Datasets:

bug-localization

/

BeetleBox

like 0

closed

apache/airflow

https://github.com/apache/airflow

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

enable_elastic_disk property incorrectly mapped when making a request to Databricks

### Apache Airflow version 2.2.2 ### What happened When using `apache-airflow-providers-databricks` in version 2.2.0 I am sending a request to databricks to submit a job. https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsCreate -> `api/2.0/jobs/runs/submit` Databricks is expecting a boolean on a property `enable_elastic_disk` while `airflow-databricks-provider` sends a string. ``` new_cluster = { "autoscale": {"min_workers": 2, "max_workers": 5}, "spark_version": "10.4.x-scala2.12", "aws_attributes": { "first_on_demand": 1, "availability": "SPOT_WITH_FALLBACK", "zone_id": "auto", "spot_bid_price_percent": 100, }, "enable_elastic_disk": True, "driver_node_type_id": "r5a.large", "node_type_id": "c5a.xlarge", "cluster_source": "JOB", } ``` And the property `enable_elastic_disk` is not set on databricks side. I did also the same request to databricks from a Postman and the property was set to `true` which means that the problem does not lie on databricks side. ``` { "name": "test", "tasks": [ { "task_key": "test-task-key", "notebook_task": { "notebook_path": "path_to_notebook" }, "new_cluster": { "autoscale": {"min_workers": 1, "max_workers": 2}, "cluster_name": "", "spark_version": "10.4.x-scala2.12", "aws_attributes": { "first_on_demand": 1, "availability": "SPOT_WITH_FALLBACK", "zone_id": "auto", "spot_bid_price_percent": 100 }, "driver_node_type_id": "r5a.large", "node_type_id": "c5a.xlarge", "enable_elastic_disk": true, "cluster_source": "JOB" } } ] } ``` I have tried to find the problem and it apparently is this line. Before executing the line `enable_elastic_disk` is `True` of type boolean but after it becomes a string `'True'` which databricks does not parse. https://github.com/apache/airflow/blob/1cb16d5588306fcb7177486dc60c1974ea3034d4/airflow/providers/databricks/operators/databricks.py#L381 ### What you think should happen instead After setting property `enable_elastic_disk` it should be propagated into databricks but it's not. ### How to reproduce Try to run: ``` new_cluster = { "autoscale": {"min_workers": 2, "max_workers": 5}, "spark_version": "10.4.x-scala2.12", "aws_attributes": { "first_on_demand": 1, "availability": "SPOT_WITH_FALLBACK", "zone_id": "auto", "spot_bid_price_percent": 100, }, "enable_elastic_disk": True, "driver_node_type_id": "r5a.large", "node_type_id": "c5a.xlarge", "cluster_source": "JOB", } notebook_task = { "notebook_path": f"/Repos/path_to_notebook"/main_asset_information", "base_parameters": {"env": env}, } asset_information = DatabricksSubmitRunOperator( task_id="task_id" databricks_conn_id="databricks", new_cluster=new_cluster, notebook_task=notebook_task, ) ``` Make sure airflow connection named `databricks` is set and check whether databricks has the property set. After executing there is a need to check whether the property is set on databricks we can do it by using endpoint: `https://DATABRICKS_HOST/api/2.1/jobs/runs/get?run_id=123` ### Operating System MWWA ### Versions of Apache Airflow Providers `apache-airflow-providers-databricks` in version 2.2.0 ### Deployment MWAA ### Deployment details _No response_ ### Anything else That's a permanent and repeatable problem. It would be great if this fix could be attached to lower versions for example `2.2.1`, because I am not sure when AWS decides to upgrade to the latest airflow code and I am also not sure if installing higher versions of databricks provider on airflow `2.2.2` will not cause 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/25232

https://github.com/apache/airflow/pull/25394

5de11e1410b432d632e8c0d1d8ca0945811a56f0

0255a0a5e7b93f2daa3a51792cd38d19d6a373c0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/datasets/manager.py", "airflow/models/dataset.py", "tests/datasets/test_manager.py", "tests/models/test_taskinstance.py"]

Many tasks updating dataset at once causes some of them to fail

### Apache Airflow version main (development) ### What happened I have 16 dags which all update the same dataset. They're set to finish at the same time (when the seconds on the clock are 00). About three quarters of them behave as expected, but the other quarter fails with errors like: ``` [2022-07-21, 06:06:00 UTC] {standard_task_runner.py:97} ERROR - Failed to execute job 8 for task increment_source ((psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint "dataset_dag_run_queue_pkey" DETAIL: Key (dataset_id, target_dag_id)=(1, simple_dataset_sink) already exists. [SQL: INSERT INTO dataset_dag_run_queue (dataset_id, target_dag_id, created_at) VALUES (%(dataset_id)s, %(target_dag_id)s, %(created_at)s)] [parameters: {'dataset_id': 1, 'target_dag_id': 'simple_dataset_sink', 'created_at': datetime.datetime(2022, 7, 21, 6, 6, 0, 131730, tzinfo=Timezone('UTC'))}] (Background on this error at: https://sqlalche.me/e/14/gkpj); 375) ``` I've prepaired a gist with the details: https://gist.github.com/MatrixManAtYrService/b5e58be0949eab9180608d0760288d4d ### What you think should happen instead All dags should succeed ### How to reproduce See this gist: https://gist.github.com/MatrixManAtYrService/b5e58be0949eab9180608d0760288d4d Summary: Unpause all of the dags which we expect to collide, wait two minutes. Some will have collided. ### Operating System docker/debian ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astro dev start` targeting commit: cff7d9194f549d801947f47dfce4b5d6870bfaaa be sure to have `pause` in requirements.txt ### 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/25210

https://github.com/apache/airflow/pull/26103

a2db8fcb7df1a266e82e17b937c9c1cf01a16a42

4dd628c26697d759aebb81a7ac2fe85a79194328

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/baseoperator.py", "airflow/models/dagrun.py", "airflow/models/taskinstance.py", "airflow/ti_deps/dep_context.py", "airflow/ti_deps/deps/trigger_rule_dep.py", "tests/models/test_dagrun.py", "tests/models/test_taskinstance.py", "tests/ti_deps/deps/test_trigger_rule_dep.py"]

DAG Run fails when chaining multiple empty mapped tasks

### Apache Airflow version 2.3.3 (latest released) ### What happened On Kubernetes Executor and Local Executor (others not tested) a significant fraction of the DAG Runs of a DAG that has two consecutive mapped tasks which are are being passed an empty list are marked as failed when all tasks are either succeeding or being skipped.  ### What you think should happen instead The DAG Run should be marked success. ### How to reproduce Run the following DAG on Kubernetes Executor or Local Executor. The real world version of this DAG has several mapped tasks that all point to the same list, and that list is frequently empty. I have made a minimal reproducible example. ```py from datetime import datetime from airflow import DAG from airflow.decorators import task with DAG(dag_id="break_mapping", start_date=datetime(2022, 3, 4)) as dag: @task def add_one(x: int): return x + 1 @task def say_hi(): print("Hi") added_values = add_one.expand(x=[]) added_more_values = add_one.expand(x=[]) say_hi() >> added_values added_values >> added_more_values ``` ### Operating System Debian Bullseye ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==1!4.0.0 apache-airflow-providers-cncf-kubernetes==1!4.1.0 apache-airflow-providers-elasticsearch==1!4.0.0 apache-airflow-providers-ftp==1!3.0.0 apache-airflow-providers-google==1!8.1.0 apache-airflow-providers-http==1!3.0.0 apache-airflow-providers-imap==1!3.0.0 apache-airflow-providers-microsoft-azure==1!4.0.0 apache-airflow-providers-mysql==1!3.0.0 apache-airflow-providers-postgres==1!5.0.0 apache-airflow-providers-redis==1!3.0.0 apache-airflow-providers-slack==1!5.0.0 apache-airflow-providers-sqlite==1!3.0.0 apache-airflow-providers-ssh==1!3.0.0 ``` ### Deployment Astronomer ### Deployment details Local was tested on docker compose (from astro-cli) ### 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/25200

https://github.com/apache/airflow/pull/25995

1e19807c7ea0d7da11b224658cd9a6e3e7a14bc5

5697e9fdfa9d5af2d48f7037c31972c2db1f4397

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/livy/hooks/livy.py", "airflow/providers/apache/livy/operators/livy.py", "airflow/providers/apache/livy/sensors/livy.py", "tests/providers/apache/livy/hooks/test_livy.py"]

Add auth_type to LivyHook

### Apache Airflow Provider(s) apache-livy ### Versions of Apache Airflow Providers apache-airflow-providers-apache-livy==3.0.0 ### Apache Airflow version 2.3.3 (latest released) ### Operating System Ubuntu 18.04 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### What happened This is a feature request as apposed to an issue. I want to use the `LivyHook` to communicate with a Kerberized cluster. As such, I am using `requests_kerberos.HTTPKerberosAuth` as the authentication type. Currently, I am implementing this as follows: ```python from airflow.providers.apache.livy.hooks.livy import LivyHook as NativeHook from requests_kerberos import HTTPKerberosAuth as NativeAuth class HTTPKerberosAuth(NativeAuth): def __init__(self, *ignore_args, **kwargs): super().__init__(**kwargs) class LivyHook(NativeHook): def __init__(self, auth_type=HTTPKerberosAuth, **kwargs): super().__init__(**kwargs) self.auth_type = auth_type ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25179

https://github.com/apache/airflow/pull/25183

ae7bf474109410fa838ab2728ae6d581cdd41808

7d3e799f7e012d2d5c1fe24ce2bea01e68a5a193

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Tasks inside of TaskGroup do not have group_id prepended to task_id

### Apache Airflow version 2.3.3 (latest released) ### What happened As the title states, if you have dynamically mapped tasks inside of a `TaskGroup`, those tasks do not get the `group_id` prepended to their respective `task_id`s. This causes at least a couple of undesirable side effects: 1. Task names are truncated in Grid/Graph* View. The tasks below are named `plus_one` and `plus_two`:   Presumably this is because the UI normally strips off the `group_id` prefix. \* Graph View was very inconsistent in my experience. Sometimes the names are truncated, and sometimes they render correctly. I haven't figured out the pattern behind this behavior. 2. Duplicate `task_id`s between groups result in a `airflow.exceptions.DuplicateTaskIdFound`, even if the `group_id` would normally disambiguate them. ### What you think should happen instead These dynamic tasks inside of a group should have the `group_id` prepended for consistent behavior. ### How to reproduce ``` #!/usr/bin/env python3 import datetime from airflow.decorators import dag, task from airflow.utils.task_group import TaskGroup @dag( start_date=datetime.datetime(2022, 7, 19), schedule_interval=None, ) def test_dag(): with TaskGroup(group_id='group'): @task def plus_one(x: int): return x + 1 plus_one.expand(x=[1, 2, 3]) with TaskGroup(group_id='ggg'): @task def plus_two(x: int): return x + 2 plus_two.expand(x=[1, 2, 3]) dag = test_dag() if __name__ == '__main__': dag.cli() ``` ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Standalone ### Anything else Possibly related: #12309 ### 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/25165

https://github.com/apache/airflow/pull/26081

6a8f0167436b8b582aeb92a93d3f69d006b36f7b

9c4ab100e5b069c86bd00bb7860794df0e32fc2e

python

closed

apache/airflow

https://github.com/apache/airflow

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

Common-SQL Operators Various Bugs

### Apache Airflow Provider(s) common-sql ### Versions of Apache Airflow Providers `apache-airflow-providers-common-sql==1.0.0` ### Apache Airflow version 2.3.3 (latest released) ### Operating System macOS Monterey 12.3.1 ### Deployment Astronomer ### Deployment details _No response_ ### What happened - `SQLTableCheckOperator` builds multiple checks in such a way that if two or more checks are given, and one is not a fully aggregated statement, then the SQL fails as it is missing a `GROUP BY` clause. - `SQLColumnCheckOperator` provides only the last SQL query built from the columns, so when a check fails, it will only give the correct SQL in the exception statement by coincidence. ### What you think should happen instead - Multiple checks should not need a `GROUP BY` clause - Either the correct SQL statement, or no SQL statement, should be returned in the exception message. ### How to reproduce For the `SQLTableCheckOperator`, using the operator like so: ``` table_cheforestfire_costs_table_checkscks = SQLTableCheckOperator( task_id="forestfire_costs_table_checks", table=SNOWFLAKE_FORESTFIRE_COST_TABLE, checks={ "row_count_check": {"check_statement": "COUNT(*) = 9"}, "total_cost_check": {"check_statement": "land_damage_cost + property_damage_cost + lost_profits_cost = total_cost"} } ) ``` For the `SQLColumnCheckOperator`, using the operator like so: ``` cost_column_checks = SQLColumnCheckOperator( task_id="cost_column_checks", table=SNOWFLAKE_COST_TABLE, column_mapping={ "ID": {"null_check": {"equal_to": 0}}, "LAND_DAMAGE_COST": {"min": {"geq_to": 0}}, "PROPERTY_DAMAGE_COST": {"min": {"geq_to": 0}}, "LOST_PROFITS_COST": {"min": {"geq_to": 0}}, } ) ``` and ensuring that any of the `ID`, `LAND_DAMAGE_COST`, or `PROPERTY_DAMAGE_COST` checks fail. An example DAG with the correct environment and data can be found [here](https://github.com/astronomer/airflow-data-quality-demo/blob/main/dags/snowflake_examples/complex_snowflake_transform.py). ### 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/25163

https://github.com/apache/airflow/pull/25164

d66e427c4d21bc479caa629299a786ca83747994

be7cb1e837b875f44fcf7903329755245dd02dc3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagbag.py", "airflow/www/security.py", "tests/models/test_dagbag.py", "tests/www/views/test_views_home.py"]

DAG.access_control can't sync when clean access_control

### Apache Airflow version 2.3.3 (latest released) ### What happened I change my DAG from ```python with DAG( 'test', access_control={'team':{'can_edit','can_read'}}, ) as dag: ... ``` to ```python with DAG( 'test', ) as dag: ... ``` Remove `access_control` arguments, Scheduler can't sync permissions to db. If we write code like this, ```python with DAG( 'test', access_control = {'team': {}} ) as dag: ... ``` It works. ### What you think should happen instead It should clear permissions to `test` DAG on Role `team`. I think we should give a consistent behaviour of permissions sync. If we give `access_control` argument, permissions assigned in Web will clear when we update DAG file. ### How to reproduce _No response_ ### Operating System CentOS Linux release 7.9.2009 (Core) ### Versions of Apache Airflow Providers ``` airflow-code-editor==5.2.2 apache-airflow==2.3.3 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-ftp==3.0.0 apache-airflow-providers-http==3.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-microsoft-psrp==2.0.0 apache-airflow-providers-microsoft-winrm==3.0.0 apache-airflow-providers-mysql==3.0.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-samba==4.0.0 apache-airflow-providers-sftp==3.0.0 apache-airflow-providers-sqlite==3.0.0 apache-airflow-providers-ssh==3.0.0 ``` ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25149

https://github.com/apache/airflow/pull/30340

97ad7cee443c7f4ee6c0fbaabcc73de16f99a5e5

2c0c8b8bfb5287e10dc40b73f326bbf9a0437bb1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/sqs.py", "airflow/providers/amazon/aws/operators/sqs.py", "tests/providers/amazon/aws/operators/test_sqs.py"]

SQSPublishOperator should allow sending messages to a FIFO Queue

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers `apache-airflow-providers-amazon==4.1.0` ### Apache Airflow version 2.2.2 ### Operating System Amazon Linux 2 ### Deployment MWAA ### Deployment details _No response_ ### What happened The current state of the [SQSPublishOperator](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/operators/sqs.py) does not support FIFO queues because: From the Boto3 documentation, a FIFO queue requires a MessageGroupId parameter -> [SQS.Client.send_message](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sqs.html#SQS.Client.send_message) The [SQSHook](https://airflow.apache.org/docs/apache-airflow-providers-amazon/2.4.0/_modules/airflow/providers/amazon/aws/hooks/sqs.html#SQSHook) calls the boto3 client with just these parameters: ``` .send_message( QueueUrl=queue_url, MessageBody=message_body, DelaySeconds=delay_seconds, MessageAttributes=message_attributes or {}, ) ``` so if `queue_url` happens to be a FIFO queue, then that API call will fail and therefore the Airflow Operator will fail too. ### What you think should happen instead The `SQSPublishOperator` should support FIFO queues by accepting the `MessageGroupId` as a parameter. ### How to reproduce 1. Setup a FIFO SQS queue 2. Setup an Airflow operator: ``` task1 = SQSPublishOperator( task_id = task1, sqs_queue = "url", message_content = "Message string", delay_seconds = 0, ) ``` ### Anything else N/A ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/25138

https://github.com/apache/airflow/pull/25171

6839813bc75e62e154fe4163ffa1bda1c8e8cc8f

47b72056c46931aef09d63d6d80fbdd3d9128b09

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "tests/api_connexion/endpoints/test_variable_endpoint.py"]

API `variables/{variable_key}` request fails if key has character `/`

### Apache Airflow version 2.3.2 ### What happened Created a variable e.g. `a/variable` and couldn't get or delete it ### What you think should happen instead i shouldn't've been allowed to create a variable with `/`, or the GET and DELETE should work ### How to reproduce   ``` DELETE /variables/{variable_key} GET /variables/{variable_key} ``` create a variable with `/`, and then try and get it. the get will 404, even after html escape. delete also fails `GET /variables/` works just fine ### Operating System astro ### 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/25103

https://github.com/apache/airflow/pull/25774

98aac5dc282b139f0e726aac512b04a6693ba83d

a1beede41fb299b215f73f987a572c34f628de36

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "airflow/models/taskreschedule.py", "airflow/serialization/serialized_objects.py", "airflow/ti_deps/deps/ready_to_reschedule.py", "tests/models/test_taskinstance.py", "tests/serialization/test_dag_serialization.py", "tests/ti_deps/deps/test_ready_to_reschedule_dep.py"]

Dynamically mapped sensor with mode='reschedule' fails with violated foreign key constraint

### Apache Airflow version 2.3.3 (latest released) ### What happened If you are using [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html) to map a Sensor with `.partial(mode='reschedule')`, and if that sensor fails its poke condition even once, the whole sensor task will immediately die with an error like: ``` [2022-07-14, 10:45:05 CDT] {standard_task_runner.py:92} ERROR - Failed to execute job 19 for task check_reschedule ((sqlite3.IntegrityError) FOREIGN KEY constraint failed [SQL: INSERT INTO task_reschedule (task_id, dag_id, run_id, map_index, try_number, start_date, end_date, duration, reschedule_date) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)] [parameters: ('check_reschedule', 'test_dag', 'manual__2022-07-14T20:44:02.708517+00:00', -1, 1, '2022-07-14 20:45:05.874988', '2022-07-14 20:45:05.900895', 0.025907, '2022-07-14 20:45:10.898820')] (Background on this error at: https://sqlalche.me/e/14/gkpj); 2973372) ``` A similar error arises when using a Postgres backend: ``` [2022-07-14, 11:09:22 CDT] {standard_task_runner.py:92} ERROR - Failed to execute job 17 for task check_reschedule ((psycopg2.errors.ForeignKeyViolation) insert or update on table "task_reschedule" violates foreign key constraint "task_reschedule_ti_fkey" DETAIL: Key (dag_id, task_id, run_id, map_index)=(test_dag, check_reschedule, manual__2022-07-14T21:08:13.462782+00:00, -1) is not present in table "task_instance". [SQL: INSERT INTO task_reschedule (task_id, dag_id, run_id, map_index, try_number, start_date, end_date, duration, reschedule_date) VALUES (%(task_id)s, %(dag_id)s, %(run_id)s, %(map_index)s, %(try_number)s, %(start_date)s, %(end_date)s, %(duration)s, %(reschedule_date)s) RETURNING task_reschedule.id] [parameters: {'task_id': 'check_reschedule', 'dag_id': 'test_dag', 'run_id': 'manual__2022-07-14T21:08:13.462782+00:00', 'map_index': -1, 'try_number': 1, 'start_date': datetime.datetime(2022, 7, 14, 21, 9, 22, 417922, tzinfo=Timezone('UTC')), 'end_date': datetime.datetime(2022, 7, 14, 21, 9, 22, 464495, tzinfo=Timezone('UTC')), 'duration': 0.046573, 'reschedule_date': datetime.datetime(2022, 7, 14, 21, 9, 27, 458623, tzinfo=Timezone('UTC'))}] (Background on this error at: https://sqlalche.me/e/14/gkpj); 2983150) ``` `mode='poke'` seems to behave as expected. As far as I can tell, this affects all Sensor types. ### What you think should happen instead This combination of features should work without error. ### How to reproduce ``` #!/usr/bin/env python3 import datetime from airflow.decorators import dag, task from airflow.sensors.bash import BashSensor @dag( start_date=datetime.datetime(2022, 7, 14), schedule_interval=None, ) def test_dag(): @task def get_tasks(): return ['(($RANDOM % 2 == 0))'] * 10 tasks = get_tasks() BashSensor.partial(task_id='check_poke', mode='poke', poke_interval=5).expand(bash_command=tasks) BashSensor.partial(task_id='check_reschedule', mode='reschedule', poke_interval=5).expand(bash_command=tasks) dag = test_dag() if __name__ == '__main__': dag.cli() ``` ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Standalone ### 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/25095

https://github.com/apache/airflow/pull/25594

84718f92334b7e43607ab617ef31f3ffc4257635

5f3733ea310b53a0a90c660dc94dd6e1ad5755b7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/mssql/hooks/mssql.py", "tests/providers/microsoft/mssql/hooks/test_mssql.py"]

MsSqlHook.get_sqlalchemy_engine uses pyodbc instead of pymssql

### Apache Airflow Provider(s) microsoft-mssql ### Versions of Apache Airflow Providers apache-airflow-providers-microsoft-mssql==2.0.1 ### Apache Airflow version 2.2.2 ### Operating System Ubuntu 20.04 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened `MsSqlHook.get_sqlalchemy_engine` uses the default mssql driver: `pyodbc` instead of `pymssql`. - If pyodbc is installed: we get `sqlalchemy.exc.InterfaceError: (pyodbc.InterfaceError)` - Otherwise we get: `ModuleNotFoundError` PS: Looking at the code it should still apply up to provider version 3.0.0 (lastest version). ### What you think should happen instead The default driver used by `sqlalchemy.create_engine` for mssql is `pyodbc`. To use `pymssql` with `create_engine` we need to have the uri start with `mssql+pymssql://` (currently the hook uses `DBApiHook.get_uri` which starts with `mssql://`. ### How to reproduce ```python >>> from contextlib import closing >>> from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook >>> >>> hook = MsSqlHook() >>> with closing(hook.get_sqlalchemy_engine().connect()) as c: >>> with closing(c.execute("SELECT SUSER_SNAME()")) as res: >>> r = res.fetchone() ``` Will raise an exception due to the wrong driver being used. ### Anything else Demo for sqlalchemy default mssql driver choice: ```bash # pip install sqlalchemy ... Successfully installed sqlalchemy-1.4.39 # pip install pymssql ... Successfully installed pymssql-2.2.5 ``` ```python >>> from sqlalchemy import create_engine >>> create_engine("mssql://test:pwd@test:1433") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<string>", line 2, in create_engine File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/deprecations.py", line 309, in warned return fn(*args, **kwargs) File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/create.py", line 560, in create_engine dbapi = dialect_cls.dbapi(**dbapi_args) File "/usr/local/lib/python3.7/site-packages/sqlalchemy/connectors/pyodbc.py", line 43, in dbapi return __import__("pyodbc") ModuleNotFoundError: No module named 'pyodbc' ``` ### 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/25092

https://github.com/apache/airflow/pull/25185

a01cc5b0b8e4ce3b24970d763e4adccfb4e69f09

df5a54d21d6991d6cae05c38e1562da2196e76aa

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py", "airflow/models/dag.py", "airflow/timetables/base.py", "airflow/timetables/simple.py", "airflow/www/views.py", "newsfragments/25090.significant.rst"]

More natural sorting of DAG runs in the grid view

### Apache Airflow version 2.3.2 ### What happened Dag with schedule to run once every hour. Dag was started manually at 12:44, lets call this run 1 At 13:00 the scheduled run started, lets call this run 2. It appears before run 1 in the grid view. See attached screenshot  ### What you think should happen instead Dags in grid view should appear in the order they are started. ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow==2.3.2 apache-airflow-client==2.1.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.0.2 apache-airflow-providers-docker==3.0.0 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-postgres==5.0.0 apache-airflow-providers-sqlite==2.1.3 ### 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/25090

https://github.com/apache/airflow/pull/25633

a1beede41fb299b215f73f987a572c34f628de36

36eea1c8e05a6791d144e74f4497855e35baeaac

python

closed

apache/airflow

https://github.com/apache/airflow

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

Test that dataset not updated when task skipped

the AIP specifies that when a task is skipped, that we don’t mark the dataset as “updated”. we should simply add a test that verifies that this is what happens (and make changes if necessary) @blag, i tried to make this an issue so i could assign to you but can't. anyway, can reference in PR with `closes`

https://github.com/apache/airflow/issues/25036

https://github.com/apache/airflow/pull/25086

808035e00aaf59a8012c50903a09d3f50bd92ca4

f0c9ac9da6db3a00668743adc9b55329ec567066

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/www/templates/airflow/dag.html", "airflow/www/templates/airflow/dags.html", "airflow/www/views.py", "tests/models/test_dag.py", "tests/www/views/test_views_base.py"]

next run should show deps fulfillment e.g. 0 of 3

on dags page (i.e. the home page) we have a "next run" column. for dataset-driven dags, since we can't know for certain when it will be, we could instead show how many deps are fulfilled, e.g. `0 of 1` and perhaps make it a link to the datasets that the dag is dependened on. here's a sample query that returns the dags which _are_ ready to run. but for this feature you'd need to get the num deps fulfilled and the total num deps. ```python # these dag ids are triggered by datasets, and they are ready to go. dataset_triggered_dag_info_list = { x.dag_id: (x.first_event_time, x.last_event_time) for x in session.query( DatasetDagRef.dag_id, func.max(DDRQ.created_at).label('last_event_time'), func.max(DDRQ.created_at).label('first_event_time'), ) .join( DDRQ, and_( DDRQ.dataset_id == DatasetDagRef.dataset_id, DDRQ.target_dag_id == DatasetDagRef.dag_id, ), isouter=True, ) .group_by(DatasetDagRef.dag_id) .having(func.count() == func.sum(case((DDRQ.target_dag_id.is_not(None), 1), else_=0))) .all() } ```

https://github.com/apache/airflow/issues/25033

https://github.com/apache/airflow/pull/25141

47b72056c46931aef09d63d6d80fbdd3d9128b09

03a81b66de408631147f9353de6ffd3c1df45dbf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/log/cloudwatch_task_handler.py", "airflow/providers/amazon/provider.yaml", "docs/apache-airflow-providers-amazon/index.rst", "generated/provider_dependencies.json", "tests/providers/amazon/aws/log/test_cloudwatch_task_handler.py"]

update watchtower version in amazon provider

### Description there is limitation to version 2 https://github.com/apache/airflow/blob/809d95ec06447c9579383d15136190c0963b3c1b/airflow/providers/amazon/provider.yaml#L48 ### Use case/motivation using up to date version of the library ### Related issues didnt find ### 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/25019

https://github.com/apache/airflow/pull/34747

7764a51ac9b021a77a57707bc7e750168e9e0da0

c01abd1c2eed8f60fec5b9d6cc0232b54efa52de

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/package.json"]

