Datasets:

bug-localization

/

BeetleBox

like 0

closed

apache/airflow

https://github.com/apache/airflow

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

"Already exists." message is missing while updating user email with existing email id through API

### Apache Airflow version 2.2.0b2 (beta snapshot) ### Operating System Debian buster ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details Astro dev start ### What happened "Already exists." error message is missing while updating user email with existing email id through API. ### What you expected to happen "Email id already exists" error message should appear ### How to reproduce 1. Use a patch request with the below URL. `{{url}}/users/{{username}}` 2. In payload use an exiting email id ``` { "username": "{{username}}", "password": "password1", "email": "{{exiting_email}}@example.com", "first_name": "{{$randomFirstName}}", "last_name": "{{$randomLastName}}", "roles":[{ "name": "Op"}] } ``` ### 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/18703

https://github.com/apache/airflow/pull/18757

cf27419cfe058750cde4247935e20deb60bda572

a36e7ba4176eeacab1aeaf72ce452d3b30f4de3c

python

closed

apache/airflow

https://github.com/apache/airflow

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

Unable to change username while updating user information through API

### Apache Airflow version 2.2.0b2 (beta snapshot) ### Operating System Debian buster ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details Astro dev start ### What happened Unable to change username while updating user information through API though it's possible from UI. ### What you expected to happen Either username should not be editable from UI or It should be editable from API. ### How to reproduce 1. Use a patch request with the below URL. `{{url}}/users/{{username}}` 2. In payload use a different username ``` { "username": "{{username1}}", "password": "password1", "email": "{{email}}@example.com", "first_name": "{{$randomFirstName}}", "last_name": "{{$randomLastName}}", "roles":[{ "name": "Op"}] } ``` ### 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/18702

https://github.com/apache/airflow/pull/18757

cf27419cfe058750cde4247935e20deb60bda572

a36e7ba4176eeacab1aeaf72ce452d3b30f4de3c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/oracle/hooks/oracle.py", "tests/providers/oracle/hooks/test_oracle.py"]

[Oracle] Oracle Hook - make it possible to define a schema in the connection parameters

### Description Currently the oracle hook it not setting a CURRENT_SCHEMA after connecting with the Database. In a lot of use cases we have production and test databases with seperate connections and database schemas e.g. TEST.Table1, PROD.Table1 "Hard-coding" the database schema in SQL Scripts is not elegant due to having different Airflow Instances for developing and Production. An Option would be to store the database schema in a airflow Variable and getting it into the sql script with JINJA. In Large SQL files with several tables it is not elegant either, because for every table a query to the metadatabase is made. Why not using the Schema parameter in the Airflow Connections and executing `ALTER SESSION SET CURRENT_SCHEMA='SCHEMA'` right after successfully connecting to the database? An alternative would be to use option `Connection.current_schema ` of Library cx_Oracle. https://cx-oracle.readthedocs.io/en/6.4.1/connection.html#Connection.current_schema ### Use case/motivation It makes Query development much easier by storing environment attributes directly in the Airflow Connection. You have full flexibility without touching your SQL Script. It makes separation of Test and Production environments and connections possible. ### 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/18664

https://github.com/apache/airflow/pull/19084

6d110b565a505505351d1ff19592626fb24e4516

471e368eacbcae1eedf9b7e1cb4290c385396ea9

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/upgrading-from-1-10/index.rst"]

Pendulum 1.x -> 2.x with Airflow 1.x -> 2.x Documentation Updates

### Describe the issue with documentation With the upgrade from Pendulum 1.x to 2.x that coincides with upgrading from Airflow 1.x to 2.x, there are some breaking changes that aren't mentioned here: [Upgrading from 1.x to 2.x](https://airflow.apache.org/docs/apache-airflow/stable/upgrading-from-1-10/index.html) The specific breaking change I experienced is the .copy() method is completely removed in Pendulum 2.x. It turns out this isn't needed in my case as each macro that provides a pendulum.Pendulum provides a brand new instance, but it still caught me by surprise. I also noticed that the stable documentation (2.1.4 at time of writing) for macros still links to Pendulum 1.x: [Macros reference](https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html) Specifically the macros: execution_date, prev_execution_date, prev_execution_date_success, prev_start_date_success, next_execution_date **EDIT** Another breaking change: .format() now defaults to the alternative formatter in 2.x, meaning `execution_date.format('YYYY-MM-DD HH:mm:ss', formatter='alternative')` now throws errors. ### How to solve the problem The macros reference links definitely need to be changed to Pendulum 2.x, maybe it would be a nice-to-have in the 1.x -> 2.x documentation but I was effectively using .copy() in a misguided way so I wouldn't say it's totally necessary. **EDIT** I think it's worth including that `execution_date.format('YYYY-MM-DD HH:mm:ss', formatter='alternative')` will throw errors in the 1.x -> 2.x upgrade 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/18634

https://github.com/apache/airflow/pull/18955

f967ca91058b4296edb507c7826282050188b501

141d9f2d5d3e47fe7beebd6a56953df1f727746e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/jest-setup.js", "airflow/www/package.json", "airflow/www/static/js/tree/useTreeData.js", "airflow/www/static/js/tree/useTreeData.test.js", "airflow/www/templates/airflow/tree.html", "airflow/www/views.py", "airflow/www/yarn.lock"]

Auto-refresh doesn't take into account the selected date

### Apache Airflow version 2.1.3 ### Operating System Debian GNU/Linux 9 (stretch) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened In the DAG tree view, if I select a custom date in the date filter (top left corner) and press "update", DAG runs are correctly filtered to the selected date and number of runs. However, if the "auto-refresh" toggle is on, when the next tick refresh happens, the date filter is no longer taken into account and the UI displays the actual **latest** x DAG runs status. However, neither the header dates (45° angle) nor the date filter reflect this new time window I investigated in the network inspector and it seems that the xhr request that fetches dag runs status doesn't contain a date parameter and thus always fetch the latest DAG run data ### What you expected to happen I expect that the auto-refresh feature preserves the selected time window ### How to reproduce Open a DAG with at least 20 dag runs Make sure autorefresh is disabled Select a filter date earlier than the 10th dag run start date and a "number of runs" value of 10, press "update" The DAG tree view should now be focused on the 10 first dag runs Now toggle autorefresh and wait for next tick The DAG tree view will now display status of the latest 10 runs but the header dates will still reflect the old start dates ### 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/18632

https://github.com/apache/airflow/pull/19605

186513e24e723b79bc57e3ca0ade3c73e4fa2f9a

d3ccc91ba4af069d3402d458a1f0ca01c3ffb863

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dags.js", "airflow/www/templates/airflow/dags.html"]

Selecting DAG in search dropdown should lead directly to DAG

### Description When searching for a DAG in the search box, the dropdown menu suggests matching DAG names. Currently, selecting a DAG from the dropdown menu initiates a search with that DAG name as the search query. However, I think it would be more intuitive to go directly to the DAG. If the user prefers to execute the search query, they can still have the option to search without selecting from the dropdown. --- Select `example_bash_operator` from dropdown menu:  --- We are taken to the search result instead of that specific DAG:  ### Use case/motivation When you select a specific DAG from the dropdown, you probably intend to go to that DAG. Even if there were another DAG that began with the name of the selected DAG, both DAGs would appear in the dropdown, so you would still be able to select the one that you want. For example:  ### 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/18600

https://github.com/apache/airflow/pull/18991

324c31c2d7ad756ce3814f74f0b6654d02f19426

4d1f14aa9033c284eb9e6b94e9913a13d990f93e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/stats.py", "tests/core/test_stats.py"]

datadog parsing error for dagrun.schedule_delay since it is not passed in float type

### Apache Airflow version 2.1.2 ### Operating System Gentoo/Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened In datadog-agent logs, got parsing error [ AGENT ] 2021-09-29 03:20:01 UTC | CORE | ERROR | (pkg/dogstatsd/server.go:411 in errLog) | Dogstatsd: error parsing metric message '"airflow.dagrun.schedule_delay.skew:<Period [2021-09-29T03:20:00+00:00 -> 2021-09-29T03:20:00.968404+00:00]>|ms"': could not parse dogstatsd metric value: strconv.ParseFloat: parsing "<Period [2021-09-29T03:20:00+00:00 -> 2021-09-29T03:20:00.968404+00:00]>": invalid syntax ### What you expected to happen since datadog agent expects a float, see https://github.com/DataDog/datadog-agent/blob/6830beaeb182faadac40368d9d781b796b4b2c6f/pkg/dogstatsd/parse.go#L119, the schedule_delay should be a float instead of timedelta. ### 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/18599

https://github.com/apache/airflow/pull/19973

dad2f8103be954afaedf15e9d098ee417b0d5d02

5d405d9cda0b88909e6b726769381044477f4678

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/configuration.py", "airflow/models/abstractoperator.py", "airflow/models/baseoperator.py", "tests/core/test_configuration.py"]

Allow to set execution_timeout default value in airflow.cfg

### Discussed in https://github.com/apache/airflow/discussions/18411 <div type='discussions-op-text'> ^{Originally posted by **alexInhert** September 21, 2021} ### Description Currently the default value of execution_timeout in base operator is None https://github.com/apache/airflow/blob/c686241f4ceb62d52e9bfa607822e4b7a3c76222/airflow/models/baseoperator.py#L502 This means that a task will run without limit till finished. ### Use case/motivation The problem is that there is no way to overwrite this default for all dags. This causes problems where we find that tasks run sometimes for 1 week!!! for no reason. They are just stuck. This mostly happens with tasks that submit work to some 3rd party resource. The 3rd party had some error and terminated but the Airflow task for some reason did not. This can be handled in cluster policy however it feels that cluster policy is more to enforce something but less about setting defaults values. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md) </div>

https://github.com/apache/airflow/issues/18578

https://github.com/apache/airflow/pull/22389

4201e6e0da58ef7fff0665af3735112244114d5b

a111a79c0d945640b073783a048c0d0a979b9d02

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Unable to add a new user when logged using LDAP auth

### Discussed in https://github.com/apache/airflow/discussions/18290 <div type='discussions-op-text'> ^{Originally posted by **pawsok** September 16, 2021} ### Apache Airflow version 2.1.4 (latest released) ### Operating System Amazon Linux AMI 2018.03 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details - AWS ECS EC2 mode - RDS PostgreSQL for DB - LDAP authentication enabled ### What happened We upgraded Airflow from 2.0.1 to 2.1.3 and now when i log into Airflow (Admin role) using LDAP authentication and go to Security --> List Users i cannot see **add button** ("plus"). **Airflow 2.0.1** (our current version):  **Airflow 2.1.3:**  ### What you expected to happen Option to add a new user (using LDAP auth). ### How to reproduce 1. Upgrade to Airflow 2.1.3 2. Log in to Airflow as LDAP user type 3. Go to Security --> List Users ### 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) </div>

https://github.com/apache/airflow/issues/18545

https://github.com/apache/airflow/pull/22619

29de8daeeb979d8f395b1e8e001e182f6dee61b8

4e4c0574cdd3689d22e2e7d03521cb82179e0909

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/renderedtifields.py"]

airflow deadlock trying to update rendered_task_instance_fields table (mysql)

### Apache Airflow version 2.1.4 (latest released) ### Operating System linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened We have been unable to reproduce this in our testing. Our dags will utilize the S3KeySensor task. Sometimes we will have up to 100 dag_runs (running the same DAG) waiting for the S3KeySensor to poke the expected S3 documents. We are regularly seeing this deadlock with mysql: ``` Exception: (MySQLdb._exceptions.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') [SQL: DELETE FROM rendered_task_instance_fields WHERE rendered_task_instance_fields.dag_id = %s AND rendered_task_instance_fields.task_id = %s AND (rendered_task_instance_fields.dag_id, rendered_task_instance_fields.task_id, rendered_task_instance_fields.execution_date) NOT IN (SELECT subq1.dag_id, subq1.task_id, subq1.execution_date FROM (SELECT rendered_task_instance_fields.dag_id AS dag_id, rendered_task_instance_fields.task_id AS task_id, rendered_task_instance_fields.execution_date AS execution_date FROM rendered_task_instance_fields WHERE rendered_task_instance_fields.dag_id = %s AND rendered_task_instance_fields.task_id = %s ORDER BY rendered_task_instance_fields.execution_date DESC LIMIT %s) AS subq1)] [parameters: ('refresh_hub', 'scorecard_wait', 'refresh_hub', 'scorecard_wait', 1)] Exception trying create activation error: 400: ``` ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else Sometimes we will wait multiple days for our S3 documents to appear, This deadlock is occurring for 30%-40% of the dag_runs for an individual dags. ### 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/18512

https://github.com/apache/airflow/pull/18616

b6aa8d52a027c75aaa1151989c68f8d6b8529107

db2d73d95e793e63e152692f216deec9b9d9bc85

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "docs/apache-airflow/howto/email-config.rst"]

apache-airflow-providers-sendgrid==2.0.1 doesn't show in the connections drop down UI

### Apache Airflow Provider(s) sendgrid ### Versions of Apache Airflow Providers I'm running this version of airflow locally to test the providers modules. I'm interested in sendgrid, however it doesn't show up as a conn type in the UI making it unusable. The other packages I've installed do show up. ``` @linux-2:~$ airflow info Apache Airflow version | 2.1.2 executor | SequentialExecutor task_logging_handler | airflow.utils.log.file_task_handler.FileTaskHandler sql_alchemy_conn | sqlite:////home/airflow/airflow.db dags_folder | /home/airflow/dags plugins_folder | /home/airflow/plugins base_log_folder | /home/airflow/logs remote_base_log_folder | System info OS | Linux architecture | x86_64 uname | uname_result(system='Linux', node='linux-2.fritz.box', release='5.11.0-34-generic', version='#36~20.04.1-Ubuntu SMP Fri Aug 27 08:06:32 UTC 2021', machine='x86_64', processor='x86_64') locale | ('en_US', 'UTF-8') python_version | 3.6.4 (default, Aug 12 2021, 10:51:13) [GCC 9.3.0] python_location |.pyenv/versions/3.6.4/bin/python3.6 Tools info git | git version 2.25.1 ssh | OpenSSH_8.2p1 Ubuntu-4ubuntu0.2, OpenSSL 1.1.1f 31 Mar 2020 kubectl | Client Version: v1.20.4 gcloud | Google Cloud SDK 357.0.0 cloud_sql_proxy | NOT AVAILABLE mysql | mysql Ver 8.0.26-0ubuntu0.20.04.2 for Linux on x86_64 ((Ubuntu)) sqlite3 | NOT AVAILABLE psql | psql (PostgreSQL) 12.8 (Ubuntu 12.8-0ubuntu0.20.04.1) Providers info apache-airflow-providers-amazon | 2.0.0 apache-airflow-providers-ftp | 2.0.1 apache-airflow-providers-google | 3.0.0 apache-airflow-providers-http | 2.0.1 apache-airflow-providers-imap | 2.0.1 apache-airflow-providers-sendgrid | 2.0.1 apache-airflow-providers-sqlite | 2.0.1 ``` ### Apache Airflow version 2.1.2 ### Operating System NAME="Ubuntu" VERSION="20.04.2 LTS (Focal Fossa)" ID=ubuntu ID_LIKE=debian PRETTY_NAME="Ubuntu 20.04.2 LTS" VERSION_ID="20.04" HOME_URL="https://www.ubuntu.com/" SUPPORT_URL="https://help.ubuntu.com/" BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/" PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy" VERSION_CODENAME=focal UBUNTU_CODENAME=focal ### Deployment Other ### Deployment details I simply run `airflow webserver --port 8080` to test this on my local machine. In production we're using a helm chart, but the package also doesn't show up there prompting me to test it out locally with no luck. ### What happened Nothing ### What you expected to happen sendgrid provider being available as conn_type ### How to reproduce run it locally using localhost ### Anything else nop ### 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/18495

https://github.com/apache/airflow/pull/18502

ac4acf9c5197bd96fbbcd50a83ef3266bfc366a7

be82001f39e5b04fd16d51ef79b3442a3aa56e88

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/timetables/interval.py"]

2.1.3/4 queued dag runs changes catchup=False behaviour

### Apache Airflow version 2.1.4 (latest released) ### Operating System Amazon Linux 2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened Say, for example, you have a DAG that has a sensor. This DAG is set to run every minute, with max_active_runs=1, and catchup=False. This sensor may pass 1 or more times per day. Previously, when this sensor is satisfied once per day, there is 1 DAG run for that given day. When the sensor is satisfied twice per day, there are 2 DAG runs for that given day. With the new queued dag run state, new dag runs will be queued for each minute (up to AIRFLOW__CORE__MAX_QUEUED_RUNS_PER_DAG), this seems to be against the spirit of catchup=False. This means that if a dag run is waiting on a sensor for longer than the schedule_interval, it will still in effect 'catchup' due to the queued dag runs. ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/18487

https://github.com/apache/airflow/pull/19130

3a93ad1d0fd431f5f4243d43ae8865e22607a8bb

829f90ad337c2ea94db7cd58ccdd71dd680ad419

python

closed

apache/airflow

https://github.com/apache/airflow

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

No SQL error shown when using the JDBC operator

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System Debian buster ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details astro dev start Dockerfile - https://gist.github.com/jyotsa09/267940333ffae4d9f3a51ac19762c094#file-dockerfile ### What happened Both connection pointed to the same database where table "Person" doesn't exist. When there is a SQL failure in PostgressOperator then logs are saying - ``` psycopg2.errors.UndefinedTable: relation "person" does not exist LINE 1: Select * from Person ``` When there is a SQL failure in JDBCOperator then logs are just saying - ``` [2021-09-23, 19:06:22 UTC] {local_task_job.py:154} INFO - Task exited with return code ``` ### What you expected to happen JDBCOperator should show "Task failed with exception" or something similar to Postgress. ### How to reproduce Use this dag - https://gist.github.com/jyotsa09/267940333ffae4d9f3a51ac19762c094 (Connection extras is in the gist.) ### Anything else Related: https://github.com/apache/airflow/issues/16564 ### 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/18482

https://github.com/apache/airflow/pull/21540

cb24ee9414afcdc1a2b0fe1ec0b9f0ba5e1bd7b7

bc1b422e1ce3a5b170618a7a6589f8ae2fc33ad6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/dag_command.py", "airflow/jobs/backfill_job.py", "airflow/models/dag.py", "tests/cli/commands/test_dag_command.py"]

CLI: `airflow dags test { dag w/ schedule_interval=None } ` error: "No run dates were found"

### Apache Airflow version 2.2.0b2 (beta snapshot) ### Operating System ubuntu 20.04 ### Versions of Apache Airflow Providers n/a ### Deployment Virtualenv installation ### Deployment details pip install /path/to/airflow/src ### What happened Given any DAG initialized with: `schedule_interval=None` Run `airflow dags test mydagname $(date +%Y-%m-%d)` and get an error: ``` INFO - No run dates were found for the given dates and dag interval. ``` This behavior changed in https://github.com/apache/airflow/pull/15397, it used to trigger a backfill dagrun at the given date. ### What you expected to happen I expected a backfill dagrun with the given date, regardless of whether it fit into the `schedule_interval`. If AIP-39 made that an unrealistic expectation, then I'd hope for some way to define unscheduled dags which can still be tested from the command line (which, so far as I know, is the fastest way to iterate on a DAG.). As it is, I keep changing `schedule_interval` back and forth depending on whether I want to iterate via `astro dev start` (which tolerates `None` but does superfluous work if the dag is scheduled) or via `airflow dags test ...` (which doesn't tolerate `None`). ### How to reproduce Initialize a DAG with: `schedule_interval=None` and run it via `airflow dags test mydagname $(date +%Y-%m-%d)` ### 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/18473

https://github.com/apache/airflow/pull/18742

5306a6071e1cf223ea6b4c8bc4cb8cacd25d370e

cfc2e1bb1cbf013cae065526578a4e8ff8c18362

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"]

Fetching the driver logs using SparkKubernetesSensor fails when the driver pod has a sidecar

### Apache Airflow version 2.1.1 ### Operating System Ubuntu ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened I tried to run a simple Spark application using the SparkKubernetesOperator and SparkKubernetesSensor. In the yaml file for the Spark Operator I added a sidecar container to the driver pod. When the job runs in Airflow the SparkKubernetesSensor step will fail with the following error: > [2021-09-23 13:24:21,547] {spark_kubernetes.py:92} WARNING - Could not read logs for pod pyspark-pi-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({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Thu, 23 Sep 2021 13:24:21 GMT', 'Content-Length': '233'}) HTTP response body: b'{"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"a container name must be specified for pod pyspark-pi-driver, choose one of: [spark-kubernetes-driver logging-sidecar]","reason":"BadRequest","code":400}\n' In my yaml file I am not setting timeToLiveSeconds so the driver pod is still around at the end of the job execution, so there should be no issues fetching the logs. I believe the error is due to the fact that in the call to **get_pod_logs**, from within SparkKubernetesSensor._log_driver, only the driver pod name is sent and not any container name. This syntax works fine if the driver container is alone in the pod, but it will throw an error if there are multiple containers inside the pod. I'm attaching my DAG and yaml files. [spark-py-pi-dag-and-yaml.tar.gz](https://github.com/apache/airflow/files/7218062/spark-py-pi-dag-and-yaml.tar.gz) ### What you expected to happen The SparkKubernetesSensor should be able to get the driver container logs even if there are sidecar containers running along side the driver. ### How to reproduce The attached YAML and DAG definition can be used to reproduce the issue. ### 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/18468

