Datasets:

bug-localization

/

BeetleBox

like 0

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dags.js", "airflow/www/views.py", "tests/www/views/test_views_acl.py"]

Airflow 2.3.0: can't filter by owner if selected from dropdown

### Apache Airflow version 2.3.0 (latest released) ### What happened On a clean install of 2.3.0, whenever I try to filter by owner, if I select it from the dropdown (which correctly detects the owner's name) it returns the following error: `DAG "ecodina" seems to be missing from DagBag.` Webserver's log: ``` 127.0.0.1 - - [12/May/2022:12:27:47 +0000] "GET /dagmodel/autocomplete?query=ecodin&status=all HTTP/1.1" 200 17 "http://localhost/home?search=ecodina" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "GET /dags/ecodina/grid?search=ecodina HTTP/1.1" 302 217 "http://localhost/home?search=ecodina" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "GET /home HTTP/1.1" 200 35774 "http://localhost/home?search=ecodina" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "POST /blocked HTTP/1.1" 200 2 "http://localhost/home" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "POST /last_dagruns HTTP/1.1" 200 402 "http://localhost/home" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "POST /dag_stats HTTP/1.1" 200 333 "http://localhost/home" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" 127.0.0.1 - - [12/May/2022:12:27:50 +0000] "POST /task_stats HTTP/1.1" 200 1194 "http://localhost/home" "Mozilla/5.0 (X11; Linux x86_64; rv:78.0) Gecko/20100101 Firefox/78.0" ``` Instead, if I write the owner's name fully and avoid selecting it from the dropdown, it works as expected since it constructs the correct URL: `my.airflow.com/home?search=ecodina` ### What you think should happen instead The DAGs table should only show the selected owner's DAGs. ### How to reproduce - Start the Airflow Webserver - Connect to the Airflow webpage - Type an owner name in the _Search DAGs_ textbox and select it from the dropdown ### Operating System CentOS Linux 8 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Installed on a conda environment, as if it was a virtualenv: - `conda create -c conda-forge -n airflow python=3.9` - `conda activate airflow` - `pip install "apache-airflow[postgres]==2.3.0" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.9.txt"` Database: PostgreSQL 13 ### 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/23670

https://github.com/apache/airflow/pull/23804

70b41e46b46e65c0446a40ab91624cb2291a5039

29afd35b9cfe141b668ce7ceccecdba60775a8ff

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/README.rst"]

Fix ./breeze build-docs command options in docs/README.rst

### What do you see as an issue? I got an error when executing `./breeze build-docs -- --help` command in docs/README.rst. ```bash % ./breeze build-docs -- --help Usage: breeze build-docs [OPTIONS] Try running the '--help' flag for more information. ╭─ Error ─────────────────────────────────────────────────╮ │ Got unexpected extra argument (--help) │ ╰─────────────────────────────────────────────────────────╯ To find out more, visit https://github.com/apache/airflow/blob/main/BREEZE.rst ``` ### Solving the problem "--" in option should be removed. ### 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/23669

https://github.com/apache/airflow/pull/23671

3138604b264878f27505223bd14c7814eacc1e57

3fa57168a520d8afe0c06d8a0200dd3517f43078

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/s3_to_sql.py", "airflow/providers/amazon/provider.yaml", "docs/apache-airflow-providers-amazon/operators/transfer/s3_to_sql.rst", "tests/providers/amazon/aws/transfers/test_s3_to_sql.py", "tests/system/providers/amazon/aws/example_s3_to_sql.py"]

Add transfers operator S3 to SQL / SQL to SQL

### Description Should we add S3 to SQL to aws transfers? ### Use case/motivation 1. After process data from spark/glue(more), we need to publish data to sql 2. Synchronize data between 2 sql databases. ### 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/23666

https://github.com/apache/airflow/pull/29085

e5730364b4eb5a3b30e815ca965db0f0e710edb6

efaed34213ad4416e2f4834d0cd2f60c41814507

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Crashes scheduler - Non Empty Return

### Apache Airflow version 2.3.0 (latest released) ### What happened I have a dag that looks like this. When I uncomment `py_job`(Dynamically mapped PythonOperator) it works well with `pull_messages` (Taskflow API). When I try to do the same with `DatabricksRunNowOperator` it crashes the scheduler with error Related issues #23486 ### Sample DAG ``` import json import pendulum from airflow.decorators import dag, task from airflow.operators.python import PythonOperator from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator @dag( schedule_interval=None, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, tags=['example'], ) def tutorial_taskflow_api_etl(): def random(*args, **kwargs): print ("==== kwargs inside random ====", args, kwargs) print ("I'm random") return 49 @task def pull_messages(): return [["hi"], ["hello"]] op = DatabricksRunNowOperator.partial( task_id = "new_job", job_id=42, notebook_params={"dry-run": "true"}, python_params=["douglas adams", "42"], spark_submit_params=["--class", "org.apache.spark.examples.SparkPi"] ).expand(jar_params=pull_messages()) # py_job = PythonOperator.partial( # task_id = 'py_job', # python_callable=random # ).expand(op_args= pull_messages()) tutorial_etl_dag = tutorial_taskflow_api_etl() ``` ### Error ``` [2022-05-11 11:46:30 +0000] [40] [INFO] Worker exiting (pid: 40) return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/usr/local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.9/site-packages/astronomer/airflow/version_check/plugin.py", line 29, in run_before fn(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 824, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 906, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1148, in _schedule_dag_run schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) 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/dagrun.py", line 522, in update_state info = self.task_instance_scheduling_decisions(session) 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/dagrun.py", line 658, in task_instance_scheduling_decisions schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( File "/usr/local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 714, in _get_ready_tis expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 609, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 595, in _resolve_map_lengths raise RuntimeError(f"Failed to populate all mapping metadata; missing: {keys}") RuntimeError: Failed to populate all mapping metadata; missing: 'jar_params' [2022-05-11 11:46:30 +0000] [31] [INFO] Shutting down: Master ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-databricks ### 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/23642

https://github.com/apache/airflow/pull/23771

5e3f652397005c5fac6c6b0099de345b5c39148d

3849ebb8d22bbc229d464c4171c9b5ff960cd089

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/trigger.py"]

Triggerer process die with DB Deadlock

### Apache Airflow version 2.2.5 ### What happened When create many Deferrable operator (eg. `TimeDeltaSensorAsync`), triggerer component died because of DB Deadlock issue. ``` [2022-05-11 02:45:08,420] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5397) starting [2022-05-11 02:45:08,421] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5398) starting [2022-05-11 02:45:09,459] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5400) starting [2022-05-11 02:45:09,461] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5399) starting [2022-05-11 02:45:10,503] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5401) starting [2022-05-11 02:45:10,504] {triggerer_job.py:358} INFO - Trigger <airflow.triggers.temporal.DateTimeTrigger moment=2022-05-13T11:10:00+00:00> (ID 5402) starting [2022-05-11 02:45:11,113] {triggerer_job.py:108} ERROR - Exception when executing TriggererJob._run_trigger_loop Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/usr/local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) MySQLdb._exceptions.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/jobs/triggerer_job.py", line 106, in _execute self._run_trigger_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/triggerer_job.py", line 127, in _run_trigger_loop Trigger.clean_unused() File "/usr/local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/trigger.py", line 91, in clean_unused session.query(TaskInstance).filter( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 4063, in update update_op.exec_() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1697, in exec_ self._do_exec() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1895, in _do_exec self._execute_stmt(update_stmt) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1702, in _execute_stmt self.result = self.query._execute_crud(stmt, self.mapper) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 3568, in _execute_crud return conn.execute(stmt, self._params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1011, in execute return meth(self, multiparams, params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 298, in _execute_on_connection return connection._execute_clauseelement(self, multiparams, params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1124, in _execute_clauseelement ret = self._execute_context( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1316, in _execute_context self._handle_dbapi_exception( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1510, in _handle_dbapi_exception util.raise_( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/usr/local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') [SQL: UPDATE task_instance SET trigger_id=%s WHERE task_instance.state != %s AND task_instance.trigger_id IS NOT NULL] [parameters: (None, <TaskInstanceState.DEFERRED: 'deferred'>)] (Background on this error at: http://sqlalche.me/e/13/e3q8) [2022-05-11 02:45:11,118] {triggerer_job.py:111} INFO - Waiting for triggers to clean up [2022-05-11 02:45:11,592] {triggerer_job.py:117} INFO - Exited trigger loop Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/usr/local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) MySQLdb._exceptions.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/bin/airflow", line 8, in <module> sys.exit(main()) File "/usr/local/lib/python3.8/site-packages/airflow/__main__.py", line 48, in main args.func(args) File "/usr/local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/cli/commands/triggerer_command.py", line 56, in triggerer job.run() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/base_job.py", line 246, in run self._execute() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/triggerer_job.py", line 106, in _execute self._run_trigger_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/triggerer_job.py", line 127, in _run_trigger_loop Trigger.clean_unused() File "/usr/local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/trigger.py", line 91, in clean_unused session.query(TaskInstance).filter( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 4063, in update update_op.exec_() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1697, in exec_ self._do_exec() File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1895, in _do_exec self._execute_stmt(update_stmt) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1702, in _execute_stmt self.result = self.query._execute_crud(stmt, self.mapper) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 3568, in _execute_crud return conn.execute(stmt, self._params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1011, in execute return meth(self, multiparams, params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 298, in _execute_on_connection return connection._execute_clauseelement(self, multiparams, params) File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1124, in _execute_clauseelement ret = self._execute_context( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1316, in _execute_context self._handle_dbapi_exception( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1510, in _handle_dbapi_exception util.raise_( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/usr/local/lib/python3.8/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/usr/local/lib/python3.8/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') [SQL: UPDATE task_instance SET trigger_id=%s WHERE task_instance.state != %s AND task_instance.trigger_id IS NOT NULL] [parameters: (None, <TaskInstanceState.DEFERRED: 'deferred'>)] (Background on this error at: http://sqlalche.me/e/13/e3q8) ``` ### What you think should happen instead Triggerer processor does not raise Deadlock error. ### How to reproduce Create "test_timedelta" DAG and run it. ```python from datetime import datetime, timedelta from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.sensors.time_delta import TimeDeltaSensorAsync default_args = { "owner": "user", "start_date": datetime(2021, 2, 8), "retries": 2, "retry_delay": timedelta(minutes=20), "depends_on_past": False, } with DAG( dag_id="test_timedelta", default_args=default_args, schedule_interval="10 11 * * *", max_active_runs=1, max_active_tasks=2, catchup=False, ) as dag: start = DummyOperator(task_id="start") end = DummyOperator(task_id="end") for idx in range(800): tx = TimeDeltaSensorAsync( task_id=f"sleep_{idx}", delta=timedelta(days=3), ) start >> tx >> end ``` ### Operating System uname_result(system='Linux', node='d2845d6331fd', release='5.10.104-linuxkit', version='#1 SMP Thu Mar 17 17:08:06 UTC 2022', machine='x86_64', processor='') ### Versions of Apache Airflow Providers apache-airflow-providers-apache-druid | 2.3.3 apache-airflow-providers-apache-hive | 2.3.2 apache-airflow-providers-apache-spark | 2.1.3 apache-airflow-providers-celery | 2.1.3 apache-airflow-providers-ftp | 2.1.2 apache-airflow-providers-http | 2.1.2 apache-airflow-providers-imap | 2.2.3 apache-airflow-providers-jdbc | 2.1.3 apache-airflow-providers-mysql | 2.2.3 apache-airflow-providers-postgres | 4.1.0 apache-airflow-providers-redis | 2.0.4 apache-airflow-providers-sqlite | 2.1.3 apache-airflow-providers-ssh | 2.4.3 ### Deployment Other Docker-based deployment ### Deployment details webserver: 1 instance scheduler: 1 instance worker: 1 instance (Celery) triggerer: 1 instance redis: 1 instance Database: 1 instance (mysql) ### Anything else webserver: 172.19.0.9 scheduler: 172.19.0.7 triggerer: 172.19.0.5 worker: 172.19.0.8 MYSQL (`SHOW ENGINE INNODB STATUS;`) ``` ------------------------ LATEST DETECTED DEADLOCK ------------------------ 2022-05-11 07:47:49 139953955817216 *** (1) TRANSACTION: TRANSACTION 544772, ACTIVE 0 sec starting index read mysql tables in use 1, locked 1 LOCK WAIT 7 lock struct(s), heap size 1128, 2 row lock(s) MySQL thread id 20, OS thread handle 139953861383936, query id 228318 172.19.0.5 airflow_user updating UPDATE task_instance SET trigger_id=NULL WHERE task_instance.state != 'deferred' AND task_instance.trigger_id IS NOT NULL *** (1) HOLDS THE LOCK(S): RECORD LOCKS space id 125 page no 231 n bits 264 index ti_state of table `airflow_db`.`task_instance` trx id 544772 lock_mode X locks rec but not gap Record lock, heap no 180 PHYSICAL RECORD: n_fields 4; compact format; info bits 0 0: len 6; hex 717565756564; asc queued;; 1: len 14; hex 746573745f74696d6564656c7461; asc test_timedelta;; 2: len 9; hex 736c6565705f323436; asc sleep_246;; 3: len 30; hex 7363686564756c65645f5f323032322d30352d30395431313a31303a3030; asc scheduled__2022-05-09T11:10:00; (total 36 bytes); *** (1) WAITING FOR THIS LOCK TO BE GRANTED: RECORD LOCKS space id 125 page no 47 n bits 128 index PRIMARY of table `airflow_db`.`task_instance` trx id 544772 lock_mode X locks rec but not gap waiting Record lock, heap no 55 PHYSICAL RECORD: n_fields 28; compact format; info bits 0 0: len 14; hex 746573745f74696d6564656c7461; asc test_timedelta;; 1: len 9; hex 736c6565705f323436; asc sleep_246;; 2: len 30; hex 7363686564756c65645f5f323032322d30352d30395431313a31303a3030; asc scheduled__2022-05-09T11:10:00; (total 36 bytes); 3: len 6; hex 000000085001; asc P ;; 4: len 7; hex 01000001411e2f; asc A /;; 5: len 7; hex 627b6a250b612d; asc b{j% a-;; 6: SQL NULL; 7: SQL NULL; 8: len 7; hex 72756e6e696e67; asc running;; 9: len 4; hex 80000001; asc ;; 10: len 12; hex 643238343564363333316664; asc d2845d6331fd;; 11: len 4; hex 726f6f74; asc root;; 12: len 4; hex 8000245e; asc $^;; 13: len 12; hex 64656661756c745f706f6f6c; asc default_pool;; 14: len 7; hex 64656661756c74; asc default;; 15: len 4; hex 80000002; asc ;; 16: len 20; hex 54696d6544656c746153656e736f724173796e63; asc TimeDeltaSensorAsync;; 17: len 7; hex 627b6a240472e0; asc b{j$ r ;; 18: SQL NULL; 19: len 4; hex 80000002; asc ;; 20: len 5; hex 80057d942e; asc } .;; 21: len 4; hex 80000001; asc ;; 22: len 4; hex 800021c7; asc ! ;; 23: len 30; hex 36353061663737642d363762372d343166382d383439342d636637333061; asc 650af77d-67b7-41f8-8494-cf730a; (total 36 bytes); 24: SQL NULL; 25: SQL NULL; 26: SQL NULL; 27: len 2; hex 0400; asc ;; *** (2) TRANSACTION: TRANSACTION 544769, ACTIVE 0 sec updating or deleting mysql tables in use 1, locked 1 LOCK WAIT 7 lock struct(s), heap size 1128, 4 row lock(s), undo log entries 2 MySQL thread id 12010, OS thread handle 139953323235072, query id 228319 172.19.0.8 airflow_user updating UPDATE task_instance SET start_date='2022-05-11 07:47:49.745773', state='running', try_number=1, hostname='d2845d6331fd', job_id=9310 WHERE task_instance.task_id = 'sleep_246' AND task_instance.dag_id = 'test_timedelta' AND task_instance.run_id = 'scheduled__2022-05-09T11:10:00+00:00' *** (2) HOLDS THE LOCK(S): RECORD LOCKS space id 125 page no 47 n bits 120 index PRIMARY of table `airflow_db`.`task_instance` trx id 544769 lock_mode X locks rec but not gap Record lock, heap no 55 PHYSICAL RECORD: n_fields 28; compact format; info bits 0 0: len 14; hex 746573745f74696d6564656c7461; asc test_timedelta;; 1: len 9; hex 736c6565705f323436; asc sleep_246;; 2: len 30; hex 7363686564756c65645f5f323032322d30352d30395431313a31303a3030; asc scheduled__2022-05-09T11:10:00; (total 36 bytes); 3: len 6; hex 000000085001; asc P ;; 4: len 7; hex 01000001411e2f; asc A /;; 5: len 7; hex 627b6a250b612d; asc b{j% a-;; 6: SQL NULL; 7: SQL NULL; 8: len 7; hex 72756e6e696e67; asc running;; 9: len 4; hex 80000001; asc ;; 10: len 12; hex 643238343564363333316664; asc d2845d6331fd;; 11: len 4; hex 726f6f74; asc root;; 12: len 4; hex 8000245e; asc $^;; 13: len 12; hex 64656661756c745f706f6f6c; asc default_pool;; 14: len 7; hex 64656661756c74; asc default;; 15: len 4; hex 80000002; asc ;; 16: len 20; hex 54696d6544656c746153656e736f724173796e63; asc TimeDeltaSensorAsync;; 17: len 7; hex 627b6a240472e0; asc b{j$ r ;; 18: SQL NULL; 19: len 4; hex 80000002; asc ;; 20: len 5; hex 80057d942e; asc } .;; 21: len 4; hex 80000001; asc ;; 22: len 4; hex 800021c7; asc ! ;; 23: len 30; hex 36353061663737642d363762372d343166382d383439342d636637333061; asc 650af77d-67b7-41f8-8494-cf730a; (total 36 bytes); 24: SQL NULL; 25: SQL NULL; 26: SQL NULL; 27: len 2; hex 0400; asc ;; *** (2) WAITING FOR THIS LOCK TO BE GRANTED: RECORD LOCKS space id 125 page no 231 n bits 264 index ti_state of table `airflow_db`.`task_instance` trx id 544769 lock_mode X locks rec but not gap waiting Record lock, heap no 180 PHYSICAL RECORD: n_fields 4; compact format; info bits 0 0: len 6; hex 717565756564; asc queued;; 1: len 14; hex 746573745f74696d6564656c7461; asc test_timedelta;; 2: len 9; hex 736c6565705f323436; asc sleep_246;; 3: len 30; hex 7363686564756c65645f5f323032322d30352d30395431313a31303a3030; asc scheduled__2022-05-09T11:10:00; (total 36 bytes); *** WE ROLL BACK TRANSACTION (1) ``` Airflow env ``` AIRFLOW__CELERY__RESULT_BACKEND=db+mysql://airflow_user:airflow_pass@mysql/airflow_db AIRFLOW__CORE__DEFAULT_TIMEZONE=KST AIRFLOW__CELERY__BROKER_URL=redis://redis:6379/0 AIRFLOW__CORE__LOAD_EXAMPLES=False AIRFLOW__WEBSERVER__DEFAULT_UI_TIMEZONE=KST AIRFLOW_HOME=/home/deploy/airflow AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL=30 AIRFLOW__CORE__EXECUTOR=CeleryExecutor AIRFLOW__WEBSERVER__SECRET_KEY=aoiuwernholo AIRFLOW__DATABASE__LOAD_DEFAULT_CONNECTIONS=False AIRFLOW__CORE__SQL_ALCHEMY_CONN=mysql+mysqldb://airflow_user:airflow_pass@mysql/airflow_db ``` ### 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/23639

https://github.com/apache/airflow/pull/24071

5087f96600f6d7cc852b91079e92d00df6a50486

d86ae090350de97e385ca4aaf128235f4c21f158

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/hooks/snowflake.py", "tests/providers/snowflake/hooks/test_snowflake.py"]

SnowflakeHook.run() raises UnboundLocalError exception if sql argument is empty

### Apache Airflow Provider(s) snowflake ### Versions of Apache Airflow Providers apache-airflow-providers-snowflake==2.3.0 ### Apache Airflow version 2.2.2 ### Operating System Amazon Linux AMI ### Deployment MWAA ### Deployment details _No response_ ### What happened If the sql parameter is an empty list, the execution_info list variable is attempted to be returned when it hasn't been initialized. The execution_info variable is [defined](https://github.com/apache/airflow/blob/2.3.0/airflow/providers/snowflake/hooks/snowflake.py#L330) only within parsing through each sql query, so if the sql queries list is empty, it never gets defined. ``` [...] snowflake_hook.run(sql=queries, autocommit=True) File "/usr/local/airflow/.local/lib/python3.7/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 304, in run return execution_info UnboundLocalError: local variable 'execution_info' referenced before assignment ``` ### What you think should happen instead The function could either return an empty list or None. Perhaps the `execution_info` variable definition could just be moved further up in the function definition so that returning it at the end doesn't raise issues. Or, there should be a check in the `run` implementation to see if the `sql` argument is empty or not, and appropriately handle what to return from there. ### How to reproduce Pass an empty list to the sql argument when calling `SnowflakeHook.run()`. ### Anything else My script that utilizes the `SnowflakeHook.run()` function is automated in a way where there isn't always a case that there are sql queries to run. Of course, on my end I would update my code to first check if the sql queries list is populated before calling the hook to run. However, it would save for unintended exceptions if the hook's `run()` function also appropriately handles what gets returned in the event that the `sql` argument is empty. ### 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/23623

https://github.com/apache/airflow/pull/23767

4c9f7560355eefd57a29afee73bf04273e81a7e8

86cfd1244a641a8f17c9b33a34399d9be264f556

python

closed

apache/airflow

https://github.com/apache/airflow

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

DatabricksSubmitRunOperator and DatabricksRunNowOperator cannot define .json as template_ext

### Apache Airflow version 2.2.2 ### What happened Introduced here https://github.com/apache/airflow/commit/0a2d0d1ecbb7a72677f96bc17117799ab40853e0 databricks operators now define template_ext property as `('.json',)`. This change broke a few dags we have currently as they basically define a config json file that needs to be posted to databricks. Example: ```python DatabricksRunNowOperator( task_id=..., job_name=..., python_params=["app.py", "--config", "/path/to/config/inside-docker-image.json"], databricks_conn_id=..., email_on_failure=..., ) ``` This snippet will make airflow to load /path/to/config/inside-docker-image.json and it is not desired. @utkarsharma2 @potiuk can this change be reverted, please? It's causing headaches when a json file is provided as part of the dag parameters. ### What you think should happen instead Use a more specific extension for databricks operators, like ```.json-tpl``` ### How to reproduce _No response_ ### Operating System Any ### Versions of Apache Airflow Providers apache-airflow-providers-databricks==2.6.0 ### 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/23622

https://github.com/apache/airflow/pull/23641

84c9f4bf70cbc2f4ba19fdc5aa88791500d4daaa

acf89510cd5a18d15c1a45e674ba0bcae9293097

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add an offload option to CloudSQLExportInstanceOperator validation specification

### 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 want to use serverless export to offload the export operation from the primary instance. https://cloud.google.com/sql/docs/mysql/import-export#serverless Used CloudSQLExportInstanceOperator with the exportContext.offload flag to perform a serverless export operation. I got the following warning: ``` {field_validator.py:266} WARNING - The field 'exportContext.offload' is in the body, but is not specified in the validation specification '[{'name': 'fileType', 'allow_empty': False}, {'name': 'uri', 'allow_empty': False}, {'name': 'databases', 'optional': True, 'type': 'list'}, {'name': 'sqlExportOptions', 'type': 'dict', 'optional': True, 'fields': [{'name': 'tables', 'optional': True, 'type': 'list'}, {'name': 'schemaOnly', 'optional': True}]}, {'name': 'csvExportOptions', 'type': 'dict', 'optional': True, 'fields': [{'name': 'selectQuery'}]}]'. This might be because you are using newer API version and new field names defined for that version. Then the warning can be safely ignored, or you might want to upgrade the operatorto the version that supports the new API version. ``` ### What you think should happen instead I think a validation specification for `exportContext.offload` should be added. ### How to reproduce Try to use `exportContext.offload`, as in the example below. ```python CloudSQLExportInstanceOperator( task_id='export_task', project_id='some_project', instance='cloud_sql_instance', body={ "exportContext": { "fileType": "csv", "uri": "gs://my-bucket/export.csv", "databases": ["some_db"], "csvExportOptions": {"selectQuery": "select * from some_table limit 10"}, "offload": True } }, ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23613

https://github.com/apache/airflow/pull/23614

1bd75ddbe3b1e590e38d735757d99b43db1725d6

74557e41e3dcedec241ea583123d53176994cccc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/celery_kubernetes_executor.py", "airflow/executors/local_kubernetes_executor.py", "tests/executors/test_celery_kubernetes_executor.py", "tests/executors/test_local_kubernetes_executor.py"]

AttributeError: 'CeleryKubernetesExecutor' object has no attribute 'send_callback'

