloc/' /home/airflow/.local/lib/python3.7/site-packages/airflow/models/dag.py ``` The DAG is deactivated as expected ### 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/30600

https://github.com/apache/airflow/pull/30608

0f3b6579cb67d3cf8bd9fa8f9abd502fc774201a

7609021ce93d61f2101f5e8cdc126bb8369d334b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/dag_processor_job_runner.py"]

After upgrading to 2.5.3, Dag Processing time increased dramatically.

### Apache Airflow version 2.5.3 ### What happened I upgraded my airflow cluster from 2.5.2 to 2.5.3 , after which strange things started happening. I'm currently using a standalone dagProcessor, and the parsing time that used to take about 2 seconds has suddenly increased to about 10 seconds. I'm thinking it's weird because I haven't made any changes other than a version up, but is there something I can look into? Thanks in advance! 🙇🏼  ### What you think should happen instead I believe that the time it takes to parse a Dag should be constant, or at least have some variability, but shouldn't take as long as it does now. ### How to reproduce If you cherrypick [this commit](https://github.com/apache/airflow/pull/30079) into 2.5.2 stable code, the issue will recur. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details - Kubernetes 1.21 Cluster - 1.7.0 helm chart - standalone dag processor - using kubernetes executor - using mysql database ### Anything else This issue still persists, and restarting the Dag Processor has not resolved the issue. ### 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/30593

https://github.com/apache/airflow/pull/30899

7ddad1a24b1664cef3827b06d9c71adbc558e9ef

00ab45ffb7dee92030782f0d1496d95b593fd4a7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/utils/db.py", "tests/utils/test_db.py"]

alembic Logging

### Apache Airflow version 2.5.3 ### What happened When I call the airflow initdb function, it outputs these lines to the log INFO [alembic.runtime.migration] Context impl PostgresqlImpl. INFO [alembic.runtime.migration] Will assume transactional DDL. ### What you think should happen instead There should be a mechanism to disable these logs, or they should just be set to WARN by default ### How to reproduce Set up a new postgres connection and call: from airflow.utils.db import initdb initdb() ### Operating System MacOS ### 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/30562

https://github.com/apache/airflow/pull/31415

c5597d1fabe5d8f3a170885f6640344d93bf64bf

e470d784627502f171819fab072e0bbab4a05492

python

closed

apache/airflow

https://github.com/apache/airflow

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

Cannot clear tasking instances on "List Task Instance" page with User role

### Apache Airflow version main (development) ### What happened Only users with the role `Admin` are allowed to use the action clear on the TaskInstance list view. ### What you think should happen instead Users with role `User` should be able to clear task instance in the Task Instance page. ### How to reproduce Try to clear Task instance while using a user with a `User` role. ### Operating System Fedora 37 ### 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/30414

https://github.com/apache/airflow/pull/30415

22bef613678e003dde9128ac05e6c45ce934a50c

b140c4473335e4e157ff2db85148dd120c0ed893

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/ci.yml", "BREEZE.rst", "dev/breeze/src/airflow_breeze/commands/testing_commands.py", "dev/breeze/src/airflow_breeze/commands/testing_commands_config.py", "dev/breeze/src/airflow_breeze/utils/selective_checks.py", "images/breeze/output-commands-hash.txt", "images/breeze/output_testing_tests.svg"]

merge breeze's --test-type and --test-types options

### Description using `breeze testing tests` recently I noticed that the way to specify which tests to run is very confusing: * `--test-type` supports specifying one type only (or `All`), allows specifying which provider tests to run in details, and is ignored if `--run-in-parallel` is provided (from what I saw) * `--test-types` (note the `s` at the end) supports a list of types, does not allow to select specific provider tests, and is ignored if `--run-in-parallel` is NOT specified. I _think_ that the two are mutually exclusive (i.e. there is no situation where one is taken into account and the other isn’t ignored), so it’d make sense to merge them. Definition of Done: - --test-type or --test-types can be used interchangeably, whether the tests are running in parallel or not (it'd be a bit like how `kubectl` allows using singular or plural for some actions, like `k get pod` == `k get pods`) - When using the type `Providers`, specific provider tests can be selected between square brackets using the current syntax (e.g. `Providers[airbyte,http]`) - several types can be specified, separated by a space (e.g. `"WWW CLI"`) - the two bullet points above can be combined (e.g. `--test-type "Always Providers[airbyte,http] WWW"`) ### Use case/motivation having a different behavior for a very similar option depending on whether we are running in parallel or not is confusing, and from a user perspective, there is no benefit to having those as separate options. ### 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/30407

