Datasets:

bug-localization

/

BeetleBox

like 0

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ti_deps/deps/prev_dagrun_dep.py", "tests/ti_deps/deps/test_prev_dagrun_dep.py"]

Dynamic task mapping does not always create mapped tasks

### Apache Airflow version 2.5.1 ### What happened Same problem as https://github.com/apache/airflow/issues/28296, but seems to happen nondeterministically, and still happens when ignoring `depends_on_past=True`. I've got a task that retrieves some filenames, which then creates dynamically mapped tasks to move the files, one per task. I'm using a similar task across multiple DAGs. However, task mapping fails on some DAG runs: it inconsistently happens per DAG run, and some DAGs do not seem to be affected at all. These seem to be the DAGs where no task was ever mapped, so that the mapped task instance ended up in a Skipped state. What happens is that multiple files will be found, but only a single dynamically mapped task will be created. This task never starts and has map_index of -1. It can be found under the "List instances, all runs" menu, but says "No Data found." under the "Mapped Tasks" tab.   When I press the "Run" button when the mapped task is selected, the following error appears: ``` Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped! ``` The previous task _has_ run however. No errors appeared in my Airflow logs. When I try to run the task with **Ignore All Deps** enabled, I get the error: ``` Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped! ``` This last bit is a contradiction, the task cannot be mapped and not mapped simultaneously. If the amount of mapped tasks is 0 while in this erroneous state, the mapped tasks will not be marked as skipped as expected. ### What you think should happen instead The mapped tasks should not get stuck with "no status". The mapped tasks should be created and ran successfully, or in the case of a 0-length list output of the upstream task they should be skipped. ### How to reproduce Run the below DAG, if it runs successfully clear several tasks out of order. This may not immediately reproduce the bug, but after some task clearing, for me it always ends up in the faulty state described above. ``` from airflow import DAG from airflow.decorators import task import datetime as dt from airflow.operators.python import PythonOperator import random @task def get_filenames_kwargs(): return [ {"file_name": i} for i in range(random.randint(0, 2)) ] def print_filename(file_name): print(file_name) with DAG( dag_id="dtm_test_2", start_date=dt.datetime(2023, 2, 10), default_args={ "owner": "airflow", "depends_on_past": True, }, schedule="@daily", ) as dag: get_filenames_task = get_filenames_kwargs.override(task_id="get_filenames_task")() print_filename_task = PythonOperator.partial( task_id="print_filename_task", python_callable=print_filename, ).expand(op_kwargs=get_filenames_task) ``` ### Operating System Amazon Linux v2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29531

https://github.com/apache/airflow/pull/32397

685328e3572043fba6db432edcaacf8d06cf88d0

73bc49adb17957e5bb8dee357c04534c6b41f9dd

python

closed

apache/airflow

https://github.com/apache/airflow

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

Hide non-used docs attributes from Task Instance Detail

### Description Inside a BashOperator, I added a markdown snippet of documentation for the "Task Instance Details" of my Airflow nodes. Now I can see my markdown, defined by the attribute "doc_md", but also Attribute: bash_command Attribute: doc Attribute: doc_json Attribute: doc_rst Attribute: doc_yaml I think it would look better if only the chosen type of docs would be shown in the Task Instance detail, instead of leaving the names of other attributes without anything added to them.  ### Use case/motivation I would like to see only the type of doc attribute that I chose to add to my task instance detail and hide all the others docs type. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29515

https://github.com/apache/airflow/pull/29545

655ffb835eb4c5343c3f2b4d37b352248f2768ef

f2f6099c5a2f3613dce0cc434a95a9479d748cf5

python

closed

apache/airflow

https://github.com/apache/airflow

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

KPO - deferrable - Invalid kube-config file. Expected key contexts in kube-config