### Apache Airflow version 2.3.0 (latest released) ### What happened The issue started to occur after upgrading airflow from v2.2.5 to v2.3.0. The schedulers are crashing when DAG's SLA is configured. Only occurred when I used `CeleryKubernetesExecutor`. Tested on `CeleryExecutor` and it works as expected. ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 824, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 919, in _do_scheduling self._send_dag_callbacks_to_processor(dag, callback_to_run) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1179, in _send_dag_callbacks_to_processor self._send_sla_callbacks_to_processor(dag) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1195, in _send_sla_callbacks_to_processor self.executor.send_callback(request) AttributeError: 'CeleryKubernetesExecutor' object has no attribute 'send_callback' ``` ### What you think should happen instead Work like previous version ### How to reproduce 1. Use `CeleryKubernetesExecutor` 2. Configure DAG's SLA DAG to reproduce: ``` # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Example DAG demonstrating the usage of the BashOperator.""" from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator DEFAULT_ARGS = { "sla": timedelta(hours=1), } with DAG( dag_id="example_bash_operator", default_args=DEFAULT_ARGS, schedule_interval="0 0 * * *", start_date=datetime(2021, 1, 1), catchup=False, dagrun_timeout=timedelta(minutes=60), tags=["example", "example2"], params={"example_key": "example_value"}, ) as dag: run_this_last = DummyOperator( task_id="run_this_last", ) # [START howto_operator_bash] run_this = BashOperator( task_id="run_after_loop", bash_command="echo 1", ) # [END howto_operator_bash] run_this >> run_this_last for i in range(3): task = BashOperator( task_id="runme_" + str(i), bash_command='echo "{{ task_instance_key_str }}" && sleep 1', ) task >> run_this # [START howto_operator_bash_template] also_run_this = BashOperator( task_id="also_run_this", bash_command='echo "run_id={{ run_id }} | dag_run={{ dag_run }}"', ) # [END howto_operator_bash_template] also_run_this >> run_this_last # [START howto_operator_bash_skip] this_will_skip = BashOperator( task_id="this_will_skip", bash_command='echo "hello world"; exit 99;', dag=dag, ) # [END howto_operator_bash_skip] this_will_skip >> run_this_last if __name__ == "__main__": dag.cli() ``` ### 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? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23610

https://github.com/apache/airflow/pull/23617

60a1d9d191fb8fc01893024c897df9632ad5fbf4

c5b72bf30c8b80b6c022055834fc7272a1a44526

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/taskInstance/taskActions/ClearInstance.tsx", "airflow/www/static/js/dag/details/taskInstance/taskActions/MarkInstanceAs.tsx"]

After upgrade from Airflow 2.2.4, grid disappears for some DAGs

### Apache Airflow version 2.3.0 (latest released) ### What happened After the upgrade from 2.2.4 to 2.3.0, some DAGs grid data seems missing and it renders the UI blank ### What you think should happen instead When I click the grid for a specific execution date, I expect to be able to click the tasks and view the log, render jinja templating, and clear status ### How to reproduce Run an upgrade from 2.2.4 to 2.3.0 with a huge database (we have ~750 DAGs with a minimum of 10 tasks each). In addition, we heavily rely on XCom. ### Operating System Ubuntu 20.04.3 LTS ### Versions of Apache Airflow Providers apache-airflow apache_airflow-2.3.0-py3-none-any.whl apache-airflow-providers-amazon apache_airflow_providers_amazon-3.3.0-py3-none-any.whl apache-airflow-providers-ftp apache_airflow_providers_ftp-2.1.2-py3-none-any.whl apache-airflow-providers-http apache_airflow_providers_http-2.1.2-py3-none-any.whl apache-airflow-providers-imap apache_airflow_providers_imap-2.2.3-py3-none-any.whl apache-airflow-providers-mongo apache_airflow_providers_mongo-2.3.3-py3-none-any.whl apache-airflow-providers-mysql apache_airflow_providers_mysql-2.2.3-py3-none-any.whl apache-airflow-providers-pagerduty apache_airflow_providers_pagerduty-2.1.3-py3-none-any.whl apache-airflow-providers-postgres apache_airflow_providers_postgres-4.1.0-py3-none-any.whl apache-airflow-providers-sendgrid apache_airflow_providers_sendgrid-2.0.4-py3-none-any.whl apache-airflow-providers-slack apache_airflow_providers_slack-4.2.3-py3-none-any.whl apache-airflow-providers-sqlite apache_airflow_providers_sqlite-2.1.3-py3-none-any.whl apache-airflow-providers-ssh apache_airflow_providers_ssh-2.4.3-py3-none-any.whl apache-airflow-providers-vertica apache_airflow_providers_vertica-2.1.3-py3-none-any.whl ### Deployment Virtualenv installation ### Deployment details Python 3.8.10 ### Anything else For the affected DAGs, all the time ### 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/23588

https://github.com/apache/airflow/pull/32992

8bfad056d8ef481cc44288c5749fa5c54efadeaa

943b97850a1e82e4da22e8489c4ede958a42213d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/grid/AutoRefresh.jsx", "airflow/www/static/js/grid/Grid.jsx", "airflow/www/static/js/grid/Grid.test.jsx", "airflow/www/static/js/grid/Main.jsx", "airflow/www/static/js/grid/ToggleGroups.jsx", "airflow/www/static/js/grid/api/useGridData.test.jsx", "airflow/www/static/js/grid/details/index.jsx", "airflow/www/static/js/grid/index.jsx", "airflow/www/static/js/grid/renderTaskRows.jsx", "airflow/www/static/js/grid/renderTaskRows.test.jsx"]

`task_id` with `.` e.g. `hello.world` is not rendered in grid view

### Apache Airflow version 2.3.0 (latest released) ### What happened `task_id` with `.` e.g. `hello.world` is not rendered in grid view. ### What you think should happen instead The task should be rendered just fine in Grid view. ### How to reproduce ``` # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Example DAG demonstrating the usage of the BashOperator.""" from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator with DAG( dag_id="example_bash_operator", schedule_interval="0 0 * * *", start_date=datetime(2021, 1, 1), catchup=False, dagrun_timeout=timedelta(minutes=60), tags=["example", "example2"], params={"example_key": "example_value"}, ) as dag: run_this_last = DummyOperator( task_id="run.this.last", ) # [START howto_operator_bash] run_this = BashOperator( task_id="run.after.loop", bash_command="echo 1", ) # [END howto_operator_bash] run_this >> run_this_last for i in range(3): task = BashOperator( task_id="runme." + str(i), bash_command='echo "{{ task_instance_key_str }}" && sleep 1', ) task >> run_this # [START howto_operator_bash_template] also_run_this = BashOperator( task_id="also.run.this", bash_command='echo "run_id={{ run_id }} | dag_run={{ dag_run }}"', ) # [END howto_operator_bash_template] also_run_this >> run_this_last # [START howto_operator_bash_skip] this_will_skip = BashOperator( task_id="this.will.skip", bash_command='echo "hello world"; exit 99;', dag=dag, ) # [END howto_operator_bash_skip] this_will_skip >> run_this_last if __name__ == "__main__": dag.cli() ``` ### 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? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23580

https://github.com/apache/airflow/pull/23590

028087b5a6e94fd98542d0e681d947979eb1011f

afdfece9372fed83602d50e2eaa365597b7d0101

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

The xmltodict 0.13.0 breaks some emr tests

### Apache Airflow version main (development) ### What happened The xmltodict 0.13.0 breaks some EMR tests (this is happening in `main` currently: Example: https://github.com/apache/airflow/runs/6343826225?check_suite_focus=true#step:9:13417 ``` tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_create_job_flow_extra_args: ValueError: Malformatted input tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_create_job_flow_uses_the_emr_config_to_create_a_cluster: ValueError: Malformatted input tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_get_cluster_id_by_name: ValueError: Malformatted input ``` Downgrading to 0.12.0 fixes the problem. ### What you think should happen instead The tests should work ### How to reproduce * Run Breeze * Run `pytest tests/providers/amazon/aws/hooks/test_emr.py` -> observe it to succeed * Run `pip install xmltodict==0.13.0` -> observe it being upgraded from 0.12.0 * Run `pytest tests/providers/amazon/aws/hooks/test_emr.py` -> observe it to fail with `Malformed input` error ### Operating System Any ### Versions of Apache Airflow Providers Latest from main ### Deployment Other ### Deployment details CI ### 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/23576

https://github.com/apache/airflow/pull/23992

614b2329c1603ef1e2199044e2cc9e4b7332c2e0

eec85d397ef0ecbbe5fd679cf5790adae2ad9c9f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/dag_processor_command.py", "tests/cli/commands/test_dag_processor_command.py"]

cli command `dag-processor` uses `[core] sql_alchemy_conn`

### Apache Airflow version 2.3.0 (latest released) ### What happened Dag processor failed to start if `[core] sql_alchemy_conn` not defined ``` airflow-local-airflow-dag-processor-1 | [2022-05-08 16:42:35,835] {configuration.py:494} WARNING - section/key [core/sql_alchemy_conn] not found in config airflow-local-airflow-dag-processor-1 | Traceback (most recent call last): airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/bin/airflow", line 8, in <module> airflow-local-airflow-dag-processor-1 | sys.exit(main()) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main airflow-local-airflow-dag-processor-1 | args.func(args) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command airflow-local-airflow-dag-processor-1 | return func(*args, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper airflow-local-airflow-dag-processor-1 | return f(*args, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/dag_processor_command.py", line 53, in dag_processor airflow-local-airflow-dag-processor-1 | sql_conn: str = conf.get('core', 'sql_alchemy_conn').lower() airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/configuration.py", line 486, in get airflow-local-airflow-dag-processor-1 | return self._get_option_from_default_config(section, key, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/configuration.py", line 496, in _get_option_from_default_config airflow-local-airflow-dag-processor-1 | raise AirflowConfigException(f"section/key [{section}/{key}] not found in config") airflow-local-airflow-dag-processor-1 | airflow.exceptions.AirflowConfigException: section/key [core/sql_alchemy_conn] not found in config ``` ### What you think should happen instead Since https://github.com/apache/airflow/pull/22284 `sql_alchemy_conn` moved to `[database]` section `dag-processor` should use this configuration ### How to reproduce Run `airflow dag-processor` without defined `[core] sql_alchemy_conn` https://github.com/apache/airflow/blob/6e5955831672c71bfc0424dd50c8e72f6fd5b2a7/airflow/cli/commands/dag_processor_command.py#L52-L53 ### Operating System Arch Linux ### 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-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-snowflake==2.6.0 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23572

https://github.com/apache/airflow/pull/23575

827bfda59b7a0db6ada697ccd01c739d37430b9a

9837e6d813744e3c5861c32e87b3aeb496d0f88d

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/values.yaml"]

Description of defaultAirflowRepository in values.yaml is misleading

### Official Helm Chart version 1.5.0 (latest released) ### Apache Airflow version 2.3.0 (latest released) ### Kubernetes Version minikube v1.25.2 ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened defaultAirflowRepository is described in ```values.yaml``` as ```yaml # Default airflow repository -- overrides all the specific images below defaultAirflowRepository: apache/airflow ``` ### What you think should happen instead ```defaultAirflowRepository``` doesn't override the specific images, it is _overridden by them_. For example, in ```_helpers.yaml``` ``` {{ define "pod_template_image" -}} {{ printf "%s:%s" (.Values.images.pod_template.repository | default .Values.defaultAirflowRepository) (.Values.images.pod_template.tag | default .Values.defaultAirflowTag) }} {{- end }} ``` Suggest updating the comment line to:- ```yaml # Default airflow repository -- overridden by all the specific images below ``` ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23566

https://github.com/apache/airflow/pull/26428

a2b186a152ade5b2932c5d01b437f5549f250a89

02d22f6ce2dbb4a1c5c5eb01dfa3070327e377bb

python

closed

apache/airflow

https://github.com/apache/airflow

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

templates_dict, op_args, op_kwargs no longer rendered in PythonVirtualenvOperator

### Apache Airflow version 2.3.0 (latest released) ### What happened Templated strings in templates_dict, op_args, op_kwargs of PythonVirtualenvOperator are no longer rendered. ### What you think should happen instead All templated strings in templates_dict, op_args and op_kwargs must be rendered, i.e. these 3 arguments must be template_fields of PythonVirtualenvOperator, as it was in Airflow 2.2.3 ### 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 This is due to template_fields class variable being set in PythonVirtualenvOperator `template_fields: Sequence[str] = ('requirements',)` that overrode class variable of PythonOperator `template_fields = ('templates_dict', 'op_args', 'op_kwargs')`. I read in some discussion that wanted to make requirements a template field for PythonVirtualenvOperator, but we must specify all template fields of parent class as well. `template_fields: Sequence[str] = ('templates_dict', 'op_args', 'op_kwargs', 'requirements',)` ### 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/23557

https://github.com/apache/airflow/pull/23559

7132be2f11db24161940f57613874b4af86369c7

1657bd2827a3299a91ae0abbbfe4f6b80bd4cdc0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Mapping is Immutable within a Run

### Apache Airflow version 2.3.0 (latest released) ### What happened Looks like mapped tasks are immutable, even when the source XCOM that created them changes. This is a problem for things like Late Arriving Data and Data Reprocessing ### What you think should happen instead Mapped tasks should change in response to a change of input ### How to reproduce Here is a writeup and MVP DAG demonstrating the issue https://gist.github.com/fritz-astronomer/d159d0e29d57458af5b95c0f253a3361 ### Operating System docker/debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else Can look into a fix - but may not be able to submit a full PR ### 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/23550

https://github.com/apache/airflow/pull/23667

ad297c91777277e2b76dd7b7f0e3e3fc5c32e07c

b692517ce3aafb276e9d23570e9734c30a5f3d1f

python

closed

apache/airflow

https://github.com/apache/airflow

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

Gantt Chart Broken After Deleting a Task

### Apache Airflow version 2.2.5 ### What happened After a task was deleted from a DAG we received the following message when visiting the gantt view for the DAG in the webserver. ``` { "detail": null, "status": 404, "title": "Task delete-me not found", "type": "https://airflow.apache.org/docs/apache-airflow/2.2.5/stable-rest-api-ref.html#section/Errors/NotFound" } ``` This was only corrected by manually deleting the offending task instances from the `task_instance` and `task_fail` tables. ### What you think should happen instead I would expect the gantt chart to load either excluding the non-existent task or flagging that the task associated with task instance no longer exists. ### How to reproduce * Create a DAG with multiple tasks. * Run the DAG. * Delete one of the tasks. * Attempt to open the gantt view for the DAG. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Custom docker container hosted on Amazon ECS. ### 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/23546

https://github.com/apache/airflow/pull/23627

e09e4635b0dc50cbd3a18f8be02ce9b2e2f3d742

4b731f440734b7a0da1bbc8595702aaa1110ad8d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/file.py", "tests/utils/test_file.py"]

Airflow .airflowignore not handling soft link properly.

### Apache Airflow version 2.3.0 (latest released) ### What happened Soft link and folder under same root folder will be handled as the same relative path. Say i have dags folder which looks like this: ``` -dags: -- .airflowignore -- folder -- soft-links-to-folder -> folder ``` and .airflowignore: ``` folder/ ``` both folder and soft-links-to-folder will be ignored. ### What you think should happen instead Only the folder should be ignored. This is the expected behavior in airflow 2.2.4, before i upgraded. ~~The root cause is that both _RegexpIgnoreRule and _GlobIgnoreRule is calling `relative_to` method to get search path.~~ ### How to reproduce check @tirkarthi comment for the test case. ### 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? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23532

https://github.com/apache/airflow/pull/23535

7ab5ea7853df9d99f6da3ab804ffe085378fbd8a

8494fc7036c33683af06a0e57474b8a6157fda05

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"]

Provide resources attribute in KubernetesPodOperator to be templated

### Description Make resources in KubernetesPodOperator as templated. We need to modify this during several runs and it needs code change for each run. ### Use case/motivation For running CPU and memory intensive workloads, we want to continuously optimise the "limt_cpu" and "limit_memory" parameters. Hence, we want to provide these parameters as a part of the pipeline definition. ### 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/23529

https://github.com/apache/airflow/pull/27457

aefadb8c5b9272613d5806b054a1b46edf29d82e

47a2b9ee7f1ff2cc1cc1aa1c3d1b523c88ba29fb

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/ci/docker-compose/integration-cassandra.yml"]

Cassandra container 3.0.26 fails to start on CI

### Apache Airflow version main (development) ### What happened Cassandra released a new image (3.0.26) on 05.05.2022 and it broke our builds, for example: * https://github.com/apache/airflow/runs/6320170343?check_suite_focus=true#step:10:6651 * https://github.com/apache/airflow/runs/6319805534?check_suite_focus=true#step:10:12629 * https://github.com/apache/airflow/runs/6319710486?check_suite_focus=true#step:10:6759 The problem was that container for cassandra did not cleanly start: ``` ERROR: for airflow Container "3bd115315ba7" is unhealthy. Encountered errors while bringing up the project. 3bd115315ba7 cassandra:3.0 "docker-entrypoint.s…" 5 minutes ago Up 5 minutes (unhealthy) 7000-7001/tcp, 7199/tcp, 9042/tcp, 9160/tcp airflow-integration-postgres_cassandra_1 ``` The logs of cassandra container do not show anything suspected, cassandra seems to start ok, but the health-checks for the : ``` INFO 08:45:22 Using Netty Version: [netty-buffer=netty-buffer-4.0.44.Final.452812a, netty-codec=netty-codec-4.0.44.Final.452812a, netty-codec-haproxy=netty-codec-haproxy-4.0.44.Final.452812a, netty-codec-http=netty-codec-http-4.0.44.Final.452812a, netty-codec-socks=netty-codec-socks-4.0.44.Final.452812a, netty-common=netty-common-4.0.44.Final.452812a, netty-handler=netty-handler-4.0.44.Final.452812a, netty-tcnative=netty-tcnative-1.1.33.Fork26.142ecbb, netty-transport=netty-transport-4.0.44.Final.452812a, netty-transport-native-epoll=netty-transport-native-epoll-4.0.44.Final.452812a, netty-transport-rxtx=netty-transport-rxtx-4.0.44.Final.452812a, netty-transport-sctp=netty-transport-sctp-4.0.44.Final.452812a, netty-transport-udt=netty-transport-udt-4.0.44.Final.452812a] INFO 08:45:22 Starting listening for CQL clients on /0.0.0.0:9042 (unencrypted)... INFO 08:45:23 Not starting RPC server as requested. Use JMX (StorageService->startRPCServer()) or nodetool (enablethrift) to start it INFO 08:45:23 Startup complete INFO 08:45:24 Created default superuser role ‘cassandra’ ``` We mitigated it by #23522 and pinned cassandra to 3.0.25 version but more investigation/reachout is needed. ### What you think should happen instead Cassandra should start properly. ### How to reproduce Revert #23522 and make. PR. The builds will start to fail with "cassandra unhealthy" ### Operating System Github Actions ### Versions of Apache Airflow Providers not relevant ### Deployment Other ### Deployment details CI ### Anything else Always. ### 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/23523

https://github.com/apache/airflow/pull/23537

953b85d8a911301c040a3467ab2a1ba2b6d37cd7

22a564296be1aee62d738105859bd94003ad9afc

python

closed

apache/airflow

https://github.com/apache/airflow

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

Json files from S3 downloading as text files

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.3.0 (latest released) ### Operating System Mac OS Mojave 10.14.6 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When I download a json file from S3 using the S3Hook: `filename=s3_hook.download_file(bucket_name=self.source_s3_bucket, key=key, local_path="./data") ` The file is being downloaded as a text file starting with `airflow_temp_`. ### What you think should happen instead It would be nice to have them download as a json file or keep the same filename as in S3. Since it requires additional code to go back and read the file as a dictionary (ast.literal_eval) and there is no guarantee that the json structure is maintained. ### How to reproduce Where s3_conn_id is the Airflow connection and s3_bucket is a bucket on AWS S3. This is the custom operator class: ``` from airflow.models.baseoperator import BaseOperator from airflow.utils.decorators import apply_defaults from airflow.hooks.S3_hook import S3Hook import logging class S3SearchFilingsOperator(BaseOperator): """ Queries the Datastore API and uploads the processed info as a csv to the S3 bucket. :param source_s3_bucket: Choose source s3 bucket :param source_s3_directory: Source s3 directory :param s3_conn_id: S3 Connection ID :param destination_s3_bucket: S3 Bucket Destination """ @apply_defaults def __init__( self, source_s3_bucket=None, source_s3_directory=True, s3_conn_id=True, destination_s3_bucket=None, destination_s3_directory=None, search_terms=[], *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.source_s3_bucket = source_s3_bucket self.source_s3_directory = source_s3_directory self.s3_conn_id = s3_conn_id self.destination_s3_bucket = destination_s3_bucket self.destination_s3_directory = destination_s3_directory def execute(self, context): """ Executes the operator. """ s3_hook = S3Hook(self.s3_conn_id) keys = s3_hook.list_keys(bucket_name=self.source_s3_bucket) for key in keys: # download file filename=s3_hook.download_file(bucket_name=self.source_s3_bucket, key=key, local_path="./data") logging.info(filename) with open(filename, 'rb') as handle: filing = handle.read() filing = pickle.loads(filing) logging.info(filing.keys()) ``` And this is the dag file: ``` from keywordSearch.operators.s3_search_filings_operator import S3SearchFilingsOperator from airflow import DAG from airflow.utils.dates import days_ago from datetime import timedelta # from aws_pull import aws_pull default_args = { "owner" : "airflow", "depends_on_past" : False, "start_date": days_ago(2), "email" : ["airflow@example.com"], "email_on_failure" : False, "email_on_retry" : False, "retries" : 1, "retry_delay": timedelta(seconds=30) } with DAG("keyword-search-full-load", default_args=default_args, description="Syntax Keyword Search", max_active_runs=1, schedule_interval=None) as dag: op3 = S3SearchFilingsOperator( task_id="s3_search_filings", source_s3_bucket="processed-filings", source_s3_directory="citations", s3_conn_id="Syntax_S3", destination_s3_bucket="keywordsearch", destination_s3_directory="results", dag=dag ) op3 ``` ### 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/23514

https://github.com/apache/airflow/pull/26886

d544e8fbeb362e76e14d7615d354a299445e5b5a

777b57f0c6a8ca16df2b96fd17c26eab56b3f268

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/webserver_command.py", "tests/cli/commands/test_webserver_command.py"]

Random "duplicate key value violates unique constraint" errors when initializing the postgres database

### Apache Airflow version 2.3.0 (latest released) ### What happened while testing airflow 2.3.0 locally (using postgresql 12.4), the webserver container shows random errors: ``` webserver_1 | + airflow db init ... webserver_1 | + exec airflow webserver ... webserver_1 | [2022-05-04 18:58:46,011] {{manager.py:568}} INFO - Added Permission menu access on Permissions to role Admin postgres_1 | 2022-05-04 18:58:46.013 UTC [41] ERROR: duplicate key value violates unique constraint "ab_permission_view_role_permission_view_id_role_id_key" postgres_1 | 2022-05-04 18:58:46.013 UTC [41] DETAIL: Key (permission_view_id, role_id)=(204, 1) already exists. postgres_1 | 2022-05-04 18:58:46.013 UTC [41] STATEMENT: INSERT INTO ab_permission_view_role (id, permission_view_id, role_id) VALUES (nextval('ab_permission_view_role_id_seq'), 204, 1) RETURNING ab_permission_view_role.id webserver_1 | [2022-05-04 18:58:46,015] {{manager.py:570}} ERROR - Add Permission to Role Error: (psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint "ab_permission_view_role_permission_view_id_role_id_key" webserver_1 | DETAIL: Key (permission_view_id, role_id)=(204, 1) already exists. webserver_1 | webserver_1 | [SQL: INSERT INTO ab_permission_view_role (id, permission_view_id, role_id) VALUES (nextval('ab_permission_view_role_id_seq'), %(permission_view_id)s, %(role_id)s) RETURNING ab_permission_view_role.id] webserver_1 | [parameters: {'permission_view_id': 204, 'role_id': 1}] ``` notes: 1. when the db is first initialized, i have ~40 errors like this (with ~40 different `permission_view_id` but always the same `'role_id': 1`) 2. when it's not the first time initializing db, i always have 1 error like this but it shows different `permission_view_id` each time 3. all these errors don't seem to have any real negative effects, the webserver is still running and airflow is still running and scheduling tasks 4. "occasionally" i do get real exceptions which render the webserver workers all dead: ``` postgres_1 | 2022-05-05 20:03:30.580 UTC [44] ERROR: duplicate key value violates unique constraint "ab_permission_view_role_permission_view_id_role_id_key" postgres_1 | 2022-05-05 20:03:30.580 UTC [44] DETAIL: Key (permission_view_id, role_id)=(214, 1) already exists. postgres_1 | 2022-05-05 20:03:30.580 UTC [44] STATEMENT: INSERT INTO ab_permission_view_role (id, permission_view_id, role_id) VALUES (nextval('ab_permission_view_role_id_seq'), 214, 1) RETURNING ab_permission_view_role.id webserver_1 | [2022-05-05 20:03:30 +0000] [121] [ERROR] Exception in worker process webserver_1 | Traceback (most recent call last): webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context webserver_1 | self.dialect.do_execute( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute webserver_1 | cursor.execute(statement, parameters) webserver_1 | psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "ab_permission_view_role_permission_view_id_role_id_key" webserver_1 | DETAIL: Key (permission_view_id, role_id)=(214, 1) already exists. webserver_1 | webserver_1 | webserver_1 | The above exception was the direct cause of the following exception: webserver_1 | webserver_1 | Traceback (most recent call last): webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/arbiter.py", line 589, in spawn_worker webserver_1 | worker.init_process() webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/workers/base.py", line 134, in init_process webserver_1 | self.load_wsgi() webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/workers/base.py", line 146, in load_wsgi webserver_1 | self.wsgi = self.app.wsgi() webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/app/base.py", line 67, in wsgi webserver_1 | self.callable = self.load() webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/app/wsgiapp.py", line 58, in load webserver_1 | return self.load_wsgiapp() webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/app/wsgiapp.py", line 48, in load_wsgiapp webserver_1 | return util.import_app(self.app_uri) webserver_1 | File "/usr/local/lib/python3.8/site-packages/gunicorn/util.py", line 412, in import_app webserver_1 | app = app(*args, **kwargs) webserver_1 | File "/usr/local/lib/python3.8/site-packages/airflow/www/app.py", line 158, in cached_app webserver_1 | app = create_app(config=config, testing=testing) webserver_1 | File "/usr/local/lib/python3.8/site-packages/airflow/www/app.py", line 146, in create_app webserver_1 | sync_appbuilder_roles(flask_app) webserver_1 | File "/usr/local/lib/python3.8/site-packages/airflow/www/app.py", line 68, in sync_appbuilder_roles webserver_1 | flask_app.appbuilder.sm.sync_roles() webserver_1 | File "/usr/local/lib/python3.8/site-packages/airflow/www/security.py", line 580, in sync_roles webserver_1 | self.update_admin_permission() webserver_1 | File "/usr/local/lib/python3.8/site-packages/airflow/www/security.py", line 562, in update_admin_permission webserver_1 | self.get_session.commit() webserver_1 | File "<string>", line 2, in commit webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1423, in commit webserver_1 | self._transaction.commit(_to_root=self.future) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 829, in commit webserver_1 | self._prepare_impl() webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl webserver_1 | self.session.flush() webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3255, in flush webserver_1 | self._flush(objects) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3395, in _flush webserver_1 | transaction.rollback(_capture_exception=True) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ webserver_1 | compat.raise_( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ webserver_1 | raise exception webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3355, in _flush webserver_1 | flush_context.execute() webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 453, in execute webserver_1 | rec.execute(self) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 576, in execute webserver_1 | self.dependency_processor.process_saves(uow, states) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/dependency.py", line 1182, in process_saves webserver_1 | self._run_crud( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/orm/dependency.py", line 1245, in _run_crud webserver_1 | connection.execute(statement, secondary_insert) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1200, in execute webserver_1 | return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS) webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 313, in _execute_on_connection webserver_1 | return connection._execute_clauseelement( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1389, in _execute_clauseelement webserver_1 | ret = self._execute_context( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1748, in _execute_context webserver_1 | self._handle_dbapi_exception( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1929, in _handle_dbapi_exception webserver_1 | util.raise_( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ webserver_1 | raise exception webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_context webserver_1 | self.dialect.do_execute( webserver_1 | File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute webserver_1 | cursor.execute(statement, parameters) webserver_1 | sqlalchemy.exc.IntegrityError: (psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint "ab_permission_view_role_permission_view_id_role_id_key" webserver_1 | DETAIL: Key (permission_view_id, role_id)=(214, 1) already exists. webserver_1 | webserver_1 | [SQL: INSERT INTO ab_permission_view_role (id, permission_view_id, role_id) VALUES (nextval('ab_permission_view_role_id_seq'), %(permission_view_id)s, %(role_id)s) RETURNING ab_permission_view_role.id] webserver_1 | [parameters: {'permission_view_id': 214, 'role_id': 1}] webserver_1 | (Background on this error at: http://sqlalche.me/e/14/gkpj) webserver_1 | [2022-05-05 20:03:30 +0000] [121] [INFO] Worker exiting (pid: 121) flower_1 | + exec airflow celery flower scheduler_1 | + exec airflow scheduler webserver_1 | [2022-05-05 20:03:31 +0000] [118] [INFO] Worker exiting (pid: 118) webserver_1 | [2022-05-05 20:03:31 +0000] [119] [INFO] Worker exiting (pid: 119) webserver_1 | [2022-05-05 20:03:31 +0000] [120] [INFO] Worker exiting (pid: 120) worker_1 | + exec airflow celery worker ``` However such exceptions are rare and pure random, i can't find a way to reproduce them consistently. ### What you think should happen instead prior to 2.3.0 there were no such errors ### How to reproduce _No response_ ### Operating System Linux Mint 20.3 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23512