Invalid `package.json` file in `airflow/www`

### Apache Airflow version 2.3.3 (latest released) ### What happened According to npm docs fields `name` and `version` are required in `package.json`, but are not present in `airflow/www/package.json` See: https://docs.npmjs.com/creating-a-package-json-file#required-name-and-version-fields This can confuse some build tools, but also is just in incorrect format. ### What you think should happen instead The fields `name` and `version` should be defined even if they contain just dummy values - we don't need real values since the package is not published in npm registry. ### How to reproduce _No response_ ### Operating System N/A ### 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/25007

https://github.com/apache/airflow/pull/25065

7af840f65813404122702c5511ec67c3b952b3c3

1fd59d61decdc1d7e493eca80a629d02533a4ba0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow doesn't set default task group while calling dag.add_tasks

### Apache Airflow version 2.3.3 (latest released) ### What happened Airflow set default task group while creating operator if dag parameter is set https://github.com/apache/airflow/blob/main/airflow/models/baseoperator.py#L236 However, It doesn't set the default task group while adding a task using dag.add_task function https://github.com/apache/airflow/blob/main/airflow/models/dag.py#L2179 This broke the code at line no https://github.com/apache/airflow/blob/main/airflow/models/taskmixin.py#L312 and getting the error Cannot check for mapped dependants when not attached to a DAG. Please add below line in dag.add_task function also: if dag: task_group = TaskGroupContext.get_current_task_group(dag) if task_group: task_id = task_group.child_id(task_id) ### What you think should happen instead It should not break if task is added using dag.add_task ### How to reproduce don't dag parameter while creating operator object. add task using add_task in dag. ### 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? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24996

https://github.com/apache/airflow/pull/25000

45e5150714e0a5a8e82e3fa6d0b337b92cbeb067

ce0a6e51c2d4ee87e008e28897b2450778b51003

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/oracle/example_dags/__init__.py", "airflow/providers/oracle/example_dags/example_oracle.py", "docs/apache-airflow-providers-oracle/index.rst", "docs/apache-airflow-providers-oracle/operators/index.rst"]

oracle hook _map_param() incorrect

### Apache Airflow Provider(s) oracle ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.3.3 (latest released) ### Operating System OEL 7.6 ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened [_map_param()](https://github.com/apache/airflow/blob/main/airflow/providers/oracle/hooks/oracle.py#L36) function from Oracle hook has an incorrect check of types: ``` PARAM_TYPES = {bool, float, int, str} def _map_param(value): if value in PARAM_TYPES: # In this branch, value is a Python type; calling it produces # an instance of the type which is understood by the Oracle driver # in the out parameter mapping mechanism. value = value() return value ``` `if value in PARAM_TYPES` never gets True for all the mentioned variables types: ``` PARAM_TYPES = {bool, float, int, str} >>> "abc" in PARAM_TYPES False >>> 123 in PARAM_TYPES False >>> True in PARAM_TYPES False >>> float(5.5) in PARAM_TYPES False ``` The correct condition would be `if type(value) in PARAM_TYPES` **But**, if we only fix this condition, next in positive case (type(value) in PARAM_TYPES = True) one more issue occurs with `value = value()` `bool`, `float`, `int` or `str` are not callable `TypeError: 'int' object is not callable` This line is probaby here for passing a python callable into sql statement of procedure params in tasks, is it? If so, need to correct: `if type(value) not in PARAM_TYPES` Here is the full fix: ``` def _map_param(value): if type(value) not in PARAM_TYPES: value = value() return value ``` Next casses are tested: ``` def oracle_callable(n=123): return n def oracle_pass(): return 123 task1 = OracleStoredProcedureOperator( task_id='task1', oracle_conn_id='oracle_conn', procedure='AIRFLOW_TEST', parameters={'var':oracle_callable} ) task2 = OracleStoredProcedureOperator( task_id='task2', oracle_conn_id='oracle_conn', procedure='AIRFLOW_TEST', parameters={'var':oracle_callable()} ) task3 = OracleStoredProcedureOperator( task_id='task3', oracle_conn_id='oracle_conn', procedure='AIRFLOW_TEST', parameters={'var':oracle_callable(456)} ) task4 = OracleStoredProcedureOperator( task_id='task4', oracle_conn_id='oracle_conn', procedure='AIRFLOW_TEST', parameters={'var':oacle_pass} ) ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24953

https://github.com/apache/airflow/pull/30979

130b6763db364426d1d794641b256d7f2ce0b93d

edebfe3f2f2c7fc2b6b345c6bc5f3a82e7d47639

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add support for dynamic databricks connection id

### Apache Airflow Provider(s) databricks ### Versions of Apache Airflow Providers apache-airflow-providers-databricks==3.0.0 # Latest ### Apache Airflow version 2.3.2 (latest released) ### Operating System Linux ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened _No response_ ### What you think should happen instead ### Motivation In a single airflow deployment, we are looking to have the ability to support multiple databricks connections ( `databricks_conn_id`) at runtime. This can be helpful to run the same DAG against multiple testing lanes(a.k.a. different development/testing Databricks environments). ### Potential Solution We can pass the connection id via the Airflow DAG run configuration at runtime. For this, `databricks_conn_id` is required to be a templated field. ### How to reproduce Minor enhancement/new feature ### 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/24938

https://github.com/apache/airflow/pull/24945

7fc5e0b24a8938906ad23eaa1262c9fb74ee2df1

8dfe7bf5ff090a675353a49da21407dffe2fc15e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_dag_decorator.py", "airflow/example_dags/example_sla_dag.py", "airflow/models/dag.py", "docs/spelling_wordlist.txt"]

Type hints for taskflow @dag decorator

### Description I find no type hints when write a DAG use TaskFlowApi. `dag` and `task` decorator is a simple wrapper without detail arguments provide in docstring. ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24936

https://github.com/apache/airflow/pull/25044

61fc4899d71821fd051944d5d9732f7d402edf6c

be63c36bf1667c8a420d34e70e5a5efd7ca42815

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add options to Docker Operator

### Description I'm trying to add options like log-opt max-size 5 and I can't. ### Use case/motivation I'm working in Hummingbot and I would like to offer the community a system to manage multiple bots, rebalance portfolio, etc. Our system needs a terminal to execute commands so currently I'm not able to use airflow to accomplish this task. ### 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/24921

https://github.com/apache/airflow/pull/26653

fd27584b3dc355eaf0c0cd7a4cd65e0e580fcf6d

19d6f54704949d017b028e644bbcf45f5b53120b

python

closed

apache/airflow

https://github.com/apache/airflow

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

Send default email if file "html_content_template" not found

### Apache Airflow version 2.3.2 (latest released) ### What happened I created a new email template to be sent when there are task failures. I accidentally added the path to the `[email] html_content_template` and `[email] subject_template` with a typo and no email was sent. The task's log is the following: ``` Traceback (most recent call last): File "/home/user/.conda/envs/airflow/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1942, in handle_failure self.email_alert(error, task) File "/home/user/.conda/envs/airflow/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2323, in email_alert subject, html_content, html_content_err = self.get_email_subject_content(exception, task=task) File "/home/user/.conda/envs/airflow/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2315, in get_email_subject_content subject = render('subject_template', default_subject) File "/home/user/.conda/envs/airflow/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2311, in render with open(path) as f: FileNotFoundError: [Errno 2] No such file or directory: '/home/user/airflow/config/templates/email_failure_subject.tmpl' ``` I've looked the TaskInstance class (https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py). I've seen that the `render` function (https://github.com/apache/airflow/blob/bcf2c418d261c6244e60e4c2d5de42b23b714bd1/airflow/models/taskinstance.py#L2271) has a `content` parameter, which is not used inside. I guess the solution to this bug is simple: just add a `try - catch` block and return the default content in the `catch` part. ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System CentOS Linux 8 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Conda environment ### 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/24919

https://github.com/apache/airflow/pull/24943

b7f51b9156b780ebf4ca57b9f10b820043f61651

fd6f537eab7430cb10ea057194bfc9519ff0bb38

python

closed

apache/airflow

https://github.com/apache/airflow

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

grid_data api keep refreshing when backfill DAG runs

### Apache Airflow version 2.3.2 (latest released) ### What happened  ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System 186-Ubuntu ### Versions of Apache Airflow Providers 2.3.2 ### 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/24844

https://github.com/apache/airflow/pull/25042

38d6c28f9cf9ee4f663d068032830911f7a8e3a3

de6938e173773d88bd741e43c7b0aa16d8a1a167

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dag disappears when DAG tag is longer than 100 char limit

### Apache Airflow version 2.2.5 ### What happened We added new DAG tags to a couple of our DAGs. In the case when the tag was longer than the 100 character limit the DAG was not showing in the UI and wasn't scheduled. It was however possible to reach it by typing in the URL to the DAG. Usually when DAGs are broken there will be an error message in the UI, but this problem did not render any error message. This problem occurred to one of our templated DAGs. Only one DAG broke and it was the one with a DAG tag which was too long. When we fixed the length, the DAG was scheduled and was visible in the UI again. ### What you think should happen instead Exclude the dag if it is over the 100 character limit or show an error message in the UI. ### How to reproduce Add a DAG tag which is longer than 100 characters. ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details Running Airflow in Kubernetes. Syncing DAGs from S3 with https://tech.scribd.com/blog/2020/breaking-up-the-dag-repo.html ### 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/24820

https://github.com/apache/airflow/pull/25196

a5cbcb56774d09b67c68f87187f2f48d6e70e5f0

4b28635b2085a07047c398be6cc1ac0252a691f7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/operators/python.py", "tests/operators/test_python.py"]

Check if virtualenv is installed fails

### Apache Airflow version 2.3.0 ### What happened When using a `PythonVirtualenvOperator` it is checked if `virtualenv` is installed by `if not shutil.which("virtualenv"):` https://github.com/apache/airflow/blob/a1679be85aa49c0d6a7ba2c31acb519a5bcdf594/airflow/operators/python.py#L398 Actually, this expression checks if `virtualenv` is on PATH. If Airflow is installed in a virtual environment itself and `virtualenv` is not installed in the environment the check might pass but `virtualenv` cannot be used as it is not present in the environment. ### What you think should happen instead It should be checked if `virtualenv` is actually available in the environment. ```python if importlib.util.find_spec("virtualenv") is None: raise AirflowException('PythonVirtualenvOperator requires virtualenv, please install it.') ``` https://stackoverflow.com/a/14050282 ### How to reproduce _No response_ ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/24783

https://github.com/apache/airflow/pull/32939

16e0830a5dfe42b9ab0bbca7f8023bf050bbced0

ddcd474a5e2ce4568cca646eb1f5bce32b4ba0ed

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/secrets/secrets_manager.py"]

AWS secret manager: AccessDeniedException is not a valid Exception

### Apache Airflow version 2.3.1 ### What happened Airflow AWS Secret manager handles `AccesssDeniedException` in [secret_manager.py](https://github.com/apache/airflow/blob/providers-amazon/4.0.0/airflow/providers/amazon/aws/secrets/secrets_manager.py#L272) whereas it's not a valid exception for the client ``` File "/usr/local/lib/python3.9/site-packages/airflow/models/variable.py", line 265, in get_variable_from_secrets var_val = secrets_backend.get_variable(key=key) File "/usr/local/lib/python3.9/site-packages/airflow/providers/amazon/aws/secrets/secrets_manager.py", line 238, in get_variable return self._get_secret(self.variables_prefix, key) File "/usr/local/lib/python3.9/site-packages/airflow/providers/amazon/aws/secrets/secrets_manager.py", line 275, in _get_secret except self.client.exceptions.AccessDeniedException: File "/home/astro/.local/lib/python3.9/site-packages/botocore/errorfactory.py", line 51, in __getattr__ raise AttributeError( AttributeError: <botocore.errorfactory.SecretsManagerExceptions object at 0x7f19cd3c09a0> object has no attribute 'AccessDeniedException'. Valid exceptions are: DecryptionFailure, EncryptionFailure, InternalServiceError, InvalidNextTokenException, InvalidParameterException, InvalidRequestException, LimitExceededException, MalformedPolicyDocumentException, PreconditionNotMetException, PublicPolicyException, ResourceExistsException, ResourceNotFoundException ``` ### What you think should happen instead Handle exception specific to [get_secret_value](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/secretsmanager.html#SecretsManager.Client.get_secret_value) ### How to reproduce This happened during a unique case where the 100s of secrets are loaded at once. I'm assuming the request is hanging over 30s ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.4.0 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24773

https://github.com/apache/airflow/pull/24898

f69e597dfcbb6fa7e7f1a3ff2b5013638567abc3

60c2a3bf82b4fe923b8006f6694f74823af87537

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/serve_logs.py", "newsfragments/24755.improvement.rst"]

Log server on celery worker does not work in IPv6-only setup

### Apache Airflow version 2.2.5 ### What happened I deployed the Airflow helm chart in a Kubernetes cluster that only allows IPv6 traffic. When I want to look at a task log in the UI there is this message: ``` *** Fetching from: http://airflow-v1-worker-0.airflow-v1-worker.airflow.svc.cluster.local:8793/log/my-dag/my-task/2022-06-28T00:00:00+00:00/1.log *** Failed to fetch log file from worker. [Errno 111] Connection refused ``` So the webserver cannot fetch the logfile from the worker. This happens in my opinion because the gunicorn application listens to `0.0.0.0` (IPv4), see [code](https://github.com/apache/airflow/blob/main/airflow/utils/serve_logs.py#L142) or worker log below, and the inter-pod communication in my cluster is IPv6. ``` ~ » k logs airflow-v1-worker-0 -c airflow-worker -p [2022-06-30 14:51:52 +0000] [49] [INFO] Starting gunicorn 20.1.0 [2022-06-30 14:51:52 +0000] [49] [INFO] Listening at: http://0.0.0.0:8793 (49) [2022-06-30 14:51:52 +0000] [49] [INFO] Using worker: sync [2022-06-30 14:51:52 +0000] [50] [INFO] Booting worker with pid: 50 [2022-06-30 14:51:52 +0000] [51] [INFO] Booting worker with pid: 51 -------------- celery@airflow-v1-worker-0 v5.2.3 (dawn-chorus) --- ***** ----- -- ******* ---- Linux-5.10.118-x86_64-with-glibc2.28 2022-06-30 14:51:53 - *** --- * --- - ** ---------- [config] - ** ---------- .> app: airflow.executors.celery_executor:0x7f73b8d23d00 - ** ---------- .> transport: redis://:**@airflow-v1-redis-master.airflow.svc.cluster.local:6379/1 - ** ---------- .> results: postgresql://airflow:**@airflow-v1-pgbouncer.airflow.svc.cluster.local:6432/airflow_backend_db - *** --- * --- .> concurrency: 16 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** ----- -------------- [queues] .> default exchange=default(direct) key=default [tasks] . airflow.executors.celery_executor.execute_command ``` ### What you think should happen instead The gunicorn webserver should (configurably) listen to IPv6 traffic. ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24755

https://github.com/apache/airflow/pull/24846

7f749b653ce363b1450346b61c7f6c406f72cd66

2f29bfefb59b0014ae9e5f641d3f6f46c4341518

python

closed

apache/airflow

https://github.com/apache/airflow

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

Allow back script_location in Glue to be None *again*

### Apache Airflow version 2.3.2 (latest released) ### What happened On this commit someone broke the AWS Glue provider by enforcing the script_location to be a string: https://github.com/apache/airflow/commit/27b77d37a9b2e63e95a123c31085e580fc82b16c Then someone realized that (see comment thread [there](https://github.com/apache/airflow/commit/27b77d37a9b2e63e95a123c31085e580fc82b16c#r72466413)) and created a new PR to allow None to be parsed again here: https://github.com/apache/airflow/pull/23357 But the parameters no longer have the `Optional[str]` typing and now the error persists with this traceback: ```Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/glue.py", line 163, in __init__ super().__init__(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/baseoperator.py", line 373, in apply_defaults raise AirflowException(f"missing keyword argument {missing_args.pop()!r}") airflow.exceptions.AirflowException: missing keyword argument 'script_location' ``` ### What you think should happen instead Please revert the change and add `Optional[str]` here: https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/operators/glue.py#L69 ### How to reproduce Use the class without a script_location ### Operating System Linux ### Versions of Apache Airflow Providers Apache airflow 2.3.2 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24753

https://github.com/apache/airflow/pull/24754

1b3905ef6eb5630e8d12975d9e91600ffb832471

49925be66483ce942bcd4827df9dbd41c3ef41cf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/kubernetes/kube_client.py", "tests/kubernetes/test_client.py"]

Configuring retry policy of the the kubernetes CoreV1Api ApiClient

### Description Can we add the option to configure the Retry policy of the kubernetes CoreV1Api? Or set it to default have some more resilient configuration. Today it appears to retry operations 3 times but with 0 backoff in between each try. Causing temporary network glitches to result in fatal errors. Following the flow below: 1. `airflow.kubernetes.kube_client.get_kube_client()` Calls `load_kube_config()` without any configuration set, this assigns a default configuration with `retries=None` to `CoreV1Api.set_default()` 1b. Creates `CoreV1Api()` with `api_client=None` 1c. `ApiClient()` default constructor creates a default configuration object via `Configuration.get_default_copy(), this is the default injected above` 2. On request, through some complicated flow inside `ApiClient` and urllib3, this `configuration.retries` eventually finds its way into urllib `HTTPConnectionPool`, where if unset, it uses `urllib3.util.Retry.DEFAULT`, this has a policy of 3x retries with 0 backoff time in between. ------ Configuring the ApiClient would mean changing the `get_kube_client()` to something roughly resembling: ``` client_config = Configuration() client_config.retries = Retry(total=3, backoff=LOAD_FROM_CONFIG) config.load_kube_config(...., client_configuration=client_config) apiclient = ApiClient(client_config) return CoreV1Api(apiclient) ``` I don't know myself how fine granularity is best to expose to be configurable from airflow. The retry object has a lot of different options, so do the rest of the kubernetes client Configuration object. Maybe it should be injected from a plugin rather than config-file? Maybe urllib or kubernets library have other ways to set default config? ### Use case/motivation Our Kubernetes API server had some unknown hickup for 10 seconds, this caused the Airflow kubernetes executor to crash, restarting airflow and then it started killing pods that were running fine, showing following log: "Reset the following 1 orphaned TaskInstances" If the retries would have had some backoff it would have likely survived this hickup. See attachment for the full stack trace, it's too long to include inline. Here is the most interesting parts: ``` 2022-06-29 21:25:49 Class={kubernetes_executor.py:111} Level=ERROR Unknown error in KubernetesJobWatcher. Failing ... 2022-06-29 21:25:49 Class={connectionpool.py:810} Level=WARNING Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fbe35de0c70>: Failed to establish a new connection: [Errno 111] Connection refused')': /api/v1/namespaces/default/pods/REDACTED 2022-06-29 21:25:49 urllib3.exceptions.ProtocolError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read)) 2022-06-29 21:25:49 Class={connectionpool.py:810} Level=WARNING Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fbe315ec040>: Failed to establish a new connection: [Errno 111] Connection refused')': /api/v1/namespaces/default/pods/REDACTED 2022-06-29 21:25:49 Class={connectionpool.py:810} Level=WARNING Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fbe315ec670>: Failed to establish a new connection: [Errno 111] Connection refused')': /api/v1/namespaces/default/pods/REDACTED ... 2022-06-29 21:25:50 Class={kubernetes_executor.py:813} Level=INFO Shutting down Kubernetes executor ... 2022-06-29 21:26:08 Class={scheduler_job.py:696} Level=INFO Starting the scheduler ... 2022-06-29 21:27:29 Class={scheduler_job.py:1285} Level=INFO Message=Reset the following 1 orphaned TaskInstances: ``` [airflowkubernetsretrycrash.log](https://github.com/apache/airflow/files/9017815/airflowkubernetsretrycrash.log) From airflow version 2.3.2 ### 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/24748

https://github.com/apache/airflow/pull/29809

440bf46ff0b417c80461cf84a68bd99d718e19a9

dcffbb4aff090e6c7b6dc96a4a68b188424ae174

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/time_sensor.py", "tests/sensors/test_time_sensor.py"]

TimeSensorAsync breaks if target_time is timezone-aware

### Apache Airflow version 2.3.2 (latest released) ### What happened `TimeSensorAsync` fails with the following error if `target_time` is aware: ``` [2022-06-29, 05:09:11 CDT] {taskinstance.py:1889} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/sensors/time_sensor.py", line 60, in execute trigger=DateTimeTrigger(moment=self.target_datetime), File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/triggers/temporal.py", line 42, in __init__ raise ValueError(f"The passed datetime must be using Pendulum's UTC, not {moment.tzinfo!r}") ValueError: The passed datetime must be using Pendulum's UTC, not Timezone('America/Chicago') ``` ### What you think should happen instead Given the fact that `TimeSensor` correctly handles timezones (#9882), this seems like a bug. `TimeSensorAsync` should be a drop-in replacement for `TimeSensor`, and therefore should have the same timezone behavior. ### How to reproduce ``` #!/usr/bin/env python3 import datetime from airflow.decorators import dag from airflow.sensors.time_sensor import TimeSensor, TimeSensorAsync import pendulum @dag( start_date=datetime.datetime(2022, 6, 29), schedule_interval='@daily', ) def time_sensor_dag(): naive_time1 = datetime.time( 0, 1) aware_time1 = datetime.time( 0, 1).replace(tzinfo=pendulum.local_timezone()) naive_time2 = pendulum.time(23, 59) aware_time2 = pendulum.time(23, 59).replace(tzinfo=pendulum.local_timezone()) TimeSensor(task_id='naive_time1', target_time=naive_time1, mode='reschedule') TimeSensor(task_id='naive_time2', target_time=naive_time2, mode='reschedule') TimeSensor(task_id='aware_time1', target_time=aware_time1, mode='reschedule') TimeSensor(task_id='aware_time2', target_time=aware_time2, mode='reschedule') TimeSensorAsync(task_id='async_naive_time1', target_time=naive_time1) TimeSensorAsync(task_id='async_naive_time2', target_time=naive_time2) TimeSensorAsync(task_id='async_aware_time1', target_time=aware_time1) # fails TimeSensorAsync(task_id='async_aware_time2', target_time=aware_time2) # fails dag = time_sensor_dag() ``` This can also happen if the `target_time` is naive and `core.default_timezone = system`. ### Operating System CentOS Stream 8 ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Standalone ### 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/24736

https://github.com/apache/airflow/pull/25221

f53bd5df2a0b370a14f811b353229ad3e9c66662

ddaf74df9b1e9a4698d719f81931e822b21b0a95

python

closed

apache/airflow

https://github.com/apache/airflow

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

Trigger DAG from templated view tab producing bad request

### Body Reproduced on main branch. The bug: When clicking Trigger DAG from templated view tab it resulted in a BAD REQUEST page however DAG run is created (it also produce the UI alert "it should start any moment now") To compare trying to trigger DAG from log tab works as expected so the issue seems to be relevant only to to the specific tab.  ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/24725

https://github.com/apache/airflow/pull/25729

f24e706ff7a84fd36ea39dc3399346c357d40bd9

69663b245a9a67b6f05324ce7b141a1bd9b05e0a

python

closed

apache/airflow

https://github.com/apache/airflow

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

Error for Hive Server2 Connection Document

### What do you see as an issue? In this Document https://airflow.apache.org/docs/apache-airflow-providers-apache-hive/stable/connections/hiveserver2.html Describe , In Extra must use the "auth_mechanism " but in the sources Code used "authMechanism". ### Solving the problem use same words. ### Anything else None ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24692

https://github.com/apache/airflow/pull/24713

13908c2c914cf08f9d962a4d3b6ae54fbdf1d223

cef97fccd511c8e5485df24f27b82fa3e46929d7

python

closed

apache/airflow

https://github.com/apache/airflow

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

Docker is not pushing last line over xcom

### Apache Airflow Provider(s) docker ### Versions of Apache Airflow Providers apache-airflow-providers-docker==2.7.0 docker==5.0.3 ### Apache Airflow version 2.3.2 (latest released) ### Operating System 20.04.4 LTS (Focal Fossa) ### Deployment Docker-Compose ### Deployment details Deployed using docker compose command ### What happened Below is my dockeroperator code ``` extract_data_from_presto = DockerOperator( task_id='download_data', image=IMAGE_NAME, api_version='auto', auto_remove=True, mount_tmp_dir=False, docker_url='unix://var/run/docker.sock', network_mode="host", tty=True, xcom_all=False, mounts=MOUNTS, environment={ "PYTHONPATH": "/opt", }, command=f"test.py", retries=3, dag=dag, ) ``` Last line printed in docker is not getting pushed over xcom. In my case last line in docker is `[2022-06-27, 08:31:34 UTC] {docker.py:312} INFO - {"day": 20220627, "batch": 1656318682, "source": "all", "os": "ubuntu"}` However the xcom value returned shown in UI is empty <img width="1329" alt="image" src="https://user-images.githubusercontent.com/25153155/175916850-8f50c579-9d26-44bc-94ae-6d072701ff0b.png"> ### What you think should happen instead It should have return the `{"day": 20220627, "batch": 1656318682, "source": "all", "os": "ubuntu"}` as output of return_value ### How to reproduce I am not able to exactly produce it with example but it's failing with my application. So I extended the DockerOperator class in my code & copy pasted the `_run_image_with_mounts` method and added 2 print statements ``` print(f"log lines from attach {log_lines}") try: if self.xcom_all: return [stringify(line).strip() for line in self.cli.logs(**log_parameters)] else: lines = [stringify(line).strip() for line in self.cli.logs(**log_parameters, tail=1)] print(f"lines from logs: {lines}") ``` Value of log_lines comes from this [line](https://github.com/apache/airflow/blob/main/airflow/providers/docker/operators/docker.py#L309) The output of this is as below. First line is last print in my docker code ``` [2022-06-27, 14:43:26 UTC] {pipeline.py:103} INFO - {"day": 20220627, "batch": 1656340990, "os": "ubuntu", "source": "all"} [2022-06-27, 14:43:27 UTC] {logging_mixin.py:115} INFO - log lines from attach ['2022-06-27, 14:43:15 UTC - root - read_from_presto - INFO - Processing datetime is 2022-06-27 14:43:10.755685', '2022-06-27, 14:43:15 UTC - pyhive.presto - presto - INFO - SHOW COLUMNS FROM <truncated data as it's too long>, '{"day": 20220627, "batch": 1656340990, "os": "ubuntu", "source": "all"}'] [2022-06-27, 14:43:27 UTC] {logging_mixin.py:115} INFO - lines from logs: ['{', '"', 'd', 'a', 'y', '"', ':', '', '2', '0', '2', '2', '0', '6', '2', '7', ',', '', '"', 'b', 'a', 't', 'c', 'h', '"', ':', '', '1', '6', '5', '6', '3', '4', '0', '9', '9', '0', ',', '', '"', 'o', 's', '"', ':', '', '"', 'u', 'b', 'u', 'n', 't', 'u', '"', ',', '', '"', 's', 'o', 'u', 'r', 'c', 'e', '"', ':', '', '"', 'a', 'l', 'l', '"', '}', '', ''] ``` From above you can see for some unknown reason `self.cli.logs(**log_parameters, tail=1)` returns array of characters. This changes was brough as part of [change](https://github.com/apache/airflow/commit/2f4a3d4d4008a95fc36971802c514fef68e8a5d4) Before that it was returning the data from log_lines My suggestion to modify the code as below ``` if self.xcom_all: return [stringify(line).strip() for line in log_lines] else: lines = [stringify(line).strip() for line in log_lines] return lines[-1] if lines else None ``` ### 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/24681

https://github.com/apache/airflow/pull/24726

6fd06fa8c274b39e4ed716f8d347229e017ba8e5

cc6a44bdc396a305fd53c7236427c578e9d4d0b7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/templates.py"]

