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BeetleBox

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closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/NOTES.txt"]

Kubernetes does not show logs for task instances if remote logging is not configured

Without configuring remote logging, logs from Kubernetes for task instances are not complete. Without remote logging configured, the logging for task instances are outputted as : logging_level: INFO ```log BACKEND=postgresql DB_HOST=airflow-postgresql.airflow.svc.cluster.local DB_PORT=5432 [2021-04-03 12:35:52,047] {dagbag.py:448} INFO - Filling up the DagBag from /opt/airflow/dags/k8pod.py /home/airflow/.local/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/backcompat/backwards_compat_converters.py:26 DeprecationWarning: This module is deprecated. Please use `kubernetes.client.models.V1Volume`. /home/airflow/.local/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/backcompat/backwards_compat_converters.py:27 DeprecationWarning: This module is deprecated. Please use `kubernetes.client.models.V1VolumeMount`. Running <TaskInstance: k8_pod_operator_xcom.task322 2021-04-03T12:25:49.515523+00:00 [queued]> on host k8podoperatorxcomtask322.7f2ee45d4d6443c5ad26bd8fbefb8292 ``` **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): Client Version: version.Info{Major:"1", Minor:"20", GitVersion:"v1.20.4", GitCommit:"e87da0bd6e03ec3fea7933c4b5263d151aafd07c", GitTreeState:"clean", BuildDate:"2021-02-18T16:12:00Z", GoVersion:"go1.15.8", Compiler:"gc", Platform:"linux/amd64"} Server Version: version.Info{Major:"1", Minor:"20", GitVersion:"v1.20.2", GitCommit:"faecb196815e248d3ecfb03c680a4507229c2a56", GitTreeState:"clean", BuildDate:"2021-01-21T01:11:42Z", GoVersion:"go1.15.5", Compiler:"gc", Platform:"linux/amd64"} **Environment**: - **OS** (e.g. from /etc/os-release): Ubuntu 20.04 **What happened**: The logs for task instances run are not shown without remote logging configured **What you expected to happen**: I expected to see complete logs for tasks **How to reproduce it**: Start airflow using the helm chart without configuring remote logging. Run a task and check the logs. It's necessary to set `delete_worker_pods` to False so you can view the logs after the task has ended

https://github.com/apache/airflow/issues/15179

https://github.com/apache/airflow/pull/16784

1eed6b4f37ddf2086bf06fb5c4475c68fadac0f9

8885fc1d9516b30b316487f21e37d34bdd21e40e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/tutorial.py", "airflow/models/baseoperator.py", "airflow/serialization/schema.json", "airflow/www/utils.py", "airflow/www/views.py", "docs/apache-airflow/concepts.rst", "tests/serialization/test_dag_serialization.py", "tests/www/test_utils.py"]

Task doc is not shown on Airflow 2.0 Task Instance Detail view

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 932f8c2e9360de6371031d4d71df00867a2776e6 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: locally run `airflow server` - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): mac - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> Task doc is shown on Airflow v1 Task Instance Detail view but not shown on v2. **What you expected to happen**: <!-- What do you think went wrong? --> Task doc is shown. **How to reproduce it**: - install airflow latest master - `airflow server` - open `tutorial_etl_dag` in `example_dags` - run dag(I don't know why but task instance detail can't open with error if no dag runs) and open task instance detail <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/15178

https://github.com/apache/airflow/pull/15191

7c17bf0d1e828b454a6b2c7245ded275b313c792

e86f5ca8fa5ff22c1e1f48addc012919034c672f

python

closed

apache/airflow

https://github.com/apache/airflow

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

scheduler does not apply ordering when querying which task instances to queue

Issue type: Bug Airflow version: 2.0.1 (although bug may have existed earlier, and master still has the bug) Issue: The scheduler sometimes queues tasks in alphabetical order instead of in priority weight and execution date order. This causes priorities to not work at all, and causes some tasks with name later in the alphabet to never run as long as new tasks with names earlier in the alphabet are ready. Where the issue is in code (I think): The scheduler will query the DB to get a set of task instances that are ready to run: https://github.com/apache/airflow/blob/2.0.1/airflow/jobs/scheduler_job.py#L915-L924 And will simply get the first `max_tis` task instances from the result (with the `limit` call in the last line of the query), where `max_tis` is computed earlier in the code as cumulative pools slots available. The code in master improved the query to filter out tasks from starved pools, but still it will get the first `max_tis` tasks only with no ordering or reasoning on which `max_tis` to take. Later, the scheduler is smart and will queue tasks based on priority and execution order: https://github.com/apache/airflow/blob/2.0.1/airflow/jobs/scheduler_job.py#L978-L980 However, the correct sorting (second code link here) will only happen on the subset picked by the query (first code link here), but the query will not pick tasks following correct sorting. This causes tasks with lower priority and / or later execution date to be queued BEFORE tasks with higher priority and / or earlier execution date, just because the first are higher in alphabet than the second, and therefore the first are returned by the unsorted limited SQL query only. Proposed fix: Add a "sort by" in the query that gets the tasks to examine (first code link here), so that tasks are sorted by priority weight and execution time (meaning, same logic as the list sorting done later). I am willing to submit a PR if at least I get some feedback on the proposed fix here.

https://github.com/apache/airflow/issues/15171

https://github.com/apache/airflow/pull/15210

4752fb3eb8ac8827e6af6022fbcf751829ecb17a

943292b4e0c494f023c86d648289b1f23ccb0ee9

python

closed

apache/airflow

https://github.com/apache/airflow

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

"duplicate key value violates unique constraint "dag_run_dag_id_execution_date_key" when triggering a 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.14 **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: [2021-04-02 07:23:30,513] [ERROR] app.py:1892 - Exception on /api/v1/dags/auto_test/dagRuns [POST] Traceback (most recent call last): File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context cursor, statement, parameters, context File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_execution_date_key" DETAIL: Key (dag_id, execution_date)=(auto_test, 1967-12-13 20:57:42.043+01) already exists. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/usr/local/lib/python3.6/site-packages/flask/_compat.py", line 39, in reraise raise value File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.6/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/decorator.py", line 48, in wrapper response = function(request) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/uri_parsing.py", line 144, in wrapper response = function(request) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/validation.py", line 184, in wrapper response = function(request) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/validation.py", line 384, in wrapper return function(request) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/response.py", line 103, in wrapper response = function(request) File "/usr/local/lib/python3.6/site-packages/connexion/decorators/parameter.py", line 121, in wrapper return function(**kwargs) File "/usr/local/lib/python3.6/site-packages/airflow/api_connexion/security.py", line 47, in decorated return func(*args, **kwargs) File "/usr/local/lib/python3.6/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.6/site-packages/airflow/api_connexion/endpoints/dag_run_endpoint.py", line 231, in post_dag_run session.commit() File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 1046, in commit self.transaction.commit() File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 504, in commit self._prepare_impl() File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 483, in _prepare_impl self.session.flush() File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 2540, in flush self._flush(objects) File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 2682, in _flush transaction.rollback(_capture_exception=True) File "/usr/local/lib/python3.6/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ with_traceback=exc_tb, File "/usr/local/lib/python3.6/site-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/session.py", line 2642, in _flush flush_context.execute() File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/unitofwork.py", line 422, in execute rec.execute(self) File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/unitofwork.py", line 589, in execute uow, File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/persistence.py", line 245, in save_obj insert, File "/usr/local/lib/python3.6/site-packages/sqlalchemy/orm/persistence.py", line 1136, in _emit_insert_statements statement, params File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1011, in execute return meth(self, multiparams, params) File "/usr/local/lib/python3.6/site-packages/sqlalchemy/sql/elements.py", line 298, in _execute_on_connection return connection._execute_clauseelement(self, multiparams, params) File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1130, in _execute_clauseelement distilled_params, File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1317, in _execute_context e, statement, parameters, cursor, context File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1511, in _handle_dbapi_exception sqlalchemy_exception, with_traceback=exc_info[2], from_=e File "/usr/local/lib/python3.6/site-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context cursor, statement, parameters, context File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.IntegrityError: (psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint "dag_run_dag_id_execution_date_key" DETAIL: Key (dag_id, execution_date)=(auto_test, 1967-12-13 20:57:42.043+01) already exists. [SQL: INSERT INTO dag_run (dag_id, execution_date, start_date, end_date, state, run_id, creating_job_id, external_trigger, run_type, conf, last_scheduling_decision, dag_hash) VALUES (%(dag_id)s, %(execution_date)s, %(start_date)s, %(end_date)s, %(state)s, %(run_id)s, %(creating_job_id)s, %(external_trigger)s, %(run_type)s, %(conf)s, %(last_scheduling_decision)s, %(dag_hash)s) RETURNING dag_run.id] [parameters: {'dag_id': 'auto_test', 'execution_date': datetime.datetime(1967, 12, 13, 19, 57, 42, 43000, tzinfo=Timezone('UTC')), 'start_date': datetime.datetime(2021, 4, 2, 7, 23, 30, 511735, tzinfo=Timezone('UTC')), 'end_date': None, 'state': 'running', 'run_id': 'dag_run_id_zstp_4435_postman11', 'creating_job_id': None, 'external_trigger': True, 'run_type': <DagRunType.MANUAL: 'manual'>, 'conf': <psycopg2.extensions.Binary object at 0x7f07b30b71e8>, 'last_scheduling_decision': None, 'dag_hash': None}] <!-- (please include exact error messages if you can) --> **What you expected to happen**: The second trigger success <!-- What do you think went wrong? --> **How to reproduce it**: Trigger a dag with the following conf: `{ "dag_run_id": "dag_run_id_zstp_4435_postman", "execution_date": "1967-12-13T19:57:42.043Z", "conf": {} }` Then trigerring the same dag only with a different dag_run_id: `{ "dag_run_id": "dag_run_id_zstp_4435_postman111", "execution_date": "1967-12-13T19:57:42.043Z", "conf": {} }` <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/15150

https://github.com/apache/airflow/pull/15174

36d9274f4ea87f28e2dcbab393b21e34a04eec30

d89bcad26445c8926093680aac84d969ac34b54c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/example_dags/example_bigquery_to_mssql.py", "airflow/providers/google/cloud/transfers/bigquery_to_mssql.py", "airflow/providers/google/provider.yaml", "tests/providers/google/cloud/transfers/test_bigquery_to_mssql.py"]

Big Query to MS SQL operator

<!-- 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** A new transfer operator for transferring records from Big Query to MSSQL. **Use case / motivation** Very similar to Bigquery to mysql, this will be an operator for transferring rows from Big Query to MSSQL. **Are you willing to submit a PR?** Yes **Related Issues** No

https://github.com/apache/airflow/issues/15145

https://github.com/apache/airflow/pull/15422

70cfe0135373d1f0400e7d9b275ebb017429794b

7f8f75eb80790d4be3167f5e1ffccc669a281d55

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/cli.py", "tests/utils/test_cli_util.py"]

airflow scheduler -p command not working in airflow 2.0.1

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: 4GB RAM, Processor - Intel(R) Core(TM) i5-8250U CPU @ 1.60GHz - **OS** (e.g. from /etc/os-release): 18.04.5 LTS (Bionic Beaver) - **Kernel** (e.g. `uname -a`): Linux 4.15.0-136-generic - **Install tools**: bare metal installation as per commands given [here](https://airflow.apache.org/docs/apache-airflow/stable/installation.html) **What happened**: On running `airflow scheduler -p` got following error:- ``` Traceback (most recent call last): File "/home/vineet/Documents/projects/venv/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/__main__.py", line 40, in main args.func(args) File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/utils/cli.py", line 86, in wrapper metrics = _build_metrics(f.__name__, args[0]) File "/home/vineet/Documents/projects/venv/lib/python3.6/site-packages/airflow/utils/cli.py", line 118, in _build_metrics full_command[idx + 1] = "*" * 8 IndexError: list assignment index out of range ``` As per docs, `-p` flag is a valid flag and gives correct result for `airflow scheduler --do-pickle` **How to reproduce it**: Install `airflow` 2.0.1 and run `airflow scheduler -p`

https://github.com/apache/airflow/issues/15131

https://github.com/apache/airflow/pull/15143

6822665102c973d6e4d5892564294489ca094580

486b76438c0679682cf98cb88ed39c4b161cbcc8

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

ImportError: cannot import name '_check_google_client_version' from 'pandas_gbq.gbq'

**What happened**: `pandas-gbq` released version [0.15.0](https://github.com/pydata/pandas-gbq/releases/tag/0.15.0) which broke `apache-airflow-backport-providers-google==2020.11.23` ``` ../lib/python3.7/site-packages/airflow/providers/google/cloud/hooks/bigquery.py:49: in <module> from pandas_gbq.gbq import ( E ImportError: cannot import name '_check_google_client_version' from 'pandas_gbq.gbq' (/usr/local/lib/python3.7/site-packages/pandas_gbq/gbq.py) ``` The fix is to pin `pandas-gpq==0.14.1`.

https://github.com/apache/airflow/issues/15113

https://github.com/apache/airflow/pull/15114

64b00896d905abcf1fbae195a29b81f393319c5f

b3b412523c8029b1ffbc600952668dc233589302

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "chart/values.yaml", "docs/docker-stack/build-arg-ref.rst", "docs/docker-stack/build.rst", "docs/docker-stack/docker-examples/extending/writable-directory/Dockerfile", "docs/docker-stack/entrypoint.rst", "scripts/in_container/prod/entrypoint_prod.sh"]

Make the entrypoint in Prod image fail in case the user/group is not properly set

Airflow Production image accepts two types of uid/gid setting: * airflow user (50000) with any GID * any other user wit GID = 0 (this is to accommodate OpenShift Guidelines https://docs.openshift.com/enterprise/3.0/creating_images/guidelines.html) We should check the uid/gid at entrypoint and fail it with clear error message if the uid/gid are set wrongly. <!-- 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** <!-- A short description of your feature --> **Use case / motivation** <!-- 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. --> **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/15107

https://github.com/apache/airflow/pull/15162

1d635ef0aefe995553059ee5cf6847cf2db65b8c

ce91872eccceb8fb6277012a909ad6b529a071d2

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow web server redirects to a non-existing log folder - v2.1.0.dev0

**Apache Airflow version**: v2.1.0.dev0 **Environment**: - **Others**: Docker + docker compose ``` docker pull apache/airflow:master-python3.8 ``` **What happened**: Once the tasks finish successfully, I click on the Logs button in the web server, then I got redirected to this URL: `http://localhost:8080/task?dag_id=testing&task_id=testing2&execution_date=2021-03-30T22%3A50%3A17.075509%2B00%3A00` Everything looks fine just for 0.5-ish seconds (the screenshots below were taken by disabling the page refreshing):   Then, it instantly gets redirected to the following URL: `http://localhost:8080/task?dag_id=testing&task_id=testing2&execution_date=2021-03-30+22%3A50%3A17%2B00%3A00# ` In which I cannot see any info:   The problems lies in the log format specified in the URL: ``` 2021-03-30T22%3A50%3A17.075509%2B00%3A00 2021-03-30+22%3A50%3A17%2B00%3A00# ``` This is my python code to run the DAG: ```python args = { 'owner': 'airflow', } dag = DAG( dag_id='testing', default_args=args, schedule_interval=None, start_date=datetime(2019,1,1), catchup=False, tags=['example'], ) task = PythonOperator( task_id="testing2", python_callable=test_python, depends_on_past=False, op_kwargs={'test': 'hello'}, dag=dag, ) ``` **Configuration details** Environment variables from docker-compose.yml file: ``` AIRFLOW__CORE__EXECUTOR: CeleryExecutor AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0 AIRFLOW__CORE__FERNET_KEY: '' AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true' AIRFLOW__CORE__LOAD_EXAMPLES: 'false' AIRFLOW_HOME: /opt/airflow AIRFLOW__CORE__DEFAULT_TIMEZONE: Europe/Madrid AIRFLOW__WEBSERVER__DEFAULT_UI_TIMEZONE: Europe/Madrid AIRFLOW__WEBSERVER__EXPOSE_CONFIG: 'true' ```

https://github.com/apache/airflow/issues/15103

https://github.com/apache/airflow/pull/15258

019241be0c839ba32361679ffecd178c0506d10d

523fb5c3f421129aea10045081dc5e519859c1ae

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/bigquery_dts.py", "airflow/providers/google/cloud/operators/bigquery_dts.py"]

GCP BigQuery Data Transfer Run Issue

**Apache Airflow version**: composer-1.15.1-airflow-1.10.14 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: Google Composer **What happened**: After a `TransferConfig` is created successfully by operator `BigQueryCreateDataTransferOperator`, and also confirmed in GCP console, the created Resource name is: ``` projects/<project-id>/locations/europe/transferConfigs/<transfer-config-id> ``` Then I use operator `BigQueryDataTransferServiceStartTransferRunsOperator` to run the transfer, I got this error: ``` 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_dts.py", line 290, in execut metadata=self.metadata File "/usr/local/lib/airflow/airflow/providers/google/common/hooks/base_google.py", line 425, in inner_wrappe return func(self, *args, **kwargs File "/usr/local/lib/airflow/airflow/providers/google/cloud/hooks/bigquery_dts.py", line 235, in start_manual_transfer_run metadata=metadata or () File "/opt/python3.6/lib/python3.6/site-packages/google/cloud/bigquery_datatransfer_v1/services/data_transfer_service/client.py", line 1110, in start_manual_transfer_run response = rpc(request, retry=retry, timeout=timeout, metadata=metadata, File "/opt/python3.6/lib/python3.6/site-packages/google/api_core/gapic_v1/method.py", line 145, in __call_ return wrapped_func(*args, **kwargs File "/opt/python3.6/lib/python3.6/site-packages/google/api_core/grpc_helpers.py", line 75, in error_remapped_callabl six.raise_from(exceptions.from_grpc_error(exc), exc File "<string>", line 3, in raise_fro google.api_core.exceptions.NotFound: 404 Requested entity was not found ``` **What you expected to happen**: `BigQueryDataTransferServiceStartTransferRunsOperator` should run the data transfer job. **How to reproduce it**: 1. Create a `cross_region_copy` TransferConfig with operator`BigQueryCreateDataTransferOperator` 2. Run the job with operator `BigQueryDataTransferServiceStartTransferRunsOperator` ```python create_transfer = BigQueryCreateDataTransferOperator( task_id=f'create_{ds}_transfer', transfer_config={ 'destination_dataset_id': ds, 'display_name': f'Copy {ds}', 'data_source_id': 'cross_region_copy', 'schedule_options': {'disable_auto_scheduling': True}, 'params': { 'source_project_id': source_project, 'source_dataset_id': ds, 'overwrite_destination_table': True }, }, project_id=target_project, ) transfer_config_id = f"{{{{ task_instance.xcom_pull('create_{ds}_transfer', key='transfer_config_id') }}}}" start_transfer = BigQueryDataTransferServiceStartTransferRunsOperator( task_id=f'start_{ds}_transfer', transfer_config_id=transfer_config_id, requested_run_time={"seconds": int(time.time() + 60)}, project_id=target_project, ) run_id = f"{{{{ task_instance.xcom_pull('start_{ds}_transfer', key='run_id') }}}}" ``` **Anything else we need to know**: So I went to the [Google's API reference page](https://cloud.google.com/bigquery-transfer/docs/reference/datatransfer/rest/v1/projects.locations.transferConfigs/startManualRuns) to run some test. When I use this parent parameter `projects/{projectId}/transferConfigs/{configId}`, it thrown the same error. But it works when I use `rojects/{projectId}/locations/{locationId}/transferConfigs/{configId}` I guess the piece of code that causes this issue is here in the hook, why does it use `projects/{projectId}/transferConfigs/{configId}` instead of `rojects/{projectId}/locations/{locationId}/transferConfigs/{configId}`? https://github.com/apache/airflow/blob/def961512904443db90e0a980c43dc4d8f8328d5/airflow/providers/google/cloud/hooks/bigquery_dts.py#L226-L232

https://github.com/apache/airflow/issues/15088

https://github.com/apache/airflow/pull/20221

bc76126a9f6172a360fd4301eeb82372d000f70a

98514cc1599751d7611b3180c60887da0a25ff5e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "airflow/cli/commands/scheduler_command.py", "chart/templates/scheduler/scheduler-deployment.yaml", "tests/cli/commands/test_scheduler_command.py"]

Run serve_logs process as part of scheduler command

<!-- 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** - The `airflow serve_logs` command has been removed from the CLI as of 2.0.0 - When using `CeleryExecutor`, the `airflow celery worker` command runs the `serve_logs` process in the background. - We should do the same with `airflow scheduler` command when using `LocalExecutor` or `SequentialExecutor` <!-- A short description of your feature --> **Use case / motivation** - This will allow for viewing task logs in the UI when using `LocalExecutor` or `SequentialExecutor` without elasticsearch configured. <!-- 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. --> **Are you willing to submit a PR?** Yes. Working with @dimberman . <!--- We accept contributions! --> **Related Issues** - https://github.com/apache/airflow/issues/14222 - https://github.com/apache/airflow/issues/13331 <!-- Is there currently another issue associated with this? -->

https://github.com/apache/airflow/issues/15071

https://github.com/apache/airflow/pull/15557

053d903816464f699876109b50390636bf617eff

414bb20fad6c6a50c5a209f6d81f5ca3d679b083

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/api_connexion/schemas/user_schema.py", "tests/api_connexion/endpoints/test_user_endpoint.py", "tests/api_connexion/schemas/test_user_schema.py"]

Remove 'user_id', 'role_id' from User and Role in OpenAPI schema

Would be good to remove the 'id' of both User and Role schemas from what is dumped in REST API endpoints. ID of User and Role table are sensitive data that would be fine to hide from the endpoints

https://github.com/apache/airflow/issues/15059

https://github.com/apache/airflow/pull/15117

b62ca0ad5d8550a72257ce59c8946e7f134ed70b

7087541a56faafd7aa4b9bf9f94eb6b75eed6851

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/api/experimental/endpoints.py", "airflow/www/templates/airflow/trigger.html", "airflow/www/views.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py", "tests/www/api/experimental/test_endpoints.py", "tests/www/views/test_views_trigger_dag.py"]

DAG task execution and API fails if dag_run.conf is provided with an array or string (instead of dict)