https://github.com/apache/airflow/pull/27297

9ab1a6a3e70b32a3cddddf0adede5d2f3f7e29ea

8f99c793ec4289f7fc28d890b6c2887f0951e09b

python

closed

apache/airflow

https://github.com/apache/airflow

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

Tasks stuck indefinitely when following container logs

### Apache Airflow version 2.2.4 ### What happened I observed that some workers hanged randomly after being running. Also, logs were not being reported. After some time, the pod status was on "Completed" when inspecting from k8s api, but wasn't on Airflow, which showed "status:running" for the pod. After some investigation, the issue is in the new kubernetes pod operator and is dependant of a current issue in the kubernetes api. When a log rotate event occurs in kubernetes, the stream we consume on fetch_container_logs(follow=True,...) is no longer being feeded. Therefore, the k8s pod operator hangs indefinetly at the middle of the log. Only a sigterm could terminate it as logs consumption is blocking execute() to finish. Ref to the issue in kubernetes: https://github.com/kubernetes/kubernetes/issues/59902 Linking to https://github.com/apache/airflow/issues/12103 for reference, as the result is more or less the same for end user (although the root cause is different) ### What you think should happen instead Pod operator should not hang. Pod operator could follow the new logs from the container - this is out of scope of airflow as ideally the k8s api does it automatically. ### Solution proposal I think there are many possibilities to walk-around this from airflow-side to not hang indefinitely (like making `fetch_container_logs` non-blocking for `execute` and instead always block until status.phase.completed as it's currently done when get_logs is not true). ### How to reproduce Running multiple tasks will sooner or later trigger this. Also, one can configure a more aggressive logs rotation in k8s so this race is triggered more often. #### Operating System Debian GNU/Linux 11 (bullseye) #### Versions of Apache Airflow Providers ``` apache-airflow==2.2.4 apache-airflow-providers-google==6.4.0 apache-airflow-providers-cncf-kubernetes==3.0.2 ``` However, this should be reproducible with master. #### Deployment Official Apache Airflow Helm Chart ### 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/23497

https://github.com/apache/airflow/pull/28336

97006910a384579c9f0601a72410223f9b6a0830

6d2face107f24b7e7dce4b98ae3def1178e1fc4c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/grid/TaskName.jsx"]

Grid View - Multilevel taskgroup shows white text on the UI

### Apache Airflow version 2.3.0 (latest released) ### What happened Blank text if there are nested Task Groups . Nested TaskGroup - Graph view:  Nested TaskGroup - Grid view:  ### What you think should happen instead We should see the text as up task group level. ### How to reproduce ### deploy below DAG: ``` from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.utils.dates import datetime from airflow.utils.task_group import TaskGroup with DAG(dag_id="grid_view_dag", start_date=datetime(2022, 5, 3, 0, 00), schedule_interval=None, concurrency=2, max_active_runs=2) as dag: parent_task_group = None for i in range(0, 10): with TaskGroup(group_id=f"tg_level_{i}", parent_group=parent_task_group) as tg: t = DummyOperator(task_id=f"task_level_{i}") parent_task_group = tg ``` ### got to grid view and expand the nodes:  #### you can see the text after text selection:  ### Operating System N/A ### Versions of Apache Airflow Providers N/A ### Deployment Docker-Compose ### Deployment details reproducible using the following docker-compose file: https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml ### 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/23476

https://github.com/apache/airflow/pull/23482

d9902958448b9d6e013f90f14d2d066f3121dcd5

14befe3ad6a03f27e20357e9d4e69f99d19a06d1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagbag.py", "airflow/security/permissions.py", "airflow/www/security.py", "tests/www/test_security.py"]

Could not get DAG access permission after upgrade to 2.3.0

### Apache Airflow version 2.3.0 (latest released) ### What happened I upgraded my airflow instance from version 2.1.3 to 2.3.0 but got issue that there are no permission for new DAGs. **The issue only happens in DAG which has dag_id contains dot symbol.** ### What you think should happen instead There should be 3 new permissions for a DAG. ### How to reproduce + Create a new DAG with id, lets say: `dag.id_1` + Go to the UI -> Security -> List Role + Edit any Role + Try to insert permissions of new DAG above to chosen role. -> Could not get any permission for created DAG above. There are 3 DAG permissions named `can_read_DAG:dag`, `can_edit_DAG:dag`, `can_delete_DAG:dag` There should be 3 new permissions: `can_read_DAG:dag.id_1`, `can_edit_DAG:dag.id_1`, `can_delete_DAG:dag.id_1` ### Operating System Kubernetes ### 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/23473

https://github.com/apache/airflow/pull/23510

ae3e68af3c42a53214e8264ecc5121049c3beaf3

cc35fcaf89eeff3d89e18088c2e68f01f8baad56

python

closed

apache/airflow

https://github.com/apache/airflow

["README.md", "breeze-complete", "dev/breeze/src/airflow_breeze/global_constants.py", "images/breeze/output-commands-hash.txt", "images/breeze/output-commands.svg", "images/breeze/output-config.svg", "images/breeze/output-shell.svg", "images/breeze/output-start-airflow.svg", "scripts/ci/libraries/_initialization.sh"]

Add Postgres 14 support

### Description _No response_ ### Use case/motivation Using Postgres 14 as backend ### 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/23460

https://github.com/apache/airflow/pull/23506

9ab9cd47cff5292c3ad602762ae3e371c992ea92

6169e0a69875fb5080e8d70cfd9d5e650a9d13ba

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/dag_processor_command.py", "tests/cli/commands/test_dag_processor_command.py"]

External DAG processor not working

### Apache Airflow version 2.3.0 (latest released) ### What happened Running a standalone Dag Processor instance with `airflow dag-processor` throws the following exception: ``` Standalone DagProcessor is not supported when using sqlite. ``` ### What you think should happen instead The `airflow dag-processor` should start without an exception in case of Postgres database ### How to reproduce The error is in the following line: https://github.com/apache/airflow/blob/6f146e721c81e9304bf7c0af66fc3d203d902dab/airflow/cli/commands/dag_processor_command.py#L53 It should be ```python sql_conn: str = conf.get('database', 'sql_alchemy_conn').lower() ``` due to the change in the configuration file done in https://github.com/apache/airflow/pull/22284 ### 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? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23447

https://github.com/apache/airflow/pull/23575

827bfda59b7a0db6ada697ccd01c739d37430b9a

9837e6d813744e3c5861c32e87b3aeb496d0f88d

python

closed

apache/airflow

https://github.com/apache/airflow

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

DataprocJobBaseOperator not compatible with TaskGroups

### Body Following Stackoverflow question: https://stackoverflow.com/questions/72091119/airflow-issues-with-calling-taskgroup The issue is that when defining task in TaskGroup the identifier of the task becomes `group_id.task_id` [DataprocJobBaseOperator](https://github.com/apache/airflow/blob/05ccfd42f28db7d0a8fe3ed023b0e7a8ec188609/airflow/providers/google/cloud/operators/dataproc.py#L836-L838) have default of using `task_id` for job name but Google doesn't allow the `.` char : `google.api_core.exceptions.InvalidArgument: 400 Job id 'weekday_analytics.avg_speed_20220502_22c11bdf' must conform to '[a-zA-Z0-9]([a-zA-Z0-9\-\_]{0,98}[a-zA-Z0-9])?' pattern` We probably should fix `DataprocJobBaseOperator` to handle cases where the task defined in task group by replacing the `.` to another char. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23439

https://github.com/apache/airflow/pull/23791

509b277dce50fb1fbc25aea565182933bb506ee2

a43e98d05047d9c4d5a7778bcb10efc4bdef7a01

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/s3.py", "airflow/providers/amazon/aws/links/emr.py", "airflow/providers/amazon/aws/sensors/emr.py", "airflow/providers/amazon/provider.yaml", "tests/providers/amazon/aws/hooks/test_s3.py", "tests/providers/amazon/aws/sensors/test_emr_base.py", "tests/providers/amazon/aws/sensors/test_emr_job_flow.py"]

Logs for EmrStepSensor

### Description Add feature to EmrStepSensor to bring back the spark task url & logs after task execution ### Use case/motivation After starting an EMR step task using EmrAddStepsOperator we generally have an EmrStepSensor to track the status of the step. The job ID is available for the sensor and is being poked at regular interval. ``` [2022-04-26, 22:07:43 UTC] {base_aws.py:100} INFO - Retrieving region_name from Connection.extra_config['region_name'] [2022-04-26, 22:07:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC [2022-04-26, 22:07:44 UTC] {emr.py:74} INFO - Job flow currently PENDING [2022-04-26, 22:08:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC [2022-04-26, 22:08:44 UTC] {emr.py:74} INFO - Job flow currently PENDING [2022-04-26, 22:09:44 UTC] {emr.py:316} INFO - Poking step s-123ABC123ABC on cluster j-123ABC123ABC [2022-04-26, 22:09:44 UTC] {emr.py:74} INFO - Job flow currently COMPLETED [2022-04-26, 22:09:44 UTC] {base.py:251} INFO - Success criteria met. Exiting. [2022-04-26, 22:09:44 UTC] {taskinstance.py:1288} INFO - Marking task as SUCCESS. dag_id=datapipeline_sample, task_id=calculate_pi_watch_step, execution_date=20220426T220739, start_date=20220426T220743, end_date=20220426T220944 ``` After the task is completed the status is displayed. If the user wants to review the logs of the task, it is a multistep process to get hold of the job logs from EMR cluster. It will be a great addition to add the log url and possibly relay the logs to Airflow EmrStepSensor post completion of the task. This will be very handy when there are failures of many tasks and will make it a great user experience. ### 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/23437

https://github.com/apache/airflow/pull/28180

9eacf607be109eb6ab80f7e27d234a17fb128ae0

fefcb1d567d8d605f7ec9b7d408831d656736541

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/base.py", "airflow/models/mappedoperator.py", "airflow/serialization/serialized_objects.py", "tests/api_connexion/endpoints/test_task_endpoint.py", "tests/models/test_taskinstance.py"]

Empty `expand()` crashes the scheduler

### Apache Airflow version 2.3.0 (latest released) ### What happened I've found a DAG that will crash the scheduler: ``` @task def hello(): return "hello" hello.expand() ``` ``` [2022-05-03 03:41:23,779] {scheduler_job.py:753} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 824, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 906, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/jobs/scheduler_job.py", line 1148, in _schedule_dag_run schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagrun.py", line 522, in update_state info = self.task_instance_scheduling_decisions(session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagrun.py", line 661, in task_instance_scheduling_decisions session=session, File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagrun.py", line 714, in _get_ready_tis expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/mappedoperator.py", line 609, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() TypeError: reduce() of empty sequence with no initial value ``` ### What you think should happen instead A user DAG shouldn't crash the scheduler. This specific case could likely be an ImportError at parse time, but it makes me think we might be missing some exception handling? ### How to reproduce _No response_ ### Operating System Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23435

https://github.com/apache/airflow/pull/23463

c9b21b8026c595878ee4cc934209fc1fc2ca2396

9214018153dd193be6b1147629f73b23d8195cce

python

closed

apache/airflow

https://github.com/apache/airflow

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

Mapping over multiple parameters results in 1 task fewer than expected

### Apache Airflow version 2.3.0 (latest released) ### What happened While testing the [example](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html#mapping-over-multiple-parameters) given for `Mapping over multiple parameters` I noticed only 5 tasks are being mapped rather than the expected 6. task example from the doc: ``` @task def add(x: int, y: int): return x + y added_values = add.expand(x=[2, 4, 8], y=[5, 10]) ``` The doc mentions: ``` # This results in the add function being called with # add(x=2, y=5) # add(x=2, y=10) # add(x=4, y=5) # add(x=4, y=10) # add(x=8, y=5) # add(x=8, y=10) ``` But when I create a DAG with the example, only 5 tasks are mapped instead of 6:  ### What you think should happen instead A task should be mapped for all 6 possible outcomes, rather than only 5 ### How to reproduce Create a DAG using the example provided [here](Mapping over multiple parameters) and check the number of mapped instances:  ### 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 Localhost instance of Astronomer Runtime 5.0.0 ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23425

https://github.com/apache/airflow/pull/23434

0fde90d92ae306f37041831f5514e9421eee676b

3fb8e0b0b4e8810bedece873949871a94dd7387a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/dag_run_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/dag_run_schema.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py"]

Add a queue DAG run endpoint to REST API

### Description Add a POST endpoint to queue a dag run like we currently do [here](https://github.com/apache/airflow/issues/23419). Url format: `api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/queue` ### 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/23420

https://github.com/apache/airflow/pull/23481

1220c1a7a9698cdb15289d7066b29c209aaba6aa

4485393562ea4151a42f1be47bea11638b236001

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/dag_run_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/dag_run_schema.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py"]

Add a DAG Run clear endpoint to REST API

### Description Add a POST endpoint to clear a dag run like we currently do [here](https://github.com/apache/airflow/blob/main/airflow/www/views.py#L2087). Url format: `api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/clear` ### 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/23419

https://github.com/apache/airflow/pull/23451

f352ee63a5d09546a7997ba8f2f8702a1ddb4af7

b83cc9b5e2c7e2516b0881861bbc0f8589cb531d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/dag_run_schema.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py", "tests/api_connexion/schemas/test_dag_run_schema.py"]

Add more fields to DAG Run API endpoints

### Description There are a few fields that would be useful to include in the REST API for getting a DAG run or list of DAG runs: `data_interval_start` `data_interval_end` `last_scheduling_decision` `run_type` as (backfill, manual and scheduled) ### Use case/motivation We use this information in the Grid view as part of `tree_data`. If we added these extra fields to the REST APi we could remove all dag run info from tree_data. ### 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/23415

https://github.com/apache/airflow/pull/23440

22b49d334ef0008be7bd3d8481b55b8ab5d71c80

6178491a117924155963586b246d2bf54be5320f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/utils.py", "airflow/migrations/versions/0110_2_3_2_add_cascade_to_dag_tag_foreignkey.py", "airflow/models/dag.py", "docs/apache-airflow/migrations-ref.rst"]

airflow db clean - Dag cleanup won't run if dag is tagged

### Apache Airflow version 2.3.0 (latest released) ### What happened When running `airflow db clean`, if a to-be-cleaned dag is also tagged, a foreign key constraint in dag_tag is violated. Full error: ``` sqlalchemy.exc.IntegrityError: (psycopg2.errors.ForeignKeyViolation) update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" DETAIL: Key (dag_id)=(some-dag-id-here) is still referenced from table "dag_tag". ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-cncf-kubernetes==4.0.1 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-oracle==2.2.3 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-samba==3.0.4 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 _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/23414

https://github.com/apache/airflow/pull/23444

e2401329345dcc5effa933b92ca969b8779755e4

8ccff9244a6d1a936d8732721373b967e95ec404

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/base.py", "tests/serialization/test_dag_serialization.py", "tests/ti_deps/deps/test_ready_to_reschedule_dep.py"]

PythonSensor is not considering mode='reschedule', instead marking task UP_FOR_RETRY

### Apache Airflow version 2.3.0 (latest released) ### What happened A PythonSensor that works on versions <2.3.0 in mode reschedule is now marking the task as `UP_FOR_RETRY` instead. Log says: ``` [2022-05-02, 15:48:23 UTC] {python.py:66} INFO - Poking callable: <function test at 0x7fd56286bc10> [2022-05-02, 15:48:23 UTC] {taskinstance.py:1853} INFO - Rescheduling task, marking task as UP_FOR_RESCHEDULE [2022-05-02, 15:48:23 UTC] {local_task_job.py:156} INFO - Task exited with return code 0 [2022-05-02, 15:48:23 UTC] {local_task_job.py:273} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` But it directly marks it as `UP_FOR_RETRY` and then follows `retry_delay` and `retries` ### What you think should happen instead It should mark the task as `UP_FOR_RESCHEDULE` and reschedule it according to the `poke_interval` ### How to reproduce ``` from datetime import datetime, timedelta from airflow import DAG from airflow.sensors.python import PythonSensor def test(): return False default_args = { "owner": "airflow", "depends_on_past": False, "start_date": datetime(2022, 5, 2), "email_on_failure": False, "email_on_retry": False, "retries": 1, "retry_delay": timedelta(minutes=1), } dag = DAG("dag_csdepkrr_development_v001", default_args=default_args, catchup=False, max_active_runs=1, schedule_interval=None) t1 = PythonSensor(task_id="PythonSensor", python_callable=test, poke_interval=30, mode='reschedule', dag=dag) ``` ### Operating System Latest Docker image ### 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-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-oracle==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==2.5.2 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Docker-Compose ### Deployment details Latest Docker compose from the documentation ### 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/23411

https://github.com/apache/airflow/pull/23674

d3b08802861b006fc902f895802f460a72d504b0

f9e2a3051cd3a5b6fcf33bca4c929d220cf5661e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py"]

Airflow 2.3.0 does not keep promised backward compatibility regarding database configuration using _CMD Env

### Apache Airflow version 2.3.0 (latest released) ### What happened We used to configure the Database using the AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD Environment variable. Now the config option moved from CORE to DATABASE. However, we intended to keep backward compatibility as stated in the [Release Notes](AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD). Upon 2.3.0 update however, the _CMD suffixed variables are no longer recognized for database configuration in Core - I think due to a missing entry here: https://github.com/apache/airflow/blob/8622808aa79531bcaa5099d26fbaf54b4afe931a/airflow/configuration.py#L135 ### What you think should happen instead We should only get a deprecation warning but the Database should be configured correctly. ### How to reproduce Configure Airflow using an external Database using the AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD environment variable. Notice that Airflow falls back to SQLight. ### Operating System kubernetes ### 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/23408

https://github.com/apache/airflow/pull/23441

6178491a117924155963586b246d2bf54be5320f

0cdd401cda61006a42afba243f1ad813315934d4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/utils/pod_manager.py"]

Airflow kubernetes pod operator fetch xcom fails

### Apache Airflow version 2.3.0 (latest released) ### What happened Airflow kubernetes pod operator load xcom fails def _exec_pod_command(self, resp, command: str) -> Optional[str]: if resp.is_open(): self.log.info('Running command... %s\n', command) resp.write_stdin(command + '\n') while resp.is_open(): resp.update(timeout=1) if resp.peek_stdout(): return resp.read_stdout() if resp.peek_stderr(): self.log.info("stderr from command: %s", resp.read_stderr()) break return None _exec_pod_command read only peek stdout doesn't read full response.This content is loaded as json file json. loads function which causes system break with error "unterminated string" ### What you think should happen instead It should not read partial content ### How to reproduce When json size is larger ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/23396

https://github.com/apache/airflow/pull/23490

b0406f58f0c51db46d2da7c7c84a0b5c3d4f09ae

faae9faae396610086d5ea18d61c356a78a3d365

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "tests/jobs/test_scheduler_job.py"]

Scheduler crashes with psycopg2.errors.DeadlockDetected exception

### Apache Airflow version 2.2.5 (latest released) ### What happened Customer has a dag that generates around 2500 tasks dynamically using a task group. While running the dag, a subset of the tasks (~1000) run successfully with no issue and (~1500) of the tasks are getting "skipped", and the dag fails. The same DAG runs successfully in Airflow v2.1.3 with same Airflow configuration. While investigating the Airflow processes, We found that both the scheduler got restarted with below error during the DAG execution. ``` [2022-04-27 20:42:44,347] {scheduler_job.py:742} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1256, in _execute_context self.dialect.do_executemany( File "/usr/local/lib/python3.9/site-packages/sqlalchemy/dialects/postgresql/psycopg2.py", line 912, in do_executemany cursor.executemany(statement, parameters) psycopg2.errors.DeadlockDetected: deadlock detected DETAIL: Process 1646244 waits for ShareLock on transaction 3915993452; blocked by process 1640692. Process 1640692 waits for ShareLock on transaction 3915992745; blocked by process 1646244. HINT: See server log for query details. CONTEXT: while updating tuple (189873,4) in relation "task_instance" ``` This issue seems to be related to #19957 ### What you think should happen instead This issue was observed while running huge number of concurrent task created dynamically by a DAG. Some of the tasks are getting skipped due to restart of scheduler with Deadlock exception. ### How to reproduce DAG file: ``` from propmix_listings_details import BUCKET, ZIPS_FOLDER, CITIES_ZIP_COL_NAME, DETAILS_DEV_LIMIT, DETAILS_RETRY, DETAILS_CONCURRENCY, get_api_token, get_values, process_listing_ids_based_zip from airflow.utils.task_group import TaskGroup from airflow import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators.python_operator import PythonOperator from datetime import datetime, timedelta default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email_on_failure': False, 'email_on_retry': False, 'retries': 0, } date = '{{ execution_date }}' email_to = ['example@airflow.com'] # Using a DAG context manager, you don't have to specify the dag property of each task state = 'Maha' with DAG('listings_details_generator_{0}'.format(state), start_date=datetime(2021, 11, 18), schedule_interval=None, max_active_runs=1, concurrency=DETAILS_CONCURRENCY, dagrun_timeout=timedelta(minutes=10), catchup=False # enable if you don't want historical dag runs to run ) as dag: t0 = DummyOperator(task_id='start') with TaskGroup(group_id='group_1') as tg1: token = get_api_token() zip_list = get_values(BUCKET, ZIPS_FOLDER+state, CITIES_ZIP_COL_NAME) for zip in zip_list[0:DETAILS_DEV_LIMIT]: details_operator = PythonOperator( task_id='details_{0}_{1}'.format(state, zip), # task id is generated dynamically pool='pm_details_pool', python_callable=process_listing_ids_based_zip, task_concurrency=40, retries=3, retry_delay=timedelta(seconds=10), op_kwargs={'zip': zip, 'date': date, 'token':token, 'state':state} ) t0 >> tg1 ``` ### Operating System kubernetes cluster running on GCP linux (amd64) ### Versions of Apache Airflow Providers pip freeze | grep apache-airflow-providers apache-airflow-providers-amazon==1!3.2.0 apache-airflow-providers-cncf-kubernetes==1!3.0.0 apache-airflow-providers-elasticsearch==1!2.2.0 apache-airflow-providers-ftp==1!2.1.2 apache-airflow-providers-google==1!6.7.0 apache-airflow-providers-http==1!2.1.2 apache-airflow-providers-imap==1!2.2.3 apache-airflow-providers-microsoft-azure==1!3.7.2 apache-airflow-providers-mysql==1!2.2.3 apache-airflow-providers-postgres==1!4.1.0 apache-airflow-providers-redis==1!2.0.4 apache-airflow-providers-slack==1!4.2.3 apache-airflow-providers-snowflake==2.6.0 apache-airflow-providers-sqlite==1!2.1.3 apache-airflow-providers-ssh==1!2.4.3 ### Deployment Astronomer ### Deployment details Airflow v2.2.5-2 Scheduler count: 2 Scheduler resources: 20AU (2CPU and 7.5GB) Executor used: Celery Worker count : 2 Worker resources: 24AU (2.4 CPU and 9GB) Termination grace period : 2mins ### Anything else This issue happens in all the dag runs. Some of the tasks are getting skipped and some are getting succeeded and the scheduler fails with the Deadlock exception error. ### 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/23361

https://github.com/apache/airflow/pull/25312

741c20770230c83a95f74fe7ad7cc9f95329f2cc

be2b53eaaf6fc136db8f3fa3edd797a6c529409a

python

closed

apache/airflow

https://github.com/apache/airflow

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

Tasks set to queued by a backfill get cleared and rescheduled by the kubernetes executor, breaking the backfill