Macro prev_execution_date is always empty

### Apache Airflow version 2.3.2 (latest released) ### What happened The variable `prev_execution_date` is empty on the first run meaning, all usage will automatically trigger a None error. ### What you think should happen instead A default date should be provided instead, either the DAG's `start_date` or a default `datetime.min` as during the first run, it will always trigger an error effectively preventing the DAG from running and hence, always returning an error. ### How to reproduce Pass the variables/macros to any Task: ``` { "execution_datetime": '{{ ts_nodash }}', "prev_execution_datetime": '{{ prev_start_date_success | ts_nodash }}' #.strftime("%Y%m%dT%H%M%S") } ``` Whilst the logical execution date (`execution_datetime`) works, the previous succesful logical execution date `prev_execution_datetime` automatically blows up when applying the `ts_nodash` filter. This effectively makes it impossible to use said macro ever, as it will always fail. ### Operating System Ubuntu ### 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/24678

https://github.com/apache/airflow/pull/25593

1594d7706378303409590c57ab1b17910e5d09e8

741c20770230c83a95f74fe7ad7cc9f95329f2cc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/operators/trigger_dagrun.py"]

Mapped TriggerDagRunOperator causes SerializationError due to operator_extra_links 'property' object is not iterable

### Apache Airflow version 2.3.2 (latest released) ### What happened Hi, I have a kind of issue with launching several subDags via mapping TriggerDagRunOperator (mapping over `conf` parameter). Here is the demo example of my typical DAG: ```python from airflow import DAG from airflow.operators.python_operator import PythonOperator from airflow.operators.trigger_dagrun import TriggerDagRunOperator from airflow import XComArg from datetime import datetime with DAG( 'triggerer', schedule_interval=None, catchup=False, start_date=datetime(2019, 12, 2) ) as dag: t1 = PythonOperator( task_id='first', python_callable=lambda : list(map(lambda i: {"x": i}, list(range(10)))), ) t2 = TriggerDagRunOperator.partial( task_id='second', trigger_dag_id='mydag' ).expand(conf=XComArg(t1)) t1 >> t2 ``` But when Airflow tries to import such DAG it throws the following SerializationError (which I observed both in UI and in $AIRFLOW_HOME/logs/scheduler/latest/<my_dag_name>.py.log): ``` Broken DAG: [/home/aliona/airflow/dags/triggerer_dag.py] Traceback (most recent call last): File "/home/aliona/airflow/venv/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 638, in _serialize_node serialize_op['_operator_extra_links'] = cls._serialize_operator_extra_links( File "/home/aliona/airflow/venv/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 933, in _serialize_operator_extra_links for operator_extra_link in operator_extra_links: TypeError: 'property' object is not iterable During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/aliona/airflow/venv/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 1106, in to_dict json_dict = {"__version": cls.SERIALIZER_VERSION, "dag": cls.serialize_dag(var)} File "/home/aliona/airflow/venv/lib/python3.10/site-packages/airflow/serialization/serialized_objects.py", line 1014, in serialize_dag raise SerializationError(f'Failed to serialize DAG {dag.dag_id!r}: {e}') airflow.exceptions.SerializationError: Failed to serialize DAG 'triggerer': 'property' object is not iterable ``` How it appears in the UI:  ### What you think should happen instead I think that TriggerDagRunOperator mapped over `conf` parameter should serialize and work well by default. During the debugging process and trying to make everything work I found out that simple non-mapped TriggerDagRunOperator has value `['Triggered DAG']` in `operator_extra_links` field, so, it is Lisr. But as for mapped TriggerDagRunOperator, it is 'property'. I don't have any idea why during the serialization process Airflow cannot get value of this property, but I tried to reinitialize this field with `['Triggered DAG']` value and finally I fixed this issue in a such way. For now, for every case of using mapped TriggerDagRunOperator I also use such code at the end of my dag file: ```python # here 'second' is the name of corresponding mapped TriggerDagRunOperator task (see demo code above) t2_patch = dag.task_dict['second'] t2_patch.operator_extra_links=['Triggered DAG'] dag.task_dict.update({'second': t2_patch}) ``` So, for every mapped TriggerDagRunOperator task I manually change value of operator_extra_links property to `['Triggered DAG']` and as a result there is no any SerializationError. I have a lot of such cases, and all of them are working good with this fix, all subDags are launched, mapped configuration is passed correctly. Also I can wait for end of their execution or not, all this options also work correctly. ### How to reproduce 1. Create dag with mapped TriggerDagRunOperator tasks (main parameters such as task_id, trigger_dag_id and others are in `partial section`, in `expand` section use conf parameter with non-empty iterable value), as, for example: ```python t2 = TriggerDagRunOperator.partial( task_id='second', trigger_dag_id='mydag' ).expand(conf=[{'x': 1}]) ``` 2. Try to serialize dag, and error will appear. The full example of failing dag file: ```python from airflow import DAG from airflow.operators.python_operator import PythonOperator from airflow.operators.trigger_dagrun import TriggerDagRunOperator from airflow import XComArg from datetime import datetime with DAG( 'triggerer', schedule_interval=None, catchup=False, start_date=datetime(2019, 12, 2) ) as dag: t1 = PythonOperator( task_id='first', python_callable=lambda : list(map(lambda i: {"x": i}, list(range(10)))), ) t2 = TriggerDagRunOperator.partial( task_id='second', trigger_dag_id='mydag' ).expand(conf=[{'a': 1}]) t1 >> t2 # uncomment these lines to fix an error # t2_patch = dag.task_dict['second'] # t2_patch.operator_extra_links=['Triggered DAG'] # dag.task_dict.update({'second': t2_patch}) ``` As subDag ('mydag') I use these DAG: ```python from airflow import DAG from airflow.operators.python_operator import PythonOperator from datetime import datetime with DAG( 'mydag', schedule_interval=None, catchup=False, start_date=datetime(2019, 12, 2) ) as dag: t1 = PythonOperator( task_id='first', python_callable=lambda : print("first"), ) t2 = PythonOperator( task_id='second', python_callable=lambda : print("second"), ) t1 >> t2 ``` ### Operating System Ubuntu 22.04 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-sqlite==2.1.3 ### Deployment Virtualenv installation ### Deployment details Python 3.10.4 pip 22.0.2 ### Anything else Currently for demonstration purposes I am using fully local Airflow installation: single node, SequentialExecutor and SQLite database backend. But such issue also appeared for multi-node installation with either CeleryExecutor or LocalExecutor and PostgreSQL database in the backend. ### 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/24653

https://github.com/apache/airflow/pull/24676

48ceda22bdbee50b2d6ca24767164ce485f3c319

8dcafdfcdddc77fdfd2401757dcbc15bfec76d6b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/oracle/hooks/oracle.py", "airflow/utils/db.py", "docs/apache-airflow-providers-oracle/connections/oracle.rst", "tests/providers/oracle/hooks/test_oracle.py"]

Failed to retrieve data from Oracle database with UTF-8 charset

### Apache Airflow Provider(s) oracle ### Versions of Apache Airflow Providers apache-airflow-providers-oracle==3.1.0 ### Apache Airflow version 2.3.2 (latest released) ### Operating System Linux 4.19.79-1.el7.x86_64 ### Deployment Docker-Compose ### Deployment details Oracle Database 12c Enterprise Edition Release 12.2.0.1.0 Python: 3.8 Oracle database charset: UTF-8 (returned by `SELECT value FROM nls_database_parameters WHERE parameter = 'NLS_NCHAR_CHARACTERSET'`) Oracle's client environment: - LC_CTYPE=C.UTF-8 - NLS_LANG=AMERICAN_AMERICA.CL8MSWIN1251 - LC_ALL=C.UTF-8 ### What happened Any query to Oracle database with UTF8 charset failed with error: > oracledb.exceptions.NotSupportedError: DPY-3012: national character set id 871 is not supported by python-oracledb in thin mode ### What you think should happen instead Definetelly, it should work, as it was in previous Oracle provider version (3.0.0). Quick search shows that `python-oracledb` package, which replaces `cx_Oracle` in 3.1.0, uses **thin** driver mode by default, and it seems that UTF-8 codepage is not supported in that mode ( [see this issue](https://stackoverflow.com/questions/72465536/python-oracledb-new-cx-oracle-connection-generating-notsupportederror-dpy-3012) ). In order to get to thick mode, a call to `oracledb.init_oracle_client()` is required before any connection made ( [see here](https://python-oracledb.readthedocs.io/en/latest/api_manual/module.html#oracledb.init_oracle_client) ). Indeed, if I add this call to `airflow/providers/oracle/hooks/oracle.py`, everything works fine. Resulting code looks like this: ``` import math import warnings from datetime import datetime from typing import Dict, List, Optional, Union import oracledb oracledb.init_oracle_client() ... ``` Downgrade to version 3.0.0 also helps, but I suppose it should be some permanent solution, like adding a configuration parameter or so. ### How to reproduce - Setup an Oracle database with UTF8 charset - Setup an Airflow connection with `oracle` type - Create an operator which issues a `SELECT` statement against the database ### Anything else Task execution log as follows: > [2022-06-23, 17:35:36 MSK] {task_command.py:370} INFO - Running <TaskInstance: nip-stage-load2.load-dict.load-sa_user scheduled__2022-06-22T00:00:00+00:00 [running]> on host dwh_develop_scheduler > [2022-06-23, 17:35:37 MSK] {taskinstance.py:1569} INFO - Exporting the following env vars: > AIRFLOW_CTX_DAG_EMAIL=airflow@example.com > AIRFLOW_CTX_DAG_OWNER=airflow > AIRFLOW_CTX_DAG_ID=nip-stage-load2 > AIRFLOW_CTX_TASK_ID=load-dict.load-sa_user > AIRFLOW_CTX_EXECUTION_DATE=2022-06-22T00:00:00+00:00 > AIRFLOW_CTX_TRY_NUMBER=1 > AIRFLOW_CTX_DAG_RUN_ID=scheduled__2022-06-22T00:00:00+00:00 > [2022-06-23, 17:35:37 MSK] {base.py:68} INFO - Using connection ID 'nip_standby' for task execution. > [2022-06-23, 17:35:37 MSK] {base.py:68} INFO - Using connection ID 'stage' for task execution. > [2022-06-23, 17:35:37 MSK] {data_transfer.py:198} INFO - Executing: > SELECT * FROM GMP.SA_USER > [2022-06-23, 17:35:37 MSK] {base.py:68} INFO - Using connection ID 'nip_standby' for task execution. > [2022-06-23, 17:35:37 MSK] {taskinstance.py:1889} ERROR - Task failed with exception > Traceback (most recent call last): > File "/home/airflow/.local/lib/python3.8/site-packages/dwh_etl/operators/data_transfer.py", line 265, in execute > if not self.no_check and self.compare_datasets(self.object_name, src, dest): > File "/home/airflow/.local/lib/python3.8/site-packages/dwh_etl/operators/data_transfer.py", line 199, in compare_datasets > src_df = src.get_pandas_df(sql) > File "/home/airflow/.local/lib/python3.8/site-packages/airflow/hooks/dbapi.py", line 128, in get_pandas_df > with closing(self.get_conn()) as conn: > File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/oracle/hooks/oracle.py", line 149, in get_conn > conn = oracledb.connect(**conn_config) > File "/home/airflow/.local/lib/python3.8/site-packages/oracledb/connection.py", line 1000, in connect > return conn_class(dsn=dsn, pool=pool, params=params, **kwargs) > File "/home/airflow/.local/lib/python3.8/site-packages/oracledb/connection.py", line 128, in __init__ > impl.connect(params_impl) > File "src/oracledb/impl/thin/connection.pyx", line 345, in oracledb.thin_impl.ThinConnImpl.connect > File "src/oracledb/impl/thin/connection.pyx", line 163, in oracledb.thin_impl.ThinConnImpl._connect_with_params > File "src/oracledb/impl/thin/connection.pyx", line 129, in oracledb.thin_impl.ThinConnImpl._connect_with_description > File "src/oracledb/impl/thin/connection.pyx", line 250, in oracledb.thin_impl.ThinConnImpl._connect_with_address > File "src/oracledb/impl/thin/protocol.pyx", line 197, in oracledb.thin_impl.Protocol._connect_phase_two > File "src/oracledb/impl/thin/protocol.pyx", line 263, in oracledb.thin_impl.Protocol._process_message > File "src/oracledb/impl/thin/protocol.pyx", line 242, in oracledb.thin_impl.Protocol._process_message > File "src/oracledb/impl/thin/messages.pyx", line 280, in oracledb.thin_impl.Message.process > File "src/oracledb/impl/thin/messages.pyx", line 2094, in oracledb.thin_impl.ProtocolMessage._process_message > File "/home/airflow/.local/lib/python3.8/site-packages/oracledb/errors.py", line 103, in _raise_err > raise exc_type(_Error(message)) from cause > oracledb.exceptions.NotSupportedError: DPY-3012: national character set id 871 is not supported by python-oracledb in thin mode ### 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/24618

https://github.com/apache/airflow/pull/26576

ee21c1bac4cb5bb1c19ea9e5e84ee9b5854ab039

b254a9f4bead4e5d4f74c633446da38550f8e0a1

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/system/providers/databricks/example_databricks_sql.py"]

Error in docstring in the example DAG for Databricks example_databricks_sql.py

### What do you see as an issue? The docstring in example_databricks_sql.py code doesn't explain the code in that file. It seems to be copied from example_databricks.py file. ### Solving the problem By putting the right docstring for the file example_databricks_sql.py ### 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/24597

https://github.com/apache/airflow/pull/26157

6045f7ad697e2bdb934add1a8aeae5a817306b22

5948d7fd100841cc623caeba438e97b640c2df90

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/airbyte/hooks/airbyte.py", "airflow/providers/airbyte/operators/airbyte.py", "tests/providers/airbyte/hooks/test_airbyte.py"]

`AirbyteHook` add cancel job option

### Apache Airflow Provider(s) airbyte ### Versions of Apache Airflow Providers I want to cancel the job if it running more than specific time . Task is getting timeout however, airbyte job was not cancelled. it seems, on kill feature has not implemented Workaround: Create a custom operator and implement cancel hook and call it in on kill function. def on_kill(self): if (self.job_id): self.log.error('on_kill: stopping airbyte Job %s',self.job_id) self.hook.cancel_job(self.job_id) ### Apache Airflow version 2.0.2 ### Operating System Linux ### Deployment MWAA ### Deployment details Airflow 2.0.2 ### What happened airbyte job was not cancelled upon timeout ### What you think should happen instead it should cancel the job ### How to reproduce Make sure job runs more than timeout sync_source_destination = AirbyteTriggerSyncOperator( task_id=f'airbyte_{key}', airbyte_conn_id='airbyte_con', connection_id=key, asynchronous=False, execution_timeout=timedelta(minutes=2) ) ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24574

https://github.com/apache/airflow/pull/24593

45b11d4ed1412c00ebf32a03ab5ea3a06274f208

c118b2836f7211a0c3762cff8634b7b9a0d1cf0b

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-snowflake/connections/snowflake.rst"]

Snowflake Provider connection documentation is misleading

### What do you see as an issue? Relevant page: https://airflow.apache.org/docs/apache-airflow-providers-snowflake/stable/connections/snowflake.html ## Behavior in the Airflow package The `SnowflakeHook` object in Airflow behaves oddly compared to some other database hooks like Postgres (so extra clarity in the documentation is beneficial). Most notably, the `SnowflakeHook` does _not_ make use of the either the `host` or `port` of the `Connection` object it consumes. It is completely pointless to specify these two fields. When constructing the URL in a runtime context, `snowflake.sqlalchemy.URL` is used for parsing. `URL()` allows for either `account` or `host` to be specified as kwargs. Either one of these 2 kwargs will correspond with what we'd conventionally call the host in a typical URL's anatomy. However, because `SnowflakeHook` never parses `host`, any `host` defined in the Connection object would never get this far into the parsing. ## Issue with the documentation Right now the documentation does not make clear that it is completely pointless to specify the `host`. The documentation correctly omits the port, but says that the host is optional. It does not warn the user about this field never being consumed at all by the `SnowflakeHook` ([source here](https://github.com/apache/airflow/blob/main/airflow/providers/snowflake/hooks/snowflake.py)). This can lead to some confusion especially because the Snowflake URI consumed by `SQLAlchemy` (which many people using Snowflake will be familiar with) uses either the "account" or "host" as its host. So a user coming from SQLAlchemy may think it is fine to post the account as the "host" and skip filling in the "account" inside the extras (after all, it's "extra"), whereas that doesn't work. I would argue that if it is correct to omit the `port` in the documentation (which it is), then `host` should also be excluded. Furthermore, the documentation reinforces this confusion with the last few lines, where an environment variable example connection is defined that uses a host. Finally, the documentation says "When specifying the connection in environment variable you should specify it using URI syntax", which is no longer true as of 2.3.0. ### Solving the problem I have 3 proposals for how the documentation should be updated to better reflect how the `SnowflakeHook` actually works. 1. The `Host` option should not be listed as part of the "Configuring the Connection" section. 2. The example URI should remove the host. The new example URI would look like this: `snowflake://user:password@/db-schema?account=account&database=snow-db&region=us-east&warehouse=snow-warehouse`. This URI with a blank host works fine; you can test this yourself: ```python from airflow.models.connection import Connection c = Connection(conn_id="foo", uri="snowflake://user:password@/db-schema?account=account&database=snow-db&region=us-east&warehouse=snow-warehouse") print(c.host) print(c.extra_dejson) ``` 3. An example should be provided of a valid Snowflake construction using the JSON. This example would not only work on its own merits of defining an environment variable connection valid for 2.3.0, but it also would highlight some of the idiosyncrasies of how Airflow defines connections to Snowflake. This would also be valuable as a reference for the AWS `SecretsManagerBackend` for when `full_url_mode` is set to `False`. ### Anything else I wasn't sure whether to label this issue as a provider issue or documentation issue; I saw templates for either but not both. ### 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/24572

https://github.com/apache/airflow/pull/24573

02d8f96bfbc43e780db0220dd7647af0c0f46093

2fb93f88b120777330b6ed13b24fa07df279c41e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/0080_2_0_2_change_default_pool_slots_to_1.py"]

Migration changes column to NOT NULL without updating NULL data first

### Apache Airflow version 2.3.2 (latest released) ### What happened During upgrade from Airflow 1.x, I've encountered migration failure in migration https://github.com/apache/airflow/blob/05c542dfa8eee9b4cdca4e9370f459ce807354b2/airflow/migrations/versions/0080_2_0_2_change_default_pool_slots_to_1.py In PR #20962 on these lines https://github.com/apache/airflow/pull/20962/files#diff-9e46226bab06a05ef0040d1f8cc08c81ba94455ca9a170a0417352466242f2c1L61-L63 the update was removed, which breaks if the original table contains nulls in that column (at least in postgres DB). ### What you think should happen instead _No response_ ### How to reproduce - Have pre 2.0.2 version deployed, where the column was nullable. - Have task instance with `pool_slots = NULL` - Try to migrate to latest version (or any version after #20962 was merged) ### Operating System Custom NixOS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details We have NixOS with Airflow installed using setup.py with postgres as a DB. ### Anything else ``` INFO [alembic.runtime.migration] Running upgrade 449b4072c2da -> 8646922c8a04, Change default ``pool_slots`` to ``1`` Traceback (most recent call last): File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) psycopg2.errors.NotNullViolation: column "pool_slots" contains null values The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/bin/.airflow-wrapped", line 9, in <module> sys.exit(main()) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/cli/commands/db_command.py", line 35, in initdb db.initdb() File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/utils/db.py", line 648, in initdb upgradedb(session=session) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/utils/db.py", line 1449, in upgradedb command.upgrade(config, revision=to_revision or 'heads') File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/command.py", line 320, in upgrade script.run_env() File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/script/base.py", line 563, in run_env util.load_python_file(self.dir, "env.py") File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/util/pyfiles.py", line 92, in load_python_file module = load_module_py(module_id, path) File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/util/pyfiles.py", line 108, in load_module_py spec.loader.exec_module(module) # type: ignore File "<frozen importlib._bootstrap_external>", line 850, in exec_module File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/migrations/env.py", line 107, in <module> run_migrations_online() File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/migrations/env.py", line 101, in run_migrations_online context.run_migrations() File "<string>", line 8, in run_migrations File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/runtime/environment.py", line 851, in run_migrations self.get_context().run_migrations(**kw) File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/runtime/migration.py", line 620, in run_migrations step.migration_fn(**kw) File "/nix/store/[redacted-hash2]-python3.9-apache-airflow-2.3.2/lib/python3.9/site-packages/airflow/migrations/versions/0080_2_0_2_change_default_pool_slots_to_1.py", line 41, in upgrade batch_op.alter_column("pool_slots", existing_type=sa.Integer, nullable=False, server_default='1') File "/nix/store/lb7982cwd56am6nzx1ix0aljz416w6mw-python3-3.9.6/lib/python3.9/contextlib.py", line 124, in __exit__ next(self.gen) File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/operations/base.py", line 374, in batch_alter_table impl.flush() File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/operations/batch.py", line 108, in flush fn(*arg, **kw) File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/ddl/postgresql.py", line 170, in alter_column super(PostgresqlImpl, self).alter_column( File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/ddl/impl.py", line 227, in alter_column self._exec( File "/nix/store/[redacted-hash3]-python3.9-alembic-1.7.7/lib/python3.9/site-packages/alembic/ddl/impl.py", line 193, in _exec return conn.execute(construct, multiparams) File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1200, in execute return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS) File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/sql/ddl.py", line 77, in _execute_on_connection return connection._execute_ddl( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1290, in _execute_ddl ret = self._execute_context( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1748, in _execute_context self._handle_dbapi_exception( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1929, in _handle_dbapi_exception util.raise_( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/nix/store/[redacted-hash1]-python3.9-SQLAlchemy-1.4.9/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.IntegrityError: (psycopg2.errors.NotNullViolation) column "pool_slots" contains null values [SQL: ALTER TABLE task_instance ALTER COLUMN pool_slots SET NOT NULL] (Background on this error at: http://sqlalche.me/e/14/gkpj) ``` ### 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/24566