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

923f1ef30e8f4c0df2845817b8f96373991ad3ce

5c97e5be484ff572070b0ad320c5936bc028be93

python

closed

apache/airflow

https://github.com/apache/airflow

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

SlackAPIFileOperator filename is not templatable but is documented as is

### Apache Airflow Provider(s) slack ### Versions of Apache Airflow Providers apache-airflow-providers-slack==3.0.0 ### Apache Airflow version 2.1.4 (latest released) ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened Using a template string for `SlackAPIFileOperator.filename` doesn't apply the templating. ### What you expected to happen `SlackAPIFileOperator.filrname` should work with a template string. ### How to reproduce Use a template string when using `SlackAPIFileOperator`. ### Anything else Re: PR: https://github.com/apache/airflow/pull/17400/files In commit: https://github.com/apache/airflow/pull/17400/commits/bbd11de2f1d37e9e2f07e5f9b4d331bf94ef6b97 `filename` was removed from `template_fields`. I believe this was because `filename` was removed as a parameter in favour of `file`. In a later commit `file` was renamed to `filename` but the `template_fields` was not put back. The documentation still states that it's a templated field. ### 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/18465

https://github.com/apache/airflow/pull/18466

958b679dae6bbd9def7c60191c3a7722ce81382a

9bf0ed2179b62f374cad74334a8976534cf1a53b

python

closed

apache/airflow

https://github.com/apache/airflow

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

BranchPythonOperator intermittently fails to find its downstream task ids

### Apache Airflow version 2.2.0b2 (beta snapshot) ### Operating System debian (docker) ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astro dev start` with dockerfile: ``` FROM quay.io/astronomer/ap-airflow-dev:2.2.0-buster-44011 ``` And dags: - [simple_branch](https://gist.github.com/MatrixManAtYrService/bb7eac11d2a60c840b02f5fc7c4e864f#file-simple_branch-py) - [branch](https://gist.github.com/MatrixManAtYrService/bb7eac11d2a60c840b02f5fc7c4e864f#file-branch-py) ### What happened Spamming the "manual trigger" button on `simple_branch` caused no trouble:  But doing the same on `branch` caused intermittent failures:  task logs: ``` *** Reading local file: /usr/local/airflow/logs/branch/decide_if_you_like_it/2021-09-23T01:31:50.261287+00:00/2.log [2021-09-23, 01:41:27 UTC] {taskinstance.py:1034} INFO - Dependencies all met for <TaskInstance: branch.decide_if_you_like_it manual__2021-09-23T01:31:50.261287+00:00 [queued]> [2021-09-23, 01:41:27 UTC] {taskinstance.py:1034} INFO - Dependencies all met for <TaskInstance: branch.decide_if_you_like_it manual__2021-09-23T01:31:50.261287+00:00 [queued]> [2021-09-23, 01:41:27 UTC] {taskinstance.py:1232} INFO - -------------------------------------------------------------------------------- [2021-09-23, 01:41:27 UTC] {taskinstance.py:1233} INFO - Starting attempt 2 of 2 [2021-09-23, 01:41:27 UTC] {taskinstance.py:1234} INFO - -------------------------------------------------------------------------------- [2021-09-23, 01:41:27 UTC] {taskinstance.py:1253} INFO - Executing <Task(BranchPythonOperator): decide_if_you_like_it> on 2021-09-23 01:31:50.261287+00:00 [2021-09-23, 01:41:27 UTC] {standard_task_runner.py:52} INFO - Started process 8584 to run task [2021-09-23, 01:41:27 UTC] {standard_task_runner.py:76} INFO - Running: ['airflow', 'tasks', 'run', 'branch', 'decide_if_you_like_it', 'manual__2021-09-23T01:31:50.261287+00:00', '--job-id', '216', '--raw', '--subdir', 'DAGS_FOLDER/branch.py', '--cfg-path', '/tmp/tmp82f81d8w', '--error-file', '/tmp/tmpco74n1v0'] [2021-09-23, 01:41:27 UTC] {standard_task_runner.py:77} INFO - Job 216: Subtask decide_if_you_like_it [2021-09-23, 01:41:27 UTC] {logging_mixin.py:109} INFO - Running <TaskInstance: branch.decide_if_you_like_it manual__2021-09-23T01:31:50.261287+00:00 [running]> on host 992912b473f9 [2021-09-23, 01:41:27 UTC] {taskinstance.py:1406} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=airflow AIRFLOW_CTX_DAG_ID=branch AIRFLOW_CTX_TASK_ID=decide_if_you_like_it AIRFLOW_CTX_EXECUTION_DATE=2021-09-23T01:31:50.261287+00:00 AIRFLOW_CTX_DAG_RUN_ID=manual__2021-09-23T01:31:50.261287+00:00 [2021-09-23, 01:41:27 UTC] {python.py:152} INFO - Done. Returned value was: None [2021-09-23, 01:41:27 UTC] {skipmixin.py:143} INFO - Following branch None [2021-09-23, 01:41:27 UTC] {taskinstance.py:1680} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1315, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1437, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1493, in _execute_task result = execute_callable(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/operators/python.py", line 181, in execute self.skip_all_except(context['ti'], branch) File "/usr/local/lib/python3.9/site-packages/airflow/models/skipmixin.py", line 147, in skip_all_except branch_task_ids = set(branch_task_ids) TypeError: 'NoneType' object is not iterable [2021-09-23, 01:41:27 UTC] {taskinstance.py:1261} INFO - Marking task as FAILED. dag_id=branch, task_id=decide_if_you_like_it, execution_date=20210923T013150, start_date=20210923T014127, end_date=20210923T014127 [2021-09-23, 01:41:27 UTC] {standard_task_runner.py:88} ERROR - Failed to execute job 216 for task decide_if_you_like_it 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 292, 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 1315, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1437, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1493, in _execute_task result = execute_callable(context=context) File "/usr/local/lib/python3.9/site-packages/airflow/operators/python.py", line 181, in execute self.skip_all_except(context['ti'], branch) File "/usr/local/lib/python3.9/site-packages/airflow/models/skipmixin.py", line 147, in skip_all_except branch_task_ids = set(branch_task_ids) TypeError: 'NoneType' object is not iterable [2021-09-23, 01:41:27 UTC] {local_task_job.py:154} INFO - Task exited with return code 1 [2021-09-23, 01:41:27 UTC] {local_task_job.py:264} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### What you expected to happen `BranchPythonOperator` should create tasks that always succeed ### How to reproduce Keep clicking manual executions of the dag called `branch` until you've triggered ten or so. At least one of them will fail with the error: ``` TypeError: 'NoneType' object is not iterable ``` ### Anything else I've also seen this behavior in image: `quay.io/astronomer/ap-airflow-dev:2.1.4-buster-44000` ### 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/18454

https://github.com/apache/airflow/pull/18471

2f44f0b023eb8d322fef9b53b609e390e57c7d45

c1578fbd80ae8be7bc73b20ec142df6c0e8b10c8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/cc1e65623dc7_add_max_tries_column_to_task_instance.py"]

Silent failure when running `airflow db init`

### Discussed in https://github.com/apache/airflow/discussions/18408 <div type='discussions-op-text'> ^{Originally posted by **ziplokk1** September 21, 2021} Hey folks, I've come across an issue where when attempting to run migrations against a clean database, but with existing dags defined in my dags folder, there's a silent failure and the migration exits with the `airflow` command usage prompt saying "invalid command 'db'". I got to digging and the culprit was the [following line](https://github.com/apache/airflow/blob/main/airflow/migrations/versions/cc1e65623dc7_add_max_tries_column_to_task_instance.py#L73). I'm almost positive that this has to do with an erroneous `.py` file in my dags folder (I have yet to dig deeper into the issue), but this begged the question of why is the migration is reading the dags to migrate existing data in the database in the first place? Shouldn't the migration simply migrate the data regardless of the state of the dag files themselves? I was able to fix the issue by changing the line in the link above to `dagbag = DagBag(read_dags_from_db=True)` but I'd like to see if there was any context behind reading the dag file instead of the database before opening a PR.</div>

https://github.com/apache/airflow/issues/18449

https://github.com/apache/airflow/pull/18450

716c6355224b1d40b6475c2fa60f0b967204fce6

7924249ce37decd5284427a916eccb83f235a4bb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/hooks/azure_batch.py", "docs/apache-airflow-providers-microsoft-azure/connections/azure_batch.rst", "tests/providers/microsoft/azure/hooks/test_azure_batch.py", "tests/providers/microsoft/azure/operators/test_azure_batch.py"]

Cannot retrieve Account URL in AzureBatchHook using the custom connection fields

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers apache-airflow-providers-microsoft-azure==2.0.0 ### Apache Airflow version 2.1.0 ### Operating System Debian GNU/Linux ### Deployment Astronomer ### Deployment details _No response_ ### What happened Related to #18124. When attempting to connect to Azure Batch, the following exception is thrown even though the corresponding "Azure Batch Account URl" field is populated in the Azure Batch connection form: ``` airflow.exceptions.AirflowException: Extra connection option is missing required parameter: `account_url` ``` Airflow Connection:  ### What you expected to happen Airflow tasks should be able to connect to Azure Batch when properly populating the custom connection form. Or, at the very least, the above exception should not be thrown when an Azure Batch Account URL is provided in the connection. ### How to reproduce 1. Install the Microsoft Azure provider and create an Airflow Connection with the type Azure Batch 2. Provide values for at least "Azure Batch Account URl" 3. Finally execute a task which uses the AzureBatchHook ### Anything else The get_required_param() method is being passed a value of "auth_method" from the `Extra` field in the connection form. The `Extra` field is no longer exposed in the connection form and would never be able to be provided. ```python def _get_required_param(name): """Extract required parameter from extra JSON, raise exception if not found""" value = conn.extra_dejson.get(name) if not value: raise AirflowException(f'Extra connection option is missing required parameter: `{name}`') return value ... batch_account_url = _get_required_param('account_url') or _get_required_param( 'extra__azure_batch__account_url' ) ``` ### 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/18442

https://github.com/apache/airflow/pull/18456

df131471999578f4824a2567ce932a3a24d7c495

1d2924c94e38ade7cd21af429c9f451c14eba183

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Can't Run Tasks from UI when using CeleryKubernetesExecutor

### Apache Airflow version 2.1.3 ### Operating System ContainerOS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.1.0 apache-airflow-providers-celery==2.0.0 apache-airflow-providers-cncf-kubernetes==2.0.2 apache-airflow-providers-databricks==2.0.1 apache-airflow-providers-docker==2.1.0 apache-airflow-providers-elasticsearch==2.0.2 apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-google==5.0.0 apache-airflow-providers-grpc==2.0.0 apache-airflow-providers-hashicorp==2.0.0 apache-airflow-providers-http==2.0.0 apache-airflow-providers-imap==2.0.0 apache-airflow-providers-microsoft-azure==3.1.0 apache-airflow-providers-mysql==2.1.0 apache-airflow-providers-papermill==2.0.1 apache-airflow-providers-postgres==2.0.0 apache-airflow-providers-redis==2.0.0 apache-airflow-providers-sendgrid==2.0.0 apache-airflow-providers-sftp==2.1.0 apache-airflow-providers-slack==4.0.0 apache-airflow-providers-sqlite==2.0.0 apache-airflow-providers-ssh==2.1.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Official Helm Chart on GKE using CeleryKubernetesExecutor. ### What happened I'm unable to Run Tasks from the UI when using CeleryKubernetesExecutor. It just shows the error "Only works with the Celery or Kubernetes executors, sorry". <img width="1792" alt="Captura de Pantalla 2021-09-22 a la(s) 17 00 30" src="https://user-images.githubusercontent.com/2586758/134413668-5b638941-bb50-44ff-9349-d53326d1f489.png"> ### What you expected to happen It should be able to run Tasks from the UI when using CeleryKubernetesExecutor, as it only is a "selective superset" (based on queue) of both the Celery and Kubernetes executors. ### How to reproduce 1. Run an Airflow instance with the CeleryKubernetes executor. 2. Go to any DAG 3. Select a Task 4. Press the "Run" button. 5. Check the error. ### Anything else I will append an example PR as a comment. ### 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/18440

https://github.com/apache/airflow/pull/18441

32947a464d174517da942a84d62dd4d0b1ff4b70

dc45f97cbb192882d628428bd6dd3ccd32128537

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/variable_list.html", "airflow/www/templates/airflow/variable_show.html", "airflow/www/templates/airflow/variable_show_widget.html", "airflow/www/views.py", "airflow/www/widgets.py"]

Add a 'View' button for Airflow Variables in the UI

### Description A 'view' (eye) button is added to the Airflow Variables in the UI. This button shall open the selected Variable for viewing (but not for editing), with the actual json formatting, in either a new page or a modal view. ### Use case/motivation I store variables in the json format, most of them have > 15 attributes. To view the content of the variables, I either have to open the Variable in editing mode, which I find dangerous since there is a chance I (or another user) accidentally delete information; or I have to copy the variable as displayed in the Airflow Variables page list and then pass it through a json formatter to get the correct indentation. I would like to have a way to (safely) view the variable in it's original format. ### 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/18436

https://github.com/apache/airflow/pull/21342

5276ef8ad9749b2aaf4878effda513ee378f4665

f0bbb9d1079e2660b4aa6e57c53faac84b23ce3d

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/flower/flower-deployment.yaml", "chart/templates/scheduler/scheduler-deployment.yaml", "chart/templates/triggerer/triggerer-deployment.yaml", "chart/templates/webserver/webserver-deployment.yaml", "chart/templates/workers/worker-deployment.yaml", "chart/tests/test_airflow_common.py", "chart/tests/test_annotations.py", "chart/values.schema.json", "chart/values.yaml"]

helm chart: allow each component to have different custom pod annotations

### Description Currently, to inject custom annotations into airflow pods, we can use such a snippet in the `values.yaml`: ``` airflowPodAnnotations: my-annotation: "foo" ``` However, this will inject the annotation to the pods of all airflow components (scheduler.. worker.. etc.). There is no way to inject different annotations to different components. It would be nice to be able to inject different annotations to different components with such a `values.yaml`: ``` workers: podAnnotations: my-annotation: "foo" scheduler: podAnnotations: my-annotation: "bar" ``` ### Use case/motivation Generally, it's not a bad idea to make things more customizable to the chart users. Specifically for my use case, the motivation is because we are using vault sidecar to inject secrets into our airflow pods: https://www.vaultproject.io/docs/platform/k8s/injector#secrets-via-annotations And we wish to have different vault connection settings for the different pods. ### 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/18400

https://github.com/apache/airflow/pull/18481

31c7ef51f47a312c3b85b081ad3b9e42cf9a1b53

2f44f0b023eb8d322fef9b53b609e390e57c7d45

python

closed

apache/airflow

https://github.com/apache/airflow

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

TriggerEvent fires, and then defers a second time (doesn't fire a second time though).

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System debian buster ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details Helm info: ``` helm repo add astronomer https://helm.astronomer.io cat <<- 'EOF' | helm install airflow astronomer/airflow --namespace airflow -f - airflowVersion: 2.2.0 defaultAirflowRepository: myrepo defaultAirflowTag: myimagetag executor: KubernetesExecutor images: airflow: repository: myrepo pullPolicy: Always pod_template: repository: myrepo pullPolicy: Always triggerer: serviceAccount: create: True EOF ``` Dockerfile: ``` FROM quay.io/astronomer/ap-airflow-dev:2.2.0-buster-43897 COPY ./dags/ ./dags/ ``` [the dag](https://gist.github.com/MatrixManAtYrService/8d1d0c978465aa249e8bd0498cc08031#file-many_triggers-py) ### What happened Six tasks deferred for a random (but deterministic) amount of time, and the triggerer fired six events. Two of those events then deferred a second time, which wasn't necessary because they had already fired. Looks like a race condition. Here are the triggerer logs: https://gist.github.com/MatrixManAtYrService/8d1d0c978465aa249e8bd0498cc08031#file-triggerer-log-L29 Note that most deferrals only appear once, but the ones for "Aligator" and "Bear" appear twice. ### What you expected to happen Six tasks deferred, six tasks fires, no extra deferrals. ### How to reproduce Run the dag in [this gist](https://gist.github.com/MatrixManAtYrService/8d1d0c978465aa249e8bd0498cc08031) with the kubernetes executor (be sure there's a triggerer running). Notice that some of the triggerer log messages appear nore than once. Those represent superfluous computation. ### 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/18392

https://github.com/apache/airflow/pull/20699

0ebd55e0f8fc7eb26a2b35b779106201ffe88f55

16b8c476518ed76e3689966ec4b0b788be935410

python

closed

apache/airflow

https://github.com/apache/airflow

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

If the DAG contains Tasks Goups, Graph view does not properly display tasks statuses

### Apache Airflow version 2.1.4 (latest released) ### Operating System Linux/Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When upgrading to latest release (2.1.4) from 2.1.3, Graph View does not properly display tasks statuses. See:  All these tasks are in succeed or failed status in the Tree View, for the same Dag Run. ### What you expected to happen Graph View to display task statuses. ### How to reproduce As I can see, DAGs without tasks groups are not affected. So, probably just create a DAG with a task group. I can reproduce this with in local with the official docker image (python 3.8). ### 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/18369

https://github.com/apache/airflow/pull/18607

afec743a60fa56bd743a21e85d718e685cad0778

26680d4a274c4bac036899167e6fea6351e73358

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/experimental/mark_tasks.py", "airflow/api_connexion/endpoints/dag_run_endpoint.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py"]

http patched an in-progress dagRun to state=failed, but the change was clobbered by a subsequent TI state change.

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System debian buster ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astro dev start` image: [quay.io/astronomer/ap-airflow-dev:2.2.0-buster-43897](https://quay.io/repository/astronomer/ap-airflow-dev?tab=tags) ### What happened I patched a dagRun's state to `failed` while it was running. ``` curl -i -X PATCH "http://localhost:8080/api/v1/dags/steady_task_stream/dagRuns/scheduled__2021-09-18T00:00:00+00:00" -H 'Content-Type: application/json' --user 'admin:admin' \ -d '{ "state": "failed" }' ``` For a brief moment, I saw my change reflected in the UI. Then auto-refresh toggled itself to disabled. When I re-enabled it, the dag_run the state: "running".  Presumably this happened because the new API **only** affects the dagRun. The tasks keep running, so they updated dagRun state and overwrite the patched value. ### What you expected to happen The UI will not let you set a dagrun's state without also setting TI states. Here's what it looks like for "failed":  Notice that it forced me to fail a task instance. This is what prevents the dag from continuing onward and clobbering my patched value. It's similar when you set it to "success", except every child task gets set to "success". At the very least, *if* the API lets me patch the dagrun state *then* it should also lets me patch the TI states. This way an API user can patch both at once and prevent the clobber. (`/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}` doesn't yet support the `patch` verb, so I can't currently do this). Better still would be for the API to make it easy for me to do what the UI does. For instance, patching a dagrun with: ``` { "state": "failed" } ``` Should return code 409 and a message like: > To patch steady_task_stream.scheduled__2021-09-18T00:00:00+00:00.state to success, you must also make the following changes: { "task_1":"success", "task_2":"success", ... }. Supply "update_tasks": "true" to do this. And updating it with: ``` { "state": "failed", "update_tasks": "true"} ``` Should succeed and provide feedback about which state changes occurred. ### How to reproduce Any dag will do here, but here's the one I used: https://gist.github.com/MatrixManAtYrService/654827111dc190407a3c81008da6ee16 Be sure to run it in an airflow that has https://github.com/apache/airflow/pull/17839, which introduced the patch functionality that makes it possible to reach this bug. - Unpause the dag - Make note of the execution date - Delete the dag and wait for it to repopulate in the UI (you may need to refresh). - Prepare this command in your terminal, you may need to tweak the dag name and execution date to match your scenario: ``` curl -i -X PATCH "http://localhost:8080/api/v1/dags/steady_task_stream/dagRuns/scheduled__2021-09-18T00:00:00+00:00" -H 'Content-Type: application/json' --user 'admin:admin' \ -d '{ "state": "failed" }' ``` - Unpause the dag again - Before it completes, run the command to patch its state to "failed" Note that as soon as the next task completes, your patched state has been overwritten and is now "running" or maybe "success" ### Anything else @ephraimbuddy, @kaxil mentioned that you might be interested in this. ### 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/18363

https://github.com/apache/airflow/pull/18370

13a558d658c3a1f6df4b2ee5d894fee65dc103db

56d1765ea01b25b42947c3641ef4a64395daec5e

python

closed

apache/airflow

https://github.com/apache/airflow

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

No Mass Delete Option for Task Instances Similar to What DAGRuns Have in UI

### Apache Airflow version 2.1.3 (latest released) ### Operating System macOS Big Sur 11.3.1 ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details default Astronomer deployment with some test DAGs ### What happened In the UI for DAGRuns there are checkboxes that allow multiple DAGRuns to be selected.  Within the Actions menu on this same view, there is a Delete option which allows multiple DAGRuns to be deleted at the same time.  Task instance view on the UI does not offer the same option, even though Task Instances can be individually deleted with the trash can button.  ### What you expected to happen I expect that the Task Instances can also be bulk deleted, in the same way that DAGRuns can be. ### How to reproduce Open up Task Instance and DAGRun views from the Browse tab and compare the options in the Actions dropdown menus. ### 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/18333

https://github.com/apache/airflow/pull/18438

db2d73d95e793e63e152692f216deec9b9d9bc85

932a2254064a860d614ba2b7c10c7cb091605c7d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/executors/kubernetes_executor.py", "airflow/kubernetes/kubernetes_helper_functions.py", "tests/executors/test_kubernetes_executor.py"]

Add task name, DAG name, try_number, and run_id to all Kubernetes executor logs

### Description Every line in the scheduler log pertaining to a particular task instance should be stamped with that task instance's identifying information. In the Kubernetes executor, some lines are stamped only with the pod name instead. ### Use case/motivation When trying to trace the lifecycle of a task in the kubernetes executor, you currently must search first for the name of the pod created by the task, then search for the pod name in the logs. This means you need to be pretty familiar with the structure of the scheduler logs in order to search effectively for the lifecycle of a task that had a problem. Some log statements like `Attempting to finish pod` do have the annotations for the pod, which include dag name, task name, and run_id, but others do not. For instance, `Event: podname-a2f2c1ac706 had an event of type DELETED` has no such annotations. ### 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/18329

https://github.com/apache/airflow/pull/29929

ffc1dbb4acc1448a9ee6576cb4d348a20b209bc5

64b0872d92609e2df465989062e39357eeef9dab

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/config_templates/default_celery.py", "chart/templates/_helpers.yaml", "chart/templates/secrets/result-backend-connection-secret.yaml", "chart/values.yaml", "tests/charts/test_basic_helm_chart.py", "tests/charts/test_rbac.py", "tests/charts/test_result_backend_connection_secret.py"]

db+ string in result backend but not metadata secret

### Official Helm Chart version 1.1.0 (latest released) ### Apache Airflow version 2.1.3 (latest released) ### Kubernetes Version 1.21 ### Helm Chart configuration data: metadataSecretName: "airflow-metadata" resultBackendSecretName: "airflow-result-backend" ### Docker Image customisations _No response_ ### What happened If we only supply 1 secret with ``` connection: postgresql://airflow:password@postgres.rds:5432/airflow?sslmode=disable ``` To use for both metadata and resultBackendConnection then we end up with a connection error because resultBackendConnection expects the string to be formatted like ``` connection: db+postgresql://airflow:password@postgres.rds:5432/airflow?sslmode=disable ``` from what i can tell ### What you expected to happen I'd expect to be able to use the same secret for both using the same format if they are using the same connection. ### How to reproduce Make a secret structured like above to look like the metadataConnection auto-generated secret. use that same secret for the result backend. deploy. ### Anything else Occurs always. To get around currently we make 2 secrets one with just the db+ prepended. ### 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/18283

https://github.com/apache/airflow/pull/24496

5f67cc0747ea661b703e4c44c77e7cd005cb9588

9312b2865a53cfcfe637605c708cf68d6df07a2c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/hooks/subprocess.py", "docs/apache-airflow/logging-monitoring/errors.rst", "tests/core/test_sentry.py"]

test_sentry.py breaks many other tests

### Apache Airflow version main (development) ### Operating System macOS Big Sur ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### What happened `breeze tests tests` fails at `tests/hooks/test_subprocess.py::TestSubprocessHook::test_env_1_empty_env` This can be easily reproduced with pytest directly just by including 2 tests `TestSentryHook::test_add_tagging` and `tests/hooks/test_subprocesses.py` pytest tests/core/test_sentry.py::TestSentryHook::test_add_tagging tests/hooks/test_subprocess.py::TestSubprocessHook::test_env_1_empty_env The test fails because after `TestSentryHook::test_add_tagging` the `subprocess.Popen` is monkey patched to sentry's own [`sentry_sdk.integrations.stdlib.Popen`](https://github.com/getsentry/sentry-python/blob/master/sentry_sdk/integrations/stdlib.py#L186) which behaves differently that the python's built-in [`subprocess.Popen`](https://docs.python.org/3/library/subprocess.html#popen-constructor). sentry's version always adds the contents of `os.environ` to the environment of the subprocess while the original stdlib version will not. The `test_env_1_empty_env` sole purpose seems to check that the `os.environ` is not passed to the subprocess if `env=={}` but unfortunately as soon as you enable sentry that is no longer true. ``` ==================================================================================================================== FAILURES ==================================================================================================================== ____________________________________________________________________________________________________ TestSubprocessHook.test_env_1_empty_env _____________________________________________________________________________________________________ a = (<tests.hooks.test_subprocess.TestSubprocessHook testMethod=test_env_1_empty_env>,) @wraps(func) def standalone_func(*a): > return func(*(a + p.args), **p.kwargs) ../../.pyenv/versions/3.8.8/envs/venv-airflow-38/lib/python3.8/site-packages/parameterized/parameterized.py:533: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.hooks.test_subprocess.TestSubprocessHook testMethod=test_env_1_empty_env>, name = 'empty env', env = {}, expected = {'SUBPROCESS_ENV_TEST': ''} @parameterized.expand( [ ('with env', {'ABC': '123', 'AAA': '456'}, {'ABC': '123', 'AAA': '456', OS_ENV_KEY: ''}), ('empty env', {}, {OS_ENV_KEY: ''}), ('no env', None, {OS_ENV_KEY: OS_ENV_VAL}), ] ) def test_env(self, name, env, expected): """ Test that env variables are exported correctly to the command environment. When ``env`` is ``None``, ``os.environ`` should be passed to ``Popen``. Otherwise, the variables in ``env`` should be available, and ``os.environ`` should not. """ hook = SubprocessHook() def build_cmd(keys, filename): """ Produce bash command to echo env vars into filename. Will always echo the special test var named ``OS_ENV_KEY`` into the file to test whether ``os.environ`` is passed or not. """ return '\n'.join(f"echo {k}=${k}>> {filename}" for k in [*keys, OS_ENV_KEY]) with TemporaryDirectory() as tmp_dir, mock.patch.dict('os.environ', {OS_ENV_KEY: OS_ENV_VAL}): tmp_file = Path(tmp_dir, 'test.txt') command = build_cmd(env and env.keys() or [], tmp_file.as_posix()) hook.run_command(command=['bash', '-c', command], env=env) actual = dict([x.split('=') for x in tmp_file.read_text().splitlines()]) > assert actual == expected E AssertionError: assert {'SUBPROCESS_ENV_TEST': 'this-is-from-os-environ'} == {'SUBPROCESS_ENV_TEST': ''} E Differing items: E {'SUBPROCESS_ENV_TEST': 'this-is-from-os-environ'} != {'SUBPROCESS_ENV_TEST': ''} E Full diff: E - {'SUBPROCESS_ENV_TEST': ''} E + {'SUBPROCESS_ENV_TEST': 'this-is-from-os-environ'} tests/hooks/test_subprocess.py:62: AssertionError ``` ### What you expected to happen I expect to be able to run `breeze tests tests` with no failing tests. Maybe test_sentry should not be run by default. I also expect/hope that enabling [`sentry_on`](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#sentry-on) on `airflow.cfg` does not cause behavioural changes, it seems to me that `test_env_1_empty_env` was added to **explicitly check that the values of certain environment variables were not leaked** to subprocesses. But I they will be leaked as soon as `sentry_on=True` ### How to reproduce pyenv virtualenv 3.8.8 venv-airflow-38 pyenv shell venv-airflow-38 breeze initialize-local-virtualenv pytest tests/core/test_sentry.py::TestSentryHook::test_add_tagging tests/hooks/test_subprocess.py::TestSubprocessHook::test_env_1_empty_env ### 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/18268

https://github.com/apache/airflow/pull/18346

7458f1e7861e5146da61cbff9393d72c88a3608c

50e2e6e5e7c6c758f051daec7eda7c147e9e8bda

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/gcs_to_s3.py", "tests/providers/amazon/aws/transfers/test_gcs_to_s3.py"]