### Apache Airflow version 2.2.5 (latest released) ### What happened A backfill launched from the scheduler pod, queues tasks as it should but while they are in the process of starting the kubernentes executor loop running in the scheduler clears these tasks and reschedules them via this function https://github.com/apache/airflow/blob/9449a107f092f2f6cfa9c8bbcf5fd62fadfa01be/airflow/executors/kubernetes_executor.py#L444 This causes the backfill to not queue any more tasks and enters an endless loop of waiting for the task it has queued to complete. The way I have mitigated this is to set the `AIRFLOW__KUBERNETES__WORKER_PODS_QUEUED_CHECK_INTERVAL` to 3600, which is not ideal ### What you think should happen instead The function clear_not_launched_queued_tasks should respect tasks launched by a backfill process and not clear them. ### How to reproduce start a backfill with large number of tasks and watch as they get queued and then subsequently rescheduled by the kubernetes executor running in the scheduler pod ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers ``` apache-airflow 2.2.5 py38h578d9bd_0 apache-airflow-providers-cncf-kubernetes 3.0.2 pyhd8ed1ab_0 apache-airflow-providers-docker 2.4.1 pyhd8ed1ab_0 apache-airflow-providers-ftp 2.1.2 pyhd8ed1ab_0 apache-airflow-providers-http 2.1.2 pyhd8ed1ab_0 apache-airflow-providers-imap 2.2.3 pyhd8ed1ab_0 apache-airflow-providers-postgres 3.0.0 pyhd8ed1ab_0 apache-airflow-providers-sqlite 2.1.3 pyhd8ed1ab_0 ``` ### Deployment Other 3rd-party Helm chart ### Deployment details Deployment is running the latest helm chart of Airflow Community Edition ### 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/23356

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

49cfb6498eed0acfc336a24fd827b69156d5e5bb

640d4f9636d3867d66af2478bca15272811329da

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/cluster_policies/__init__.py", "tests/dags_corrupted/test_nonstring_owner.py", "tests/models/test_dagbag.py"]

Silent DAG import error by making owner a list

### Apache Airflow version 2.2.5 (latest released) ### What happened If the argument `owner` is unhashable, such as a list, the DAG will fail to be imported, but will also not report as an import error. If the DAG is new, it will simply be missing. If this is an update to the existing DAG, the webserver will continue to show the old version. ### What you think should happen instead A DAG import error should be raised. ### How to reproduce Set the `owner` argument for a task to be a list. See this minimal reproduction DAG. ``` from datetime import datetime from airflow.decorators import dag, task @dag( schedule_interval="@daily", start_date=datetime(2021, 1, 1), catchup=False, default_args={"owner": ["person"]}, tags=['example']) def demo_bad_owner(): @task() def say_hello(): print("hello") demo_bad_owner() ``` ### Operating System Debian Bullseye ### Versions of Apache Airflow Providers None needed. ### Deployment Astronomer ### Deployment details _No response_ ### Anything else The worker appears to still be able to execute the tasks when updating an existing DAG. Not sure how that's possible. Also reproduced on 2.3.0rc2. ### 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/23343

https://github.com/apache/airflow/pull/23359

9a0080c20bb2c4a9c0f6ccf1ece79bde895688ac

c4887bcb162aab9f381e49cecc2f212600c493de

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSTransformOperator: provide Jinja templating in source and destination object names

### Description Provide an option to receive the source_object and destination_object via Jinja params. ### Use case/motivation Usecase: Need to execute a DAG to fetch a video from GCS bucket based on paramater and then transform it and store it back. ### 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/23327

https://github.com/apache/airflow/pull/23328

505af06303d8160c71f6a7abe4792746f640083d

c82b3b94660a38360f61d47676ed180a0d32c189

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/dot_renderer.py", "tests/utils/test_dot_renderer.py"]

`airflow dags show` Exception: "The node ... should be TaskGroup and is not"

### Apache Airflow version main (development) ### What happened This happens for any dag with a task expansion. For instance: ```python from datetime import datetime from airflow import DAG from airflow.operators.bash import BashOperator with DAG( dag_id="simple_mapped", start_date=datetime(1970, 1, 1), schedule_interval=None, ) as dag: BashOperator.partial(task_id="hello_world").expand( bash_command=["echo hello", "echo world"] ) ``` I ran `airflow dags show simple_mapped` and instead of graphviz DOT notation, I saw this: ``` {dagbag.py:507} INFO - Filling up the DagBag from /Users/matt/2022/04/27/dags Traceback (most recent call last): File .../bin/airflow", line 8, in <module> sys.exit(main()) File ... lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File ... lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File ... lib/python3.9/site-packages/airflow/cli/commands/dag_command.py", line 205, in dag_show dot = render_dag(dag) File ... lib/python3.9/site-packages/airflow/utils/dot_renderer.py", line 188, in render_dag _draw_nodes(dag.task_group, dot, states_by_task_id) File ... lib/python3.9/site-packages/airflow/utils/dot_renderer.py", line 125, in _draw_nodes _draw_task_group(node, parent_graph, states_by_task_id) File ... lib/python3.9/site-packages/airflow/utils/dot_renderer.py", line 110, in _draw_task_group _draw_nodes(child, parent_graph, states_by_task_id) File ... lib/python3.9/site-packages/airflow/utils/dot_renderer.py", line 121, in _draw_nodes raise AirflowException(f"The node {node} should be TaskGroup and is not") airflow.exceptions.AirflowException: The node <Mapped(BashOperator): hello_world> should be TaskGroup and is not ``` ### What you think should happen instead I should see something about the dag structure. ### How to reproduce run `airflow dags show` for any dag with a task expansion ### Operating System MacOS, venv ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details ``` ❯ airflow version 2.3.0.dev0 ``` cloned at 4f6fe727a ### Anything else There's a related card on this board https://github.com/apache/airflow/projects/12 > Support Mapped task groups in the DAG "dot renderer" (i.e. backfill job with --show-dagrun) But I don't think that functionality is making it into 2.3.0, so maybe we need to add a fix here in the meantime? ### 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/23315

https://github.com/apache/airflow/pull/23339

d3028e1e9036a3c67ec4477eee6cd203c12f7f5c

59e93106d55881163a93dac4a5289df1ba6e1db5

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/helm-chart/production-guide.rst"]

Helm chart production guide fails to inform resultBackendSecretName parameter should be used

### What do you see as an issue? The [production guide](https://airflow.apache.org/docs/helm-chart/stable/production-guide.html) indicates that the code below is what is necessary for deploying with secrets. But `resultBackendSecretName` should also be filled, or Airflow wont start. ``` data: metadataSecretName: mydatabase ``` In addition to that, the expected URL is different in both variables. `resultBackendSecretName` expects a url that starts with `db+postgresql://`, while `metadataSecretName` expects `postgresql://` or `postgres://` and wont work with `db+postgresql://`. To solve this, it might be necessary to create multiple secrets. Just in case this is relevant, I'm using CeleryKubernetesExecutor. ### Solving the problem Docs should warn about the issue above. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23306

https://github.com/apache/airflow/pull/23307

3977e1798d8294ba628b5f330f43702c1a5c79fc

48915bd149bd8b58853880d63b8c6415688479ec

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/ISSUE_TEMPLATE/airflow_doc_issue_report.yml", "README.md"]

Fix typos in README.md and airflow_doc_issue_report.yml

### What do you see as an issue? Just found small typos as below: 1) Missing a period symbol right after the sentence - File Location: README.md - Simply added a period at the end of the sentence: "...it is effectively removed when we release the first new MINOR (Or MAJOR if there is no new MINOR version) of Airflow." 2) Typo in Airflow Doc issue report - File Location: .github/ISSUE_TEMPLATE/airflow_doc_issue_report.yml - Changed "eequest" to "request" ### Solving the problem Simply fix them as explained above and will make a PR for this! ### 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/23293

https://github.com/apache/airflow/pull/23294

97ad3dbab59407fde97367fe7c0c4602c1d3452f

c26796e31a9543cd8b45b50264128ac17455002c

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCP Composer v1.18.6 and 2.0.10 incompatible with CloudSqlProxyRunner

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers 6.6.0 or above ### Apache Airflow version 2.2.3 ### Operating System n/a ### Deployment Composer ### Deployment details _No response_ ### What happened Hi! A [user on StackOverflow](https://stackoverflow.com/questions/71975635/gcp-composer-v1-18-6-and-2-0-10-incompatible-with-cloudsqlproxyrunner ) and some Cloud SQL engineers at Google noticed that the CloudSQLProxyRunner was broken by [this commit](https://github.com/apache/airflow/pull/22127/files#diff-5992ce7fff93c23c57833df9ef892e11a023494341b80a9fefa8401f91988942L454) ### What you think should happen instead Ideally DAGs should continue to work as they did before ### How to reproduce Make a DAG that connects to Cloud SQL using the CloudSQLProxyRunner in Composer 1.18.6 or above using the google providers 6.6.0 or above and see a 404 ### 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/23292

https://github.com/apache/airflow/pull/23299

0c9c1cf94acc6fb315a9bc6f5bf1fbd4e4b4c923

1f3260354988b304cf31d5e1d945ce91798bed48

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskmixin.py", "airflow/utils/edgemodifier.py", "airflow/utils/task_group.py", "tests/utils/test_edgemodifier.py"]

Cycle incorrectly detected in DAGs when using Labels within Task Groups

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened When attempting to create a DAG containing Task Groups and in those Task Groups there are Labels between nodes, the DAG fails to import due to cycle detection. Consider this DAG: ```python from pendulum import datetime from airflow.decorators import dag, task, task_group from airflow.utils.edgemodifier import Label @task def begin(): ... @task def end(): ... @dag(start_date=datetime(2022, 1, 1), schedule_interval=None) def task_groups_with_edge_labels(): @task_group def group(): begin() >> Label("label") >> end() group() _ = task_groups_with_edge_labels() ``` When attempting to import the DAG, this error message is displayed: <img width="1395" alt="image" src="https://user-images.githubusercontent.com/48934154/165566299-3dd65cff-5e36-47d3-a243-7bc33d4344d6.png"> This also occurs on the `main` branch as well. ### What you think should happen instead Users should be able to specify Labels between tasks within a Task Group. ### How to reproduce - Use the DAG mentioned above and try to import into an Airflow environment - Or, create a simple unit test of the following and execute said test. ```python def test_cycle_task_group_with_edge_labels(self): from airflow.models.baseoperator import chain from airflow.utils.task_group import TaskGroup from airflow.utils.edgemodifier import Label dag = DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) with dag: with TaskGroup(group_id="task_group") as task_group: op1 = EmptyOperator(task_id='A') op2 = EmptyOperator(task_id='B') op1 >> Label("label") >> op2 assert not check_cycle(dag) ``` A `AirflowDagCycleException` should be thrown: ``` tests/utils/test_dag_cycle.py::TestCycleTester::test_cycle_task_group_with_edge_labels FAILED [100%] =============================================================================================== FAILURES =============================================================================================== ________________________________________________________________________ TestCycleTester.test_cycle_task_group_with_edge_labels ________________________________________________________________________ self = <tests.utils.test_dag_cycle.TestCycleTester testMethod=test_cycle_task_group_with_edge_labels> def test_cycle_task_group_with_edge_labels(self): from airflow.models.baseoperator import chain from airflow.utils.task_group import TaskGroup from airflow.utils.edgemodifier import Label dag = DAG('dag', start_date=DEFAULT_DATE, default_args={'owner': 'owner1'}) with dag: with TaskGroup(group_id="task_group") as task_group: op1 = EmptyOperator(task_id='A') op2 = EmptyOperator(task_id='B') op1 >> Label("label") >> op2 > assert not check_cycle(dag) tests/utils/test_dag_cycle.py:168: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/utils/dag_cycle_tester.py:76: in check_cycle child_to_check = _check_adjacent_tasks(current_task_id, task) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ task_id = 'task_group.B', current_task = <Task(EmptyOperator): task_group.B> def _check_adjacent_tasks(task_id, current_task): """Returns first untraversed child task, else None if all tasks traversed.""" for adjacent_task in current_task.get_direct_relative_ids(): if visited[adjacent_task] == CYCLE_IN_PROGRESS: msg = f"Cycle detected in DAG. Faulty task: {task_id}" > raise AirflowDagCycleException(msg) E airflow.exceptions.AirflowDagCycleException: Cycle detected in DAG. Faulty task: task_group.B airflow/utils/dag_cycle_tester.py:62: AirflowDagCycleException ---------------------------------------------------------------------------------------- Captured stdout setup ----------------------------------------------------------------------------------------- ========================= AIRFLOW ========================== Home of the user: /root Airflow home /root/airflow Skipping initializing of the DB as it was initialized already. You can re-initialize the database by adding --with-db-init flag when running tests. ======================================================================================= short test summary info ======================================================================================== FAILED tests/utils/test_dag_cycle.py::TestCycleTester::test_cycle_task_group_with_edge_labels - airflow.exceptions.AirflowDagCycleException: Cycle detected in DAG. Faulty task: task_group.B ==================================================================================== 1 failed, 2 warnings in 1.08s ===================================================================================== ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers N/A ### Deployment Astronomer ### Deployment details This issue also occurs on the `main` branch using Breeze. ### Anything else Possibly related to #21404 When the Label is removed, no cycle is detected. ```python from pendulum import datetime from airflow.decorators import dag, task, task_group from airflow.utils.edgemodifier import Label @task def begin(): ... @task def end(): ... @dag(start_date=datetime(2022, 1, 1), schedule_interval=None) def task_groups_with_edge_labels(): @task_group def group(): begin() >> end() group() _ = task_groups_with_edge_labels() ``` <img width="1437" alt="image" src="https://user-images.githubusercontent.com/48934154/165566908-a521d685-a032-482e-9e6b-ef85f0743e64.png"> ### 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/23285

https://github.com/apache/airflow/pull/23291

726b27f86cf964924e5ee7b29a30aefe24dac45a

3182303ce50bda6d5d27a6ef4e19450fb4e47eea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/task_schema.py", "tests/api_connexion/endpoints/test_task_endpoint.py", "tests/api_connexion/schemas/test_task_schema.py"]

Get DAG tasks in REST API does not include is_mapped

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened The rest API endpoint for get [/dags/{dag_id}/tasks](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/get_tasks) does not include `is_mapped`. Example: `consumer` is mapped but I have no way to tell that from the API response: <img width="306" alt="Screen Shot 2022-04-27 at 11 35 54 AM" src="https://user-images.githubusercontent.com/4600967/165556420-f8ade6e6-e904-4be0-a759-5281ddc04cba.png"> <img width="672" alt="Screen Shot 2022-04-27 at 11 35 25 AM" src="https://user-images.githubusercontent.com/4600967/165556310-742ec23d-f5a8-4cae-bea1-d00fd6c6916f.png"> ### What you think should happen instead Someone should be able to know if a task from get /tasks is mapped or not. ### How to reproduce call get /tasks on a dag with mapped tasks. see there is no way to determine if it is mapped from the response body. ### Operating System Mac OSX ### 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/23284

https://github.com/apache/airflow/pull/23319

98ec8c6990347fda60cbad33db915dc21497b1f0

f3d80c2a0dce93b908d7c9de30c9cba673eb20d5

python

closed

apache/airflow

https://github.com/apache/airflow

["breeze-legacy"]

Breeze-legacy missing flag_build_docker_images

### Apache Airflow version main (development) ### What happened Running `./breeze-legacy` warns about a potential issue: ```shell ❯ ./breeze-legacy --help Good version of docker 20.10.13. ./breeze-legacy: line 1434: breeze::flag_build_docker_images: command not found ... ``` And sure enough, `flag_build_docker_images` is referenced but not defined anywhere: ```shell ❯ ag flag_build_docker_images breeze-legacy 1433:$(breeze::flag_build_docker_images) ``` And I believe that completely breaks `breeze-legacy`: ```shell ❯ ./breeze-legacy Good version of docker 20.10.13. ERROR: Allowed platform: [ ]. Passed: 'linux/amd64' Switch to supported value with --platform flag. ERROR: The previous step completed with error. Please take a look at output above ``` ### What you think should happen instead Breeze-legacy should still work. Bash functions should be defined if they are still in use. ### How to reproduce Pull `main` branch. Run `./breeze-legacy`. ### Operating System macOS 11.6.4 Big Sur (Intel) ### 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/23272

https://github.com/apache/airflow/pull/23276

1e87f51d163a8db7821d3a146c358879aff7ec0e

aee40f82ccec7651abe388d6a2cbac35f5f4c895

python

closed

apache/airflow

https://github.com/apache/airflow

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

wasb hook not using AZURE_CLIENT_ID environment variable as client_id for ManagedIdentityCredential

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers apache-airflow-providers-microsoft-azure==3.8.0 ### Apache Airflow version 2.2.4 ### Operating System Ubuntu 20.04.2 LTS ### Deployment Official Apache Airflow Helm Chart ### Deployment details Have deployed airflow using the official helm chart on aks cluster. ### What happened I have deployed apache airflow using the official helm chart on an AKS cluster. The pod has multiple user assigned identity assigned to it. i have set the AZURE_CLIENT_ID environment variable to the client id that i want to use for authentication. _Airflow connection:_ wasb_default = '{"login":"storageaccountname"}' **Env** AZURE_CLIENT_ID="user-managed-identity-client-id" _**code**_ ``` # suppress azure.core logs import logging logger = logging.getLogger("azure.core") logger.setLevel(logging.ERROR) from airflow.providers.microsoft.azure.hooks.wasb import WasbHook conn_id = 'wasb-default' hook = WasbHook(conn_id) for blob_name in hook.get_blobs_list("testcontainer"): print(blob_name) ``` **error** ``` azure.core.exceptions.ClientAuthenticationError: Unexpected content type "text/plain; charset=utf-8" Content: failed to get service principal token, error: adal: Refresh request failed. Status Code = '400'. Response body: {"error":"invalid_request","error_description":"Multiple user assigned identities exist, please specify the clientId / resourceId of the identity in the token request"} Endpoint http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https%3A%2F%2Fstorage.azure.com ``` **trace** ``` [2022-04-26 16:37:23,446] {environment.py:103} WARNING - Incomplete environment configuration. These variables are set: AZURE_CLIENT_ID [2022-04-26 16:37:23,446] {managed_identity.py:89} INFO - ManagedIdentityCredential will use IMDS [2022-04-26 16:37:23,605] {chained.py:84} INFO - DefaultAzureCredential acquired a token from ManagedIdentityCredential #Note: azure key vault azure.secrets.key_vault.AzureKeyVaultBackend uses DefaultAzureCredential to get the connection [2022-04-26 16:37:23,687] {base.py:68} INFO - Using connection ID 'wasb-default' for task execution. [2022-04-26 16:37:23,687] {managed_identity.py:89} INFO - ManagedIdentityCredential will use IMDS [2022-04-26 16:37:23,688] {wasb.py:155} INFO - Using managed identity as credential Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/policies/_universal.py", line 561, in deserialize_from_text return json.loads(data_as_str) File "/usr/local/lib/python3.10/json/__init__.py", line 346, in loads return _default_decoder.decode(s) File "/usr/local/lib/python3.10/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/usr/local/lib/python3.10/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_internal/managed_identity_client.py", line 51, in _process_response content = ContentDecodePolicy.deserialize_from_text( File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/policies/_universal.py", line 563, in deserialize_from_text raise DecodeError(message="JSON is invalid: {}".format(err), response=response, error=err) azure.core.exceptions.DecodeError: JSON is invalid: Expecting value: line 1 column 1 (char 0) The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_credentials/imds.py", line 97, in _request_token token = self._client.request_token(*scopes, headers={"Metadata": "true"}) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_internal/managed_identity_client.py", line 126, in request_token token = self._process_response(response, request_time) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_internal/managed_identity_client.py", line 59, in _process_response six.raise_from(ClientAuthenticationError(message=message, response=response.http_response), ex) File "<string>", line 3, in raise_from azure.core.exceptions.ClientAuthenticationError: Unexpected content type "text/plain; charset=utf-8" Content: failed to get service principal token, error: adal: Refresh request failed. Status Code = '400'. Response body: {"error":"invalid_request","error_description":"Multiple user assigned identities exist, please specify the clientId / resourceId of the identity in the token request"} Endpoint http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https%3A%2F%2Fstorage.azure.com The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/tmp/test.py", line 7, in <module> for blob_name in hook.get_blobs_list("test_container"): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/microsoft/azure/hooks/wasb.py", line 231, in get_blobs_list for blob in blobs: File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/paging.py", line 129, in __next__ return next(self._page_iterator) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/paging.py", line 76, in __next__ self._response = self._get_next(self.continuation_token) File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_list_blobs_helper.py", line 79, in _get_next_cb process_storage_error(error) File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_shared/response_handlers.py", line 89, in process_storage_error raise storage_error File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_list_blobs_helper.py", line 72, in _get_next_cb return self._command( File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_generated/operations/_container_operations.py", line 1572, in list_blob_hierarchy_segment pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 211, in run return first_node.send(pipeline_request) # type: ignore File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) [Previous line repeated 2 more times] File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/policies/_redirect.py", line 158, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py", line 515, in send raise err File "/home/airflow/.local/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py", line 489, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/_base.py", line 71, in send response = self.next.send(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/policies/_authentication.py", line 117, in send self.on_request(request) File "/home/airflow/.local/lib/python3.10/site-packages/azure/core/pipeline/policies/_authentication.py", line 94, in on_request self._token = self._credential.get_token(*self._scopes) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_internal/decorators.py", line 32, in wrapper token = fn(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_credentials/managed_identity.py", line 123, in get_token return self._credential.get_token(*scopes, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_internal/get_token_mixin.py", line 76, in get_token token = self._request_token(*scopes, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/azure/identity/_credentials/imds.py", line 111, in _request_token six.raise_from(ClientAuthenticationError(message=ex.message, response=ex.response), ex) File "<string>", line 3, in raise_from azure.core.exceptions.ClientAuthenticationError: Unexpected content type "text/plain; charset=utf-8" Content: failed to get service principal token, error: adal: Refresh request failed. Status Code = '400'. Response body: {"error":"invalid_request","error_description":"Multiple user assigned identities exist, please specify the clientId / resourceId of the identity in the token request"} Endpoint http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https%3A%2F%2Fstorage.azure.com ``` ### What you think should happen instead The wasb hook should be able to authenticate using the user identity specified in the AZURE_CLIENT_ID and list the blobs ### How to reproduce In an environment with multiple user assigned identity. ``` import logging logger = logging.getLogger("azure.core") logger.setLevel(logging.ERROR) from airflow.providers.microsoft.azure.hooks.wasb import WasbHook conn_id = 'wasb-default' hook = WasbHook(conn_id) for blob_name in hook.get_blobs_list("testcontainer"): print(blob_name) ``` ### Anything else the issue is caused because we are not passing client_id to ManagedIdentityCredential in [azure.hooks.wasb.WasbHook](https://github.com/apache/airflow/blob/1d875a45994540adef23ad6f638d78c9945ef873/airflow/providers/microsoft/azure/hooks/wasb.py#L153-L160) ``` if not credential: credential = ManagedIdentityCredential() self.log.info("Using managed identity as credential") return BlobServiceClient( account_url=f"https://{conn.login}.blob.core.windows.net/", credential=credential, **extra, ) ``` solution 1: instead of ManagedIdentityCredential use [Azure.identity.DefaultAzureCredential](https://github.com/Azure/azure-sdk-for-python/blob/aa35d07aebf062393f14d147da54f0342e6b94a8/sdk/identity/azure-identity/azure/identity/_credentials/default.py#L32) solution 2: pass the client id from env [as done in DefaultAzureCredential](https://github.com/Azure/azure-sdk-for-python/blob/aa35d07aebf062393f14d147da54f0342e6b94a8/sdk/identity/azure-identity/azure/identity/_credentials/default.py#L104-L106): `ManagedIdentityCredential(client_id=os.environ.get("AZURE_CLIENT_ID")` ### 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/23266