https://github.com/apache/airflow/pull/24585

75db755f4b06b4cfdd3eb2651dbf88ddba2d831f

9f58e823329d525c0e2b3950ada7e0e047ee7cfd

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/installation/upgrading.rst", "docs/spelling_wordlist.txt"]

upgrading from 2.2.3 or 2.2.5 to 2.3.2 fails on migration-job

### Apache Airflow version 2.3.2 (latest released) ### What happened Upgrade Airflow 2.2.3 or 2.2.5 -> 2.3.2 fails on migration-job. **first time upgrade execution:** ``` Referencing column 'task_id' and referenced column 'task_id' in foreign key constraint 'task_map_task_instance_fkey' are incompatible.") [SQL: CREATE TABLE task_map ( dag_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, task_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, run_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, map_index INTEGER NOT NULL, length INTEGER NOT NULL, `keys` JSON, PRIMARY KEY (dag_id, task_id, run_id, map_index), CONSTRAINT task_map_length_not_negative CHECK (length >= 0), CONSTRAINT task_map_task_instance_fkey FOREIGN KEY(dag_id, task_id, run_id, map_index) REFERENCES task_instance (dag_id, task_id, run_id, map_index) ON DELETE CASCADE ) ] ``` **after the first failed execution (should be due to the first failed execution):** ``` Can't DROP 'task_reschedule_ti_fkey'; check that column/key exists") [SQL: ALTER TABLE task_reschedule DROP FOREIGN KEY task_reschedule_ti_fkey[] ``` ### What you think should happen instead The migration-job shouldn't fail ;) ### How to reproduce Everytime in my environment just need to create a snapshot from last working DB-Snapshot (Airflow Version 2.2.3) and then deploy Airflow 2.3.2. I can update in between to 2.2.5 but ran into the same issue by update to 2.3.2. ### Operating System Debian GNU/Linux 10 (buster) - apache/airflow:2.3.2-python3.8 (hub.docker.com) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.4.0 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-cncf-kubernetes==2.2.0 apache-airflow-providers-docker==2.3.0 apache-airflow-providers-elasticsearch==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==6.2.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-microsoft-azure==3.4.0 apache-airflow-providers-mysql==2.1.1 apache-airflow-providers-odbc==2.0.1 apache-airflow-providers-postgres==2.4.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.3.0 apache-airflow-providers-slack==4.1.0 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.3.0 apache-airflow-providers-tableau==2.1.4 ### Deployment Official Apache Airflow Helm Chart ### Deployment details - K8s Rev: v1.21.12-eks-a64ea69 - helm chart version: 1.6.0 - Database: AWS RDS MySQL 8.0.28 ### Anything else Full error Log **first** execution: ``` /home/airflow/.local/lib/python3.8/site-packages/airflow/configuration.py:529: DeprecationWarning: The auth_backend option in [api[] has been renamed to auth_backends - the old setting has been used, but please update your config. option = self._get_option_from_config_file(deprecated_key, deprecated_section, key, kwargs, section) /home/airflow/.local/lib/python3.8/site-packages/airflow/configuration.py:356: FutureWarning: The auth_backends setting in [api[] has had airflow.api.auth.backend.session added in the running config, which is needed by the UI. Please update your config before Apache Airflow 3.0. warnings.warn( DB: mysql+mysqldb://airflow:***@test-airflow2-db-blue.fsgfsdcfds76.eu-central-1.rds.amazonaws.com:3306/airflow Performing upgrade with database mysql+mysqldb://airflow:***@test-airflow2-db-blue.fsgfsdcfds76.eu-central-1.rds.amazonaws.com:3306/airflow [2022-06-17 12:19:59,724[] {db.py:920} WARNING - Found 33 duplicates in table task_fail. Will attempt to move them. [2022-06-17 12:36:18,813[] {db.py:1448} INFO - Creating tables INFO [alembic.runtime.migration[] Context impl MySQLImpl. INFO [alembic.runtime.migration[] Will assume non-transactional DDL. INFO [alembic.runtime.migration[] Running upgrade be2bfac3da23 -> c381b21cb7e4, Create a ``session`` table to store web session data INFO [alembic.runtime.migration[] Running upgrade c381b21cb7e4 -> 587bdf053233, Add index for ``dag_id`` column in ``job`` table. INFO [alembic.runtime.migration[] Running upgrade 587bdf053233 -> 5e3ec427fdd3, Increase length of email and username in ``ab_user`` and ``ab_register_user`` table to ``256`` characters INFO [alembic.runtime.migration[] Running upgrade 5e3ec427fdd3 -> 786e3737b18f, Add ``timetable_description`` column to DagModel for UI. INFO [alembic.runtime.migration[] Running upgrade 786e3737b18f -> f9da662e7089, Add ``LogTemplate`` table to track changes to config values ``log_filename_template`` INFO [alembic.runtime.migration[] Running upgrade f9da662e7089 -> e655c0453f75, Add ``map_index`` column to TaskInstance to identify task-mapping, and a ``task_map`` table to track mapping values from XCom. Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) MySQLdb._exceptions.OperationalError: (3780, "Referencing column 'task_id' and referenced column 'task_id' in foreign key constraint 'task_map_task_instance_fkey' are incompatible.") The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/commands/db_command.py", line 82, in upgradedb db.upgradedb(to_revision=to_revision, from_revision=from_revision, show_sql_only=args.show_sql_only) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/db.py", line 1449, in upgradedb command.upgrade(config, revision=to_revision or 'heads') File "/home/airflow/.local/lib/python3.8/site-packages/alembic/command.py", line 322, in upgrade script.run_env() File "/home/airflow/.local/lib/python3.8/site-packages/alembic/script/base.py", line 569, in run_env util.load_python_file(self.dir, "env.py") File "/home/airflow/.local/lib/python3.8/site-packages/alembic/util/pyfiles.py", line 94, in load_python_file module = load_module_py(module_id, path) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/util/pyfiles.py", line 110, in load_module_py spec.loader.exec_module(module) # type: ignore File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/env.py", line 107, in <module> run_migrations_online() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/env.py", line 101, in run_migrations_online context.run_migrations() File "<string>", line 8, in run_migrations File "/home/airflow/.local/lib/python3.8/site-packages/alembic/runtime/environment.py", line 853, in run_migrations self.get_context().run_migrations(**kw) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/runtime/migration.py", line 623, in run_migrations step.migration_fn(**kw) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/versions/0100_2_3_0_add_taskmap_and_map_id_on_taskinstance.py", line 75, in upgrade op.create_table( File "<string>", line 8, in create_table File "<string>", line 3, in create_table File "/home/airflow/.local/lib/python3.8/site-packages/alembic/operations/ops.py", line 1254, in create_table return operations.invoke(op) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/operations/base.py", line 394, in invoke return fn(self, operation) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/operations/toimpl.py", line 114, in create_table operations.impl.create_table(table) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/ddl/impl.py", line 354, in create_table self._exec(schema.CreateTable(table)) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/ddl/impl.py", line 195, in _exec return conn.execute(construct, multiparams) File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1200, in execute return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS) File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/sql/ddl.py", line 77, in _execute_on_connection return connection._execute_ddl( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1290, in _execute_ddl ret = self._execute_context( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1748, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1929, in _handle_dbapi_exception util.raise_( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (3780, "Referencing column 'task_id' and referenced column 'task_id' in foreign key constraint 'task_map_task_instance_fkey' are incompatible.") [SQL: CREATE TABLE task_map ( dag_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, task_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, run_id VARCHAR(250) COLLATE utf8mb3_bin NOT NULL, map_index INTEGER NOT NULL, length INTEGER NOT NULL, `keys` JSON, PRIMARY KEY (dag_id, task_id, run_id, map_index), CONSTRAINT task_map_length_not_negative CHECK (length >= 0), CONSTRAINT task_map_task_instance_fkey FOREIGN KEY(dag_id, task_id, run_id, map_index) REFERENCES task_instance (dag_id, task_id, run_id, map_index) ON DELETE CASCADE ) ] (Background on this error at: http://sqlalche.me/e/14/e3q8) ``` Full error Log **after** first execution (should caused by first execution): ``` /home/airflow/.local/lib/python3.8/site-packages/airflow/configuration.py:529: DeprecationWarning: The auth_backend option in [api[] has been renamed to auth_backends - the old setting has been used, but please update your config. option = self._get_option_from_config_file(deprecated_key, deprecated_section, key, kwargs, section) /home/airflow/.local/lib/python3.8/site-packages/airflow/configuration.py:356: FutureWarning: The auth_backends setting in [api[] has had airflow.api.auth.backend.session added in the running config, which is needed by the UI. Please update your config before Apache Airflow 3.0. warnings.warn( DB: mysql+mysqldb://airflow:***@test-airflow2-db-blue.cndbtlpttl69.eu-central-1.rds.amazonaws.com:3306/airflow Performing upgrade with database mysql+mysqldb://airflow:***@test-airflow2-db-blue.cndbtlpttl69.eu-central-1.rds.amazonaws.com:3306/airflow [2022-06-17 12:41:53,882[] {db.py:1448} INFO - Creating tables INFO [alembic.runtime.migration[] Context impl MySQLImpl. INFO [alembic.runtime.migration[] Will assume non-transactional DDL. INFO [alembic.runtime.migration[] Running upgrade f9da662e7089 -> e655c0453f75, Add ``map_index`` column to TaskInstance to identify task-mapping, and a ``task_map`` table to track mapping values from XCom. Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) MySQLdb._exceptions.OperationalError: (1091, "Can't DROP 'task_reschedule_ti_fkey'; check that column/key exists") The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/commands/db_command.py", line 82, in upgradedb db.upgradedb(to_revision=to_revision, from_revision=from_revision, show_sql_only=args.show_sql_only) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/db.py", line 1449, in upgradedb command.upgrade(config, revision=to_revision or 'heads') File "/home/airflow/.local/lib/python3.8/site-packages/alembic/command.py", line 322, in upgrade script.run_env() File "/home/airflow/.local/lib/python3.8/site-packages/alembic/script/base.py", line 569, in run_env util.load_python_file(self.dir, "env.py") File "/home/airflow/.local/lib/python3.8/site-packages/alembic/util/pyfiles.py", line 94, in load_python_file module = load_module_py(module_id, path) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/util/pyfiles.py", line 110, in load_module_py spec.loader.exec_module(module) # type: ignore File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/env.py", line 107, in <module> run_migrations_online() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/env.py", line 101, in run_migrations_online context.run_migrations() File "<string>", line 8, in run_migrations File "/home/airflow/.local/lib/python3.8/site-packages/alembic/runtime/environment.py", line 853, in run_migrations self.get_context().run_migrations(**kw) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/runtime/migration.py", line 623, in run_migrations step.migration_fn(**kw) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/migrations/versions/0100_2_3_0_add_taskmap_and_map_id_on_taskinstance.py", line 49, in upgrade batch_op.drop_index("idx_task_reschedule_dag_task_run") File "/usr/local/lib/python3.8/contextlib.py", line 120, in __exit__ next(self.gen) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/operations/base.py", line 376, in batch_alter_table impl.flush() File "/home/airflow/.local/lib/python3.8/site-packages/alembic/operations/batch.py", line 111, in flush fn(*arg, **kw) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/ddl/mysql.py", line 155, in drop_constraint super(MySQLImpl, self).drop_constraint(const) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/ddl/impl.py", line 338, in drop_constraint self._exec(schema.DropConstraint(const)) File "/home/airflow/.local/lib/python3.8/site-packages/alembic/ddl/impl.py", line 195, in _exec return conn.execute(construct, multiparams) File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1200, in execute return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS) File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/sql/ddl.py", line 77, in _execute_on_connection return connection._execute_ddl( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1290, in _execute_ddl ret = self._execute_context( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1748, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1929, in _handle_dbapi_exception util.raise_( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (1091, "Can't DROP 'task_reschedule_ti_fkey'; check that column/key exists") [SQL: ALTER TABLE task_reschedule DROP FOREIGN KEY task_reschedule_ti_fkey[] (Background on this error at: http://sqlalche.me/e/14/e3q8) ``` ### 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/24526

https://github.com/apache/airflow/pull/25938

994f18872af8d2977d78e6d1a27314efbeedb886

e2592628cb0a6a37efbacc64064dbeb239e83a50

python

closed

apache/airflow

https://github.com/apache/airflow

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

mini-scheduler raises AttributeError: 'NoneType' object has no attribute 'keys'

### Apache Airflow version 2.3.2 (latest released) ### What happened The mini-scheduler run after a task finishes sometimes fails with an error "AttributeError: 'NoneType' object has no attribute 'keys'"; see full traceback below. ### What you think should happen instead _No response_ ### How to reproduce The minimal reproducing example I could find is this: ```python import pendulum from airflow.models import BaseOperator from airflow.utils.task_group import TaskGroup from airflow.decorators import task from airflow import DAG @task def task0(): pass class Op0(BaseOperator): template_fields = ["some_input"] def __init__(self, some_input, **kwargs): super().__init__(**kwargs) self.some_input = some_input if __name__ == "__main__": with DAG("dag0", start_date=pendulum.now()) as dag: with TaskGroup(group_id="tg1"): Op0(task_id="task1", some_input=task0()) dag.partial_subset("tg1.task1") ``` Running this script with airflow 2.3.2 produces this traceback: ``` Traceback (most recent call last): File "/app/airflow-bug-minimal.py", line 22, in <module> dag.partial_subset("tg1.task1") File "/venv/lib/python3.10/site-packages/airflow/models/dag.py", line 2013, in partial_subset dag.task_dict = { File "/venv/lib/python3.10/site-packages/airflow/models/dag.py", line 2014, in <dictcomp> t.task_id: _deepcopy_task(t) File "/venv/lib/python3.10/site-packages/airflow/models/dag.py", line 2011, in _deepcopy_task return copy.deepcopy(t, memo) File "/usr/local/lib/python3.10/copy.py", line 153, in deepcopy y = copier(memo) File "/venv/lib/python3.10/site-packages/airflow/models/baseoperator.py", line 1156, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "/venv/lib/python3.10/site-packages/airflow/models/baseoperator.py", line 1000, in __setattr__ self.set_xcomargs_dependencies() File "/venv/lib/python3.10/site-packages/airflow/models/baseoperator.py", line 1107, in set_xcomargs_dependencies XComArg.apply_upstream_relationship(self, arg) File "/venv/lib/python3.10/site-packages/airflow/models/xcom_arg.py", line 186, in apply_upstream_relationship op.set_upstream(ref.operator) File "/venv/lib/python3.10/site-packages/airflow/models/taskmixin.py", line 241, in set_upstream self._set_relatives(task_or_task_list, upstream=True, edge_modifier=edge_modifier) File "/venv/lib/python3.10/site-packages/airflow/models/taskmixin.py", line 185, in _set_relatives dags: Set["DAG"] = {task.dag for task in [*self.roots, *task_list] if task.has_dag() and task.dag} File "/venv/lib/python3.10/site-packages/airflow/models/taskmixin.py", line 185, in <setcomp> dags: Set["DAG"] = {task.dag for task in [*self.roots, *task_list] if task.has_dag() and task.dag} File "/venv/lib/python3.10/site-packages/airflow/models/dag.py", line 508, in __hash__ val = tuple(self.task_dict.keys()) AttributeError: 'NoneType' object has no attribute 'keys' ``` Note that the call to `dag.partial_subset` usually happens in the mini-scheduler: https://github.com/apache/airflow/blob/2.3.2/airflow/jobs/local_task_job.py#L253 ### Operating System Linux (Debian 9) ### 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/24525

https://github.com/apache/airflow/pull/24865

17564a40a7b8b5dee878cc634077e0a2e63e36fb

c23b31cd786760da8a8e39ecbcf2c0d31e50e594

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic mapping over KubernetesPodOperator results produces triplicate child tasks

### Apache Airflow version 2.3.2 (latest released) ### What happened Attempting to use [dynamic task mapping](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html#mapping-over-result-of-classic-operators) on the results of a `KubernetesPodOperator` (or `GKEStartPodOperator`) produces 3x as many downstream task instances as it should. Two-thirds of the downstream tasks fail more or less instantly. ### What you think should happen instead The problem is that the number of downstream tasks is calculated by counting XCOMs associated with the upstream task, assuming that each `task_id` has a single XCOM: https://github.com/apache/airflow/blob/fe5e689adfe3b2f9bcc37d3975ae1aea9b55e28a/airflow/models/mappedoperator.py#L606-L615 However the `KubernetesPodOperator` pushes two XCOMs in its `.execute()` method: https://github.com/apache/airflow/blob/fe5e689adfe3b2f9bcc37d3975ae1aea9b55e28a/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py#L425-L426 So the number of downstream tasks ends up being 3x what it should. ### How to reproduce Reproducing the behavior requires access to a kubernetes cluster, but in psedo-code, a dag like this should demonstrate the behavior: ``` with DAG(...) as dag: # produces an output list with N elements first_pod = GKEStartPodOperator(..., do_xcom_push=True) # produces 1 output per input, so N task instances are created each with a single output second_pod = GKEStartPodOperator.partial(..., do_xcom_push=True).expand(id=XComArg(first_pod)) # should have N task instances created, but actually gets 3N task instances created third_pod = GKEStartPodOperator.partial(..., do_xcom_push=True).expand(id=XComArg(second_pod)) ``` ### Operating System macOS 12.4 ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==4.1.0 apache-airflow-providers-google==8.0.0 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else When I edit `mappedoperator.py` in my local deployment to filter on the XCOM key things behave as expected: ``` # Collect lengths from mapped upstreams. xcom_query = ( session.query(XCom.task_id, func.count(XCom.map_index)) .group_by(XCom.task_id) .filter( XCom.dag_id == self.dag_id, XCom.run_id == run_id, XCom.key == 'return_value', <------- added this line XCom.task_id.in_(mapped_dep_keys), XCom.map_index >= 0, ) ) ``` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24487

https://github.com/apache/airflow/pull/24530

df388a3d5364b748993e61b522d0b68ff8b8124a

a69095fea1722e153a95ef9da93b002b82a02426

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/0111_2_3_3_add_indexes_for_cascade_deletes.py", "airflow/models/taskfail.py", "airflow/models/taskreschedule.py", "airflow/models/xcom.py", "docs/apache-airflow/migrations-ref.rst"]

`airflow db clean task_instance` takes a long time

### Apache Airflow version 2.3.1 ### What happened When I ran the `airflow db clean task_instance` command, it can take up to 9 hours to complete. The database around 3215220 rows in the `task_instance` table and 51602 rows in the `dag_run` table. The overall size of the database is around 1 TB. I believe the issue is because of the cascade constraints on others tables as well as the lack of indexes on task_instance foreign keys. Running delete on a small number of rows gives this shows most of the time is spent in xcom and task_fail tables ``` explain (analyze,buffers,timing) delete from task_instance t1 where t1.run_id = 'manual__2022-05-11T01:09:05.856703+00:00'; rollback; Trigger for constraint task_reschedule_ti_fkey: time=3.208 calls=23 Trigger for constraint task_map_task_instance_fkey: time=1.848 calls=23 Trigger for constraint xcom_task_instance_fkey: time=4457.779 calls=23 Trigger for constraint rtif_ti_fkey: time=3.135 calls=23 Trigger for constraint task_fail_ti_fkey: time=1164.183 calls=23 ``` I temporarily fixed it by adding these indexes. ``` create index idx_task_reschedule_dr_fkey on task_reschedule (dag_id, run_id); create index idx_xcom_ti_fkey on xcom (dag_id, task_id, run_id, map_index); create index idx_task_fail_ti_fkey on task_fail (dag_id, task_id, run_id, map_index); ``` ### What you think should happen instead It should not take 9 hours to complete a clean up process. Before upgrading to 2.3.x, it was taking no more than 5 minutes. ### How to reproduce _No response_ ### Operating System N/A ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24484

https://github.com/apache/airflow/pull/24488

127f8f4de02422ade8f2c84f84d3262d6efde185

677c42227c08f705142f298ab88915f133cd94e5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/bigquery.py", "airflow/providers/google/cloud/operators/bigquery.py", "airflow/providers/google/cloud/triggers/bigquery.py", "docs/apache-airflow-providers-google/operators/cloud/bigquery.rst", "tests/providers/google/cloud/hooks/test_bigquery.py", "tests/providers/google/cloud/operators/test_bigquery.py"]

let BigQueryGetData operator take a query string and as_dict flag

### Description Today the BigQueryGetData airflow.providers.google.cloud.operators.bigquery.BigQueryGetDataOperator only allows you to point to a specific dataset and table and how many rows you want. It already sets up a BigQueryHook so it very easy to implement custom query from a string as well. It would also be very efficient to have a as_dict flag to return the result as a list of dicts. I am not an expert in python but here is my atempt at a modification of the current code (from 8.0.0rc2) ``` python class BigQueryGetDataOperatorX(BaseOperator): """ Fetches the data from a BigQuery table (alternatively fetch data for selected columns) and returns data in a python list. The number of elements in the returned list will be equal to the number of rows fetched. Each element in the list will again be a list where element would represent the columns values for that row. **Example Result**: ``[['Tony', '10'], ['Mike', '20'], ['Steve', '15']]`` .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:BigQueryGetDataOperator` .. note:: If you pass fields to ``selected_fields`` which are in different order than the order of columns already in BQ table, the data will still be in the order of BQ table. For example if the BQ table has 3 columns as ``[A,B,C]`` and you pass 'B,A' in the ``selected_fields`` the data would still be of the form ``'A,B'``. **Example**: :: get_data = BigQueryGetDataOperator( task_id='get_data_from_bq', dataset_id='test_dataset', table_id='Transaction_partitions', max_results=100, selected_fields='DATE', gcp_conn_id='airflow-conn-id' ) :param dataset_id: The dataset ID of the requested table. (templated) :param table_id: The table ID of the requested table. (templated) :param max_results: The maximum number of records (rows) to be fetched from the table. (templated) :param selected_fields: List of fields to return (comma-separated). If unspecified, all fields are returned. :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param location: The location used for the operation. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). :param query: (Optional) A sql query to execute instead :param as_dict: if True returns the result as a list of dictionaries. default to False """ template_fields: Sequence[str] = ( 'dataset_id', 'table_id', 'max_results', 'selected_fields', 'impersonation_chain', ) ui_color = BigQueryUIColors.QUERY.value def __init__( self, *, dataset_id: Optional[str] = None, table_id: Optional[str] = None, max_results: Optional[int] = 100, selected_fields: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, query: Optional[str] = None, as_dict: bool = False, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.table_id = table_id self.max_results = int(max_results) self.selected_fields = selected_fields self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.location = location self.impersonation_chain = impersonation_chain self.query = query self.as_dict = as_dict if not query and not table_id: self.log.error('Table_id or query not set. Please provide either a dataset_id + table_id or a query string') def execute(self, context: 'Context') -> list: self.log.info( 'Fetching Data from %s.%s max results: %s', self.dataset_id, self.table_id, self.max_results ) hook = BigQueryHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, location=self.location, ) if not self.query: if not self.selected_fields: schema: Dict[str, list] = hook.get_schema( dataset_id=self.dataset_id, table_id=self.table_id, ) if "fields" in schema: self.selected_fields = ','.join([field["name"] for field in schema["fields"]]) with hook.list_rows( dataset_id=self.dataset_id, table_id=self.table_id, max_results=self.max_results, selected_fields=self.selected_fields ) as rows: if self.as_dict: table_data = [json.dumps(dict(zip(self.selected_fields, row))).encode('utf-8') for row in rows] else: table_data = [row.values() for row in rows] else: with hook.get_conn().cursor().execute(self.query) as cursor: if self.as_dict: table_data = [json.dumps(dict(zip(self.keys,row))).encode('utf-8') for row in cursor.fetchmany(self.max_results)] else: table_data = [row for row in cursor.fetchmany(self.max_results)] self.log.info('Total extracted rows: %s', len(table_data)) return table_data ``` ### Use case/motivation This would simplify getting data from BigQuery into airflow instead of having to first store the data in a separat table with BigQueryInsertJob and then fetch that. Also simplifies handling the data with as_dict in the same way that many other database connectors in python does. ### 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/24460

https://github.com/apache/airflow/pull/30887

dff7e0de362e4cd318d7c285ec102923503eceb3

b8f73768ec13f8d4cc1605cca3fa93be6caac473

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/abstractoperator.py", "airflow/models/baseoperator.py", "airflow/models/mappedoperator.py", "airflow/models/taskinstance.py", "airflow/utils/context.py", "tests/decorators/test_python.py", "tests/models/test_mappedoperator.py"]