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): Tried both pip install and k8s image **Environment**: Dev Workstation of K8s execution - both the same - **OS** (e.g. from /etc/os-release): Ubuntu 20.04 LTS - **Others**: Python 3.6 **What happened**: We use Airflow 1.10.14 currently in production and have a couple of DAGs defined today which digest a batch call. We implemented the batch (currently) in a way that the jobs are provided as dag_run.conf as an array of dicts, e.g. "[ { "job": "1" }, { "job": "2" } ]". Trying to upgrade to Airflow 2.0.1 we see that such calls are still possible to submit but all further actions are failing: - It is not possible to query status via REST API, generates a HTTP 500 - DAG starts but all tasks fail. - Logs can not be displayed (actually there are none produced on the file system) - Error logging is a bit complex, Celery worker does not provide meaningful logs on console nor produces log files, running a scheduler as SequentialExecutor reveals at least one meaningful sack trace as below - (probably a couple of other internal logic is also failing - Note that the dag_run.conf can be seen as submitted (so is correctly received) in Browse--> DAG Runs menu As a regression using the same dag and passing a dag_run.conf = "{ "batch": [ { "job": "1" }, { "job": "2" } ] }" as well as "{}". Example (simple) DAG to reproduce: ``` from airflow import DAG from airflow.operators.bash import BashOperator from airflow.utils.dates import days_ago from datetime import timedelta dag = DAG( 'test1', description='My first DAG', default_args={ 'owner': 'jscheffl', 'email': ['***@***.de'], 'email_on_failure': True, 'email_on_retry': True, 'retries': 5, 'retry_delay': timedelta(minutes=5), }, start_date=days_ago(2) ) hello_world = BashOperator( task_id='hello_world', bash_command='echo hello world', dag=dag, ) ``` Stack trace from SequentialExecutor: ``` Traceback (most recent call last): File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/__main__.py", line 40, in main args.func(args) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/cli.py", line 89, in wrapper return f(*args, **kwargs) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 225, in task_run ti.init_run_context(raw=args.raw) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1987, in init_run_context self._set_context(self) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/log/logging_mixin.py", line 54, in _set_context set_context(self.log, context) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/log/logging_mixin.py", line 174, in set_context handler.set_context(value) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 56, in set_context local_loc = self._init_file(ti) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 245, in _init_file relative_path = self._render_filename(ti, ti.try_number) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 77, in _render_filename jinja_context = ti.get_template_context() File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1606, in get_template_context self.overwrite_params_with_dag_run_conf(params=params, dag_run=dag_run) File "/home/jscheffl/Programmieren/Python/Airflow/syncignore/airflow-venv/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1743, in overwrite_params_with_dag_run_conf params.update(dag_run.conf) ValueError: dictionary update sequence element #0 has length 4; 2 is required {sequential_executor.py:66} ERROR - Failed to execute task Command '['airflow', 'tasks', 'run', 'test1', 'hello_world', '2021-03-25T22:22:36.732899+00:00', '--local', '--pool', 'default_pool', '--subdir', '/home/jscheffl/Programmieren/Python/Airflow/airflow-home/dags/test1.py']' returned non-zero exit status 1.. [2021-03-25 23:42:47,209] {scheduler_job.py:1199} INFO - Executor reports execution of test1.hello_world execution_date=2021-03-25 22:22:36.732899+00:00 exited with status failed for try_number 5 ``` **What you expected to happen**: - EITHER the submission of arrays as dag_run.conf is supported like in 1.10.14 - OR I would expect that the submission contains a validation if array values are not supported by Airflow (which it seems it was at least working in 1.10) **How to reproduce it**: See DAG code above, reproduce the error e.g. by triggering with "[ "test" ]" as dag_run.conf **Anything else we need to know**: I assume not :-)

https://github.com/apache/airflow/issues/15023

https://github.com/apache/airflow/pull/15057

eeb97cff9c2cef46f2eb9a603ccf7e1ccf804863

01c9818405107271ee8341c72b3d2d1e48574e08

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ui/src/api/index.ts", "airflow/ui/src/components/TriggerRunModal.tsx", "airflow/ui/src/interfaces/api.ts", "airflow/ui/src/utils/memo.ts", "airflow/ui/src/views/Pipelines/Row.tsx", "airflow/ui/src/views/Pipelines/index.tsx", "airflow/ui/test/Pipelines.test.tsx"]

Establish mutation patterns via the API

https://github.com/apache/airflow/issues/15019

https://github.com/apache/airflow/pull/15068

794922649982b2a6c095f7fa6be4e5d6a6d9f496

9ca49b69113bb2a1eaa0f8cec2b5f8598efc19ea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ui/package.json", "airflow/ui/src/api/index.ts", "airflow/ui/src/components/Table.tsx", "airflow/ui/src/interfaces/react-table-config.d.ts", "airflow/ui/src/views/Pipelines/PipelinesTable.tsx", "airflow/ui/src/views/Pipelines/Row.tsx", "airflow/ui/test/Pipelines.test.tsx", "airflow/ui/yarn.lock"]

Build out custom Table components

https://github.com/apache/airflow/issues/15018

https://github.com/apache/airflow/pull/15805

65519ab83ddf4bd6fc30c435b5bfccefcb14d596

2c6b003fbe619d5d736cf97f20a94a3451e1a14a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_local.py", "tests/providers/google/cloud/transfers/test_gcs_to_local.py"]

`GCSToLocalFilesystemOperator` unnecessarily downloads objects when it checks object size

`GCSToLocalFilesystemOperator` in `airflow/providers/google/cloud/transfers/gcs_to_local.py` checks the file size if `store_to_xcom_key` is `True`. https://github.com/apache/airflow/blob/b40dffa08547b610162f8cacfa75847f3c4ca364/airflow/providers/google/cloud/transfers/gcs_to_local.py#L137-L142 How it checks size is to download the object as `bytes` then check the size. This unnecessarily downloads the objects. `google.cloud.storage.blob.Blob` itself already has a `size` property ([documentation reference](https://googleapis.dev/python/storage/1.30.0/blobs.html#google.cloud.storage.blob.Blob.size)), and it should be used instead. In extreme cases, if the object is big size, it adds unnecessary burden on the instance resources. A new method, `object_size()`, can be added to `GCSHook`, then this can be addressed in `GCSToLocalFilesystemOperator`.

https://github.com/apache/airflow/issues/15005

https://github.com/apache/airflow/pull/16171

19eb7ef95741e10d712845bc737b86615cbb8e7a

e1137523d4e9cb5d5cfe8584963620677a4ad789

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/sensors/s3_prefix.py", "tests/providers/amazon/aws/sensors/test_s3_prefix.py"]

S3MultipleKeysSensor operator

**Description** Currently we have an operator, S3KeySensor which polls for the given prefix in the bucket. At times, there is need to poll for multiple prefixes in given bucket in one go. To have that - I propose to have a S3MultipleKeysSensor, which would poll for multiple prefixes in the given bucket in one go. **Use case / motivation** To make it easier for users to poll multiple S3 prefixes in a given bucket. **Are you willing to submit a PR?** Yes, I have an implementation ready for that. **Related Issues** NA

https://github.com/apache/airflow/issues/15001

https://github.com/apache/airflow/pull/18807

ec31b2049e7c3b9f9694913031553f2d7eb66265

176165de3b297c0ed7d2b60cf6b4c37fc7a2337f

python

closed

apache/airflow

https://github.com/apache/airflow

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

When an ECS Task fails to start, ECS Operator raises a CloudWatch exception

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 1.10.13 **Environment**: - **Cloud provider or hardware configuration**:AWS - **OS** (e.g. from /etc/os-release): Amazon Linux 2 - **Kernel** (e.g. `uname -a`): 4.14.209-160.339.amzn2.x86_64 - **Install tools**: pip - **Others**: **What happened**: When an ECS Task exits with `stopCode: TaskFailedToStart`, the ECS Operator will exit with a ResourceNotFoundException for the GetLogEvents operation. This is because the task has failed to start, so no log is created. ``` [2021-03-14 02:32:49,792] {ecs_operator.py:147} INFO - ECS Task started: {'tasks': [{'attachments': [], 'availabilityZone': 'ap-northeast-1c', 'clusterArn': 'arn:aws:ecs:ap-northeast-1:xxxx:cluster/ecs-cluster', 'containerInstanceArn': 'arn:aws:ecs:ap-northeast-1:xxxx:container-instance/ecs-cluster/xxxx', 'containers': [{'containerArn': 'arn:aws:ecs:ap-northeast-1:xxxx:container/xxxx', 'taskArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task/ecs-cluster/xxxx', 'name': 'container_image', 'image': 'xxxx.dkr.ecr.ap-northeast-1.amazonaws.com/ecr/container_image:latest', 'lastStatus': 'PENDING', 'networkInterfaces': [], 'cpu': '128', 'memoryReservation': '128'}], 'cpu': '128', 'createdAt': datetime.datetime(2021, 3, 14, 2, 32, 49, 770000, tzinfo=tzlocal()), 'desiredStatus': 'RUNNING', 'group': 'family:task', 'lastStatus': 'PENDING', 'launchType': 'EC2', 'memory': '128', 'overrides': {'containerOverrides': [{'name': 'container_image', 'command': ['/bin/bash', '-c', 'xxxx']}], 'inferenceAcceleratorOverrides': []}, 'startedBy': 'airflow', 'tags': [], 'taskArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task/ecs-cluster/xxxx', 'taskDefinitionArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task-definition/task:1', 'version': 1}], 'failures': [], 'ResponseMetadata': {'RequestId': 'xxxx', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'xxxx', 'content-type': 'application/x-amz-json-1.1', 'content-length': '1471', 'date': 'Sun, 14 Mar 2021 02:32:48 GMT'}, 'RetryAttempts': 0}} [2021-03-14 02:34:15,022] {ecs_operator.py:168} INFO - ECS Task stopped, check status: {'tasks': [{'attachments': [], 'availabilityZone': 'ap-northeast-1c', 'clusterArn': 'arn:aws:ecs:ap-northeast-1:xxxx:cluster/ecs-cluster', 'connectivity': 'CONNECTED', 'connectivityAt': datetime.datetime(2021, 3, 14, 2, 32, 49, 770000, tzinfo=tzlocal()), 'containerInstanceArn': 'arn:aws:ecs:ap-northeast-1:xxxx:container-instance/ecs-cluster/xxxx', 'containers': [{'containerArn': 'arn:aws:ecs:ap-northeast-1:xxxx:container/xxxx', 'taskArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task/ecs-cluster/xxxx', 'name': 'container_image', 'image': 'xxxx.dkr.ecr.ap-northeast-1.amazonaws.com/ecr/container_image:latest', 'lastStatus': 'STOPPED', 'reason': 'CannotPullContainerError: failed to register layer: Error processing tar file(exit status 1): write /var/lib/xxxx: no space left on device', 'networkInterfaces': [], 'healthStatus': 'UNKNOWN', 'cpu': '128', 'memoryReservation': '128'}], 'cpu': '128', 'createdAt': datetime.datetime(2021, 3, 14, 2, 32, 49, 770000, tzinfo=tzlocal()), 'desiredStatus': 'STOPPED', 'executionStoppedAt': datetime.datetime(2021, 3, 14, 2, 34, 12, 810000, tzinfo=tzlocal()), 'group': 'family:task', 'healthStatus': 'UNKNOWN', 'lastStatus': 'STOPPED', 'launchType': 'EC2', 'memory': '128', 'overrides': {'containerOverrides': [{'name': 'container_image', 'command': ['/bin/bash', '-c', 'xxxx']}], 'inferenceAcceleratorOverrides': []}, 'pullStartedAt': datetime.datetime(2021, 3, 14, 2, 32, 51, 68000, tzinfo=tzlocal()), 'pullStoppedAt': datetime.datetime(2021, 3, 14, 2, 34, 13, 584000, tzinfo=tzlocal()), 'startedBy': 'airflow', 'stopCode': 'TaskFailedToStart', 'stoppedAt': datetime.datetime(2021, 3, 14, 2, 34, 12, 821000, tzinfo=tzlocal()), 'stoppedReason': 'Task failed to start', 'stoppingAt': datetime.datetime(2021, 3, 14, 2, 34, 12, 821000, tzinfo=tzlocal()), 'tags': [], 'taskArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task/ecs-cluster/xxxx', 'taskDefinitionArn': 'arn:aws:ecs:ap-northeast-1:xxxx:task-definition/task:1', 'version': 2}], 'failures': [], 'ResponseMetadata': {'RequestId': 'xxxx', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'xxxx', 'content-type': 'application/x-amz-json-1.1', 'content-length': '1988', 'date': 'Sun, 14 Mar 2021 02:34:14 GMT'}, 'RetryAttempts': 0}} [2021-03-14 02:34:15,024] {ecs_operator.py:172} INFO - ECS Task logs output: [2021-03-14 02:34:15,111] {credentials.py:1094} INFO - Found credentials in environment variables. [2021-03-14 02:34:15,416] {taskinstance.py:1150} ERROR - An error occurred (ResourceNotFoundException) when calling the GetLogEvents operation: The specified log stream does not exist. Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 984, in _run_raw_task result = task_copy.execute(context=context) File "/usr/local/lib/python3.7/site-packages/airflow/contrib/operators/ecs_operator.py", line 152, in execute self._check_success_task() File "/usr/local/lib/python3.7/site-packages/airflow/contrib/operators/ecs_operator.py", line 175, in _check_success_task for event in self.get_logs_hook().get_log_events(self.awslogs_group, stream_name): File "/usr/local/lib/python3.7/site-packages/airflow/contrib/hooks/aws_logs_hook.py", line 85, in get_log_events **token_arg) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 357, in _api_call return self._make_api_call(operation_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 676, in _make_api_call raise error_class(parsed_response, operation_name) botocore.errorfactory.ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the GetLogEvents operation: The specified log stream does not exist. ``` <!-- (please include exact error messages if you can) --> **What you expected to happen**: ResourceNotFoundException is misleading because it feels like a problem with CloudWatchLogs. Expect AirflowException to indicate that the task has failed. <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> This can be reproduced by running an ECS Task that fails to start, for example by specifying a non-existent entry_point. **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> --> I suspect Issue #11663 has the same problem, i.e. it's not a CloudWatch issue, but a failure to start an ECS Task.

https://github.com/apache/airflow/issues/15000

https://github.com/apache/airflow/pull/18733

a192b4afbd497fdff508b2a06ec68cd5ca97c998

767a4f5207f8fc6c3d8072fa780d84460d41fc7a

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/ci/libraries/_md5sum.sh", "scripts/ci/libraries/_verify_image.sh", "scripts/docker/compile_www_assets.sh"]

Static file not being loaded in web server in docker-compose

Apache Airflow version: apache/airflow:master-python3.8 Environment: Cloud provider or hardware configuration: OS (e.g. from /etc/os-release): Mac OS 10.16.5 Kernel (e.g. uname -a): Darwin Kernel Version 19.6.0 Browser: Google Chrome Version 89.0.4389.90 What happened: I am having an issue with running `apache/airflow:master-python3.8` with docker-compose. The log of the webserver says` Please make sure to build the frontend in static/ directory and restart the server` when it is running. Due to static files not being loaded, login and dags are not working. What you expected to happen: static files being loaded correctly. How to reproduce it: My docker-compose is based on the official example. https://github.com/apache/airflow/blob/master/docs/apache-airflow/start/docker-compose.yaml Anything else we need to know: It used to work until 2 days ago when the new docker image was released. Login prompt looks like this. 

https://github.com/apache/airflow/issues/14991

https://github.com/apache/airflow/pull/14995

775ee51d0e58aeab5d29683dd2ff21b8c9057095

5dc634bf74bbec68bbe1c7b6944d0a9efd85181d

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/ci.yml", "docs/exts/docs_build/fetch_inventories.py", "scripts/ci/docs/ci_docs.sh", "scripts/ci/docs/ci_docs_prepare.sh"]

Make Docs builds fallback in case external docs sources are missing

Every now and then our docs builds start to fail because of external dependency (latest example here #14985). And while we are doing caching now of that information, it does not help when the initial retrieval fails. This information does not change often but with the number of dependencies we have it will continue to fail regularly simply because many of those depenencies are not very reliable - they are just a web page hosted somewhere. They are nowhere near the stabilty of even PyPI or Apt sources and we have no mirroring in case of problem. Maybe we could a) see if we can use some kind of mirroring scheme (do those sites have mirrrors ? ) b) if not, simply write a simple script that will dump the cached content for those to S3, refresh it in the CI scheduled (nightly) master builds ad have a fallback mechanism to download that from there in case of any problems in CI?

https://github.com/apache/airflow/issues/14989

https://github.com/apache/airflow/pull/15109

2ac4638b7e93d5144dd46f2c09fb982c374db79e

8cc8d11fb87d0ad5b3b80907874f695a77533bfa

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/exts/docs_build/fetch_inventories.py", "docs/exts/docs_build/third_party_inventories.py"]

Docs build are failing with `requests.exceptions.TooManyRedirects` error

Our docs build is failing with the following error on PRs and locally, example https://github.com/apache/airflow/pull/14983/checks?check_run_id=2185523525#step:4:282: ``` Fetched inventory: https://googleapis.dev/python/videointelligence/latest/objects.inv Traceback (most recent call last): File "/opt/airflow/docs/build_docs.py", line 278, in <module> main() File "/opt/airflow/docs/build_docs.py", line 218, in main priority_packages = fetch_inventories() File "/opt/airflow/docs/exts/docs_build/fetch_inventories.py", line 126, in fetch_inventories failed, success = list(failed), list(failed) File "/usr/local/lib/python3.6/concurrent/futures/_base.py", line 586, in result_iterator yield fs.pop().result() File "/usr/local/lib/python3.6/concurrent/futures/_base.py", line 425, in result return self.__get_result() File "/usr/local/lib/python3.6/concurrent/futures/_base.py", line 384, in __get_result raise self._exception File "/usr/local/lib/python3.6/concurrent/futures/thread.py", line 56, in run result = self.fn(*self.args, **self.kwargs) File "/opt/airflow/docs/exts/docs_build/fetch_inventories.py", line 53, in _fetch_file response = session.get(url, allow_redirects=True, stream=True) File "/usr/local/lib/python3.6/site-packages/requests/sessions.py", line 555, in get return self.request('GET', url, **kwargs) File "/usr/local/lib/python3.6/site-packages/requests/sessions.py", line 542, in request resp = self.send(prep, **send_kwargs) File "/usr/local/lib/python3.6/site-packages/requests/sessions.py", line 677, in send history = [resp for resp in gen] File "/usr/local/lib/python3.6/site-packages/requests/sessions.py", line 677, in <listcomp> history = [resp for resp in gen] File "/usr/local/lib/python3.6/site-packages/requests/sessions.py", line 166, in resolve_redirects raise TooManyRedirects('Exceeded {} redirects.'.format(self.max_redirects), response=resp) requests.exceptions.TooManyRedirects: Exceeded 30 redirects. ########################################################################################### ``` To reproduce locally run: ``` ./breeze build-docs -- --package-filter apache-airflow ```

https://github.com/apache/airflow/issues/14985

https://github.com/apache/airflow/pull/14986

a2b285825323da5a72dc0201ad6dc7d258771d0d

f6a1774555341f6a82c7cae1ce65903676bde61a

python

closed

apache/airflow

https://github.com/apache/airflow

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

Support all terminus task states for Docker Swarm Operator

**Apache Airflow version**: latest **What happened**: There are more terminus task states than the ones we currently check in Docker Swarm Operator. This makes the operator run infinitely when the service goes into these states. **What you expected to happen**: The operator should terminate. **How to reproduce it**: Run a Airflow task via the Docker Swarm operator and return failed status code from it. **Anything else we need to know**: So as a fix I have added the complete list of tasks from the Docker reference (https://docs.docker.com/engine/swarm/how-swarm-mode-works/swarm-task-states/) We would like to send this patch back upstream to Apache Airflow.

https://github.com/apache/airflow/issues/14959

https://github.com/apache/airflow/pull/14960

6b78394617c7e699dda1acf42e36161d2fc29925

ab477176998090e8fb94d6f0e6bf056bad2da441

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/ci.yml", ".pre-commit-config.yaml", "BREEZE.rst", "STATIC_CODE_CHECKS.rst", "breeze-complete", "scripts/ci/selective_ci_checks.sh", "scripts/ci/static_checks/eslint.sh"]

Run selective CI pipeline for UI-only PRs

For PRs that only touch files in `airflow/ui/` we'd like to run a selective set of CI actions. We only need linting and UI tests. Additionally, this update should pull the test runs out of the pre-commit.

https://github.com/apache/airflow/issues/14957

https://github.com/apache/airflow/pull/15009

a2d99293c9f5bdf1777fed91f1c48230111f53ac

7417f81d36ad02c2a9d7feb9b9f881610f50ceba

python

closed

apache/airflow

https://github.com/apache/airflow

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

A Small Exception Message Typo

I guess, line 59 in file "[airflow/airflow/task/task_runner/__init__.py](https://github.com/apache/airflow/blob/5a864f0e456348e0a871cf4678e1ffeec541c52d/airflow/task/task_runner/__init__.py#L59)" should change like: **OLD**: f'The task runner could not be loaded. Please check "executor" key in "core" section.' **NEW**: f'The task runner could not be loaded. Please check "task_runner" key in "core" section.' So, "executor" should be replaced with "task_runner".

https://github.com/apache/airflow/issues/14933

https://github.com/apache/airflow/pull/15067

6415489390c5ec3679f8d6684c88c1dd74414951

794922649982b2a6c095f7fa6be4e5d6a6d9f496

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/cli.py", "airflow/utils/log/file_processor_handler.py", "airflow/utils/log/file_task_handler.py", "airflow/utils/log/non_caching_file_handler.py"]

Scheduler Memory Leak in Airflow 2.0.1

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.17.4 **Environment**: Dev - **OS** (e.g. from /etc/os-release): RHEL7 **What happened**: After running fine for some time my airflow tasks got stuck in scheduled state with below error in Task Instance Details: "All dependencies are met but the task instance is not running. In most cases this just means that the task will probably be scheduled soon unless: - The scheduler is down or under heavy load If this task instance does not start soon please contact your Airflow administrator for assistance." **What you expected to happen**: I restarted the scheduler then it started working fine. When i checked my metrics i realized the scheduler has a memory leak and over past 4 days it has reached up to 6GB of memory utilization In version >2.0 we don't even have the run_duration config option to restart scheduler periodically to avoid this issue until it is resolved. **How to reproduce it**: I saw this issue in multiple dev instances of mine all running Airflow 2.0.1 on kubernetes with KubernetesExecutor. Below are the configs that i changed from the default config. max_active_dag_runs_per_dag=32 parallelism=64 dag_concurrency=32 sql_Alchemy_pool_size=50 sql_Alchemy_max_overflow=30 **Anything else we need to know**: The scheduler memory leaks occurs consistently in all instances i have been running. The memory utilization keeps growing for scheduler.

https://github.com/apache/airflow/issues/14924

https://github.com/apache/airflow/pull/18054

6acb9e1ac1dd7705d9bfcfd9810451dbb549af97

43f595fe1b8cd6f325d8535c03ee219edbf4a559

python

closed

apache/airflow

https://github.com/apache/airflow

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

S3ToRedshiftOperator is not transaction safe for truncate

**Apache Airflow version**: 2.0.0 **Environment**: - **Cloud provider or hardware configuration**: AWS - **OS** (e.g. from /etc/os-release): Amazon Linux 2 **What happened**: The TRUNCATE operation has a fine print in Redshift that it is committing the transaction. See https://docs.aws.amazon.com/redshift/latest/dg/r_TRUNCATE.html > However, be aware that TRUNCATE commits the transaction in which it is run. and > The TRUNCATE command commits the transaction in which it is run; therefore, you can't roll back a TRUNCATE operation, and a TRUNCATE command may commit other operations when it commits itself. Currently with truncate=True, the operator would generate a statement like: ```sql BEGIN; TRUNCATE TABLE schema.table; -- this commits the transaction --- the table is now empty for any readers until the end of the copy COPY .... COMMIT; ``` **What you expected to happen**: Replacing with a DELETE operation would solve the problem, in a normal database it is not considered a fast operation but with Redshift, a 1B+ rows table is deleted in less than 5 seconds on a 2-node ra3.xlplus. (not counting vacuum or analyze) and a vacuum of the empty table taking less than 3 minutes. ```sql BEGIN; DELETE FROM schema.table; COPY .... COMMIT; ``` It should be mentioned in the documentation that a delete is done for the reason above and that vacuum and analyze operations are left to manage. **How often does this problem occur? Once? Every time etc?** Always.