https://github.com/apache/airflow/pull/23394

fcfaa8307ac410283f1270a0df9e557570e5ffd3

8f181c10344bd319ac5f6aeb102ee3c06e1f1637

python

closed

apache/airflow

https://github.com/apache/airflow

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

Pool option does not work in backfill command

### Apache Airflow version 2.2.4 ### What happened Discussion Ref: https://github.com/apache/airflow/discussions/22201 Added the pool option to the backfill command, but only uses default_pool. The log appears as below, but if you check the Task Instance Details / List Pool UI, default_pool is used. ```-------------------------------------------------------------------------------- [2022-03-12, 20:03:44 KST] {taskinstance.py:1244} INFO - Starting attempt 1 of 1 [2022-03-12, 20:03:44 KST] {taskinstance.py:1245} INFO - -------------------------------------------------------------------------------- [2022-03-12, 20:03:44 KST] {taskinstance.py:1264} INFO - Executing <Task(BashOperator): runme_0> on 2022-03-05 00:00:00+00:00 [2022-03-12, 20:03:44 KST] {standard_task_runner.py:52} INFO - Started process 555 to run task [2022-03-12, 20:03:45 KST] {standard_task_runner.py:76} INFO - Running: ['***', 'tasks', 'run', 'example_bash_operator', 'runme_0', 'backfill__2022-03-05T00:00:00+00:00', '--job-id', '127', '--pool', 'backfill_pool', '--raw', '--subdir', '/home/***/.local/lib/python3.8/site-packages/***/example_dags/example_bash_operator.py', '--cfg-path', '/tmp/tmprhjr0bc_', '--error-file', '/tmp/tmpkew9ufim'] [2022-03-12, 20:03:45 KST] {standard_task_runner.py:77} INFO - Job 127: Subtask runme_0 [2022-03-12, 20:03:45 KST] {logging_mixin.py:109} INFO - Running <TaskInstance: example_bash_operator.runme_0 backfill__2022-03-05T00:00:00+00:00 [running]> on host 56d55382c860 [2022-03-12, 20:03:45 KST] {taskinstance.py:1429} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=*** AIRFLOW_CTX_DAG_ID=example_bash_operator AIRFLOW_CTX_TASK_ID=runme_0 AIRFLOW_CTX_EXECUTION_DATE=2022-03-05T00:00:00+00:00 AIRFLOW_CTX_DAG_RUN_ID=backfill__2022-03-05T00:00:00+00:00 [2022-03-12, 20:03:45 KST] {subprocess.py:62} INFO - Tmp dir root location: /tmp [2022-03-12, 20:03:45 KST] {subprocess.py:74} INFO - Running command: ['bash', '-c', 'echo "example_bash_operator__runme_0__20220305" && sleep 1'] [2022-03-12, 20:03:45 KST] {subprocess.py:85} INFO - Output: [2022-03-12, 20:03:46 KST] {subprocess.py:89} INFO - example_bash_operator__runme_0__20220305 [2022-03-12, 20:03:47 KST] {subprocess.py:93} INFO - Command exited with return code 0 [2022-03-12, 20:03:47 KST] {taskinstance.py:1272} INFO - Marking task as SUCCESS. dag_id=example_bash_operator, task_id=runme_0, execution_date=20220305T000000, start_date=20220312T110344, end_date=20220312T110347 [2022-03-12, 20:03:47 KST] {local_task_job.py:154} INFO - Task exited with return code 0 [2022-03-12, 20:03:47 KST] {local_task_job.py:264} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you think should happen instead The backfill task instance should use a slot in the backfill_pool. ### How to reproduce 1. Create a backfill_pool in UI. 2. Run the backfill command on the example dag. ``` $ docker exec -it airflow_airflow-scheduler_1 /bin/bash $ airflow dags backfill example_bash_operator -s 2022-03-05 -e 2022-03-06 \ --pool backfill_pool --reset-dagruns -y [2022-03-12 11:03:52,720] {backfill_job.py:386} INFO - [backfill progress] | finished run 0 of 2 | tasks waiting: 2 | succeeded: 8 | running: 2 | failed: 0 | skipped: 2 | deadlocked: 0 | not ready: 2 [2022-03-12 11:03:57,574] {dagrun.py:545} INFO - Marking run <DagRun example_bash_operator @ 2022-03-05T00:00:00+00:00: backfill__2022-03-05T00:00:00+00:00, externally triggered: False> successful [2022-03-12 11:03:57,575] {dagrun.py:590} INFO - DagRun Finished: dag_id=example_bash_operator, execution_date=2022-03-05T00:00:00+00:00, run_id=backfill__2022-03-05T00:00:00+00:00, run_start_date=2022-03-12 11:03:37.530158+00:00, run_end_date=2022-03-12 11:03:57.575869+00:00, run_duration=20.045711, state=success, external_trigger=False, run_type=backfill, data_interval_start=2022-03-05T00:00:00+00:00, data_interval_end=2022-03-06 00:00:00+00:00, dag_hash=None [2022-03-12 11:03:57,582] {dagrun.py:545} INFO - Marking run <DagRun example_bash_operator @ 2022-03-06T00:00:00+00:00: backfill__2022-03-06T00:00:00+00:00, externally triggered: False> successful [2022-03-12 11:03:57,583] {dagrun.py:590} INFO - DagRun Finished: dag_id=example_bash_operator, execution_date=2022-03-06T00:00:00+00:00, run_id=backfill__2022-03-06T00:00:00+00:00, run_start_date=2022-03-12 11:03:37.598927+00:00, run_end_date=2022-03-12 11:03:57.583295+00:00, run_duration=19.984368, state=success, external_trigger=False, run_type=backfill, data_interval_start=2022-03-06 00:00:00+00:00, data_interval_end=2022-03-07 00:00:00+00:00, dag_hash=None [2022-03-12 11:03:57,584] {backfill_job.py:386} INFO - [backfill progress] | finished run 2 of 2 | tasks waiting: 0 | succeeded: 10 | running: 0 | failed: 0 | skipped: 4 | deadlocked: 0 | not ready: 0 [2022-03-12 11:03:57,589] {backfill_job.py:851} INFO - Backfill done. Exiting. ``` ### Operating System MacOS BigSur, docker-compose ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details Follow the guide - [Running Airflow in Docker]. Use CeleryExecutor. https://airflow.apache.org/docs/apache-airflow/stable/start/docker.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/23249

https://github.com/apache/airflow/pull/23258

511d0ee256b819690ccf0f6b30d12340b1dd7f0a

3970ea386d5e0a371143ad1e69b897fd1262842d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add api call for changing task instance status

### Description In the UI you can change the status of a task instance, but there is no API call available for the same feature. It would be nice to have an api call for this as well. ### Use case/motivation I found a solution on stack-overflow on [How to add manual tasks in an Apache Airflow Dag]. There is a suggestion to set a task on failed and change it manually to succeed when the task is done. Our project has many manual tasks. This suggestions seems like a good option, but there is no api call yet to call instead of change all status manually. I would like to use an api call for this instead. You can change the status of on a dag run so it also seems natural to have something similar for task instances. ### 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/23246

https://github.com/apache/airflow/pull/26165

5c37b503f118b8ad2585dff9949dd8fdb96689ed

1e6f1d54c54e5dc50078216e23ba01560ebb133c

python

closed

apache/airflow

https://github.com/apache/airflow

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

Ability to clear a specific DAG Run's task instances via REST APIs

### Discussed in https://github.com/apache/airflow/discussions/23220 <div type='discussions-op-text'> ^{Originally posted by **yashk97** April 25, 2022} Hi, My use case is in case multiple DAG Runs fail on some task (not the same one in all of them), I want to individually re-trigger each of these DAG Runs. Currently, I have to rely on the Airflow UI (attached screenshots) where I select the failed task and clear its state (along with the downstream tasks) to re-run from that point. While this works, it becomes tedious if the number of failed DAG runs is huge. I checked the REST API Documentation and came across the clear Task Instances API with the following URL: /api/v1/dags/{dag_id}/clearTaskInstances However, it filters task instances of the specified DAG in a given date range. I was wondering if, for a specified DAG Run, we can clear a task along with its downstream tasks irrespective of the states of the tasks or the DAG run through REST API. This will give us more granular control over re-running DAGs from the point of failure.   </div>

https://github.com/apache/airflow/issues/23227

https://github.com/apache/airflow/pull/23516

3221ed5968423ea7a0dc7e1a4b51084351c2d56b

eceb4cc5888a7cf86a9250fff001fede2d6aba0f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/utils.py", "airflow/migrations/versions/0110_2_3_2_add_cascade_to_dag_tag_foreignkey.py", "airflow/models/dag.py", "docs/apache-airflow/migrations-ref.rst"]

UI shows Foreign Key Error when deleting a dag

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened I tried to delete a dag from the grid view, and I saw this instead ``` Ooops! Something bad has happened. ... Python version: 3.7.13 Airflow version: 2.3.0b1 Node: airflow-webserver-7c4f49f5dd-h74w2 ------------------------------------------------------------------------------- Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1706, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) psycopg2.errors.ForeignKeyViolation: update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" DETAIL: Key (dag_id)=(core_todo) is still referenced from table "dag_tag". The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/home/airflow/.local/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise raise value File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/auth.py", line 40, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/decorators.py", line 80, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/views.py", line 1812, in delete delete_dag.delete_dag(dag_id) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/api/common/delete_dag.py", line 80, in delete_dag .delete(synchronize_session='fetch') File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3111, in delete execution_options={"synchronize_session": synchronize_session}, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 1670, in execute result = conn._execute_20(statement, params or {}, execution_options) File "/home/airflow/.local/lib/python3.7/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/python3.7/site-packages/sqlalchemy/sql/elements.py", line 314, in _execute_on_connection self, multiparams, params, execution_options File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1399, in _execute_clauseelement cache_hit=cache_hit, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1749, in _execute_context e, statement, parameters, cursor, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1930, in _handle_dbapi_exception sqlalchemy_exception, with_traceback=exc_info[2], from_=e File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1706, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 716, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.IntegrityError: (psycopg2.errors.ForeignKeyViolation) update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" DETAIL: Key (dag_id)=(core_todo) is still referenced from table "dag_tag". [SQL: DELETE FROM dag WHERE dag.dag_id IN (%(dag_id_1_1)s) RETURNING dag.dag_id] [parameters: {'dag_id_1_1': 'core_todo'}] (Background on this error at: http://sqlalche.me/e/14/gkpj) ``` Also, here are the database pod logs: ``` │ 2022-04-25 01:42:14.185 GMT [155] STATEMENT: INSERT INTO log (dttm, dag_id, task_id, map_index, event, execution_date, owner, extra) VALUES ('2022-04-25T01:42:14.178085+00:00'::timestamptz, NULL, NULL, NULL, 'cli_upgradedb', NULL, ' │ │ 2022-04-25 01:42:14.371 GMT [155] ERROR: relation "connection" does not exist at character 55 │ │ 2022-04-25 01:42:14.371 GMT [155] STATEMENT: SELECT connection.conn_id AS connection_conn_id │ │ FROM connection GROUP BY connection.conn_id │ │ HAVING count(*) > 1 │ │ 2022-04-25 01:42:14.372 GMT [155] ERROR: relation "connection" does not exist at character 55 │ │ 2022-04-25 01:42:14.372 GMT [155] STATEMENT: SELECT connection.conn_id AS connection_conn_id │ │ FROM connection │ │ WHERE connection.conn_type IS NULL │ │ 2022-04-25 01:42:16.489 GMT [158] ERROR: relation "log" does not exist at character 13 │ │ 2022-04-25 01:42:16.489 GMT [158] STATEMENT: INSERT INTO log (dttm, dag_id, task_id, map_index, event, execution_date, owner, extra) VALUES ('2022-04-25T01:42:16.482543+00:00'::timestamptz, NULL, NULL, NULL, 'cli_check', NULL, 'airf │ │ 2022-04-25 01:42:17.917 GMT [160] ERROR: column "map_index" of relation "log" does not exist at character 41 │ │ 2022-04-25 01:42:17.917 GMT [160] STATEMENT: INSERT INTO log (dttm, dag_id, task_id, map_index, event, execution_date, owner, extra) VALUES ('2022-04-25T01:42:17.910396+00:00'::timestamptz, NULL, NULL, NULL, 'cli_flower', NULL, 'air │ │ 2022-04-25 03:18:33.631 GMT [24494] ERROR: update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" │ │ 2022-04-25 03:18:33.631 GMT [24494] DETAIL: Key (dag_id)=(core_todo) is still referenced from table "dag_tag". │ │ 2022-04-25 03:18:33.631 GMT [24494] STATEMENT: DELETE FROM dag WHERE dag.dag_id IN ('core_todo') RETURNING dag.dag_id │ │ 2022-04-25 03:31:18.858 GMT [24760] ERROR: update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" │ │ 2022-04-25 03:31:18.858 GMT [24760] DETAIL: Key (dag_id)=(core_todo) is still referenced from table "dag_tag". │ │ 2022-04-25 03:31:18.858 GMT [24760] STATEMENT: DELETE FROM dag WHERE dag.dag_id IN ('core_todo') RETURNING dag.dag_id ``` ### What you think should happen instead The dag gets deleted, no error ### How to reproduce I'm not sure if I can replicate it, but I'll report back here if I can. So far as I remember the steps were: 1. run a (large) dag 2. the dag failed for unrelated reasons 3. delete the dag from the grid view 4. see error page ### Operating System kubernetes/debian ### Versions of Apache Airflow Providers n/a ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deployed via helm into a microk8s cluster, which was running in a VM, which was deployed by CircleCI. ### 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/23206

https://github.com/apache/airflow/pull/23444

e2401329345dcc5effa933b92ca969b8779755e4

8ccff9244a6d1a936d8732721373b967e95ec404

python

closed

apache/airflow

https://github.com/apache/airflow

["CONTRIBUTORS_QUICK_START.rst", "CONTRIBUTORS_QUICK_START_CODESPACES.rst", "CONTRIBUTORS_QUICK_START_GITPOD.rst", "CONTRIBUTORS_QUICK_START_PYCHARM.rst", "CONTRIBUTORS_QUICK_START_VSCODE.rst"]

Some links in contributor's quickstart table of contents are broken

### What do you see as an issue? In `CONTRIBUTORS_QUICK_START.rst`, the links in the table of contents that direct users to parts of the guide that are hidden by the drop down don't work if the drop down isn't expanded. For example, clicking "[Setup Airflow with Breeze](https://github.com/apache/airflow/blob/main/CONTRIBUTORS_QUICK_START.rst#setup-airflow-with-breeze)" does nothing until you open the appropriate drop down `Setup and develop using <PyCharm, Visual Studio Code, Gitpod>` ### Solving the problem Instead of having the entire documentation blocks under `Setup and develop using {method}` dropdowns, there could be drop downs under each section so that the guide remains concise without sacrificing the functionality of the table of contents. ### Anything else I'm happy to submit a PR eventually, but I might not be able to get around to it for a bit if anyone else wants to handle it real quick. ### 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/23174

https://github.com/apache/airflow/pull/23762

e08b59da48743ff0d0ce145d1bc06bb7b5f86e68

1bf6dded9a5dcc22238b8943028b08741e36dfe5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/mark_tasks.py", "airflow/models/dag.py", "tests/models/test_dag.py", "tests/test_utils/mapping.py"]

Mark Success on a mapped task, reruns other failing mapped tasks

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened Have a DAG with mapped tasks. Mark at least two mapped tasks as failed. Mark one of the failures as success. See the other task(s) switch to `no_status` and rerun.  ### What you think should happen instead Marking a single mapped task as a success probably shouldn't affect other failed mapped tasks. ### How to reproduce _No response_ ### Operating System OSX ### 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/23171

https://github.com/apache/airflow/pull/23177

d262a72ca7ab75df336b93cefa338e7ba3f90ebb

26a9ec65816e3ec7542d63ab4a2a494931a06c9b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/schemas/connection_schema.py", "tests/api_connexion/endpoints/test_connection_endpoint.py"]

Getting error "Extra Field may not be null" while hitting create connection api with extra=null

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened Getting error "Extra Field may not be null" while hitting create connection api with extra=null ``` { "detail": "{'extra': ['Field may not be null.']}", "status": 400, "title": "Bad Request", "type": "http://apache-airflow-docs.s3-website.eu-central-1.amazonaws.com/docs/apache-airflow/latest/stable-rest-api-ref.html#section/Errors/BadRequest" } ``` ### What you think should happen instead I should be able to create connection through API ### How to reproduce Steps to reproduce: 1. Hit connection end point with json body Api Endpoint - api/v1/connections HTTP Method - Post Json Body - ``` { "connection_id": "string6", "conn_type": "string", "host": "string", "login": null, "schema": null, "port": null, "password": "pa$$word", "extra":null } ``` ### Operating System debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details Astro dev start ### Anything else As per code I am assuming it may be null. ``` Connection: description: Full representation of the connection. allOf: - $ref: '#/components/schemas/ConnectionCollectionItem' - type: object properties: password: type: string format: password writeOnly: true description: Password of the connection. extra: type: string nullable: true description: Other values that cannot be put into another field, e.g. RSA keys. ``` ### 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/23168

https://github.com/apache/airflow/pull/23183

b33cd10941dd10d461023df5c2d3014f5dcbb7ac

b45240ad21ca750106931ba2b882b3238ef2b37d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_gcs.py", "tests/providers/google/cloud/transfers/test_gcs_to_gcs.py"]

GCSToGCSOperator ignores replace parameter when there is no wildcard

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers Latest ### Apache Airflow version 2.2.5 (latest released) ### Operating System MacOS 12.2.1 ### Deployment Composer ### Deployment details _No response_ ### What happened Ran the same DAG twice with 'replace = False', in the second run files are overwritten anyway. source_object does not include wildcard. Not sure whether this incorrect behavior happens to "with wildcard" scenario, but from source code https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/gcs_to_gcs.py in line 346 (inside _copy_source_with_wildcard) we have if not self.replace: but in _copy_source_without_wildcard we don't check self.replace at all. ### What you think should happen instead When 'replace = False', the second run should skip copying files since they are already there. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23162

https://github.com/apache/airflow/pull/23340

03718194f4fa509f16fcaf3d41ff186dbae5d427

82c244f9c7f24735ee952951bcb5add45422d186

python

closed

apache/airflow

https://github.com/apache/airflow

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

docker container still running while dag run failed

### Apache Airflow version 2.1.4 ### What happened I have operator run with docker . When dag run failed , docker.py try to remove container but remove failed and got the following error: `2022-04-20 00:03:50,381] {taskinstance.py:1463} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/docker/operators/docker.py", line 301, in _run_image_with_mounts for line in lines: File "/home/airflow/.local/lib/python3.8/site-packages/docker/types/daemon.py", line 32, in __next__ return next(self._stream) File "/home/airflow/.local/lib/python3.8/site-packages/docker/api/client.py", line 412, in <genexpr> gen = (data for (_, data) in gen) File "/home/airflow/.local/lib/python3.8/site-packages/docker/utils/socket.py", line 92, in frames_iter_no_tty (stream, n) = next_frame_header(socket) File "/home/airflow/.local/lib/python3.8/site-packages/docker/utils/socket.py", line 64, in next_frame_header data = read_exactly(socket, 8) File "/home/airflow/.local/lib/python3.8/site-packages/docker/utils/socket.py", line 49, in read_exactly next_data = read(socket, n - len(data)) File "/home/airflow/.local/lib/python3.8/site-packages/docker/utils/socket.py", line 29, in read select.select([socket], [], []) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1238, in signal_handler raise AirflowException("Task received SIGTERM signal") airflow.exceptions.AirflowException: Task received SIGTERM signal During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/docker/api/client.py", line 268, in _raise_for_status response.raise_for_status() File "/home/airflow/.local/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 409 Client Error: Conflict for url: http+docker://localhost/v1.35/containers/de4cd812f8b0dcc448d591d1bd28fa736b1712237c8c8848919be512938bd515?v=False&link=False&force=False During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1165, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1283, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1313, in _execute_task result = task_copy.execute(context=context) File "/usr/local/airflow/dags/operators/byx_base_operator.py", line 611, in execute raise e File "/usr/local/airflow/dags/operators/byx_base_operator.py", line 591, in execute self.execute_job(context) File "/usr/local/airflow/dags/operators/byx_datax_operator.py", line 93, in execute_job result = call_datax.execute(context) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/docker/operators/docker.py", line 343, in execute return self._run_image() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/docker/operators/docker.py", line 265, in _run_image return self._run_image_with_mounts(self.mounts, add_tmp_variable=False) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/docker/operators/docker.py", line 317, in _run_image_with_mounts self.cli.remove_container(self.container['Id']) File "/home/airflow/.local/lib/python3.8/site-packages/docker/utils/decorators.py", line 19, in wrapped return f(self, resource_id, *args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/docker/api/container.py", line 1010, in remove_container self._raise_for_status(res) File "/home/airflow/.local/lib/python3.8/site-packages/docker/api/client.py", line 270, in _raise_for_status raise create_api_error_from_http_exception(e) File "/home/airflow/.local/lib/python3.8/site-packages/docker/errors.py", line 31, in create_api_error_from_http_exception raise cls(e, response=response, explanation=explanation) docker.errors.APIError: 409 Client Error for http+docker://localhost/v1.35/containers/de4cd812f8b0dcc448d591d1bd28fa736b1712237c8c8848919be512938bd515?v=False&link=False&force=False: Conflict ("You cannot remove a running container de4cd812f8b0dcc448d591d1bd28fa736b1712237c8c8848919be512938bd515. Stop the container before attempting removal or force remove") ` ### What you think should happen instead the container should removed successful when dag run failed ### How to reproduce step 1: create a dag with execute DockerOperator operation step 2: trigger dag step 3: mark dag run to failed simulate dag run failed, and the remove container failed error will appear and the docker container still running. ### Operating System NAME="Amazon Linux" VERSION="2" ID="amzn" ID_LIKE="centos rhel fedora" VERSION_ID="2" PRETTY_NAME="Amazon Linux 2" ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23159

https://github.com/apache/airflow/pull/23160

5d5d62e41e93fe9845c96ab894047422761023d8

237d2225d6b92a5012a025ece93cd062382470ed

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/sensors/bigquery_dts.py", "tests/providers/google/cloud/sensors/test_bigquery_dts.py"]

location is missing in BigQueryDataTransferServiceTransferRunSensor

### Apache Airflow version 2.2.3 ### What happened Location is missing in [BigQueryDataTransferServiceTransferRunSensor](airflow/providers/google/cloud/sensors/bigquery_dts.py). This forces us to execute data transfers only in the us. When starting a transfer the location can be provided. ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Google Cloud Composer ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23146

https://github.com/apache/airflow/pull/23166

692a0899430f86d160577c3dd0f52644c4ffad37

967140e6c3bd0f359393e018bf27b7f2310a2fd9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Task stuck in "scheduled" when running in backfill job

### Apache Airflow version 2.2.4 ### What happened We are running airflow 2.2.4 with KubernetesExecutor. I have created a dag to run airflow backfill command with SubprocessHook. What was observed is that when I started to backfill a few days' dagruns the backfill would get stuck with some dag runs having tasks staying in the "scheduled" state and never getting running. We are using the default pool and the pool is totoally free when the tasks got stuck. I could find some logs saying: `TaskInstance: <TaskInstance: test_dag_2.task_1 backfill__2022-03-29T00:00:00+00:00 [queued]> found in queued state but was not launched, rescheduling` and nothing else in the log. ### What you think should happen instead The tasks stuck in "scheduled" should start running when there is free slot in the pool. ### How to reproduce Airflow 2.2.4 with python 3.8.13, KubernetesExecutor running in AWS EKS. One backfill command example is: `airflow dags backfill test_dag_2 -s 2022-03-01 -e 2022-03-10 --rerun-failed-tasks` The test_dag_2 dag is like: ``` 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': 1, '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="@daily", catchup=True, max_active_runs=1, ) as dag: t1 = BashOperator( task_id='task_1', depends_on_past=False, bash_command='sleep 30' ) t2 = PythonOperator( task_id='get_execution_date', python_callable=get_execution_date ) t1 >> t2 ``` ### Operating System Debian GNU/Linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.0.0 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-cncf-kubernetes==3.0.2 apache-airflow-providers-docker==2.4.1 apache-airflow-providers-elasticsearch==2.2.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==6.4.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.1 apache-airflow-providers-http==2.0.3 apache-airflow-providers-imap==2.2.0 apache-airflow-providers-microsoft-azure==3.6.0 apache-airflow-providers-microsoft-mssql==2.1.0 apache-airflow-providers-odbc==2.0.1 apache-airflow-providers-postgres==3.0.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.4.1 apache-airflow-providers-slack==4.2.0 apache-airflow-providers-snowflake==2.5.0 apache-airflow-providers-sqlite==2.1.0 apache-airflow-providers-ssh==2.4.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23145

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

49cfb6498eed0acfc336a24fd827b69156d5e5bb

640d4f9636d3867d66af2478bca15272811329da

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler deadlock error when mapping over empty list