GCSToS3Operator : files already uploaded because of wrong prefix

### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian 10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### What happened The operator airflow.providers.amazon.aws.transfers.gcs_to_s3.GCSToS3Operator is buggy because if the argument replace=False and the task is retried then the task will always fail : ``` Traceback (most recent call last): File "/usr/local/lib/airflow/airflow/models/taskinstance.py", line 985, in _run_raw_task result = task_copy.execute(context=context) File "/home/airflow/gcs/plugins/birdz/operators/gcs_to_s3_operator.py", line 159, in execute cast(bytes, file_bytes), key=dest_key, replace=self.replace, acl_policy=self.s3_acl_policy File "/usr/local/lib/airflow/airflow/providers/amazon/aws/hooks/s3.py", line 61, in wrapper return func(*bound_args.args, **bound_args.kwargs) File "/usr/local/lib/airflow/airflow/providers/amazon/aws/hooks/s3.py", line 90, in wrapper return func(*bound_args.args, **bound_args.kwargs) File "/usr/local/lib/airflow/airflow/providers/amazon/aws/hooks/s3.py", line 608, in load_bytes self._upload_file_obj(file_obj, key, bucket_name, replace, encrypt, acl_policy) File "/usr/local/lib/airflow/airflow/providers/amazon/aws/hooks/s3.py", line 653, in _upload_file_obj raise ValueError(f"The key {key} already exists.") ``` Furthermore I noted that the argument "prefix" that correspond to the GCS prefix to search in the bucket is keeped in the destination S3 ### What you expected to happen I expect the operator to print "In sync, no files needed to be uploaded to S3" if a retry is done in case of replace=false And I expect to keep or not the GCS prefix to the destination S3 key. It's already handled by other transfer operator like 'airflow.providers.google.cloud.transfers.gcs_to_sftp.GCSToSFTPOperator' which implement a keep_directory_structure argument to keep the folder structure between source and destination ### How to reproduce Retry the operator with replace=False ### 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/18267

https://github.com/apache/airflow/pull/22071

184a46fc93cf78e6531f25d53aa022ee6fd66496

c7286e53064d717c97807f7ccd6cad515f88fe52

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/settings.py", "airflow/utils/state.py", "docs/apache-airflow/howto/customize-ui.rst", "tests/www/views/test_views_home.py"]

Deferred status color not distinct enough

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System Mac OSX 11.5.2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened In the tree view, the status color for deferred is very difficult to distinguish from up_for_reschedule and is generally not very distinct. I can’t imagine it’s good for people with colorblindness either. ### What you expected to happen I expect the status colors to form a distinct palette. While there is already some crowding in the greens, I don't want to see it get worse with the new status. ### How to reproduce Make a deferred task and view in tree view. ### Anything else I might suggest something in the purple range like [BlueViolet #8A2BE2](https://www.color-hex.com/color/8a2be2) ### 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/18245

https://github.com/apache/airflow/pull/18414

cfc2e1bb1cbf013cae065526578a4e8ff8c18362

e351eada1189ed50abef8facb1036599ae96399d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/task.html", "airflow/www/views.py", "tests/www/views/test_views_task_norun.py"]

Rendered Template raise general error if TaskInstance not exists

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System Linux ### Versions of Apache Airflow Providers n/a ### Deployment Docker-Compose ### Deployment details Docker Image: apache/airflow:2.2.0b1-python3.8 ```shell $ docker version Client: Version: 20.10.8 API version: 1.41 Go version: go1.16.6 Git commit: 3967b7d28e Built: Wed Aug 4 10:59:01 2021 OS/Arch: linux/amd64 Context: default Experimental: true Server: Engine: Version: 20.10.8 API version: 1.41 (minimum version 1.12) Go version: go1.16.6 Git commit: 75249d88bc Built: Wed Aug 4 10:58:48 2021 OS/Arch: linux/amd64 Experimental: false containerd: Version: v1.5.5 GitCommit: 72cec4be58a9eb6b2910f5d10f1c01ca47d231c0.m runc: Version: 1.0.2 GitCommit: v1.0.2-0-g52b36a2d docker-init: Version: 0.19.0 GitCommit: de40ad0 ``` ```shell $ docker-compose version docker-compose version 1.29.2, build unknown docker-py version: 5.0.2 CPython version: 3.9.6 OpenSSL version: OpenSSL 1.1.1l 24 Aug 2021 ``` ### What happened Rendered Template endpoint raise error if not Task Instances exists (e.g. fresh DAG) ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/home/airflow/.local/lib/python3.8/site-packages/flask/_compat.py", line 39, in reraise raise value File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/auth.py", line 51, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/decorators.py", line 72, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 1055, in rendered_templates ti = dag_run.get_task_instance(task_id=task.task_id, session=session) AttributeError: 'NoneType' object has no attribute 'get_task_instance' ``` ### What you expected to happen I understand that probably no way to render this value anymore by airflow webserver. So basically it can be replaced by the same warning/error which throw when we tried to access to XCom for TaskInstance which not exists (Task Instance -> XCom -> redirect to /home -> In top of the page Task Not exists)  However it would be nice if we have same as we have in 2.1 (and probably earlier in 2.x) ### How to reproduce 1. Create new DAG 2. Go to Graph View 3. Try to access Rendered Tab 4. Get an error  ### Anything else Every time. Sample url: `http://127.0.0.1:8081/rendered-templates?dag_id=sample_dag&task_id=sample_task&execution_date=2021-09-14T10%3A10%3A23.484252%2B00%3A00` Discuss with @uranusjr ### 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/18242

https://github.com/apache/airflow/pull/18244

ed10edd20b3c656fe1cd9b1c17468c0026f075e2

c313febf63ba534c97955955273d3faec583cfd9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Azure wasb hook is creating a container when getting a blob client

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers apache-airflow-providers-microsoft-azure==3.1.1 ### Apache Airflow version 2.1.2 ### Operating System OSX ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened `_get_blob_client` method in the wasb hook is trying to create a container ### What you expected to happen I expect to get a container client of an existing container ### How to reproduce _No response_ ### Anything else I believe the fix is minor, e.g. ``` def _get_blob_client(self, container_name: str, blob_name: str) -> BlobClient: """ Instantiates a blob client :param container_name: The name of the blob container :type container_name: str :param blob_name: The name of the blob. This needs not be existing :type blob_name: str """ container_client = self.create_container(container_name) return container_client.get_blob_client(blob_name) ``` should be changed to ``` def _get_blob_client(self, container_name: str, blob_name: str) -> BlobClient: """ Instantiates a blob client :param container_name: The name of the blob container :type container_name: str :param blob_name: The name of the blob. This needs not be existing :type blob_name: str """ container_client = self._get_container_client(container_name) return container_client.get_blob_client(blob_name) ``` ### 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/18237

https://github.com/apache/airflow/pull/18287

f8ba4755ae77f3e08275d18e5df13c368363066b

2dac083ae241b96241deda20db7725e2fcf3a93e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/local_task_job.py", "tests/jobs/test_local_task_job.py"]

When I set wait_for_downstream=True, the task will not wait, and a failure will be reported

Version: 2.1.3 OS: CentOS 7 Executor: CeleryExecutor I update airflow to 2.1.3 from 1.10.15（New environment redeployment），I found the problem still unsolved，My dag args is: ``` default_args = { 'owner': 'admin', 'depends_on_past': True, 'start_date': start_time, 'wait_for_downstream': True, 'on_failure_callback': failure_callback, 'on_retry_callback': retry_callback, 'retries': 1, 'retry_delay': timedelta(seconds=60) } ``` The DAG will be scheduled hourly Assuming that task is A >> B >> C >> D >> E >> F now A B C is success and D is running，the next batch task A B is success，i think the next batch task C will wait for run until the D is success, because set wait_for_downstream=True，In fact，the task C report a failure ```log *** Reading local file: /home/hadoop/bi/log/airflow/dag_data_etl_1h/ods.session_logs/2021-09-13T06:03:00+00:00/2.log [2021-09-14 15:22:04,492] {__init__.py:51} DEBUG - Loading core task runner: StandardTaskRunner [2021-09-14 15:22:04,501] {base_task_runner.py:62} DEBUG - Planning to run as the user [2021-09-14 15:22:04,503] {taskinstance.py:618} DEBUG - Refreshing TaskInstance <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> from DB [2021-09-14 15:22:04,509] {taskinstance.py:656} DEBUG - Refreshed TaskInstance <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> [2021-09-14 15:22:04,516] {taskinstance.py:918} DEBUG - <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> dependency 'Task Instance State' PASSED: True, Task state queued was valid. [2021-09-14 15:22:04,516] {taskinstance.py:918} DEBUG - <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> dependency 'Not In Retry Period' PASSED: True, The task instance was not marked for retrying. [2021-09-14 15:22:04,516] {taskinstance.py:918} DEBUG - <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> dependency 'Task Instance Not Running' PASSED: True, Task is not in running state. [2021-09-14 15:22:04,524] {taskinstance.py:918} DEBUG - <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]> dependency 'Previous Dagrun State' PASSED: False, The tasks downstream of the previous task instance <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13 05:03:00+00:00 [success]> haven't completed (and wait_for_downstream is True). [2021-09-14 15:22:04,524] {taskinstance.py:897} INFO - Dependencies not met for <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13T06:03:00+00:00 [queued]>, dependency 'Previous Dagrun State' FAILED: The tasks downstream of the previous task instance <TaskInstance: dag_data_etl_1h.ods.session_logs 2021-09-13 05:03:00+00:00 [success]> haven't completed (and wait_for_downstream is True). [2021-09-14 15:22:04,525] {local_task_job.py:96} INFO - Task is not able to be run ``` There is no abnormality, the task is reported inexplicably wrong  Earlier this year, I also upgraded the 2.x version, but this problem has not been resolved. _Originally posted by @pysenic in https://github.com/apache/airflow/discussions/18227_

https://github.com/apache/airflow/issues/18229

https://github.com/apache/airflow/pull/18310

3f493beedebf389000526d1a1a205038dbce29ff

18fef9bb13a2cbd8cc09484082648aec78610d52

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg", "setup.py", "tests/core/test_providers_manager.py"]

tests/core/test_providers_manager.py::TestProviderManager::test_hooks broken on 3.6

### Apache Airflow version main (development) ### Operating System Any ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### What happened `tests/core/test_providers_manager.py::TestProviderManager::test_hooks` is broken on 3.6 by #18209 since newer `importlib-resources` versions uses a different implementation and the mocks no longer works. This was actually visible before merging; all and only 3.6 checks in the PR failed. Let’s be more careful identifying CI failure patterns in the future 🙂 Not exactly sure how to fix yet. I believe the breaking changes were introduced in [importlib-resources 5.2](https://github.com/python/importlib_resources/blob/v5.2.2/CHANGES.rst#v520), but restricting `<5.2` is not a long-term fix since the same version is also in the Python 3.10 stdlib and will bite us again very soon. ### What you expected to happen _No response_ ### How to reproduce ``` $ ./breeze tests -- tests/core/test_providers_manager.py::TestProviderManager::test_hooks ... tests/core/test_providers_manager.py F [100%] ================================================== FAILURES ================================================== _______________________________________ TestProviderManager.test_hooks _______________________________________ self = <test_providers_manager.TestProviderManager testMethod=test_hooks> mock_import_module = <MagicMock name='import_module' id='139679504564520'> @patch('airflow.providers_manager.importlib.import_module') def test_hooks(self, mock_import_module): # Compat with importlib_resources mock_import_module.return_value.__spec__ = Mock() with pytest.warns(expected_warning=None) as warning_records: with self._caplog.at_level(logging.WARNING): > provider_manager = ProvidersManager() tests/core/test_providers_manager.py:123: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/providers_manager.py:242: in __init__ self._provider_schema_validator = _create_provider_info_schema_validator() airflow/providers_manager.py:92: in _create_provider_info_schema_validator schema = json.loads(importlib_resources.read_text('airflow', 'provider_info.schema.json')) /usr/local/lib/python3.6/site-packages/importlib_resources/_legacy.py:46: in read_text with open_text(package, resource, encoding, errors) as fp: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ package = 'airflow', resource = 'provider_info.schema.json', encoding = 'utf-8', errors = 'strict' def open_text( package: Package, resource: Resource, encoding: str = 'utf-8', errors: str = 'strict', ) -> TextIO: """Return a file-like object opened for text reading of the resource.""" > return (_common.files(package) / _common.normalize_path(resource)).open( 'r', encoding=encoding, errors=errors ) E TypeError: unsupported operand type(s) for /: 'Mock' and 'str' ``` ### 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/18225

https://github.com/apache/airflow/pull/18228

c313febf63ba534c97955955273d3faec583cfd9

21d53ed2ab4e246890da56698d1146a2287b932d

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/UPDATING.rst", "chart/templates/NOTES.txt", "chart/templates/flower/flower-ingress.yaml", "chart/templates/webserver/webserver-ingress.yaml", "chart/tests/test_ingress_flower.py", "chart/tests/test_ingress_web.py", "chart/values.schema.json", "chart/values.yaml"]

Helm chart ingress support multiple hostnames

### Description When the official airflow helm chart is used to install airflow in k8s, I want to be able to access the airflow UI from multiple hostnames. Currently given how the ingress resource is structured it doesn't seem possible, and modifying it needs to take into account backwards compatibility concerns. ### Use case/motivation Given my company's DNS structure, I need to be able to access the airflow UI running in kubernetes from multiple hostnames. ### 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/18216

https://github.com/apache/airflow/pull/18257

4308a8c364d410ea8c32d2af7cc8ca3261054696

516d6d86064477b1e2044a92bffb33bf9d7fb508

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/user_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "tests/api_connexion/endpoints/test_user_endpoint.py", "tests/test_utils/api_connexion_utils.py"]

POST /api/v1/users fails with exception

### Apache Airflow version main (development) ### Operating System From Astronomer’s QA team ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### What happened When adding a new user, The following exception is emitted: ``` Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/usr/local/lib/python3.9/site-packages/flask/_compat.py", line 39, in reraise raise value File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/usr/local/lib/python3.9/site-packages/airflow/_vendor/connexion/decorators/decorator.py", line 48, in wrapper response = function(request) File "/usr/local/lib/python3.9/site-packages/airflow/_vendor/connexion/decorators/uri_parsing.py", line 144, in wrapper response = function(request) File "/usr/local/lib/python3.9/site-packages/airflow/_vendor/connexion/decorators/validation.py", line 184, in wrapper response = function(request) File "/usr/local/lib/python3.9/site-packages/airflow/_vendor/connexion/decorators/response.py", line 103, in wrapper response = function(request) File "/usr/local/lib/python3.9/site-packages/airflow/_vendor/connexion/decorators/parameter.py", line 121, in wrapper return function(**kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/api_connexion/security.py", line 47, in decorated return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/api_connexion/endpoints/user_endpoint.py", line 105, in post_user user.roles.extend(roles_to_add) AttributeError: 'bool' object has no attribute 'roles' ``` The immediate cause to this exception is F.A.B. returns `False` when it fails to add a new user. The problem, however, is _why_ excactly it failed. This is the payload used: ```json { "username": "username6", "password": "password1", "email": "username5@example.com", "first_name": "user2", "last_name": "test1", "roles":[{"name":"Admin"},{"name":"Viewer"}] } ``` This went through validation, therefore we know 1. The POST-ing user has permission to create a new user. 2. The format is correct (including the nested roles). 3. There is not already an existing `username6` in the database. 4. All listed roles exist. (All these are already covered by unit tests.) Further complicating the issue is F.A.B.’s security manager swallows an exception when this happens, and only logs the exception to the server. And we’re having trouble locating that line of log. It’s quite difficult to diagnose further, so I’m posting this hoping someone has better luck reproducing this. I will submit a fix to correct the immediate issue, making the API emit 500 with something like “Failed to create user for unknown reason” to make the failure _slightly_ less confusing. ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/18204

https://github.com/apache/airflow/pull/18224

a9776d36ca46d57a8da2fe931ce91a2847322345

7a1912437ca8dddf668eaa4ca2448dc958e77697

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/task.html", "airflow/www/views.py", "tests/www/views/test_views_task_norun.py"]

NoResultFound raises in UI when access to Task Instance Details

### Apache Airflow version 2.2.0b1 (beta snapshot) ### Operating System Linux (`5.13.13-zen1-1-zen #1 ZEN SMP PREEMPT Thu, 26 Aug 2021 19:14:35 +0000 x86_64 GNU/Linux`) ### Versions of Apache Airflow Providers <details><summary> pip freeze | grep apache-airflow-providers</summary> apache-airflow-providers-amazon==2.1.0<br> apache-airflow-providers-celery==2.0.0<br> apache-airflow-providers-cncf-kubernetes==2.0.1<br> apache-airflow-providers-docker==2.1.0<br> apache-airflow-providers-elasticsearch==2.0.2<br> apache-airflow-providers-ftp==2.0.0<br> apache-airflow-providers-google==5.0.0<br> apache-airflow-providers-grpc==2.0.0<br> apache-airflow-providers-hashicorp==2.0.0<br> apache-airflow-providers-http==2.0.0<br> apache-airflow-providers-imap==2.0.0<br> apache-airflow-providers-microsoft-azure==3.1.0<br> apache-airflow-providers-mysql==2.1.0<br> apache-airflow-providers-postgres==2.0.0<br> apache-airflow-providers-redis==2.0.0<br> apache-airflow-providers-sendgrid==2.0.0<br> apache-airflow-providers-sftp==2.1.0<br> apache-airflow-providers-slack==4.0.0<br> apache-airflow-providers-sqlite==2.0.0<br> apache-airflow-providers-ssh==2.1.0<br> </details> ### Deployment Docker-Compose ### Deployment details Docker Image: apache/airflow:2.2.0b1-python3.8 ```shell $ docker version Client: Version: 20.10.8 API version: 1.41 Go version: go1.16.6 Git commit: 3967b7d28e Built: Wed Aug 4 10:59:01 2021 OS/Arch: linux/amd64 Context: default Experimental: true Server: Engine: Version: 20.10.8 API version: 1.41 (minimum version 1.12) Go version: go1.16.6 Git commit: 75249d88bc Built: Wed Aug 4 10:58:48 2021 OS/Arch: linux/amd64 Experimental: false containerd: Version: v1.5.5 GitCommit: 72cec4be58a9eb6b2910f5d10f1c01ca47d231c0.m runc: Version: 1.0.2 GitCommit: v1.0.2-0-g52b36a2d docker-init: Version: 0.19.0 GitCommit: de40ad0 ``` ```shell $ docker-compose version docker-compose version 1.29.2, build unknown docker-py version: 5.0.2 CPython version: 3.9.6 OpenSSL version: OpenSSL 1.1.1l 24 Aug 2021 ``` ### What happened I've tested current project for compatibility to migrate to 2.2.x in the future. As soon as i tried to access from UI to Task Instance Details or Rendered Template for DAG _which never started before_ I've got this error ``` Python version: 3.8.11 Airflow version: 2.2.0b1 Node: e70bca1d41d3 ------------------------------------------------------------------------------- Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/home/airflow/.local/lib/python3.8/site-packages/flask/_compat.py", line 39, in reraise raise value File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/auth.py", line 51, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/decorators.py", line 72, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 1368, in task session.query(TaskInstance) File "/home/airflow/.local/lib/python3.8/site-packages/sqlalchemy/orm/query.py", line 3500, in one raise orm_exc.NoResultFound("No row was found for one()") sqlalchemy.orm.exc.NoResultFound: No row was found for one() ``` ### What you expected to happen On previous version (Apache Airflow 2.1.2) it show information even if DAG never started. If it's new behavior of Airflow for Task Instances in UI it would be nice get information specific to this error rather than generic error ### How to reproduce 1. Use Apache Airflow: 2.2.0b1 2. Create new DAG 3. In web server try to access to Task Instance Details (`/task` entrypoint) or Rendered Template (`rendered-templates` entrypoint) ### Anything else As soon as DAG started at least once this kind of errors gone when access Task Instance Details or Rendered Template for this DAG Tasks Seems like this kind of errors happen after #17719 ### 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/18188

https://github.com/apache/airflow/pull/18244

ed10edd20b3c656fe1cd9b1c17468c0026f075e2

c313febf63ba534c97955955273d3faec583cfd9

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/core/test_providers_manager.py"]

Upgrade `importlib-resources` version

### Description The version for `importlib-resources` constraint sets it to be [v1.5.0](https://github.com/python/importlib_resources/tree/v1.5.0) which is over a year old. For compatibility sake (for instance with something like Datapane) I would suggest upgrading it. ### Use case/motivation Upgrade a an old dependency to keep code up to date. ### Related issues Not that I am aware of, maybe somewhat #12120, or #15991. ### 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/18155

https://github.com/apache/airflow/pull/18215

dd313a57721918577b6465cd00d815a429a8f240

b7f366cd68b3fed98a4628d5aa15a1e8da7252a3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Deferred TI's `next_method` and `next_kwargs` not cleared on retries

### Apache Airflow version main (development) ### Operating System macOS 11.5.2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened If your first try fails and you have retries enabled, the subsequent tries skip right to the final `next_method(**next_kwargs)` instead of starting with `execute` again. ### What you expected to happen Like we reset things like start date, we should wipe `next_method` and `next_kwargs` so we can retry the task from the beginning. ### 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/18146

https://github.com/apache/airflow/pull/18210

9c8f7ac6236bdddd979bb6242b6c63003fae8490

9d497729184711c33630dec993b88603e0be7248

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/rbac/security-context-constraint-rolebinding.yaml", "chart/tests/test_scc_rolebinding.py", "chart/values.schema.json", "chart/values.yaml", "docs/helm-chart/production-guide.rst"]