https://github.com/apache/airflow/pull/30424

90ba6fe070d903bca327b52b2f61468408d0d96a

20606438c27337c20aa9aff8397dfa6f286f03d3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/kubernetes_executor.py"]

ERROR - Unknown error in KubernetesJobWatcher

### Official Helm Chart version 1.7.0 ### Apache Airflow version 2.4.0 ### Kubernetes Version K3s Kubernetes Version: v1.24.2+k3s2 ### Helm Chart configuration _No response_ ### Docker Image customizations _No response_ ### What happened Same as https://github.com/apache/airflow/issues/12229 ``` [2023-03-31T17:47:08.304+0000] {kubernetes_executor.py:112} ERROR - Unknown error in KubernetesJobWatcher. Failing Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 103, in run self.resource_version = self._run( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 148, in _run for event in list_worker_pods(): File "/home/airflow/.local/lib/python3.10/site-packages/kubernetes/watch/watch.py", line 182, in stream raise client.rest.ApiException( kubernetes.client.exceptions.ApiException: (410) Reason: Expired: too old resource version: 202541353 (202544371) Process KubernetesJobWatcher-3: Traceback (most recent call last): File "/usr/local/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 103, in run self.resource_version = self._run( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 148, in _run for event in list_worker_pods(): File "/home/airflow/.local/lib/python3.10/site-packages/kubernetes/watch/watch.py", line 182, in stream raise client.rest.ApiException( kubernetes.client.exceptions.ApiException: (410) Reason: Expired: too old resource version: 202541353 (202544371) [2023-03-31T17:47:08.832+0000] {kubernetes_executor.py:291} ERROR - Error while health checking kube watcher process. Process died for unknown reasons ``` ### What you think should happen instead no errors in the logs? ### How to reproduce appears soon after AF-scheduler pod restart ### 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/30400

https://github.com/apache/airflow/pull/30425

cce9b2217b86a88daaea25766d0724862577cc6c

9e5fabecb05e83700688d940d31a0fbb49000d64

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/sql_to_s3.py", "docs/apache-airflow-providers-amazon/transfer/sql_to_s3.rst", "tests/providers/amazon/aws/transfers/test_sql_to_s3.py", "tests/system/providers/amazon/aws/example_sql_to_s3.py"]

SqlToS3Operator not able to write data with partition_cols provided.