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened manually triggered this dag: ```python from datetime import datetime from airflow import DAG with DAG( dag_id="null_mapped_2", start_date=datetime(1970, 1, 1), schedule_interval=None, ) as dag: @dag.task def empty(): return [] @dag.task def print_it(thing): print(thing) print_it.expand(thing=empty()) ``` scheduler logs (whitespace added for emphasis): ``` ____________ _____________ ____ |__( )_________ __/__ /________ __ ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ [2022-04-20 20:46:24,760] {scheduler_job.py:697} INFO - Starting the scheduler [2022-04-20 20:46:24,761] {scheduler_job.py:702} INFO - Processing each file at most -1 times [2022-04-20 20:46:24 +0000] [28] [INFO] Starting gunicorn 20.1.0 [2022-04-20 20:46:24 +0000] [28] [INFO] Listening at: http://0.0.0.0:8793 (28) [2022-04-20 20:46:24 +0000] [28] [INFO] Using worker: sync [2022-04-20 20:46:24 +0000] [29] [INFO] Booting worker with pid: 29 [2022-04-20 20:46:24,782] {executor_loader.py:106} INFO - Loaded executor: LocalExecutor [2022-04-20 20:46:24 +0000] [78] [INFO] Booting worker with pid: 78 [2022-04-20 20:46:24,953] {manager.py:156} INFO - Launched DagFileProcessorManager with pid: 166 [2022-04-20 20:46:24,962] {settings.py:55} INFO - Configured default timezone Timezone('UTC') /usr/local/lib/python3.9/site-packages/airflow/configuration.py:466 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. [2022-04-20 20:46:24,988] {scheduler_job.py:1231} INFO - Resetting orphaned tasks for active dag runs /usr/local/lib/python3.9/site-packages/airflow/configuration.py:466 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. [2022-04-20 20:46:32,124] {update_checks.py:128} INFO - Checking for new version of Astronomer Certified Airflow, previous check was performed at None [2022-04-20 20:46:32,441] {update_checks.py:84} INFO - Check finished, next check in 86400.0 seconds /usr/local/lib/python3.9/site-packages/airflow/configuration.py:466 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. [2022-04-20 20:47:45,296] {scheduler_job.py:354} INFO - 1 tasks up for execution: <TaskInstance: null_mapped_2.empty manual__2022-04-20T20:47:45.075321+00:00 [scheduled]> [2022-04-20 20:47:45,297] {scheduler_job.py:419} INFO - DAG null_mapped_2 has 0/16 running and queued tasks [2022-04-20 20:47:45,297] {scheduler_job.py:505} INFO - Setting the following tasks to queued state: <TaskInstance: null_mapped_2.empty manual__2022-04-20T20:47:45.075321+00:00 [scheduled]> [2022-04-20 20:47:45,300] {scheduler_job.py:547} INFO - Sending TaskInstanceKey(dag_id='null_mapped_2', task_id='empty', run_id='manual__2022-04-20T20:47:45.075321+00:00', try_number=1, map_index=-1) to executor with priority 2 and queue default [2022-04-20 20:47:45,300] {base_executor.py:88} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'null_mapped_2', 'empty', 'manual__2022-04-20T20:47:45.075321+00:00', '--local', '--subdir', 'DAGS_FOLDER/null_mapped_2.py'] [2022-04-20 20:47:45,303] {local_executor.py:79} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'null_mapped_2', 'empty', 'manual__2022-04-20T20:47:45.075321+00:00', '--local', '--subdir', 'DAGS_FOLDER/null_mapped_2.py'] [2022-04-20 20:47:45,340] {dagbag.py:507} INFO - Filling up the DagBag from /usr/local/airflow/dags/null_mapped_2.py /usr/local/lib/python3.9/site-packages/airflow/configuration.py:466 DeprecationWarning: The sql_alchemy_conn option in [core] has been moved to the sql_alchemy_conn option in [database] - the old setting has been used, but please update your config. [2022-04-20 20:47:45,432] {task_command.py:369} INFO - Running <TaskInstance: null_mapped_2.empty manual__2022-04-20T20:47:45.075321+00:00 [queued]> on host 7ec8e95b149d [2022-04-20 20:47:46,796] {dagrun.py:583} ERROR - Deadlock; marking run <DagRun null_mapped_2 @ 2022-04-20 20:47:45.075321+00:00: manual__2022-04-20T20:47:45.075321+00:00, externally triggered: True> failed [2022-04-20 20:47:46,797] {dagrun.py:607} INFO - DagRun Finished: dag_id=null_mapped_2, execution_date=2022-04-20 20:47:45.075321+00:00, run_id=manual__2022-04-20T20:47:45.075321+00:00, run_start_date=2022-04-20 20:47:45.254176+00:00, run_end_date=2022-04-20 20:47:46.797390+00:00, run_duration=1.543214, state=failed, external_trigger=True, run_type=manual, data_interval_start=2022-04-20 20:47:45.075321+00:00, data_interval_end=2022-04-20 20:47:45.075321+00:00, dag_hash=8476a887126cf6d52573ee41fa81c637 [2022-04-20 20:47:46,801] {dag.py:2894} INFO - Setting next_dagrun for null_mapped_2 to None, run_after=None [2022-04-20 20:47:46,818] {scheduler_job.py:600} INFO - Executor reports execution of null_mapped_2.empty run_id=manual__2022-04-20T20:47:45.075321+00:00 exited with status success for try_number 1 [2022-04-20 20:47:46,831] {scheduler_job.py:644} INFO - TaskInstance Finished: dag_id=null_mapped_2, task_id=empty, run_id=manual__2022-04-20T20:47:45.075321+00:00, map_index=-1, run_start_date=2022-04-20 20:47:45.491953+00:00, run_end_date=2022-04-20 20:47:45.760654+00:00, run_duration=0.268701, state=success, executor_state=success, try_number=1, max_tries=0, job_id=2, pool=default_pool, queue=default, priority_weight=2, operator=_PythonDecoratedOperator, queued_dttm=2022-04-20 20:47:45.297857+00:00, queued_by_job_id=1, pid=250 ``` The dagrun fails, even though none of its tasks get set to failed: <img width="319" alt="Screen Shot 2022-04-20 at 2 43 15 PM" src="https://user-images.githubusercontent.com/5834582/164320377-663fcc0a-0bc4-4edc-8cc3-91884b84748d.png"> ### What you think should happen instead Mapping over a 0 length list should create no map_index'ed tasks, but the parent task should succeed because none of the 0 tasks failed. ### How to reproduce Run the dag above ### Operating System debian (docker) ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astrocloud dev start`, image contains version 2.3.0.dev20220414 ### 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/23131

https://github.com/apache/airflow/pull/23134

af45483b95896033ba1937a2037a8e0a6db1bff0

03f7d857e940b9c719975e72ded4a89f183b0100

python

closed

apache/airflow

https://github.com/apache/airflow

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

SparkKubernetesSensor Cannot Attach Log When There Are Sidecars in the Driver Pod

### Apache Airflow Provider(s) cncf-kubernetes ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes==3.0.0 ### Apache Airflow version 2.2.5 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When using `SparkKubernetesSensor` with `attach_log=True`, it cannot get the log correctly with the below error: ``` [2022-04-20, 08:42:04 UTC] {spark_kubernetes.py:95} WARNING - Could not read logs for pod spark-pi-0.4753748373914717-1-driver. It may have been disposed. Make sure timeToLiveSeconds is set on your SparkApplication spec. underlying exception: (400) Reason: Bad Request HTTP response headers: HTTPHeaderDict({'Audit-Id': '29ac5abb-452d-4411-a420-8d74155e187d', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 20 Apr 2022 08:42:04 GMT', 'Content-Length': '259'}) HTTP response body: b'{"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"a container name must be specified for pod spark-pi-0.4753748373914717-1-driver, choose one of: [istio-init istio-proxy spark-kubernetes-driver]","reason":"BadRequest","code":400}\n' ``` It is because no container is specified when calling kubernetes hook.get_pod_logs https://github.com/apache/airflow/blob/501a3c3fbefbcc0d6071a00eb101110fc4733e08/airflow/providers/cncf/kubernetes/sensors/spark_kubernetes.py#L85 ### What you think should happen instead It should get the log of container `spark-kubernetes-driver` ### 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/23114

https://github.com/apache/airflow/pull/26560

923f1ef30e8f4c0df2845817b8f96373991ad3ce

5c97e5be484ff572070b0ad320c5936bc028be93

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/s3.py", "airflow/providers/amazon/aws/operators/s3.py", "airflow/providers/amazon/aws/sensors/s3.py", "airflow/providers/amazon/aws/transfers/local_to_s3.py", "tests/providers/amazon/aws/hooks/test_s3.py", "tests/providers/amazon/aws/operators/test_s3_object.py", "tests/providers/amazon/aws/sensors/test_s3_key.py", "tests/providers/amazon/aws/transfers/test_local_to_s3.py"]

LocalFilesystemToS3Operator dest_key can not be a full s3:// style url

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-mongo==2.3.3 apache-airflow-providers-sqlite==2.1.3 ### Apache Airflow version 2.3.0b1 (pre-release) ### Operating System Arch Linux ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened `LocalFilesystemToS3Operator` does not accept full s3:// style url as `dest_key`, although it states, that it should: ``` :param dest_key: The key of the object to copy to. (templated) It can be either full s3:// style url or relative path from root level. When it's specified as a full s3:// url, including dest_bucket results in a TypeError. ``` ### What you think should happen instead `LocalFilesystemToS3Operator` should behave as documented. ### How to reproduce A modification of an existing UT: ``` @mock_s3 def test_execute_with_only_key(self): conn = boto3.client('s3') conn.create_bucket(Bucket=self.dest_bucket) operator = LocalFilesystemToS3Operator( task_id='s3_to_file_sensor', dag=self.dag, filename=self.testfile1, dest_key=f's3://dummy/{self.dest_key}', **self._config, ) operator.execute(None) objects_in_dest_bucket = conn.list_objects(Bucket=self.dest_bucket, Prefix=self.dest_key) # there should be object found, and there should only be one object found assert len(objects_in_dest_bucket['Contents']) == 1 # the object found should be consistent with dest_key specified earlier assert objects_in_dest_bucket['Contents'][0]['Key'] == self.dest_key ``` `FAILED tests/providers/amazon/aws/transfers/test_local_to_s3.py::TestFileToS3Operator::test_execute_with_only_key - TypeError: expected string or bytes-like object` ### 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/23111

https://github.com/apache/airflow/pull/23180

e2c7847c6bf73685f0576364787fab906397a6fe

27a80511ec3ffcf036354741bd0bfe18d4b4a471

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/processor.py", "airflow/models/taskfail.py", "airflow/models/taskinstance.py", "tests/api/common/test_delete_dag.py", "tests/callbacks/test_callback_requests.py", "tests/jobs/test_scheduler_job.py"]

Mapped KubernetesPodOperator "fails" but UI shows it is as still running

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened This dag has a problem. The `name` kwarg is missing from one of the mapped instances. ```python3 from datetime import datetime from airflow import DAG from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import ( KubernetesPodOperator, ) from airflow.configuration import conf namespace = conf.get("kubernetes", "NAMESPACE") with DAG( dag_id="kpo_mapped", start_date=datetime(1970, 1, 1), schedule_interval=None, ) as dag: KubernetesPodOperator( task_id="cowsay_static_named", name="cowsay_statc", namespace=namespace, image="docker.io/rancher/cowsay", cmds=["cowsay"], arguments=["moo"], ) KubernetesPodOperator.partial( task_id="cowsay_mapped", # name="cowsay_mapped", # required field missing image="docker.io/rancher/cowsay", namespace=namespace, cmds=["cowsay"], ).expand(arguments=[["mooooove"], ["cow"], ["get out the way"]]) KubernetesPodOperator.partial( task_id="cowsay_mapped_named", name="cowsay_mapped", namespace=namespace, image="docker.io/rancher/cowsay", cmds=["cowsay"], ).expand(arguments=[["mooooove"], ["cow"], ["get out the way"]]) ``` If you omit that field in an unmapped task, you get a dag parse error, which is appropriate. But omitting it from the mapped task gives you this runtime error in the task logs: ``` [2022-04-20, 05:11:02 UTC] {standard_task_runner.py:52} INFO - Started process 60 to run task [2022-04-20, 05:11:02 UTC] {standard_task_runner.py:79} INFO - Running: ['airflow', 'tasks', 'run', 'kpo_mapped', 'cowsay_mapped', 'manual__2022-04-20T05:11:01+00:00', '--job-id', '12', '--raw', '--subdir', 'DAGS_FOLDER/dags/taskmap/kpo_mapped.py', '--cfg-path', '/tmp/tmp_g3sj496', '--map-index', '0', '--error-file', '/tmp/tmp2_313wxj'] [2022-04-20, 05:11:02 UTC] {standard_task_runner.py:80} INFO - Job 12: Subtask cowsay_mapped [2022-04-20, 05:11:02 UTC] {task_command.py:369} INFO - Running <TaskInstance: kpo_mapped.cowsay_mapped manual__2022-04-20T05:11:01+00:00 map_index=0 [running]> on host airflow-worker-65f9fd9d5b-vpgnk [2022-04-20, 05:11:02 UTC] {taskinstance.py:1863} WARNING - We expected to get frame set in local storage but it was not. Please report this as an issue with full logs at https://github.com/apache/airflow/issues/new Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1440, 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 1544, 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 2210, in render_templates rendered_task = self.task.render_template_fields(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 722, in render_template_fields unmapped_task = self.unmap(unmap_kwargs=kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 508, in unmap op = self.operator_class(**unmap_kwargs, _airflow_from_mapped=True) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 390, in apply_defaults result = func(self, **kwargs, default_args=default_args) File "/usr/local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 259, in __init__ self.name = self._set _name(name) File "/usr/local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 442, in _set_name raise AirflowException("`name` is required unless `pod_template_file` or `full_pod_spec` is set") airflow.exceptions.AirflowException: `name` is required unless `pod_template_file` or `full_pod_spec` is set ``` But rather than failing the task, Airflow just thinks that the task is still running: <img width="833" alt="Screen Shot 2022-04-19 at 11 13 47 PM" src="https://user-images.githubusercontent.com/5834582/164156155-41986d3a-d171-4943-8443-a0fc3c542988.png"> ### What you think should happen instead Ideally this error would be surfaced when the dag is first parsed. If that's not possible, then it should fail the task completely (i.e. a red square should show up in the grid view). ### How to reproduce Run the dag above ### Operating System ubuntu (microk8s) ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes | 4.0.0 ### Deployment Astronomer ### Deployment details Deployed via the astronomer airflow helm chart, values: ``` airflow: airflowHome: /usr/local/airflow defaultAirflowRepository: 172.28.11.191:30500/airflow defaultAirflowTag: tb11c-inner-operator-expansion env: - name: AIRFLOW__CORE__DAGBAG_IMPORT_ERROR_TRACEBACK_DEPTH value: '99' executor: CeleryExecutor gid: 50000 images: airflow: pullPolicy: Always repository: 172.28.11.191:30500/airflow flower: pullPolicy: Always pod_template: pullPolicy: Always logs: persistence: enabled: true size: 2Gi scheduler: livenessProbe: timeoutSeconds: 45 triggerer: livenessProbe: timeoutSeconds: 45 ``` Image base: `quay.io/astronomer/ap-airflow-dev:main` Airflow version: `2.3.0.dev20220414` ### 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/23107

https://github.com/apache/airflow/pull/23119

1e8ac47589967f2a7284faeab0f65b01bfd8202d

91b82763c5c17e8ab021f2d4f2a5681ea90adf6b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/css/bootstrap-theme.css"]

UI: Transparent border causes dropshadow to render 1px away from Action dropdown menu in Task Instance list

### Apache Airflow version 2.2.5 (latest released) ### What happened Airflow: > Astronomer Certified: v2.2.5.post1 based on Apache Airflow v2.2.5 > Git Version: .release:2.2.5+astro.1+90fc013e6e4139e2d4bfe438ad46c3af1d523668 Due to this CSS in `airflowDefaultTheme.ce329611a683ab0c05fd.css`: ```css .dropdown-menu { background-clip: padding-box; background-color: #fff; border: 1px solid transparent; /* <-- transparent border */ } ``` the dropdown border and dropshadow renders...weirdly:  Zoomed in - take a close look at the border and how the contents underneath the dropdown bleed through the border, making the dropshadow render 1px away from the dropdown menu:  ### What you think should happen instead When I remove the abberrant line of CSS above, it cascades to this in `bootstrap.min.css`: ```css .dropdown-menu { ... border: 1px solid rgba(0,0,0,.15); ... } ``` which renders the border as gray:  So I think we should not use a transparent border, or we should remove the explicit border from the dropdown and let Bootstrap control it. ### How to reproduce Spin up an instance of Airflow with `astro dev start`, trigger a DAG, inspect the DAG details, and list all task instances of a DAG run. Then click the Actions dropdown menu. ### Operating System macOS 11.6.4 Big Sur (Intel) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Astro installed via Homebrew: > Astro CLI Version: 0.28.1, Git Commit: 980c0d7bd06b818a2cb0e948bb101d0b27e3a90a > Astro Server Version: 0.28.4-rc9 ### 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/23092

https://github.com/apache/airflow/pull/27789

8b1ebdacd8ddbe841a74830f750ed8f5e6f38f0a

d233c12c30f9a7f3da63348f3f028104cb14c76b

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "TESTING.rst", "dev/breeze/src/airflow_breeze/commands/testing.py", "dev/breeze/src/airflow_breeze/shell/enter_shell.py", "dev/breeze/src/airflow_breeze/utils/docker_command_utils.py", "images/breeze/output-commands.svg", "images/breeze/output-tests.svg"]

Breeze: Running integration tests in Breeze

We should be able to run integration tests with Breeze - this is extension of `test` unit tests command that should allow to enable --integrations (same as in Shell) and run the tests with only the integration tests selected.

https://github.com/apache/airflow/issues/23083

https://github.com/apache/airflow/pull/23445

83784d9e7b79d2400307454ccafdacddaee16769

7ba4e35a9d1b65b4c1a318ba4abdf521f98421a2

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "TESTING.rst", "dev/breeze/src/airflow_breeze/commands/testing.py", "dev/breeze/src/airflow_breeze/shell/enter_shell.py", "dev/breeze/src/airflow_breeze/utils/docker_command_utils.py", "images/breeze/output-commands.svg", "images/breeze/output-tests.svg"]

Breeze: Add running unit tests with Breeze

We should be able to run unit tests automatically from breeze (`test` command in legacy-breeze)

https://github.com/apache/airflow/issues/23082

https://github.com/apache/airflow/pull/23445

83784d9e7b79d2400307454ccafdacddaee16769

7ba4e35a9d1b65b4c1a318ba4abdf521f98421a2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/vertex_ai/endpoint_service.py", "airflow/providers/google/cloud/operators/vertex_ai/endpoint_service.py", "tests/providers/google/cloud/hooks/vertex_ai/test_endpoint_service.py", "tests/providers/google/cloud/operators/test_vertex_ai.py"]

Add `endpoint_id` arg to `vertex_ai.endpoint_service.CreateEndpointOperator`

### Description Add the optional argument `endpoint_id` to `google.cloud.operators.vertex_ai.endpoint_service.CreateEndpointOperator` class and `google.cloud.hooks.vertex_ai.endpoint_service.EndpointServiceHook.create_endpoint` method. ### Use case/motivation `google.cloud.operators.vertex_ai.endpoint_service.CreateEndpointOperator` class and `google.cloud.hooks.vertex_ai.endpoint_service.EndpointServiceHook.create_endpoint` method do not have `endpoint_id` argument. They internally use [`CreateEndpointRequest`](https://github.com/googleapis/python-aiplatform/blob/v1.11.0/google/cloud/aiplatform_v1/types/endpoint_service.py#L43), which accepts `endpoint_id`. Hence, I'd like them to accept `endpoint_id` argument and pass it to [`CreateEndpointRequest`](https://github.com/googleapis/python-aiplatform/blob/v1.11.0/google/cloud/aiplatform_v1/types/endpoint_service.py#L43). If this is satisfied, we can create Vertex Endpoints with a specific Endpoint ID. Then, an Endpoint will be created with the specified Endpoint ID. Without it, an Endpoint will be created with an ID generated randomly. ### 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/23074

https://github.com/apache/airflow/pull/23070

6b459995b260cc7023e4720974ef4f59893cd283

d4a33480550db841657b998c0b4464feffec0ef9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/tree/InstanceTooltip.jsx", "airflow/www/static/js/tree/details/content/dagRun/index.jsx", "airflow/www/static/js/tree/details/content/taskInstance/Details.jsx", "airflow/www/static/js/tree/details/content/taskInstance/MappedInstances.jsx", "airflow/www/utils.py"]

Grid view: "duration" shows 00:00:00

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened Run [a dag with an expanded TimedeltaSensor and a normal TimedeltaSensor](https://gist.github.com/MatrixManAtYrService/051fdc7164d187ab215ff8087e4db043), and navigate to the corresponding entries in the grid view. While the dag runs: - The unmapped task shows its "duration" to be increasing - The mapped task shows a blank entry for the duration Once the dag has finished: - both show `00:00:00` for the duration ### What you think should happen instead I'm not sure what it should show, probably time spent running? Or maybe queued + running? Whatever it should be, 00:00:00 doesn't seem right if it spent 90 seconds waiting around (e.g. in the "running" state) Also, if we're going to update duration continuously while the normal task is running, we should do the same for the expanded task. ### How to reproduce run a dag with expanded sensors, notice 00:00:00 duration ### Operating System debian (docker) ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astrocloud dev start` Dockerfile: ``` FROM quay.io/astronomer/ap-airflow-dev:main ``` image at airflow version 6d6ac2b2bcbb0547a488a1a13fea3cb1a69d24e8 ### 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/23068

https://github.com/apache/airflow/pull/23259

511ea702d5f732582d018dad79754b54d5e53f9d

9e2531fa4d9890f002d184121e018e3face5586b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/presto/hooks/presto.py", "airflow/providers/trino/hooks/trino.py"]

Presto hook is broken in the latest provider release (2.2.0)

### Apache Airflow version 2.2.5 (latest released) ### What happened The latest presto provider release https://pypi.org/project/apache-airflow-providers-presto/ is broken due to: ``` File "/usr/local/lib/python3.8/site-packages/airflow/providers/presto/hooks/presto.py", line 117, in get_conn http_headers = {"X-Presto-Client-Info": generate_presto_client_info()} File "/usr/local/lib/python3.8/site-packages/airflow/providers/presto/hooks/presto.py", line 56, in generate_presto_client_info 'try_number': context_var['try_number'], KeyError: 'try_number' ``` ### What you think should happen instead This is due to the latest airflow release 2.2.5 does not include this PR: https://github.com/apache/airflow/pull/22297/ the presto hook changes were introduced in this pr https://github.com/apache/airflow/pull/22416 ### How to reproduce _No response_ ### Operating System Mac ### Versions of Apache Airflow Providers https://pypi.org/project/apache-airflow-providers-presto/ version: 2.2.0 ### Deployment Other ### Deployment details local ### 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) cc @levyitay

https://github.com/apache/airflow/issues/23059

https://github.com/apache/airflow/pull/23061

b24650c0cc156ceb5ef5791f1647d4d37a529920

5164cdbe98ad63754d969b4b300a7a0167565e33

python

closed

apache/airflow

https://github.com/apache/airflow

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

Graph view: Nodes arrows are cut

### Body <img width="709" alt="Screen Shot 2022-04-15 at 17 37 37" src="https://user-images.githubusercontent.com/45845474/163584251-f1ea5bc7-e132-41c4-a20c-cc247b81b899.png"> Reproduce example using [example_emr_job_flow_manual_steps ](https://github.com/apache/airflow/blob/b3cae77218788671a72411a344aab42a3c58e89c/airflow/providers/amazon/aws/example_dags/example_emr_job_flow_manual_steps.py)in AWS provider Already discussed with @bbovenzi this issue will be fixed after 2.3.0 as it requires quite a bit of changes... also this is not a regression and it's just a "comsitic" issue in very specific DAGs. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/23042

https://github.com/apache/airflow/pull/23044

749e53def43055225a2e5d09596af7821d91b4ac

028087b5a6e94fd98542d0e681d947979eb1011f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/mssql_to_gcs.py", "airflow/providers/google/cloud/transfers/mysql_to_gcs.py", "airflow/providers/google/cloud/transfers/oracle_to_gcs.py", "airflow/providers/google/cloud/transfers/postgres_to_gcs.py", "airflow/providers/google/cloud/transfers/presto_to_gcs.py", "airflow/providers/google/cloud/transfers/sql_to_gcs.py", "airflow/providers/google/cloud/transfers/trino_to_gcs.py", "tests/providers/google/cloud/transfers/test_postgres_to_gcs.py", "tests/providers/google/cloud/transfers/test_sql_to_gcs.py"]

PostgresToGCSOperator does not allow nested JSON

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==6.3.0 ### Apache Airflow version 2.1.4 ### Operating System macOS Big Sur version 11.6.1 ### Deployment Composer ### Deployment details _No response_ ### What happened Postgres JSON column output contains extra `\`: `{"info": "{\"phones\": [{\"type\": \"mobile\", \"phone\": \"001001\"}, {\"type\": \"fix\", \"phone\": \"002002\"}]}", "name": null}` While in the previous version the output looks like `{"info": {"phones": [{"phone": "001001", "type": "mobile"}, {"phone": "002002", "type": "fix"}]}, "name": null}` The introduced extra `\` will cause JSON parsing error in following `GCSToBigQueryOperator` ### What you think should happen instead The output should NOT contain extra `\`: `{"info": {"phones": [{"phone": "001001", "type": "mobile"}, {"phone": "002002", "type": "fix"}]}, "name": null}` It is caused by this new code change in https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/transfers/postgres_to_gcs.py should comment out this block > if isinstance(value, dict): > return json.dumps(value) ### How to reproduce Try to output a Postgres table with JSON column --- you may use the the `info` above as example. ### 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/23040

https://github.com/apache/airflow/pull/23063

ca3fbbbe14203774a16ddd23e82cfe652b22eb4a

766726f2e3a282fcd2662f5dc6e9926dc38a6540

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers_manager.py", "tests/core/test_providers_manager.py"]

providers_manager | Exception when importing 'apache-airflow-providers-google' package ModuleNotFoundError: No module named 'airflow.providers.mysql'

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened ```shell airflow users create -r Admin -u admin -e admin@example.com -f admin -l user -p admin ``` give ```log [2022-04-15 07:08:30,801] {manager.py:807} WARNING - No user yet created, use flask fab command to do it. [2022-04-15 07:08:31,024] {manager.py:585} INFO - Removed Permission menu access on Permissions to role Admin [2022-04-15 07:08:31,049] {manager.py:543} INFO - Removed Permission View: menu_access on Permissions [2022-04-15 07:08:31,149] {manager.py:508} INFO - Created Permission View: menu access on Permissions [2022-04-15 07:08:31,160] {manager.py:568} INFO - Added Permission menu access on Permissions to role Admin [2022-04-15 07:08:32,250] {providers_manager.py:237} WARNING - Exception when importing 'airflow.providers.google.cloud.hooks.cloud_sql.CloudSQLHook' from 'apache-airflow-providers-google' package Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/providers_manager.py", line 215, in _sanity_check imported_class = import_string(class_name) File "/usr/local/lib/python3.8/site-packages/airflow/utils/module_loading.py", line 32, in import_string module = import_module(module_path) File "/usr/local/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 783, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/cloud_sql.py", line 52, in <module> from airflow.providers.mysql.hooks.mysql import MySqlHook ModuleNotFoundError: No module named 'airflow.providers.mysql' [2022-04-15 07:29:12,007] {manager.py:213} INFO - Added user admin User "admin" created with role "Admin" ``` ### What you think should happen instead it do not log this warning with ``` apache-airflow==2.2.5 apache-airflow-providers-google==6.7.0 ``` ```log [2022-04-15 07:44:45,962] {manager.py:779} WARNING - No user yet created, use flask fab command to do it. [2022-04-15 07:44:46,304] {manager.py:512} WARNING - Refused to delete permission view, assoc with role exists DAG Runs.can_create Admin [2022-04-15 07:44:48,310] {manager.py:214} INFO - Added user admin User "admin" created with role "Admin" ``` ### How to reproduce _No response_ ### Operating System ubuntu ### Versions of Apache Airflow Providers requirements.txt : ``` apache-airflow-providers-google==6.8.0 ``` pip install -r requirements.txt --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0b1/constraints-3.8.txt" ### Deployment Other Docker-based deployment ### Deployment details pip install apache-airflow[postgres]==2.3.0b1 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0b1/constraints-3.8.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/23033

https://github.com/apache/airflow/pull/23037

4fa718e4db2daeb89085ea20e8b3ce0c895e415c

8dedd2ac13a6cdc0c363446985f492e0f702f639

python

closed

apache/airflow

https://github.com/apache/airflow

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

`airflow tasks states-for-dag-run` has no `map_index` column

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened I ran: ``` $ airflow tasks states-for-dag-run taskmap_xcom_pull 'manual__2022-04-14T13:27:04.958420+00:00' dag_id | execution_date | task_id | state | start_date | end_date ==================+==================================+===========+=========+==================================+================================= taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | foo | success | 2022-04-14T13:27:05.343134+00:00 | 2022-04-14T13:27:05.598641+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | bar | success | 2022-04-14T13:27:06.256684+00:00 | 2022-04-14T13:27:06.462664+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.480364+00:00 | 2022-04-14T13:27:07.713226+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.512084+00:00 | 2022-04-14T13:27:07.768716+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.546097+00:00 | 2022-04-14T13:27:07.782719+00:00 ``` ...targeting a dagrun for which `identity` had three expanded tasks. All three showed up, but the output didn't show me enough to know which one was which. ### What you think should happen instead There should be a `map_index` column so that I know which one is which. ### How to reproduce Run a dag with expanded tasks, then try to view their states via the cli ### Operating System debian (docker) ### 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/23028

https://github.com/apache/airflow/pull/23030

10c9cb5318fd8a9e41a7b4338e5052c8feece7ae

b24650c0cc156ceb5ef5791f1647d4d37a529920

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/backfill_job.py", "airflow/models/mappedoperator.py", "airflow/models/taskinstance.py", "airflow/models/taskmixin.py", "airflow/models/xcom_arg.py", "tests/models/test_taskinstance.py"]

A task's returned object should not be checked for mappability if the dag doesn't use it in an expansion.