### Apache Airflow Provider(s) cncf-kubernetes ### Versions of Apache Airflow Providers 5.2.0 ### Apache Airflow version 2.5.1 ### Operating System linux 5.15.0-60-generic - Ubuntu 22.04 ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened **the exact same task in sync mode ( normal ) work but in async (deferable) it fail** airflow connection - kubernetes_default -> ```json { "conn_type": "kubernetes", "extra": "{\"extra__kubernetes__in_cluster\": false, \"extra__kubernetes__kube_config_path\": \"/opt/airflow/include/.kube/config\", \"extra__kubernetes__namespace\": \"default\", \"extra__kubernetes__cluster_context\": \"kind-kind\", \"extra__kubernetes__disable_verify_ssl\": false, \"extra__kubernetes__disable_tcp_keepalive\": false}" } ``` ```python from pendulum import today from airflow import DAG from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator dag = DAG( dag_id="kubernetes_dag", schedule_interval="0 0 * * *", start_date=today("UTC").add(days=-1) ) with dag: cmd = "echo toto && sleep 10 && echo finish" KubernetesPodOperator( task_id="task-a", namespace="default", kubernetes_conn_id="kubernetes_default", name="airflow-test-pod", image="alpine:3.16.2", cmds=["sh", "-c", cmd], is_delete_operator_pod=True, deferrable=True, get_logs=True, ) KubernetesPodOperator( task_id="task-B", namespace="default", kubernetes_conn_id="kubernetes_default", name="airflow-test-pod", image="alpine:3.16.2", cmds=["sh", "-c", cmd], is_delete_operator_pod=True, get_logs=True, ) ``` ```log [2023-02-12, 09:53:24 UTC] {taskinstance.py:1768} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 611, in execute_complete raise AirflowException(event["message"]) airflow.exceptions.AirflowException: Invalid kube-config file. Expected key contexts in kube-config During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 630, in execute_complete self.post_complete_action( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 654, in post_complete_action self.cleanup( File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 673, in cleanup raise AirflowException( airflow.exceptions.AirflowException: Pod airflow-test-pod-vw8fxf25 returned a failure: ``` ``` ``` ### What you think should happen instead deferrable KPO should work same as KPO and not fail ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md) 

https://github.com/apache/airflow/issues/29488

https://github.com/apache/airflow/pull/29498

155ef09721e5af9a8be8841eb0e690edbfe36188

b5296b74361bfe2449033eca5f732c4a4377f6bb

python

closed

apache/airflow

https://github.com/apache/airflow

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

TaskFlow API `multiple_outputs` inferral causes import errors when using TYPE_CHECKING

### Apache Airflow version 2.5.1 ### What happened When using the TaskFlow API, I like to generally keep a good practice of adding type annotations in the TaskFlow functions so others reading the DAG and task code have better context around inputs/outputs, keep imports solely used for typing behind `typing.TYPE_CHECKING`, and utilize PEP 563 for forwarding annotation evaluations. Unfortunately, when using ~PEP 563 _and_ `TYPE_CHECKING`~ just TYPE_CHECKING, DAG import errors occur with a "NameError: <name> is not defined." exception. ### What you think should happen instead Users should be free to use ~PEP 563 and~ `TYPE_CHECKING` when using the TaskFlow API and not hit DAG import errors along the way. ### How to reproduce Using a straightforward use case of transforming a DataFrame, let's assume this toy example: ```py from __future__ import annotations from typing import TYPE_CHECKING, Any from pendulum import datetime from airflow.decorators import dag, task if TYPE_CHECKING: from pandas import DataFrame @dag(start_date=datetime(2023, 1, 1), schedule=None) def multiple_outputs(): @task() def transform(df: DataFrame) -> dict[str, Any]: ... transform() multiple_outputs() ``` Add this DAG to your DAGS_FOLDER and the following import error should be observed: <img width="641" alt="image" src="https://user-images.githubusercontent.com/48934154/217713685-ec29d5cc-4a48-4049-8dfa-56cbd76cddc3.png"> ### Operating System Debian GNU/Linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.2.0 apache-airflow-providers-apache-hive==5.1.1 apache-airflow-providers-apache-livy==3.2.0 apache-airflow-providers-celery==3.1.0 apache-airflow-providers-cncf-kubernetes==5.1.1 apache-airflow-providers-common-sql==1.3.3 apache-airflow-providers-databricks==4.0.0 apache-airflow-providers-dbt-cloud==2.3.1 apache-airflow-providers-elasticsearch==4.3.3 apache-airflow-providers-ftp==3.3.0 apache-airflow-providers-google==8.8.0 apache-airflow-providers-http==4.1.1 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-microsoft-azure==5.1.0 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-redis==3.1.0 apache-airflow-providers-sftp==4.2.1 apache-airflow-providers-snowflake==4.0.2 apache-airflow-providers-sqlite==3.3.1 apache-airflow-providers-ssh==3.4.0 astronomer-providers==1.14.0 ### Deployment Astronomer ### Deployment details OOTB local Airflow install with LocalExecutor built with the Astro CLI. ### Anything else - This behavior/error was not observed using Airflow 2.4.3. - As a workaround, `multiple_outputs` can be explicitly set on the TaskFlow function to skip the inferral. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29435

https://github.com/apache/airflow/pull/29445

f9e9d23457cba5d3e18b5bdb7b65ecc63735b65b

b1306065054b98a63c6d3ab17c84d42c2d52809a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/mappedoperator.py", "tests/models/test_mappedoperator.py", "tests/test_utils/mock_operators.py"]

Jinja templating doesn't work with container_resources when using dymanic task mapping with Kubernetes Pod Operator