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers I am using the standard operator version which comes with apache/airflow:2.5.2. ### Apache Airflow version 2.5.2 ### Operating System Ubuntu 22.04.2 LTS ### Deployment Official Apache Airflow Helm Chart ### Deployment details I have used a simple docker compose setup can using the same in my local. ### What happened I am using SqlToS3Operator in my Dag. I need to store the data using the partition col. The operator writes the data in a temporary file but in my case it should be a folder. I am getting the below error for the same. ``` [2023-03-31, 03:47:57 UTC] {sql_to_s3.py:175} INFO - Writing data to temp file [2023-03-31, 03:47:57 UTC] {taskinstance.py:1775} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/transfers/sql_to_s3.py", line 176, in execute getattr(data_df, file_options.function)(tmp_file.name, **self.pd_kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/pandas/util/_decorators.py", line 207, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/frame.py", line 2685, in to_parquet **kwargs, File "/home/airflow/.local/lib/python3.7/site-packages/pandas/io/parquet.py", line 423, in to_parquet **kwargs, File "/home/airflow/.local/lib/python3.7/site-packages/pandas/io/parquet.py", line 190, in write **kwargs, File "/home/airflow/.local/lib/python3.7/site-packages/pyarrow/parquet/__init__.py", line 3244, in write_to_dataset max_rows_per_group=row_group_size) File "/home/airflow/.local/lib/python3.7/site-packages/pyarrow/dataset.py", line 989, in write_dataset min_rows_per_group, max_rows_per_group, create_dir File "pyarrow/_dataset.pyx", line 2775, in pyarrow._dataset._filesystemdataset_write File "pyarrow/error.pxi", line 113, in pyarrow.lib.check_status NotADirectoryError: [Errno 20] Cannot create directory '/tmp/tmp3z4dpv_p.parquet/application_createdAt=2020-06-05 11:47:44.000000000'. Detail: [errno 20] Not a directory ``` ### What you think should happen instead The Operator should have supported the partition col as well. ### How to reproduce I am using the below code snipet for the same. ``` sql_to_s3_task = SqlToS3Operator( task_id="sql_to_s3_task", sql_conn_id="mysql_con", query=sql, s3_bucket=Variable.get("AWS_S3_BUCKET"), aws_conn_id="aws_con", file_format="parquet", s3_key="Fact_applications", pd_kwargs={ "partition_cols":['application_createdAt'] }, replace=True, ) ``` This could be using to reproduce the same. ### Anything else I believe [this](https://github.com/apache/airflow/blob/6e751812d2e48b743ae1bc375e3bebb8414b4a0e/airflow/providers/amazon/aws/transfers/sql_to_s3.py#L173) logic should be updated for the same. ### 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/30382

https://github.com/apache/airflow/pull/30460

372a0881d9591f6d69105b1ab6709f5f42560fb6

d7cef588d6f6a749bd5e8fbf3153a275f4120ee8

python

closed

apache/airflow

https://github.com/apache/airflow

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

Need an REST API or/and Airflow CLI to fetch last parsed time of a given DAG

### Description We need to access the time at which a given DAG was parsed last. Airflow Version : 2.2.2 and above. ### Use case/motivation End users want to run a given DAG post applying the changes they have done on them. This would mean that the DAG should be parsed post the edits done to it. Right now the last parsed time is available by accessing the airflow database only. Querying the database directly is not the best solution to the problem. Ideally airflow should be exposing APIs that end users can consume that can help provide the last parsed time for a given DAG. ### Related issues Not Aware. ### 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/30365

https://github.com/apache/airflow/pull/30432

c5b685e88dd6ecf56d96ef4fefa6c409f28e2b22

7074167d71c93b69361d24c1121adc7419367f2a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/s3_to_redshift.py", "tests/providers/amazon/aws/transfers/test_s3_to_redshift.py"]

S3ToRedshiftOperator does not support default values on upsert

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers 7.2.1 ### Apache Airflow version 2.5.1 ### Operating System Ubuntu ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened I am trying to use the `S3ToRedshiftOperator` to copy data into an existing table which has a column defined as non-null with default. The copy fails with the following error: ``` redshift_connector.error.ProgrammingError: {'S': 'ERROR', 'C': '42601', 'M': 'NOT NULL column without DEFAULT must be included in column list', 'F': '../src/pg/src/backend/commands/commands_copy.c', 'L': '2727', 'R': 'DoTheCopy'} ``` This is happening because when using the `UPSERT` method, the operator first creates a temporary table with this statement ([here](https://github.com/apache/airflow/blob/47cf233ccd612a68bea1ad3898f06b91c63c1964/airflow/providers/amazon/aws/transfers/s3_to_redshift.py#L173)): ``` CREATE TABLE #bar (LIKE foo.bar); ``` And then attempts to copy data into this temporary table. By default, `CREATE TABLE ... LIKE` does not include default values: _The default behavior is to exclude default expressions, so that all columns of the new table have null defaults._ (from the [docs](https://docs.aws.amazon.com/redshift/latest/dg/r_CREATE_TABLE_NEW.html)). ### What you think should happen instead We should be able to include default values when creating the temporary table. ### How to reproduce * Create a table with a column defined as non-null with default value * Use the operator to copy data into it using the `UPSERT` method ### 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/30341

https://github.com/apache/airflow/pull/32558

bf68e1060b0214ee195c61f9d7be992161e25589

145b16caaa43f0c42bffd97344df916c602cddde

python