Allow airflow standard images to run in openshift utilising the official helm chart

### Description Airflow helm chart is very powerful and configurable, however in order to run it in a on-premises openshift 4 environment, one must manually create security context constraints or extra RBAC rules in order to permit the pods to start with arbitrary user ids. ### Use case/motivation I would like to be able to run Airflow using the provided helmchart in a on-premises Openshift 4 installation. ### 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/18136

https://github.com/apache/airflow/pull/18147

806e4bce9bf827869b4066a14861f791c46179c8

45e8191f5c07a1db83c54cf892767ae71a295ba0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/hooks/adx.py", "tests/providers/microsoft/azure/hooks/test_adx.py"]

Cannot retrieve Authentication Method in AzureDataExplorerHook using the custom connection fields

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers apache-airflow-providers-microsoft-azure==1!3.1.1 ### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian GNU/Linux ### Deployment Astronomer ### Deployment details _No response_ ### What happened When attempting to connect to Azure Data Explorer, the following exception is thrown even though the corresponding "Authentication Method" field is populated in the Azure Data Explorer connection form: ``` airflow.exceptions.AirflowException: Extra connection option is missing required parameter: `auth_method` ``` Airflow Connection:  ### What you expected to happen Airflow tasks should be able to connect to Azure Data Explorer when properly populating the custom connection form. Or, at the very least, the above exception should not be thrown when an Authentication Method is provided in the connection. ### How to reproduce 1. Install the Microsoft Azure provider and create an Airflow Connection with the type `Azure Data Explorer`. 2. Provide all values for "Auth Username", "Auth Password", "Tenant ID", and "Authentication Method". 3. Finally execute a task which uses the `AzureDataExplorerHook` ### Anything else Looks like there are a few issues in the `AzureDataExplorerHook`: - The `get_required_param()` method is being passed a value of "auth_method" from the `Extras` field in the connection form. The `Extras` field is no longer exposed in the connection form and would never be able to be provided. ```python def get_required_param(name: str) -> str: """Extract required parameter from extra JSON, raise exception if not found""" value = conn.extra_dejson.get(name) if not value: raise AirflowException(f'Extra connection option is missing required parameter: `{name}`') return value auth_method = get_required_param('auth_method') or get_required_param( 'extra__azure_data_explorer__auth_method' ) ``` - The custom fields mapping for "Tenant ID" and "Authentication Method" are switched so even if these values are provided in the connection form they will not be used properly in the hook. ```python @staticmethod def get_connection_form_widgets() -> Dict[str, Any]: """Returns connection widgets to add to connection form""" from flask_appbuilder.fieldwidgets import BS3PasswordFieldWidget, BS3TextFieldWidget from flask_babel import lazy_gettext from wtforms import PasswordField, StringField return { "extra__azure_data_explorer__auth_method": StringField( lazy_gettext('Tenant ID'), widget=BS3TextFieldWidget() ), "extra__azure_data_explorer__tenant": StringField( lazy_gettext('Authentication Method'), widget=BS3TextFieldWidget() ), ... ``` ### 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/18124

https://github.com/apache/airflow/pull/18203

d9c0e159dbe670458b89a47d81f49d6a083619a2

410e6d7967c6db0a968f26eb903d072e356f1348

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sentry.py"]

Exception within LocalTaskJob._run_mini_scheduler_on_child_tasks brakes Sentry Handler

### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon @ file:///root/.cache/pypoetry/artifacts/7f/f7/23/fc7fd3543aa486275ef0385c29063ff0dc391b0fc95dc5aa6cab2cf4e5/apache_airflow_providers_amazon-2.2.0-py3-none-any.whl apache-airflow-providers-celery @ file:///root/.cache/pypoetry/artifacts/14/80/39/0d9d57205da1d24189ac9c18eb3477664ed2c2618c1467c9809b9a2fbf/apache_airflow_providers_celery-2.0.0-py3-none-any.whl apache-airflow-providers-ftp @ file:///root/.cache/pypoetry/artifacts/a5/13/da/bf14abc40193a1ee1b82bbd800e3ac230427d7684b9d40998ac3684bef/apache_airflow_providers_ftp-2.0.1-py3-none-any.whl apache-airflow-providers-http @ file:///root/.cache/pypoetry/artifacts/fc/d7/d2/73c89ef847bbae1704fa403d7e92dba1feead757aae141613980db40ff/apache_airflow_providers_http-2.0.0-py3-none-any.whl apache-airflow-providers-imap @ file:///root/.cache/pypoetry/artifacts/af/5d/de/21c10bfc7ac076a415dcc3fc909317547e77e38c005487552cf40ddd97/apache_airflow_providers_imap-2.0.1-py3-none-any.whl apache-airflow-providers-postgres @ file:///root/.cache/pypoetry/artifacts/50/27/e0/9b0d8f4c0abf59967bb87a04a93d73896d9a4558994185dd8bc43bb67f/apache_airflow_providers_postgres-2.2.0-py3-none-any.whl apache-airflow-providers-redis @ file:///root/.cache/pypoetry/artifacts/7d/95/03/5d2a65ace88ae9a9ce9134b927b1e9639c8680c13a31e58425deae55d1/apache_airflow_providers_redis-2.0.1-py3-none-any.whl apache-airflow-providers-sqlite @ file:///root/.cache/pypoetry/artifacts/ec/e6/a3/e0d81fef662ccf79609e7d2c4e4440839a464771fd2a002d252c9a401d/apache_airflow_providers_sqlite-2.0.1-py3-none-any.whl ``` ### Deployment Other Docker-based deployment ### Deployment details We are using the Sentry integration ### What happened An exception within LocalTaskJobs mini scheduler was handled incorrectly by the Sentry integrations 'enrich_errors' method. This is because it assumes its applied to a method of a TypeInstance task ``` TypeError: cannot pickle 'dict_keys' object File "airflow/sentry.py", line 166, in wrapper return func(task_instance, *args, **kwargs) File "airflow/jobs/local_task_job.py", line 241, in _run_mini_scheduler_on_child_tasks partial_dag = task.dag.partial_subset( File "airflow/models/dag.py", line 1487, in partial_subset dag.task_dict = { File "airflow/models/dag.py", line 1488, in <dictcomp> t.task_id: copy.deepcopy(t, {id(t.dag): dag}) # type: ignore File "copy.py", line 153, in deepcopy y = copier(memo) File "airflow/models/baseoperator.py", line 970, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "copy.py", line 161, in deepcopy rv = reductor(4) AttributeError: 'LocalTaskJob' object has no attribute 'task' File "airflow", line 8, in <module> sys.exit(main()) File "airflow/__main__.py", line 40, in main args.func(args) File "airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "airflow/utils/cli.py", line 91, in wrapper return f(*args, **kwargs) File "airflow/cli/commands/task_command.py", line 238, in task_run _run_task_by_selected_method(args, dag, ti) File "airflow/cli/commands/task_command.py", line 64, in _run_task_by_selected_method _run_task_by_local_task_job(args, ti) File "airflow/cli/commands/task_command.py", line 121, in _run_task_by_local_task_job run_job.run() File "airflow/jobs/base_job.py", line 245, in run self._execute() File "airflow/jobs/local_task_job.py", line 128, in _execute self.handle_task_exit(return_code) File "airflow/jobs/local_task_job.py", line 166, in handle_task_exit self._run_mini_scheduler_on_child_tasks() File "airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "airflow/sentry.py", line 168, in wrapper self.add_tagging(task_instance) File "airflow/sentry.py", line 119, in add_tagging task = task_instance.task ``` ### What you expected to happen The error to be handled correctly and passed on to Sentry without raising another exception within the error handling system ### How to reproduce In this case we were trying to backfill task for a DAG that at that point had a compilation error. This is quite an edge case yes :-) ### 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/18118

https://github.com/apache/airflow/pull/18119

c9d29467f71060f14863ca3508cb1055572479b5

f97ddf10e148bd18b6a09ec96f1901068c8684f0

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/jobs/create-user-job.yaml", "chart/templates/jobs/migrate-database-job.yaml", "chart/tests/test_basic_helm_chart.py"]

Helm Chart Jobs (apache-airflow-test-run-airflow-migrations) does not Pass Labels to Pod

### Official Helm Chart version 1.1.0 (latest released) ### Apache Airflow version 2.1.3 (latest released) ### Kubernetes Version 1.18 ### Helm Chart configuration Any configuration should replicate this. Here is a simple example: ``` statsd: enabled: false redis: enabled: false postgresql: enabled: false dags: persistence: enabled: true labels: sidecar.istio.iok/inject: "false" ``` ### Docker Image customisations Base image that comes with helm ### What happened When I installed istio (/w istio proxy etc) on my namespace I noticed that the "apache-airflow-test-run-airflow-migrations" job would never fully complete. I was investigating and it seemed like the issue was with Istio so I tried creating a label in my values.yaml (as seen above) to disable to the istio inject - ``` labels: sidecar.istio.iok/inject: "false" ``` The job picked up this label but when the job created the pod, the pod did not have this label. It appears there's a mismatch between the job's and the pods' labels that it creates. ### What you expected to happen I was thinking that any labels associated to job (from values.yaml) would be inherited in the corresponding pods created ### How to reproduce 1. Install istio on your cluster 2. Create a namespace and add this label to the namespace- `istio-injection: enabled` 3. Add this in your values.yaml `sidecar.istio.iok/inject: "false"` and deploy your helm chart 4. Ensure that your apache-airflow-test-run-airflow-migrations job has the istio inject label, but the corresponding apache-airflow-test-run-airflow-migrations-... pod does **not** ### Anything else I have a fix for this issue in my forked repo [here](https://github.com/jlouk/airflow/commit/c4400493da8b774c59214078eed5cf7d328844ea) I've tested this on my cluster and have ensured this removes the mismatch of labels between job and pod. The helm install continues without error. Let me know if there are any other tests you'd like me to run. Thanks, John ### 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/18103

https://github.com/apache/airflow/pull/18403

43bd351df17084ec8c670e712da0193503519b74

a91d9a7d681a5227d7d72876110e31b448383c20

python

closed

apache/airflow

https://github.com/apache/airflow

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

DAG Import Errors Broken DAG Dropdown Arrow Icon Switched

### Apache Airflow version 2.1.3 (latest released) ### Operating System macOS Big Sur 11.3.1 ### Versions of Apache Airflow Providers N/A ### Deployment Virtualenv installation ### Deployment details Default installation, with one DAG added for testing. ### What happened The arrow icons used when a DAG has an import error there is a drop down that will tell you the errors with the import.   Once the arrow is pressed it flips from facing right to facing down, and the specific tracebacks are displayed.   By clicking the arrow on the traceback, it displays more information about the error that occurred. That arrow then flips from facing down to facing right.   Notice how the arrows in the UI display conflicting information depending on the direction of the arrow and the element that contains that arrow. For the parent -- DAG Import Errors -- when the arrow is right-facing, it hides information in the child elements and when the arrow is down-facing it displays the child elements. For the child elements, the arrow behavior is the opposite of expected: right-facing shows the information, and left-facing hides it. ### What you expected to happen I expect for both the parent and child elements of the DAG Import Errors banner, when the arrow in the GUI is facing right the information is hidden, and when the arrow faces down the information is shown. ### How to reproduce Create a DAG with an import error and the banner will be displayed. ### 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/18100

https://github.com/apache/airflow/pull/18207

8ae2bb9bfa8cfd62a8ae5f6edabce47800ccb140

d3d847acfdd93f8d1d8dc1495cf5b3ca69ae5f78

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/celery_command.py", "airflow/cli/commands/scheduler_command.py", "tests/cli/commands/test_celery_command.py", "tests/cli/commands/test_scheduler_command.py"]

Scheduler process not gracefully exiting on major exceptions

### Apache Airflow version 2.1.3 (latest released) ### Operating System Ubuntu 20.04.3 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.2.0 apache-airflow-providers-cncf-kubernetes==2.0.2 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==5.1.0 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-postgres==2.2.0 apache-airflow-providers-sftp==2.1.1 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.1.1 ### Deployment Other Docker-based deployment ### Deployment details docker-compose version 1.29.2, build 5becea4c Docker version 20.10.8, build 3967b7d ### What happened When a major exception occurs in the scheduler (such as a RDBMS backend failure/restart) or basically anything triggering a failure of the JobScheduler, if the log_serving is on, then although the JobScheduler process is stopped, the exception cascades all the way up to the CLI, but the shutdown of the `serve_logs` process is never triggered [here](https://github.com/apache/airflow/blob/ff64fe84857a58c4f6e47ec3338b576125c4223f/airflow/cli/commands/scheduler_command.py#L72), causing the parent process to wait idle, therefore making any watchdog/restart of the process fail with a downed scheduler in such instances. ### What you expected to happen I expect the whole CLI `airflow scheduler` to exit with an error so it can be safely restarted immediately without any external intervention. ### How to reproduce Launch any scheduler from the CLI with the log serving on (no `--skip-serve-logs`), then shutdown the RDBMS. This will cause the scheduler to exit, but the process will still be running, which is not the expected behavior. ### Anything else I already have a PR to submit for a fix on this issue, care to take a look at it ? Will link it in the issue. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/18096

https://github.com/apache/airflow/pull/18092

0b7b13372f6dbf18a35d5346d3955f65b31dd00d

9a63bf2efb7b45ededbe84039d5f3cf6c2cfb853

python

closed

apache/airflow

https://github.com/apache/airflow

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

KeyError when ECS failed to start image

### Apache Airflow version 2.1.3 (latest released) ### Operating System Linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==1.4.0 apache-airflow-providers-docker==1.2.0 apache-airflow-providers-ftp==1.1.0 apache-airflow-providers-http==1.1.1 apache-airflow-providers-imap==1.0.1 apache-airflow-providers-postgres==2.0.0 apache-airflow-providers-sqlite==1.0.2 ### Deployment Other ### Deployment details _No response_ ### What happened [2021-09-08 00:30:00,035] {taskinstance.py:1462} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1164, 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 1282, 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 1312, in _execute_task result = task_copy.execute(context=context) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 230, in execute self._check_success_task() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/amazon/aws/operators/ecs.py", line 380, in _check_success_task if container.get('lastStatus') == 'STOPPED' and container['exitCode'] != 0: KeyError: 'exitCode' ### What you expected to happen I Expect to see the error reason reported by the ECS operator not the KeyError when the code tries to access the exit code from the container. Maybe something like: ` if container.get('lastStatus') == 'STOPPED' and container.get('exitCode', 1) != 0:` to avoid the exception and some error handling that would make is easier to find the reason the container failed. currently we have to search through the big huge info log with the Failed INFO. ### How to reproduce Run ECS Task with bad creds to pull docker image from gitlab repo ### 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/18089

https://github.com/apache/airflow/pull/20264

38fd65dcfe85149170d6be640c23018e0065cf7c

206cce971da6941e8c1b0d3c4dbf4fa8afe0fba4

python

closed

apache/airflow

https://github.com/apache/airflow

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

Incorrect parameter posted to last_dagruns, task_stats, blocked etc

### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers `apache-airflow-providers-amazon @ file:///root/.cache/pypoetry/artifacts/7f/f7/23/fc7fd3543aa486275ef0385c29063ff0dc391b0fc95dc5aa6cab2cf4e5/apache_airflow_providers_amazon-2.2.0-py3-none-any.whl apache-airflow-providers-celery @ file:///root/.cache/pypoetry/artifacts/14/80/39/0d9d57205da1d24189ac9c18eb3477664ed2c2618c1467c9809b9a2fbf/apache_airflow_providers_celery-2.0.0-py3-none-any.whl apache-airflow-providers-ftp @ file:///root/.cache/pypoetry/artifacts/a5/13/da/bf14abc40193a1ee1b82bbd800e3ac230427d7684b9d40998ac3684bef/apache_airflow_providers_ftp-2.0.1-py3-none-any.whl apache-airflow-providers-http @ file:///root/.cache/pypoetry/artifacts/fc/d7/d2/73c89ef847bbae1704fa403d7e92dba1feead757aae141613980db40ff/apache_airflow_providers_http-2.0.0-py3-none-any.whl apache-airflow-providers-imap @ file:///root/.cache/pypoetry/artifacts/af/5d/de/21c10bfc7ac076a415dcc3fc909317547e77e38c005487552cf40ddd97/apache_airflow_providers_imap-2.0.1-py3-none-any.whl apache-airflow-providers-postgres @ file:///root/.cache/pypoetry/artifacts/77/15/08/a8b670fb068b3135f97d1d343e96d48a43cbf7f6ecd0d3006ba37d90bb/apache_airflow_providers_postgres-2.2.0rc1-py3-none-any.whl apache-airflow-providers-redis @ file:///root/.cache/pypoetry/artifacts/7d/95/03/5d2a65ace88ae9a9ce9134b927b1e9639c8680c13a31e58425deae55d1/apache_airflow_providers_redis-2.0.1-py3-none-any.whl apache-airflow-providers-sqlite @ file:///root/.cache/pypoetry/artifacts/ec/e6/a3/e0d81fef662ccf79609e7d2c4e4440839a464771fd2a002d252c9a401d/apache_airflow_providers_sqlite-2.0.1-py3-none-any.whl ` ### Deployment Docker-Compose ### Deployment details Issue is agnostic to the deployment as long as you have more dags in your system than will fit on the first page in airflow home ### What happened The blocked, last_dagrun, dag_stats, task_stats endpoints are being sent the incorrect form field. The field used to be `dag_ids` and now it seems to be `dagIds` (on the JS side) https://github.com/apache/airflow/blame/2.1.3/airflow/www/static/js/dags.js#L89 and https://github.com/apache/airflow/blob/2.1.3/airflow/www/views.py#L1659. This causes the end point to attempt to return all dags which in our case is 13000+. The fall back behaviour of returning all dags the user has permission for is a bad idea if i am honest. Perhaps it can be removed? ### What you expected to happen The correct form field be posted and only the dags relevant to the page be returned ### How to reproduce 1. Create more dags with task runs than's available on one page (suggest lowering page size) 2. Enable js debugging 3. Refresh /home 4. Inspect response from /blocked /task_stats /last_dagruns and observe it returns dags which aren't on the page ### 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/18083

https://github.com/apache/airflow/pull/18085

3fe948a860a6eed2ee51a6f1be658a3ba260683f

d6e48cd98a1d82829326d4c48a80688866563f3e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/experimental/trigger_dag.py", "tests/api/client/test_local_client.py", "tests/operators/test_trigger_dagrun.py", "tests/www/api/experimental/test_dag_runs_endpoint.py"]

TriggerDagRunOperator start_date is not set

### Apache Airflow version 2.1.3 (latest released) ### Operating System ubuntu, macos ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==1.4.0 apache-airflow-providers-ftp==1.1.0 apache-airflow-providers-http==1.1.1 apache-airflow-providers-imap==1.0.1 apache-airflow-providers-postgres==1.0.2 apache-airflow-providers-slack==4.0.0 apache-airflow-providers-sqlite==1.0.2 ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened We are using TriggerDagRunOperator in the end of DAG to retrigger current DAG: `TriggerDagRunOperator(task_id=‘trigger_task’, trigger_dag_id=‘current_dag’)` Everything works fine, except we have missing duration in UI and ​warnings in scheduler : `[2021-09-07 15:33:12,890] {dagrun.py:604} WARNING - Failed to record duration of <DagRun current_dag @ 2021-09-07 12:32:17.035471+00:00: manual__2021-09-07T12:32:16.956461+00:00, externally triggered: True>: start_date is not set.` And in web UI we can't see duration in TreeView and DAG has no started and duration values. ### What you expected to happen Correct behaviour with start date and duration metrics in web UI. ### How to reproduce ``` import pendulum import pytz from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.operators.trigger_dagrun import TriggerDagRunOperator with DAG( start_date=pendulum.datetime(year=2021, month=7, day=1).astimezone(pytz.utc), schedule_interval='@daily', default_args={}, max_active_runs=1, dag_id='current_dag' ) as dag: step1 = DummyOperator(task_id='dummy_task') trigger_self = TriggerDagRunOperator(task_id='trigger_self', trigger_dag_id='current_dag') step1 >> trigger_self ``` `[2021-09-08 12:53:35,094] {dagrun.py:604} WARNING - Failed to record duration of <DagRun current_dag @ 2021-01-04 03:00:11+00:00: backfill__2021-01-04T03:00:11+00:00, externally triggered: False>: start_date is not set.` ### 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/18082

https://github.com/apache/airflow/pull/18226

1d2924c94e38ade7cd21af429c9f451c14eba183

6609e9a50f0ab593e347bfa92f56194334f5a94d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/microsoft/azure/operators/container_instances.py"]

AzureContainerInstancesOperator is not working due to argument error

### Apache Airflow Provider(s) microsoft-azure ### Versions of Apache Airflow Providers 3.1.0 ### Apache Airflow version 2.1.3 (latest released) ### Operating System Ubuntu ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened I'm getting this error when trying to run AzureContainerInstancesOperator. I believe its due to AzureContainerInstanceHook initialization when passing self.ci_conn_id as a positional argument. https://github.com/apache/airflow/blob/79d85573591f641db4b5f89a12213e799ec6dea1/airflow/providers/microsoft/azure/operators/azure_container_instances.py#L202 Should this be? ```py self._ci_hook = AzureContainerInstanceHook(conn_id=self.ci_conn_id) ``` Error: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1164, 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 1282, 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 1312, in _execute_task result = task_copy.execute(context=context) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/microsoft/azure/operators/azure_container_instances.py", line 202, in execute self._ci_hook = AzureContainerInstanceHook(self.ci_conn_id) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/microsoft/azure/hooks/azure_container_instance.py", line 45, in __init__ super().__init__(sdk_client=ContainerInstanceManagementClient, *args, **kwargs) TypeError: __init__() got multiple values for argument 'sdk_client' ``` ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/18074

https://github.com/apache/airflow/pull/19668

7b700bbe32e18708bd0affbc59c43ce9b3420e28

e25446a8b19197e55989174f210e1c94ae5ff65f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/www/static/js/graph.js", "airflow/www/static/js/tree.js", "airflow/www/templates/airflow/graph.html", "airflow/www/templates/airflow/tree.html", "airflow/www/views.py"]

Move auto-refresh interval to config variable

### Description Auto-refresh is an awesome new feature! Right now it occurs every [3 seconds](https://github.com/apache/airflow/blob/79d85573591f641db4b5f89a12213e799ec6dea1/airflow/www/static/js/tree.js#L463), and this interval is not configurable. Making this interval configurable would be useful. ### Use case/motivation On a DAG with many tasks and many active DAG runs, auto-refresh can be a significant strain on the webserver. We observed our production webserver reach 100% CPU today when we had about 30 running DAG runs of a DAG containing about 50 tasks. Being able to increase the interval to guard against this would be helpful. ### 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/18069

https://github.com/apache/airflow/pull/18107

a3c4784cdc22744aa8bf2634526805cf07da5152

a77379454c7841bef619523819edfb92795cb597

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "chart/templates/workers/worker-deployment.yaml", "docs/docker-stack/entrypoint.rst"]