https://github.com/apache/airflow/issues/14888

https://github.com/apache/airflow/pull/17117

32582b5bf1432e7c7603b959a675cf7edd76c9e6

f44d7bd9cfe00b1409db78c2a644516b0ab003e9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/slack/operators/slack.py", "tests/providers/slack/operators/test_slack.py"]

SlackAPIFileOperator is broken

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Environment**: Docker - **Cloud provider or hardware configuration**: Local file system - **OS** (e.g. from /etc/os-release): Arch Linux - **Kernel** (e.g. `uname -a`): 5.11.5-arch1-1 **What happened**: I tried to post a file from a long Python string to a Slack channel through the SlackAPIFileOperator. I defined the operator this way: ``` SlackAPIFileOperator( task_id="{}-notifier".format(self.task_id), channel="#alerts-metrics", token=MY_TOKEN, initial_comment=":warning: alert", filename="{{ ds }}.csv", filetype="csv", content=df.to_csv() ) ``` Task failed with the following error: ``` DEBUG - Sending a request - url: https://www.slack.com/api/files.upload, query_params: {}, body_params: {}, files: {}, json_body: {'channels': '#alerts-metrics', 'content': '<a long pandas.DataFrame.to_csv output>', 'filename': '{{ ds }}.csv', 'filetype': 'csv', 'initial_comment': ':warning: alert'}, headers: {'Content-Type': 'application/json;charset=utf-8', 'Authorization': '(redacted)', 'User-Agent': 'Python/3.6.12 slackclient/3.3.2 Linux/5.11.5-arch1-1'} DEBUG - Received the following response - status: 200, headers: {'date': 'Thu, 18 Mar 2021 13:28:44 GMT', 'server': 'Apache', 'x-xss-protection': '0', 'pragma': 'no-cache', 'cache-control': 'private, no-cache, no-store, must-revalidate', 'access-control-allow-origin': '*', 'strict-transport-security': 'max-age=31536000; includeSubDomains; preload', 'x-slack-req-id': '0ff5fd17ca7e2e8397559b6347b34820', 'x-content-type-options': 'nosniff', 'referrer-policy': 'no-referrer', 'access-control-expose-headers': 'x-slack-req-id, retry-after', 'x-slack-backend': 'r', 'x-oauth-scopes': 'incoming-webhook,files:write,chat:write', 'x-accepted-oauth-scopes': 'files:write', 'expires': 'Mon, 26 Jul 1997 05:00:00 GMT', 'vary': 'Accept-Encoding', 'access-control-allow-headers': 'slack-route, x-slack-version-ts, x-b3-traceid, x-b3-spanid, x-b3-parentspanid, x-b3-sampled, x-b3-flags', 'content-type': 'application/json; charset=utf-8', 'x-envoy-upstream-service-time': '37', 'x-backend': 'files_normal files_bedrock_normal_with_overflow files_canary_with_overflow files_bedrock_canary_with_overflow files_control_with_overflow files_bedrock_control_with_overflow', 'x-server': 'slack-www-hhvm-files-iad-xg4a', 'x-via': 'envoy-www-iad-xvw3, haproxy-edge-lhr-u1ge', 'x-slack-shared-secret-outcome': 'shared-secret', 'via': 'envoy-www-iad-xvw3', 'connection': 'close', 'transfer-encoding': 'chunked'}, body: {'ok': False, 'error': 'no_file_data'} [2021-03-18 13:28:43,601] {taskinstance.py:1455} ERROR - The request to the Slack API failed. The server responded with: {'ok': False, 'error': 'no_file_data'} ``` **What you expected to happen**: I expect the operator to succeed and see a new message in Slack with a snippet of a downloadable CSV file. **How to reproduce it**: Just declare a DAG this way: ``` from airflow import DAG from airflow.providers.slack.operators.slack import SlackAPIFileOperator from pendulum import datetime with DAG(dag_id="SlackFile", default_args=dict(start_date=datetime(2021, 1, 1), owner='airflow', catchup=False)) as dag: SlackAPIFileOperator( task_id="Slack", token=YOUR_TOKEN, content="test-content" ) ``` And try to run it. **Anything else we need to know**: This seems to be a known issue: https://apache-airflow.slack.com/archives/CCQ7EGB1P/p1616079965083200 I workaround it with this following re-implementation: ``` from typing import Optional, Any from airflow import AirflowException from airflow.providers.slack.hooks.slack import SlackHook from airflow.providers.slack.operators.slack import SlackAPIOperator from airflow.utils.decorators import apply_defaults class SlackAPIFileOperator(SlackAPIOperator): """ Send a file to a slack channel Examples: .. code-block:: python slack = SlackAPIFileOperator( task_id="slack_file_upload", dag=dag, slack_conn_id="slack", channel="#general", initial_comment="Hello World!", file="hello_world.csv", filename="hello_world.csv", filetype="csv", content="hello,world,csv,file", ) :param channel: channel in which to sent file on slack name (templated) :type channel: str :param initial_comment: message to send to slack. (templated) :type initial_comment: str :param file: the file (templated) :type file: str :param filename: name of the file (templated) :type filename: str :param filetype: slack filetype. (templated) - see https://api.slack.com/types/file :type filetype: str :param content: file content. (templated) :type content: str """ template_fields = ('channel', 'initial_comment', 'file', 'filename', 'filetype', 'content') ui_color = '#44BEDF' @apply_defaults def __init__( self, channel: str = '#general', initial_comment: str = 'No message has been set!', file: Optional[str] = None, filename: str = 'default_name.csv', filetype: str = 'csv', content: Optional[str] = None, **kwargs, ) -> None: if (content is None) and (file is None): raise AirflowException('At least one of "content" or "file" should be defined.') self.method = 'files.upload' self.channel = channel self.initial_comment = initial_comment self.file = file self.filename = filename self.filetype = filetype self.content = content super().__init__(method=self.method, **kwargs) def execute(self, **kwargs): slack = SlackHook(token=self.token, slack_conn_id=self.slack_conn_id) args = dict( channels=self.channel, filename=self.filename, filetype=self.filetype, initial_comment=self.initial_comment ) if self.content is not None: args['content'] = self.content elif self.file is not None: args['file'] = self.content slack.call(self.method, data=args) def construct_api_call_params(self) -> Any: pass ``` Maybe it is not the best solution as it does not leverage work from `SlackAPIOperator`. But at least, it fullfill my use case.

https://github.com/apache/airflow/issues/14880

https://github.com/apache/airflow/pull/17247

797b515a23136d1f00c6bd938960882772c1c6bd

07c8ee01512b0cc1c4602e356b7179cfb50a27f4

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/boring-cyborg.yml"]

Add area:providers for general providers to boring-cyborg

**Description** Add label **area-providers** to general providers. For example the **provider:Google** https://github.com/apache/airflow/blob/16f43605f3370f20611ba9e08b568ff8a7cd433d/.github/boring-cyborg.yml#L21-L25 **Use case / motivation** Help better issue/pull request monitoring. **Are you willing to submit a PR?** Yes **Related Issues** <!-- Is there currently another issue associated with this? -->

https://github.com/apache/airflow/issues/14879

https://github.com/apache/airflow/pull/14941

01a5d36e6bbc1d9e7afd4e984376301ea378a94a

3bbf9aea0b54a7cb577eb03f805e0b0566b759c3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Using TaskGroup without context manager (Graph view visual bug)

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **What happened**: When I do not use the context manager for the task group and instead call the add function to add the tasks, those tasks show up on the Graph view.  However, when I click on the task group item on the Graph UI, it will fix the issue. When I close the task group item, the tasks will not be displayed as expected.  **What you expected to happen**: I expected the tasks inside the task group to not display on the Graph view.  **How to reproduce it**: Render this DAG in Airflow ```python from airflow.models import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator from airflow.utils.task_group import TaskGroup from datetime import datetime with DAG(dag_id="example_task_group", start_date=datetime(2021, 1, 1), tags=["example"], catchup=False) as dag: start = BashOperator(task_id="start", bash_command='echo 1; sleep 10; echo 2;') tg = TaskGroup("section_1", tooltip="Tasks for section_1") task_1 = DummyOperator(task_id="task_1") task_2 = BashOperator(task_id="task_2", bash_command='echo 1') task_3 = DummyOperator(task_id="task_3") tg.add(task_1) tg.add(task_2) tg.add(task_3) ```

https://github.com/apache/airflow/issues/14864

https://github.com/apache/airflow/pull/23071

9caa511387f92c51ab4fc42df06e0a9ba777e115

337863fa35bba8463d62e5cf0859f2bb73cf053a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/mysql/hooks/mysql.py", "tests/providers/mysql/hooks/test_mysql.py"]

MySQL hook uses wrong autocommit calls for mysql-connector-python

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **Environment**: * **Cloud provider or hardware configuration**: WSL2/Docker running `apache/airflow:2.0.1-python3.7` image * **OS** (e.g. from /etc/os-release): Host: Ubuntu 20.04 LTS, Docker Image: Debian GNU/Linux 10 (buster) * **Kernel** (e.g. `uname -a`): 5.4.72-microsoft-standard-WSL2 x86_64 * **Others**: Docker version 19.03.8, build afacb8b7f0 **What happened**: Received a `'bool' object is not callable` error when attempting to use the mysql-connector-python client for a task: ``` [2021-03-17 10:20:13,247] {{taskinstance.py:1455}} ERROR - 'bool' object is not callable Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1112, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1285, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1310, in _execute_task result = task_copy.execute(context=context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/mysql/operators/mysql.py", line 74, in execute hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/hooks/dbapi.py", line 175, in run self.set_autocommit(conn, autocommit) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/mysql/hooks/mysql.py", line 55, in set_autocommit conn.autocommit(autocommit) ``` **What you expected to happen**: The task to run without complaints. **How to reproduce it**: Create and use a MySQL connection with `{"client": "mysql-connector-python"}` specified in the Extra field. **Anything else we need to know**: The MySQL hook seems to be using `conn.get_autocommit()` and `conn.autocommit()` to get/set the autocommit flag for both mysqlclient and mysql-connector-python. These method don't actually exist in mysql-connector-python as it uses autocommit as a property rather than a method. I was able to work around it by adding an `if not callable(conn.autocommit)` condition to detect when mysql-connector-python is being used, but I'm sure there's probably a more elegant way of detecting which client is being used. mysql-connector-python documentation: https://dev.mysql.com/doc/connector-python/en/connector-python-api-mysqlconnection-autocommit.html Autocommit calls: https://github.com/apache/airflow/blob/2a2adb3f94cc165014d746102e12f9620f271391/airflow/providers/mysql/hooks/mysql.py#L55 https://github.com/apache/airflow/blob/2a2adb3f94cc165014d746102e12f9620f271391/airflow/providers/mysql/hooks/mysql.py#L66

https://github.com/apache/airflow/issues/14857

https://github.com/apache/airflow/pull/14869

b8cf46a12fba5701d9ffc0b31aac8375fbca37f9

9b428bbbdf4c56f302a1ce84f7c2caf34b81ffa0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Enabling Datadog to tag metrics results in AttributeError

**Apache Airflow version**: 2.0.1 **Python version**: 3.8 **Cloud provider or hardware configuration**: AWS **What happened**: In order to add tags to [Airflow metrics,](https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html), it's required to set `AIRFLOW__METRICS__STATSD_DATADOG_ENABLED` to `True` and add tags in the `AIRFLOW__METRICS__STATSD_DATADOG_TAGS` variable. We were routing our statsd metrics to Datadog anyway, so this should theoretically have not changed anything other than the addition of any specified tags. Setting the environment variable `AIRFLOW__METRICS__STATSD_DATADOG_ENABLED` to `True` (along with the other required statsd connection variables) results in the following error, which causes the process to terminate. This is from the scheduler, but this would apply anywhere that `Stats.timer()` is being called. ``` AttributeError: 'DogStatsd' object has no attribute 'timer' return Timer(self.dogstatsd.timer(stat, *args, tags=tags, **kwargs)) File "/usr/local/lib/python3.8/site-packages/airflow/stats.py", line 345, in timer return fn(_self, stat, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/stats.py", line 233, in wrapper timer = Stats.timer('scheduler.critical_section_duration') File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1538, in _do_scheduling num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1382, in _run_scheduler_loop self._run_scheduler_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1280, in _execute Traceback (most recent call last): ``` **What you expected to happen**: The same default Airflow metrics get sent by connecting to datadog, tagged with the metrics specified in `AIRFLOW__METRICS__STATSD_DATADOG_TAGS`. **What do you think went wrong?**: There is a bug in the implementation of the `Timer` method of `SafeDogStatsdLogger`. https://github.com/apache/airflow/blob/master/airflow/stats.py#L341-L347 `DogStatsd` has no method called `timer`. Instead it should be `timed`: https://datadogpy.readthedocs.io/en/latest/#datadog.dogstatsd.base.DogStatsd.timed **How to reproduce it**: Set the environment variables (or their respective config values) `AIRFLOW__METRICS__STATSD_ON`, `AIRFLOW__METRICS__STATSD_HOST`, `AIRFLOW__METRICS__STATSD_PORT`, and then set `AIRFLOW__METRICS__STATSD_DATADOG_ENABLED` to `True` and start up Airflow. **Anything else we need to know**: How often does this problem occur? Every time

https://github.com/apache/airflow/issues/14839

https://github.com/apache/airflow/pull/15132

3a80b7076da8fbee759d9d996bed6e9832718e55

b7cd2df056ac3ab113d77c5f6b65f02a77337907

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/endpoints/role_and_permission_endpoint.py", "airflow/api_connexion/openapi/v1.yaml", "airflow/security/permissions.py", "tests/api_connexion/endpoints/test_role_and_permission_endpoint.py"]

Add Create/Update/Delete API endpoints for Roles

To be able to fully manage the permissions in the UI we will need to be able to modify the roles and the permissions they have. It probably makes sense to have one PR that adds CUD (Read is already done) endpoints for Roles. Permissions are not createable via anything but code, so we only need these endpoints for Roles, but not Permissions.

https://github.com/apache/airflow/issues/14830

https://github.com/apache/airflow/pull/14840

266384a63f4693b667f308d49fcbed9a10a41fce

6706b67fecc00a22c1e1d6658616ed9dd96bbc7b

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg"]

Latest SQLAlchemy (1.4) Incompatible with latest sqlalchemy_utils

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): N/A **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): Mac OS Big Sur - **Kernel** (e.g. `uname -a`): - **Install tools**: pip 20.1.1 - **Others**: **What happened**: Our CI environment broke due to the release of SQLAlchemy 1.4, which is incompatible with the latest version of sqlalchemy-utils. ([Related issue](https://github.com/kvesteri/sqlalchemy-utils/issues/505)) Partial stacktrace: ``` File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/airflow/www/utils.py", line 27, in <module> from flask_appbuilder.models.sqla.interface import SQLAInterface File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/flask_appbuilder/models/sqla/interface.py", line 16, in <module> from sqlalchemy_utils.types.uuid import UUIDType File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy_utils/__init__.py", line 1, in <module> from .aggregates import aggregated # noqa File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy_utils/aggregates.py", line 372, in <module> from .functions.orm import get_column_key File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy_utils/functions/__init__.py", line 1, in <module> from .database import ( # noqa File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy_utils/functions/database.py", line 11, in <module> from .orm import quote File "/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy_utils/functions/orm.py", line 14, in <module> from sqlalchemy.orm.query import _ColumnEntity ImportError: cannot import name '_ColumnEntity' from 'sqlalchemy.orm.query' (/Users/samwheating/Desktop/tmp_venv/lib/python3.7/site-packages/sqlalchemy/orm/query.py) ``` I'm not sure what the typical procedure is in the case of breaking changes to dependencies, but seeing as there's an upcoming release I thought it might be worth pinning sqlalchemy to 1.3.x? (Or pin the version of sqlalchemy-utils to a compatible version if one is released before Airflow 2.0.2) **What you expected to happen**: `airflow db init` to run successfully. <!-- What do you think went wrong? --> **How to reproduce it**: 1) Create a new virtualenv 2) `pip install apache-airflow` 3) `airflow db init` <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. --->

https://github.com/apache/airflow/issues/14811

https://github.com/apache/airflow/pull/14812

251eb7d170db3f677e0c2759a10ac1e31ac786eb

c29f6fb76b9d87c50713ae94fda805b9f789a01d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ui/package.json", "airflow/ui/src/components/AppContainer/AppHeader.tsx", "airflow/ui/src/components/AppContainer/TimezoneDropdown.tsx", "airflow/ui/src/components/MultiSelect.tsx", "airflow/ui/src/providers/TimezoneProvider.tsx", "airflow/ui/src/views/Pipelines/PipelinesTable.tsx", "airflow/ui/src/views/Pipelines/index.tsx", "airflow/ui/test/TimezoneDropdown.test.tsx", "airflow/ui/test/utils.tsx", "airflow/ui/yarn.lock"]

Design/build timezone switcher modal

- Once we have the current user's preference set and available in Context, add a modal that allows the preferred display timezone to be changed. - Modal will be triggered by clicking the time/TZ in the global navigation.

https://github.com/apache/airflow/issues/14807

https://github.com/apache/airflow/pull/15674

46d62782e85ff54dd9dc96e1071d794309497983

3614910b4fd32c90858cd9731fc0421078ca94be

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images-workflow-run.yml", ".github/workflows/ci.yml", "scripts/ci/tools/ci_free_space_on_ci.sh", "tests/cli/commands/test_jobs_command.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_taskinstance.py", "tests/test_utils/asserts.py"]

[QUARANTINED] The test_scheduler_verify_pool_full occasionally fails

Probably same root cause as in #14773 and #14772 but there is another test that fails occassionally: https://github.com/apache/airflow/runs/2106723579?check_suite_focus=true#step:6:10811 ``` _______________ TestSchedulerJob.test_scheduler_verify_pool_full _______________ self = <tests.jobs.test_scheduler_job.TestSchedulerJob testMethod=test_scheduler_verify_pool_full> def test_scheduler_verify_pool_full(self): """ Test task instances not queued when pool is full """ dag = DAG(dag_id='test_scheduler_verify_pool_full', start_date=DEFAULT_DATE) BashOperator( task_id='dummy', dag=dag, owner='airflow', pool='test_scheduler_verify_pool_full', bash_command='echo hi', ) dagbag = DagBag( dag_folder=os.path.join(settings.DAGS_FOLDER, "no_dags.py"), include_examples=False, read_dags_from_db=True, ) dagbag.bag_dag(dag=dag, root_dag=dag) dagbag.sync_to_db() session = settings.Session() pool = Pool(pool='test_scheduler_verify_pool_full', slots=1) session.add(pool) session.flush() dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag)) scheduler = SchedulerJob(executor=self.null_exec) scheduler.processor_agent = mock.MagicMock() # Create 2 dagruns, which will create 2 task instances. dr = dag.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE, state=State.RUNNING, ) > scheduler._schedule_dag_run(dr, {}, session) tests/jobs/test_scheduler_job.py:2586: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/jobs/scheduler_job.py:1688: in _schedule_dag_run dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session) airflow/utils/session.py:62: in wrapper return func(*args, **kwargs) airflow/models/dagbag.py:178: in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <airflow.models.dagbag.DagBag object at 0x7f45f1959a90> dag_id = 'test_scheduler_verify_pool_full' session = <sqlalchemy.orm.session.Session object at 0x7f45f19eef70> def _add_dag_from_db(self, dag_id: str, session: Session): """Add DAG to DagBag from DB""" from airflow.models.serialized_dag import SerializedDagModel row = SerializedDagModel.get(dag_id, session) if not row: > raise SerializedDagNotFound(f"DAG '{dag_id}' not found in serialized_dag table") E airflow.exceptions.SerializedDagNotFound: DAG 'test_scheduler_verify_pool_full' not found in serialized_dag table airflow/models/dagbag.py:234: SerializedDagNotFound ``` <!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> **What you expected to happen**: <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14778

https://github.com/apache/airflow/pull/14792

3f61df11e7e81abc0ac4495325ccb55cc1c88af4

45cf89ce51b203bdf4a2545c67449b67ac5e94f1

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images-workflow-run.yml", ".github/workflows/ci.yml", "scripts/ci/tools/ci_free_space_on_ci.sh", "tests/cli/commands/test_jobs_command.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_taskinstance.py", "tests/test_utils/asserts.py"]

[QUARANTINE] The test_verify_integrity_if_dag_changed occasionally fails

https://github.com/apache/airflow/issues/14773

https://github.com/apache/airflow/pull/14792

3f61df11e7e81abc0ac4495325ccb55cc1c88af4

45cf89ce51b203bdf4a2545c67449b67ac5e94f1

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images-workflow-run.yml", ".github/workflows/ci.yml", "scripts/ci/tools/ci_free_space_on_ci.sh", "tests/cli/commands/test_jobs_command.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_taskinstance.py", "tests/test_utils/asserts.py"]

[QUARANTINE] Occasional failures from test_scheduler_verify_pool_full_2_slots_per_task

The test occasionally fails with: DAG 'test_scheduler_verify_pool_full_2_slots_per_task' not found in serialized_dag table https://github.com/apache/airflow/pull/14531/checks?check_run_id=2106170551#step:6:10314 ``` ______ TestSchedulerJob.test_scheduler_verify_pool_full_2_slots_per_task _______ self = <tests.jobs.test_scheduler_job.TestSchedulerJob testMethod=test_scheduler_verify_pool_full_2_slots_per_task> def test_scheduler_verify_pool_full_2_slots_per_task(self): """ Test task instances not queued when pool is full. Variation with non-default pool_slots """ dag = DAG(dag_id='test_scheduler_verify_pool_full_2_slots_per_task', start_date=DEFAULT_DATE) BashOperator( task_id='dummy', dag=dag, owner='airflow', pool='test_scheduler_verify_pool_full_2_slots_per_task', pool_slots=2, bash_command='echo hi', ) dagbag = DagBag( dag_folder=os.path.join(settings.DAGS_FOLDER, "no_dags.py"), include_examples=False, read_dags_from_db=True, ) dagbag.bag_dag(dag=dag, root_dag=dag) dagbag.sync_to_db() session = settings.Session() pool = Pool(pool='test_scheduler_verify_pool_full_2_slots_per_task', slots=6) session.add(pool) session.commit() dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag)) scheduler = SchedulerJob(executor=self.null_exec) scheduler.processor_agent = mock.MagicMock() # Create 5 dagruns, which will create 5 task instances. date = DEFAULT_DATE for _ in range(5): dr = dag.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=date, state=State.RUNNING, ) > scheduler._schedule_dag_run(dr, {}, session) tests/jobs/test_scheduler_job.py:2641: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ airflow/jobs/scheduler_job.py:1688: in _schedule_dag_run dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session) airflow/utils/session.py:62: in wrapper return func(*args, **kwargs) airflow/models/dagbag.py:178: in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <airflow.models.dagbag.DagBag object at 0x7f05c934f5b0> dag_id = 'test_scheduler_verify_pool_full_2_slots_per_task' session = <sqlalchemy.orm.session.Session object at 0x7f05c838cb50> def _add_dag_from_db(self, dag_id: str, session: Session): """Add DAG to DagBag from DB""" from airflow.models.serialized_dag import SerializedDagModel row = SerializedDagModel.get(dag_id, session) if not row: > raise SerializedDagNotFound(f"DAG '{dag_id}' not found in serialized_dag table") E airflow.exceptions.SerializedDagNotFound: DAG 'test_scheduler_verify_pool_full_2_slots_per_task' not found in serialized_dag table ``` <!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> **What you expected to happen**: <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14772

https://github.com/apache/airflow/pull/14792

3f61df11e7e81abc0ac4495325ccb55cc1c88af4

45cf89ce51b203bdf4a2545c67449b67ac5e94f1

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images-workflow-run.yml", ".github/workflows/ci.yml", "scripts/ci/tools/ci_free_space_on_ci.sh", "tests/cli/commands/test_jobs_command.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_taskinstance.py", "tests/test_utils/asserts.py"]