### Apache Airflow version main (development) ### What happened Here's a dag: ```python3 with DAG(...) as dag: @dag.task def foo(): return "foo" @dag.task def identity(thing): return thing foo() >> identity.expand(thing=[1, 2, 3]) ``` `foo` fails with these task logs: ``` [2022-04-14, 14:15:26 UTC] {python.py:173} INFO - Done. Returned value was: foo [2022-04-14, 14:15:26 UTC] {taskinstance.py:1837} WARNING - We expected to get frame set in local storage but it was not. Please report this as an issue with full logs at https://github.com/apache/airflow/issues/new Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1417, 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 1564, in _execute_task_with_callbacks result = self._execute_task(context, task_orig) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1634, in _execute_task self._record_task_map_for_downstreams(task_orig, result, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2314, in _record_task_map_for_downstreams raise UnmappableXComTypePushed(value) airflow.exceptions.UnmappableXComTypePushed: unmappable return type 'str' ``` ### What you think should happen instead Airflow shouldn't bother checking `foo`'s return type for mappability because its return value is never used in an expansion. ### How to reproduce Run the dag, notice the failure ### Operating System debian (docker) ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details using image with ref: e5dd6fdcfd2f53ed90e29070711c121de447b404 ### 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/23018

https://github.com/apache/airflow/pull/23053

b8bbfd4b318108b4fdadc78cd46fd1735da243ae

197cff3194e855b9207c3c0da8ae093a0d5dda55

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst"]

Breeze: Add uninstallation instructions for Breeze

We should have information how to uninstall Breeze: * in the cheatsheet * in BREEZE.rst

https://github.com/apache/airflow/issues/23005

https://github.com/apache/airflow/pull/23045

2597ea47944488f3756a84bd917fa780ff5594da

2722c42659100474b21aae3504ee4cbe24f72ab4

python

closed

apache/airflow

https://github.com/apache/airflow

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

Invalid execution_date crashes pages accepting the query parameter

### Apache Airflow version 2.2.5 (latest released) ### What happened Invalid execution_date in query parameter will crash durations page since pendulum parsing exception is not handled in several views ### What you think should happen instead On `ParseError` the page should resort to some default value like in grid page or show an error flash message instead of crash. ### How to reproduce 1. Visit a dag duration page with invalid date in URL : http://localhost:8080/dags/raise_exception/duration?days=30&root=&num_runs=25&base_date=2022-04-12+16%3A29%3A21%2B05%3A30er 2. Stacktrace ```python Python version: 3.10.4 Airflow version: 2.3.0.dev0 Node: laptop ------------------------------------------------------------------------------- Traceback (most recent call last): File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/pendulum/parsing/__init__.py", line 131, in _parse dt = parser.parse( File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/dateutil/parser/_parser.py", line 1368, in parse return DEFAULTPARSER.parse(timestr, **kwargs) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/dateutil/parser/_parser.py", line 643, in parse raise ParserError("Unknown string format: %s", timestr) dateutil.parser._parser.ParserError: Unknown string format: 2022-04-12 16:29:21+05:30er During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/_compat.py", line 39, in reraise raise value File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/home/karthikeyan/stuff/python/airflow/airflow/www/auth.py", line 40, in decorated return func(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/airflow/www/decorators.py", line 80, in wrapper return f(*args, **kwargs) File "/home/karthikeyan/stuff/python/airflow/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/home/karthikeyan/stuff/python/airflow/airflow/www/views.py", line 2870, in duration base_date = timezone.parse(base_date) File "/home/karthikeyan/stuff/python/airflow/airflow/utils/timezone.py", line 205, in parse return pendulum.parse(string, tz=timezone or TIMEZONE, strict=False) # type: ignore File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/pendulum/parser.py", line 29, in parse return _parse(text, **options) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/pendulum/parser.py", line 45, in _parse parsed = base_parse(text, **options) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/pendulum/parsing/__init__.py", line 74, in parse return _normalize(_parse(text, **_options), **_options) File "/home/karthikeyan/stuff/python/airflow/.env/lib/python3.10/site-packages/pendulum/parsing/__init__.py", line 135, in _parse raise ParserError("Invalid date string: {}".format(text)) pendulum.parsing.exceptions.ParserError: Invalid date string: 2022-04-12 16:29:21+05:30er ``` ### 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/22969

https://github.com/apache/airflow/pull/23161

6f82fc70ec91b493924249f062306330ee929728

9e25bc211f6f7bba1aff133d21fe3865dabda53d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/hooks/dbapi.py"]

closing connection chunks in DbApiHook.get_pandas_df

### Apache Airflow version 2.2.5 (latest released) ### What happened Hi all, Please be patient with me, it's my first Bugreport in git at all :) **Affected function:** DbApiHook.get_pandas_df **Short description**: If I use DbApiHook.get_pandas_df with parameter "chunksize" the connection is lost **Error description** I tried using the DbApiHook.get_pandas_df function instead of pandas.read_sql. Without the parameter "chunksize" both functions work the same. But as soon as I add the parameter chunksize to get_pandas_df, I lose the connection in the first iteration. This happens both when querying Oracle and Mysql (Mariadb) databases. During my research I found a comment on a closed issue that describes the same -> [#8468 ](https://github.com/apache/airflow/issues/8468) My Airflow version: 2.2.5 I think it's something to do with the "with closing" argument, because when I remove that argument, the chunksize argument was working. ``` def get_pandas_df(self, sql, parameters=None, **kwargs): """ Executes the sql and returns a pandas dataframe :param sql: the sql statement to be executed (str) or a list of sql statements to execute :param parameters: The parameters to render the SQL query with. :param kwargs: (optional) passed into pandas.io.sql.read_sql method """ try: from pandas.io import sql as psql except ImportError: raise Exception("pandas library not installed, run: pip install 'apache-airflow[pandas]'.") # Not working with closing(self.get_conn()) as conn: return psql.read_sql(sql, con=conn, params=parameters, **kwargs) # would working # return psql.read_sql(sql, con=conn, params=parameters, **kwargs)_ ``` ### What you think should happen instead It should give me a chunk of DataFrame ### How to reproduce **not working** ``` src_hook = OracleHook(oracle_conn_id='oracle_source_conn_id') query = "select * from example_table" for chunk in src_hook.get_pandas_df(query,chunksize=2): print(chunk.head()) ``` **works** ``` for chunk in src_hook.get_pandas_df(query): print(chunk.head()) ``` **works** ``` for chunk in pandas.read_sql(query,src_hook.get_conn(),chunksize=2): print(chunk.head()) ``` ### Operating System MacOS Monetäre ### Versions of Apache Airflow Providers apache-airflow 2.2.5 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-mongo 2.3.3 apache-airflow-providers-mysql 2.2.3 apache-airflow-providers-oracle 2.2.3 apache-airflow-providers-salesforce 3.4.3 apache-airflow-providers-sftp 2.5.2 apache-airflow-providers-sqlite 2.1.3 apache-airflow-providers-ssh 2.4.3 ### 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/22947

https://github.com/apache/airflow/pull/23452

41e94b475e06f63db39b0943c9d9a7476367083c

ab1f637e463011a34d950c306583400b7a2fceb3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Deferrable operator trigger event payload is not persisted in db and not passed to completion method

### Apache Airflow version 2.2.5 (latest released) ### What happened When trigger is fired, event payload is added in next_kwargs with 'event' key. This gets persisted in db when next_kwargs are not provided by operator. but when present due to modification of existing dict its not persisted in db ### What you think should happen instead It should persist trigger event payload in db even when next kwargs are provided ### How to reproduce _No response_ ### Operating System any ### 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/22942

https://github.com/apache/airflow/pull/22944

a801ea3927b8bf3ca154fea3774ebf2d90e74e50

bab740c0a49b828401a8baf04eb297d083605ae8

python

closed

apache/airflow

https://github.com/apache/airflow

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

XCom is cleared when a task resumes from deferral.

### Apache Airflow version 2.2.5 (latest released) ### What happened A task's XCom value is cleared when a task is rescheduled after being deferred. ### What you think should happen instead XCom should not be cleared in this case, as it is still the same task run. ### How to reproduce ``` from datetime import datetime, timedelta from airflow import DAG from airflow.models import BaseOperator from airflow.triggers.temporal import TimeDeltaTrigger class XComPushDeferOperator(BaseOperator): def execute(self, context): context["ti"].xcom_push("test", "test_value") self.defer( trigger=TimeDeltaTrigger(delta=timedelta(seconds=10)), method_name="next", ) def next(self, context, event=None): pass with DAG( "xcom_clear", schedule_interval=None, start_date=datetime(2022, 4, 11), ) as dag: XComPushDeferOperator(task_id="xcom_push") ``` ### Operating System macOS ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [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/22931

https://github.com/apache/airflow/pull/22932

4291de218e0738f32f516afe0f9d6adce7f3220d

8b687ec82a7047fc35410f5c5bb0726de434e749

python

closed

apache/airflow

https://github.com/apache/airflow

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

Text wrap for task group tooltips

### Description Improve the readability of task group tooltips by wrapping the text after a certain number of characters. ### Use case/motivation When tooltips have a lot of words in them, and your computer monitor is fairly large, Airflow will display the task group tooltip on one very long line. This can be difficult to read. It would be nice if after, say, 60 characters, additional tooltip text would be displayed on a new line. ### 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/22912

https://github.com/apache/airflow/pull/22978

0cd8833df74f4b0498026c4103bab130e1fc1068

2f051e303fd433e64619f931eab2180db44bba23

python

closed

apache/airflow

https://github.com/apache/airflow

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

Invalid JSON metadata in get_logs_with_metadata causes server error.

### Apache Airflow version 2.2.5 (latest released) ### What happened Invalid JSON metadata in get_logs_with_metadata causes server error. The `json.loads` exception is not handled like validation in other endpoints. http://127.0.0.1:8080/get_logs_with_metadata?execution_date=2015-11-16T14:34:15+00:00&metadata=invalid ### What you think should happen instead A proper error message can be returned ### How to reproduce Accessing below endpoint with invalid metadata payload http://127.0.0.1:8080/get_logs_with_metadata?execution_date=2015-11-16T14:34:15+00:00&metadata=invalid ### 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/22897

https://github.com/apache/airflow/pull/22898

8af77127f1aa332c6e976c14c8b98b28c8a4cd26

a3dd8473e4c5bbea214ebc8d5545b75281166428

python

closed

apache/airflow

https://github.com/apache/airflow

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

ECS operator throws an error on attempting to reattach to ECS tasks

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon 3.2.0 ### Apache Airflow version 2.2.5 (latest released) ### Operating System Linux / ECS ### Deployment Other Docker-based deployment ### Deployment details We are running Docker on Open Shift 4 ### What happened There seems to be a bug in the code for ECS operator, during the "reattach" flow. We are running into some instability issues that cause our Airflow scheduler to restart. When the scheduler restarts while a task is running using ECS, the ECS operator will try to reattach to the ECS task once the Airflow scheduler restarts. The code works fine finding the ECS task and attaching to it, but then when it tries to fetch the logs, it throws the following error: `Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1334, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1460, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1516, in _execute_task result = execute_callable(context=context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 295, in execute self.task_log_fetcher = self._get_task_log_fetcher() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 417, in _get_task_log_fetcher log_stream_name = f"{self.awslogs_stream_prefix}/{self.ecs_task_id}" AttributeError: 'EcsOperator' object has no attribute 'ecs_task_id'` At this point, the operator will fail and the task will be marked for retries and eventually gets marked as failed, while on the ECS side, the ECS task is running fine. The manual way to fix this would be to wait for the ECS task to complete, then mark the task as successful and trigger downstream tasks. This is not very practical, since the task can take a long time (in our case the task can take hours) ### What you think should happen instead I expect that the ECS operator should be able to reattach and pull the logs as normal. ### How to reproduce Configure a task that would run using the ECS operator, and make sure it takes a very long time. Start the task, and once the logs starts flowing to Airflow, restart the Airflow scheduler. Wait for the scheduler to restart and check that upon retry, the task would be able to attach and fetch the logs. ### Anything else When restarting Airflow, it tries to kill the task at hand. In our case, we didn't give the permission to the AWS role to kill the running ECS tasks, and therefore the ECS tasks keep running during the restart of Airflow. Others might not have this setup, and therefore they won't run into the "reattach" flow, and they won't encounter the issue reported here. This is not a good option for us, since our tasks can take hours to complete, and we don't want to interfere with their execution. We also need to improve the stability of the Open Shift infrastructure where Airflow is running, so that the scheduler doesn't restart so often, but that is a different story. ### 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/22878

https://github.com/apache/airflow/pull/23370

3f6d5eef427f3ea33d0cd342143983f54226bf05

d6141c6594da86653b15d67eaa99511e8fe37a26

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "scripts/docker/entrypoint_prod.sh"]

There is no handler for BACKEND=sqs in entrypoint_prod.sh function wait_for_connection

### Apache Airflow version 2.2.5 (latest released) ### What happened Actually this is looking more like a bug see error below. I think I am configuring it correctly. Might be a configuration issue see https://github.com/apache/airflow/issues/22863 From a docker container running on an EC2 I'm trying to use AWS sqs as my celery broker. I'm using ec2 IAM credentials so I set broker_url = sqs:// According to https://docs.celeryq.dev/en/latest/getting-started/backends-and-brokers/sqs.html If you are using IAM roles on instances, you can set the BROKER_URL to: sqs:// and kombu will attempt to retrieve access tokens from the instance metadata. The error I get is: airflow-worker-1_1 | airflow-worker-1_1 | ### BACKEND=sqs airflow-worker-1_1 | DB_HOST=None airflow-worker-1_1 | DB_PORT= airflow-worker-1_1 | .................... airflow-worker-1_1 | ERROR! Maximum number of retries (20) reached. airflow-worker-1_1 | airflow-worker-1_1 | Last check result: airflow-worker-1_1 | $ run_nc 'None' '' airflow-worker-1_1 | Traceback (most recent call last): airflow-worker-1_1 | File "", line 1, in airflow-worker-1_1 | socket.gaierror: [Errno -5] No address associated with hostname airflow-worker-1_1 | Can't parse as an IP address I traced the source of the error to entrypoint_prod.sh. FROM function wait_for_connection { echo BACKEND="${BACKEND:=${detected_backend}}" readonly BACKEND if [[ -z "${detected_port=}" ]]; then if [[ ${BACKEND} == "postgres"* ]]; then detected_port=5432 elif [[ ${BACKEND} == "mysql"* ]]; then detected_port=3306 elif [[ ${BACKEND} == "mssql"* ]]; then detected_port=1433 elif [[ ${BACKEND} == "redis"* ]]; then detected_port=6379 elif [[ ${BACKEND} == "amqp"* ]]; then detected_port=5672 fi fi There is no handler for ### BACKEND=sqs Verified the ### BACKEND=sqs is comming from the broker_url = sqs:// ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu container ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/22868

https://github.com/apache/airflow/pull/22883

ee449fec6ca855aff3c4830c6758a9d5e5db1a2d

0ae0f7e2448e05917e51e29b854ad60463378fbe

python

closed

apache/airflow

https://github.com/apache/airflow

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

When passing the 'False' value to the parameters of a decorated dag function I get this traceback

### Apache Airflow version 2.2.3 ### What happened When passing the `False` value to a decorated dag function I get this traceback below. Also the default value is not shown when clicking 'trigger dag w/ config'. ```[2022-04-07, 20:08:57 UTC] {taskinstance.py:1259} INFO - Executing <Task(_PythonDecoratedOperator): value_consumer> on 2022-04-07 20:08:56.914410+00:00 [2022-04-07, 20:08:57 UTC] {standard_task_runner.py:52} INFO - Started process 2170 to run task [2022-04-07, 20:08:57 UTC] {standard_task_runner.py:76} INFO - Running: ['airflow', 'tasks', 'run', 'check_ui_config', 'value_consumer', 'manual__2022-04-07T20:08:56.914410+00:00', '--job-id', '24', '--raw', '--subdir', 'DAGS_FOLDER/check_ui_config.py', '--cfg-path', '/tmp/tmpww9euksv', '--error-file', '/tmp/tmp7kjdfks5'] [2022-04-07, 20:08:57 UTC] {standard_task_runner.py:77} INFO - Job 24: Subtask value_consumer [2022-04-07, 20:08:57 UTC] {logging_mixin.py:109} INFO - Running <TaskInstance: check_ui_config.value_consumer manual__2022-04-07T20:08:56.914410+00:00 [running]> on host a643f8828615 [2022-04-07, 20:08:57 UTC] {taskinstance.py:1700} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1329, in _run_raw_task self._execute_task_with_callbacks(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1418, in _execute_task_with_callbacks self.render_templates(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1992, in render_templates self.task.render_template_fields(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1061, in render_template_fields self._do_render_template_fields(self, self.template_fields, context, jinja_env, set()) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1074, in _do_render_template_fields rendered_content = self.render_template(content, context, jinja_env, seen_oids) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1125, in render_template return tuple(self.render_template(element, context, jinja_env) for element in content) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1125, in <genexpr> return tuple(self.render_template(element, context, jinja_env) for element in content) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1116, in render_template return content.resolve(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/param.py", line 226, in resolve raise AirflowException(f'No value could be resolved for parameter {self._name}') airflow.exceptions.AirflowException: No value could be resolved for parameter test [2022-04-07, 20:08:57 UTC] {taskinstance.py:1267} INFO - Marking task as FAILED. dag_id=check_ui_config, task_id=value_consumer, execution_date=20220407T200856, start_date=20220407T200857, end_date=20220407T200857 [2022-04-07, 20:08:57 UTC] {standard_task_runner.py:89} ERROR - Failed to execute job 24 for task value_consumer Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/task/task_runner/standard_task_runner.py", line 85, in _start_by_fork args.func(args, dag=self.dag) File "/usr/local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 298, in task_run _run_task_by_selected_method(args, dag, ti) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 107, in _run_task_by_selected_method _run_raw_task(args, ti) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 180, in _run_raw_task ti._run_raw_task( File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1329, in _run_raw_task self._execute_task_with_callbacks(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1418, in _execute_task_with_callbacks self.render_templates(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1992, in render_templates self.task.render_template_fields(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1061, in render_template_fields self._do_render_template_fields(self, self.template_fields, context, jinja_env, set()) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1074, in _do_render_template_fields rendered_content = self.render_template(content, context, jinja_env, seen_oids) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1125, in render_template return tuple(self.render_template(element, context, jinja_env) for element in content) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1125, in <genexpr> return tuple(self.render_template(element, context, jinja_env) for element in content) File "/usr/local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1116, in render_template return content.resolve(context) File "/usr/local/lib/python3.9/site-packages/airflow/models/param.py", line 226, in resolve raise AirflowException(f'No value could be resolved for parameter {self._name}') airflow.exceptions.AirflowException: No value could be resolved for parameter test ``` ### What you think should happen instead I think airflow should be able to handle the False value when passing it as a dag param. ### How to reproduce ``` from airflow.decorators import dag, task from airflow.models.param import Param from datetime import datetime, timedelta @task def value_consumer(val): print(val) @dag( start_date=datetime(2021, 1, 1), schedule_interval=timedelta(days=365, hours=6) ) def check_ui_config(test): value_consumer(test) the_dag = check_ui_config(False) ``` ### Operating System Docker (debian:buster) ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details Astro cli with this image: quay.io/astronomer/ap-airflow-dev:2.2.3-2 ### Anything else  ### 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/22843

https://github.com/apache/airflow/pull/22964

e09b4f144d1edefad50a58ebef56bd40df4eb39c

a0f7e61497d547b82edc1154d39535d79aaedff3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Allow mapped Task as input to another mapped task

This dag ```python with DAG(dag_id="simple_mapping", start_date=pendulum.DateTime(2022, 4, 6), catchup=True) as d3: @task(email='a@b.com') def add_one(x: int): return x + 1 two_three_four = add_one.expand(x=[1, 2, 3]) three_four_five = add_one.expand(x=two_three_four) ``` Fails with this error: ``` File "/home/ash/code/airflow/airflow/airflow/models/taskinstance.py", line 2239, in _record_task_map_for_downstreams raise UnmappableXComTypePushed(value) airflow.exceptions.UnmappableXComTypePushed: unmappable return type 'int' ```

https://github.com/apache/airflow/issues/22833

https://github.com/apache/airflow/pull/22849

1a8b8f521c887716d7e0c987a58e8e5c3b62bdaa

8af77127f1aa332c6e976c14c8b98b28c8a4cd26

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/jira/sensors/jira.py"]

JiraTicketSensor duplicates TaskId

### Apache Airflow Provider(s) jira ### Versions of Apache Airflow Providers apache-airflow-providers-jira==2.0.1 ### Apache Airflow version 2.2.2 ### Operating System Amazon Linux 2 ### Deployment MWAA ### Deployment details _No response_ ### What happened I've been trying to use the Jira Operator to create a Ticket from Airflow and use the JiraTicketSensor to check if the ticket was resolved. Creating the task works fine, but I can't get the Sensor to work. If I don't provide the method_name I get an error that it is required, if I provide it as None, I get an error saying the Task id has already been added to the DAG. ```text Broken DAG: [/usr/local/airflow/dags/jira_ticket_sensor.py] Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/models/baseoperator.py", line 553, in __init__ task_group.add(self) File "/usr/local/lib/python3.7/site-packages/airflow/utils/task_group.py", line 175, in add raise DuplicateTaskIdFound(f"Task id '{key}' has already been added to the DAG") airflow.exceptions.DuplicateTaskIdFound: Task id 'jira_sensor' has already been added to the DAG ``` ### What you think should happen instead _No response_ ### How to reproduce use this dag ```python from datetime import datetime from airflow import DAG from airflow.providers.jira.sensors.jira import JiraTicketSensor with DAG( dag_id='jira_ticket_sensor', schedule_interval=None, start_date=datetime(2021, 1, 1), catchup=False ) as dag: jira_sensor = JiraTicketSensor( task_id='jira_sensor', jira_conn_id='jira_default', ticket_id='TEST-1', field='status', expected_value='Completed', method_name='issue', poke_interval=600 ) ``` ### Anything else This error occurs every time ### 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/22810

https://github.com/apache/airflow/pull/23046

e82a2fdf841dd571f3b8f456c4d054cf3a94fc03

bf10545d8358bcdb9ca5dacba101482296251cab

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/secrets/metadata-connection-secret.yaml", "tests/charts/test_basic_helm_chart.py"]