Chart version 1.1.0 does not gracefully shutdown workers

### Official Helm Chart version 1.1.0 (latest released) ### Apache Airflow version 2.1.3 (latest released) ### Kubernetes Version 1.19.13 ### Helm Chart configuration ```yaml executor: "CeleryExecutor" workers: # Number of airflow celery workers in StatefulSet replicas: 1 # Below is the default value, it does not work command: ~ args: - "bash" - "-c" - |- exec \ airflow celery worker ``` ### Docker Image customisations ```dockerfile FROM apache/airflow:2.1.3-python3.7 ENV AIRFLOW_HOME=/opt/airflow USER root RUN set -ex \ && buildDeps=' \ python3-dev \ libkrb5-dev \ libssl-dev \ libffi-dev \ build-essential \ libblas-dev \ liblapack-dev \ libpq-dev \ gcc \ g++ \ ' \ && apt-get update -yqq \ && apt-get upgrade -yqq \ && apt-get install -yqq --no-install-recommends \ $buildDeps \ libsasl2-dev \ libsasl2-modules \ apt-utils \ curl \ vim \ rsync \ netcat \ locales \ sudo \ patch \ libpq5 \ && apt-get autoremove -yqq --purge\ && apt-get clean \ && rm -rf /var/lib/apt/lists/* USER airflow COPY --chown=airflow:root requirements*.txt /tmp/ RUN pip install -U pip setuptools wheel cython \ && pip install -r /tmp/requirements_providers.txt \ && pip install -r /tmp/requirements.txt COPY --chown=airflow:root setup.py /tmp/custom_operators/ COPY --chown=airflow:root custom_operators/ /tmp/custom_operators/custom_operators/ RUN pip install /tmp/custom_operatos COPY --chown=airflow:root entrypoint*.sh / COPY --chown=airflow:root config/ ${AIRFLOW_HOME}/config/ COPY --chown=airflow:root airflow.cfg ${AIRFLOW_HOME}/ COPY --chown=airflow:root dags/ ${AIRFLOW_HOME}/dags ``` ### What happened Using CeleryExecutor whenever I kill a worker pod that is running a task with `kubectl delete pod` or a `helm upgrade` the pod gets instantly killed and does not wait for the task to finish or the end of terminationGracePeriodSeconds. ### What you expected to happen I expect the worker to finish all it's tasks inside the grace period before being killed Killing the pod when it's running a task throws this ```bash k logs -f airflow-worker-86d78f7477-rjljs * Serving Flask app "airflow.utils.serve_logs" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off [2021-09-07 16:26:25,612] {_internal.py:113} INFO - * Running on http://0.0.0.0:8793/ (Press CTRL+C to quit) /home/airflow/.local/lib/python3.7/site-packages/celery/platforms.py:801 RuntimeWarning: You're running the worker with superuser privileges: this is absolutely not recommended! Please specify a different user using the --uid option. User information: uid=50000 euid=50000 gid=0 egid=0 [2021-09-07 16:28:11,021: WARNING/ForkPoolWorker-1] Running <TaskInstance: test-long-running.long-long 2021-09-07T16:28:09.148524+00:00 [queued]> on host airflow-worker-86d78f7477-rjljs worker: Warm shutdown (MainProcess) [2021-09-07 16:28:32,919: ERROR/MainProcess] Process 'ForkPoolWorker-2' pid:20 exited with 'signal 15 (SIGTERM)' [2021-09-07 16:28:32,930: ERROR/MainProcess] Process 'ForkPoolWorker-1' pid:19 exited with 'signal 15 (SIGTERM)' [2021-09-07 16:28:33,183: ERROR/MainProcess] Task handler raised error: WorkerLostError('Worker exited prematurely: signal 15 (SIGTERM) Job: 0.') Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/celery/worker/worker.py", line 208, in start self.blueprint.start(self) File "/home/airflow/.local/lib/python3.7/site-packages/celery/bootsteps.py", line 119, in start step.start(parent) File "/home/airflow/.local/lib/python3.7/site-packages/celery/bootsteps.py", line 369, in start return self.obj.start() File "/home/airflow/.local/lib/python3.7/site-packages/celery/worker/consumer/consumer.py", line 318, in start blueprint.start(self) File "/home/airflow/.local/lib/python3.7/site-packages/celery/bootsteps.py", line 119, in start step.start(parent) File "/home/airflow/.local/lib/python3.7/site-packages/celery/worker/consumer/consumer.py", line 599, in start c.loop(*c.loop_args()) File "/home/airflow/.local/lib/python3.7/site-packages/celery/worker/loops.py", line 83, in asynloop next(loop) File "/home/airflow/.local/lib/python3.7/site-packages/kombu/asynchronous/hub.py", line 303, in create_loop poll_timeout = fire_timers(propagate=propagate) if scheduled else 1 File "/home/airflow/.local/lib/python3.7/site-packages/kombu/asynchronous/hub.py", line 145, in fire_timers entry() File "/home/airflow/.local/lib/python3.7/site-packages/kombu/asynchronous/timer.py", line 68, in __call__ return self.fun(*self.args, **self.kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/kombu/asynchronous/timer.py", line 130, in _reschedules return fun(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/celery/worker/consumer/gossip.py", line 167, in periodic for worker in values(workers): File "/home/airflow/.local/lib/python3.7/site-packages/kombu/utils/functional.py", line 109, in _iterate_values for k in self: File "/home/airflow/.local/lib/python3.7/site-packages/kombu/utils/functional.py", line 95, in __iter__ def __iter__(self): File "/home/airflow/.local/lib/python3.7/site-packages/celery/apps/worker.py", line 285, in _handle_request raise exc(exitcode) celery.exceptions.WorkerShutdown: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/billiard/pool.py", line 1267, in mark_as_worker_lost human_status(exitcode), job._job), billiard.exceptions.WorkerLostError: Worker exited prematurely: signal 15 (SIGTERM) Job: 0. -------------- celery@airflow-worker-86d78f7477-rjljs v4.4.7 (cliffs) --- ***** ----- -- ******* ---- Linux-5.4.129-63.229.amzn2.x86_64-x86_64-with-debian-10.10 2021-09-07 16:26:26 - *** --- * --- - ** ---------- [config] - ** ---------- .> app: airflow.executors.celery_executor:0x7ff517d78d90 - ** ---------- .> transport: redis://:**@airflow-redis:6379/0 - ** ---------- .> results: postgresql+psycopg2://airflow:**@db-host:5432/airflow - *** --- * --- .> concurrency: 2 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** ----- -------------- [queues] .> default exchange=default(direct) key=default ``` ### How to reproduce Run a dag with this airflow configuration ```yaml executor: "CeleryExecutor" workers: replicas: 1 command: ~ args: - "bash" - "-c" # The format below is necessary to get `helm lint` happy - |- exec \ airflow celery worker ``` and kill the worker pod ### Anything else Overwriting the official entrypoint seems to solve the issue ```yaml workers: # To gracefully shutdown workers I have to overwrite the container entrypoint command: ["airflow"] args: ["celery", "worker"] ``` When the worker gets killed another worker pod comes online and the old one stays in status `Terminating`, all new tasks go to the new worker. Below are the logs when the worker gets killed ```bash k logs -f airflow-worker-5ff95df84f-fznk7 * Serving Flask app "airflow.utils.serve_logs" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off [2021-09-07 16:42:42,399] {_internal.py:113} INFO - * Running on http://0.0.0.0:8793/ (Press CTRL+C to quit) /home/airflow/.local/lib/python3.7/site-packages/celery/platforms.py:801 RuntimeWarning: You're running the worker with superuser privileges: this is absolutely not recommended! Please specify a different user using the --uid option. User information: uid=50000 euid=50000 gid=0 egid=0 [2021-09-07 16:42:53,133: WARNING/ForkPoolWorker-1] Running <TaskInstance: test-long-running.long-long 2021-09-07T16:28:09.148524+00:00 [queued]> on host airflow-worker-5ff95df84f-fznk7 worker: Warm shutdown (MainProcess) -------------- celery@airflow-worker-5ff95df84f-fznk7 v4.4.7 (cliffs) --- ***** ----- -- ******* ---- Linux-5.4.129-63.229.amzn2.x86_64-x86_64-with-debian-10.10 2021-09-07 16:42:43 - *** --- * --- - ** ---------- [config] - ** ---------- .> app: airflow.executors.celery_executor:0x7f69aaa90d50 - ** ---------- .> transport: redis://:**@airflow-redis:6379/0 - ** ---------- .> results: postgresql+psycopg2://airflow:**@db-host:5432/airflow - *** --- * --- .> concurrency: 2 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** ----- -------------- [queues] .> default exchange=default(direct) key=default rpc error: code = Unknown desc = Error: No such container: efe5ce470f5bd5b7f84479c1a8f5dc1d5d92cb1ad6b16696fa5a1ca9610602ee% ``` There is no timestamp but it waits for the task to finish before writing `worker: Warm shutdown (MainProcess)` Another option I tried was using this as the entrypoint and it also works ```bash #!/usr/bin/env bash handle_worker_term_signal() { # Remove worker from queue celery -b $AIRFLOW__CELERY__BROKER_URL -d celery@$HOSTNAME control cancel_consumer default while [ $(airflow jobs check --hostname $HOSTNAME | grep "Found one alive job." | wc -l) -eq 1 ]; do echo 'Finishing jobs!' airflow jobs check --hostname $HOSTNAME --limit 100 --allow-multiple sleep 60 done echo 'All jobs finished! Terminating worker' kill $pid exit 0 } trap handle_worker_term_signal SIGTERM airflow celery worker & pid="$!" wait $pid ``` Got the idea from this post: https://medium.com/flatiron-engineering/upgrading-airflow-with-zero-downtime-8df303760c96 Thanks! ### 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/18066

https://github.com/apache/airflow/pull/18068

491d81893b0afb80b5f9df191369875bce6e2aa0

9e13e450032f4c71c54d091e7f80fe685204b5b4

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images.yml"]

The current CI requires rebasing to main far too often

### Body I was wondering why people have to rebase their PRs so often now, this was not the case for quite a while, but it seems that now, the need to rebase to latest `main` is far more frequent than it should be. I think also we have a number of cases where PR build succeeds but it breaks main after merging. This also did not use to be that often as we observe now.. I looked a bit closer and I believe the problem was #15944 PR streamlined some of the code of our CI I just realised it also removed an important feature of the previous setup - namely using the MERGE commit rather than original commit from the PR. Previously, when we built the image, we have not used the original commit from the incoming PR, but the merge commit that GitHub generates. Whenever there is no conflict, GitHub performs automatic merge with `main` and by default PR during the build uses that 'merge' PR and not the original commit. This means that all the PRs - even if they can be cleanly rebased - are using the original commit now and they are built "as if they were built using original branch point". Unfortunately as far as I checked, there is no "merge commit hash" in the "pull_request_target" workflow. Previously, the "build image" workflow used my custom "get_workflow_origin" action to find the merge commit via GitHub API. This has much better user experience because the users do not have to rebase to main nearly as often as they do now. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/18055

https://github.com/apache/airflow/pull/18060

64d2f5488f6764194a2f4f8a01f961990c75b840

1bfb5722a8917cbf770922a26dc784ea97aacf33

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/hashicorp/hooks/vault.py", "tests/providers/hashicorp/hooks/test_vault.py"]

`VaultHook` AppRole authentication fails when using a conn_uri

### Apache Airflow version 2.1.3 (latest released) ### Operating System MacOS Big Sur ### Versions of Apache Airflow Providers apache-airflow-providers-hashicorp==2.0.0 ### Deployment Astronomer ### Deployment details _No response_ ### What happened When trying to to use the VaultHook with AppRole authentication via a connection defined as a conn_uri, I was unable to establish a connection in a custom operator due the 'role_id' not being provided despite it being an optional argument. https://github.com/apache/airflow/blob/main/airflow/providers/hashicorp/hooks/vault.py#L124 ### What you expected to happen So, with a connection defined as a URI as follows: ``` http://[ROLE_ID]:[SECRET_ID]@https://[VAULT_URL]?auth_type=approle ``` I receive the following error when trying to run a task in a custom operator. ``` [2021-09-07 08:30:36,877] {base.py:79} INFO - Using connection to: id: vault. Host: https://[VAULT_URL], Port: None, Schema: None, Login: [ROLE_ID], Password: ***, extra: {'auth_type': 'approle'} [2021-09-07 08:30:36,879] {taskinstance.py:1462} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1164, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1282, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1312, in _execute_task result = task_copy.execute(context=context) File "/usr/local/airflow/plugins/operators/my_vault_operator.py", line 28, in execute vaulthook = VaultHook(vault_conn_id=self.vault_conn_id) File "/usr/local/lib/python3.7/site-packages/airflow/providers/hashicorp/hooks/vault.py", line 216, in __init__ radius_port=radius_port, File "/usr/local/lib/python3.7/site-packages/airflow/providers/hashicorp/_internal_client/vault_client.py", line 153, in __init__ raise VaultError("The 'approle' authentication type requires 'role_id'") hvac.exceptions.VaultError: The 'approle' authentication type requires 'role_id', on None None ``` Given that the ROLE_ID and SECRET_ID are part of the connection, I was expecting that the hook should retrieve this from `self.connection.login`. ### How to reproduce 1. Create a connection to Vault defined as a `conn_uri`, e.g. `http://[ROLE_ID]:[SECRET_ID]@https://[VAULT_URL]?auth_type=approle` 2. Create a simple custom operator as follows: ``` from airflow.models import BaseOperator from airflow.providers.hashicorp.hooks.vault import VaultHook class MyVaultOperator(BaseOperator): def __init__(self, vault_conn_id=None, *args, **kwargs): super(MyVaultOperator, self).__init__(*args, **kwargs) self.vault_conn_id = vault_conn_id def execute(self, context): vaulthook = VaultHook(vault_conn_id=self.vault_conn_id) ``` 3. Create a simple DAG to use this operator ``` from datetime import datetime from airflow import models from operators.my_vault_operator import MyVaultOperator default_args = { 'owner': 'airflow', 'start_date': datetime(2011, 1, 1), } dag_name = 'my_vault_dag' with models.DAG( dag_name, default_args=default_args ) as dag: my_vault_task = MyVaultOperator( task_id='vault_task', vault_conn_id='vault', ) ``` 4. Run this DAG via `airflow tasks test my_vault_dag vault_task 2021-09-06` ### 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/18053

https://github.com/apache/airflow/pull/18064

9e13e450032f4c71c54d091e7f80fe685204b5b4

476ae0eb588a5bfbc3d415a34cf4f0262f53888e

python

closed

apache/airflow

https://github.com/apache/airflow

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

Duplicating the same connection twice gives "Integrity error, probably unique constraint"

### Apache Airflow version 2.2.0 ### Operating System Debian buster ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details astro dev start ### What happened When I have tried to duplicate a connection first time it has created a connection with hello_copy1 and duplicating the same connection second time giving me an error of ```Connection hello_copy2 can't be added. Integrity error, probably unique constraint.``` ### What you expected to happen It should create a connection with name hello_copy3 or the error message should be more user friendly. ### How to reproduce https://github.com/apache/airflow/pull/15574#issuecomment-912438705 ### Anything else Suggested Error ``` Connection hello_copy2 can't be added because it already exists. ``` or Change the number logic in _copyN so the new name will be always unique. ### 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/18050

https://github.com/apache/airflow/pull/18161

f248a215aa341608e2bc7d9083ca9d18ab756ac4

3ddb36578c5020408f89f5532b21dc0c38e739fb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/neo4j/hooks/neo4j.py", "tests/providers/neo4j/hooks/test_neo4j.py", "tests/providers/neo4j/operators/test_neo4j.py"]

Neo4j Hook does not return query response

### Apache Airflow Provider(s) neo4j ### Versions of Apache Airflow Providers apache-airflow-providers-neo4j version 2.0.0 ### Apache Airflow version 2.1.0 ### Operating System macOS Big Sur Version 11.4 ### Deployment Docker-Compose ### Deployment details docker-compose version 1.29.2, build 5becea4c Docker version 20.10.7, build f0df350 ### What happened ```python neo4j = Neo4jHook(conn_id=self.neo4j_conn_id) sql = "MATCH (n) RETURN COUNT(n)" result = neo4j.run(sql) logging.info(result) ``` If i run the code snippet above i always get an empty response. ### What you expected to happen i would like to have the query response. ### How to reproduce ```python from airflow.models.baseoperator import BaseOperator from airflow.providers.neo4j.hooks.neo4j import Neo4jHook import logging class testNeo4jHookOperator(BaseOperator): def __init__( self, neo4j_conn_id: str, **kwargs) -> None: super().__init__(**kwargs) self.neo4j_conn_id = neo4j_conn_id def execute(self, context): neo4j = Neo4jHook(conn_id=self.neo4j_conn_id) sql = "MATCH (n) RETURN COUNT(n)" result = neo4j.run(sql) logging.info(result) return result ``` I created this custom operator to test the bug. ### Anything else The bug is related to the way the hook is implemented and how the Neo4j driver works: ```python def run(self, query) -> Result: """ Function to create a neo4j session and execute the query in the session. :param query: Neo4j query :return: Result """ driver = self.get_conn() if not self.connection.schema: with driver.session() as session: result = session.run(query) else: with driver.session(database=self.connection.schema) as session: result = session.run(query) return result ``` In my opinion, the above hook run method should be changed to: ```python def run(self, query) -> Result: """ Function to create a neo4j session and execute the query in the session. :param query: Neo4j query :return: Result """ driver = self.get_conn() if not self.connection.schema: with driver.session() as session: result = session.run(query) return result.data() else: with driver.session(database=self.connection.schema) as session: result = session.run(query) return result.data() ``` This is because when trying to get result.data (or result in general) externally the session is always empty. I tried this solution and it seems to work. ### 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/18005

https://github.com/apache/airflow/pull/18007

0dba2e0d644ab0bd2512144231b56463218a3b74

5d2b056f558f3802499eb6d98643433c31d8534c

python

closed

apache/airflow

https://github.com/apache/airflow

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

CloudSqlProxyRunner doesn't support connections from secrets backends

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google~=5.0 ### Apache Airflow version 2.1.3 (latest released) ### Operating System MacOS Big Sur ### Deployment Astronomer ### Deployment details _No response_ ### What happened The CloudSqlProxyRunner queries the Airflow DB directly to retrieve the GCP connection. https://github.com/apache/airflow/blob/7b3a5f95cd19667a683e92e311f6c29d6a9a6a0b/airflow/providers/google/cloud/hooks/cloud_sql.py#L521 ### What you expected to happen In order to not break when using connections stored in external secrets backends, it should use standardized methods to retrieve connections and not query the Airflow DB directly. ### How to reproduce Use a CloudSQLDatabaseHook with `use_proxy=True` while using an external secrets backend. ### Anything else Every time ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/18003

https://github.com/apache/airflow/pull/18006

867e9305f08bf9580f25430d8b6e84071c59f9e6

21348c194d4149237e357e0fff9ed444d27fa71d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/dag_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/exceptions.py", "airflow/www/views.py"]

Delete DAG REST API Functionality

### Description The stable REST API seems to be missing the functionality to delete a DAG. This would mirror clicking the "Trash Can" on the UI (and could maybe eventually power it)  ### 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/17969

https://github.com/apache/airflow/pull/17980

b6a962ca875bc29aa82a252f5c179faff601780b

2cace945cd35545385d83090c8319525d91f8efd

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/task/task_runner/standard_task_runner.py"]

lack of definitive error message if task launch fails

### Apache Airflow version 2.1.2 ### Operating System Centos docker image ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Self built docker image on kubernetes ### What happened If airflow fails to launch a task (Kubernetes worker pod in my case), the task logs with a simple error message "task exited with return code 1". This is problematic for developers working on developing platforms around airflow. ### What you expected to happen Provide a proper error message when a task exits with return code 1 from standard task runner. ### 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/17966

https://github.com/apache/airflow/pull/17967

bff580602bc619afe1bee2f7a5c3ded5fc6e39dd

b6a962ca875bc29aa82a252f5c179faff601780b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/www/views.py", "docs/apache-airflow/security/webserver.rst", "tests/www/views/test_views_base.py", "tests/www/views/test_views_robots.py"]

Warn if robots.txt is accessed

### Description https://github.com/apache/airflow/pull/17946 implements a `/robots.txt` endpoint to block search engines crawling Airflow - in the cases where it is (accidentally) exposed to the public Internet. If we record any GET requests to that end-point we'd have a strong warning flag that the deployment is exposed, and could issue a warning in the UI, or even enable some kill-switch on the deployment. Some deployments are likely intentionally available and rely on auth mechanisms on the `login` endpoint, so there should be a config option to suppress the warnings. An alternative approach would be to monitor for requests from specific user-agents used by crawlers for the same reasons ### Use case/motivation People who accidentally expose airflow have a slightly higher chance of realising they've done so and tighten their security. ### 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/17962

https://github.com/apache/airflow/pull/18557

7c45bc35e767ff21982636fa2e36bc07c97b9411

24a53fb6476a3f671859451049407ba2b8d931c8

python

closed

apache/airflow

https://github.com/apache/airflow

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

template_ext in task attributes shows incorrect value

### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian Buster ### Versions of Apache Airflow Providers _No response_ ### Deployment Managed Services (Astronomer, Composer, MWAA etc.) ### Deployment details Running `quay.io/astronomer/ap-airflow:2.1.3-buster` Docker image ### What happened In the task attributes view of a BashOperator, the `template_ext` row doesn't show any value, while I would expect to see `('.sh', '.bash')`.  ### What you expected to happen Expect to see correct `template_ext` value ### How to reproduce Run a BashOperator and browse to Instance Details --> scroll down to `template_ext` row ### Anything else Spent a little time trying to figure out what could be the reason, but haven't found the problem yet. Therefore created issue to track. ### 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/17932

https://github.com/apache/airflow/pull/17985

f7276353ccd5d15773eea6c0d90265650fd22ae3

ca4f99d349e664bbcf58d3c84139b5f4919f6c8e

python

closed

apache/airflow

https://github.com/apache/airflow

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

Timetable registration a la OperatorLinks

Currently (as implemented in #17414), timetables are serialised by their classes’ full import path. This works most of the time, but not in some cases, including: * Nested in class or function * Declared directly in a DAG file without a valid import name (e.g. `12345.py`) It’s fundamentally impossible to fix some of the cases (e.g. function-local class declaration) due to how Python works, but by requiring the user to explicitly register the timetable class, we can at least expose that problem so users don’t attempt to do that. However, since the timetable actually would work a lot of times without any additional mechanism, I’m also wondering if we should _require_ registration. 1. Always require registration. A DAG using an unregistered timetable class fails to serialise. 2. Only require registration when the timetable class has wonky import path. “Normal” classes work out of the box without registering, and user sees a serialisation error asking for registration otherwise. 3. Don’t require registration. If a class cannot be correctly serialised, tell the user we can’t do it and the timetable must be declared another way.

https://github.com/apache/airflow/issues/17931

https://github.com/apache/airflow/pull/17989

31b15c94886c6083a6059ca0478060e46db67fdb

be7efb1d30929a7f742f5b7735a3d6fbadadd352

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/providers/amazon/aws/utils/emailer.py", "airflow/utils/email.py", "docs/apache-airflow/howto/email-config.rst", "tests/providers/amazon/aws/utils/test_emailer.py", "tests/utils/test_email.py"]

Emails notifications with AWS SES not working due to missing "from:" field

### Apache Airflow version main (development) ### Operating System macOsS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon (main) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened https://github.com/apache/airflow/blob/098765e227d4ab7873a2f675845b50c633e356da/airflow/providers/amazon/aws/utils/emailer.py#L40-L41 ``` hook.send_email( mail_from=None, to=to, subject=subject, html_content=html_content, files=files, cc=cc, bcc=bcc, mime_subtype=mime_subtype, mime_charset=mime_charset, ) ``` the `mail_from=None` will trigger an error when sending the mail, when using AWS Simple Email Service you need to provide a from address and has to be already verified in AWS SES > "Verified identities" ### What you expected to happen I expected it to send a mail but it doesn't because ### How to reproduce _No response_ ### Anything else This is easily solved by providing a from address like in: ``` smtp_mail_from = conf.get('smtp', 'SMTP_MAIL_FROM') hook.send_email( mail_from=smtp_mail_from, ``` the problem is: Can we reuse the smtp.SMTP_MAIL_FROM or do we need to create a new configuration parameter like email.email_from_address ? * smtp uses its own config smtp.smtp_mail_from * sendgrid uses an environment variable `SENDGRID_MAIL_FROM` (undocumented by the way) So, my personal proposal is to * introduce an email.email_from_email and email.email_from_name * read those new configuration parameters at [utils.email.send_email](https://github.com/apache/airflow/blob/098765e227d4ab7873a2f675845b50c633e356da/airflow/utils/email.py#L50-L67 ) * pass those as arguments to the backend (kwargs `from_email`, `from_name`) . the [sendgrid backend can already read those ](https://github.com/apache/airflow/blob/098765e227d4ab7873a2f675845b50c633e356da/airflow/providers/sendgrid/utils/emailer.py#L70-L71) although seems unused at the momemt. ### 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/17930

https://github.com/apache/airflow/pull/18042

0649688e15b9763e91b4cd2963cdafea1eb8219d

1543dc28f4a2f1631dfaedd948e646a181ccf7ee

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/models/base.py", "docs/apache-airflow/howto/set-up-database.rst", "tests/models/test_base.py"]