[QUARANTINE] Test_retry_still_in_executor sometimes fail

Occasional failures: https://github.com/apache/airflow/pull/14531/checks?check_run_id=2106170532#step:6:10454 ``` ________________ TestSchedulerJob.test_retry_still_in_executor _________________ self = <tests.jobs.test_scheduler_job.TestSchedulerJob testMethod=test_retry_still_in_executor> def test_retry_still_in_executor(self): """ Checks if the scheduler does not put a task in limbo, when a task is retried but is still present in the executor. """ executor = MockExecutor(do_update=False) dagbag = DagBag(dag_folder=os.path.join(settings.DAGS_FOLDER, "no_dags.py"), include_examples=False) dagbag.dags.clear() dag = DAG(dag_id='test_retry_still_in_executor', start_date=DEFAULT_DATE, schedule_interval="@once") dag_task1 = BashOperator( task_id='test_retry_handling_op', bash_command='exit 1', retries=1, dag=dag, owner='airflow' ) dag.clear() dag.is_subdag = False with create_session() as session: orm_dag = DagModel(dag_id=dag.dag_id) orm_dag.is_paused = False session.merge(orm_dag) dagbag.bag_dag(dag=dag, root_dag=dag) dagbag.sync_to_db() @mock.patch('airflow.jobs.scheduler_job.DagBag', return_value=dagbag) def do_schedule(mock_dagbag): # Use a empty file since the above mock will return the # expected DAGs. Also specify only a single file so that it doesn't # try to schedule the above DAG repeatedly. scheduler = SchedulerJob( num_runs=1, executor=executor, subdir=os.path.join(settings.DAGS_FOLDER, "no_dags.py") ) scheduler.heartrate = 0 scheduler.run() do_schedule() # pylint: disable=no-value-for-parameter with create_session() as session: ti = ( session.query(TaskInstance) .filter( TaskInstance.dag_id == 'test_retry_still_in_executor', TaskInstance.task_id == 'test_retry_handling_op', ) .first() ) ti.task = dag_task1 def run_with_error(ti, ignore_ti_state=False): try: ti.run(ignore_ti_state=ignore_ti_state) except AirflowException: pass assert ti.try_number == 1 # At this point, scheduler has tried to schedule the task once and # heartbeated the executor once, which moved the state of the task from # SCHEDULED to QUEUED and then to SCHEDULED, to fail the task execution # we need to ignore the TaskInstance state as SCHEDULED is not a valid state to start # executing task. run_with_error(ti, ignore_ti_state=True) assert ti.state == State.UP_FOR_RETRY assert ti.try_number == 2 with create_session() as session: ti.refresh_from_db(lock_for_update=True, session=session) ti.state = State.SCHEDULED session.merge(ti) # To verify that task does get re-queued. executor.do_update = True do_schedule() # pylint: disable=no-value-for-parameter ti.refresh_from_db() > assert ti.state == State.SUCCESS E AssertionError: assert None == 'success' E + where None = <TaskInstance: test_retry_still_in_executor.test_retry_handling_op 2016-01-01 00:00:00+00:00 [None]>.state E + and 'success' = State.SUCCESS tests/jobs/test_scheduler_job.py:2934: AssertionError ``` <!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> **What you expected to happen**: <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14771

https://github.com/apache/airflow/pull/14792

3f61df11e7e81abc0ac4495325ccb55cc1c88af4

45cf89ce51b203bdf4a2545c67449b67ac5e94f1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/smart_sensor.py"]

[Smart sensor] Runtime error: dictionary changed size during iteration

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **What happened**: <!-- (please include exact error messages if you can) --> Smart Sensor TI crashes with a Runtime error. Here's the logs: ``` RuntimeError: dictionary changed size during iteration File "airflow/sentry.py", line 159, in wrapper return func(task_instance, *args, session=session, **kwargs) File "airflow/models/taskinstance.py", line 1112, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "airflow/models/taskinstance.py", line 1285, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "airflow/models/taskinstance.py", line 1315, in _execute_task result = task_copy.execute(context=context) File "airflow/sensors/smart_sensor.py", line 736, in execute self.flush_cached_sensor_poke_results() File "airflow/sensors/smart_sensor.py", line 681, in flush_cached_sensor_poke_results for ti_key, sensor_exception in self.cached_sensor_exceptions.items(): ``` **What you expected to happen**: <!-- What do you think went wrong? --> Smart sensor should always execute without any runtime error. **How to reproduce it**: I haven't been able to reproduce it consistently since it sometimes works and sometimes errors. **Anything else we need to know**: It's a really noisy error in Sentry. In just 4 days, 3.8k events were reported in Sentry. <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14770

https://github.com/apache/airflow/pull/14774

2ab2cbf93df9eddfb527fcfd9d7b442678a57662

4aec25a80e3803238cf658c416c8e6d3975a30f6

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/jobs/test_backfill_job.py"]

[QUARANTINE] Backfill depends on past test is flaky

Test backfill_depends_on_past is flaky. The whole Backfill class was in Heisentest but I believe this is the only one that is problematic now so I remove the class from heisentests and move the depends_on_past to quarantine. <!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> **What you expected to happen**: <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14755

https://github.com/apache/airflow/pull/19862

5ebd63a31b5bc1974fc8974f137b9fdf0a5f58aa

a804666347b50b026a8d3a1a14c0b2e27a369201

python

closed

apache/airflow

https://github.com/apache/airflow

["breeze"]

Breeze fails to run on macOS with the old version of bash

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: master/HEAD (commit 99c74968180ab7bc6d7152ec4233440b62a07969) **Environment**: - **Cloud provider or hardware configuration**: MacBook Air (13-inch, Early 2015) - **OS** (e.g. from /etc/os-release): macOS Big Sur version 11.2.2 - **Install tools**: git - **Others**: GNU bash, version 3.2.57(1)-release (x86_64-apple-darwin20) Copyright (C) 2007 Free Software Foundation, Inc. **What happened**: When I executed `./breeze` initially following this section https://github.com/apache/airflow/blob/master/BREEZE.rst#installation, I got the following error. ``` $ ./breeze ./breeze: line 28: @: unbound variable ``` **What you expected to happen**: > The First time you run Breeze, it pulls and builds a local version of Docker images. It should start to pull and build a local version of Docker images. **How to reproduce it**: ``` git clone git@github.com:apache/airflow.git cd airflow ./breeze ``` **Anything else we need to know**: The old version of bash reports the error when there's no argument with `${@}`. https://github.com/apache/airflow/blob/master/breeze#L28

https://github.com/apache/airflow/issues/14754

https://github.com/apache/airflow/pull/14787

feb6b8107e1e01aa1dae152f7b3861fe668b3008

c613384feb52db39341a8d3a52b7f47695232369

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst"]

Less confusable description about Cleaning the environment for new comers

**Description** I think the section of Cleaning the environment in this document is confusable for new comers. https://github.com/apache/airflow/blob/master/BREEZE.rst#cleaning-the-environment Actual Suddenly, `./breeze stop` appears in the document. It's not necessary for those who first run breeze. Exepected It's better to write the condition like below. Stop Breeze with `./breeze stop.` (If Breeze is already running) Use case / motivation Because `./breeze stop.` is not required for new comers, it's better to write the condition. Are you willing to submit a PR? Yes, I'm ready.

https://github.com/apache/airflow/issues/14752

https://github.com/apache/airflow/pull/14753

b9e8ca48e61fdb8d80960981de0ee5409e3a6df9

3326babd02d02c87ec80bf29439614de4e636e10

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst"]

Better description about Getopt and gstat when runnin Breeze

**Description** For me, the section of Getopt and gstat in this document doesn't have enough description to run Breeze. https://github.com/apache/airflow/blob/master/BREEZE.rst#getopt-and-gstat Actual After executing the following commands quoted from https://github.com/apache/airflow/blob/master/BREEZE.rst#getopt-and-gstat, I cannot know that the commads enabled the PATH properly. ``` echo 'export PATH="/usr/local/opt/gnu-getopt/bin:$PATH"' >> ~/.zprofile . ~/.zprofile ``` Exepected It's better to write commands for checking that `getopt` and `gstat` are succesfully installed. ``` $ getopt --version getopt from util-linux 2.36.2 $ gstat --version stat (GNU coreutils) 8.32 Copyright (C) 2020 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <https://gnu.org/licenses/gpl.html>. This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law. Written by Michael Meskes. ``` **Use case / motivation** Because I'm not familiar with unix shell, with the exisiting document, I couldn't know those commands are properly installed or not. **Are you willing to submit a PR?** Yes, I'm ready.

https://github.com/apache/airflow/issues/14750

https://github.com/apache/airflow/pull/14751

99c74968180ab7bc6d7152ec4233440b62a07969

b9e8ca48e61fdb8d80960981de0ee5409e3a6df9

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "BREEZE.rst", "STATIC_CODE_CHECKS.rst", "airflow/ui/package.json", "airflow/ui/yarn.lock", "breeze-complete"]

Add precommit linting and testing to the new /ui

**Description** We just initialized the new UI for AIP-38 under `/airflow/ui`. To continue development, it would be best to add a pre-commit hook to run the linting and testing commands for the new project. **Use case / motivation** The new UI already has linting and testing setup with `yarn lint` and `yarn test`. We just need a pre-commit hook for them. **Are you willing to submit a PR?** Yes **Related Issues** no

https://github.com/apache/airflow/issues/14726

https://github.com/apache/airflow/pull/14836

5f774fae530577e302c153cc8726c93040ebbde0

e395fcd247b8aa14dbff2ee979c1a0a17c42adf4

python

closed

apache/airflow

https://github.com/apache/airflow

["UPDATING.md", "airflow/cli/cli_parser.py", "airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/config_templates/default_celery.py", "airflow/config_templates/default_test.cfg", "airflow/configuration.py", "airflow/models/baseoperator.py", "chart/values.yaml", "docs/apache-airflow/executor/celery.rst"]

Decouple default_queue from celery config section

**Description** We are using a 3rd party executor which has the ability to use multiple queues, however the `default_queue` is defined under the `celery` heading and is used regardless of the executor that you are using. See: https://github.com/apache/airflow/blob/2.0.1/airflow/models/baseoperator.py#L366 It would be nice to decouple the default_queue configuration argument away from the celery section to allow other executors to utilise this functionality with less confusion. **Are you willing to submit a PR?** Yep! Will open a pull request shortly **Related Issues** Not that I'm aware of.

https://github.com/apache/airflow/issues/14696

https://github.com/apache/airflow/pull/14699

7757fe32e0aa627cb849f2d69fbbb01f1d180a64

1d0c1684836fb0c3d1adf86a8f93f1b501474417

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/local_to_s3.py", "airflow/providers/google/cloud/transfers/azure_fileshare_to_gcs.py", "airflow/providers/google/cloud/transfers/s3_to_gcs.py", "tests/providers/amazon/aws/transfers/test_local_to_s3.py"]

The S3ToGCSOperator fails on templated `dest_gcs` URL

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: Docker **What happened**: When passing a templatized `dest_gcs` argument to the `S3ToGCSOperator` operator, the DAG fails to import because the constructor attempts to test the validity of the URL before the template has been populated in `execute`. The error is: ``` Broken DAG: [/opt/airflow/dags/bad_gs_dag.py] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/gcs.py", line 1051, in gcs_object_is_directory _, blob = _parse_gcs_url(bucket) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/gcs.py", line 1063, in _parse_gcs_url raise AirflowException('Please provide a bucket name') airflow.exceptions.AirflowException: Please provide a bucket name ``` **What you expected to happen**: The DAG should successfully parse when using a templatized `dest_gcs` value. **How to reproduce it**: Instantiating a `S3ToGCSOperator` task with `dest_gcs="{{ var.gcs_url }}"` fails. <details> ```python from airflow.decorators import dag from airflow.utils.dates import days_ago from airflow.providers.google.cloud.transfers.s3_to_gcs import S3ToGCSOperator @dag( schedule_interval=None, description="Demo S3-to-GS Bug", catchup=False, start_date=days_ago(1), ) def demo_bug(): S3ToGCSOperator( task_id="transfer_task", bucket="example_bucket", prefix="fake/prefix", dest_gcs="{{ var.gcs_url }}", ) demo_dag = demo_bug() ``` </details> **Anything else we need to know**: Should be fixable by moving the code that evaluates whether the URL is a folder to `execute()`.

https://github.com/apache/airflow/issues/14682

https://github.com/apache/airflow/pull/19048

efdfd15477f92da059fa86b4fa18b6f29cb97feb

3c08c025c5445ffc0533ac28d07ccf2e69a19ca8

python

closed

apache/airflow

https://github.com/apache/airflow

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

TriggerDagRunOperator OperatorLink doesn't work when HTML base url doesn't match the Airflow base url

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **What happened**: When I click on the "Triggered DAG" Operator Link in TriggerDagRunOperator, I am redirected with a relative link.  The redirect uses the HTML base URL and not the airflow base URL. This is only an issue if the URLs do not match. **What you expected to happen**: I expect the link to take me to the Triggered DAG tree view (default view) instead of the base url of the service hosting the webserver. **How to reproduce it**: Create an airflow deployment where the HTML base url doesn't match the airflow URL.

https://github.com/apache/airflow/issues/14675

https://github.com/apache/airflow/pull/14990

62aa7965a32f1f8dde83cb9c763deef5b234092b

aaa3bf6b44238241bd61178426b692df53770c22

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "docs/apache-airflow/concepts/connections.rst", "docs/apache-airflow/macros-ref.rst", "tests/models/test_taskinstance.py"]

Provide jinja template syntax to access connections

**Description** Expose the connection into the jinja template context via `conn.value.<connectionname>.{host,port,login,password,extra_config,etc}` Today is possible to conveniently access [airflow's variables](https://airflow.apache.org/docs/apache-airflow/stable/concepts.html#variables) in jinja templates using `{{ var.value.<variable_name> }}`. There is no equivalent (to my knowledge for [connections](https://airflow.apache.org/docs/apache-airflow/stable/concepts.html#connections)), I understand that most of the time connection are used programmatically in Operators and Hooks source code, but there are use cases where the connection info has to be pass as parameters to the operators and then it becomes cumbersome to do it without jinja template syntax. I seen workarounds like using [user defined macros to provide get_login(my_conn_id)](https://stackoverflow.com/questions/65826404/use-airflow-connection-from-a-jinja-template/65873023#65873023 ), but I'm after a consistent interface for accessing both variables and connections in the same way **Workaround** The following `user_defined_macro` (from my [stackoverflow answer](https://stackoverflow.com/a/66471911/90580)) provides the suggested syntax `connection.mssql.host` where `mssql` is the connection name: ``` class ConnectionGrabber: def __getattr__(self, name): return Connection.get_connection_from_secrets(name) dag = DAG( user_defined_macros={'connection': ConnectionGrabber()}, ...) task = BashOperator(task_id='read_connection', bash_command='echo {{connection.mssql.host }}', dag=dag) ``` This macro can be added to each DAG individually or to all DAGs via an Airflow's Plugin. What I suggest is to make this macro part of the default. **Use case / motivation** For example, passing credentials to a [KubernetesPodOperator](https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/operators.html#howto-operator-kubernetespodoperator) via [env_vars](https://cloud.google.com/composer/docs/how-to/using/using-kubernetes-pod-operator) today has to be done like this: ``` connection = Connection.get_connection_from_secrets('somecredentials') k = KubernetesPodOperator( task_id='task1', env_vars={'MY_VALUE': '{{ var.value.my_value }}', 'PWD': conn.password,}, ) ``` where I would prefer to use consistent syntax for both variables and connections like this: ``` # not needed anymore: connection = Connection.get_connection_from_secrets('somecredentials') k = KubernetesPodOperator( task_id='task1', env_vars={'MY_VALUE': '{{ var.value.my_value }}', 'PWD': '{{ conn.somecredentials.password }}',}, ) ``` The same applies to `BashOperator` where I sometimes feel the need to pass connection information to the templated script. **Are you willing to submit a PR?** yes, I can write the PR. **Related Issues**

https://github.com/apache/airflow/issues/14597

https://github.com/apache/airflow/pull/16686

5034414208f85a8be61fe51d6a3091936fe402ba

d3ba80a4aa766d5eaa756f1fa097189978086dac

python

closed

apache/airflow

https://github.com/apache/airflow

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

Unreachable Secrets Backend Causes Web Server Crash

**Apache Airflow version**: 1.10.12 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **Environment**: - **Cloud provider or hardware configuration**: Amazon MWAA - **OS** (e.g. from /etc/os-release): Amazon Linux (latest) - **Kernel** (e.g. `uname -a`): n/a - **Install tools**: n/a **What happened**: If an unreachable secrets.backend is specified in airflow.cfg the web server crashes **What you expected to happen**: An invalid secrets backend should be ignored with a warning, and the system should default back to the metadatabase secrets **How to reproduce it**: In an environment without access to AWS Secrets Manager, add the following to your airflow.cfg: ``` [secrets] backend = airflow.contrib.secrets.aws_secrets_manager.SecretsManagerBackend ``` **or** an environment without access to SSM specifiy: ``` [secrets] backend = airflow.contrib.secrets.aws_systems_manager.SystemsManagerParameterStoreBackend ``` Reference: https://airflow.apache.org/docs/apache-airflow/1.10.12/howto/use-alternative-secrets-backend.html#aws-ssm-parameter-store-secrets-backend

https://github.com/apache/airflow/issues/14592

https://github.com/apache/airflow/pull/16404

4d4830599578ae93bb904a255fb16b81bd471ef1

0abbd2d918ad9027948fd8a33ebb42487e4aa000

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/celery_executor.py"]

AttributeError: 'DatabaseBackend' object has no attribute 'task_cls'

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.18.15 **Environment**: - **Cloud provider or hardware configuration**: AWS - **OS** (e.g. from /etc/os-release): ubuntu 18.04 - **Kernel** (e.g. `uname -a`): Linux mlb-airflow-infra-workers-75f589bcd9-wtls8 4.9.0-9-amd64 #1 SMP Debian 4.9.168-1 (2019-04-12) x86_64 x86_64 x86_64 GNU/Linux - **Install tools**: - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> Scheduler is constantly restarting due to this error: ``` [2021-03-03 17:31:19,393] {scheduler_job.py:1298} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1280, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1384, in _run_scheduler_loop self.executor.heartbeat() File "/usr/local/lib/python3.6/dist-packages/airflow/executors/base_executor.py", line 162, in heartbeat self.sync() File "/usr/local/lib/python3.6/dist-packages/airflow/executors/celery_executor.py", line 340, in sync self.update_all_task_states() File "/usr/local/lib/python3.6/dist-packages/airflow/executors/celery_executor.py", line 399, in update_all_task_states state_and_info_by_celery_task_id = self.bulk_state_fetcher.get_many(self.tasks.values()) File "/usr/local/lib/python3.6/dist-packages/airflow/executors/celery_executor.py", line 552, in get_many result = self._get_many_from_db_backend(async_results) File "/usr/local/lib/python3.6/dist-packages/airflow/executors/celery_executor.py", line 570, in _get_many_from_db_backend task_cls = app.backend.task_cls AttributeError: 'DatabaseBackend' object has no attribute 'task_cls' [2021-03-03 17:31:20,396] {process_utils.py:100} INFO - Sending Signals.SIGTERM to GPID 3852 [2021-03-03 17:31:20,529] {process_utils.py:66} INFO - Process psutil.Process(pid=3996, status='terminated', started='17:31:19') (3996) terminated with exit code None [2021-03-03 17:31:20,533] {process_utils.py:66} INFO - Process psutil.Process(pid=3997, status='terminated', started='17:31:19') (3997) terminated with exit code None [2021-03-03 17:31:20,533] {process_utils.py:206} INFO - Waiting up to 5 seconds for processes to exit... [2021-03-03 17:31:20,540] {process_utils.py:66} INFO - Process psutil.Process(pid=3852, status='terminated', exitcode=0, started='17:31:13') (3852) terminated with exit code 0 [2021-03-03 17:31:20,540] {scheduler_job.py:1301} INFO - Exited execute loop ``` **What you expected to happen**: Scheduler running <!-- What do you think went wrong? --> **How to reproduce it**: Install airflow 2.0.1 in an ubuntu 18.04 instance: `pip install apache-airflow[celery,postgres,s3,crypto,jdbc,google_auth,redis,slack,ssh,sentry,kubernetes,statsd]==2.0.1` Use the following variables: ``` AIRFLOW__CORE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:<pwd>@<DB>:<port>/airflow AIRFLOW__CORE__DEFAULT_TIMEZONE=<TZ> AIRFLOW__CORE__LOAD_DEFAULTS=false AIRFLOW__CELERY__BROKER_URL=sqs:// AIRFLOW__CELERY__DEFAULT_QUEUE=<queue> AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER=s3://<bucket>/<prefix>/ AIRFLOW__CORE__LOAD_EXAMPLES=false AIRFLOW__CORE__REMOTE_LOGGING=True AIRFLOW__CORE__FERNET_KEY=<fernet_key> AIRFLOW__CORE__EXECUTOR=CeleryExecutor AIRFLOW__CELERY__BROKER_TRANSPORT_OPTIONS__REGION=<region> AIRFLOW__CELERY__RESULT_BACKEND=db+postgresql://airflow:<pwd>@<DB>:<port>/airflow ``` Start airflow components (webserver, celery workers, scheduler). With no DAGs running everything is stable and not failing but once a DAGs start running the scheduler starts getting the error and from then it constantly reboots until crash. <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: This happens every time and the scheduler keeps restarting <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14586

https://github.com/apache/airflow/pull/14612

511f0426530bfabd9d93f4737df7add1080b4e8d

33910d6c699b5528db4be40d31199626dafed912

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_external_task_marker_dag.py", "airflow/models/dag.py", "airflow/sensors/external_task.py", "docs/apache-airflow/howto/operator/external_task_sensor.rst", "tests/sensors/test_external_task_sensor.py"]

TaskGroup Sensor

<!-- 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** Enable the ability for a task in a DAG to wait upon the successful completion of an entire TaskGroup. **Use case / motivation** TaskGroups provide a great mechanism for authoring DAGs, however there are situations where it might be necessary for a task in an external DAG to wait upon the the completion of the TaskGroup as a whole. At the moment this is only possible with one of the following workarounds: 1. Add an external task sensor for each task in the group. 2. Add a Dummy task after the TaskGroup which the external task sensor waits on. I would envisage either adapting `ExternalTaskSensor` to also work with TaskGroups or creating a new `ExternalTaskGroupSensor`. **Are you willing to submit a PR?** Time permitting, yes! **Related Issues** <!-- Is there currently another issue associated with this? -->

https://github.com/apache/airflow/issues/14563

https://github.com/apache/airflow/pull/24902

0eb0b543a9751f3d458beb2f03d4c6ff22fcd1c7

bc04c5ff0fa56e80d3d5def38b798170f6575ee8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/cheat_sheet_command.py", "airflow/cli/commands/info_command.py", "airflow/cli/simple_table.py"]