Unable to access operator attrs within Jinja context for mapped tasks

### Apache Airflow version 2.3.2 (latest released) ### What happened When attempting to generate mapped SQL tasks using a Jinja-templated query that access operator attributes, an exception like the following is thrown: `jinja2.exceptions.UndefinedError: 'airflow.models.mappedoperator.MappedOperator object' has no attribute '<operator attribute>'` For example, when attempting to map `SQLValueCheckOperator` tasks with respect to `database` using a query of `SELECT COUNT(*) FROM {{ task.database }}.tbl;`: `jinja2.exceptions.UndefinedError: 'airflow.models.mappedoperator.MappedOperator object' has no attribute 'database'` Or, when using `SnowflakeOperator` and mapping via `parameters` of a query like `SELECT * FROM {{ task.parameters.tbl }};`: `jinja2.exceptions.UndefinedError: 'airflow.models.mappedoperator.MappedOperator object' has no attribute 'parameters'` ### What you think should happen instead When using Jinja-template SQL queries, the attribute that is being using for the mapping should be accessible via `{{ task.<operator attribute> }}`. Executing the same SQL query with classic, non-mapped tasks allows for this operator attr access from the `task` context object. Ideally, the same interface should apply for both non-mapped and mapped tasks. Also with the preference of using `parameters` over `params` in SQL-type operators, having the ability to map over `parameters` will help folks move from using `params` to `parameters`. ### How to reproduce Consider the following DAG: ```python from pendulum import datetime from airflow.decorators import dag from airflow.operators.sql import SQLValueCheckOperator from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator CORE_SQL = "SELECT COUNT(*) FROM {{ task.database }}.tbl;" SNOWFLAKE_SQL = """SELECT * FROM {{ task.parameters.tbl }};""" @dag(dag_id="map-city", start_date=datetime(2022, 6, 7), schedule_interval=None) def map_city(): classic_sql_value_check = SQLValueCheckOperator( task_id="classic_sql_value_check", conn_id="snowflake", sql=CORE_SQL, database="dev", pass_value=20000, ) mapped_value_check = SQLValueCheckOperator.partial( task_id="check_row_count", conn_id="snowflake", sql=CORE_SQL, pass_value=20000, ).expand(database=["dev", "production"]) classic_snowflake_task = SnowflakeOperator( task_id="classic_snowflake_task", snowflake_conn_id="snowflake", sql=SNOWFLAKE_SQL, parameters={"tbl": "foo"}, ) mapped_snowflake_task = SnowflakeOperator.partial( task_id="mapped_snowflake_task", snowflake_conn_id="snowflake", sql=SNOWFLAKE_SQL ).expand( parameters=[ {"tbl": "foo"}, {"tbl": "bar"}, ] ) _ = map_city() ``` **`SQLValueCheckOperator` tasks** The logs for the "classic_sql_value_check", non-mapped task show the query executing as expected: `[2022-06-11, 02:01:03 UTC] {sql.py:204} INFO - Executing SQL check: SELECT COUNT(*) FROM dev.tbl;` while the mapped "check_row_count" task fails with the following exception: ```bash [2022-06-11, 02:01:03 UTC] {standard_task_runner.py:79} INFO - Running: ['airflow', 'tasks', 'run', 'map-city', 'check_row_count', 'manual__2022-06-11T02:01:01.831761+00:00', '--job-id', '350', '--raw', '--subdir', 'DAGS_FOLDER/map_city.py', '--cfg-path', '/tmp/tmpm5bg9mt5', '--map-index', '0', '--error-file', '/tmp/tmp2kbilt2l'] [2022-06-11, 02:01:03 UTC] {standard_task_runner.py:80} INFO - Job 350: Subtask check_row_count [2022-06-11, 02:01:03 UTC] {task_command.py:370} INFO - Running <TaskInstance: map-city.check_row_count manual__2022-06-11T02:01:01.831761+00:00 map_index=0 [running]> on host 569596df5be5 [2022-06-11, 02:01:03 UTC] {taskinstance.py:1889} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1451, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1555, in _execute_task_with_callbacks task_orig = self.render_templates(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2212, in render_templates rendered_task = self.task.render_template_fields(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 726, in render_template_fields self._do_render_template_fields( File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/abstractoperator.py", line 344, in _do_render_template_fields rendered_content = self.render_template( File "/usr/local/lib/python3.9/site-packages/airflow/models/abstractoperator.py", line 391, in render_template return render_template_to_string(template, context) File "/usr/local/lib/python3.9/site-packages/airflow/utils/helpers.py", line 296, in render_template_to_string return render_template(template, context, native=False) File "/usr/local/lib/python3.9/site-packages/airflow/utils/helpers.py", line 291, in render_template return "".join(nodes) File "<template>", line 13, in root File "/usr/local/lib/python3.9/site-packages/jinja2/runtime.py", line 903, in _fail_with_undefined_error raise self._undefined_exception(self._undefined_message) jinja2.exceptions.UndefinedError: 'airflow.models.mappedoperator.MappedOperator object' has no attribute 'database' ``` **`SnowflakeOperator` tasks** Similarly, the "classic_snowflake_task" non-mapped task is able to execute the SQL query as expected: `[2022-06-11, 02:01:04 UTC] {snowflake.py:324} INFO - Running statement: SELECT * FROM foo;, parameters: {'tbl': 'foo'}` while the mapped "mapped_snowflake_task task fails to execute the query: ```bash [2022-06-11, 02:01:03 UTC] {standard_task_runner.py:79} INFO - Running: ['airflow', 'tasks', 'run', 'map-city', 'mapped_snowflake_task', 'manual__2022-06-11T02:01:01.831761+00:00', '--job-id', '347', '--raw', '--subdir', 'DAGS_FOLDER/map_city.py', '--cfg-path', '/tmp/tmp6kmqs5ew', '--map-index', '0', '--error-file', '/tmp/tmpkufg9xqx'] [2022-06-11, 02:01:03 UTC] {standard_task_runner.py:80} INFO - Job 347: Subtask mapped_snowflake_task [2022-06-11, 02:01:03 UTC] {task_command.py:370} INFO - Running <TaskInstance: map-city.mapped_snowflake_task manual__2022-06-11T02:01:01.831761+00:00 map_index=0 [running]> on host 569596df5be5 [2022-06-11, 02:01:03 UTC] {taskinstance.py:1889} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1451, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1555, in _execute_task_with_callbacks task_orig = self.render_templates(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2212, in render_templates rendered_task = self.task.render_template_fields(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 726, in render_template_fields self._do_render_template_fields( File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/abstractoperator.py", line 344, in _do_render_template_fields rendered_content = self.render_template( File "/usr/local/lib/python3.9/site-packages/airflow/models/abstractoperator.py", line 391, in render_template return render_template_to_string(template, context) File "/usr/local/lib/python3.9/site-packages/airflow/utils/helpers.py", line 296, in render_template_to_string return render_template(template, context, native=False) File "/usr/local/lib/python3.9/site-packages/airflow/utils/helpers.py", line 291, in render_template return "".join(nodes) File "<template>", line 13, in root File "/usr/local/lib/python3.9/site-packages/jinja2/sandbox.py", line 326, in getattr value = getattr(obj, attribute) File "/usr/local/lib/python3.9/site-packages/jinja2/runtime.py", line 910, in __getattr__ return self._fail_with_undefined_error() File "/usr/local/lib/python3.9/site-packages/jinja2/runtime.py", line 903, in _fail_with_undefined_error raise self._undefined_exception(self._undefined_message) jinja2.exceptions.UndefinedError: 'airflow.models.mappedoperator.MappedOperator object' has no attribute 'parameters' ``` ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-snowflake==2.7.0 ### Deployment Astronomer ### Deployment details Astronomer Runtime 5.0.3 ### Anything else Even though using the `{{ task.<operator attr> }}` method does not work for mapped tasks, there is a workaround. Given the `SnowflakeOperator` example from above attempting to execute the query: `SELECT * FROM {{ task.parameters.tbl }};`, users can modify the templated query to `SELECT * FROM {{ task.mapped_kwargs.parameters[ti.map_index].tbl }};` for successful execution. This workaround isn't very obvious though and requires from solid digging into the new 2.3.0 code. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24388

https://github.com/apache/airflow/pull/26702

ed494594ef213b3633aa3972e1b8b4ad18b88e42

5560a46bfe8a14205c5e8a14f0b5c2ae74ee100c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/transfers/s3_to_snowflake.py", "airflow/providers/snowflake/utils/__init__.py", "airflow/providers/snowflake/utils/common.py", "docs/apache-airflow-providers-snowflake/operators/s3_to_snowflake.rst", "tests/providers/snowflake/transfers/test_s3_to_snowflake.py", "tests/providers/snowflake/utils/__init__.py", "tests/providers/snowflake/utils/test_common.py", "tests/system/providers/snowflake/example_snowflake.py"]

Pattern parameter in S3ToSnowflakeOperator

### Description I would like to propose to add a pattern parameter to allow loading only those files that satisfy the given regex pattern. This function is supported on the Snowflake side, it just requires passing a parameter to the COPY INTO command. [Snowflake documentation/](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html#loading-using-pattern-matching) ### Use case/motivation I have multiple files with different schema in one folder. I would like to move to Snowflake only files which meet the given name filter, and I am not able to do it with the prefix parameter. ### Related issues I am not aware ### 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/24360

https://github.com/apache/airflow/pull/24571

5877f45d65d5aa864941efebd2040661b6f89cb1

66e84001df069c76ba8bfefe15956c4018844b92

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/gcs.py", "tests/providers/google/cloud/operators/test_gcs.py"]

GCSDeleteObjectsOperator raises unexpected ValueError for prefix set as empty string

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers All versions. ``` apache-airflow-providers-google>=1.0.0b1 apache-airflow-backport-providers-google>=2020.5.20rc1 ``` ### Apache Airflow version 2.3.2 (latest released) ### Operating System macOS 12.3.1 ### Deployment Composer ### Deployment details _No response_ ### What happened I'm currently doing the upgrade check in Airflow 1.10.15 and one of the topics is to change the import locations from contrib to the specific provider. While replacing: `airflow.contrib.operators.gcs_delete_operator.GoogleCloudStorageDeleteOperator` By: `airflow.providers.google.cloud.operators.gcs.GCSDeleteObjectsOperator` An error appeared in the UI: `Broken DAG: [...] Either object or prefix should be set. Both are None` --- Upon further investigation, I found out that while the `GoogleCloudStorageDeleteOperator` from contrib module had this parameter check (as can be seen [here](https://github.com/apache/airflow/blob/v1-10-stable/airflow/contrib/operators/gcs_delete_operator.py#L63)): ```python assert objects is not None or prefix is not None ``` The new `GCSDeleteObjectsOperator` from Google provider module have the following (as can be seen [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/gcs.py#L308-L309)): ```python if not objects and not prefix: raise ValueError("Either object or prefix should be set. Both are None") ``` --- As it turns out, these conditions are not equivalent, because a variable `prefix` containing the value of an empty string won't raise an error on the first case, but will raise it in the second one. ### What you think should happen instead This behavior does not match with the documentation description, since using a prefix as an empty string is perfectly valid in case the user wants to delete all objects within the bucket. Furthermore, there were no philosophical changes within the API in that timeframe. This code change happened in [this commit](https://github.com/apache/airflow/commit/25e9047a4a4da5fad4f85c366e3a6262c0a4f68e#diff-c45d838a139b258ab703c23c30fd69078108f14a267731bd2be5cc1c8a7c02f5), where the developer's intent was clearly to remove assertions, not to change the logic behind the validation. In fact, it even relates to a PR for [this Airflow JIRA ticket](https://issues.apache.org/jira/browse/AIRFLOW-6193). ### How to reproduce Add a `GCSDeleteObjectsOperator` with a parameter `prefix=""` to a DAG. Example: ```python from datetime import datetime, timedelta from airflow import DAG from airflow.providers.google.cloud.operators.gcs import GCSDeleteObjectsOperator with DAG('test_dag', schedule_interval=timedelta(days=1), start_date=datetime(2022, 1, 1)) as dag: task = GCSDeleteObjectsOperator( task_id='task_that_generates_ValueError', bucket_name='some_bucket', prefix='' ) ``` ### Anything else In my opinion, the error message wasn't very accurate as well, since it just breaks the DAG without pointing out which task is causing the issue. It took me 20 minutes to pinpoint the exact task in my case, since I was dealing with a DAG with a lot of tasks. Adding the `task_id` to the error message could improve the developer experience in that case. ### 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/24352

https://github.com/apache/airflow/pull/24353

dd35fdaf35b6e46fd69a1b1da36ae7ffc0505dcb

e7a1c50d62680a521ef90a424b7eff03635081d5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db.py"]

Add salesforce_default to List Connection

### Apache Airflow version 2.1.2 ### What happened salesforce_default is not in the list of Connection. ### What you think should happen instead Should be added salesforce_default to ListConnection. ### How to reproduce After resetting DB, look at List Connection. ### Operating System GCP Container ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details composer-1.17.1-airflow-2.1.2 ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24346

https://github.com/apache/airflow/pull/24347

e452949610cff67c0e0a9918a8fefa7e8cc4b8c8

6d69dd062f079a8fbf72563fd218017208bfe6c1

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQueryCreateEmptyTableOperator do not deprecated bigquery_conn_id yet

### Apache Airflow version 2.3.2 (latest released) ### What happened `bigquery_conn_id` is deprecated for other operators like `BigQueryDeleteTableOperator` and replaced by `gcp_conn_id` but it's not the case for `BigQueryCreateEmptyTableOperator` ### 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/24343

https://github.com/apache/airflow/pull/24376

dd78e29a8c858769c9c21752f319e19af7f64377

8e0bddaea69db4d175f03fa99951f6d82acee84d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/exceptions.py", "airflow/models/xcom_arg.py", "tests/decorators/test_python.py"]

TaskFlow AirflowSkipException causes downstream step to fail

### Apache Airflow version 2.3.2 (latest released) ### What happened Using TaskFlow API and have 2 tasks that lead to the same downstream task. These tasks check for new data and when found will set an XCom entry of the new filename for the downstream to handle. If no data is found the upstream tasks raise a skip exception. The downstream task has the trigger_rule = none_failed_min_one_success. Problem is that a task which is set to Skip doesn't set any XCom. When the downstream task starts it raises the error: `airflow.exceptions.AirflowException: XComArg result from task2 at airflow_2_3_xcomarg_render_error with key="return_value" is not found!` ### What you think should happen instead Based on trigger rule of "none_failed_min_one_success", expectation is that an upstream task should be allowed to skip and the downstream task will still run. While the downstream does try to start based on trigger rules, it never really gets to run since the error is raised when rendering the arguments. ### How to reproduce Example dag will generate the error if run. ``` from airflow.decorators import dag, task from airflow.exceptions import AirflowSkipException @task def task1(): return "example.csv" @task def task2(): raise AirflowSkipException() @task(trigger_rule="none_failed_min_one_success") def downstream_task(t1, t2): print("task ran") @dag( default_args={"owner": "Airflow", "start_date": "2022-06-07"}, schedule_interval=None, ) def airflow_2_3_xcomarg_render_error(): t1 = task1() t2 = task2() downstream_task(t1, t2) example_dag = airflow_2_3_xcomarg_render_error() ``` ### Operating System Ubuntu 20.04.4 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24338

https://github.com/apache/airflow/pull/25661

c7215a28f9df71c63408f758ed34253a4dfaa318

a4e38978194ef46565bc1e5ba53ecc65308d09aa

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/example_dags/README.md", "dev/example_dags/update_example_dags_paths.py"]

"Example DAGs" link under kubernetes-provider documentation is broken. Getting 404

### What do you see as an issue? _Example DAGs_ folder is not available for _apache-airflow-providers-cncf-kubernetes_ , which results in broken link on documentation page ( https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/index.html ). Getting 404 error when clicked on _Example DAGs_ link (https://github.com/apache/airflow/tree/main/airflow/providers/cncf/kubernetes/example_dags ) <img width="1464" alt="Screenshot 2022-06-08 at 9 01 56 PM" src="https://user-images.githubusercontent.com/11991059/172657376-8a556e9e-72e5-4aab-9c71-b1da239dbf5c.png"> <img width="1475" alt="Screenshot 2022-06-08 at 9 01 39 PM" src="https://user-images.githubusercontent.com/11991059/172657413-c72d14f2-071f-4452-baf7-0f41504a5a3a.png"> ### Solving the problem Folder named _example_dags_ should be created under link (https://github.com/apache/airflow/tree/main/airflow/providers/cncf/kubernetes/) which should include kubernetes specific DAG examples. ### 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/24331

https://github.com/apache/airflow/pull/24348

74ac9f788c31512b1fcd9254282905f34cc40666

85c247ae10da5ee93f26352d369f794ff4f2e47c

python

closed

apache/airflow

https://github.com/apache/airflow

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

`TI.log_url` is incorrect with mapped tasks

### Apache Airflow version 2.3.0 ### What happened I had an `on_failure_callback` that sent a `task_instance.log_url` to slack, it no longer behaves correctly - giving me a page with no logs rendered instead of the logs for my task. (Example of failure, URL like: https://XYZ.astronomer.run/dhp2pmdd/log?execution_date=2022-06-05T00%3A00%3A00%2B00%3A00&task_id=create_XXX_zip_files_and_upload&dag_id=my_dag )  ### What you think should happen instead The correct behavior would be the URL: https://XYZ.astronomer.run/dhp2pmdd/log?execution_date=2022-06-05T00%3A00%3A00%2B00%3A00&task_id=create_XXX_zip_files_and_upload&dag_id=my_dag&map_index=0 as exemplified:  ### How to reproduce _No response_ ### Operating System Debian/Docker ### 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/24328

https://github.com/apache/airflow/pull/24335

a9c350762db4dca7ab5f6c0bfa0c4537d697b54c

48a6155bb1478245c1dd8b6401e4cce00e129422

python

closed

apache/airflow

https://github.com/apache/airflow

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

S3KeySensor wildcard_match only matching key prefixes instead of full patterns

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers 3.4.0 ### Apache Airflow version 2.3.2 (latest released) ### Operating System Debian GNU/Linux 10 ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened For patterns like "*.zip" the S3KeySensor succeeds for all files, does not take full pattern into account i.e. the ".zip" part). Bug introduced in https://github.com/apache/airflow/pull/22737 ### What you think should happen instead Full pattern match as in version 3.3.0 (in S3KeySensor poke()): ``` ... if self.wildcard_match: return self.get_hook().check_for_wildcard_key(self.bucket_key, self.bucket_name) ... ``` alternatively the files obtained by `files = self.get_hook().get_file_metadata(prefix, bucket_name)` which only match the prefix should be further filtered. ### How to reproduce create a DAG with a key sensor task containing wildcard and suffix, e.g. the following task should succeed only if any ZIP-files are available in "my-bucket", but succeeds for all instead: `S3KeySensor(task_id="wait_for_file", bucket_name="my-bucket", bucket_key="*.zip", wildcard_match=True)` ### Anything else Not directly part of this issue but at the same time I would suggest to include additional file attributes in method _check_key, e.g. the actual key of the files. This way more filters, e.g. exclude specific keys, could be implemented by using the check_fn. ### 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/24321

https://github.com/apache/airflow/pull/24378

f8e106a531d2dc502bdfe47c3f460462ab0a156d

7fed7f31c3a895c0df08228541f955efb16fbf79

python

closed

apache/airflow

https://github.com/apache/airflow

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

`EmrCreateJobFlowOperator` does not work if emr_conn_id param contain credential

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.3.2 (latest released) ### Operating System os ### Deployment Other ### Deployment details _No response_ ### What happened EmrCreateJobFlowOperator currently have two params for connection `emr_conn_id` and `aws_conn_id`. So it works only when I set `aws_conn_id` containing credentials and an empty `emr_conn_id` and it does not work in the below case - when I set both aws_conn_id and emr_conn_id in the operator and both connection contains credentials i.e it has aws_access_key_id and other params in airflow connection extra ``` Unknown parameter in input: "aws_access_key_id", must be one of: Name, LogUri, LogEncryptionKmsKeyId, AdditionalInfo, AmiVersion, ReleaseLabel, Instances, Steps, BootstrapActions, SupportedProducts, NewSupportedProducts, Applications, Configurations, VisibleToAllUsers, JobFlowRole, ServiceRole, Tags, SecurityConfiguration, AutoScalingRole, ScaleDownBehavior, CustomAmiId, EbsRootVolumeSize, RepoUpgradeOnBoot, KerberosAttributes, StepConcurrencyLevel, ManagedScalingPolicy, PlacementGroupConfigs, AutoTerminationPolicy, OSReleaseLabel ``` - when I set both aws_conn_id and emr_conn_id in the operator and only emr_conn_id connection contains credentials i.e it has aws_access_key_id and other params in airflow connection extra ``` [2022-06-07, 20:49:19 UTC] {taskinstance.py:1826} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/airflow/airflow/providers/amazon/aws/operators/emr.py", line 324, in execute response = emr.create_job_flow(job_flow_overrides) File "/opt/airflow/airflow/providers/amazon/aws/hooks/emr.py", line 87, in create_job_flow response = self.get_conn().run_job_flow(**job_flow_overrides) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 508, in _api_call return self._make_api_call(operation_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 895, in _make_api_call operation_model, request_dict, request_context File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 917, in _make_request return self._endpoint.make_request(operation_model, request_dict) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in make_request return self._send_request(request_dict, operation_model) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 195, in _send_request request = self.create_request(request_dict, operation_model) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 134, in create_request operation_name=operation_model.name, File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 412, in emit return self._emitter.emit(aliased_event_name, **kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 256, in emit return self._emit(event_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 239, in _emit response = handler(**kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/signers.py", line 103, in handler return self.sign(operation_name, request) File "/usr/local/lib/python3.7/site-packages/botocore/signers.py", line 187, in sign auth.add_auth(request) File "/usr/local/lib/python3.7/site-packages/botocore/auth.py", line 405, in add_auth raise NoCredentialsError() botocore.exceptions.NoCredentialsError: Unable to locate credentials ``` - When I set only aws_conn_id in the operator and it contains credentials ``` Traceback (most recent call last): File "/opt/airflow/airflow/providers/amazon/aws/operators/emr.py", line 324, in execute response = emr.create_job_flow(job_flow_overrides) File "/opt/airflow/airflow/providers/amazon/aws/hooks/emr.py", line 90, in create_job_flow emr_conn = self.get_connection(self.emr_conn_id) File "/opt/airflow/airflow/hooks/base.py", line 67, in get_connection conn = Connection.get_connection_from_secrets(conn_id) File "/opt/airflow/airflow/models/connection.py", line 430, in get_connection_from_secrets raise AirflowNotFoundException(f"The conn_id `{conn_id}` isn't defined") ``` - When I set only emr_conn_id in the operator and it contains credentials ``` [2022-06-07, 20:49:19 UTC] {taskinstance.py:1826} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/airflow/airflow/providers/amazon/aws/operators/emr.py", line 324, in execute response = emr.create_job_flow(job_flow_overrides) File "/opt/airflow/airflow/providers/amazon/aws/hooks/emr.py", line 87, in create_job_flow response = self.get_conn().run_job_flow(**job_flow_overrides) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 508, in _api_call return self._make_api_call(operation_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 895, in _make_api_call operation_model, request_dict, request_context File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 917, in _make_request return self._endpoint.make_request(operation_model, request_dict) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in make_request return self._send_request(request_dict, operation_model) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 195, in _send_request request = self.create_request(request_dict, operation_model) File "/usr/local/lib/python3.7/site-packages/botocore/endpoint.py", line 134, in create_request operation_name=operation_model.name, File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 412, in emit return self._emitter.emit(aliased_event_name, **kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 256, in emit return self._emit(event_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/hooks.py", line 239, in _emit response = handler(**kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/signers.py", line 103, in handler return self.sign(operation_name, request) File "/usr/local/lib/python3.7/site-packages/botocore/signers.py", line 187, in sign auth.add_auth(request) File "/usr/local/lib/python3.7/site-packages/botocore/auth.py", line 405, in add_auth raise NoCredentialsError() botocore.exceptions.NoCredentialsError: Unable to locate credentials ``` - When I set aws_conn_id having credential and emr_conn_id having no credential i.e empty extra field in airflow connection then it work ### What you think should happen instead It should work with even one connection id i.e with aws_conn_id or emr_conn_id and should not fail even emr_conn_id has credentials ### How to reproduce - Create EmrCreateJobFlowOperator and pass both aws_conn_id and emr_conn_id or - Create EmrCreateJobFlowOperator and pass aws_conn_id or emr_conn_id ### 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/24318

https://github.com/apache/airflow/pull/24306

4daf51a2c388b41201a0a8095e0a97c27d6704c8

99d98336312d188a078721579a3f71060bdde542

python

closed

apache/airflow

https://github.com/apache/airflow

["breeze"]

Fix command in breeze

### What do you see as an issue? There is a mistake in the command displayed in breeze. ``` The answer is 'no'. Skipping Installing pipx?. Please run those commands manually (you might need to restart shell between them):i pip -m install pipx pipx ensurepath pipx install -e '/Users/ishiis/github/airflow/dev/breeze/' breeze setup-autocomplete --force After that, both pipx and breeze should be available on your path ``` There is no -m option in pip. ```bash % pip -m install pipx Usage: pip <command> [options] no such option: -m ``` ### Solving the problem Fix the command. ### 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/24281

https://github.com/apache/airflow/pull/24282

6dc474fc82aa9325081b0c5f2b92c948e2f16f74

69ca427754c54c5496bf90b7fc70fdd646bc92e5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py"]

KubernetesPodOperator rendered template tab does not pretty print `env_vars`

### Apache Airflow version 2.2.5 ### What happened I am using the `KubernetesPodOperator` for airflow tasks in `Airflow 2.2.5` and it doesnot render the `env_vars` in the `rendered template` in a easily human consumable format as it did in `Airflow 1.10.x`  ### What you think should happen instead The `env_vars` should be pretty printed in human legible form. ### How to reproduce Create a task with the `KubernetesPodOperator` and check the `Rendered template` tab of the task instance. ### Operating System Docker ### Versions of Apache Airflow Providers 2.2.5 ### 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/24197

https://github.com/apache/airflow/pull/25850

1a087bca3d6ecceab96f9ab818b3b75262222d13

db5543ef608bdd7aefdb5fefea150955d369ddf4

python

closed

apache/airflow

https://github.com/apache/airflow

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

`BigQueryCreateExternalTableOperator` uses deprecated function

### Body The `BigQueryCreateExternalTableOperator` uses `create_external_table`: https://github.com/apache/airflow/blob/cd49a8b9f64c57b5622025baee9247712c692e72/airflow/providers/google/cloud/operators/bigquery.py#L1131-L1147 this function is deprecated: https://github.com/apache/airflow/blob/511d0ee256b819690ccf0f6b30d12340b1dd7f0a/airflow/providers/google/cloud/hooks/bigquery.py#L598-L602 **The task:** Refactor/change the operator to replace `create_external_table` with `create_empty_table`. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/24160

https://github.com/apache/airflow/pull/24363

626d9db2908563c4b7675db5de2cb1e3acde82e9

c618da444e841afcfd73eeb0bce9c87648c89140

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/workers/worker-kedaautoscaler.yaml", "chart/values.schema.json", "chart/values.yaml", "tests/charts/test_keda.py"]