Dag tags are not refreshing if case sensitivity changed

### Apache Airflow version 2.1.3 (latest released) ### Operating System Debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Custom docker image on k8s ### What happened New dag version was written in same named dag. The only difference was one of specified tags got changed in case, for ex. "test" to "Test". With huge amount of dags this causes scheduler immediately to crash due to constraint violation. ### What you expected to happen Tags are refreshed correctly without crash of scheduler. ### How to reproduce 1. Create dag with tags in running airflow cluster 2. Update dag with change of case of one of tags for ex. "test" to "Test" 3. Watch scheduler crash continuously ### Anything else Alternative option is to make tags case sensitive... ### 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/17897

https://github.com/apache/airflow/pull/18072

79d85573591f641db4b5f89a12213e799ec6dea1

b658a4243fb5b22b81677ac0f30c767dfc3a1b4b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/state.py", "tests/utils/test_state.py"]

Cleared tasks get literal 'DagRunState.QUEUED' instead of the value 'queued'

### Apache Airflow version 2.1.3 (latest released) ### Operating System CentOS Stream release 8 ### Versions of Apache Airflow Providers None of them are relevant ### Deployment Virtualenv installation ### Deployment details mkdir /srv/airflow cd /srv/airflow virtualenv venv source venv/bin/activate pip install apache-airflow==2.1.3 AIRFLOW_HOME and AIRFLOW_CONFIG is specified via environment variables in /etc/sysconfig/airflow, which is in turn used as EnvironmentFile in systemd service files. systemctl start airflow-{scheduler,webserver,kerberos} Python version: 3.9.2 LocalExecutors are used ### What happened On the Web UI, I had cleared failed tasks, which have been cleared properly, but the DagRun became black with a literal value of "DagRunState.QUEUED", therefore it can't be scheduled again. ### What you expected to happen DagRun state should be 'queued'. ### How to reproduce Just clear any tasks on the Web UI. I wonder how could it be that nobody noticed this issue. ### Anything else Here's a patch to fix it. Maybe the __str__ method should be different, or the database/persistence layer should handle this, but for now, this solves the problem. ```patch --- airflow/models/dag.py.orig 2021-08-28 09:48:05.465542450 +0200 +++ airflow/models/dag.py 2021-08-28 09:47:34.272639960 +0200 @@ -1153,7 +1153,7 @@ confirm_prompt=False, include_subdags=True, include_parentdag=True, - dag_run_state: DagRunState = DagRunState.QUEUED, + dag_run_state: DagRunState = DagRunState.QUEUED.value, dry_run=False, session=None, get_tis=False, @@ -1369,7 +1369,7 @@ confirm_prompt=False, include_subdags=True, include_parentdag=False, - dag_run_state=DagRunState.QUEUED, + dag_run_state=DagRunState.QUEUED.value, dry_run=False, ): ``` ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/17879

https://github.com/apache/airflow/pull/17886

332406dae9f6b08de0d43576c4ed176eb49b8ed0

a3f9c690aa80d12ff1d5c42eaaff4fced07b9429

python

closed

apache/airflow

https://github.com/apache/airflow

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

Is it possible to extend the window with parameters in the UI?

### Description Is it possible to extend the window with parameters in the UI? - I have simple parameters and they do not fit ( ### Use case/motivation I have simple parameters and they do not fit ( ### Related issues yt ### 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/17874

https://github.com/apache/airflow/pull/20052

9083ecd928a0baba43e369e2c65225e092a275ca

abe842cf6b68472cc4f84dcec1a5ef94ff98ba5b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/configuration.py", "airflow/www/extensions/init_views.py", "docs/apache-airflow/security/api.rst"]

More than one url in cors header

**Description** Enabled list of cors headers origin allowed **Use case / motivation** I'm currently developping Django application working with apache airflow for some external work through api. From Django, user run dag and a js request wait for dag end. By this way, I enabled cors headers in airflow.cfg with correct url. But now, I've environment (dev & prod). It could be useful to be able to set more than one url in airflow.cfg cors header origin allowed Thank's in advance :)

https://github.com/apache/airflow/issues/17869

https://github.com/apache/airflow/pull/17941

ace2374c20a9dc4b004237bfd600dd6eaa0f91b4

a88115ea24a06f8706886a30e4f765aa4346ccc3

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/templates-ref.rst", "docs/apache-airflow/timezone.rst"]

Macros reference point to outdated pendulum information

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.x **Description**: The documentation on Macros Reference is outdated when it comes to `pendulum`. This commit: https://github.com/apache/airflow/commit/c41192fa1fc5c2b3e7b8414c59f656ab67bbef28 updated Pendulum to `~=2.0` but the [documentation](https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html) still refers to `pendulum.Pendulum` (which doesn't exist in 2.x). The link to the docs refers to 2.x, but it gives me a 404 when I try to open it? https://github.com/apache/airflow/blame/98d45cbe503241798653d47192364b1ffb633f35/docs/apache-airflow/templates-ref.rst#L178 I think these macros are all of type `pendulum.DateTime` now **Are you willing to submit a PR?** Yes, I'll add a PR asap

https://github.com/apache/airflow/issues/17853

https://github.com/apache/airflow/pull/18766

c9bf5f33e5d5bcbf7d31663a8571628434d7073f

cf27419cfe058750cde4247935e20deb60bda572

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/howto/connection.rst"]

Connections created via AIRFLOW_CONN_ enviroment variables do not show up in the Admin > Connections or airflow connections list

The connections created using [environment variables like AIRFLOW_CONN_MYCONNID](https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html#storing-a-connection-in-environment-variables) do not show up in the UI. They don't show up in `airflow connections list` either, although if you know the conn_id you can `airflow connections get conn_id` and it will find it.

https://github.com/apache/airflow/issues/17852

https://github.com/apache/airflow/pull/17915

b5da846dd1f27d798dc7dc4f4227de4418919874

e9bf127a651794b533f30a77dc171e0ea5052b4f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/base_aws.py", "airflow/providers/amazon/aws/utils/connection_wrapper.py", "docs/apache-airflow-providers-amazon/connections/aws.rst", "tests/providers/amazon/aws/utils/test_connection_wrapper.py"]

AwsBaseHook isn't using connection.host, using connection.extra.host instead

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: Version: v2.1.2 Git Version: .release:2.1.2+d25854dd413aa68ea70fb1ade7fe01425f456192 **OS**: PRETTY_NAME="Debian GNU/Linux 10 (buster)" NAME="Debian GNU/Linux" VERSION_ID="10" VERSION="10 (buster)" VERSION_CODENAME=buster ID=debian HOME_URL="https://www.debian.org/" SUPPORT_URL="https://www.debian.org/support" BUG_REPORT_URL="https://bugs.debian.org/" **Apache Airflow Provider versions**: **Deployment**: Docker compose using reference docker compose from here: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html **What happened**: Connection specifications have a host field. This is declared as a member variable of the connection class here: https://github.com/apache/airflow/blob/96f7e3fec76a78f49032fbc9a4ee9a5551f38042/airflow/models/connection.py#L101 It's correctly used in the BaseHook class, eg here: https://github.com/apache/airflow/blob/96f7e3fec76a78f49032fbc9a4ee9a5551f38042/airflow/hooks/base.py#L69 However in AwsBaseHook, it's expected to be in extra, here: https://github.com/apache/airflow/blob/96f7e3fec76a78f49032fbc9a4ee9a5551f38042/airflow/providers/amazon/aws/hooks/base_aws.py#L404-L406 This should be changed to `endpoint_url = connection_object.host` **What you expected to happen**: AwsBaseHook should use the connection.host value, not connection.extra.host **How to reproduce it**: See above code **Anything else we need to know**: **Are you willing to submit a PR?** Probably. It'd also be good for someone inexperienced. This would represent a backwards-incompatible change, any problem with that? <!--- This is absolutely not required, but we are happy to guide you in contribution process especially if you already have a good understanding of how to implement the fix. Airflow is a community-managed project and we love to bring new contributors in. Find us in #airflow-how-to-pr on Slack! -->

https://github.com/apache/airflow/issues/17833

https://github.com/apache/airflow/pull/25494

2682dd6b2c2f2de95cda435984832e8421d0497b

a0212a35930f44d88e12f19e83ec5c9caa0af82a

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/in_container/prod/entrypoint_prod.sh"]

Docker image entrypoint does not parse redis URLs correctly with no password

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: <!-- AIRFLOW VERSION IS MANDATORY --> 2.1.3 **Deployment**: <!-- e.g. Virtualenv / VM / Docker-compose / K8S / Helm Chart / Managed Airflow Service --> <!-- Please include your deployment tools and versions: docker-compose, k8s, helm, etc --> ECS, docker-compose **What happened**: A redis connection URL of the form: ``` redis://<host>:6379/1 ``` is parsed incorrectly by this pattern: https://github.com/apache/airflow/blob/2.1.3/scripts/in_container/prod/entrypoint_prod.sh#L96 - the host is determined to be `localhost` as there is no `@` character. Adding an `@` character, e.g. `redis://@redis-analytics-airflow.signal:6379/1` results in correct parsing, but cannot connect to redis as auth fails. Logs: ``` BACKEND=redis DB_HOST=localhost DB_PORT=6379 ``` and eventually ``` ERROR! Maximum number of retries (20) reached. Last check result: $ run_nc 'localhost' '6379' (UNKNOWN) [127.0.0.1] 6379 (?) : Connection refused sent 0, rcvd 0 ``` **How to reproduce it**: Run airflow with an official docker image and ` AIRFLOW__CELERY__BROKER_URL: redis://<ANY_REDIS_HOSTNAME>:6379/1` and observe that it parses the host as `localhost`. **Anything else we need to know**: This was working without issue in Airflow 2.1.2 - weirdly I cannot see changes to the entrypoint, but we have not changed the URL `AIRFLOW__CELERY__BROKER_URL: redis://redis-analytics-airflow.signal:6379/1`.

https://github.com/apache/airflow/issues/17828

https://github.com/apache/airflow/pull/17847

a2fd67dc5e52b54def97ea9bb61c8ba3557179c6

275e0d1b91949ad1a1b916b0ffa27009e0797fea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/css/dags.css", "airflow/www/templates/airflow/dags.html", "airflow/www/views.py", "docs/apache-airflow/core-concepts/dag-run.rst", "tests/www/views/test_views_home.py"]

Add another tab in Dags table that shows running now

The DAGs main page has All/Active/Paused. It would be nice if we can have also Running Now which will show all the DAGs that have at least 1 in Running of DAG runs:  For the above example the new DAGs tab of Running Now should show only 2 rows. The use case is to see easily what DAGs are currently in progress.

https://github.com/apache/airflow/issues/17826

https://github.com/apache/airflow/pull/30429

c25251cde620481592392e5f82f9aa8a259a2f06

dbe14c31d52a345aa82e050cc0a91ee60d9ee567

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dags.js", "airflow/www/templates/airflow/dags.html"]

"Runs" column being cut off by "Schedule" column in web UI

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.1.3 **OS**: CentOS Linux 7 **Apache Airflow Provider versions**: Standard provider package that comes with Airflow 2.1.3 via Docker container **Deployment**: docker-compose version 1.29.2, build 5becea4c **What happened**: Updated container from Airflow 2.1.2 --> 2.1.3 without an specific error message **What you expected to happen**: I did not expect an overlap of these two columns. **How to reproduce it**: Update Airflow image from 2.1.2 to 2.1.3 using Google Chrome Version 92.0.4515.159 (Official Build) (x86_64). **Anything else we need to know**: Tested in Safari Version 14.1.2 (16611.3.10.1.6) and the icons do not even load. The loading "dots" animate and do not load "Runs" and "Schedule" icons. No special plugins used and adblockers shut off. Refresh does not help; rebuild does not help. Widened window. Does not fix issue as other column adjust with change in window width. **Are you willing to submit a PR?** I am rather new to Airflow and do not have enough experience in fixing Airflow bugs. 

https://github.com/apache/airflow/issues/17810

https://github.com/apache/airflow/pull/17817

06e53f26e5cb2f1ad4aabe05fa12d2db9c66e282

96f7e3fec76a78f49032fbc9a4ee9a5551f38042

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/webserver_command.py", "setup.cfg"]

Webserver fail to start with virtualenv: No such file or directory: 'gunicorn': 'gunicorn'

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.1.2 <!-- AIRFLOW VERSION IS MANDATORY --> **OS**: CentOS Linux release 8.2.2004 (Core) <!-- MANDATORY! You can get it via `cat /etc/oss-release` for example --> **Apache Airflow Provider versions**: apache-airflow-providers-celery==2.0.0 apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-http==2.0.0 apache-airflow-providers-imap==2.0.0 apache-airflow-providers-mysql==2.1.0 apache-airflow-providers-redis==2.0.0 apache-airflow-providers-sftp==2.1.0 apache-airflow-providers-sqlite==2.0.0 apache-airflow-providers-ssh==2.1.0 <!-- You can use `pip freeze | grep apache-airflow-providers` (you can leave only relevant ones)--> **Deployment**: Virtualenv 3.7.5 <!-- e.g. Virtualenv / VM / Docker-compose / K8S / Helm Chart / Managed Airflow Service --> <!-- Please include your deployment tools and versions: docker-compose, k8s, helm, etc --> **What happened**: Got error when I try to start a webserver: ```bash $ $VENV_PATH/bin/airflow webserver ... ================================================================= Traceback (most recent call last): File "/VENV_PATH/bin/airflow", line 8, in <module> sys.exit(main()) File "/VENV_PATH/lib/python3.7/site-packages/airflow/__main__.py", line 40, in main args.func(args) File "/VENV_PATH/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/VENV_PATH/lib/python3.7/site-packages/airflow/utils/cli.py", line 91, in wrapper return f(*args, **kwargs) File "/VENV_PATH/lib/python3.7/site-packages/airflow/cli/commands/webserver_command.py", line 483, in webserver with subprocess.Popen(run_args, close_fds=True) as gunicorn_master_proc: File "/home/user/.pyenv/versions/3.7.5/lib/python3.7/subprocess.py", line 800, in __init__ restore_signals, start_new_session) File "/home/user/.pyenv/versions/3.7.5/lib/python3.7/subprocess.py", line 1551, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: 'gunicorn': 'gunicorn' [user@xxxxx xxxxxxxxxx]$ /VENV_PATH/bin/python Python 3.7.5 (default, Aug 20 2021, 17:16:24) ``` <!-- Please include exact error messages if you can --> **What you expected to happen**: Airflow start webserver success <!-- What do you think went wrong? --> **How to reproduce it**: assume we already have `AIRFLOW_HOME` 1. create a venv: `virtualenv xxx` 2. `xxx/bin/pip install apache-airflow` 3. `xxx/bin/airflow webserver` <!-- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If this is a UI bug, please provide a screenshot of the bug or a link to a youtube video of the bug in action You can include images/screen-casts etc. by drag-dropping the image here. --> **Anything else we need to know**: `cli/commands/webserver_command.py:394` use `gunicorn` as subprocess command, assume it's in PATH. `airflow webserver` runs well after `export PATH=$PATH:$VENV_PATH/bin`. <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here inside fenced ``` ``` blocks or inside a foldable details tag if it's long: <details><summary>x.log</summary> lots of stuff </details> --> **Are you willing to submit a PR?** I'm not good at python so maybe it's not a good idea for me to submit a PR. <!--- This is absolutely not required, but we are happy to guide you in contribution process especially if you already have a good understanding of how to implement the fix. Airflow is a community-managed project and we love to bring new contributors in. Find us in #airflow-how-to-pr on Slack! -->

https://github.com/apache/airflow/issues/17804

https://github.com/apache/airflow/pull/17805

5019916b7b931a13ad7131f0413601b0db475b77

c32ab51ef8819427d8af0b297a80b99e2f93a953

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/gcs.py", "airflow/providers/google/cloud/operators/bigquery.py", "airflow/providers/google/cloud/utils/mlengine_operator_utils.py", "tests/providers/google/cloud/hooks/test_gcs.py", "tests/providers/google/cloud/operators/test_mlengine_utils.py", "tests/providers/google/cloud/utils/test_mlengine_operator_utils.py"]

BigQueryCreateExternalTableOperator from providers package fails to get schema from GCS object

**Apache Airflow version**: 1.10.15 **OS**: Linux 5.4.109+ **Apache Airflow Provider versions**: apache-airflow-backport-providers-apache-beam==2021.3.13 apache-airflow-backport-providers-cncf-kubernetes==2021.3.3 apache-airflow-backport-providers-google==2021.3.3 **Deployment**: Cloud Composer 1.16.6 (Google Cloud Managed Airflow Service) **What happened**: BigQueryCreateExternalTableOperator from the providers package ([airflow.providers.google.cloud.operators.bigquery](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py)) fails with correct _schema_object_ parameter. **What you expected to happen**: I expected the DAG to succesfully run, as I've previously tested it with the deprecated operator from the contrib package ([airflow.contrib.operators.bigquery_operator](https://github.com/apache/airflow/blob/5786dcdc392f7a2649f398353a0beebef01c428e/airflow/contrib/operators/bigquery_operator.py#L476)), using the same parameters. Debbuging the DAG execution log, I saw the providers operator generated a wrong call to the Cloud Storage API: it mixed up the bucket and object parameters, according the stack trace bellow. ``` [2021-08-23 23:17:22,316] {taskinstance.py:1152} ERROR - 404 GET https://storage.googleapis.com/download/storage/v1/b/foo/bar/schema.json/o/mybucket?alt=media: Not Found: ('Request failed with status code', 404, 'Expected one of', <HTTPStatus.OK: 200>, <HTTPStatus.PARTIAL_CONTENT: 206>) Traceback (most recent call last) File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/client.py", line 728, in download_blob_to_fil checksum=checksum File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/blob.py", line 956, in _do_downloa response = download.consume(transport, timeout=timeout File "/opt/python3.6/lib/python3.6/site-packages/google/resumable_media/requests/download.py", line 168, in consum self._process_response(result File "/opt/python3.6/lib/python3.6/site-packages/google/resumable_media/_download.py", line 186, in _process_respons response, _ACCEPTABLE_STATUS_CODES, self._get_status_cod File "/opt/python3.6/lib/python3.6/site-packages/google/resumable_media/_helpers.py", line 104, in require_status_cod *status_code google.resumable_media.common.InvalidResponse: ('Request failed with status code', 404, 'Expected one of', <HTTPStatus.OK: 200>, <HTTPStatus.PARTIAL_CONTENT: 206> During handling of the above exception, another exception occurred Traceback (most recent call last) File "/usr/local/lib/airflow/airflow/models/taskinstance.py", line 985, in _run_raw_tas result = task_copy.execute(context=context File "/usr/local/lib/airflow/airflow/providers/google/cloud/operators/bigquery.py", line 1178, in execut schema_fields = json.loads(gcs_hook.download(self.bucket, self.schema_object) File "/usr/local/lib/airflow/airflow/providers/google/cloud/hooks/gcs.py", line 301, in downloa return blob.download_as_string( File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/blob.py", line 1391, in download_as_strin timeout=timeout File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/blob.py", line 1302, in download_as_byte checksum=checksum File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/client.py", line 731, in download_blob_to_fil _raise_from_invalid_response(exc File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/storage/blob.py", line 3936, in _raise_from_invalid_respons raise exceptions.from_http_status(response.status_code, message, response=response google.api_core.exceptions.NotFound: 404 GET https://storage.googleapis.com/download/storage/v1/b/foo/bar/schema.json/o/mybucket?alt=media: Not Found: ('Request failed with status code', 404, 'Expected one of', <HTTPStatus.OK: 200>, <HTTPStatus.PARTIAL_CONTENT: 206> ``` PS: the bucket (_mybucket_) and object path (_foo/bar/schema.json_) were masked for security reasons. I believe the error appears on the [following](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L1183) line, although the bug itself is probably located on the [gcs_hook.download()](https://github.com/apache/airflow/blob/0264fea8c2024d7d3d64aa0ffa28a0cfa48839cd/airflow/providers/google/cloud/hooks/gcs.py#L266) method: `schema_fields = json.loads(gcs_hook.download(self.bucket, self.schema_object))` **How to reproduce it**: Create a DAG using both operators and the same parameters, as the example bellow. The task using the contrib version of the operator should work, while the task using the providers version should fail. ``` from airflow.contrib.operators.bigquery_operator import BigQueryCreateExternalTableOperator as BQExtTabOptContrib from airflow.providers.google.cloud.operators.bigquery import BigQueryCreateExternalTableOperator as BQExtTabOptProviders #TODO: default args and DAG definition create_landing_external_table_contrib = BQExtTabOptContrib( task_id='create_landing_external_table_contrib', bucket='mybucket', source_objects=['foo/bar/*.csv'], destination_project_dataset_table='project.dataset.table', schema_object='foo/bar/schema_file.json', ) create_landing_external_table_providers = BQExtTabOptProviders( task_id='create_landing_external_table_providers', bucket='mybucket', source_objects=['foo/bar/*.csv'], destination_project_dataset_table='project.dataset.table', schema_object='foo/bar/schema_file.json', ) ``` **Anything else we need to know**: The [*gcs_hook.download()*](https://github.com/apache/airflow/blob/0264fea8c2024d7d3d64aa0ffa28a0cfa48839cd/airflow/providers/google/cloud/hooks/gcs.py#L313) method is using the deprecated method _download_as_string()_ from the Cloud Storage API (https://googleapis.dev/python/storage/latest/blobs.html). It should be changed to _download_as_bytes()_. Also, comparing the [providers version](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L1183) of the operator to the [contrib version](https://github.com/apache/airflow/blob/5786dcdc392f7a2649f398353a0beebef01c428e/airflow/contrib/operators/bigquery_operator.py#L621), I observed there is also a missing decode operation: `.decode("utf-8")` **Are you willing to submit a PR?** Yes

https://github.com/apache/airflow/issues/17800

https://github.com/apache/airflow/pull/20091

3a0f5545a269b35cf9ccc845c4ec9397d9376b70

50bf5366564957cc0f057ca923317c421fffdeaa

python

closed

apache/airflow

https://github.com/apache/airflow

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

Circular Dependency in Apache Airflow 2.1.3

**Apache Airflow version**: 2.1.3 **OS**: Ubuntu 20.04 LTS **Deployment**: Bazel **What happened**: When I tried to bump my Bazel monorepo from 2.1.2 to 2.1.3, Bazel complains that there is the following circular dependency. ``` ERROR: /github/home/.cache/bazel/_bazel_bookie/c5c5e4532705a81d38d884f806d2bf84/external/pip/pypi__apache_airflow/BUILD:11:11: in py_library rule @pip//pypi__apache_airflow:pypi__apache_airflow: cycle in dependency graph: //wager/publish/airflow:airflow .-> @pip//pypi__apache_airflow:pypi__apache_airflow | @pip//pypi__apache_airflow_providers_http:pypi__apache_airflow_providers_http `-- @pip//pypi__apache_airflow:pypi__apache_airflow ``` **What you expected to happen**: No dependency cycles. **How to reproduce it**: A concise reproduction will require some effort. I am hoping that there is a quick resolution to this, but I am willing to create a reproduction if it is required to determine the root cause. **Anything else we need to know**: Perhaps related to apache/airflow#14128. **Are you willing to submit a PR?** Yes.