Airflow info command doesn't work properly with pbcopy on Mac OS

Hello, Mac OS has a command for copying data to the clipboard - `pbcopy`. Unfortunately, with the [introduction of more fancy tables](https://github.com/apache/airflow/pull/12689) to this command, we can no longer use it together. For example: ```bash airflow info | pbcopy ``` <details> <summary>Clipboard content</summary> ``` Apache Airflow: 2.1.0.dev0 System info | Mac OS | x86_64 | uname_result(system='Darwin', node='Kamils-MacBook-Pro.local', | release='20.3.0', version='Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 | PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64', machine='x86_64', | processor='i386') | (None, 'UTF-8') | 3.8.7 (default, Feb 14 2021, 09:58:39) [Clang 12.0.0 (clang-1200.0.32.29)] | /Users/kamilbregula/.pyenv/versions/3.8.7/envs/airflow-py38/bin/python3.8 Tools info | git version 2.24.3 (Apple Git-128) | OpenSSH_8.1p1, LibreSSL 2.7.3 | Client Version: v1.19.3 | Google Cloud SDK 326.0.0 | NOT AVAILABLE | NOT AVAILABLE | 3.32.3 2020-06-18 14:16:19 | 02c344aceaea0d177dd42e62c8541e3cab4a26c757ba33b3a31a43ccc7d4aapl | psql (PostgreSQL) 13.2 Paths info | /Users/kamilbregula/airflow | /Users/kamilbregula/.pyenv/versions/airflow-py38/bin:/Users/kamilbregula/.pye | v/libexec:/Users/kamilbregula/.pyenv/plugins/python-build/bin:/Users/kamilbre | ula/.pyenv/plugins/pyenv-virtualenv/bin:/Users/kamilbregula/.pyenv/plugins/py | nv-update/bin:/Users/kamilbregula/.pyenv/plugins/pyenv-installer/bin:/Users/k | milbregula/.pyenv/plugins/pyenv-doctor/bin:/Users/kamilbregula/.pyenv/plugins | python-build/bin:/Users/kamilbregula/.pyenv/plugins/pyenv-virtualenv/bin:/Use | s/kamilbregula/.pyenv/plugins/pyenv-update/bin:/Users/kamilbregula/.pyenv/plu | ins/pyenv-installer/bin:/Users/kamilbregula/.pyenv/plugins/pyenv-doctor/bin:/ | sers/kamilbregula/.pyenv/plugins/pyenv-virtualenv/shims:/Users/kamilbregula/. | yenv/shims:/Users/kamilbregula/.pyenv/bin:/usr/local/opt/gnu-getopt/bin:/usr/ | ocal/opt/mysql@5.7/bin:/usr/local/opt/mysql-client@5.7/bin:/usr/local/opt/ope | ssl/bin:/Users/kamilbregula/Library/Python/2.7/bin/:/Users/kamilbregula/bin:/ | sers/kamilbregula/google-cloud-sdk/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin | /sbin:/Users/kamilbregula/.cargo/bin | /Users/kamilbregula/.pyenv/versions/3.8.7/envs/airflow-py38/bin:/Users/kamilb | egula/.pyenv/versions/3.8.7/lib/python38.zip:/Users/kamilbregula/.pyenv/versi | ns/3.8.7/lib/python3.8:/Users/kamilbregula/.pyenv/versions/3.8.7/lib/python3. | /lib-dynload:/Users/kamilbregula/.pyenv/versions/3.8.7/envs/airflow-py38/lib/ | ython3.8/site-packages:/Users/kamilbregula/devel/airflow/airflow:/Users/kamil | regula/airflow/dags:/Users/kamilbregula/airflow/config:/Users/kamilbregula/ai | flow/plugins | True Config info | SequentialExecutor | airflow.utils.log.file_task_handler.FileTaskHandler | sqlite:////Users/kamilbregula/airflow/airflow.db | /Users/kamilbregula/airflow/dags | /Users/kamilbregula/airflow/plugins | /Users/kamilbregula/airflow/logs Providers info | 1.2.0 | 1.0.1 | 1.0.1 | 1.1.0 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.1 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.2 | 1.0.2 | 1.1.1 | 1.0.1 | 1.0.1 | 2.1.0 | 1.0.1 | 1.0.1 | 1.1.1 | 1.0.1 | 1.0.1 | 1.1.0 | 1.0.1 | 1.2.0 | 1.0.1 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.1 | 1.1.1 | 1.0.1 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.1 | 1.0.1 | 1.0.2 | 1.0.2 | 1.0.1 | 2.0.0 | 1.0.1 | 1.0.1 | 1.0.2 | 1.1.1 | 1.0.1 | 3.0.0 | 1.0.2 | 1.2.0 | 1.0.0 | 1.0.2 | 1.0.1 | 1.0.1 | 1.0.1 ``` </details> CC: @turbaszek

https://github.com/apache/airflow/issues/14518

https://github.com/apache/airflow/pull/14528

1b0851c9b75f0d0a15427898ae49a2f67d076f81

a1097f6f29796bd11f8ed7b3651dfeb3e40eec09

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "airflow/cli/simple_table.py", "docs/apache-airflow/usage-cli.rst", "docs/spelling_wordlist.txt"]

The tables are not parsable by standard linux utilities.

Hello, I changed the format of the tables a long time ago so that they could be parsed in standard Linux tools such as AWK. https://github.com/apache/airflow/pull/8409 For example, to list the files that contain the DAG, I could run the command below. ``` airflow dags list | grep -v "dag_id" | awk '{print $2}' | sort | uniq ``` To pause all dags: ```bash airflow dags list | awk '{print $1}' | grep -v "dag_id"| xargs airflow dags pause ``` Unfortunately [that has changed](https://github.com/apache/airflow/pull/12704) and we now have more fancy tables, but harder to use in standard Linux tools. Alternatively, we can use JSON output, but I don't always have JQ installed on production environment, so performing administrative tasks is difficult for me. ```bash $ docker run apache/airflow:2.0.1 bash -c "jq" /bin/bash: jq: command not found ``` Best regards, Kamil Breguła CC: @turbaszek

https://github.com/apache/airflow/issues/14517

https://github.com/apache/airflow/pull/14546

8801a0cc3b39cf3d2a3e5ef6af004d763bdb0b93

0ef084c3b70089b9b061090f7d88ce86e3651ed4

python

closed

apache/airflow

https://github.com/apache/airflow

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

Tasks in an infinite slots pool are never scheduled

**Apache Airflow version**: v2.0.0 and up **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): not tested with K8 **Environment**: all **What happened**: Executing the unit test included below, or create an infinite pool ( `-1` slots ) and tasks that should be executed in that pool. ``` INFO airflow.jobs.scheduler_job.SchedulerJob:scheduler_job.py:991 Not scheduling since there are -1 open slots in pool test_scheduler_verify_infinite_pool ``` **What you expected to happen**: To schedule tasks, or to drop support for infinite slots pools? **How to reproduce it**: easiest one is this unit test: ``` def test_scheduler_verify_infinite_pool(self): """ Test that TIs are still scheduled if we only have one infinite pool. """ dag = DAG(dag_id='test_scheduler_verify_infinite_pool', start_date=DEFAULT_DATE) BashOperator( task_id='test_scheduler_verify_infinite_pool_t0', dag=dag, owner='airflow', pool='test_scheduler_verify_infinite_pool', bash_command='echo hi', ) dagbag = DagBag( dag_folder=os.path.join(settings.DAGS_FOLDER, "no_dags.py"), include_examples=False, read_dags_from_db=True, ) dagbag.bag_dag(dag=dag, root_dag=dag) dagbag.sync_to_db() session = settings.Session() pool = Pool(pool='test_scheduler_verify_infinite_pool', slots=-1) session.add(pool) session.commit() dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag)) scheduler = SchedulerJob(executor=self.null_exec) scheduler.processor_agent = mock.MagicMock() dr = dag.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE, state=State.RUNNING, ) scheduler._schedule_dag_run(dr, {}, session) task_instances_list = scheduler._executable_task_instances_to_queued(max_tis=32, session=session) # Let's make sure we don't end up with a `max_tis` == 0 assert len(task_instances_list) >= 1 ``` **Anything else we need to know**: Overall I'm not sure whether it's worth fixing in those various spots: https://github.com/bperson/airflow/blob/master/airflow/jobs/scheduler_job.py#L908 https://github.com/bperson/airflow/blob/master/airflow/jobs/scheduler_job.py#L971 https://github.com/bperson/airflow/blob/master/airflow/jobs/scheduler_job.py#L988 https://github.com/bperson/airflow/blob/master/airflow/jobs/scheduler_job.py#L1041 https://github.com/bperson/airflow/blob/master/airflow/jobs/scheduler_job.py#L1056 Or whether to restrict `-1` ( infinite ) slots in pools: https://github.com/bperson/airflow/blob/master/airflow/models/pool.py#L49

https://github.com/apache/airflow/issues/14515

https://github.com/apache/airflow/pull/15247

90f0088c5752b56177597725cc716f707f2f8456

96f764389eded9f1ea908e899b54bf00635ec787

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/ssh/CHANGELOG.rst", "airflow/providers/ssh/hooks/ssh.py", "airflow/providers/ssh/provider.yaml"]

Add a retry with wait interval for SSH operator

<!-- 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** currently SSH operator fails if authentication fails without retying. We can add two more parameters as mentioned below which will retry in case of Authentication failure after waiting for configured time. - max_retry - maximum time SSH operator should retry in case of exception - wait - how many seconds it should wait before next retry <!-- A short description of your feature --> **Use case / motivation** We are using SSH operator heavily in our production jobs, And what I have noticed is sometimes SSH operator fails to authenticate, however open re-running jobs it run successfully. And this happens ofthen. We have ended up writing our own custom operator for this. However, if we can implement this, this could help others as well. <!-- 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. --> Implement the suggested feature for ssh operator. **Are you willing to submit a PR?** I will submit the PR if feature gets approval. <!--- We accept contributions! --> **Related Issues** N/A <!-- Is there currently another issue associated with this? --> No

https://github.com/apache/airflow/issues/14489

https://github.com/apache/airflow/pull/19981

4a73d8f3d1f0c2cb52707901f9e9a34198573d5e

b6edc3bfa1ed46bed2ae23bb2baeefde3f9a59d3

python

closed

apache/airflow

https://github.com/apache/airflow

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

tree view task instances have too much left padding in webserver UI

**Apache Airflow version**: 2.0.1 Here is tree view of a dag with one task:  For some reason the task instances render partially off the page, and there's a large amount of empty space that could have been used instead. **Environment** MacOS Chrome

https://github.com/apache/airflow/issues/14486

https://github.com/apache/airflow/pull/14566

8ef862eee6443cc2f34f4cc46425357861e8b96c

3f7ebfdfe2a1fa90b0854028a5db057adacd46c1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/schemas/dag_schema.py", "tests/api_connexion/endpoints/test_dag_endpoint.py", "tests/api_connexion/schemas/test_dag_schema.py"]

DAG /details endpoint returning empty array objects

When testing the following two endpoints, I get different results for the array of owners and tags. The former should be identical to the response of the latter endpoint. `/api/v1/dags/{dag_id}/details`: ```json { "owners": [], "tags": [ {}, {} ], } ``` `/api/v1/dags/{dag_id}`: ```json { "owners": [ "airflow" ], "tags": [ { "name": "example" }, { "name": "example2" } ] } ```

https://github.com/apache/airflow/issues/14481

https://github.com/apache/airflow/pull/14490

9c773bbf0174a8153720d594041f886b2323d52f

4424d10f05fa268b54c81ef8b96a0745643690b6

python

closed

apache/airflow

https://github.com/apache/airflow

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

DagRun duration not visible in tree view tooltip if not currently running

On airflow 2.0.1 On tree view if dag run is running, duration shows as expected:  But if dag run is complete, duration is null: 

https://github.com/apache/airflow/issues/14473

https://github.com/apache/airflow/pull/14566

8ef862eee6443cc2f34f4cc46425357861e8b96c

3f7ebfdfe2a1fa90b0854028a5db057adacd46c1

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg"]

Upgrade Flask-AppBuilder to 3.2.0 for improved OAUTH/LDAP

Version `3.2.0` of Flask-AppBuilder added support for LDAP group binding (see PR: https://github.com/dpgaspar/Flask-AppBuilder/pull/1374), we should update mainly for the `AUTH_ROLES_MAPPING` feature, which lets users bind to RBAC roles based on their LDAP/OAUTH group membership. Here are the docs about Flask-AppBuilder security: https://flask-appbuilder.readthedocs.io/en/latest/security.html#authentication-ldap This will resolve https://github.com/apache/airflow/issues/8179

https://github.com/apache/airflow/issues/14469

https://github.com/apache/airflow/pull/14665

b718495e4caecb753742c3eb22919411a715f24a

97b5e4cd6c001ec1a1597606f4e9f1c0fbea20d2

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "breeze", "breeze-complete", "scripts/ci/libraries/_initialization.sh", "scripts/ci/libraries/_verbosity.sh"]

breeze: build-image output docker build parameters to be used in scripts

<!-- 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** I would like to see a `--print-docker-args` option for `breeze build-image` **Use case / motivation** `breeze build-image --production-image --additional-extras microsoft.mssql --print-docker-args` should print something like ```--build-arg XXXX=YYYY --build-arg WWW=ZZZZ``` I would like to use that output in a script so that I can use `kaniko-executor` to build the image instead of `docker build` . <!-- 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. --> **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/14460

https://github.com/apache/airflow/pull/14468

4a54292b69bb9a68a354c34246f019331270df3d

aa28e4ed77d8be6558dbeb8161a5af82c4395e99

python

closed

apache/airflow

https://github.com/apache/airflow

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

on_failure_callback does not seem to fire on pod deletion/eviction

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.16.x **Environment**: KubernetesExecutor with single scheduler pod **What happened**: On all previous versions we used (from 1.10.x to 2.0.0), evicting or deleting a running task pod triggered the `on_failure_callback` from `BaseOperator`. We use this functionality quite a lot to detect eviction and provide work carry-over and automatic task clear. We've recently updated our dev environment to 2.0.1 and it seems that now `on_failure_callback` is only fired when pod completes naturally, i.e. not evicted / deleted with kubectl Everything looks the same on task log level when pod is removed with `kubectl delete pod...`: ``` Received SIGTERM. Terminating subprocesses Sending Signals.SIGTERM to GPID 16 Received SIGTERM. Terminating subprocesses. Task received SIGTERM signal Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1112, 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 1285, 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 1315, in _execute_task result = task_copy.execute(context=context) File "/usr/local/lib/python3.7/site-packages/li_airflow_common/custom_operators/li_operator.py", line 357, in execute self.operator_task_code(context) File "/usr/local/lib/python3.7/site-packages/li_airflow_common/custom_operators/mapreduce/yarn_jar_operator.py", line 62, in operator_task_code ssh_connection=_ssh_con File "/usr/local/lib/python3.7/site-packages/li_airflow_common/custom_operators/mapreduce/li_mapreduce_cluster_operator.py", line 469, in watch_application existing_apps=_associated_applications.keys() File "/usr/local/lib/python3.7/site-packages/li_airflow_common/custom_operators/mapreduce/li_mapreduce_cluster_operator.py", line 376, in get_associated_application_info logger=self.log File "/usr/local/lib/python3.7/site-packages/li_airflow_common/custom_operators/mapreduce/yarn_api/yarn_api_ssh_client.py", line 26, in send_request _response = requests.get(request) File "/usr/local/lib/python3.7/site-packages/requests/api.py", line 76, in get return request('get', url, params=params, **kwargs) File "/usr/local/lib/python3.7/site-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, **kwargs) File "/usr/local/lib/python3.7/site-packages/requests/sessions.py", line 542, in request resp = self.send(prep, **send_kwargs) File "/usr/local/lib/python3.7/site-packages/requests/sessions.py", line 655, in send r = adapter.send(request, **kwargs) File "/usr/local/lib/python3.7/site-packages/requests/adapters.py", line 449, in send timeout=timeout File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 677, in urlopen chunked=chunked, File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 392, in _make_request conn.request(method, url, **httplib_request_kw) File "/usr/local/lib/python3.7/http/client.py", line 1277, in request self._send_request(method, url, body, headers, encode_chunked) File "/usr/local/lib/python3.7/http/client.py", line 1323, in _send_request self.endheaders(body, encode_chunked=encode_chunked) File "/usr/local/lib/python3.7/http/client.py", line 1272, in endheaders self._send_output(message_body, encode_chunked=encode_chunked) File "/usr/local/lib/python3.7/http/client.py", line 1032, in _send_output self.send(msg) File "/usr/local/lib/python3.7/http/client.py", line 972, in send self.connect() File "/usr/local/lib/python3.7/site-packages/urllib3/connection.py", line 187, in connect conn = self._new_conn() File "/usr/local/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn (self._dns_host, self.port), self.timeout, **extra_kw File "/usr/local/lib/python3.7/site-packages/urllib3/util/connection.py", line 74, in create_connection sock.connect(sa) File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1241, in signal_handler raise AirflowException("Task received SIGTERM signal") airflow.exceptions.AirflowException: Task received SIGTERM signal Marking task as FAILED. dag_id=mock_dag_limr, task_id=SetupMockScaldingDWHJob, execution_date=20190910T000000, start_date=20210224T162811, end_date=20210224T163044 Process psutil.Process(pid=16, status='terminated', exitcode=1, started='16:28:10') (16) terminated with exit code 1 ``` But `on_failure_callback` is not triggered. For simplicity, let's assume the callback does this: ``` def act_on_failure(context): send_slack_message( message=f"{context['task_instance_key_str']} fired failure callback", channel=get_stored_variable('slack_log_channel') ) def get_stored_variable(variable_name, deserialize=False): try: return Variable.get(variable_name, deserialize_json=deserialize) except KeyError: if os.getenv('PYTEST_CURRENT_TEST'): _root_dir = str(Path(__file__).parent) _vars_path = os.path.join(_root_dir, "vars.json") _vars_json = json.loads(open(_vars_path, 'r').read()) if deserialize: return _vars_json.get(variable_name, {}) else: return _vars_json.get(variable_name, "") else: raise def send_slack_message(message, channel): _web_hook_url = get_stored_variable('slack_web_hook') post = { "text": message, "channel": channel } try: json_data = json.dumps(post) req = request.Request( _web_hook_url, data=json_data.encode('ascii'), headers={'Content-Type': 'application/json'} ) request.urlopen(req) except request.HTTPError as em: print('Failed to send slack messsage to the hook {hook}: {msg}, request: {req}'.format( hook=_web_hook_url, msg=str(em), req=str(post) )) ``` Scheduler logs related to this event: ``` 21-02-24 16:33:04,968] {kubernetes_executor.py:147} INFO - Event: mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3 had an event of type MODIFIED [2021-02-24 16:33:04,968] {kubernetes_executor.py:202} INFO - Event: mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3 Pending [2021-02-24 16:33:04,979] {kubernetes_executor.py:147} INFO - Event: mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3 had an event of type DELETED [2021-02-24 16:33:04,979] {kubernetes_executor.py:197} INFO - Event: Failed to start pod mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3, will reschedule [2021-02-24 16:33:05,406] {kubernetes_executor.py:354} INFO - Attempting to finish pod; pod_id: mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3; state: up_for_reschedule; annotations: {'dag_id': 'mock_dag_limr', 'task_id': 'SetupMockSparkDwhJob', 'execution_date': '2019-09-10T00:00:00+00:00', 'try_number': '9'} [2021-02-24 16:33:05,419] {kubernetes_executor.py:528} INFO - Changing state of (TaskInstanceKey(dag_id='mock_dag_limr', task_id='SetupMockSparkDwhJob', execution_date=datetime.datetime(2019, 9, 10, 0, 0, tzinfo=tzlocal()), try_number=9), 'up_for_reschedule', 'mockdaglimrsetupmocksparkdwhjob.791032759a764d8bae66fc7bd7ab2db3', 'airflow', '173647183') to up_for_reschedule [2021-02-24 16:33:05,422] {scheduler_job.py:1206} INFO - Executor reports execution of mock_dag_limr.SetupMockSparkDwhJob execution_date=2019-09-10 00:00:00+00:00 exited with status up_for_reschedule for try_number 9 ``` However task stays in failed state (not what scheduler says) When pod completes on its own (fails, exits with 0), callbacks are triggered correctly **What you expected to happen**: `on_failure_callback` is called regardless of how pod exists, including SIGTERM-based interruptions: pod eviction, pod deletion <!-- What do you think went wrong? --> Not sure really. We believe this code is executed since we get full stack trace https://github.com/apache/airflow/blob/2.0.1/airflow/models/taskinstance.py#L1149 But then it is unclear why `finally` clause here does not run: https://github.com/apache/airflow/blob/master/airflow/models/taskinstance.py#L1422 **How to reproduce it**: With Airflow 2.0.1 running KubernetesExecutor, execute `kubectl delete ...` on any running task pod. Task operator should define `on_failure_callback`. In order to check that it is/not called, send data from it to any external logging system **Anything else we need to know**: Problem is persistent and only exists in 2.0.1 version

https://github.com/apache/airflow/issues/14422

https://github.com/apache/airflow/pull/15172

e5d69ad6f2d25e652bb34b6bcf2ce738944de407

def1e7c5841d89a60f8972a84b83fe362a6a878d

python

closed

apache/airflow

https://github.com/apache/airflow

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

NULL values in the operator column of task_instance table cause API validation failures

**Apache Airflow version**: 2.0.1 **Environment**: Docker on Linux Mint 20.1, image based on apache/airflow:2.0.1-python3.8 **What happened**: I'm using the airflow API and the following exception occurred: ```python >>> import json >>> import requests >>> from requests.auth import HTTPBasicAuth >>> payload = {"dag_ids": ["{my_dag_id}"]} >>> r = requests.post("https://localhost:8080/api/v1/dags/~/dagRuns/~/taskInstances/list", auth=HTTPBasicAuth('username', 'password'), data=json.dumps(payload), headers={'Content-Type': 'application/json'}) >>> r.status_code 500 >>> print(r.text) { "detail": "None is not of type 'string'\n\nFailed validating 'type' in schema['allOf'][0]['properties'][ 'task_instances']['items']['properties']['operator']:\n {'type': 'string'}\n\nOn instance['task_instanc es'][5]['operator']:\n None", "status": 500, "title": "Response body does not conform to specification", "type": "https://airflow.apache.org/docs/2.0.1/stable-rest-api-ref.html#section/Errors/Unknown" } None is not of type 'string' Failed validating 'type' in schema['allOf'][0]['properties']['task_instances']['items']['properties']['ope rator']: {'type': 'string'} On instance['task_instances'][5]['operator']: None ``` This happens on all the "old" task instances before upgrading to 2.0.0 There is no issue with new task instances created after the upgrade. <!-- (please include exact error messages if you can) --> **What do you think went wrong?**: The `operator` column was introduced in 2.0.0. But during migration, all the existing database entries are filled with `NULL` values. So I had to execute this manually in my database ```sql UPDATE task_instance SET operator = 'NoOperator' WHERE operator IS NULL; ``` **How to reproduce it**: * Run airflow 1.10.14 * Create a DAG with multiple tasks and run them * Upgrade airflow to 2.0.0 or 2.0.1 * Make the API call as above **Anything else we need to know**: Similar to https://github.com/apache/airflow/issues/13799 but not exactly the same

https://github.com/apache/airflow/issues/14421

https://github.com/apache/airflow/pull/16516

60925453b1da9fe54ca82ed59889cd65a0171516

087556f0c210e345ac1749933ff4de38e40478f6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/druid/operators/druid.py", "tests/providers/apache/druid/operators/test_druid.py"]