Add support for KEDA HPA Config to Helm Chart

### Description > When managing the scale of a group of replicas using the HorizontalPodAutoscaler, it is possible that the number of replicas keeps fluctuating frequently due to the dynamic nature of the metrics evaluated. This is sometimes referred to as thrashing, or flapping. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#flapping Sometimes clusters need to restrict the flapping of Airflow worker replicas. KEDA supports [`advanced.horizontalPodAutoscalerConfig`](https://keda.sh/docs/1.4/concepts/scaling-deployments/). It would be great if the users would have the option in the helm chart to configure scale down behavior. ### Use case/motivation KEDA currently cannot set advanced options. We want to set advanced options like `scaleDown.stabilizationWindowSeconds`, `scaleDown.policies`. ### 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/24103

https://github.com/apache/airflow/pull/24220

97948ecae7fcbb7dfdfb169cfe653bd20a108def

8639c70f187a7d5b8b4d2f432d2530f6d259eceb

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/exts/exampleinclude.py"]

Fix style of example-block

### What do you see as an issue? Style of example-block in the document is broken. <img width="810" alt="example-block" src="https://user-images.githubusercontent.com/12693596/171412272-70ca791b-c798-4080-83ab-e358f290ac31.png"> This problem occurs when browser width is between 1000px and 1280px. See: https://airflow.apache.org/docs/apache-airflow-providers-http/stable/operators.html ### Solving the problem The container class should be removed. ```html <div class="example-block-wrapper docutils container"> ^^^^^^^^^ ... </div> ``` ### 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/24077

https://github.com/apache/airflow/pull/24078

e41b5a012427b5e7eab49de702b83dba4fc2fa13

5087f96600f6d7cc852b91079e92d00df6a50486

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db.py", "tests/utils/test_db.py"]

`airflow db check-migrations -t 0` fails to check migrations in airflow 2.3

### Apache Airflow version 2.3.1 (latest released) ### What happened As of Airflow 2.3.0 the `airflow db check-migrations -t 0` command will ALWAYS think there are unapplied migrations (even if there are none to apply), whereas, in Airflow 2.2.5, a single check would be run succeessfully. This was caused by PR https://github.com/apache/airflow/pull/18439, which updated the loop from [`while True`](https://github.com/apache/airflow/blob/2.2.5/airflow/utils/db.py#L638) (which always loops at least once) to [`for ticker in range(timeout)`](https://github.com/apache/airflow/blob/2.3.0/airflow/utils/db.py#L696) (which will NOT loop if timeout=0). ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System All ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/24060

https://github.com/apache/airflow/pull/24068

841ed271017ff35a3124f1d1a53a5c74730fed60

84d7b5ba39b3ff1fb5b856faec8fd4e731d3f397

python

closed

apache/airflow

https://github.com/apache/airflow

[".gitignore", "chart/.gitignore", "chart/Chart.lock", "chart/Chart.yaml", "chart/INSTALL", "chart/NOTICE", "chart/charts/postgresql-10.5.3.tgz", "scripts/ci/libraries/_kind.sh", "tests/charts/conftest.py"]

Frequent failures of helm chart tests

### Apache Airflow version main (development) ### What happened We keep on getting very frequent failures of Helm Chart tests and seems that a big number of those errors are because of errors when pulling charts from bitnami for postgres: Example here (but I saw it happening very often recently): https://github.com/apache/airflow/runs/6666449965?check_suite_focus=true#step:9:314 ``` Save error occurred: could not find : chart postgresql not found in https://charts.bitnami.com/bitnami: looks like "https://charts.bitnami.com/bitnami" is not a valid chart repository or cannot be reached: stream error: stream ID 1; INTERNAL_ERROR Deleting newly downloaded charts, restoring pre-update state Error: could not find : chart postgresql not found in https://charts.bitnami.com/bitnami: looks like "https://charts.bitnami.com/bitnami" is not a valid chart repository or cannot be reached: stream error: stream ID 1; INTERNAL_ERROR Dumping logs from KinD ``` It is not only a problem for our CI but it might be similar problem for our users who want to install the chart - they might also get the same kinds of error. I guess we should either make it more resilient to intermittent problems with bitnami charts or use another chart (or maybe even host the chart ourselves somewhere within apache infrastructure. While the postgres chart is not really needed for most "production" users, it is still a dependency of our chart and it makes our chart depend on external and apparently flaky service. ### What you think should happen instead We should find (or host ourselves) more stable dependency or get rid of it. ### How to reproduce Look at some recent CI builds and see that they often fail in K8S tests and more often than not the reason is missing postgresql chart. ### Operating System any ### Versions of Apache Airflow Providers not relevant ### Deployment Other ### Deployment details CI ### Anything else Happy to make the change once we agree what's the best way :). ### 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/24037

https://github.com/apache/airflow/pull/24395

5d5976c08c867b8dbae8301f46e0c422d4dde1ed

779571b28b4ae1906b4a23c031d30fdc87afb93e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/hdfs/hooks/webhdfs.py", "tests/providers/apache/hdfs/hooks/test_webhdfs.py"]

Make port as optional argument

### Apache Airflow version 2.3.1 (latest released) ### What happened my webhdfs is running behind a proxy. I need to give a complicated URL and do not want to add port in front of the URL So there are two options 1) use Schema to add stuff before webhdfs/v1 2) make adding of port optional so that complete url before webhdfs can be provided in the hostname ### What you think should happen instead error while connecting to webhdfs [2022-05-27, 15:24:43 UTC] {webhdfs.py:89} INFO - Read operation on namenode abc.xyz.com failed with error: b'' ### How to reproduce run webhdfs behind a proxy without exposing the actual servers ### Operating System Debian ### Versions of Apache Airflow Providers 2.2.3 apache-airflow-providers-apache-hdfs ### 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/24022

https://github.com/apache/airflow/pull/24550

ed37c3a0e87f64e6942497c5d4c15078a5e02d16

d8ec1ec8ecae64dbe8591496e8bec71d5e3ca25a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", "tests/providers/cncf/kubernetes/operators/test_kubernetes_pod.py"]

KubernetesPodOperator/KubernetesExecutor: Failed to adopt pod 422

### Apache Airflow version 2.3.0 ### What happened Here i provide steps to reproduce this. Goal of this: to describe how to reproduce the "Failed to Adopt pod" error condition. The DAG->step Described Below should be of type KubernetesPodOperator NOTE: under normal operation, (where the MAIN_AIRFLOW_POD is never recycled by k8s, we will never see this edge-case) (it is only when the workerPod is still running, but the MAIN_AIRFLOW_POD is suddenly restarted/stopped) (that we would see orphan->workerPods) 1] Implement a contrived-DAG, with a single step -> which is long-running (e.g. 6 minutes) 2] Deploy your airflow-2.1.4 / airfow-2.3.0 together with the contrived-DAG 3] Run your contrived-DAG. 4] in the middle of running the single-step, check via "kubectl" that your Kubernetes->workerPod has been created / running 5] while workerPod still running, do "kubectl delete pod <OF_MAIN_AIRFLOW_POD>". This will mean that the workerPod becomes an orphan. 6] the workerPod still continues to run through to completion. after which the K8S->status of the pod will be Completed, however the pod doesn't shut down itself. 7] "kubectl" start up a new <MAIN_AIRFLOW_POD> so the web-ui is running again. 8] MAIN_AIRFLOW_POD->webUi - Run your contrived-DAG again 9] while the contrived-DAG is starting/tryingToStart etc, you will see in the logs printed out "Failed to adopt pod" -> with 422 error code. The step-9 with the error message, you will find two appearances of this error msg in the airflow-2.1.4, airflow-2.3.0 source-code. The step-7 may also - general logging from the MAIN_APP - may also output the "Failed to adopt pod" error message also. ### What you think should happen instead On previous versions of airflow e.g. 1.10.x, the orphan-workerPods would be adopted by the 2nd run-time of the airflowMainApp and either used to continue the same DAG and/or cleared away when complete. This is not happening with the newer airflow 2.1.4 / 2.3.0 (presumably because the code changed), and upon the 2nd run-time of the airflowMainApp - it would seem to try to adopt-workerPod but fails at that point ("Failed to adopt pod" in the logs and hence it cannot clear away orphan pods). Given this is an edge-case only, (i.e. we would not expect k8s to be recycling the main airflowApp/pod anyway), it doesn't seem totally urgent bug. However, the only reason for me raising this issue with yourselves is that given any k8s->namespace, in particular in PROD, over time (e.g. 1 month?) the namespace will slowly be being filled up with orphanPods and somebody would need to manually log-in to delete old pods. ### How to reproduce Here i provide steps to reproduce this. Goal of this: to describe how to reproduce the "Failed to Adopt pod" error condition. The DAG->step Described Below should be of type KubernetesPodOperator NOTE: under normal operation, (where the MAIN_AIRFLOW_POD is never recycled by k8s, we will never see this edge-case) (it is only when the workerPod is still running, but the MAIN_AIRFLOW_POD is suddenly restarted/stopped) (that we would see orphan->workerPods) 1] Implement a contrived-DAG, with a single step -> which is long-running (e.g. 6 minutes) 2] Deploy your airflow-2.1.4 / airfow-2.3.0 together with the contrived-DAG 3] Run your contrived-DAG. 4] in the middle of running the single-step, check via "kubectl" that your Kubernetes->workerPod has been created / running 5] while workerPod still running, do "kubectl delete pod <OF_MAIN_AIRFLOW_POD>". This will mean that the workerPod becomes an orphan. 6] the workerPod still continues to run through to completion. after which the K8S->status of the pod will be Completed, however the pod doesn't shut down itself. 7] "kubectl" start up a new <MAIN_AIRFLOW_POD> so the web-ui is running again. 8] MAIN_AIRFLOW_POD->webUi - Run your contrived-DAG again 9] while the contrived-DAG is starting/tryingToStart etc, you will see in the logs printed out "Failed to adopt pod" -> with 422 error code. The step-9 with the error message, you will find two appearances of this error msg in the airflow-2.1.4, airflow-2.3.0 source-code. The step-7 may also - general logging from the MAIN_APP - may also output the "Failed to adopt pod" error message also. ### Operating System kubernetes ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details nothing special. it (CI/CD pipeline) builds the app. using requirements.txt to pull-in all the required python dependencies (including there is a dependency for the airflow-2.1.4 / 2.3.0) it (CI/CD pipeline) packages the app as an ECR image & then deploy directly to k8s namespace. ### Anything else this is 100% reproducible each & every time. i have tested this multiple times. also - i tested this on the old airflow-1.10.x a couple of times to verify that the bug did not exist previously ### 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/24015

https://github.com/apache/airflow/pull/29279

05fb80ee9373835b2f72fad3e9976cf29aeca23b

d26dc223915c50ff58252a709bb7b33f5417dfce

python

closed

apache/airflow

https://github.com/apache/airflow

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

Set color of Operators in cloud_sql.py

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==5.0.0 ### Apache Airflow version 2.1.2 ### Operating System GCP Container ### Deployment Composer ### Deployment details composer-1.17.1-airflow-2.1.2 ### What happened No color is set for the GraphView legends. All CloudSQLOperator legends are white. <img width="970" alt="legends" src="https://user-images.githubusercontent.com/12693596/170856092-0f9af91a-fbe9-47d9-9a9e-6e572b45fdfc.png"> ### What you think should happen instead A color should be set to the Operators in cloud_sql.py. ### How to reproduce Add example_cloud_sql.py to Dags and look at GraphView. ### 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/23999

https://github.com/apache/airflow/pull/24000

65ad2aed26f7572ba0d3b04a33f9144989ac7117

3dd7b1ddbaa3170fbda30a8323286abf075f30ba

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/serve_logs.py", "tests/utils/test_serve_logs.py"]

serve_logs.py should respect the logging config's task handler's base_log_folder value

### Apache Airflow version 2.2.5 ### What happened in a worker container, a flask app is spun up to serve task log files that is read by the webserver and rendered to the user in the UI. The log files cannot be read if you overwrite the task handler's base_log_folder value. (404 error) ie. in the airflow.cfg, the base_log_folder = `foo/bar/logs`, and the task handler uses `{base_log_folder}/dags` ### What you think should happen instead in https://github.com/apache/airflow/blob/main/airflow/utils/serve_logs.py#L33, it should read the logging config's task handler log location. ### How to reproduce use a custom logging config, override the task's base log folder. Run a dag and try to view logs in the ui, you will get a 404 ``` LOGGING_CONFIG["handlers"].update( { "task": { "class": "airflow.utils.log.file_task_handler.FileTaskHandler", "formatter": "airflow", "base_log_folder": f"{BASE_LOG_FOLDER}/dags", "filename_template": FILENAME_TEMPLATE, "filters": ["mask_secrets"], }, } ``` ### 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/23967

https://github.com/apache/airflow/pull/32781

9e3104b5effc56f77833772d86e7201bcc50b8c9

579ce065c5b0865b4aea580ee2d05c89680b7a8b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py"]

Add missing parameter documentation for `KubernetesHook` and `KubernetesPodOperator`

### Body Currently the following modules are missing certain parameters in their docstrings. Because of this, these parameters are not captured in the [Python API docs for the Kubernetes provider](https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/_api/airflow/providers/cncf/kubernetes/index.html). - [ ] KubernetesHook: `in_cluster`, `config_file`, `cluster_context`, `client_configuration` - [ ] KubernetesPodOperator: `env_from`, `node_selectors`, `pod_runtime_info_envs`, `configmaps` ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23955

https://github.com/apache/airflow/pull/24054

203fe71b49da760968c26752957f765c4649423b

98b4e48fbc1262f1381e7a4ca6cce31d96e6f5e9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/databricks/operators/databricks.py", "airflow/providers/databricks/operators/databricks_sql.py", "docs/apache-airflow-providers-databricks/operators/submit_run.rst", "docs/spelling_wordlist.txt"]

Add missing parameter documentation in `DatabricksSubmitRunOperator` and `DatabricksSqlOperator`

### Body Currently the following modules are missing certain parameters in their docstrings. Because of this, these parameters are not captured in the [Python API docs for the Databricks provider](https://airflow.apache.org/docs/apache-airflow-providers-databricks/stable/_api/airflow/providers/databricks/index.html). - [ ] DatabricksSubmitRunOperator: `tasks` - [ ] DatabricksSqlOperator: `do_xcom_push` - Granted this is really part of the `BaseOperator`, but this operator specifically sets the default value to False so it would be good if this was explicitly listed for users. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23954

https://github.com/apache/airflow/pull/24599

2e5737df531410d2d678d09b5d2bba5d37a06003

82f842ffc56817eb039f1c4f1e2c090e6941c6af

python

closed

apache/airflow

https://github.com/apache/airflow

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

Only autorefresh active dags on home page

### Description In https://github.com/apache/airflow/pull/22900, we added auto-refresh for the home page. Right now, we pass all dag_ids to the `last_dagruns`, `dag_stats` and `task_stats` endpoints. During auto-refresh, we should only request info for dags that are not paused. On page load, we still want to check all three endpoints for all dags in view. But for subsequent auto-refresh requests we should only check active dags. See [here](https://github.com/apache/airflow/blob/main/airflow/www/static/js/dags.js#L429) for where the homepage auto-refresh lives. ### Use case/motivation Smaller requests should make a faster home page. ### 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/23949

https://github.com/apache/airflow/pull/24770

e9d19a60a017224165e835949f623f106b97e1cb

2a1472a6bef57fc57cfe4577bcbed5ba00521409

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/grid/dagRuns/Bar.jsx", "airflow/www/static/js/grid/dagRuns/Tooltip.jsx", "airflow/www/static/js/grid/details/Header.jsx", "airflow/www/static/js/grid/details/content/dagRun/index.jsx"]

Add backfill icon to grid view dag runs

### Description In the grid view, we use a play icon to indicate manually triggered dag runs. We should do the same for a backfilled dag run. Possible icons can be found [here](https://react-icons.github.io/react-icons). Note: We use the manual run icon in both the [dag run bar component](https://github.com/apache/airflow/blob/main/airflow/www/static/js/grid/dagRuns/Bar.jsx) and in the [details panel header](https://github.com/apache/airflow/blob/main/airflow/www/static/js/grid/details/Header.jsx) ### Use case/motivation Help users quickly differentiate between manual, scheduled and backfill runs. ### 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/23945

https://github.com/apache/airflow/pull/23970

6962d8a3556999af2eec459c944417ddd6d2cfb3

d470a8ef8df152eceee88b95365ff923db7cb2d7

python

closed

apache/airflow

https://github.com/apache/airflow

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

No option to set blocksize when retrieving a file in ftphook

### Apache Airflow version 2.0.0 ### What happened using ftphook, im trying to download a file in chunks but the deafult blocksize is 8192 and cannot be changed. retrieve_file code is calling conn.retrbinary(f'RETR {remote_file_name}', callback) but no blocksize is passed while this function is declared: def retrbinary(self, cmd, callback, blocksize=8192, rest=None): ### What you think should happen instead allow passing a blocksize ### How to reproduce _No response_ ### Operating System gcp ### 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/23935

https://github.com/apache/airflow/pull/24860

2f29bfefb59b0014ae9e5f641d3f6f46c4341518

64412ee867fe0918cc3b616b3fb0b72dcd88125c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/fab_security/manager.py"]

Airflow 2.3.1 - gunicorn keeps removing and adding Permission menu access on Permissions to role Admin

### Apache Airflow version 2.3.1 (latest released) ### What happened gunicorn log file is full of these messages. This behavior was present also in release 2.3.0 I don't think that was a problem in Airflow 2.2.x The problem is that web server now is much weekier than before and it is the second time in a week that I found it not running. ``` [2022-05-26 05:50:55,083] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:50:55,098] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:50:55,159] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:50:55,164] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-05-26 05:51:25,688] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:51:25,704] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:51:25,763] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:51:25,769] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-05-26 05:51:56,283] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:51:56,297] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:51:56,353] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:51:56,358] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-05-26 05:52:26,954] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:52:26,971] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:52:27,033] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:52:27,040] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-05-26 05:52:57,595] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:52:57,610] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:52:57,679] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:52:57,684] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-05-26 05:53:28,130] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-05-26 05:53:28,148] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-05-26 05:53:28,209] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-05-26 05:53:28,214] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin ``` ### What you think should happen instead Log must be clear and gunicorn stable as a rock. ### How to reproduce Just launch gunicorn ### Operating System Ubuntu 20.04.4 LTS ### Versions of Apache Airflow Providers ``` apache-airflow-providers-celery==2.1.4 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-mssql==2.1.3 apache-airflow-providers-microsoft-winrm==2.0.5 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-openfaas==2.0.3 apache-airflow-providers-oracle==2.2.3 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-samba==3.0.4 apache-airflow-providers-sftp==2.6.0 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.4 ``` ### Deployment Virtualenv installation ### Deployment details gunicorn is configured with AUTH_LDAP option ### Anything else no ### 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/23926

https://github.com/apache/airflow/pull/24065

75fdbf01dd91cc65d8289b1d2f7183b1db08dae1

be21e08e1bb6202626c12b2375f24167cf22838a

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

Wrong dependecy version of requests for Databricks provider

### Apache Airflow Provider(s) databricks ### Versions of Apache Airflow Providers apache-airflow-providers-databricks==2.7.0 ### Apache Airflow version 2.2.2 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Other ### Deployment details _No response_ ### What happened There was added import statement from the `requests` library to `airflow/providers/databricks/hooks/databricks_base.py` by [this MR](https://github.com/apache/airflow/pull/22422/files#diff-bfbc446378c91e1c398eb07d02dc333703bb1dda4cbe078193b16199f11db8a5R34). But minimal version of requests wasn't bumped up to `>=2.27.0`, as we can see[ it's still >=2.26.0](https://github.com/apache/airflow/blob/main/setup.py#L269), but that JSONDecodeError class added to `requests` library starting from [2.27.0 (see release notes)](https://github.com/psf/requests/releases/tag/v2.27.0). It brings inconsistency between libraries and leads to following error: ``` File "/usr/local/lib/python3.7/site-packages/airflow/providers/databricks/hooks/databricks.py", line 33, in <module> from airflow.providers.databricks.hooks.databricks_base import BaseDatabricksHook File "/usr/local/lib/python3.7/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 34, in <module> from requests.exceptions import JSONDecodeError ImportError: cannot import name 'JSONDecodeError' from 'requests.exceptions' ``` ### What you think should happen instead it breaks DAGs ### How to reproduce Use any Databricks operator with `requests==2.26.0`, which is defined is minimal compatible version ### 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/23917

https://github.com/apache/airflow/pull/23927

b170dc7d66a628e405a824bfbc9fb48a3b3edd63

80c3fcd097c02511463b2c4f586757af0e5f41b2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/manager.py", "airflow/utils/process_utils.py", "tests/utils/test_process_utils.py"]

`dag-processor` failed to start in docker

### Apache Airflow version 2.3.0 (latest released) ### What happened Standalone DagProcessor which run in Apache Airflow Production Docker Image failed with error ``` airflow-dag-processor_1 | airflow-dag-processor_1 | Traceback (most recent call last): airflow-dag-processor_1 | File "/home/airflow/.local/bin/airflow", line 8, in <module> airflow-dag-processor_1 | sys.exit(main()) airflow-dag-processor_1 | File "/home/airflow/.local/lib/python3.7/site-packages/airflow/__main__.py", line 38, in main airflow-dag-processor_1 | args.func(args) airflow-dag-processor_1 | File "/home/airflow/.local/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 51, in command airflow-dag-processor_1 | return func(*args, **kwargs) airflow-dag-processor_1 | File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/cli.py", line 99, in wrapper airflow-dag-processor_1 | return f(*args, **kwargs) airflow-dag-processor_1 | File "/home/airflow/.local/lib/python3.7/site-packages/airflow/cli/commands/dag_processor_command.py", line 80, in dag_processor airflow-dag-processor_1 | manager.start() airflow-dag-processor_1 | File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 475, in start airflow-dag-processor_1 | os.setpgid(0, 0) airflow-dag-processor_1 | PermissionError: [Errno 1] Operation not permitted ``` This error not happen if directly run in host system by `airflow dag-processor` Seems like this issue happen because when we run in Apache Airflow Production Docker Image `airflow` process is session leader, and according to `man setpgid` ``` ERRORS setpgid() will fail and the process group will not be altered if: [EPERM] The process indicated by the pid argument is a session leader. ``` ### What you think should happen instead `dag-processor` should start in docker without error ### How to reproduce 1. Use simple docker-compose file which use official Airflow 2.3.0 image ```yaml # docker-compose-dag-processor.yaml version: '3' volumes: postgres-db-volume: aiflow-logs-volume: x-airflow-common: &airflow-common image: apache/airflow:2.3.0-python3.7 environment: AIRFLOW__SCHEDULER__STANDALONE_DAG_PROCESSOR: 'True' AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:insecurepassword@postgres/airflow volumes: - aiflow-logs-volume:/opt/airflow/logs - ${PWD}/dags:/opt/airflow/dags user: "${AIRFLOW_UID:-50000}:0" extra_hosts: - "host.docker.internal:host-gateway" services: postgres: image: postgres:13 environment: POSTGRES_USER: airflow POSTGRES_PASSWORD: insecurepassword POSTGRES_DB: airflow ports: - 55432:5432 volumes: - postgres-db-volume:/var/lib/postgresql/data healthcheck: test: ["CMD", "pg_isready", "-U", "airflow"] interval: 5s retries: 5 restart: unless-stopped airflow-upgrade-db: <<: *airflow-common command: ["db", "upgrade"] depends_on: postgres: condition: service_healthy airflow-dag-processor: <<: *airflow-common command: dag-processor restart: unless-stopped depends_on: airflow-upgrade-db: condition: service_completed_successfully ``` 2. `docker-compose -f docker-compose-dag-processor.yaml up` ### Operating System macOS Monterey 12.3.1 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details Docker: **20.10.12** docker-compose: **1.29.2** ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23871

https://github.com/apache/airflow/pull/23872

8ccff9244a6d1a936d8732721373b967e95ec404

9216489d9a25f56f7a55d032b0ebfc1bf0bf4a83

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/commands/testing_commands.py"]

Don’t show traceback on 'breeze tests' subprocess returning non-zero

### Body Currently, if any tests fail when `breeze tests` is run, Breeze 2 would emit a traceback pointing to the `docker-compose` subprocess call. This is due to Docker propagating the exit call of the underlying `pytest` subprocess. While it is technically correct to emit an exception, the traceback is useless in this context, and only clutters output. It may be a good idea to add a special case for this and suppress the exception. A similar situation can be observed with `breeze shell` if you run `exit 1` in the shell. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23868

https://github.com/apache/airflow/pull/23897

1bf6dded9a5dcc22238b8943028b08741e36dfe5

d788f4b90128533b1ac3a0622a8beb695b52e2c4

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/commands/ci_image_commands.py", "dev/breeze/src/airflow_breeze/utils/md5_build_check.py", "images/breeze/output-commands-hash.txt"]

Don’t prompt for 'breeze build-image'