https://github.com/apache/airflow/issues/17795

https://github.com/apache/airflow/pull/17796

36c5fd3df9b271702e1dd2d73c579de3f3bd5fc0

0264fea8c2024d7d3d64aa0ffa28a0cfa48839cd

python

closed

apache/airflow

https://github.com/apache/airflow

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

get_pandas_df() fails when it tries to read an empty table

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.2.0.dev0 **OS**: Debian GNU/Linux **Apache Airflow Provider versions**: <!-- You can use `pip freeze | grep apache-airflow-providers` (you can leave only relevant ones)--> **Deployment**: docker-compose version 1.29.2 **What happened**: Currently in hive hooks, when [get_pandas_df()](https://github.com/apache/airflow/blob/main/airflow/providers/apache/hive/hooks/hive.py#L1039) is used to create a dataframe, the first step creates the dataframe, the second step creates the columns. The step to add columns throws an exception when the table is empty. ``` hh = HiveServer2Hook() sql = "SELECT * FROM <table> WHERE 1=0" df = hh.get_pandas_df(sql) [2021-08-15 21:10:15,282] {{hive.py:449}} INFO - SELECT * FROM <table> WHERE 1=0 Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/venv/lib/python3.7/site-packages/airflow/providers/apache/hive/hooks/hive.py", line 1073, in get_pandas_df df.columns = [c[0] for c in res['header']] File "/venv/lib/python3.7/site-packages/pandas/core/generic.py", line 5154, in __setattr__ return object.__setattr__(self, name, value) File "pandas/_libs/properties.pyx", line 66, in pandas._libs.properties.AxisProperty.__set__ File "/venv/lib/python3.7/site-packages/pandas/core/generic.py", line 564, in _set_axis self._mgr.set_axis(axis, labels) File "/venv/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 227, in set_axis f"Length mismatch: Expected axis has {old_len} elements, new " ValueError: Length mismatch: Expected axis has 0 elements, new values have 1 elements ``` **What you expected to happen**: When an empty table is read, get_pandas_df() should return an empty dataframe, with the columns listed in the empty dataframe. Ideally, `len(df.index)` produces `0` and `df.columns` lists the columns. **How to reproduce it**: Use your own hive server... ``` from airflow.providers.apache.hive.hooks.hive import HiveServer2Hook hh = HiveServer2Hook() sql = "SELECT * FROM <table> WHERE 1=0" df = hh.get_pandas_df(sql) ``` Where \<table> can be any table. **Anything else we need to know**: Here's a potential fix ... ``` #df = pandas.DataFrame(res['data'], **kwargs) #df.columns = [c[0] for c in res['header']] df = pandas.DataFrame(res['data'], columns=[c[0] for c in res['header']], **kwargs) ``` A PR is more or less ready but someone else should confirm this is an actual bug. I can trigger the bug on an actual hive server. I cannot trigger the bug using [test_hive.py](https://github.com/apache/airflow/blob/main/tests/providers/apache/hive/hooks/test_hive.py). Also, if a PR Is needed, and a new test is needed, then I may need some help updating test_hive.py. **Are you willing to submit a PR?** Yes.

https://github.com/apache/airflow/issues/17765

https://github.com/apache/airflow/pull/17777

1d71441ceca304b9f6c326a4381626a60164b774

da99c3fa6c366d762bba9fbf3118cc3b3d55f6b4

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/in_container/prod/clean-logs.sh"]

Scheduler-gc fails on helm chart when running 'find'

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.1.3 **OS**: centos-release-7-8.2003.0.el7.centos.x86_64 **Apache Airflow Provider versions**: Irrelevant **Deployment**: Helm Chart **Versions**: ```go Client Version: version.Info{Major:"1", Minor:"16+", GitVersion:"v1.16.15-eks-ad4801", GitCommit:"ad4801fd44fe0f125c8d13f1b1d4827e8884476d", GitTreeState:"clean", BuildDate:"2020-10-20T23:30:20Z", GoVersion:"go1.13.15", Compiler:"gc", Platform:"linux/amd64"} Server Version: version.Info{Major:"1", Minor:"17+", GitVersion:"v1.17.17-eks-087e67", GitCommit:"087e67e479962798594218dc6d99923f410c145e", GitTreeState:"clean", BuildDate:"2021-07-31T01:39:55Z", GoVersion:"go1.13.15", Compiler:"gc", Platform:"linux/amd64"} version.BuildInfo{Version:"v3.6.3", GitCommit:"d506314abfb5d21419df8c7e7e68012379db2354", GitTreeState:"clean", GoVersion:"go1.16.5"} ``` **What happened**: Helm deployment succeeds but scheduler pod fails with error: ```log Cleaning logs every 900 seconds Trimming airflow logs to 15 days. find: The -delete action automatically turns on -depth, but -prune does nothing when -depth is in effect. If you want to carry on anyway, just explicitly use the -depth option. ``` **What you expected to happen**: I expect the `scheduler-gc` to run successfully and if it fails, helm deployment should fail. **How to reproduce it**: 1. Build container using breeze from [reference `719709b6e994a99ad2cb8f90042a19a7924acb8e`](https://github.com/apache/airflow/commit/719709b6e994a99ad2cb8f90042a19a7924acb8e) 2. Deploy to helm using this container. 3. Run kubectl describe pod airflow-schduler-XXXX -c sheduler-gc` to see the error. For minimal version: ```shell git clone git@github.com:apache/airflow.git cd airflow ./breeze build-image --image-tag airflow --production-image docker run --entrypoint=bash airflow /clean-logs echo $? ``` **Anything else we need to know**: It appears the changes was made in order to support a specific kubernetes volume protocol: https://github.com/apache/airflow/pull/17547. I ran this command locally and achieved the same message: ```console $ find "${DIRECTORY}"/logs -type d -name 'lost+found' -prune -name '*.log' -delete find: The -delete action automatically turns on -depth, but -prune does nothing when -depth is in effect. If you want to carry on anyway, just explicitly use the -depth option. $ echo $? 1 ```` **Are you willing to submit a PR?** Maybe. I am not very familiar with the scheduler-gc service so I will have to look into it.

https://github.com/apache/airflow/issues/17733

https://github.com/apache/airflow/pull/17739

af9dc8b3764fc0f4630c0a83f1f6a8273831c789

f654db8327cdc5c6c1f26517f290227cbc752a3c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/_messages.html", "airflow/www/templates/airflow/dags.html", "airflow/www/templates/airflow/main.html", "airflow/www/templates/appbuilder/flash.html", "airflow/www/views.py", "tests/www/views/test_views.py"]

Duplicated general notifications in Airflow UI above DAGs list

**Apache Airflow version**: 2.2.0.dev0 (possible older versions too) **OS**: Linux Debian 11 (BTW Here we have a typo in comment inside bug report template in `.github/ISSUE_TEMPLATE/bug_report.md`: `cat /etc/oss-release` <- double 's'.) **Apache Airflow Provider versions**: - **Deployment**: Docker-compose 1.25.0 **What happened**: The issue is related to Airflow UI and it shows duplicated notifications when removing all the tags from the input after filtering DAGs. **What you expected to happen**: The notifications should not be duplicated. **How to reproduce it**: In the main view, above the list of the DAGs (just below the top bar menu), there is a place where notifications appear. Suppose that there are 2 notifications (no matter which). Now try to search DAGs by tag using 'Filter DAGs by tag' input and use a valid tag. After the filtering is done, clear the input either by clicking on 'x' next to the tag or on the 'x' near the right side of the input. Notice that the notifications are duplicated and now you have 4 instead of 2 (each one is displayed twice). The input id is `s2id_autogen1`. This bug happens only if all the tags are removed from the filtering input. If you remove the tag while there is still another one in the input, the bug will not appear. Also, it is not present while searching DAGs by name using the input 'Search DAGs'. After search, before removing tags from input:  Duplicated notifications after removing tag from input:  **Are you willing to submit a PR?** I can try.

https://github.com/apache/airflow/issues/17727

https://github.com/apache/airflow/pull/18462

3857aa822df5a057e0db67b2342ef769c012785f

18e91bcde0922ded6eed724924635a31578d8230

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add SQL Check Operators to Snowflake Provider

Add SnowflakeCheckOperator, SnowflakeValueCheckOperator, and SnowflakeIntervalCheckOperator to Snowflake provider. **Use case / motivation** The SQL operator, as well as other provider DB operators, already support this functionality. It can prove useful for data quality use cases with Snowflake. It should be relatively easy to implement in the same fashion as [BigQuery's versions](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L101). **Are you willing to submit a PR?** Yep! **Related Issues** Not that I could find.

https://github.com/apache/airflow/issues/17694

https://github.com/apache/airflow/pull/17741

19454940d45486a925a6d890da017a22b6b962de

a8970764d98f33a54be0e880df27f86b311038ac

python

closed

apache/airflow

https://github.com/apache/airflow

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

Kubernetes Executor: Tasks Stuck in Queued State indefinitely (or until scheduler restart).

**Apache Airflow version**: 2.1.3 **OS**: Custom Docker Image built from python:3.8-slim **Apache Airflow Provider versions**: apache-airflow-providers-cncf-kubernetes==2.0.1 apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-http==2.0.0 apache-airflow-providers-imap==2.0.0 **Deployment**: Self managed on EKS, manifests templated and rendered using krane, dags mounted via PVC & EFS **What happened**: Task remains in queued state <img width="1439" alt="Screen Shot 2021-08-18 at 12 17 28 PM" src="https://user-images.githubusercontent.com/47788186/129936170-e16e1362-24ca-4ce9-b2f7-978f2642d388.png"> **What you expected to happen**: Task starts running **How to reproduce it**: I believe it is because a node is removed. I've attached both the scheduler/k8s executor logs and the kubernetes logs. I deployed a custom executor with an extra line in the KubernetesJobWatcher.process_status that logged the Event type for the pod. ``` date,name,message 2021-09-08T01:06:35.083Z,KubernetesJobWatcher,Event: <pod_id> had an event of type ADDED 2021-09-08T01:06:35.084Z,KubernetesJobWatcher,Event: <pod_id> Status: Pending 2021-09-08T01:06:35.085Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:06:35.085Z,KubernetesJobWatcher,Event: <pod_id> Status: Pending 2021-09-08T01:06:43.390Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:06:43.390Z,KubernetesJobWatcher,Event: <pod_id> Status: Pending 2021-09-08T01:06:43.390Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:06:43.391Z,KubernetesJobWatcher,Event: <pod_id> Status: Pending 2021-09-08T01:06:45.392Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:06:45.393Z,KubernetesJobWatcher,Event: <pod_id> Status: is Running 2021-09-08T01:07:33.185Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:07:33.186Z,KubernetesJobWatcher,Event: <pod_id> Status: is Running 2021-09-08T01:09:50.478Z,KubernetesJobWatcher,Event: <pod_id> had an event of type MODIFIED 2021-09-08T01:09:50.479Z,KubernetesJobWatcher,Event: <pod_id> Status: is Running 2021-09-08T01:09:50.479Z,KubernetesJobWatcher,Event: <pod_id> had an event of type DELETED 2021-09-08T01:09:50.480Z,KubernetesJobWatcher,Event: <pod_id> Status: is Running ``` Here are the corresponding EKS Logs ``` @timestamp,@message 2021-09-08 01:06:34 1 factory.go:503] pod etl/<pod_id> is already present in the backoff queue 2021-09-08 01:06:43 1 scheduler.go:742] pod etl/<pod_id> is bound successfully on node ""ip-10-0-133-132.ec2.internal"", 19 nodes evaluated, 1 nodes were found feasible." 2021-09-08 01:07:32 1 controller_utils.go:122] Update ready status of pods on node [ip-10-0-133-132.ec2.internal] 2021-09-08 01:07:32 1 controller_utils.go:139] Updating ready status of pod <pod_id> to false 2021-09-08 01:07:38 1 node_lifecycle_controller.go:889] Node ip-10-0-133-132.ec2.internal is unresponsive as of 2021-09-08 01:07:38.017788167 2021-09-08 01:08:51 1 node_tree.go:100] Removed node "ip-10-0-133-132.ec2.internal" in group "us-east-1:\x00:us-east-1b" from NodeTree 2021-09-08 01:08:53 1 node_lifecycle_controller.go:789] Controller observed a Node deletion: ip-10-0-133-132.ec2.internal 2021-09-08 01:09:50 1 gc_controller.go:185] Found orphaned Pod etl/<pod_id> assigned to the Node ip-10-0-133-132.ec2.internal. Deleting. 2021-09-08 01:09:50 1 gc_controller.go:189] Forced deletion of orphaned Pod etl/<pod_id> succeeded ``` **Are you willing to submit a PR?** I think there is a state & event combination missing from the process_state function in the KubernetesJobWatcher. I will submit a PR to fix.

https://github.com/apache/airflow/issues/17693

https://github.com/apache/airflow/pull/18095

db72f40707c041069f0fbb909250bde6a0aea53d

e2d069f3c78a45ca29bc21b25a9e96b4e36a5d86

python

closed

apache/airflow

https://github.com/apache/airflow

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

Role permissions and displaying dag load errors on webserver UI

Running Airflow 2.1.2. Setup using oauth authentication (azure). With the public permissions role if a user did not have a valid role in Azure AD or did not login I got all sorts of redirect loops and errors. So I updated the public permissions to: [can read on Website, menu access on My Profile, can read on My Profile, menu access on Documentation, menu access on Docs]. So basically a user not logged in can view a nearly empty airflow UI and click the "Login" button to login. I also added a group to my Azure AD with the role public that includes all users in the subscription. So users can login and it will create an account in airflow for them and they see the same thing as if they are not logged in. Then if someone added them in to a role in the azure enterprise application with a different role when the login they will have what they need. Keeps everything clean and no redirect errors, etc... always just nice airflow screens. Now one issue I noticed is with the "can read on Website" permission added to a public role the dag errors appears and are not hidden. Since the errors are related to dags I and the user does not have any dag related permissions I would think the errors would not be displayed. I'm wondering if this is an issue or more of something that should be a new feature? Cause if I can't view the dag should I be able to view the errors for it? Like adding a role "can read on Website Errors" if a new feature or update the code to tie the display of there errors into a role permission that they are related to like can view one of the dags permissions. Not logged in or logged in as a user who is defaulted to "Public" role and you can see the red errors that can be expanded and see line reference details of the errors: 

https://github.com/apache/airflow/issues/17684

https://github.com/apache/airflow/pull/17835

a759499c8d741b8d679c683d1ee2757dd42e1cd2

45e61a965f64feffb18f6e064810a93b61a48c8a

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/scheduler/scheduler-deployment.yaml"]

Airflow not running as root when deployed to k8s via helm

**Apache Airflow version**: v2.1.2 **Deployment**: Helm Chart + k8s **What happened**: helm install with values: uid=0 gid=0 Airflow pods must run as root. error: from container's bash: root@airflow-xxx-scheduler-7f49549459-w9s67:/opt/airflow# airflow ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 5, in <module> from airflow.__main__ import main ModuleNotFoundError: No module named 'airflow' ``` **What you expected to happen**: should run as root using airflow's helm only in pod's describe I see: securityContext: fsGroup: 0 runAsUser: 0

https://github.com/apache/airflow/issues/17679

https://github.com/apache/airflow/pull/17688

4e59741ff9be87d6aced1164812ab03deab259c8

986381159ee3abf3442ff496cb553d2db004e6c4

python

closed

apache/airflow

https://github.com/apache/airflow

["CONTRIBUTING.rst"]

Documentation "how to sync your fork" is wrong, should be updated.

**Description** From documentation , "CONTRIBUTING.rst" : _There is also an easy way using ``Force sync main from apache/airflow`` workflow. You can go to "Actions" in your repository and choose the workflow and manually trigger the workflow using "Run workflow" command._ This doesn't work in the current GitHub UI, there is no such option. **Use case / motivation** Should be changed to reflect current github options, selecting ""Fetch upstream" -> "Fetch and merge X upstream commits from apache:main" **Are you willing to submit a PR?** I will need help with wording **Related Issues** No.

https://github.com/apache/airflow/issues/17665

https://github.com/apache/airflow/pull/17675

7c96800426946e563d12fdfeb80b20f4c0002aaf

1cd3d8f94f23396e2f1367822fecc466db3dd170

python

closed

apache/airflow

https://github.com/apache/airflow

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

Subdags permissions not getting listed on Airflow UI

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.0.2 and 2.1.1 <!-- AIRFLOW VERSION IS MANDATORY --> **OS**: Debian <!-- MANDATORY! You can get it via `cat /etc/oss-release` for example --> **Apache Airflow Provider versions**: apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-imap==2.0.0 apache-airflow-providers-sqlite==2.0.0 <!-- You can use `pip freeze | grep apache-airflow-providers` (you can leave only relevant ones)--> **Deployment**: docker-compose <!-- e.g. Virtualenv / VM / Docker-compose / K8S / Helm Chart / Managed Airflow Service --> <!-- Please include your deployment tools and versions: docker-compose, k8s, helm, etc --> **What happened**: <!-- Please include exact error messages if you can --> **What you expected to happen**: Permissions for subdags should get listed on list roles on Airflow UI but only the parent dag get listed but not the children dags. <!-- What do you think went wrong? --> **How to reproduce it**: Run airflow 2.0.2 or 2.1.1 with a subdag in the dags directory. And then try to find subdag listing on list roles on Airflow UI.  <!-- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If this is a UI bug, please provide a screenshot of the bug or a link to a youtube video of the bug in action You can include images/screen-casts etc. by drag-dropping the image here. --> **Anything else we need to know**: It appears this problem is there most of the time. <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here inside fenced ``` ``` blocks or inside a foldable details tag if it's long: <details><summary>x.log</summary> lots of stuff </details> --> <!--- This is absolutely not required, but we are happy to guide you in contribution process especially if you already have a good understanding of how to implement the fix. Airflow is a community-managed project and we love to bring new contributors in. Find us in #airflow-how-to-pr on Slack! -->

https://github.com/apache/airflow/issues/17652

https://github.com/apache/airflow/pull/18160

3d6c86c72ef474952e352e701fa8c77f51f9548d

3e3c48a136902ac67efce938bd10930e653a8075

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml"]

Airflow API (Stable) (1.0.0) Update a DAG endpoint documentation shows you can update is_active, but the api does not accept it

**Apache Airflow version**: v2.1.1+astro.2 **OS**: Ubuntu v18.04 **Apache Airflow Provider versions**: <!-- You can use `pip freeze | grep apache-airflow-providers` (you can leave only relevant ones)--> **Deployment**: VM Ansible **What happened**: PATCH call to api/v1/dags/{dagID} gives the following response when is_active is included in the update mask and/or body: { "detail": "Only `is_paused` field can be updated through the REST API", "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" } The API spec clearly indicates is_active is a modifiable field: https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/patch_dag **What you expected to happen**: Expected the is_active field to be updated in the DAG. Either fix the endpoint or fix the documentation. **How to reproduce it**: Send PATCH call to api/v1/dags/{dagID} with "is_active": true/false in the body **Anything else we need to know**: Occurs every time. **Are you willing to submit a PR?** No

https://github.com/apache/airflow/issues/17639

https://github.com/apache/airflow/pull/17667

cbd9ad2ffaa00ba5d99926b05a8905ed9ce4e698

83a2858dcbc8ecaa7429df836b48b72e3bbc002a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/hooks/dbapi.py", "airflow/providers/sqlite/example_dags/example_sqlite.py", "airflow/providers/sqlite/hooks/sqlite.py", "docs/apache-airflow-providers-sqlite/operators.rst", "tests/providers/sqlite/hooks/test_sqlite.py"]

SqliteHook invalid insert syntax

<!-- Welcome to Apache Airflow! Please complete the next sections or the issue will be closed. --> **Apache Airflow version**: 2.1.0 <!-- AIRFLOW VERSION IS MANDATORY --> **OS**: Raspbian GNU/Linux 10 (buster) <!-- MANDATORY! You can get it via `cat /etc/oss-release` for example --> **Apache Airflow Provider versions**: apache-airflow-providers-sqlite==1.0.2 <!-- You can use `pip freeze | grep apache-airflow-providers` (you can leave only relevant ones)--> **Deployment**: Virtualenv <!-- e.g. Virtualenv / VM / Docker-compose / K8S / Helm Chart / Managed Airflow Service --> <!-- Please include your deployment tools and versions: docker-compose, k8s, helm, etc --> **What happened**: When using the insert_rows function of the sqlite hook the generated parametrized sql query has an invalid syntax. It will generate queries with the %s placeholder which result in the following error: *Query*: INSERT INTO example_table (col1, col2) VALUES (%s,%s) *Error*: sqlite3.OperationalError: near "%": syntax error *Stacktrace*: File "/home/airflow/.pyenv/versions/3.8.10/envs/airflow_3.8.10/lib/python3.8/site-packages/airflow/hooks/dbapi.py", line 307, in insert_rows cur.execute(sql, values) I replaced the placeholder with "?" in the _generate_insert_sql function DbApiHook as a test. This solved the issue. <!-- Please include exact error messages if you can --> **What you expected to happen**: Every inherited method of the DbApiHook should work for the SqliteHook. Using the insert_rows method should generate the correct parametrized query and insert rows as expected. <!-- What do you think went wrong? --> **How to reproduce it**: 1. Create an instance of the SqliteHook 2. Use the insert_rows method of the SqliteHook <!-- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If this is a UI bug, please provide a screenshot of the bug or a link to a youtube video of the bug in action You can include images/screen-casts etc. by drag-dropping the image here. -->

https://github.com/apache/airflow/issues/17632

https://github.com/apache/airflow/pull/17695

986381159ee3abf3442ff496cb553d2db004e6c4

9b2e593fd4c79366681162a1da43595584bd1abd

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/providers/alibaba/cloud/operators/test_oss.py", "tests/providers/alibaba/cloud/sensors/test_oss_key.py"]

Switch Alibaba tests to use Mocks

We should also take a look if there is a way to mock the calls rather than having to communicate with the reaal "Alibaba Cloud". This way our unit tests would be even more stable. For AWS we are using `moto` library. Seems that Alibaba has built-in way to use Mock server as backend (?) https://www.alibabacloud.com/help/doc-detail/48978.htm CC: @Gabriel39 -> would you like to pick on that task? We had a number of transient failures of the unit tests over the last week - cause the unit tests were actually reaching out to cn-hengzou Alibaba region's servers, which has a lot of stability issues. I am switching to us-east-1 in https://github.com/apache/airflow/pull/17616 but ideally we should not reach out to "real" services in unit tests (we are working on standardizing system tests that will do that and reach-out to real services but it should not be happening for unit tests).

https://github.com/apache/airflow/issues/17617

https://github.com/apache/airflow/pull/22178

9e061879f92f67304f1afafdebebfd7ee2ae8e13

03d0c702cf5bb72dcb129b86c219cbe59fd7548b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/helpers.py", "airflow/utils/task_group.py", "tests/utils/test_helpers.py"]