DruidOperator failing to submit ingestion tasks : Getting 500 error code from Druid

Issue : When trying to submit an ingestion task using DruidOperator, getting 500 error code in response from Druid. And can see no task submitted in Druid console. In Airflow 1.10.x, everything is working fine. But when upgraded to 2.0.1, it is failing to submit the task. There is absolutely no change in the code/files except the import statements. Resolution : I compared DruidOperator code for both Airflow 1.10.x & 2.0.1 and found one line causing the issue. In Airflow 2.0.x, before submitting the indexing job json string is converted to python object. But it should be json string only. In Airflow 1.10.x there is no conversion happening and hence it is working fine. (Please see below code snippets.) I have already tried this change in my setup and re-ran the ingestion tasks. It is all working fine. ~~hook.submit_indexing_job(json.loads(self.json_index_file))~~ **hook.submit_indexing_job(self.json_index_file)** Airflow 1.10.x - airflow/contrib/operators/druid_operator.py ``` def execute(self, context): hook = DruidHook( druid_ingest_conn_id=self.conn_id, max_ingestion_time=self.max_ingestion_time ) self.log.info("Submitting %s", self.index_spec_str) hook.submit_indexing_job(self.index_spec_str) ``` Airflow 2.0.1 - airflow/providers/apache/druid/operators/druid.py ``` def execute(self, context: Dict[Any, Any]) -> None: hook = DruidHook(druid_ingest_conn_id=self.conn_id, max_ingestion_time=self.max_ingestion_time) self.log.info("Submitting %s", self.json_index_file) hook.submit_indexing_job(json.loads(self.json_index_file)) ``` **Apache Airflow version**: 2.0.x **Error Logs**: ``` [2021-02-24 06:42:24,287] {{connectionpool.py:452}} DEBUG - http://druid-master:8081 "POST /druid/indexer/v1/task HTTP/1.1" 500 15714 [2021-02-24 06:42:24,287] {{taskinstance.py:570}} DEBUG - Refreshing TaskInstance <TaskInstance: druid_compact_daily 2021-02-23T01:20:00+00:00 [running]> from DB [2021-02-24 06:42:24,296] {{taskinstance.py:605}} DEBUG - Refreshed TaskInstance <TaskInstance: druid_compact_daily 2021-02-23T01:20:00+00:00 [running]> [2021-02-24 06:42:24,298] {{taskinstance.py:1455}} ERROR - Did not get 200 when submitting the Druid job to http://druid-master:8081/druid/indexer/v1/task ```

https://github.com/apache/airflow/issues/14417

https://github.com/apache/airflow/pull/14418

c2a0cb958835d0cecd90f82311e2aa8b1bbd22a0

59065400ff6333e3ff085f3d9fe9005a0a849aef

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/ci/libraries/_build_images.sh"]

breeze: it relies on GNU date which is not the default on macOS

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **OS** macOS Catalina **What happened**: `breeze build-image` gives ``` Pulling the image apache/airflow:master-python3.6-ci master-python3.6-ci: Pulling from apache/airflow Digest: sha256:92351723f04bec6e6ef27c0be2b5fbbe7bddc832911988b2d18b88914bcc1256 Status: Downloaded newer image for apache/airflow:master-python3.6-ci docker.io/apache/airflow:master-python3.6-ci date: illegal option -- - usage: date [-jnRu] [-d dst] [-r seconds] [-t west] [-v[+|-]val[ymwdHMS]] ... [-f fmt date | [[[mm]dd]HH]MM[[cc]yy][.ss]] [+format] ``` As I explained in [Slack](https://apache-airflow.slack.com/archives/CQ9QHSFQX/p1614092075007500) this is caused by https://github.com/apache/airflow/blob/b9951279a0007db99a6f4c52197907ebfa1bf325/scripts/ci/libraries/_build_images.sh#L770 ``` --build-arg AIRFLOW_IMAGE_DATE_CREATED="$(date --rfc-3339=seconds | sed 's/ /T/')" \ ``` `--rfc-3339` is an option supported by GNU date but not by the regular `date` command present macOS. **What you expected to happen**: `--rfc-3339` is an option supported by GNU date but not by the regular `date` command present macOS. **How to reproduce it**: on macOS: `breeze build-image` <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: It happens every time I think this can be solve either by checking for the presence of `gdate` and use that if present or by adhering to POSIX date options (I'm not 100% but I do believe the regular POSIX options are available in macOS's `date`) <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14393

https://github.com/apache/airflow/pull/14458

997a009715fb82c241a47405cc8647d23580af25

64cf2aedd94d27be3ab7829b7c92bd6b1866295b

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "breeze"]

breeze: capture output to a file

Reference: [Slack conversation with Jarek Potiuk](https://apache-airflow.slack.com/archives/CQ9QHSFQX/p1614073317003900) @potiuk **Description** Suggestion: it would be great if breeze captured the output into a log file by default (for example when running breeze build-image) so that is easier to review the build process, I have seen at least errors invoking date utility and now I would need to run the whole thing again to capture that. I **Use case / motivation** I want to be able to review what happened during breeze command that output lots of text and take a lot of time like `breeze build-image`. Ideally I want to happen automatically as it's very time consuming to rerun the `breeze` command to get the error. **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/14390

https://github.com/apache/airflow/pull/14470

8ad2f9c64e9ce89c252cc61f450947d53935e0f2

4a54292b69bb9a68a354c34246f019331270df3d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler ocassionally crashes with a TypeError when updating DagRun state

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): N/A **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: Python 3.8 **What happened**: Occasionally, the Airflow scheduler crashes with the following exception: ``` Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1280, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1382, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1521, in _do_scheduling self._schedule_dag_run(dag_run, active_runs_by_dag_id.get(dag_run.dag_id, set()), session) File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1760, in _schedule_dag_run schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) File "/usr/local/lib/python3.8/site-packages/airflow/utils/session.py", line 62, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/dagrun.py", line 478, in update_state self._emit_duration_stats_for_finished_state() File "/usr/local/lib/python3.8/site-packages/airflow/models/dagrun.py", line 615, in _emit_duration_stats_for_finished_state duration = self.end_date - self.start_date TypeError: unsupported operand type(s) for -: 'datetime.datetime' and 'NoneType' ``` Just before this error surfaces, Airflow typically logs either one of the following messages: ``` Marking run <DagRun sessionization @ 2021-02-15 08:05:00+00:00: scheduled__2021-02-15T08:05:00+00:00, externally triggered: False> failed ``` or ``` Marking run <DagRun sessionization @ 2021-02-16 08:05:00+00:00: scheduled__2021-02-16T08:05:00+00:00, externally triggered: False> successful ``` The cause of this issue appears to be that the scheduler is attempting to update the state of a `DagRun` instance that is in a _running_ state, but does **not** have a `start_date` set. This will eventually cause a `TypeError` to be raised at L615 in `_emit_duration_stats_for_finished_state()` because `None` is subtracted from a `datetime` object. During my testing I was able to resolve the issue by manually updating any records in the `DagRun` table which are missing a `start_date`. However, it is a bit unclear to me _how_ it is possible for a DagRun instance to be transitioned into a `running` state, without having a `start_date` set. I spent some time digging through the code, and I believe the only code path that would allow a `DagRun` to end up in such a scenario is the state transition that occurs at L475 in [DagRun](https://github.com/apache/airflow/blob/2.0.1/airflow/models/dagrun.py#L475) where `DagRun.set_state(State.RUNNING)` is invoked without verifying that a `start_date` is set. **What you expected to happen**: I expect the Airflow scheduler not to crash, and to handle this scenario gracefully. I have the impression that this is an edge-case, and even handling a missing `start_date` to be equal to a set `end_date` in `_emit_duration_stats_for_finished_state()` seems like a more favorable solution than raising a `TypeError` in the scheduler to me. **How to reproduce it**: I haven't been able to figure out a scenario which allows me to reproduce this issue reliably. We've had this issue surface for a fairly complex DAG twice in a time span of 5 days. We run over 25 DAGs on our Airflow instance, and so far the issue seems to be isolated to a single DAG. **Anything else we need to know**: While I'm unclear on what exactly causes a DagRun instance to not have a `start_date` set, the problem seems to be isolated to a single DAG on our Airflow instance. This DAG is fairly complex in the sense that it contains a number of root tasks, that have SubDagOperators set as downstream (leaf) dependencies. These SubDagOperators each represent a SubDag containing between 2 and 12 tasks. The SubDagOperator's have `depends_on_past` set to True, and `catchup` is enabled for the parent DAG. The parent DAG also has `max_active_runs` set to limit concurrency. I also have the impression, that this issue mainly surfaces when there are multiple DagRuns running concurrently for this DAG, but I don't have hard evidence for this. I did at some point clear task instance states, and transition the DagRun's state from `failed` back to `running` through the Web UI around the period that some of these issues arose. I've also been suspecting that this [block of code](https://github.com/apache/airflow/blob/2.0.1/airflow/jobs/scheduler_job.py#L1498-L1511) in `_do_scheduling` may be related to this issue, in the sense that I've been suspecting that there exists an edge case in which Task Instances may be considered active for a particular `execution_date`, but for which the DagRun object itself is not "active". My hypothesis is that this would eventually cause the "inactive" DagRun to be transitioned to `running` in `DagRun.set_state()` without ensuring that a `start_date` was set for the DagRun. I haven't been able to gather strong evidence for this hypothesis yet, though, and I'm hoping that someone more familiar with the implementation will be able to provide some guidance as to whether that hypothesis makes sense or not.

https://github.com/apache/airflow/issues/14384

https://github.com/apache/airflow/pull/14452

258ec5d95e98eac09ecc7658dcd5226c9afe14c6

997a009715fb82c241a47405cc8647d23580af25

python

closed

apache/airflow

https://github.com/apache/airflow

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

Missing schedule_delay metric

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.0 but applicable to master **Environment**: Running on ECS but not relevant to question **What happened**: I am not seeing the metric dagrun.schedule_delay.<dag_id> being reported. A search in the codebase seems to reveal that it no longer exists. It was originally added in https://github.com/apache/airflow/pull/5050. <!-- (please include exact error messages if you can) --> <!-- What do you think went wrong? --> I suspect either: 1. This metric was intentionally removed, in which case the docs should be updated to remove it. 2. It was unintentionally removed during a refactor, in which case we should add it back. 3. I am bad at searching through code, and someone could hopefully point me to where it is reported from now. **How to reproduce it**: https://github.com/apache/airflow/search?q=schedule_delay <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14364

https://github.com/apache/airflow/pull/15105

441b4ef19f07d8c72cd38a8565804e56e63b543c

ca4c4f3d343dea0a034546a896072b9c87244e71

python

closed

apache/airflow

https://github.com/apache/airflow

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

Argument order change in airflow.providers.google.cloud.hooks.gcs.GCSHook:download appears to be a mistake.

### Description https://github.com/apache/airflow/blob/6019c78cb475800f58714a9dabb747b9415599c8/airflow/providers/google/cloud/hooks/gcs.py#L262-L265 Was this order swap of the `object_name` and `bucket_name` arguments in the `GCSHook.download` a mistake? The `upload` and `delete` methods still use `bucket_name` first and the commit where this change was made `1845cd11b77f302777ab854e84bef9c212c604a0` was supposed to just add strict type checking. The docstring also appears to reference the old order.

https://github.com/apache/airflow/issues/14363

https://github.com/apache/airflow/pull/14497

77f5629a80cfec643bd3811bc92c48ef4ec39ceb

bfef559cf6138eec3ac77c64289fb1d45133d8be

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/utils/state.py", "tests/api_connexion/endpoints/test_task_instance_endpoint.py"]

Airflow stable API taskInstance call fails if a task is removed from running DAG

**Apache Airflow version**: 2.0.1 **Environment**: Docker on Win 10 with WSL, image based on `apache/airflow:2.0.1-python3.8` **What happened**: I'm using the airflow API and the following (what I believe to be a) bug popped up: ```Python >>> import requests >>> r = requests.get("http://localhost:8084/api/v1/dags/~/dagRuns/~/taskInstances", auth=HTTPBasicAuth('username', 'password')) >>> r.status_code 500 >>> print(r.text) { "detail": "'removed' is not one of ['success', 'running', 'failed', 'upstream_failed', 'skipped', 'up_for_retry', 'up_for_reschedule', 'queued', 'none', 'scheduled']\n\nFailed validating 'enum' in schema['allOf'][0]['properties']['task_instances']['items']['properties']['state']:\n {'description': 'Task state.',\n 'enum': ['success',\n 'running',\n 'failed',\n 'upstream_failed',\n 'skipped',\n 'up_for_retry',\n 'up_for_reschedule',\n 'queued',\n 'none',\n 'scheduled'],\n 'nullable': True,\n 'type': 'string',\n 'x-scope': ['',\n '#/components/schemas/TaskInstanceCollection',\n '#/components/schemas/TaskInstance']}\n\nOn instance['task_instances'][16]['state']:\n 'removed'", "status": 500, "title": "Response body does not conform to specification", "type": "https://airflow.apache.org/docs/2.0.1rc2/stable-rest-api-ref.html#section/Errors/Unknown" } >>> print(r.json()["detail"]) 'removed' is not one of ['success', 'running', 'failed', 'upstream_failed', 'skipped', 'up_for_retry', 'up_for_reschedule', 'queued', 'none', 'scheduled'] Failed validating 'enum' in schema['allOf'][0]['properties']['task_instances']['items']['properties']['state']: {'description': 'Task state.', 'enum': ['success', 'running', 'failed', 'upstream_failed', 'skipped', 'up_for_retry', 'up_for_reschedule', 'queued', 'none', 'scheduled'], 'nullable': True, 'type': 'string', 'x-scope': ['', '#/components/schemas/TaskInstanceCollection', '#/components/schemas/TaskInstance']} On instance['task_instances'][16]['state']: 'removed' ``` This happened after I changed a DAG in the corresponding instance, thus a task was removed from a DAG while the DAG was running. **What you expected to happen**: Give me all task instances, whether including the removed ones or not is up to the airflow team to decide (no preferences from my side, though I'd guess it makes more sense to supply all data as it is available). **How to reproduce it**: - Run airflow - Create a DAG with multiple tasks - While the DAG is running, remove one of the tasks (ideally one that did not yet run) - Make the API call as above

https://github.com/apache/airflow/issues/14331

https://github.com/apache/airflow/pull/14381

ea7118316660df43dd0ac0a5e72283fbdf5f2396

7418679591e5df4ceaab6c471bc6d4a975201871

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/json.py"]

Kubernetes Objects are not serializable and break Graph View in UI

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.17.12 **Environment**: - **Cloud provider or hardware configuration**: AWS EKS - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: When you click on Graph view for some DAGs in 2.0.1 the UI errors out (logs below). <!-- (please include exact error messages if you can) --> **What you expected to happen**: The Graph view to display <!-- What do you think went wrong? --> **How to reproduce it**: It is not clear to me. This only happening on a handle of our DAGs. Also the tree view displays fine. <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> --> ``` Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.8/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.8/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/usr/local/lib/python3.8/site-packages/flask/_compat.py", line 39, in reraise raise value File "/usr/local/lib/python3.8/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.8/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/usr/local/lib/python3.8/site-packages/airflow/www/auth.py", line 34, in decorated return func(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/www/decorators.py", line 97, in view_func return f(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/www/decorators.py", line 60, in wrapper return f(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/www/views.py", line 2080, in graph return self.render_template( File "/usr/local/lib/python3.8/site-packages/airflow/www/views.py", line 396, in render_template return super().render_template( File "/usr/local/lib/python3.8/site-packages/flask_appbuilder/baseviews.py", line 280, in render_template return render_template( File "/usr/local/lib/python3.8/site-packages/flask/templating.py", line 137, in render_template return _render( File "/usr/local/lib/python3.8/site-packages/flask/templating.py", line 120, in _render rv = template.render(context) File "/usr/local/lib/python3.8/site-packages/jinja2/environment.py", line 1090, in render self.environment.handle_exception() File "/usr/local/lib/python3.8/site-packages/jinja2/environment.py", line 832, in handle_exception reraise(*rewrite_traceback_stack(source=source)) File "/usr/local/lib/python3.8/site-packages/jinja2/_compat.py", line 28, in reraise raise value.with_traceback(tb) File "/usr/local/lib/python3.8/site-packages/airflow/www/templates/airflow/graph.html", line 21, in top-level template code {% from 'appbuilder/loading_dots.html' import loading_dots %} File "/usr/local/lib/python3.8/site-packages/airflow/www/templates/airflow/dag.html", line 21, in top-level template code {% from 'appbuilder/dag_docs.html' import dag_docs %} File "/usr/local/lib/python3.8/site-packages/airflow/www/templates/airflow/main.html", line 20, in top-level template code {% extends 'appbuilder/baselayout.html' %} File "/usr/local/lib/python3.8/site-packages/flask_appbuilder/templates/appbuilder/baselayout.html", line 2, in top-level template code {% import 'appbuilder/baselib.html' as baselib %} File "/usr/local/lib/python3.8/site-packages/flask_appbuilder/templates/appbuilder/init.html", line 60, in top-level template code {% block tail %} File "/usr/local/lib/python3.8/site-packages/airflow/www/templates/airflow/graph.html", line 145, in block "tail" var task_instances = {{ task_instances|tojson }}; File "/usr/local/lib/python3.8/site-packages/flask/json/__init__.py", line 376, in tojson_filter return Markup(htmlsafe_dumps(obj, **kwargs)) File "/usr/local/lib/python3.8/site-packages/flask/json/__init__.py", line 290, in htmlsafe_dumps dumps(obj, **kwargs) File "/usr/local/lib/python3.8/site-packages/flask/json/__init__.py", line 211, in dumps rv = _json.dumps(obj, **kwargs) File "/usr/local/lib/python3.8/json/__init__.py", line 234, in dumps return cls( File "/usr/local/lib/python3.8/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/usr/local/lib/python3.8/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/usr/local/lib/python3.8/site-packages/airflow/utils/json.py", line 74, in _default raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable") TypeError: Object of type 'V1ResourceRequirements' is not JSON serializable ```

https://github.com/apache/airflow/issues/14327

https://github.com/apache/airflow/pull/15199

6706b67fecc00a22c1e1d6658616ed9dd96bbc7b

7b577c35e241182f3f3473ca02da197f1b5f7437

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/kubernetes/pod_generator.py", "tests/kubernetes/test_pod_generator.py"]

Task Instances stuck in "scheduled" state

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.17.12 **Environment**: - **Cloud provider or hardware configuration**: AWS EKS - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: Several Task Instances get `scheduled` but never move to `queued` or `running`. They then become orphan tasks. <!-- (please include exact error messages if you can) --> **What you expected to happen**: Tasks to get scheduled and run :) <!-- What do you think went wrong? --> **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: I believe the issue is caused by this [limit](https://github.com/apache/airflow/blob/2.0.1/airflow/jobs/scheduler_job.py#L923). If we have more Task Instances than Pool Slots Free then some Task Instances may never show up in this query. <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14326

https://github.com/apache/airflow/pull/14703

b1ce429fee450aef69a813774bf5d3404d50f4a5

b5e7ada34536259e21fca5032ef67b5e33722c05

python

closed

apache/airflow

https://github.com/apache/airflow

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

UI: Start Date is incorrect in "DAG Details" view

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): Ubuntu - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: The start date in the "DAG Details" view `{AIRFLOW_URL}/dag_details?dag_id={dag_id}` is incorrect if there's a schedule for the DAG. **What you expected to happen**: Start date should be the same as the specified date in the DAG. **How to reproduce it**: For example, I created a DAG with a start date of `2019-07-09` but the DAG details view shows:  Minimal code block to reproduce: ``` from datetime import datetime, timedelta START_DATE = datetime(2019, 7, 9) DAG_ID = '*redacted*' dag = DAG( dag_id=DAG_ID, description='*redacted*', catchup=False, start_date=START_DATE, schedule_interval=timedelta(weeks=1), ) start = DummyOperator(task_id='start', dag=dag) ```

https://github.com/apache/airflow/issues/14299

https://github.com/apache/airflow/pull/16206

78c4f1a46ce74f13a99447207f8cdf0fcfc7df95

ebc03c63af7282c9d826054b17fe7ed50e09fe4e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/example_dags/example_s3_bucket.py", "airflow/providers/amazon/aws/example_dags/example_s3_bucket_tagging.py", "airflow/providers/amazon/aws/hooks/s3.py", "airflow/providers/amazon/aws/operators/s3_bucket.py", "airflow/providers/amazon/aws/operators/s3_bucket_tagging.py", "airflow/providers/amazon/provider.yaml", "docs/apache-airflow-providers-amazon/operators/s3.rst", "tests/providers/amazon/aws/hooks/test_s3.py", "tests/providers/amazon/aws/operators/test_s3_bucket_tagging.py", "tests/providers/amazon/aws/operators/test_s3_bucket_tagging_system.py"]

Add AWS S3 Bucket Tagging Operator

**Description** Add the missing AWS Operators for the three (get/put/delete) AWS S3 bucket tagging APIs, including testing. **Use case / motivation** I am looking to add an Operator that will implement the existing API functionality to manage the tags on an AWS S3 bucket. **Are you willing to submit a PR?** Yes **Related Issues** None that I saw

https://github.com/apache/airflow/issues/14279

https://github.com/apache/airflow/pull/14402

f25ec3368348be479dde097efdd9c49ce56922b3

8ced652ecf847ed668e5eed27e3e47a51a27b1c8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/task/task_runner/standard_task_runner.py", "tests/task/task_runner/test_standard_task_runner.py"]

Specify that exit code -9 is due to RAM

Related to https://github.com/apache/airflow/issues/9655 It would be nice to add a message when you get this error with some info, like 'This probably is because a lack of RAM' or something like that. I have found the code where the -9 is assigned but have no idea how to add a logging message. self.process = None if self._rc is None: # Something else reaped it before we had a chance, so let's just "guess" at an error code. self._rc = -9

https://github.com/apache/airflow/issues/14270

https://github.com/apache/airflow/pull/15207

eae22cec9c87e8dad4d6e8599e45af1bdd452062

18e2c1de776c8c3bc42c984ea0d31515788b6572

python

closed

apache/airflow

https://github.com/apache/airflow

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

AirflowException: The external DAG was deleted when external_dag_id references zipped DAG