Helm deployment fails when postgresql.nameOverride is used

### Apache Airflow version 2.2.5 (latest released) ### What happened Helm installation fails with the following config: ``` postgresql: enabled: true nameOverride: overridename ``` The problem is manifested in the `-airflow-metadata` secret where the connection string will be generated without respect to the `nameOverride` With the example config the generated string should be: `postgresql://postgres:postgres@myrelease-overridename:5432/postgres?sslmode=disable` but the actual string generated is: `postgresql://postgres:postgres@myrelease-overridename.namespace:5432/postgres?sslmode=disable` ### What you think should happen instead Installation should succeed with correctly generated metadata connection string ### How to reproduce To reproduce just set the following in values.yaml and attempt `helm install` ``` postgresql: enabled: true nameOverride: overridename ``` ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details using helm with kind cluster ### 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/22790

https://github.com/apache/airflow/pull/29214

338a633fc9faab54e72c408e8a47eeadb3ad55f5

56175e4afae00bf7ccea4116ecc09d987a6213c3

python

closed

apache/airflow

https://github.com/apache/airflow

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

ExternalTaskSensor does not properly expand templates in external_task_id(s)

### Apache Airflow version 2.2.4 ### What happened When using `ExternalTaskSensor`, if a Jinja template is used in `external_task_id` or `external_task_ids`, that template will not be expanded, causing the sensor to always fail. ### What you think should happen instead Ideally the template should be expanded. If we can't make that work for whatever reason, we should remove `external_task_id` from the list of valid template fields. ### How to reproduce ``` #!/usr/bin/env python3 from datetime import datetime from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.sensors.external_task import ExternalTaskSensor with DAG('dag1', start_date=datetime(2022, 4, 1), schedule_interval='@daily', is_paused_upon_creation=False) as dag1: DummyOperator(task_id='task_123') with DAG('dag2', start_date=datetime(2022, 4, 1), schedule_interval='@daily', is_paused_upon_creation=False) as dag2: ExternalTaskSensor( task_id='not_using_params', external_dag_id='dag1', external_task_id='task_123', check_existence=True, ) ExternalTaskSensor( task_id='using_external_task_id', external_dag_id='{{ params.dag_name }}', external_task_id='{{ params.task_name }}', check_existence=True, params={ 'dag_name': 'dag1', 'task_name': 'task_123', }, ) ExternalTaskSensor( task_id='using_external_task_ids', external_dag_id='{{ params.dag_name }}', external_task_ids=['{{ params.task_name }}'], check_existence=True, params={ 'dag_name': 'dag1', 'task_name': 'task_123', }, ) ``` Here are some relevant snippets from the task logs: 'not_using_params': ``` [2022-04-06, 04:25:40 CDT] {external_task.py:169} INFO - Poking for tasks ['task_123'] in dag dag1 on 2022-04-01T00:00:00+00:00 ... ``` 'using_external_task_id': ``` [2022-04-06, 04:25:41 CDT] {external_task.py:169} INFO - Poking for tasks ['{{ params.task_name }}'] in dag dag1 on 2022-04-01T00:00:00+00:00 ... ``` 'using_external_task_ids': ``` [2022-04-06, 04:25:43 CDT] {external_task.py:169} INFO - Poking for tasks ['{{ params.task_name }}'] in dag dag1 on 2022-04-01T00:00:00+00:00 ... ``` As we can see, the templated versions correctly expand the `dag_name` parameter, but not `task_name`. ### Operating System CentOS 7.4 ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details Standalone ### Anything else Maybe a separate issue, but worth noting: `ExternalTaskSensor` does not even list `external_task_ids` as a valid template field, though it seems like it should. In the above example, the "Rendered Template" works correctly for `'using_external_task_id'`, but not for `'using_external_task_ids'`. ### 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/22782

https://github.com/apache/airflow/pull/22809

aa317d92ea4dd38fbc27501048ee78b1c0c0aeb5

7331eefc393b8f1fae6f3cf061cf17eb5eaa3fc8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "airflow/utils/log/secrets_masker.py", "tests/utils/log/test_secrets_masker.py"]

Webserver doesn't mask rendered fields for pending tasks

### Apache Airflow version 2.2.5 (latest released) ### What happened When triggering a new dagrun the webserver will not mask secrets in the rendered fields for that dagrun's tasks which didn't start yet. Tasks which have completed or are in state running are not affected by this. ### What you think should happen instead The webserver should mask all secrets for tasks which have started or not started. <img width="628" alt="Screenshot 2022-04-04 at 15 36 29" src="https://user-images.githubusercontent.com/7921017/161628806-c2c579e2-faea-40cc-835c-ac6802d15dc1.png"> . ### How to reproduce Create a variable `my_secret` and run this DAG ```python from datetime import timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.sensors.time_delta import TimeDeltaSensor from airflow.utils.dates import days_ago with DAG( "secrets", start_date=days_ago(1), schedule_interval=None, ) as dag: wait = TimeDeltaSensor( task_id="wait", delta=timedelta(minutes=1), ) task = wait >> BashOperator( task_id="secret_task", bash_command="echo '{{ var.value.my_secret }}'", ) ``` While the first task `wait` is running, displaying rendered fields for the second task `secret_task` will show the unmasked secret variable. <img width="1221" alt="Screenshot 2022-04-04 at 15 33 43" src="https://user-images.githubusercontent.com/7921017/161628734-b7b13190-a3fe-4898-8fa9-ff7537245c1c.png"> ### Operating System Debian (Astronomer Airflow Docker image) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==1!3.2.0 apache-airflow-providers-cncf-kubernetes==1!3.0.0 apache-airflow-providers-elasticsearch==1!3.0.2 apache-airflow-providers-ftp==1!2.1.2 apache-airflow-providers-google==1!6.7.0 apache-airflow-providers-http==1!2.1.2 apache-airflow-providers-imap==1!2.2.3 apache-airflow-providers-microsoft-azure==1!3.7.2 apache-airflow-providers-mysql==1!2.2.3 apache-airflow-providers-postgres==1!4.1.0 apache-airflow-providers-redis==1!2.0.4 apache-airflow-providers-slack==1!4.2.3 apache-airflow-providers-sqlite==1!2.1.3 apache-airflow-providers-ssh==1!2.4.3 ``` ### Deployment Astronomer ### Deployment details _No response_ ### Anything else We have seen this issue also in Airflow 2.2.3. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/22738

https://github.com/apache/airflow/pull/23807

10a0d8e7085f018b7328533030de76b48de747e2

2dc806367c3dc27df5db4b955d151e789fbc78b0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/models/taskmixin.py", "airflow/serialization/serialized_objects.py", "airflow/utils/task_group.py", "airflow/www/views.py", "tests/models/test_dag.py", "tests/serialization/test_dag_serialization.py", "tests/utils/test_task_group.py"]

Fix the order that tasks are displayed in Grid view

The order that tasks are displayed in Grid view do not correlate with the order that the tasks would be expected to execute in the DAG. See `example_bash_operator` below: <img width="335" alt="Screen Shot 2022-04-04 at 11 47 31 AM" src="https://user-images.githubusercontent.com/4600967/161582603-dffea697-68d9-4145-909d-3240f3a65ad2.png"> <img width="426" alt="Screen Shot 2022-04-04 at 11 47 36 AM" src="https://user-images.githubusercontent.com/4600967/161582604-d59885cc-2c71-4a7d-b332-e439115d8c4c.png"> We should update the [task_group_to_tree](https://github.com/apache/airflow/blob/main/airflow/www/views.py#L232) function in views.py to better approximate the order that tasks would be run.

https://github.com/apache/airflow/issues/22731

https://github.com/apache/airflow/pull/22741

e9df0f2de95bb69490d9530d5a27d7b05b71c32e

34154803ac73d62d3e969e480405df3073032622

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/dbt/cloud/hooks/dbt.py", "tests/providers/dbt/cloud/hooks/test_dbt_cloud.py", "tests/providers/dbt/cloud/sensors/test_dbt_cloud.py"]

dbt Cloud Provider only works for Multi-tenant instances

### Apache Airflow Provider(s) dbt-cloud ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.2.5 (latest released) ### Operating System any ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened Some dbt Cloud deployments require the setting of a different base URL (could be X.getdbt.com) or cloud.X.getdbt.com) Relevant line: https://github.com/apache/airflow/blame/436c17c655494eff5724df98d1a231ffa2142253/airflow/providers/dbt/cloud/hooks/dbt.py#L154 self.base_url = "https://cloud.getdbt.com/api/v2/accounts/" ### What you think should happen instead A runtime paramater that defaults to cloud.getdbt ### 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/22730

https://github.com/apache/airflow/pull/24264

98b4e48fbc1262f1381e7a4ca6cce31d96e6f5e9

7498fba826ec477b02a40a2e23e1c685f148e20f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/local_to_gcs.py", "tests/providers/google/cloud/transfers/test_local_to_gcs.py"]

LocalFileSystemToGCSOperator give false positive while copying file from src to dest, even when src has no file

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==6.4.0 ### Apache Airflow version 2.1.4 ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened When you run LocalFilesSystemToGCSOperator with the params for src and dest, the operator reports a false positive when there are no files present under the specified src directory. I expected it to fail stating the specified directory doesn't have any file. [2022-03-15 14:26:15,475] {taskinstance.py:1107} INFO - Executing <Task(LocalFilesystemToGCSOperator): upload_files_to_GCS> on 2022-03-15T14:25:59.554459+00:00 [2022-03-15 14:26:15,484] {standard_task_runner.py:52} INFO - Started process 709 to run task [2022-03-15 14:26:15,492] {standard_task_runner.py:76} INFO - Running: ['***', 'tasks', 'run', 'dag', 'upload_files_to_GCS', '2022-03-15T14:25:59.554459+00:00', '--job-id', '1562', '--pool', 'default_pool', '--raw', '--subdir', 'DAGS_FOLDER/dag.py', '--cfg-path', '/tmp/tmp_e9t7pl9', '--error-file', '/tmp/tmpyij6m4er'] [2022-03-15 14:26:15,493] {standard_task_runner.py:77} INFO - Job 1562: Subtask upload_files_to_GCS [2022-03-15 14:26:15,590] {logging_mixin.py:104} INFO - Running <TaskInstance: dag.upload_files_to_GCS 2022-03-15T14:25:59.554459+00:00 [running]> on host 653e566fd372 [2022-03-15 14:26:15,752] {taskinstance.py:1300} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=jet2 AIRFLOW_CTX_DAG_ID=dag AIRFLOW_CTX_TASK_ID=upload_files_to_GCS AIRFLOW_CTX_EXECUTION_DATE=2022-03-15T14:25:59.554459+00:00 AIRFLOW_CTX_DAG_RUN_ID=manual__2022-03-15T14:25:59.554459+00:00 [2022-03-15 14:26:19,357] {taskinstance.py:1204} INFO - Marking task as SUCCESS. gag, task_id=upload_files_to_GCS, execution_date=20220315T142559, start_date=20220315T142615, end_date=20220315T142619 [2022-03-15 14:26:19,422] {taskinstance.py:1265} INFO - 1 downstream tasks scheduled from follow-on schedule check [2022-03-15 14:26:19,458] {local_task_job.py:149} INFO - Task exited with return code 0 ### What you think should happen instead The operator should at least info that no files were copied than just making it successful. ### How to reproduce - create a Dag with LocalFilesSystemToGCSOperator - specify an empty directory as src and a gcp bucket as bucket_name, dest param(can be blank). - run the dag ### 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/22705

https://github.com/apache/airflow/pull/22772

921ccedf7f90f15e8d18c27a77b29d232be3c8cb

838cf401b9a424ad0fbccd5fb8d3040a8f4a7f44

python

closed

apache/airflow

https://github.com/apache/airflow

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

KubernetesPodOperator failure email alert with actual error log from command executed

### Description When a command executed using KubernetesPodOperator fails, the alert email only says: `Exception: Pod Launching failed: Pod pod_name_xyz returned a failure` along with other parameters supplied to the operator but doesn't contain actual error message thrown by the command. ~~I am thinking similar to how xcom works with KubernetesPodOperator, if the command could write the error log in sidecar container in /airflow/log/error.log and airflow picks that up, then it could be included in the alert email (probably at the top). It can use same sidecar as for xcom (if that is easier to maintain) but write in different folder.~~ Looks like kubernetes has a way to send termination message. https://kubernetes.io/docs/tasks/debug-application-cluster/determine-reason-pod-failure/ Just need to pull that from container status message and include it in failure message at the top. ### Use case/motivation Similar to how email alert for most other operator includes key error message right there without having to login to airflow to see the logs, i am expecting similar functionality from KubernetesPodOperator too. ### 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/22693

https://github.com/apache/airflow/pull/22871

ddb5d9b4a2b4e6605f66f82a6bec30393f096c05

d81703c5778e13470fcd267578697158776b8318

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-apache-hdfs/index.rst"]

HDFS provider causes TypeError: __init__() got an unexpected keyword argument 'encoding'

### Discussed in https://github.com/apache/airflow/discussions/22301 <div type='discussions-op-text'> ^{Originally posted by **frankie1211** March 16, 2022} I build the custom container image, below is my Dockerfile. ```dockerfile FROM apache/airflow:2.2.4-python3.9 USER root RUN apt-get update \ && apt-get install -y gcc g++ vim libkrb5-dev build-essential libsasl2-dev \ && apt-get autoremove -yqq --purge \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* USER airflow RUN pip install --upgrade pip RUN pip install apache-airflow-providers-apache-spark --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.2.4/constraints-3.9.txt RUN pip install apache-airflow-providers-apache-hdfs --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.2.4/constraints-3.9.txt" ``` But i got the error when i run the container ``` airflow-init_1 | The container is run as root user. For security, consider using a regular user account. airflow-init_1 | .................... airflow-init_1 | ERROR! Maximum number of retries (20) reached. airflow-init_1 | airflow-init_1 | Last check result: airflow-init_1 | $ airflow db check airflow-init_1 | Traceback (most recent call last): airflow-init_1 | File "/home/airflow/.local/bin/airflow", line 5, in <module> airflow-init_1 | from airflow.__main__ import main airflow-init_1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 28, in <module> airflow-init_1 | from airflow.cli import cli_parser airflow-init_1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 621, in <module> airflow-init_1 | type=argparse.FileType('w', encoding='UTF-8'), airflow-init_1 | TypeError: __init__() got an unexpected keyword argument 'encoding' airflow-init_1 | airflow_airflow-init_1 exited with code 1 ``` </div>

https://github.com/apache/airflow/issues/22689

https://github.com/apache/airflow/pull/29614

79c07e3fc5d580aea271ff3f0887291ae9e4473f

0a4184e34c1d83ad25c61adc23b838e994fc43f1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_gcs.py", "tests/providers/google/cloud/transfers/test_gcs_to_gcs.py"]

GCSToGCSOperator cannot copy a single file/folder without copying other files/folders with that prefix

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.2.4 (latest released) ### Operating System MacOS 12.2.1 ### Deployment Composer ### Deployment details _No response_ ### What happened I have file "hourse.jpeg" and "hourse.jpeg.copy" and a folder "hourse.jpeg.folder" in source bucket. I use the following code to try to copy only "hourse.jpeg" to another bucket. gcs_to_gcs_op = GCSToGCSOperator( task_id="gcs_to_gcs", source_bucket=my_source_bucket, source_object="hourse.jpeg", destination_bucket=my_destination_bucket ) The result is the two files and one folder mentioned above are copied. From the source code it seems there is no way to do what i want. ### What you think should happen instead Only the file specified should be copied, that means we should treat source_object as exact match instead of prefix. To accomplish the current behavior as prefix, the user can/should use wild char source_object="hourse.jpeg*" ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/22675

https://github.com/apache/airflow/pull/24039

5e6997ed45be0972bf5ea7dc06e4e1cef73b735a

ec84ffe71cfa8246155b9b4cb10bf2167e75adcf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/mappedoperator.py"]

Superfluous TypeError when passing not-iterables to `expand()`

### Apache Airflow version main (development) ### What happened Here's a problematic dag. `False` is invalid here. ```python3 from airflow.models import DAG from airflow.operators.python import PythonOperator from datetime import datetime, timedelta with DAG( dag_id="singleton_expanded", schedule_interval=timedelta(days=365), start_date=datetime(2001, 1, 1), ) as dag: # has problem PythonOperator.partial( task_id="foo", python_callable=lambda x: "hi" if x else "bye", ).expand(op_args=False) ``` When I check for errors like `python dags/the_dag.py` I get the following error: ``` Traceback (most recent call last): File "/Users/matt/2022/03/30/dags/the_dag.py", line 13, in <module> PythonOperator.partial( File "/Users/matt/src/airflow/airflow/models/mappedoperator.py", line 187, in expand validate_mapping_kwargs(self.operator_class, "expand", mapped_kwargs) File "/Users/matt/src/airflow/airflow/models/mappedoperator.py", line 116, in validate_mapping_kwargs raise ValueError(error) ValueError: PythonOperator.expand() got an unexpected type 'bool' for keyword argument op_args Exception ignored in: <function OperatorPartial.__del__ at 0x10c63b1f0> Traceback (most recent call last): File "/Users/matt/src/airflow/airflow/models/mappedoperator.py", line 182, in __del__ warnings.warn(f"{self!r} was never mapped!") File "/usr/local/Cellar/python@3.9/3.9.10/Frameworks/Python.framework/Versions/3.9/lib/python3.9/warnings.py", line 109, in _showwarnmsg sw(msg.message, msg.category, msg.filename, msg.lineno, File "/Users/matt/src/airflow/airflow/settings.py", line 115, in custom_show_warning from rich.markup import escape File "<frozen importlib._bootstrap>", line 1007, in _find_and_load File "<frozen importlib._bootstrap>", line 982, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 925, in _find_spec File "<frozen importlib._bootstrap_external>", line 1414, in find_spec File "<frozen importlib._bootstrap_external>", line 1380, in _get_spec TypeError: 'NoneType' object is not iterable ``` ### What you think should happen instead I'm not sure what's up with that type error, the ValueError is what I needed to see. So I expected this: ``` Traceback (most recent call last): File "/Users/matt/2022/03/30/dags/the_dag.py", line 13, in <module> PythonOperator.partial( File "/Users/matt/src/airflow/airflow/models/mappedoperator.py", line 187, in expand validate_mapping_kwargs(self.operator_class, "expand", mapped_kwargs) File "/Users/matt/src/airflow/airflow/models/mappedoperator.py", line 116, in validate_mapping_kwargs raise ValueError(error) ValueError: PythonOperator.expand() got an unexpected type 'bool' for keyword argument op_args ``` ### How to reproduce _No response_ ### Operating System Mac OS ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details - cloned main at 327eab3e2 - created fresh venv and used pip to install - `airflow info` - `airflow db init` - add the dag - `python dags/the_dag.py` ### 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/22665

https://github.com/apache/airflow/pull/22678

9583c1cab65d28146e73aab0993304886c724bf3

17cf6367469c059c82bb7fa4289645682ef22dda

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/flower/flower-ingress.yaml", "chart/templates/webserver/webserver-ingress.yaml"]

Wrong apiVersion Detected During Ingress Creation

### Official Helm Chart version 1.5.0 (latest released) ### Apache Airflow version 2.2.4 (latest released) ### Kubernetes Version microk8s 1.23/stable ### Helm Chart configuration ``` executor: KubernetesExecutor ingress: enabled: true ## airflow webserver ingress configs web: annotations: kubernetes.io/ingress.class: public hosts: -name: "example.com" path: "/airflow" ## Disabled due to using KubernetesExecutor as recommended in the documentation flower: enabled: false ## Disabled due to using KubernetesExecutor as recommended in the documentation redis: enabled: false ``` ### Docker Image customisations No customization required to recreate, the default image has the same behavior. ### What happened Installation notes below, as displayed the install fails due to the web ingress chart attempting a semVerCompare to check that the kube version is greater than 1.19 and, if it's not, it defaults back to the v1beta networking version. The microk8s install exceeds this version so I would expect the Webserver Ingress version to utilize "networking.k8s.io/v1" instead of the beta version. Airflow installation ``` $: helm install airflow apache-airflow/airflow --namespace airflow --values ./custom-values.yaml Error: unable to build kubernetes objects from release manifest: unable to recognize "": no matches for kind "Ingress" in version "networking.k8s.io/v1beta1" ``` microk8s installation ``` $: kubectl version Client Version: version.Info{Major:"1", Minor:"23+", GitVersion:"v1.23.5-2+c812603a312d2b", GitCommit:"c812603a312d2b0c59687a1be1ae17c0878104cc", GitTreeState:"clean", BuildDate:"2022-03-17T16:14:08Z", GoVersion:"go1.17.8", Compiler:"gc", Platform:"linux/amd64"} Server Version: version.Info{Major:"1", Minor:"23+", GitVersion:"v1.23.5-2+c812603a312d2b", GitCommit:"c812603a312d2b0c59687a1be1ae17c0878104cc", GitTreeState:"clean", BuildDate:"2022-03-17T16:11:06Z", GoVersion:"go1.17.8", Compiler:"gc", Platform:"linux/amd64"} ``` ### What you think should happen instead The Webserver Ingress chart should detect that the kube version is greater than 1.19 and utilize the version ```networking.k8s.io/v1```. ### How to reproduce On Ubuntu 18.04, run: 1. ```sudo snap install microk8s --classic``` 2. ```microk8s status --wait-ready``` 3. ```microk8s enable dns ha-cluster helm3 ingress metrics-server storage``` 4. ```microk8s helm3 repo add apache-airflow https://airflow.apache.org``` 5. ```microk8s kubectl create namespace airflow``` 6. ```touch ./custom-values.yaml``` 7. ```vi ./custom-values.yaml``` and insert the values.yaml contents from above 8. ```microk8s helm3 install airflow apache-airflow/airflow --namespace airflow --values ./custom-values.yaml``` ### Anything else This problem can be reproduced consistently. ### 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/22657

https://github.com/apache/airflow/pull/28461

e377e869da9f0e42ac1e0a615347cf7cd6565d54

5c94ef0a77358dbee8ad8735a132b42d78843df7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/sqlalchemy.py"]

SAWarning: TypeDecorator UtcDateTime(timezone=True) will not produce a cache key because the ``cache_ok`` attribute is not set to True

### Apache Airflow version 2.2.4 (latest released) ### What happened Error ``` [2022-03-31, 11:47:06 UTC] {warnings.py:110} WARNING - /home/ec2-user/.local/lib/python3.7/site-packages/airflow/models/xcom.py:437: SAWarning: TypeDecorator UtcDateTime(timezone=True) will not produce a cache key because the ``cache_ok`` attribute is not set to True. This can have significant performance implications including some performance degradations in comparison to prior SQLAlchemy versions. Set this attribute to True if this type object's state is safe to use in a cache key, or False to disable this warning. (Background on this error at: https://sqlalche.me/e/14/cprf) return query.delete() [2022-03-31, 11:47:06 UTC] {warnings.py:110} WARNING - /home/ec2-user/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py:2214: SAWarning: TypeDecorator UtcDateTime(timezone=True) will not produce a cache key because the ``cache_ok`` attribute is not set to True. This can have significant performance implications including some performance degradations in comparison to prior SQLAlchemy versions. Set this attribute to True if this type object's state is safe to use in a cache key, or False to disable this warning. (Background on this error at: https://sqlalche.me/e/14/cprf) for result in query.with_entities(XCom.task_id, XCom.value) [2022-03-31, 11:47:06 UTC] {warnings.py:110} WARNING - /home/ec2-user/.local/lib/python3.7/site-packages/airflow/models/renderedtifields.py:126: SAWarning: TypeDecorator UtcDateTime(timezone=True) will not produce a cache key because the ``cache_ok`` attribute is not set to True. This can have significant performance implications including some performance degradations in comparison to prior SQLAlchemy versions. Set this attribute to True if this type object's state is safe to use in a cache key, or False to disable this warning. (Background on this error at: https://sqlalche.me/e/14/cprf) session.merge(self) [2022-03-31, 11:47:06 UTC] {warnings.py:110} WARNING - /home/ec2-user/.local/lib/python3.7/site-packages/airflow/models/renderedtifields.py:162: SAWarning: Coercing Subquery object into a select() for use in IN(); please pass a select() construct explicitly tuple_(cls.dag_id, cls.task_id, cls.execution_date).notin_(subq1), [2022-03-31, 11:47:06 UTC] {warnings.py:110} WARNING - /home/ec2-user/.local/lib/python3.7/site-packages/airflow/models/renderedtifields.py:163: SAWarning: TypeDecorator UtcDateTime(timezone=True) will not produce a cache key because the ``cache_ok`` attribute is not set to True. This can have significant performance implications including some performance degradations in comparison to prior SQLAlchemy versions. Set this attribute to True if this type object's state is safe to use in a cache key, or False to disable this warning. (Background on this error at: https://sqlalche.me/e/14/cprf) ).delete(synchronize_session=False) ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System NAME="Amazon Linux" VERSION="2" ID="amzn" ID_LIKE="centos rhel fedora" VERSION_ID="2" PRETTY_NAME="Amazon Linux 2" ANSI_COLOR="0;33" CPE_NAME="cpe:2.3:o:amazon:amazon_linux:2" HOME_URL="https://amazonlinux.com/" ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.0.0 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.3 apache-airflow-providers-imap==2.2.0 apache-airflow-providers-postgres==3.0.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sqlite==2.1.0 ### Deployment Other ### Deployment details Pip package ### 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/22647

https://github.com/apache/airflow/pull/24499

cc6a44bdc396a305fd53c7236427c578e9d4d0b7

d9694733cafd9a3d637eb37d5154f0e1e92aadd4

python