Airflow all scheduler crashed and cannot restore because of incorrect Group name

**Apache Airflow docker image version**: apache/airflow:2.1.1-python3.8 ** Environment: Kubernetes **What happened**: I set up a DAG with the group `Create Grafana alert` and inside the group a run a for loop for creating task Then all schedulers crash because of the above error, K8s tried to restart but the scheduler still met this error inspire I fixed it on DAG. My code: ``` with TaskGroup(group_id='Grafana grafana alert') as tg4: config_alerts = [] for row in CONFIG: task_id = 'alert_' + row['file_name_gcs'].upper() alert_caller = SimpleHttpOperator( task_id=task_id, http_conn_id='escalation_webhook', endpoint='api/json/send', data=json.dumps({ "payload": { "text": "Hello escalation", } }), headers={ "Content-Type": "application/json", "Authorization": "foo" }, response_check=health_check ) config_alerts.append(alert_caller) config_alerts ``` Finally, I deleted DAG on UI after that scheduler created a new one and can start. I think this can be an error but I cannot crash all schedulers and cannot back to normal until delete DAG. ``` Traceback (most recent call last): 8/12/2021 2:25:29 PM File "/home/airflow/.local/bin/airflow", line 8, in <module> 8/12/2021 2:25:29 PM sys.exit(main()) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/__main__.py", line 40, in main 8/12/2021 2:25:29 PM args.func(args) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 48, in command 8/12/2021 2:25:29 PM return func(*args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/cli.py", line 91, in wrapper 8/12/2021 2:25:29 PM return f(*args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/cli/commands/scheduler_command.py", line 64, in scheduler 8/12/2021 2:25:29 PM job.run() 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/base_job.py", line 237, in run 8/12/2021 2:25:29 PM self._execute() 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1303, in _execute 8/12/2021 2:25:29 PM self._run_scheduler_loop() 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1396, in _run_scheduler_loop 8/12/2021 2:25:29 PM num_queued_tis = self._do_scheduling(session) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1535, in _do_scheduling 8/12/2021 2:25:29 PM self._schedule_dag_run(dag_run, active_runs_by_dag_id.get(dag_run.dag_id, set()), session) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1706, in _schedule_dag_run 8/12/2021 2:25:29 PM dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 67, in wrapper 8/12/2021 2:25:29 PM return func(*args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/dagbag.py", line 186, in get_dag 8/12/2021 2:25:29 PM self._add_dag_from_db(dag_id=dag_id, session=session) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/dagbag.py", line 261, in _add_dag_from_db 8/12/2021 2:25:29 PM dag = row.dag 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/serialized_dag.py", line 175, in dag 8/12/2021 2:25:29 PM dag = SerializedDAG.from_dict(self.data) # type: Any 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/serialization/serialized_objects.py", line 792, in from_dict 8/12/2021 2:25:29 PM return cls.deserialize_dag(serialized_obj['dag']) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/serialization/serialized_objects.py", line 716, in deserialize_dag 8/12/2021 2:25:29 PM v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/serialization/serialized_objects.py", line 716, in <dictcomp> 8/12/2021 2:25:29 PM v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/serialization/serialized_objects.py", line 446, in deserialize_operator 8/12/2021 2:25:29 PM op = SerializedBaseOperator(task_id=encoded_op['task_id']) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 185, in apply_defaults 8/12/2021 2:25:29 PM result = func(self, *args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/serialization/serialized_objects.py", line 381, in __init__ 8/12/2021 2:25:29 PM super().__init__(*args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 185, in apply_defaults 8/12/2021 2:25:29 PM result = func(self, *args, **kwargs) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 527, in __init__ 8/12/2021 2:25:29 PM validate_key(task_id) 8/12/2021 2:25:29 PM File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/helpers.py", line 44, in validate_key 8/12/2021 2:25:29 PM raise AirflowException( 8/12/2021 2:25:29 PM airflow.exceptions.AirflowException: The key (Create grafana alert.alert_ESCALATION_AGING_FORWARD) has to be made of alphanumeric characters, dashes, dots and underscores exclusively ```

https://github.com/apache/airflow/issues/17568

https://github.com/apache/airflow/pull/17578

7db43f770196ad9cdebded97665d6efcdb985a18

833e1094a72b5a09f6b2249001b977538f139a19

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/settings.py"]

ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block

I am getting this error in a lot of DAGs that seem very random. The full trace of the error is: ``` Traceback (most recent call last): [2021-08-11 09:29:19,497] INFO - File "/home/***/.local/lib/python3.8/site-packages/MySQLdb/__init__.py", line 18, in <module> [2021-08-11 09:29:19,498] INFO - from . import _mysql [2021-08-11 09:29:19,499] INFO - ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block [2021-08-11 09:29:19,500] INFO - [2021-08-11 09:29:19,501] INFO - During handling of the above exception, another exception occurred: [2021-08-11 09:29:19,502] INFO - [2021-08-11 09:29:19,503] INFO - Traceback (most recent call last): [2021-08-11 09:29:19,504] INFO - File "/opt/***/dags/import_apple_app_store/py_s3_apple.py", line 7, in <module> [2021-08-11 09:29:19,505] INFO - from ***.models import Variable [2021-08-11 09:29:19,506] INFO - File "/home/***/.local/lib/python3.8/site-packages/***/__init__.py", line 46, in <module> [2021-08-11 09:29:19,506] INFO - settings.initialize() [2021-08-11 09:29:19,507] INFO - File "/home/***/.local/lib/python3.8/site-packages/***/settings.py", line 445, in initialize [2021-08-11 09:29:19,508] INFO - configure_adapters() [2021-08-11 09:29:19,508] INFO - File "/home/***/.local/lib/python3.8/site-packages/***/settings.py", line 325, in configure_adapters [2021-08-11 09:29:19,509] INFO - import MySQLdb.converters [2021-08-11 09:29:19,510] INFO - File "/home/***/.local/lib/python3.8/site-packages/MySQLdb/__init__.py", line 24, in <module> [2021-08-11 09:29:19,510] INFO - version_info, _mysql.version_info, _mysql.__file__ [2021-08-11 09:29:19,511] INFO - NameError: name '_mysql' is not defined [2021-08-11 09:29:19,647] {python_file_operator.py:118} INFO - Command exited with return code 1 ``` Characteristics: - Official Docker image apache/airflow:2.1.2. - I add to the official image the following: ` RUN sudo apt-get update && sudo apt-get install -y build-essential gconf-service libasound2 libatk1.0-0 libcairo2 libcups2 libfontconfig1 libgdk-pixbuf2.0-0 libgtk-3-0 libnspr4 libpango-1.0-0 libxss1 fonts-liberation libappindicator1 libnss3 lsb-release xdg-utils wget libappindicator3-1 libgbm1` - Also I have a requirements file. The only mysql-related library is the provider one. - Prod env in an Ubuntu 18.04 AWS EC2 of 128GB RAM - Local env MacOS 10GB allocated to Docker. I have this parameter in the docker-compose `AIRFLOW__OPERATORS__DEFAULT_RAM: 2048` - All errors occurred in python scripts executed by BashOperators - All errors happen during the imports, not even a single line of code after the imports is executed - No DAGs with the error uses any MySql library First, in prod, with Python 3.6, I had the error on several DAGs (let's say DAGs A and B worked, and C and D did not). In both A and B, there was a common point, importing Variable before SnowflakeHook. So the code was: ``` from snowflake_hook import SnowflakeHook (this file does NOT make use of Variable anywhere in the code) from airflow.models import Variable ``` And changing the order solved the problem: ``` from airflow.models import Variable from snowflake_hook import SnowflakeHook ``` Now, in local, I was testing updating the python version using the official images. Starting from Python 3.9, suddenly, the dag A, which was already fixed in prod, stopped working. Then I change to Python 3.8 and the dag A starts working again. However, dag C, which never had a problem, start failing with the same error. In this case, there were no import of SnowflakeHook, just common python libraries imports. Anyway, I change the code from: ``` import logging import pandas as pd import requests from airflow.models import Variable ``` to ``` from airflow.models import Variable import logging import pandas as pd import requests ``` And it works. Furthermore, dag D, which also didn't have a problem, fails also. I try to change the orders of the imports, and in this case, it does not work. At the time of failing, there were only 5 dags active and just 1 of them (light one) running, so it does not seem to be a RAM problem. So I am very lost about how to deal with this error. The docker-compose is very similar to the one in the official docs.

https://github.com/apache/airflow/issues/17546

https://github.com/apache/airflow/pull/18745

326e2b1272688e55fd76a85de399d0672aabe92c

d75cf4d60ddbff5b88bfe348cb83f9d173187744

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/tree.js", "airflow/www/templates/airflow/tree.html"]

Tree-view auto refresh ignores requested root task

Airflow 2.1.2 In Tree View, when you click on a task in a middle of a DAG, then click "Filter Upstream" in a popup, the webpage will reload the tree view with `root` argument in URL. However, when clicking a small Refresh button right, or enabling auto-refresh, the whole dag will load back. It ignores `root`.

https://github.com/apache/airflow/issues/17533

https://github.com/apache/airflow/pull/17633

84de7c53c2ebabef98e0916b13b239a8e4842091

c645d7ac2d367fd5324660c616618e76e6b84729

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/client/json_client.py"]

Broken json_client

**Apache Airflow version**: 2.1.2 **Apache Airflow Provider versions** (please include all providers that are relevant to your bug): apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-imap==2.0.0 apache-airflow-providers-sqlite==2.0.0 **Environment**: - **OS** (e.g. from /etc/os-release): Ubuntu - **Python**: python 3.8 - **httpx**: httpx==0.6.8, httpx==0.18.2 **What happened**: ``` Traceback (most recent call last): File "./bug.py", line 6, in <module> response = airflow_client.trigger_dag( File "/home/user/my_project/venv38/lib/python3.8/site-packages/airflow/api/client/json_client.py", line 54, in trigger_dag data = self._request( File "/home/user/my_project/venv38/lib/python3.8/site-packages/airflow/api/client/json_client.py", line 38, in _request if not resp.ok: AttributeError: 'Response' object has no attribute 'ok' ``` Json Client depends on httpx, which doesn't provide the attribute 'ok' and according to their spec (https://www.python-httpx.org/compatibility/) they are not going to do that. In the consequence handling the response crashes. **What you expected to happen**: The expected outcome is to handle the response smoothly. **How to reproduce it**: ``` #!/usr/bin/env python3 from airflow.api.client import json_client airflow_client = json_client.Client("http://rnd.airflow.mydomain.com", None) response = airflow_client.trigger_dag("my_dag", execution_date="2021-04-27T00:00:00") ``` **Anything else we need to know**: The problem always occurs.

https://github.com/apache/airflow/issues/17527

https://github.com/apache/airflow/pull/17529

663f1735c75aaa2690483f0880a8555015ed1eeb

fbddefe5cbc0fe163c42ab070bc0d7e44c5cef5b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/processor.py", "airflow/models/dag.py", "tests/dag_processing/test_processor.py", "tests/www/views/test_views_home.py"]

Removed dynamically generated DAG doesn't always remove from Airflow metadb

**Apache Airflow version**: 2.1.0 **What happened**: We use a python script that reads JSON configs from db to dynamically generate DAGs. Sometimes, when JSON Configs is updated, we expect DAGs to be removed in Airflow. This doesn't always happen. From my observation, one of the following happens: 1) DAG is removed because the python script no longer generates it. 2) DAG still exists but run history is empty. When trigger the DAG, first task is stuck in `queued` status indefinitely. **What you expected to happen**: I expect the right behavior should be >1) DAG is removed because the python script no longer generates it. **How to reproduce it**: * Create a python script in DAG folder that dynamically generates multiple DAGs * Execute these dynamically generated DAGs a few times * Use Airflow Variable to toggle (reduce) the # of DAGs generated * Examine if number of dynamically generated DAGs in the web UI

https://github.com/apache/airflow/issues/17516

https://github.com/apache/airflow/pull/17121

dc94ee26653ee4d3446210520036cc1f0eecfd81

e81f14b85e2609ce0f40081caa90c2a6af1d2c65

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/manager.py", "tests/dag_processing/test_manager.py"]

dag_processing.last_duration metric has random holes

**Apache Airflow version**: 2.1.2 **Apache Airflow Provider versions** (please include all providers that are relevant to your bug): 2.0 apache-airflow-providers-apache-hive==2.0.0 apache-airflow-providers-celery==2.0.0 apache-airflow-providers-cncf-kubernetes==1.2.0 apache-airflow-providers-docker==2.0.0 apache-airflow-providers-elasticsearch==1.0.4 apache-airflow-providers-ftp==2.0.0 apache-airflow-providers-imap==2.0.0 apache-airflow-providers-jdbc==2.0.0 apache-airflow-providers-microsoft-mssql==2.0.0 apache-airflow-providers-mysql==2.0.0 apache-airflow-providers-oracle==2.0.0 apache-airflow-providers-postgres==2.0.0 apache-airflow-providers-sqlite==2.0.0 apache-airflow-providers-ssh==2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.17 **Environment**: - **Install tools**: https://github.com/prometheus/statsd_exporter **What happened**: We are using an [statsd_exporter](https://github.com/prometheus/statsd_exporter) to store statsd metrics in prometheus. And we came across strange behavior, the metric dag_processing.last_duration. <dag_file> for different dags is drawn with holes at random intervals.    **What you expected to happen**: Metrics should be sent with the frequency specified in the config in the AIRFLOV__SHEDULER__PRINT_STATS_INTERVAL parameter and the default value is 30, and this happens in the[ _log_file_processing_stats method](https://github.com/apache/airflow/blob/2fea4cdceaa12b3ac13f24eeb383af624aacb2e7/airflow/dag_processing/manager.py#L696), the problem is that the initial time is taken using the [get_start_time](https://github.com/apache/airflow/blob/2fea4cdceaa12b3ac13f24eeb383af624aacb2e7/airflow/dag_processing/manager.py#L827) function which looks only at the probability of active processes that some of the dags will have time to be processed in 30 seconds and removed from self._processors [file_path] and thus the metrics for them will not be sent to the statsd. While for outputting to the log, [lasttime](https://github.com/apache/airflow/blob/2fea4cdceaa12b3ac13f24eeb383af624aacb2e7/airflow/dag_processing/manager.py#L783) is used in which information on the last processing is stored and from which it would be nice to send metrics to the statistics.

https://github.com/apache/airflow/issues/17513

https://github.com/apache/airflow/pull/17769

ffb81eae610f738fd45c88cdb27d601c0edf24fa

1a929f73398bdd201c8c92a08b398b41f9c2f591

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/elasticsearch/log/es_task_handler.py", "tests/providers/elasticsearch/log/test_es_task_handler.py"]

Invalid log order with ElasticsearchTaskHandler

**Apache Airflow version**: 2.* **Apache Airflow Provider versions** (please include all providers that are relevant to your bug): providers-elasticsearch==1.*/2.* **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.17 **What happened**: When using the ElasticsearchTaskHandler I see the broken order of the logs in the interface.  raw log entries: {"asctime": "2021-07-16 09:37:22,278", "filename": "pod_launcher_deprecated.py", "lineno": 131, "levelname": "WARNING", "message": "Pod not yet started: a3ca15e631a7f67697e10b23bae82644.6314229ca0c84d7ba29c870edc39a268", "exc_text": null, "dag_id": "dag_name_AS10_A_A_A_A", "task_id": "k8spod_make_upload_dir_1_2", "execution_date": "2021_07_16T09_10_00_000000", "try_number": "1", "log_id": "dag_name_AS10_A_A_A_A-k8spod_make_upload_dir_1_2-2021_07_16T09_10_00_000000-1", "offset": 1626427744958776832} {"asctime": "2021-07-16 09:37:26,485", "filename": "taskinstance.py", "lineno": 1191, "levelname": "INFO", "message": "Marking task as SUCCESS. dag_id=pod_gprs_raw_from_nfs_AS10_A_A_A_A, task_id=k8spod_make_upload_dir_1_2, execution_date=20210716T091000, start_date=20210716T092905, end_date=20210716T093726", "exc_text": null, "dag_id": "dag_name_AS10_A_A_A_A", "task_id": "k8spod_make_upload_dir_1_2", "execution_date": "2021_07_16T09_10_00_000000", "try_number": "1", "log_id": "dag_name_AS10_A_A_A_A-k8spod_make_upload_dir_1_2-2021_07_16T09_10_00_000000-1", "offset": 1626427744958776832} **What you expected to happen**: The problem lies in the method [set_context](https://github.com/apache/airflow/blob/662cb8c6ac8becb26ff405f8b21acfccdd8de2ae/airflow/providers/elasticsearch/log/es_task_handler.py#L271) that is set on the instance of the class and then all entries in the log go with the same offset, which is used to select for display in the interface. When we redefined method emit and put the offset to the record, the problem disappeared **How to reproduce it**: Run a long-lived task that generates logs, in our case it is a spark task launched from a docker container

https://github.com/apache/airflow/issues/17512

https://github.com/apache/airflow/pull/17551

a1834ce873d28c373bc11d0637bda57c17c79189

944dc32c2b4a758564259133a08f2ea8d28dcb6c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "airflow/utils/log/secrets_masker.py", "tests/cli/commands/test_task_command.py", "tests/utils/log/test_secrets_masker.py"]

Sensitive variables don't get masked when rendered with airflow tasks test

**Apache Airflow version**: 2.1.2 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): No **Environment**: - **Cloud provider or hardware configuration**: No - **OS** (e.g. from /etc/os-release): MacOS Big Sur 11.4 - **Kernel** (e.g. `uname -a`): - - **Install tools**: - - **Others**: - **What happened**: With the following code: ``` from airflow import DAG from airflow.models import Variable from airflow.operators.python import PythonOperator from datetime import datetime, timedelta def _extract(): partner = Variable.get("my_dag_partner_secret") print(partner) with DAG("my_dag", start_date=datetime(2021, 1 , 1), schedule_interval="@daily") as dag: extract = PythonOperator( task_id="extract", python_callable=_extract ) ``` By executing the command ` airflow tasks test my_dag extract 2021-01-01 ` The value of the variable my_dag_partner_secret gets rendered in the logs whereas it shouldn't ``` [2021-08-06 19:05:30,088] {taskinstance.py:1303} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=airflow AIRFLOW_CTX_DAG_ID=my_dag AIRFLOW_CTX_TASK_ID=extract AIRFLOW_CTX_EXECUTION_DATE=2021-01-01T00:00:00+00:00 partner_a [2021-08-06 19:05:30,091] {python.py:151} INFO - Done. Returned value was: None [2021-08-06 19:05:30,096] {taskinstance.py:1212} INFO - Marking task as SUCCESS. dag_id=my_dag, task_id=extract, execution_date=20210101T000000, start_date=20210806T131013, end_date=20210806T190530 ``` **What you expected to happen**: The value should be masked like on the UI or in the logs **How to reproduce it**: DAG given above **Anything else we need to know**: Nop

https://github.com/apache/airflow/issues/17476

https://github.com/apache/airflow/pull/24362

770ee0721263e108c7c74218fd583fad415e75c1

3007159c2468f8e74476cc17573e03655ab168fa

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"]

Label in front of TaskGroup breaks task dependencies

**Apache Airflow version**: 2.1.0 **What happened**: Using a Label infront of a TaskGroup breaks task dependencies, as tasks before the TaskGroup will now point to the last task in the TaskGroup. **What you expected to happen**: Task dependencies with or without Labels should be the same (and preferably the Label should be visible outside of the TaskGroup). Behavior without a Label is as follows: ```python with TaskGroup("tg1", tooltip="Tasks related to task group") as tg1: DummyOperator(task_id="b") >> DummyOperator(task_id="c") DummyOperator(task_id="a") >> tg1 ```  **How to reproduce it**: ```python with TaskGroup("tg1", tooltip="Tasks related to task group") as tg1: DummyOperator(task_id="b") >> DummyOperator(task_id="c") DummyOperator(task_id="a") >> Label("testlabel") >> tg1 ``` 

https://github.com/apache/airflow/issues/17469

https://github.com/apache/airflow/pull/29410

d0657f5539722257657f84837936c51ac1185fab

4b05468129361946688909943fe332f383302069

python

closed

apache/airflow

https://github.com/apache/airflow

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

Missing menu items in navigation panel for "Op" role

**Apache Airflow version**: 2.1.1 **What happened**: For user with "Op" role following menu items are not visible in navigation panel, however pages are accessible (roles has access to it): - "Browse" -> "DAG Dependencies" - "Admin" -> "Configurations" **What you expected to happen**: available menu items in navigation panel

https://github.com/apache/airflow/issues/17449

https://github.com/apache/airflow/pull/17450

9a0c10ba3fac3bb88f4f103114d4590b3fb191cb

4d522071942706f4f7c45eadbf48caded454cb42

python

closed

apache/airflow

https://github.com/apache/airflow

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

Deleted DAG raises SerializedDagNotFound exception when accessing webserver

**Apache Airflow version**: 2.1.2 **Environment**: - **Docker image**: official apache/airflow:2.1.2 extended with pip installed packages - **Executor**: CeleryExecutor - **Database**: PostgreSQL (engine version: 12.5) **What happened**: I've encountered `SerializedDagNotFound: DAG 'my_dag_id' not found in serialized_dag table` after deleting DAG via web UI. Exception is raised each time when UI is accessed. The exception itself does not impact UI, but still an event is sent to Sentry each time I interact with it. **What you expected to happen**: After DAG deletion I've expected that all records of it apart from logs would be deleted, but it's DAG run was still showing up in Webserver UI (even though, I couldn't find any records of DAG in the metadb, apart from dag_tag table still containing records related to deleted DAG (Which may be a reason for a new issue), but manual deletion of those records had no impact. After I've deleted DAG Run via Web UI, the exception is no longer raised. **How to reproduce it**: To this moment, I've only encountered this with one DAG, which was used for debugging purposes. It consists of multiple PythonOperators and it uses the same boilerplate code I use for dynamic DAG generation (return DAG object from `create_dag` function, add it to globals), but in it I've replaced dynamic generation logic with just `dag = create_dag(*my_args, **my_kwargs)`. I think this may have been caused by DAG run still running, when DAG was deleted, but cannot support this theory with actual information. <details> <summary>Traceback</summary> ```python SerializedDagNotFound: DAG 'my_dag_id' not found in serialized_dag table File "flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "flask/_compat.py", line 39, in reraise raise value File "flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "airflow/www/auth.py", line 34, in decorated return func(*args, **kwargs) File "airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "airflow/www/views.py", line 1679, in blocked dag = current_app.dag_bag.get_dag(dag_id) File "airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "airflow/models/dagbag.py", line 186, in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) File "airflow/models/dagbag.py", line 258, in _add_dag_from_db raise SerializedDagNotFound(f"DAG '{dag_id}' not found in serialized_dag table") ``` </details>

https://github.com/apache/airflow/issues/17442

https://github.com/apache/airflow/pull/17544

e69c6a7a13f66c4c343efa848b9b8ac2ad93b1f5

60ddcd10bbe5c2375b14307456b8e5f76c1d0dcd

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/operators/trigger_dagrun.py", "tests/operators/test_trigger_dagrun.py"]

TriggerDagRunOperator to configure the run_id of the triggered dag

<!-- Welcome to Apache Airflow! For a smooth issue process, try to answer the following questions. Don't worry if they're not all applicable; just try to include what you can :-) If you need to include code snippets or logs, please put them in fenced code blocks. If they're super-long, please use the details tag like <details><summary>super-long log</summary> lots of stuff </details> Please delete these comment blocks before submitting the issue. --> **Description** Airflow 1.10 support providing a run_id to TriggerDagRunOperator using [DagRunOrder](https://github.com/apache/airflow/blob/v1-10-stable/airflow/operators/dagrun_operator.py#L30-L33) object that will be returned after calling [TriggerDagRunOperator#python_callable](https://github.com/apache/airflow/blob/v1-10-stable/airflow/operators/dagrun_operator.py#L88-L95). With https://github.com/apache/airflow/pull/6317 (Airflow 2.0), this behavior changed and one could not provide run_id anymore to the triggered dag, which is very odd to say the least. <!-- A short description of your feature --> **Use case / motivation** I want to be able to provide run_id for the TriggerDagRunOperator. In my case, I use the TriggerDagRunOperator to trigger 30K dag_run daily and will be so annoying to see them having unidentifiable run_id. <!-- What do you want to happen? Rather than telling us how you might implement this solution, try to take a step back and describe what you are trying to achieve. --> After discussing with @ashb **Suggested fixes can be** * provide a templated run_id as a parameter to TriggerDagRunOperator. * restore the old behavior of Airflow 1.10, where DagRunOrder holds the run_id and dag_run_conf of the triggered dag * create a new sub-class of TriggerDagRunOperator to fully customize it **Are you willing to submit a PR?** <!--- We accept contributions! --> **Related Issues** <!-- Is there currently another issue associated with this? -->

https://github.com/apache/airflow/issues/17438

https://github.com/apache/airflow/pull/18788

5bc64fb923d7afcab420a1b4a6d9f6cc13362f7a

cdaa9aac80085b157c606767f2b9958cd6b2e5f0

python