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.16.3 **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): Debian GNU/Linux 10 (buster) - **Kernel** (e.g. `uname -a`): Linux airflow 3.10.0-1127.10.1.el7.x86_64 #1 SMP Tue May 26 15:05:43 EDT 2020 x86_64 GNU/Linux - **Install tools**: - **Others**: **What happened**: ExternalTaskSensor with check_existence=True referencing an external DAG inside a .zip file raises the following exception: ``` ERROR - The external DAG dag_a /opt/airflow-data/dags/my_dags.zip/dag_a.py was deleted. Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1086, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1260, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1300, in _execute_task result = task_copy.execute(context=context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/sensors/base.py", line 228, in execute while not self.poke(context): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/sensors/external_task.py", line 159, in poke self._check_for_existence(session=session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/sensors/external_task.py", line 184, in _check_for_existence raise AirflowException(f'The external DAG {self.external_dag_id} was deleted.') airflow.exceptions.AirflowException: The external DAG dag_a /opt/airflow-data/dags/my_dags.zip/dag_a.py was deleted. ``` **What you expected to happen**: The existence check should PASS. **How to reproduce it**: 1. Create a file *dag_a.py* with the following contents: ``` from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.utils.timezone import datetime DEFAULT_DATE = datetime(2015, 1, 1) with DAG("dag_a", start_date=DEFAULT_DATE, schedule_interval="@daily") as dag: task_a = DummyOperator(task_id="task_a", dag=dag) ``` 2. Create a file *dag_b.py* with contents: ``` from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.sensors.external_task import ExternalTaskSensor from airflow.utils.timezone import datetime DEFAULT_DATE = datetime(2015, 1, 1) with DAG("dag_b", start_date=DEFAULT_DATE, schedule_interval="@daily") as dag: sense_a = ExternalTaskSensor( task_id="sense_a", external_dag_id="dag_a", external_task_id="task_a", check_existence=True ) task_b = DummyOperator(task_id="task_b", dag=dag) sense_a >> task_b ``` 3. `zip my_dags.zip dag_a.py dag_b.py` 4. Load *my_dags.zip* into airflow and run *dag_b* 5. Task *sense_a* will fail with exception above. **Anything else we need to know**:

https://github.com/apache/airflow/issues/14264

https://github.com/apache/airflow/pull/27056

911d90d669ab5d1fe1f5edb1d2353c7214611630

99a6bf783412432416813d1c4bb41052054dd5c6

python

closed

apache/airflow

https://github.com/apache/airflow

["UPDATING.md", "airflow/api_connexion/endpoints/task_instance_endpoint.py", "airflow/models/dag.py", "airflow/models/taskinstance.py", "tests/sensors/test_external_task_sensor.py"]

Clearing using ExternalTaskMarker will not activate external DagRuns

**Apache Airflow version**: 2.0.1 **What happened**: When clearing task across dags using `ExternalTaskMarker` the dag state of the external `DagRun` is not set to active. So cleared tasks in the external dag will not automatically start if the `DagRun` is a Failed or Succeeded state. **What you expected to happen**: The external `DagRun` run should also be set to Running state. **How to reproduce it**: Clear tasks in an external dag using an ExternalTaskMarker. **Anything else we need to know**: Looking at the code is has: https://github.com/apache/airflow/blob/b23fc137812f5eabf7834e07e032915e2a504c17/airflow/models/dag.py#L1323-L1335 It seems like it intentionally calls the dag member method `set_dag_run_state` instead of letting the helper function `clear_task_insntances` set the `DagRun` state. But the member method will only change the state of `DagRun`s of dag where the original task is, while I believe `clear_task_instances` would correctly change the state of all involved `DagRun`s.

https://github.com/apache/airflow/issues/14260

https://github.com/apache/airflow/pull/15382

f75dd7ae6e755dad328ba6f3fd462ade194dab25

2bca8a5425c234b04fdf32d6c50ae3a91cd08262

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/baseoperator.py", "tests/core/test_core.py"]

Unable to clear Failed task with retries

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): NA **Environment**: Windows WSL2 (Ubuntu) Local - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): Ubuntu 18.04 - **Kernel** (e.g. `uname -a`): Linux d255bce4dcd5 5.4.72-microsoft-standard-WSL2 - **Install tools**: Docker -compose - **Others**: **What happened**: I have a dag with tasks: Task1 - Get Date Task 2 - Get data from Api call (Have set retires to 3) Task 3 - Load Data Task 2 had failed after three attempts. I am unable to clear the task Instance and get the below error in UI. [Dag Code](https://github.com/anilkulkarni87/airflow-docker/blob/master/dags/covidNyDaily.py) ``` Python version: 3.8.7 Airflow version: 2.0.1rc2 Node: d255bce4dcd5 ------------------------------------------------------------------------------- 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 34, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/decorators.py", line 60, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 1547, in clear return self._clear_dag_tis( File "/home/airflow/.local/lib/python3.8/site-packages/airflow/www/views.py", line 1475, in _clear_dag_tis count = dag.clear( File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/dag.py", line 1324, in clear clear_task_instances( File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 160, in clear_task_instances ti.max_tries = ti.try_number + task_retries - 1 TypeError: unsupported operand type(s) for +: 'int' and 'str' ``` **What you expected to happen**: I expected to clear the Task Instance so that the task could be scheduled again. **How to reproduce it**: 1) Clone the repo link shared above 2) Follow instructions to setup cluster. 3) Change code to enforce error in Task 2 4) Execute and try to clear task instance after three attempts. 

https://github.com/apache/airflow/issues/14252

https://github.com/apache/airflow/pull/16415

643f3c35a6ba3def40de7db8e974c72e98cfad44

15ff2388e8a52348afcc923653f85ce15a3c5f71

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagrun.py"]

both airflow dags test and airflow backfill cli commands got same error in airflow Version 2.0.1

**Apache Airflow version: 2.0.1** **What happened:** Running an airflow dags test or backfill CLI command shown in tutorial, produces the same error. **dags test cli command result：** ``` (airflow_venv) (base) app@lunar_01:airflow$ airflow dags test tutorial 2015-06-01 [2021-02-16 04:29:22,355] {dagbag.py:448} INFO - Filling up the DagBag from /home/app/Lunar/src/airflow/dags [2021-02-16 04:29:22,372] {example_kubernetes_executor_config.py:174} WARNING - Could not import DAGs in example_kubernetes_executor_config.py: No module named 'kubernetes' [2021-02-16 04:29:22,373] {example_kubernetes_executor_config.py:175} WARNING - Install kubernetes dependencies with: pip install apache-airflow['cncf.kubernetes'] Traceback (most recent call last): File "/home/app/airflow_venv/bin/airflow", line 10, in <module> sys.exit(main()) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/__main__.py", line 40, in main args.func(args) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/cli.py", line 89, in wrapper return f(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/cli/commands/dag_command.py", line 389, in dag_test dag.run(executor=DebugExecutor(), start_date=args.execution_date, end_date=args.execution_date) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/models/dag.py", line 1706, in run job.run() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/base_job.py", line 237, in run self._execute() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 805, in _execute session=session, File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 62, in wrapper return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 715, in _execute_for_run_dates tis_map = self._task_instances_for_dag_run(dag_run, session=session) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 62, in wrapper return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 359, in _task_instances_for_dag_run dag_run.refresh_from_db() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/models/dagrun.py", line 178, in refresh_from_db DR.run_id == self.run_id, File "/home/app/airflow_venv/lib/python3.7/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() ``` **backfill cli command result:** ``` (airflow_venv) (base) app@lunar_01:airflow$ airflow dags backfill tutorial --start-date 2015-06-01 --end-date 2015-06-07 /home/app/airflow_venv/lib/python3.7/site-packages/airflow/cli/commands/dag_command.py:62 PendingDeprecationWarning: --ignore-first-depends-on-past is deprecated as the value is always set to True [2021-02-16 04:30:16,979] {dagbag.py:448} INFO - Filling up the DagBag from /home/app/Lunar/src/airflow/dags [2021-02-16 04:30:16,996] {example_kubernetes_executor_config.py:174} WARNING - Could not import DAGs in example_kubernetes_executor_config.py: No module named 'kubernetes' [2021-02-16 04:30:16,996] {example_kubernetes_executor_config.py:175} WARNING - Install kubernetes dependencies with: pip install apache-airflow['cncf.kubernetes'] Traceback (most recent call last): File "/home/app/airflow_venv/bin/airflow", line 10, in <module> sys.exit(main()) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/__main__.py", line 40, in main args.func(args) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/cli.py", line 89, in wrapper return f(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/cli/commands/dag_command.py", line 116, in dag_backfill run_backwards=args.run_backwards, File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/models/dag.py", line 1706, in run job.run() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/base_job.py", line 237, in run self._execute() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 805, in _execute session=session, File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 62, in wrapper return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 715, in _execute_for_run_dates tis_map = self._task_instances_for_dag_run(dag_run, session=session) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 62, in wrapper return func(*args, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/jobs/backfill_job.py", line 359, in _task_instances_for_dag_run dag_run.refresh_from_db() File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/app/airflow_venv/lib/python3.7/site-packages/airflow/models/dagrun.py", line 178, in refresh_from_db DR.run_id == self.run_id, File "/home/app/airflow_venv/lib/python3.7/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() ```

https://github.com/apache/airflow/issues/14249

https://github.com/apache/airflow/pull/16809

2b7c59619b7dd6fd5031745ade7756466456f803

faffaec73385db3c6910d31ccea9fc4f9f3f9d42

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py", "docs/apache-airflow/howto/set-up-database.rst"]

Python 3.8 - Sqlite3 version error

python 3.8 centos 7 Docker image Can't update sqlite3. Tried Airflow 2.0.1 and 2.0.0. Same issue on Python 3.6 with Airflow 2.0.0. I was able to force the install on 2.0.0 but when running a task it failed because of the sqlite3 version mismatch. Am I just stupid? #13496 #``` (app-root) airflow db init Traceback (most recent call last): File "/opt/app-root/bin/airflow", line 5, in <module> from airflow.__main__ import main File "/opt/app-root/lib64/python3.8/site-packages/airflow/__init__.py", line 34, in <module> from airflow import settings File "/opt/app-root/lib64/python3.8/site-packages/airflow/settings.py", line 37, in <module> from airflow.configuration import AIRFLOW_HOME, WEBSERVER_CONFIG, conf # NOQA F401 File "/opt/app-root/lib64/python3.8/site-packages/airflow/configuration.py", line 1007, in <module> conf.validate() File "/opt/app-root/lib64/python3.8/site-packages/airflow/configuration.py", line 209, in validate self._validate_config_dependencies() File "/opt/app-root/lib64/python3.8/site-packages/airflow/configuration.py", line 246, in _validate_config_dependencies raise AirflowConfigException(f"error: cannot use sqlite version < {min_sqlite_version}") airflow.exceptions.AirflowConfigException: error: cannot use sqlite version < 3.15.0 (app-root) python -c "import sqlite3; print(sqlite3.sqlite_version)" 3.7.17 (app-root) python --version Python 3.8.6 (app-root) pip install --upgrade sqlite3 ERROR: Could not find a version that satisfies the requirement sqlite3 (from versions: none) ERROR: No matching distribution found for sqlite3 ```

https://github.com/apache/airflow/issues/14208

https://github.com/apache/airflow/pull/14209

59c94c679e996ab7a75b4feeb1755353f60d030f

4c90712f192dd552d1791712a49bcdc810ebe82f

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler in helm chart cannot access DAG with git sync

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **What happened**: When dags `git-sync` is `true` and `persistent` is `false`, `airflow dags list` returns nothing and the `DAGS Folder` is empty <!-- (please include exact error messages if you can) --> **What you expected to happen**: Scheduler container should still have a volumeMount to read the volume `dags` populated by the `git-sync` container <!-- What do you think went wrong? --> **How to reproduce it**: ``` --set dags.persistence.enabled=false \ --set dags.gitSync.enabled=true \ ``` Scheduler cannot access git-sync DAG as Scheduler's configured `DAGS Folder` path isn't mounted on the volume `dags` <!--- ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14202

https://github.com/apache/airflow/pull/14203

8f21fb1bf77fc67e37dc13613778ff1e6fa87cea

e164080479775aca53146331abf6f615d1f03ff0

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/best-practices.rst", "docs/apache-airflow/security/index.rst", "docs/apache-airflow/security/secrets/secrets-backend/index.rst"]

Update Best practises doc

Update https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html#variables to use Secret Backend (especially Environment Variables) as it asks user not to use Variable in top level

https://github.com/apache/airflow/issues/14200

https://github.com/apache/airflow/pull/17319

bcf719bfb49ca20eea66a2527307968ff290c929

2c1880a90712aa79dd7c16c78a93b343cd312268

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler dies if executor_config isnt passed a dict when using K8s executor

**Apache Airflow version**: 2.0.1 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): 1.15 **Environment**: - **Cloud provider or hardware configuration**: k8s on bare metal - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: pip3 - **Others**: **What happened**: Scheduler dies with ``` [2021-02-10 21:09:27,469] {scheduler_job.py:1298} ERROR - Exception when executing SchedulerJob._run_schedu ler_loop Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1280, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1384, in _run_scheduler _loop self.executor.heartbeat() File "/usr/local/lib/python3.8/site-packages/airflow/executors/base_executor.py", line 158, in heartbeat self.trigger_tasks(open_slots) File "/usr/local/lib/python3.8/site-packages/airflow/executors/base_executor.py", line 188, in trigger_ta sks self.execute_async(key=key, command=command, queue=None, executor_config=ti.executor_config) File "/usr/local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 493, in exec ute_async kube_executor_config = PodGenerator.from_obj(executor_config) File "/usr/local/lib/python3.8/site-packages/airflow/kubernetes/pod_generator.py", line 175, in from_obj k8s_legacy_object = obj.get("KubernetesExecutor", None) AttributeError: 'V1Pod' object has no attribute 'get' [2021-02-10 21:09:28,475] {process_utils.py:100} INFO - Sending Signals.SIGTERM to GPID 60 [2021-02-10 21:09:29,222] {process_utils.py:66} INFO - Process psutil.Process(pid=66, status='terminated', started='21:09:27') (66) terminated with exit code None [2021-02-10 21:09:29,697] {process_utils.py:206} INFO - Waiting up to 5 seconds for processes to exit... [2021-02-10 21:09:29,716] {process_utils.py:66} INFO - Process psutil.Process(pid=75, status='terminated', started='21:09:28') (75) terminated with exit code None [2021-02-10 21:09:29,717] {process_utils.py:66} INFO - Process psutil.Process(pid=60, status='terminated', exitcode=0, started='21:09:27') (60) terminated with exit code 0 [2021-02-10 21:09:29,717] {scheduler_job.py:1301} INFO - Exited execute loop ``` **What you expected to happen**: DAG loading fails, producing an error for just that DAG, instead of crashing the scheduler. **How to reproduce it**: Create a task like ``` test = DummyOperator(task_id="new-pod-spec", executor_config=k8s.V1Pod( spec=k8s.V1PodSpec( containers=[ k8s.V1Container( name="base", image="myimage", image_pull_policy="Always" ) ] ))) ``` or ``` test = DummyOperator(task_id="new-pod-spec", executor_config={"KubernetesExecutor": k8s.V1Pod( spec=k8s.V1PodSpec( containers=[ k8s.V1Container( name="base", image="myimage", image_pull_policy="Always" ) ] ))}) ``` essentially anything where it expects a dict but gets something else, and run the scheduler using the kubernetes executor

https://github.com/apache/airflow/issues/14182

https://github.com/apache/airflow/pull/14323

68ccda38a7877fdd0c3b207824c11c9cd733f0c6

e0ee91e15f8385e34e3d7dfc8a6365e350ea7083

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/configmaps/configmap.yaml", "chart/templates/configmaps/webserver-configmap.yaml", "chart/templates/webserver/webserver-deployment.yaml", "chart/tests/test_webserver_deployment.py"]

Split out Airflow Configmap (webserver_config.py)

**Description** Although the changes to FAB that include the [syncing of roles on login](https://github.com/dpgaspar/Flask-AppBuilder/commit/dbe1eded6369c199b777836eb08d829ba37634d7) hasn't been officially released, I'm proposing that we make some changes to the [airflow configmap](https://github.com/apache/airflow/blob/master/chart/templates/configmaps/configmap.yaml) in preparation for it. Currently, this configmap contains the `airflow.cfg`, `webserver_config.py`, `airflow_local_settings.py`, `known_hosts`, `pod_template_file.yaml`, and the `krb5.conf`. With all of these tied together, changes to any of the contents across the listed files will force a restart for the flower deployment, scheduler deployment, worker deployment, and the webserver deployment through the setting of the `checksum/airflow-config` in each. The reason I would like to split out at _least_ the `webserver_config.py` from the greater configmap is that I would like to have the opportunity to make incremental changes to the [AUTH_ROLES_MAPPING](https://github.com/dpgaspar/Flask-AppBuilder/blob/dbe1eded6369c199b777836eb08d829ba37634d7/docs/config.rst#configuration-keys) in that config without having to force restarts for all of the previously listed services apart from the webserver. Currently, if I were to add an additional group mapping that has no bearing on the workers/schedulers/flower these services would incur some down time despite not even mounting in this specific file to their pods.

https://github.com/apache/airflow/issues/14178

https://github.com/apache/airflow/pull/14353

a48bedf26d0f04901555187aed83296190604813

0891a8ea73813d878c0d00fbfdb59fa360e8d1cf

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/celery_executor.py", "tests/executors/test_celery_executor.py"]

TypeError: object of type 'map' has no len(): When celery executor multi-processes to get Task Instances

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.1 **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): "18.04.1 LTS (Bionic Beaver)" - **Kernel** (e.g. `uname -a`): 4.15.0-130-generic #134-Ubuntu - **Install tools**: - **Others**: **What happened**: I observe the following exception, in the scheduler intermittently: ``` [2021-02-10 03:51:26,651] {scheduler_job.py:1298} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1280, in _execute self._run_scheduler_loop() File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1354, in _run_scheduler_loop self.adopt_or_reset_orphaned_tasks() File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/utils/session.py", line 65, in wrapper return func(*args, session=session, **kwargs) File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1837, in adopt_or_reset_orphaned_tasks for attempt in run_with_db_retries(logger=self.log): File "/home/foo/bar/.env38/lib/python3.8/site-packages/tenacity/__init__.py", line 390, in __iter__ do = self.iter(retry_state=retry_state) File "/home/foo/bar/.env38/lib/python3.8/site-packages/tenacity/__init__.py", line 356, in iter return fut.result() File "/home/foo/py_src/lib/python3.8/concurrent/futures/_base.py", line 432, in result return self.__get_result() File "/home/foo/py_src/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result raise self._exception File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 1882, in adopt_or_reset_orphaned_tasks to_reset = self.executor.try_adopt_task_instances(tis_to_reset_or_adopt) File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 478, in try_adopt_task_instances states_by_celery_task_id = self.bulk_state_fetcher.get_many( File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 554, in get_many result = self._get_many_using_multiprocessing(async_results) File "/home/foo/bar/.env38/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 595, in _get_many_using_multiprocessing num_process = min(len(async_results), self._sync_parallelism) TypeError: object of type 'map' has no len() ``` **What you expected to happen**: I think the `len` should not be called on the `async_results`, or `map` should not be used in `try_adopt_task_instances`. **How to reproduce it**: Not sure how I can reproduce it. But, here are the offending lines: https://github.com/apache/airflow/blob/90ab60bba877c65cb93871b97db13a179820d28b/airflow/executors/celery_executor.py#L479 Then, this branch gets hit: https://github.com/apache/airflow/blob/90ab60bba877c65cb93871b97db13a179820d28b/airflow/executors/celery_executor.py#L554 The, we see the failure, here: https://github.com/apache/airflow/blob/90ab60bba877c65cb93871b97db13a179820d28b/airflow/executors/celery_executor.py#L595

https://github.com/apache/airflow/issues/14163

https://github.com/apache/airflow/pull/14883

aebacd74058d01cfecaf913c04c0dbc50bb188ea

4ee442970873ba59ee1d1de3ac78ef8e33666e0f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/lineage/__init__.py", "airflow/lineage/backend.py", "docs/apache-airflow/lineage.rst", "tests/lineage/test_lineage.py"]

Lineage Backend removed for no reason

<!-- 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** The possibility to add a lineage backend was removed in https://github.com/apache/airflow/pull/6564 but was never reintroduced. Now that this code is in 2.0, the lineage information is only in the xcoms and the only way to get it is through an experimental API that isn't very practical either. **Use case / motivation** A custom callback at the time lineage gets pushed is enough to send the lineage information to whatever lineage backend the user has. **Are you willing to submit a PR?** I'd be willing to make a PR recovering the LineageBackend and add changes if needed, unless there is a different plan for lineage from the maintainers.

https://github.com/apache/airflow/issues/14106

https://github.com/apache/airflow/pull/14146

9ac1d0a3963b0e152cb2ba4a58b14cf6b61a73a0

af2d11e36ed43b0103a54780640493b8ae46d70e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg"]

BACKEND: Unbound Variable issue in docker entrypoint

This is NOT a bug in Airflow, I'm writing this issue for documentation should someone come across this same issue and need to identify how to solve it. Please tag as appropriate. **Apache Airflow version**: Docker 2.0.1rc2 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): N/A **Environment**: Dockered - **Cloud provider or hardware configuration**: VMWare VM - **OS** (e.g. from /etc/os-release): Ubuntu 18.04.5 LTS - **Kernel** (e.g. `uname -a`): 4.15.0-128-generic - **Install tools**: Just docker/docker-compose - **Others**: **What happened**: Worker, webserver and scheduler docker containers do not start, errors: <details><summary>/entrypoint: line 71: BACKEND: unbound variable</summary> af_worker | /entrypoint: line 71: BACKEND: unbound variable af_worker | /entrypoint: line 71: BACKEND: unbound variable af_worker exited with code 1 af_webserver | /entrypoint: line 71: BACKEND: unbound variable af_webserver | /entrypoint: line 71: BACKEND: unbound variable af_webserver | /entrypoint: line 71: BACKEND: unbound variable af_webserver | /entrypoint: line 71: BACKEND: unbound variable </details> **What you expected to happen**: Docker containers to start **How to reproduce it**: What ever docker-compose file I was copying, has a MySQL Connection String not compatible with: https://github.com/apache/airflow/blob/bc026cf6961626dd01edfaf064562bfb1f2baf42/scripts/in_container/prod/entrypoint_prod.sh#L58 -- Specifically, the connection string in the docker-compose did not have a password, and no : separator for a blank password. Original Connection String: `mysql://root@mysql/airflow?charset=utf8mb4` **Anything else we need to know**: The solution is to use a password, or at the very least add the : to the user:password section Fixed Connection String: `mysql://root:@mysql/airflow?charset=utf8mb4`

https://github.com/apache/airflow/issues/14104

https://github.com/apache/airflow/pull/14124

d77f79d134e0d14443f75325b24dffed4b779920

b151b5eea5057f167bf3d2f13a84ab4eb8e42734

python

closed

apache/airflow

https://github.com/apache/airflow

["UPDATING.md", "airflow/contrib/sensors/gcs_sensor.py", "airflow/providers/google/BACKPORT_PROVIDER_README.md", "airflow/providers/google/cloud/sensors/gcs.py", "tests/always/test_project_structure.py", "tests/deprecated_classes.py", "tests/providers/google/cloud/sensors/test_gcs.py"]

Typo in Sensor: GCSObjectsWtihPrefixExistenceSensor (should be GCSObjectsWithPrefixExistenceSensor)