### Body Currently, running the (new) `breeze build-image` brings up two prompts if any of the meta files are outdated: ``` $ breeze build-image Good version of Docker: 20.10.14. Good version of docker-compose: 2.5.1 The following important files are modified in ./airflow since last time image was built: * setup.py * Dockerfile.ci * scripts/docker/common.sh * scripts/docker/install_additional_dependencies.sh * scripts/docker/install_airflow.sh * scripts/docker/install_airflow_dependencies_from_branch_tip.sh * scripts/docker/install_from_docker_context_files.sh Likely CI image needs rebuild Do you want to build the image (this works best when you have good connection and can take usually from 20 seconds to few minutes depending how old your image is)? Press y/N/q. Auto-select n in 10 seconds (add `--answer n` to avoid delay next time): y This might take a lot of time (more than 10 minutes) even if you havea good network connection. We think you should attempt to rebase first. But if you really, really want - you can attempt it. Are you really sure? Press y/N/q. Auto-select n in 10 seconds (add `--answer n` to avoid delay next time): y ``` While the prompts are shown in good nature, they don’t really make sense for `build-image` since the user already gave an explicit answer by running `build-image`. They should be suppressed. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23867

https://github.com/apache/airflow/pull/23898

cac7ab5c4f4239b04d7800712ee841f0e6f6ab86

90940b529340ef7f9b8c51d5c7d9b6a848617dea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/mappedoperator.py", "tests/serialization/test_dag_serialization.py"]

AIP-45 breaks follow-on mini scheduler for mapped tasks

I've just noticed that this causes a problem for the follow-on mini scheduler for mapped tasks. I guess that code path wasn't sufficiently unit tested. DAG ```python import csv import io import os import json from datetime import datetime from airflow import DAG from airflow.decorators import task from airflow.models.xcom_arg import XComArg from airflow.providers.amazon.aws.hooks.s3 import S3Hook from airflow.providers.amazon.aws.operators.s3 import S3ListOperator with DAG(dag_id='mapped_s3', start_date=datetime(2022, 5, 19)) as dag: files = S3ListOperator( task_id="get_inputs", bucket="airflow-summit-2022", prefix="data_provider_a/{{ data_interval_end | ds }}/", delimiter='/', do_xcom_push=True, ) @task def csv_to_json(aws_conn_id, input_bucket, key, output_bucket): hook = S3Hook(aws_conn_id=aws_conn_id) csv_data = hook.read_key(key, input_bucket) reader = csv.DictReader(io.StringIO(csv_data)) output = io.BytesIO() for row in reader: output.write(json.dumps(row, indent=None).encode('utf-8')) output.write(b"\n") output.seek(0, os.SEEK_SET) hook.load_file_obj(output, key=key.replace(".csv", ".json"), bucket_name=output_bucket) csv_to_json.partial( aws_conn_id="aws_default", input_bucket=files.bucket, output_bucket="airflow-summit-2022-processed" ).expand(key=XComArg(files)) ``` Error: ``` File "/home/ash/code/airflow/airflow/airflow/jobs/local_task_job.py", line 253, in _run_mini_scheduler_on_child_tasks info = dag_run.task_instance_scheduling_decisions(session) File "/home/ash/code/airflow/airflow/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/ash/code/airflow/airflow/airflow/models/dagrun.py", line 658, in task_instance_scheduling_decisions schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( File "/home/ash/code/airflow/airflow/airflow/models/dagrun.py", line 714, in _get_ready_tis expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) File "/home/ash/code/airflow/airflow/airflow/models/mappedoperator.py", line 609, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() File "/home/ash/code/airflow/airflow/airflow/models/mappedoperator.py", line 591, in _resolve_map_lengths expansion_kwargs = self._get_expansion_kwargs() File "/home/ash/code/airflow/airflow/airflow/models/mappedoperator.py", line 526, in _get_expansion_kwargs return getattr(self, self._expansion_kwargs_attr) AttributeError: 'MappedOperator' object has no attribute 'mapped_op_kwargs' ``` _Originally posted by @ashb in https://github.com/apache/airflow/issues/21877#issuecomment-1133409500_

https://github.com/apache/airflow/issues/23838

https://github.com/apache/airflow/pull/24772

1abdf3fd1e048f53e061cc9ad59177be7b5245ad

6fd06fa8c274b39e4ed716f8d347229e017ba8e5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/base.py", "airflow/models/mappedoperator.py", "airflow/serialization/serialized_objects.py", "tests/serialization/test_dag_serialization.py"]

Dynamic Task Mapping not working with op_kwargs in PythonOperator

### Apache Airflow version 2.3.0 (latest released) ### What happened The following DAG was written and expected to generate 3 tasks (one for each string in the list) **dag_code** ```python import logging from airflow.decorators import dag, task from airflow.operators.python import PythonOperator from airflow.utils.dates import datetime def log_strings_operator(string, *args, **kwargs): logging.info("we've made it into the method") logging.info(f"operator log - {string}") @dag( dag_id='dynamic_dag_test', schedule_interval=None, start_date=datetime(2021, 1, 1), catchup=False, tags=['example', 'dynamic_tasks'] ) def tutorial_taskflow_api_etl(): op2 = (PythonOperator .partial(task_id="logging_with_operator_task", python_callable=log_strings_operator) .expand(op_kwargs=[{"string": "a"}, {"string": "b"}, {"string": "c"}])) return op2 tutorial_etl_dag = tutorial_taskflow_api_etl() ``` **error message** ```python Broken DAG: [/usr/local/airflow/dags/dynamic_dag_test.py] Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 343, in _serialize return SerializedBaseOperator.serialize_mapped_operator(var) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 608, in serialize_mapped_operator assert op_kwargs[Encoding.TYPE] == DAT.DICT TypeError: list indices must be integers or slices, not Encoding During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1105, in to_dict json_dict = {"__version": cls.SERIALIZER_VERSION, "dag": cls.serialize_dag(var)} File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1013, in serialize_dag raise SerializationError(f'Failed to serialize DAG {dag.dag_id!r}: {e}') airflow.exceptions.SerializationError: Failed to serialize DAG 'dynamic_dag_test': list indices must be integers or slices, not Encoding ``` ### What you think should happen instead Dag should contain 1 task `logging_with_operator_task` that contains 3 indices ### How to reproduce copy/paste dag code into a dag file and run on airflow 2.3.0. Airflow UI will flag the error ### Operating System Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23833

https://github.com/apache/airflow/pull/23860

281e54b442f0a02bda53ae847aae9f371306f246

5877f45d65d5aa864941efebd2040661b6f89cb1

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQueryInsertJobOperator is broken on any type of job except `query`

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==7.0.0 ### Apache Airflow version 2.2.5 ### Operating System MacOS 12.2.1 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened We are using `BigQueryInsertJobOperator` to load data from parquet files in Google Cloud Storage with this kind of configuration: ``` BigQueryInsertJobOperator( task_id="load_to_bq", configuration={ "load": { "writeDisposition": "WRITE_APPEND", "createDisposition": "CREATE_IF_NEEDED", "destinationTable": destination_table, "sourceUris": source_files "sourceFormat": "PARQUET" } } ``` After upgrade to `apache-airflow-providers-google==7.0.0` all load jobs are now broken. I believe that problem lies in this line: https://github.com/apache/airflow/blob/5bfacf81c63668ea63e7cb48f4a708a67d0ac0a2/airflow/providers/google/cloud/operators/bigquery.py#L2170 So it's trying to get the destination table from `query` job config and makes it impossible to use any other type of job. ### What you think should happen instead _No response_ ### How to reproduce Use BigQueryInsertJobOperator to submit any type of job except `query` ### Anything else ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/operators/bigquery.py", line 2170, in execute table = job.to_api_repr()["configuration"]["query"]["destinationTable"] KeyError: 'query' ``` ### 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/23826

https://github.com/apache/airflow/pull/24165

389e858d934a7813c7f15ab4e46df33c5720e415

a597a76e8f893865e7380b072de612763639bfb9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Race condition between Triggerer and Scheduler

### Apache Airflow version 2.2.5 ### What happened Deferable tasks, that trigger instantly after getting defered, might get its state set to `FAILED` by the scheduler. The triggerer can fire the trigger and scheduler can re-queue the task instance before it has a chance to process the executor event for when the ti got defered. ### What you think should happen instead This code block should not run in this instance: https://github.com/apache/airflow/blob/5bfacf81c63668ea63e7cb48f4a708a67d0ac0a2/airflow/jobs/scheduler_job.py#L667-L692 ### How to reproduce Most importantly have a trigger, that instantly fires. I'm not sure if the executor type is important - I'm running `CeleryExecutor`. Also having two schedulers might be important. ### Operating System Arch Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/23824

https://github.com/apache/airflow/pull/23846

94f4f81efb8c424bee8336bf6b8720821e48898a

66ffe39b0b3ae233aeb80e77eea1b2b867cc8c45

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers_manager.py"]

ModuleNotFoundExceptions not matched as optional features

### Apache Airflow version 2.3.0 (latest released) ### What happened The `providers_manager.py` logs an import warning with stack trace (see example) for optional provider features instead of an info message noting the optional feature is disabled. Sample message: ``` [2022-05-19 21:46:53,065] {providers_manager.py:223} WARNING - Exception when importing 'airflow.providers.google.cloud.hooks.compute_ssh.ComputeEngineSSHHook' from 'apache-airflow-providers-google' package Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/providers_manager.py", line 257, in _sanity_check imported_class = import_string(class_name) File "/usr/local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 32, in import_string module = import_module(module_path) File "/usr/local/lib/python3.9/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1030, in _gcd_import File "<frozen importlib._bootstrap>", line 1007, in _find_and_load File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 680, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 850, in exec_module File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed File "/usr/local/lib/python3.9/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 23, in <module> import paramiko ModuleNotFoundError: No module named 'paramiko' ``` ### What you think should happen instead There is explicit code for catching `ModuleNotFoundException`s so these import errors should be logged as info messages like: ``` [2022-05-20 08:18:54,680] {providers_manager.py:215} INFO - Optional provider feature disabled when importing 'airflow.providers.google.cloud.hooks.compute_ssh.ComputeEngineSSHHook' from 'apache-airflow-providers-google' package ``` ### How to reproduce Install the `google` provider but do not install the `ssh` submodule (or alternatively the `mysql` module). Various airflow components will produce the above warning logs. ### Operating System Debian bullseye ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23823

https://github.com/apache/airflow/pull/23825

5bfacf81c63668ea63e7cb48f4a708a67d0ac0a2

6f5749c0d04bd732b320fcbe7713f2611e3d3629

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add an AWS operator for Create RDS Database

### Description @eladkal suggested we add the operator and then incorporate it into https://github.com/apache/airflow/pull/23681. I have a little bit of a backlog right now trying to get the System Tests up and running for AWS, but if someone wants to get to it before me, it should be a pretty easy first contribution. The required API call is documented [here](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/rds.html#RDS.Client.create_db_instance) and I'm happy to help with any questions and./or help review it if someone wants to take a stab at it before I get the time. ### Use case/motivation _No response_ ### Related issues Could be used to simplify https://github.com/apache/airflow/pull/23681 ### 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/23822

https://github.com/apache/airflow/pull/24099

c7feb31786c7744da91d319f499d9f6015d82454

bf727525e1fd777e51cc8bc17285f6093277fdef

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-google/operators/cloud/cloud_sql.rst"]

Fix typo in cloud_sql.rst

### What do you see as an issue? CloudSqlInstanceImportOperator does not exist. https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/cloud_sql.html#cloudsqlinstanceimportoperator <img width="1040" alt="image" src="https://user-images.githubusercontent.com/12693596/169287518-45e8dc8b-7e57-4357-ae11-ffa2642e1498.png"> ### Solving the problem Update CloudSqlInstanceImportOperator to CloudSQLImportInstanceOperator. ### 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/23799

https://github.com/apache/airflow/pull/23800

8f3ce33e921056db3bac34dcf38b238cbf417279

54aa23405240b9db553bf639de85b2d86364420b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/airflow_local_settings.py", "airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/utils/log/colored_log.py", "airflow/utils/log/timezone_aware.py", "airflow/www/static/js/grid/details/content/taskInstance/Logs/utils.js", "airflow/www/static/js/ti_log.js", "newsfragments/24373.significant.rst"]

Webserver shows wrong datetime (timezone) in log

### Apache Airflow version 2.3.0 (latest released) ### What happened same as #19401 , when I open task`s log in web interface, it shifts this time forward by 8 hours (for Asia/Shanghai), but it's already in Asia/Shanghai. here is the log in web: ``` *** Reading local file: /opt/airflow/logs/forecast/cal/2022-05-18T09:50:00+00:00/1.log [2022-05-19, 13:54:52] {taskinstance.py:1037} INFO ... ``` As you seee, UTC time is 2022-05-18T09:50:00，and My timezone is Asia/Shanghai(should shift forward 8 hours)，but it shift forward 16hours! ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye)(docker) ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details 1. build my docker image from apache/airflow:2.3.0 to change timezone ```Dockerfile FROM apache/airflow:2.3.0 # bugfix of log UI in web, here I change ti_log.js file by following on #19401 COPY ./ti_log.js /home/airflow/.local/lib/python3.7/site-packages/airflow/www/static/js/ti_log.js USER root # container timezone changed to CST time RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime \ && rm -rf /etc/timezone \ && echo Asia/Shanghai >> /etc/timezone \ && chown airflow /home/airflow/.local/lib/python3.7/site-packages/airflow/www/static/js/ti_log.js USER airflow ``` 2. use my image to run airflow by docker-compose 3. check task logs in web Although I have changed the file `airflow/www/static/js/ti_log.js`, but it did not work! Then check source from Web, I found another file : `airflow/www/static/dist/tiLog.e915520196109d459cf8.js`, then I replace "+00:00" by "+08:00" in this file. Finally it works! ```js # origin tiLog.e915520196109d459cf8.js replaceAll(c,(e=>`<time datetime="${e}+00:00" data-with-tz="true">${Object(a.f)(`${e}+00:00`)}</time>`)) ``` ```js # what I changed replaceAll(c,(e=>`<time datetime="${e}+08:00" data-with-tz="true">${Object(a.f)(`${e}+08:00`)}</time>`)) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23796

https://github.com/apache/airflow/pull/24373

5a8209e5096528b6f562efebbe71b6b9c378aaed

7de050ceeb381fb7959b65acd7008e85b430c46f

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic task mapping creates too many mapped instances when task pushed non-default XCom

### Apache Airflow version 2.3.0 (latest released) ### What happened Excess tasks are created when using dynamic task mapping with KubernetesPodOperator, but only in certain cases which I do not understand. I have a simple working example of this where the flow is: - One task that returns a list XCom (list of lists, since I'm partial-ing to `KubernetesPodOperator`'s `arguments`) of length 3. This looks like `[["a"], ["b"], ["c"]]` - A `partial` from this, which is expanded on the above's result. Each resulting task has an XCom of a single element list that looks like `["d"]`. We expect the `expand` to result in 3 tasks, which it does. So far so good. Why doesn't the issue occur at this stage? No clue. - A `partial` from the above. We expect 3 tasks in this final stage, but get 9. 3 succeed and 6 fail consistently. This 3x rule scales to as many tasks as you define in step 2 (e.g. 2 tasks in step 2 -> 6 tasks in step 3, where 4 fail)  If I recreate this using the TaskFlow API with `PythonOperator`s, I get the expected result of 1 task -> 3 tasks -> 3 tasks  Futhermore, if I attempt to look at the `Rendered Template` of the failed tasks in the `KubernetesPodOperator` implementation (first image), I consistently get `Error rendering template` and all the fields are `None`. The succeeded tasks look normal.  Since the `Rendered Template` view fails to load, I can't confirm what is actually getting provided to these failing tasks' `argument` parameter. If there's a way I can query the meta database to see this, I'd be glad to if given instruction. ### What you think should happen instead I think this has to do with how XComs are specially handled with the `KubernetesPodOperator`. If we look at the XComs tab of the upstream task (`some-task-2` in the above images), we see that the return value specifies `pod_name` and `pod_namespace` along with `return_value`.  Whereas in the `t2` task of the TaskFlow version, it only contains `return_value`.  I haven't dug through the code to verify, but I have a strong feeling these extra values `pod_name` and `pod_namespace` are being used to generate the `OperatorPartial`/`MappedOperator` as well when they shouldn't be. ### How to reproduce Run this DAG in a k8s context: ``` from datetime import datetime from airflow import XComArg from airflow.models import DAG from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator def make_operator( **kwargs ): return KubernetesPodOperator( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) def make_partial_operator( **kwargs ): return KubernetesPodOperator.partial( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) with DAG(dag_id='test-pod-xcoms', schedule_interval=None, start_date=datetime(2020, 1, 1), max_active_tasks=20) as dag: op1 = make_operator( cmds=['python3', '-c' 'import json;f=open("/airflow/xcom/return.json", "w");f.write(json.dumps([["a"], ["b"], ["c"]]))'], image='python:3.9-alpine', name='airflow-private-image-pod-1', task_id='some-task-1', do_xcom_push=True ) op2 = make_partial_operator( cmds=['python3', '-c' 'import json;f=open("/airflow/xcom/return.json", "w");f.write(json.dumps(["d"]))'], image='python:3.9-alpine', name='airflow-private-image-pod-2', task_id='some-task-2', do_xcom_push=True ) op3 = make_partial_operator( cmds=['echo', 'helloworld'], image='alpine:latest', name='airflow-private-image-pod-3', task_id='some-task-3', ) op3.expand(arguments=XComArg(op2.expand(arguments=XComArg(op1)))) ``` For the TaskFlow version of this that works, run this DAG (doesn't have to be k8s context): ``` from datetime import datetime from airflow.decorators import task from airflow.models import DAG, Variable @task def t1(): return [[1], [2], [3]] @task def t2(val): return val @task def t3(val): print(val) with DAG(dag_id='test-mapping', schedule_interval=None, start_date=datetime(2020, 1, 1)) as dag: t3.partial().expand(val=t2.partial().expand(val=t1())) ``` ### Operating System MacOS 11.6.5 ### Versions of Apache Airflow Providers Relevant: ``` apache-airflow-providers-cncf-kubernetes==4.0.1 ``` Full: ``` apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-docker==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-grpc==2.0.4 apache-airflow-providers-hashicorp==2.2.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-odbc==2.0.4 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-sendgrid==2.0.4 apache-airflow-providers-sftp==2.6.0 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details Docker (Docker Desktop) - Server Version: 20.10.13 - API Version: 1.41 - Builder: 2 Kubernetes (Docker Desktop) - Env: docker-desktop - Context: docker-desktop - Cluster Name: docker-desktop - Namespace: default - Container Runtime: docker - Version: v1.22.5 Helm: - version.BuildInfo{Version:"v3.6.3", GitCommit:"d506314abfb5d21419df8c7e7e68012379db2354", GitTreeState:"dirty", GoVersion:"go1.16.5"} ### Anything else _No response_ ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23792

https://github.com/apache/airflow/pull/24530

df388a3d5364b748993e61b522d0b68ff8b8124a

a69095fea1722e153a95ef9da93b002b82a02426

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/utils.py", "airflow/www/views.py"]

DAG Loading Slow with Dynamic Tasks - Including Test Results and Benchmarking

### Apache Airflow version 2.3.0 (latest released) ### What happened The web UI is slow to load the default (grid) view for DAGs when there are mapped tasks with a high number of expansions. I did some testing with DAGs that have a variable number of tasks, along with changing the webserver resources to see how this affects the load times. Here is a graph showing that testing. Let me know if you have any other questions about this. <img width="719" alt="image" src="https://user-images.githubusercontent.com/89415310/169158337-ffb257ae-21bc-4c19-aaec-b29873d9fe93.png"> My findings based on what I'm seeing here: The jump from 5->10 AUs makes a difference but 10 to 20 does not make a difference. There are diminishing returns when bumping up the webserver resources which leads me to believe that this could be a factor of database performance after the webserver is scaled to a certain point. If we look at the graph on a log scale, it's almost perfectly linear for the 10 and 20AU lines on the plot. This leads me to believe that the time that it takes to load is a direct function of the number of task expansions that we have for a mapped task. ### What you think should happen instead Web UI loads in a reasonable amount of time, anything less than 10 seconds would be acceptable relatively speaking with the performance that we're getting now, ideally somewhere under 2-3 second I think would be best, if possible. ### How to reproduce ``` from datetime import datetime from airflow.models import DAG from airflow.operators.empty import EmptyOperator from airflow.operators.python import PythonOperator default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email_on_failure': False, 'email_on_retry': False, } initial_scale = 7 max_scale = 12 scaling_factor = 2 for scale in range(initial_scale, max_scale + 1): dag_id = f"dynamic_task_mapping_{scale}" with DAG( dag_id=dag_id, default_args=default_args, catchup=False, schedule_interval=None, start_date=datetime(1970, 1, 1), render_template_as_native_obj=True, ) as dag: start = EmptyOperator(task_id="start") mapped = PythonOperator.partial( task_id="mapped", python_callable=lambda m: print(m), ).expand( op_args=[[x] for x in list(range(2**scale))] ) end = EmptyOperator(task_id="end") start >> mapped >> end globals()[dag_id] = dag ``` ### Operating System Debian ### Versions of Apache Airflow Providers n/a ### 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/23786

https://github.com/apache/airflow/pull/23813

86cfd1244a641a8f17c9b33a34399d9be264f556

7ab5ea7853df9d99f6da3ab804ffe085378fbd8a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", "kubernetes_tests/test_kubernetes_pod_operator.py", "kubernetes_tests/test_kubernetes_pod_operator_backcompat.py"]

Partial of a KubernetesPodOperator does not allow for limit_cpu and limit_memory in the resources argument

### Apache Airflow version 2.3.0 (latest released) ### What happened When performing dynamic task mapping and providing Kubernetes limits to the `resources` argument, the DAG raises an import error: ``` Broken DAG: [/opt/airflow/dags/bulk_image_processing.py] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/baseoperator.py", line 287, in partial partial_kwargs["resources"] = coerce_resources(partial_kwargs["resources"]) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/baseoperator.py", line 133, in coerce_resources return Resources(**resources) TypeError: __init__() got an unexpected keyword argument 'limit_cpu' ``` The offending code is: ``` KubernetesPodOperator.partial( get_logs: True, in_cluster: True, is_delete_operator_pod: True, namespace: settings.namespace, resources={'limit_cpu': settings.IMAGE_PROCESSING_OPERATOR_CPU, 'limit_memory': settings.IMAGE_PROCESSING_OPERATOR_MEMORY}, service_account_name: settings.SERVICE_ACCOUNT_NAME, startup_timeout_seconds: 600, **kwargs, ) ``` But you can see this in any DAG utilizing a `KubernetesPodOperator.partial` where the `partial` contains the `resources` argument. ### What you think should happen instead The `resources` argument should be taken at face value and applied to the `OperatorPartial` and subsequently the `MappedOperator`. ### How to reproduce Try to import this DAG using Airflow 2.3.0: ``` from datetime import datetime from airflow import XComArg from airflow.models import DAG from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator def make_operator( **kwargs ): return KubernetesPodOperator( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) def make_partial_operator( **kwargs ): return KubernetesPodOperator.partial( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) with DAG(dag_id='bulk_image_processing', schedule_interval=None, start_date=datetime(2020, 1, 1), max_active_tasks=20) as dag: op1 = make_operator( arguments=['--bucket-name', f'{{{{ dag_run.conf.get("bucket", "some-fake-default") }}}}'], cmds=['python3', 'some_entrypoint'], image='some-image', name='airflow-private-image-pod-1', task_id='some-task', do_xcom_push=True ) op2 = make_partial_operator( image='another-image', name=f'airflow-private-image-pod-2', resources={'limit_cpu': '2000m', 'limit_memory': '16Gi'}, task_id='another-task', cmds=[ 'some', 'set', 'of', 'cmds' ] ).expand(arguments=XComArg(op1)) ``` ### Operating System MacOS 11.6.5 ### Versions of Apache Airflow Providers Relevant: ``` apache-airflow-providers-cncf-kubernetes==4.0.1 ``` Full: ``` apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-docker==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-grpc==2.0.4 apache-airflow-providers-hashicorp==2.2.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-odbc==2.0.4 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-sendgrid==2.0.4 apache-airflow-providers-sftp==2.6.0 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details Docker (Docker Desktop) - Server Version: 20.10.13 - API Version: 1.41 - Builder: 2 Kubernetes (Docker Desktop) - Env: docker-desktop - Context: docker-desktop - Cluster Name: docker-desktop - Namespace: default - Container Runtime: docker - Version: v1.22.5 Helm: - version.BuildInfo{Version:"v3.6.3", GitCommit:"d506314abfb5d21419df8c7e7e68012379db2354", GitTreeState:"dirty", GoVersion:"go1.16.5"} ### Anything else You can get around this by creating the `partial` first without calling `expand` on it, setting the resources via the `kwargs` parameter, then calling `expand`. Example: ``` from datetime import datetime from airflow import XComArg from airflow.models import DAG from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator def make_operator( **kwargs ): return KubernetesPodOperator( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) def make_partial_operator( **kwargs ): return KubernetesPodOperator.partial( **{ 'get_logs': True, 'in_cluster': True, 'is_delete_operator_pod': True, 'namespace': 'default', 'startup_timeout_seconds': 600, **kwargs, } ) with DAG(dag_id='bulk_image_processing', schedule_interval=None, start_date=datetime(2020, 1, 1), max_active_tasks=20) as dag: op1 = make_operator( arguments=['--bucket-name', f'{{{{ dag_run.conf.get("bucket", "some-fake-default") }}}}'], cmds=['python3', 'some_entrypoint'], image='some-image', name='airflow-private-image-pod-1', task_id='some-task', do_xcom_push=True ) op2 = make_partial_operator( image='another-image', name=f'airflow-private-image-pod-2', task_id='another-task', cmds=[ 'some', 'set', 'of', 'cmds' ] ) op2.kwargs['resources'] = {'limit_cpu': '2000m', 'limit_memory': '16Gi'} op2.expand(arguments=XComArg(op1)) ``` ### 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/23783

https://github.com/apache/airflow/pull/24673

40f08900f2d1fb0d316b40dde583535a076f616b

45f4290712f5f779e57034f81dbaab5d77d5de85

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/utils.py", "airflow/www/views.py"]

New grid view in Airflow 2.3.0 has very slow performance on large DAGs relative to tree view in 2.2.5