Typo in Google Cloud Storage sensor: airflow/providers/google/cloud/sensors/gcs/GCSObjectsWithPrefixExistenceSensor The word _With_ is spelled incorrectly. It should be: GCSObjects**With**PrefixExistenceSensor **Apache Airflow version**: 2.0.0 **Environment**: - **Cloud provider or hardware configuration**: Google Cloud - **OS** (e.g. from /etc/os-release): Mac OS BigSur

https://github.com/apache/airflow/issues/14097

https://github.com/apache/airflow/pull/14179

6dc6339635f41a9fa50a987c4fdae5af0bae9fdc

e3bcaa3ba351234effe52ad380345c4e39003fcb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/s3.py", "airflow/providers/amazon/aws/log/s3_task_handler.py", "tests/providers/amazon/aws/hooks/test_s3.py", "tests/providers/amazon/aws/log/test_s3_task_handler.py"]

S3 Remote Logging Kubernetes Executor worker task keeps waiting to send log: "acquiring 0"

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.16.15 **Environment**: - **Cloud provider or hardware configuration**: AWS - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: Airflow Helm Chart - **Others**: **What happened**: <!-- (please include exact error messages if you can) --> A running task in a worker created from the Kubernetes Executor is constantly running with no progress being made. I checked the log and I see that it is "stuck" with a `[2021-02-05 01:07:17,316] {utils.py:580} DEBUG - Acquiring 0` I see it is able to talk to S3, in particular it does a HEAD request to see if the key exists in S3, and I get a 404, which means the object does not exist in S3. And then, the logs just stop and seems to be waiting. No more logs show up about what is going on. I am using an access point for the s3 remote base log folder, and that works in Airflow 1.10.14. Running the following, a simple dag that should just prints a statement: ``` airflow@testdag2dbdbscouter-b7f961ff64d6490e80c5cfa2fd33a37c:/opt/airflow$ airflow tasks run test_dag-2 dbdb-scouter now --local --pool default_pool --subdir /usr/airflow/dags/monitoring_and_alerts/test_dag2.py [2021-02-05 02:04:22,962] {settings.py:208} DEBUG - Setting up DB connection pool (PID 185) [2021-02-05 02:04:22,963] {settings.py:279} DEBUG - settings.prepare_engine_args(): Using pool settings. pool_size=5, max_overflow=10, pool_recycle=1800, pid=185 [2021-02-05 02:04:23,164] {cli_action_loggers.py:40} DEBUG - Adding <function default_action_log at 0x7f0984c30290> to pre execution callback [2021-02-05 02:04:30,379] {cli_action_loggers.py:66} DEBUG - Calling callbacks: [<function default_action_log at 0x7f0984c30290>] [2021-02-05 02:04:30,499] {settings.py:208} DEBUG - Setting up DB connection pool (PID 185) [2021-02-05 02:04:30,499] {settings.py:241} DEBUG - settings.prepare_engine_args(): Using NullPool [2021-02-05 02:04:30,500] {dagbag.py:440} INFO - Filling up the DagBag from /usr/airflow/dags/monitoring_and_alerts/test_dag2.py [2021-02-05 02:04:30,500] {dagbag.py:279} DEBUG - Importing /usr/airflow/dags/monitoring_and_alerts/test_dag2.py [2021-02-05 02:04:30,511] {dagbag.py:405} DEBUG - Loaded DAG <DAG: test_dag-2> [2021-02-05 02:04:30,567] {plugins_manager.py:270} DEBUG - Loading plugins [2021-02-05 02:04:30,567] {plugins_manager.py:207} DEBUG - Loading plugins from directory: /opt/airflow/plugins [2021-02-05 02:04:30,567] {plugins_manager.py:184} DEBUG - Loading plugins from entrypoints [2021-02-05 02:04:30,671] {plugins_manager.py:414} DEBUG - Integrate DAG plugins Running <TaskInstance: test_dag-2.dbdb-scouter 2021-02-05T02:04:23.265117+00:00 [None]> on host testdag2dbdbscouter-b7f961ff64d6490e80c5cfa2fd33a37c ``` If I check the logs directory, and open the log, I see that the log ``` [2021-02-05 01:07:17,314] {retryhandler.py:187} DEBUG - No retry needed. [2021-02-05 01:07:17,314] {hooks.py:210} DEBUG - Event needs-retry.s3.HeadObject: calling handler <bound method S3RegionRedirector.redirect_from_error of <botocore.utils.S3RegionRedirector object at 0x7f3e27182b80>> [2021-02-05 01:07:17,314] {utils.py:1186} DEBUG - S3 request was previously to an accesspoint, not redirecting. [2021-02-05 01:07:17,316] {utils.py:580} DEBUG - Acquiring 0 ``` If I do a manual keyboard interrupt to terminate the running task, I see the following: ``` [2021-02-05 02:11:30,103] {hooks.py:210} DEBUG - Event needs-retry.s3.HeadObject: calling handler <botocore.retryhandler.RetryHandler object at 0x7f097f048110> [2021-02-05 02:11:30,103] {retryhandler.py:187} DEBUG - No retry needed. [2021-02-05 02:11:30,103] {hooks.py:210} DEBUG - Event needs-retry.s3.HeadObject: calling handler <bound method S3RegionRedirector.redirect_from_error of <botocore.utils.S3RegionRedirector object at 0x7f097f0293d0>> [2021-02-05 02:11:30,103] {utils.py:1187} DEBUG - S3 request was previously to an accesspoint, not redirecting. [2021-02-05 02:11:30,105] {utils.py:580} DEBUG - Acquiring 0 [2021-02-05 02:11:30,105] {futures.py:277} DEBUG - TransferCoordinator(transfer_id=0) cancel(cannot schedule new futures after interpreter shutdown) called [2021-02-05 02:11:30,105] {s3_task_handler.py:193} ERROR - Could not write logs to s3://arn:aws:s3:us-west-2:<ACCOUNT>:accesspoint:<BUCKET,PATH>/2021-02-05T02:04:23.265117+00:00/1.log Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/log/s3_task_handler.py", line 190, in s3_write encrypt=conf.getboolean('logging', 'ENCRYPT_S3_LOGS'), File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/s3.py", line 61, in wrapper return func(*bound_args.args, **bound_args.kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/s3.py", line 90, in wrapper return func(*bound_args.args, **bound_args.kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/s3.py", line 547, in load_string self._upload_file_obj(file_obj, key, bucket_name, replace, encrypt, acl_policy) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/s3.py", line 638, in _upload_file_obj client.upload_fileobj(file_obj, bucket_name, key, ExtraArgs=extra_args) File "/home/airflow/.local/lib/python3.7/site-packages/boto3/s3/inject.py", line 538, in upload_fileobj extra_args=ExtraArgs, subscribers=subscribers) File "/home/airflow/.local/lib/python3.7/site-packages/s3transfer/manager.py", line 313, in upload call_args, UploadSubmissionTask, extra_main_kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/s3transfer/manager.py", line 471, in _submit_transfer main_kwargs=main_kwargs File "/home/airflow/.local/lib/python3.7/site-packages/s3transfer/futures.py", line 467, in submit future = ExecutorFuture(self._executor.submit(task)) File "/usr/local/lib/python3.7/concurrent/futures/thread.py", line 165, in submit raise RuntimeError('cannot schedule new futures after ' RuntimeError: cannot schedule new futures after interpreter shutdown ``` My Airflow config: ``` [logging] base_log_folder = /opt/airflow/logs remote_logging = True remote_log_conn_id = S3Conn google_key_path = remote_base_log_folder = s3://arn:aws:s3:us-west-2:<ACCOUNT>:accesspoint:<BUCKET>/logs encrypt_s3_logs = False logging_level = DEBUG fab_logging_level = WARN ``` **What you expected to happen**: <!-- What do you think went wrong? --> I expected for the log to be sent to S3, but the task just waits at this point. **How to reproduce it**: <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> Extended the docker image, and baked in the test dag: ``` FROM apache/airflow:2.0.0-python3.7 COPY requirements.txt /requirements.txt RUN pip install --user -r /requirements.txt ENV AIRFLOW_DAG_FOLDER="/usr/airflow" COPY --chown=airflow:root ./airflow ${AIRFLOW_DAG_FOLDER} ``` **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14089

https://github.com/apache/airflow/pull/14414

3dc762c8177264001793e20543c24c6414c14960

0d6cae4172ff185ec4c0fc483bf556ce3252b7b0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/marketing_platform/hooks/display_video.py", "airflow/providers/google/marketing_platform/operators/display_video.py", "tests/providers/google/marketing_platform/hooks/test_display_video.py", "tests/providers/google/marketing_platform/operators/test_display_video.py"]

GoogleDisplayVideo360Hook.download_media does not pass the resourceName correctly

**Apache Airflow version**: 1.10.12 **Environment**: Google Cloud Composer 1.13.3 - **Cloud provider or hardware configuration**: - Google Cloud Composer **What happened**: The GoogleDisplayVideo360Hook.download_media hook tries to download media using the "resource_name" argument. However, [per the API spec](https://developers.google.com/display-video/api/reference/rest/v1/media/download) it should pass "resourceName" Thus, it breaks every time and can never download media. Error: `ERROR - Got an unexpected keyword argument "resource_name"` **What you expected to happen**: The hook should pass in the correct resourceName and then download the media file. **How to reproduce it**: Run any workflow that tries to download any DV360 media. **Anything else we need to know**: I have written a patch that fixes the issue and will submit it shortly.

https://github.com/apache/airflow/issues/14077

https://github.com/apache/airflow/pull/20528

af4a2b0240fbf79a0a6774a9662243050e8fea9c

a6e60ce25d9f3d621a7b4089834ca5e50cd123db

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/jenkins/operators/jenkins_job_trigger.py", "tests/providers/jenkins/operators/test_jenkins_job_trigger.py"]

Add support for UNSTABLE Jenkins status

**Description** Don't mark dag as `failed` when `UNSTABLE` status received from Jenkins. It can be done by adding `allow_unstable: bool` or `success_status_values: list` parameter to `JenkinsJobTriggerOperator.__init__`. For now `SUCCESS` status is hardcoded, any other lead to fail. **Use case / motivation** I want to restart a job (`retries` parameter) only if I get `FAILED` status. `UNSTABLE` is okay for me and it's no need to restart. **Are you willing to submit a PR?** Yes **Related Issues** No

https://github.com/apache/airflow/issues/14071

https://github.com/apache/airflow/pull/14131

f180fa13bf2a0ffa31b30bb21468510fe8a20131

78adaed5e62fa604d2ef2234ad560eb1c6530976

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/samba/hooks/samba.py", "docs/apache-airflow-providers-samba/index.rst", "setup.py", "tests/providers/samba/hooks/test_samba.py"]

SambaHook using old unmaintained library

**Description** The [SambaHook](https://github.com/apache/airflow/blob/master/airflow/providers/samba/hooks/samba.py#L26) currently using [pysmbclient](https://github.com/apache/airflow/blob/master/setup.py#L408) this library hasn't been updated since 2017 https://pypi.org/project/PySmbClient/ I think worth moving to https://pypi.org/project/smbprotocol/ which newer and maintained.

https://github.com/apache/airflow/issues/14054

https://github.com/apache/airflow/pull/17273

6cc252635db6af6b0b4e624104972f0567f21f2d

f53dace36c707330e01c99204e62377750a5fb1f

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/build_docs.py", "docs/exts/docs_build/spelling_checks.py", "docs/spelling_wordlist.txt"]

Docs Builder creates SpellingError for Sphinx error unrelated to spelling issues

<!-- 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. --> <!-- IMPORTANT!!! PLEASE CHECK "SIMILAR TO X EXISTING ISSUES" OPTION IF VISIBLE NEXT TO "SUBMIT NEW ISSUE" BUTTON!!! PLEASE CHECK IF THIS ISSUE HAS BEEN REPORTED PREVIOUSLY USING SEARCH!!! Please complete the next sections or the issue will be closed. These questions are the first thing we need to know to understand the context. --> **Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **Environment**: - **Cloud provider or hardware configuration**: n/a - **OS** (e.g. from /etc/os-release): n/a - **Kernel** (e.g. `uname -a`): n/a - **Install tools**: n/a - **Others**: n/a **What happened**: A sphinx warning unrelated to spelling issues running `sphinx-build` resulted in an instance of `SpellingError` to cause a docs build failure. ``` SpellingError( file_path=None, line_no=None, spelling=None, suggestion=None, context_line=None, message=( f"Sphinx spellcheck returned non-zero exit status: {completed_proc.returncode}." ) ) # sphinx.errors.SphinxWarning: /opt/airflow/docs/apache-airflow-providers-google/_api/drive/index.rst:document isn't included in any toctree ``` The actual issue was that I failed to include an `__init__.py` file in a directory that I created. <!-- (please include exact error messages if you can) --> **What you expected to happen**: I think an exception should be raised unrelated to a spelling error. Preferably one that would indicate that there's a directory that's missing an init file, but at least a generic error unrelated to spelling <!-- What do you think went wrong? --> **How to reproduce it**: Create a new plugin directory (e.g. `airflow/providers/google/suite/sensors`) and don't include an `__init__.py` file, and run `./breeze build-docs -- --docs-only -v` <!--- As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags. If you are using kubernetes, please attempt to recreate the issue using minikube or kind. ## Install minikube/kind - Minikube https://minikube.sigs.k8s.io/docs/start/ - Kind https://kind.sigs.k8s.io/docs/user/quick-start/ 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 using the .md style of  To record a screencast, mac users can use QuickTime and then create an unlisted youtube video with the resulting .mov file. ---> **Anything else we need to know**: I'm specifically referring to lines 139 to 150 in `docs/exts/docs_build/docs_builder.py` <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14051

https://github.com/apache/airflow/pull/14196

e31b27d593f7379f38ced34b6e4ce8947b91fcb8

cb4a60e9d059eeeae02909bb56a348272a55c233

python

closed

apache/airflow

https://github.com/apache/airflow

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

SLA mechanism does not work

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: I have the following DAG: ```py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash_operator import BashOperator with DAG( dag_id="sla_trigger", schedule_interval="* * * * *", start_date=datetime(2020, 2, 3), ) as dag: BashOperator( task_id="bash_task", bash_command="sleep 30", sla=timedelta(seconds=2), ) ``` And in my understanding this dag should result in SLA miss every time it is triggered (every minute). However, after few minutes of running I don't see any SLA misses... **What you expected to happen**: I expect to see SLA if task takes longer than expected. **How to reproduce it**: Use the dag from above. **Anything else we need to know**: N/A

https://github.com/apache/airflow/issues/14050

https://github.com/apache/airflow/pull/14056

914e9ce042bf29dc50d410f271108b1e42da0add

604a37eee50715db345c5a7afed085c9afe8530d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Day change flag is in wrong place

**Apache Airflow version**: 2.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: In tree view, the "day marker" is shifted and one last dagrun of previous day is included in new day. See: <img width="398" alt="Screenshot 2021-02-03 at 14 14 55" src="https://user-images.githubusercontent.com/9528307/106752180-7014c100-662a-11eb-9342-661a237ed66c.png"> The tooltip is on 4th dagrun, but the day flag in the same line as the 3rd one. **What you expected to happen**: I expect the to see the day flag between two days not earlier. **How to reproduce it**: Create a DAG with `schedule_interval="5 8-23/1 * * *"` **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14046

https://github.com/apache/airflow/pull/14141

0f384f0644c8cfe55ca4c75d08b707be699b440f

6dc6339635f41a9fa50a987c4fdae5af0bae9fdc

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/stable-rest-api-ref.rst"]

Inexistant reference in docs/apache-airflow/stable-rest-api-ref.rst

**Apache Airflow version**: 2.1.0.dev0 (master branch) **What happened**: in file `docs/apache-airflow/stable-rest-api-ref.rst` there is a reference to a file that does not longer exist: `/docs/exts/sphinx_redoc.py`. The whole text: ``` It's a stub file. It will be converted automatically during the build process to the valid documentation by the Sphinx plugin. See: /docs/exts/sphinx_redoc.py ``` **What you expected to happen**: A reference to `docs/conf.py`, where I think is where the contents are now replaced during the build process. **How to reproduce it**: Go to the file in question. **Anything else we need to know**: I would've made a PR but I'm not 100% sure this is wrong or I just do not find the file referenced.

https://github.com/apache/airflow/issues/14045

https://github.com/apache/airflow/pull/14079

2bc9b9ce2b9fdca2d29565fc833ddc3a543daaa7

e8c7dc3f7a81fb3a7179e154920b2350f4e992c6

python

closed

apache/airflow

https://github.com/apache/airflow

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

Order of items not preserved in Task instance view

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): **Environment**: - **Cloud provider or hardware configuration**: - **OS** (e.g. from /etc/os-release): - **Kernel** (e.g. `uname -a`): - **Install tools**: - **Others**: **What happened**: The order of items is not preserved in Task Instance information: <img width="542" alt="Screenshot 2021-02-01 at 16 49 09" src="https://user-images.githubusercontent.com/9528307/106482104-6a45a100-64ad-11eb-8d2f-e478c267bce9.png"> <img width="542" alt="Screenshot 2021-02-01 at 16 49 43" src="https://user-images.githubusercontent.com/9528307/106482167-7df10780-64ad-11eb-9434-ba3e54d56dec.png"> **What you expected to happen**: I expect that the order will be always the same. Otherwise the UX is bad. **How to reproduce it**: Seems to happen randomly. But once seen the order is then consistent for given TI. **Anything else we need to know**: <!-- How often does this problem occur? Once? Every time etc? Any relevant logs to include? Put them here in side a detail tag: <details><summary>x.log</summary> lots of stuff </details> -->

https://github.com/apache/airflow/issues/14010

https://github.com/apache/airflow/pull/14036

68758b826076e93fadecf599108a4d304dd87ac7

fc67521f31a0c9a74dadda8d5f0ac32c07be218d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/telegram/operators/telegram.py", "tests/providers/telegram/operators/test_telegram.py"]

AttributeError: 'TelegramOperator' object has no attribute 'text'

Hi there 👋 I was playing with the **TelegramOperator** and stumbled upon a bug with the `text` field. It is supposed to be a template field but in reality the instance of the **TelegramOperator** does not have this attribute thus every time I try to execute code I get the error: > AttributeError: 'TelegramOperator' object has no attribute 'text' ```python TelegramOperator( task_id='send_message_telegram', telegram_conn_id='telegram_conn_id', text='Hello from Airflow!' ) ```

https://github.com/apache/airflow/issues/13989

https://github.com/apache/airflow/pull/13990

9034f277ef935df98b63963c824ba71e0dcd92c7

106d2c85ec4a240605830bf41962c0197b003135

python

closed

apache/airflow

https://github.com/apache/airflow

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

List and Dict template fields are rendered as JSON.

**Apache Airflow version**: 2.0.0 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): n/a **Environment**: Linux - **Cloud provider or hardware configuration**: amd64 - **OS** (e.g. from /etc/os-release): Centos 7 - **Kernel** (e.g. `uname -a`): - **Install tools**: pip - **Others**: **What happened**: The field `sql` is rendered as a serialized json `["select 1 from dual", "select 2 from dual"]` instead of a list of syntax-highlighted SQL statements.  **What you expected to happen**: `lists` and `dicts` should be rendered as lists and dicts rather than serialized json unless the `template_field_renderer` is `json`  **How to reproduce it**: ``` from airflow import DAG from airflow.providers.oracle.operators.oracle import OracleOperator with DAG("demo", default_args={owner='airflow'}, start_date= pendulum.yesterday(), schedule_interval='@daily',) as dag: OracleOperator(task_id='single', sql='select 1 from dual') OracleOperator(task_id='list', sql=['select 1 from dual', 'select 2 from dual']) ``` **Anything else we need to know**: Introduced by #11061, . A quick and dirty work-around: Edit file [airflow/www/views.py](https://github.com/PolideaInternal/airflow/blob/13ba1ec5494848d4a54b3291bd8db5841bfad72e/airflow/www/views.py#L673) ``` if renderer in renderers: - if isinstance(content, (dict, list)): + if isinstance(content, (dict, list)) and renderer is renderers['json']: content = json.dumps(content, sort_keys=True, indent=4) html_dict[template_field] = renderers[renderer](content) ```

https://github.com/apache/airflow/issues/13988

https://github.com/apache/airflow/pull/14024

84ef24cae657babe3882d7ad6eecc9be9967e08f

e2a06a32c87d99127d098243b311bd6347ff98e9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Can't save any connection if provider-provided connection form widgets have fields marked as InputRequired

**Apache Airflow version**: 2.0.0 with the following patch: https://github.com/apache/airflow/pull/13640 **Kubernetes version (if you are using kubernetes)** (use `kubectl version`): N/A **Environment**: - **Cloud provider or hardware configuration**: AMD Ryzen 3900X (12C/24T), 64GB RAM - **OS** (e.g. from /etc/os-release): Ubuntu 20.04.1 LTS - **Kernel** (e.g. `uname -a`): 5.9.8-050908-generic - **Install tools**: N/A - **Others**: N/A **What happened**: If there are custom hooks that implement the `get_connection_form_widgets` method that return fields using the `InputRequired` validator, saving breaks for all types of connections on the "Edit Connections" page. In Chrome, the following message is logged to the browser console: ``` An invalid form control with name='extra__hook_name__field_name' is not focusable. ``` This happens because the field is marked as `<input required>` but is hidden using CSS when the connection type exposed by the custom hook is not selected. **What you expected to happen**: Should be able to save other types of connections. In particular, either one of the following should happen: 1. The fields not belonging to the currently selected connection type should not just be hidden using CSS, but should be removed from the DOM entirely. 2. Remove the `required` attribute if the form field is hidden. **How to reproduce it**: Create a provider, and add a hook with something like: ```python @staticmethod def get_connection_form_widgets() -> Dict[str, Any]: """Returns connection widgets to add to connection form.""" return { 'extra__my_hook__client_id': StringField( lazy_gettext('OAuth2 Client ID'), widget=BS3TextFieldWidget(), validators=[wtforms.validators.InputRequired()], ), } ``` Go to the Airflow Web UI, click the "Add" button in the connection list page, then choose a connection type that's not the type exposed by the custom hook. Fill in details and click "Save". **Anything else we need to know**: N/A

https://github.com/apache/airflow/issues/13985

https://github.com/apache/airflow/pull/14052

f9c9e9c38f444a39987478f3d1a262db909de8c4

98bbe5aec578a012c1544667bf727688da1dadd4

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/in_container/_in_container_utils.sh"]

Improve error messages and propagation in CI builds

Airflow version: dev The error information in `Backport packages: wheel` is not that easy to find. Here is the end of the step that failed and end of its log: <img width="1151" alt="Screenshot 2021-01-27 at 12 02 01" src="https://user-images.githubusercontent.com/9528307/105982515-aa64e800-6097-11eb-91c8-9911448d1301.png"> but in fact the error happen some 500 lines earlier: <img width="1151" alt="Screenshot 2021-01-27 at 12 01 47" src="https://user-images.githubusercontent.com/9528307/105982504-a769f780-6097-11eb-8873-02c1d9b2d670.png"> **What you expect to happen?** I would expect that the error is at the end of the step. Otherwise the message `The previous step completed with error. Please take a look at output above ` is slightly miss-leading.

https://github.com/apache/airflow/issues/13924

https://github.com/apache/airflow/pull/15190

041a09f3ee6bc447c3457b108bd5431a2fd70ad9

7c17bf0d1e828b454a6b2c7245ded275b313c792

python