### Apache Airflow version 2.3.0 (latest released) ### What happened I upgraded a local dev deployment of Airflow from 2.2.5 to 2.3.0, then loaded the new `/dags/<dag_id>/grid` page for a few dag ids. On a big DAG, I’m seeing 30+ second latency on the `/grid` API, followed by a 10+ second delay each time I click a green rectangle. For a smaller DAG I tried, the page was pretty snappy. I went back to 2.2.5 and loaded the tree view for comparison, and saw that the `/tree/` endpoint on the large DAG had 9 seconds of latency, and clicking a green rectangle had instant responsiveness. This is slow enough that it would be a blocker for my team to upgrade. ### What you think should happen instead The grid view should be equally performant to the tree view it replaces ### How to reproduce Generate a large DAG. Mine looks like the following: - 900 tasks - 150 task groups - 25 historical runs Compare against a small DAG, in my case: - 200 tasks - 36 task groups - 25 historical runs The large DAG is unusable, the small DAG is usable. ### Operating System Ubuntu 20.04.3 LTS (Focal Fossa) ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details Docker-compose deployment on an EC2 instance running ubuntu. Airflow web server is nearly stock image from `apache/airflow:2.3.0-python3.9` ### Anything else Screenshot of load time: <img width="1272" alt="image" src="https://user-images.githubusercontent.com/643593/168957215-74eefcb0-578e-46c9-92b8-74c4a6a20769.png"> GIF of click latency:  ### 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/23772

https://github.com/apache/airflow/pull/23947

5ab58d057abb6b1f28eb4e3fb5cec7dc9850f0b0

1cf483fa0c45e0110d99e37b4e45c72c6084aa97

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add support of tags assignment to job runs in EmrContainerOperator

### Description AWS allows to assign the tags to the individual EMR job runs on a virtual cluster. However, the [EmrContainerOperator](https://github.com/apache/airflow/blob/820d8eb7c631eea7f9d5cbd23840d026e99ae304/airflow/providers/amazon/aws/operators/emr.py#L121) currently does not provide the ability to pass the custom tags for the job run. Related docs:https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr-containers.html#EMRContainers.Client.start_job_run > tags (dict) -- > The tags assigned to job runs. ### Use case/motivation **Motivation** In our deployment, we have compliance requirements which require us to specify a custom set of tags for each job submitted on the EMR virtual cluster(a shared cluster). There is an alternative way to archive this, but we would like to have this as a feature. ### 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/23765

https://github.com/apache/airflow/pull/23769

65f3b18fc1142c0d23e715fa1a98f21662df9584

e54ca47262579742fc3c297c7f8d4c48f2437f82

python

closed

apache/airflow

https://github.com/apache/airflow

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

Amazon EKS example DAG raises warnning

### Body ``` Importing package: airflow.providers.amazon.aws.example_dags [2022-05-17 15:53:10,675] {eks.py:287} WARNING - The nodegroup_subnets should be List or string representing Python list and is {{ dag_run.conf['nodegroup_subnets'] }}. Defaulting to [] ``` https://github.com/apache/airflow/blob/dec05fb6b29f2aff454bcbc3939b3b78ba5b785f/airflow/providers/amazon/aws/example_dags/example_eks_templated.py#L82 example for waning raising in the CI: https://github.com/apache/airflow/runs/6474027737?check_suite_focus=true#step:12:1010 We should fix it. The example DAG should not yield warnings. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23756

https://github.com/apache/airflow/pull/23849

6150d283234b48f86362fd4da856e282dd91ebb4

5d2296becb9401df6ca58bb7d15d6655eb168aed

python

closed

apache/airflow

https://github.com/apache/airflow

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

Task Instance pop-up menu - some buttons not always clickable

### Apache Airflow version 2.3.0 (latest released) ### What happened See recorded screencap - in the task instance pop-up menu, sometimes the top menu options aren't clickable until you move the mouse around a bit and find an area where it will allow you to click This only seems to affect the `Instance Details`, `Rendered, Log`, and `XCom` options - but not `List Instances, all runs`, or `Filter Upstream` https://user-images.githubusercontent.com/15913202/168657933-532f58c6-7f33-4693-80cf-26436ff78ceb.mp4 ### What you think should happen instead The entire 'bubble' for the options such as 'XCom' should always be clickable, without having to find a 'sweet spot' ### How to reproduce I am using Astro Runtime 5.0.0 in a localhost environment ### Operating System macOS 11.5.2 ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-databricks==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-snowflake==2.6.0 apache-airflow-providers-sqlite==2.1.3 ### Deployment Astronomer ### Deployment details _No response_ ### Anything else I experience this in an Astro deployment as well (not just localhost) using the same runtime 5.0.0 image ### 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/23733

https://github.com/apache/airflow/pull/23736

71e4deb1b093b7ad9320eb5eb34eca8ea440a238

239a9dce5b97d45620862b42fd9018fdc9d6d505

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/exceptions.py", "airflow/executors/kubernetes_executor.py", "airflow/kubernetes/pod_generator.py", "airflow/models/taskinstance.py", "tests/executors/test_kubernetes_executor.py", "tests/kubernetes/test_pod_generator.py", "tests/models/test_taskinstance.py"]

Airflow 2.3 scheduler error: 'V1Container' object has no attribute '_startup_probe'

### Apache Airflow version 2.3.0 (latest released) ### What happened After migrating from Airflow 2.2.4 to 2.3.0 scheduler fell into crash loop throwing: ``` --- Logging error --- Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 826, in _run_scheduler_loop self.executor.heartbeat() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/base_executor.py", line 171, in heartbeat self.sync() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 613, in sync self.kube_scheduler.run_next(task) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 300, in run_next self.log.info('Kubernetes job is %s', str(next_job).replace("\n", " ")) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 214, in __repr__ return self.to_str() File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 210, in to_str return pprint.pformat(self.to_dict()) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 196, in to_dict result[attr] = value.to_dict() File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 1070, in to_dict result[attr] = list(map( File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 1071, in <lambda> lambda x: x.to_dict() if hasattr(x, "to_dict") else x, File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_container.py", line 672, in to_dict value = getattr(self, attr) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_container.py", line 464, in startup_probe return self._startup_probe AttributeError: 'V1Container' object has no attribute '_startup_probe' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.9/logging/__init__.py", line 1083, in emit msg = self.format(record) File "/usr/local/lib/python3.9/logging/__init__.py", line 927, in format return fmt.format(record) File "/usr/local/lib/python3.9/logging/__init__.py", line 663, in format record.message = record.getMessage() File "/usr/local/lib/python3.9/logging/__init__.py", line 367, in getMessage msg = msg % self.args File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 214, in __repr__ return self.to_str() File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 210, in to_str return pprint.pformat(self.to_dict()) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod.py", line 196, in to_dict result[attr] = value.to_dict() File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 1070, in to_dict result[attr] = list(map( File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 1071, in <lambda> lambda x: x.to_dict() if hasattr(x, "to_dict") else x, File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_container.py", line 672, in to_dict value = getattr(self, attr) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_container.py", line 464, in startup_probe return self._startup_probe AttributeError: 'V1Container' object has no attribute '_startup_probe' Call stack: File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 757, in _execute self.executor.end() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 809, in end self._flush_task_queue() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 767, in _flush_task_queue self.log.warning('Executor shutting down, will NOT run task=%s', task) Unable to print the message and arguments - possible formatting error. Use the traceback above to help find the error. ``` kubernetes python library version was exactly as specified in constraints file: https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.9.txt ### What you think should happen instead Scheduler should work ### How to reproduce Not 100% sure but: 1. Run Airflow 2.2.4 using official Helm Chart 2. Run some dags to have some records in DB 3. Migrate to 2.3.0 (replace 2.2.4 image with 2.3.0 one) ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers irrelevant ### Deployment Official Apache Airflow Helm Chart ### Deployment details KubernetesExecutor PostgreSQL (RDS) as Airflow DB Python 3.9 Docker images build from `apache/airflow:2.3.0-python3.9` (some additional libraries installed) ### 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/23727

https://github.com/apache/airflow/pull/24117

c8fa9e96e29f9f8b4ff9c7db416097fb70a87c2d

0c41f437674f135fe7232a368bf9c198b0ecd2f0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/cloud_sql.py", "tests/providers/google/cloud/operators/test_cloud_sql.py"]

Add fields to CLOUD_SQL_EXPORT_VALIDATION

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==5.0.0 ### Apache Airflow version 2.1.2 ### Operating System GCP Container ### Deployment Composer ### Deployment details composer-1.17.1-airflow-2.1.2 ### What happened I got a validation warning. Same as #23613. ### What you think should happen instead The following fields are not implemented in CLOUD_SQL_EXPORT_VALIDATION. The following fields should be added to CLOUD_SQL_EXPORT_VALIDATION. - sqlExportOptions - mysqlExportOptions - masterData - csvExportOptions - escapeCharacter - quoteCharacter - fieldsTerminatedBy - linesTerminatedBy These are all the fields that have not been added. https://cloud.google.com/sql/docs/mysql/admin-api/rest/v1beta4/operations#exportcontext ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23722

https://github.com/apache/airflow/pull/23724

9e25bc211f6f7bba1aff133d21fe3865dabda53d

3bf9a1df38b1ccfaf965a207d047b30452df1ba5

python

closed

apache/airflow

https://github.com/apache/airflow

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

Adding PodAnnotations for Redis Statefulset

### Description Most Airflow services come with the ability of adding annotation, a part from Redis. This feature request adds this capability into the Redis helm template as well. ### Use case/motivation Specifically for us, annotations and labels are used to integrate Airflow with external services, such as Datadog, and without it, the integration becomes a bit more complex. ### 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/23705

https://github.com/apache/airflow/pull/23708

ef79a0d1c4c0a041d7ebf83b93cbb25aa3778a70

2af19f16a4d94e749bbf6c7c4704e02aac35fc11

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db_cleanup.py"]

airflow db clean - table missing exception not captured

### Apache Airflow version 2.3.0 (latest released) ### What happened I am running on the Kubernetes Executor, so Celery-related tables were never created. I am using PostgreSQL as the database. When I ran `airflow db clean`, it gave me the following exception: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) psycopg2.errors.UndefinedTable: relation "celery_taskmeta" does not exist LINE 3: FROM celery_taskmeta ^ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/db_command.py", line 195, in cleanup_tables run_cleanup( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/db_cleanup.py", line 311, in run_cleanup _cleanup_table( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/db_cleanup.py", line 228, in _cleanup_table _print_entities(query=query, print_rows=False) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/db_cleanup.py", line 137, in _print_entities num_entities = query.count() File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/query.py", line 3062, in count return self._from_self(col).enable_eagerloads(False).scalar() File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/query.py", line 2803, in scalar ret = self.one() File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/query.py", line 2780, in one return self._iter().one() File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/query.py", line 2818, in _iter result = self.session.execute( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/session.py", line 1670, in execute result = conn._execute_20(statement, params or {}, execution_options) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1520, in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/sql/elements.py", line 313, in _execute_on_connection return connection._execute_clauseelement( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1389, in _execute_clauseelement ret = self._execute_context( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1748, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1929, in _handle_dbapi_exception util.raise_( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.ProgrammingError: (psycopg2.errors.UndefinedTable) relation "celery_taskmeta" does not exist LINE 3: FROM celery_taskmeta ^ [SQL: SELECT count(*) AS count_1 FROM (SELECT celery_taskmeta.id AS celery_taskmeta_id, celery_taskmeta.task_id AS celery_taskmeta_task_id, celery_taskmeta.status AS celery_taskmeta_status, celery_taskmeta.result AS celery_taskmeta_result, celery_taskmeta.date_done AS celery_taskmeta_date_done, celery_taskmeta.traceback AS celery_taskmeta_traceback FROM celery_taskmeta WHERE celery_taskmeta.date_done < %(date_done_1)s) AS anon_1] [parameters: {'date_done_1': DateTime(2022, 1, 1, 0, 0, 0, tzinfo=Timezone('UTC'))}] (Background on this error at: http://sqlalche.me/e/14/f405) ``` ### What you think should happen instead _No response_ ### How to reproduce 1. Use an executor that do not require Celery 2. Use PostgreSQL as the database 3. Run `airflow db clean` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23698

https://github.com/apache/airflow/pull/23699

252ef66438ecda87a8aac4beed1f689f14ee8bec

a80b2fcaea984813995d4a2610987a1c9068fdb5

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow backfill command not working; Tasks are stuck in scheduled forever.

### Apache Airflow version 2.2.1 ### What happened When I try to backfill using `airflow dags backfill ...`, the 'Run' shows running but the tasks are stuck in scheduled state forever. 1. Code for DAG to reproduce the problem ```python import time from datetime import timedelta import pendulum from airflow import DAG from airflow.decorators import task from airflow.models.dag import dag from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator from airflow.operators.python import PythonOperator default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email': ['airflow@example.com'], 'email_on_failure': True, 'email_on_retry': False, 'retries': 2, 'retry_delay': timedelta(minutes=5), } def get_execution_date(**kwargs): ds = kwargs['ds'] print(ds) with DAG( 'test_dag_2', default_args=default_args, description='Testing dag', start_date=pendulum.datetime(2022, 4, 2, tz='UTC'), schedule_interval="@hourly", max_active_runs=5, concurrency=10, ) as dag: t1 = BashOperator( task_id='task_1', depends_on_past=False, bash_command='sleep 5' ) t2 = PythonOperator( task_id='get_execution_date', python_callable=get_execution_date ) t1 >> t2 ``` 2. Airflow backfill CLI ```bash $ airflow dags backfill --subdir /opt/airflow/dags/repo --reset-dagruns -s "2022-03-01 01:00:00" -e "2022-03-02 01:00:00" test_dag_2 ``` 3. Result <img width="472" alt="image" src="https://user-images.githubusercontent.com/36870121/168201159-fcdf7640-2bf9-428f-849e-25f18dd2b504.png"> 4. Discussion The 'Run' shows running but the tasks are stuck in scheduled state forever. Here are some variants what I have checked. (but still have this issue.) - max_active_runs, concurrency - -x option for backfill command - --subdir option for backfill command - --reset-dagruns for backfill command - schedule_interval to `None` and `@once` - Restart the Airflow scheduler pods. - **Upgrade Airflow version to 2.3.0.** 5. Related issue https://github.com/apache/airflow/issues/13542 ### What you think should happen instead The backfill jobs should successfully done from the past. ### How to reproduce _No response_ ### Operating System CentOS Linux release 7.9.2009 (Core) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon 3.3.0 apache-airflow-providers-celery 2.1.0 apache-airflow-providers-cncf-kubernetes 3.0.2 apache-airflow-providers-docker 2.6.0 apache-airflow-providers-elasticsearch 3.0.3 apache-airflow-providers-ftp 2.1.2 apache-airflow-providers-grpc 2.0.4 apache-airflow-providers-hashicorp 2.2.0 apache-airflow-providers-http 2.0.2 apache-airflow-providers-imap 2.2.3 apache-airflow-providers-postgres 4.1.0 apache-airflow-providers-redis 2.0.4 apache-airflow-providers-sendgrid 2.0.4 apache-airflow-providers-sftp 2.6.0 apache-airflow-providers-slack 4.2.3 apache-airflow-providers-sqlite 2.1.3 apache-airflow-providers-ssh 2.4.3 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else Here are some logs from scheduler pod, found kubernetes_executor try to delete worker pod. ```bash [2022-05-13 02:48:08,417] {kubernetes_executor.py:147} INFO - Event: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a had an event of type MODIFIED [2022-05-13 02:48:08,417] {kubernetes_executor.py:206} INFO - Event: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a Succeeded [2022-05-13 02:48:08,423] {kubernetes_executor.py:147} INFO - Event: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a had an event of type DELETED [2022-05-13 02:48:08,423] {kubernetes_executor.py:206} INFO - Event: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a Succeeded [2022-05-13 02:48:08,497] {backfill_job.py:397} INFO - [backfill progress] | finished run 0 of 25 | tasks waiting: 5 | succeeded: 0 | running: 5| failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 5 [2022-05-13 02:48:13,390] {kubernetes_executor.py:375} INFO - Attempting to finish pod; pod_id: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a; state: None; annotations: {'dag_id': 'test_dag_2', 'task_id': 'task_1', 'execution_date': None, 'run_id': 'backfill__2022-03-03T02:00:00+00:00', 'try_number': '1'} [2022-05-13 02:48:13,391] {kubernetes_executor.py:375} INFO - Attempting to finish pod; pod_id: testdag2task1.34ae944f04ee4a18a65f559163ca0d1a; state: None; annotations: {'dag_id': 'test_dag_2', 'task_id': 'task_1', 'execution_date': None, 'run_id': 'backfill__2022-03-03T02:00:00+00:00', 'try_number': '1'} [2022-05-13 02:48:13,392] {kubernetes_executor.py:576} INFO - Changing state of (TaskInstanceKey(dag_id='test_dag_2', task_id='task_1', run_id='backfill__2022-03-03T02:00:00+00:00', try_number=1), None, 'testdag2task1.34ae944f04ee4a18a65f559163ca0d1a', 'jutopia-chlee-test-chlee-backfill-test', '3559062271') to None [2022-05-13 02:48:13,396] {kubernetes_executor.py:661} INFO - Deleted pod: TaskInstanceKey(dag_id='test_dag_2', task_id='task_1', run_id='backfill__2022-03-03T02:00:00+00:00', try_number=1) in namespace jutopia-chlee-test-chlee-backfill-test ``` ### 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/23693

https://github.com/apache/airflow/pull/23720

49cfb6498eed0acfc336a24fd827b69156d5e5bb

640d4f9636d3867d66af2478bca15272811329da

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/extra-packages-ref.rst"]

Conflicts with airflow constraints for airflow 2.3.0 python 3.9

### Apache Airflow version 2.3.0 (latest released) ### What happened When installing airflow 2.3.0 using pip command with "all" it fails on dependency google-ads `pip install "apache-airflow[all]==2.3.0" -c "https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.9.txt"` > The conflict is caused by: apache-airflow[all] 2.3.0 depends on google-ads>=15.1.1; extra == "all" The user requested (constraint) google-ads==14.0.0 I changed the version of google-ads to 15.1.1, but then it failed on dependency databricks-sql-connector > The conflict is caused by: apache-airflow[all] 2.3.0 depends on databricks-sql-connector<3.0.0 and >=2.0.0; extra == "all" The user requested (constraint) databricks-sql-connector==1.0.2 and then on different dependencies... ### What you think should happen instead _No response_ ### How to reproduce (venv) [root@localhost]# `python -V` Python 3.9.7 (venv) [root@localhost]# `pip install "apache-airflow[all]==2.3.0" -c "https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.9.txt"` ### Operating System CentOS 7 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/23692

https://github.com/apache/airflow/pull/23697

4afa8e3cecf1e4a2863715d14a45160034ad31a6

310002e44887847991b0864bbf9a921c7b11e930

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/timetables/_cron.py", "airflow/timetables/interval.py", "airflow/timetables/trigger.py", "tests/timetables/test_interval_timetable.py"]

Data Interval wrong when manually triggering with a specific logical date

### Apache Airflow version 2.2.5 ### What happened When I use the date picker in the “Trigger DAG w/ config” page to choose a specific logical date for some reason on a scheduled daily DAG the Data Interval Start (circled in red) is 2 days before the logical date (circled in blue), instead of the same as the logical date. And the Data Interval End is one day before the logical date. So the interval is the correct length, but on wrong days.  I encountered this with a DAG with a daily schedule which typically runs at 09:30 UTC. I am testing this in a dev environment (with catchup off) and trying to trigger a run for 2022-05-09 09:30:00. I would expect the data interval to start at that same time and the data interval end to be 1 day after. It has nothing to do with the previous run since that was way back on 2022-04-26 ### What you think should happen instead The data interval start date should be the same as the logical date (if it is a custom logical date) ### How to reproduce I made a sample DAG as shown below: ```python import pendulum from airflow.models import DAG from airflow.operators.python import PythonOperator def sample(data_interval_start, data_interval_end): return "data_interval_start: {}, data_interval_end: {}".format(str(data_interval_start), str(data_interval_end)) args = { 'start_date': pendulum.datetime(2022, 3, 10, 9, 30) } with DAG( dag_id='sample_data_interval_issue', default_args=args, schedule_interval='30 9 * * *' # 09:30 UTC ) as sample_data_interval_issue: task = PythonOperator( task_id='sample', python_callable=sample ) ``` I then start it to start a scheduled DAG run (`2022-05-11, 09:30:00 UTC`), and the `data_interval_start` is the same as I expect, `2022-05-11T09:30:00+00:00`. However, when I went to "Trigger DAG w/ config" page and in the date chooser choose `2022-05-09 09:30:00+00:00`, and then triggered that. It shows the run datetime is `2022-05-09, 09:30:00 UTC`, but the `data_interval_start` is incorrectly set to `2022-05-08T09:30:00+00:00`, 2 days before the date I choose. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers N/A ### 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/23689

https://github.com/apache/airflow/pull/22658

026f1bb98cd05a26075bd4e4fb68f7c3860ce8db

d991d9800e883a2109b5523ae6354738e4ac5717

python

closed

apache/airflow

https://github.com/apache/airflow

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

_TaskDecorator has no __wrapped__ attribute in v2.3.0

### Apache Airflow version 2.3.0 (latest released) ### What happened I run a unit test on a task which is defined using the task decorator. In the unit test, I unwrap the task decorator with the `__wrapped__` attribute, but this no longer works in v2.3.0. It works in v2.2.5. ### What you think should happen instead I expect the wrapped function to be returned. This was what occurred in v2.2.5 When running pytest on the airflow v2.3.0 the following error is thrown: ```AttributeError: '_TaskDecorator' object has no attribute '__wrapped__'``` ### How to reproduce Here's a rough outline of the code. A module `hello.py` contains the task definition: ``` from airflow.decorators import task @task def hello_airflow(): print('hello airflow') ``` and the test contains ``` from hello import hello_airflow def test_hello_airflow(): hello_airflow.__wrapped__() ``` Then run pytest ### Operating System Rocky Linux 8.5 (Green Obsidian) ### 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/23688

https://github.com/apache/airflow/pull/23830

a8445657996f52b3ac5ce40a535d9c397c204d36

a71e4b789006b8f36cd993731a9fb7d5792fccc2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml.schema.json", "airflow/configuration.py", "tests/config_templates/deprecated.cfg", "tests/config_templates/deprecated_cmd.cfg", "tests/config_templates/deprecated_secret.cfg", "tests/config_templates/empty.cfg", "tests/core/test_configuration.py", "tests/utils/test_config.py"]

exceptions.DagRunNotFound: DagRun for example_bash_operator with run_id or execution_date of

### Apache Airflow version main (development) ### What happened trying to run airflow tasks run command locally and force `StandardTaskRunner` to use `_start_by_exec` instead of `_start_by_fork` ``` airflow tasks run example_bash_operator also_run_this scheduled__2022-05-08T00:00:00+00:00 --job-id 237 --local --subdir /Users/ping_zhang/airlab/repos/airflow/airflow/example_dags/example_bash_operator.py -f -i ``` However, it always errors out: see https://user-images.githubusercontent.com/8662365/168164336-a75bfac8-cb59-43a9-b9f3-0c345c5da79f.png ``` [2022-05-12 12:08:32,893] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this Traceback (most recent call last): [2022-05-12 12:08:32,893] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/miniforge3/envs/apache-***/bin/***", line 33, in <module> [2022-05-12 12:08:32,893] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this sys.exit(load_entry_point('apache-***', 'console_scripts', '***')()) [2022-05-12 12:08:32,893] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/__main__.py", line 38, in main [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this args.func(args) [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/cli/cli_parser.py", line 51, in command [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this return func(*args, **kwargs) [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/utils/cli.py", line 99, in wrapper [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this return f(*args, **kwargs) [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/cli/commands/task_command.py", line 369, in task_run [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this ti, _ = _get_ti(task, args.execution_date_or_run_id, args.map_index, pool=args.pool) [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/utils/session.py", line 71, in wrapper [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this return func(*args, session=session, **kwargs) [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/cli/commands/task_command.py", line 152, in _get_ti [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this dag_run, dr_created = _get_dag_run( [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this File "/Users/ping_zhang/airlab/repos/***/***/cli/commands/task_command.py", line 112, in _get_dag_run [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this raise DagRunNotFound( [2022-05-12 12:08:32,894] {base_task_runner.py:109} INFO - Job 265: Subtask also_run_this ***.exceptions.DagRunNotFound: DagRun for example_bash_operator with run_id or execution_date of 'scheduled__2022-05-08T00:00:00+00:00' not found [2022-05-12 12:08:33,014] {local_task_job.py:163} INFO - Task exited with return code 1 [2022-05-12 12:08:33,048] {local_task_job.py:265} INFO - 0 downstream tasks scheduled from follow-on schedule check [2022-05-12 12:11:30,742] {taskinstance.py:1120} INFO - Dependencies not met for <TaskInstance: example_bash_operator.also_run_this scheduled__2022-05-08T00:00:00+00:00 [running]>, dependency 'Task Instance Not Running' FAILED: Task is in the running state [2022-05-12 12:11:30,743] {local_task_job.py:102} INFO - Task is not able to be run ``` i have checked the dag_run does exist in my db:  ### What you think should happen instead _No response_ ### How to reproduce pull the latest main branch with this commit: `7277122ae62305de19ceef33607f09cf030a3cd4` run airflow scheduler, webserver and worker locally with `CeleryExecutor`. ### Operating System Apple M1 Max, version: 12.2 ### Versions of Apache Airflow Providers NA ### Deployment Other ### Deployment details on my local mac with latest main branch, latest commit: `7277122ae62305de19ceef33607f09cf030a3cd4` ### Anything else Python version: Python 3.9.7 ### 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/23679

https://github.com/apache/airflow/pull/23723

ce8ea6691820140a0e2d9a5dad5254bc05a5a270

888bc2e233b1672a61433929e26b82210796fd71

python