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Google Cloud Composer Version - 2.1.5 Airflow Version - 2.4.3 We are trying to use dynamic task mapping with Kubernetes Pod Operator. Our use-case is to return the pod's CPU and memory requirements from a function which is included as a macro in DAG Without dynamic task mapping it works perfectly, but when used with the dynamic task mapping, it is unable to recognize the macro. container_resources is a templated field as per the [docs](https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/_api/airflow/providers/cncf/kubernetes/operators/kubernetes_pod/index.html#airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator), the feature was introduced in this [PR](https://github.com/apache/airflow/pull/27457). We also tried the toggling the boolean `render_template_as_native_obj`, but still no luck. Providing below a trimmed version of our DAG to help reproduce the issue. (function to return cpu and memory is trivial here just to show example) ### What you think should happen instead It should have worked similar with or without dynamic task mapping. ### How to reproduce Deployed the following DAG in Google Cloud Composer. ``` import datetime import os from airflow import models from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import ( KubernetesPodOperator, ) from kubernetes.client import models as k8s_models dvt_image = os.environ.get("DVT_IMAGE") default_dag_args = {"start_date": datetime.datetime(2022, 1, 1)} def pod_mem(): return "4000M" def pod_cpu(): return "1000m" with models.DAG( "sample_dag", schedule_interval=None, default_args=default_dag_args, render_template_as_native_obj=True, user_defined_macros={ "pod_mem": pod_mem, "pod_cpu": pod_cpu, }, ) as dag: task_1 = KubernetesPodOperator( task_id="task_1", name="task_1", namespace="default", image=dvt_image, cmds=["bash", "-cx"], arguments=["echo hello"], service_account_name="sa-k8s", container_resources=k8s_models.V1ResourceRequirements( limits={ "memory": "{{ pod_mem() }}", "cpu": "{{ pod_cpu() }}", } ), startup_timeout_seconds=1800, get_logs=True, image_pull_policy="Always", config_file="/home/airflow/composer_kube_config", dag=dag, ) task_2 = KubernetesPodOperator.partial( task_id="task_2", name="task_2", namespace="default", image=dvt_image, cmds=["bash", "-cx"], service_account_name="sa-k8s", container_resources=k8s_models.V1ResourceRequirements( limits={ "memory": "{{ pod_mem() }}", "cpu": "{{ pod_cpu() }}", } ), startup_timeout_seconds=1800, get_logs=True, image_pull_policy="Always", config_file="/home/airflow/composer_kube_config", dag=dag, ).expand(arguments=[["echo hello"]]) task_1 >> task_2 ``` task_1 (without dynamic task mapping) completes successfully, while task_2(with dynamic task mapping) fails. Looking at the error logs, it failed while rendering the Pod spec since the calls to pod_cpu() and pod_mem() are unresolved. Here is the traceback: Exception when attempting to create Namespaced Pod: { "apiVersion": "v1", "kind": "Pod", "metadata": { "annotations": {}, "labels": { "dag_id": "sample_dag", "task_id": "task_2", "run_id": "manual__2023-02-08T183926.890852Z-eee90e4ee", "kubernetes_pod_operator": "True", "map_index": "0", "try_number": "2", "airflow_version": "2.4.3-composer", "airflow_kpo_in_cluster": "False" }, "name": "task-2-46f76eb0432d42ae9a331a6fc53835b3", "namespace": "default" }, "spec": { "affinity": {}, "containers": [ { "args": [ "echo hello" ], "command": [ "bash", "-cx" ], "env": [], "envFrom": [], "image": "us.gcr.io/ams-e2e-testing/edw-dvt-tool", "imagePullPolicy": "Always", "name": "base", "ports": [], "resources": { "limits": { "memory": "{{ pod_mem() }}", "cpu": "{{ pod_cpu() }}" } }, "volumeMounts": [] } ], "hostNetwork": false, "imagePullSecrets": [], "initContainers": [], "nodeSelector": {}, "restartPolicy": "Never", "securityContext": {}, "serviceAccountName": "sa-k8s", "tolerations": [], "volumes": [] } } Traceback (most recent call last): File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/utils/pod_manager.py", line 143, in run_pod_async resp = self._client.create_namespaced_pod( File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 7356, in create_namespaced_pod return self.create_namespaced_pod_with_http_info(namespace, body, **kwargs) # noqa: E501 File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 7455, in create_namespaced_pod_with_http_info return self.api_client.call_api( File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 348, in call_api return self.__call_api(resource_path, method, File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 180, in __call_api response_data = self.request( File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 391, in request return self.rest_client.POST(url, File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/rest.py", line 275, in POST return self.request("POST", url, File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/rest.py", line 234, in request raise ApiException(http_resp=r) kubernetes.client.exceptions.ApiException: (400) Reason: Bad Request HTTP response headers: HTTPHeaderDict({'Audit-Id': '1ef20c0b-6980-4173-b9cc-9af5b4792e86', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Kubernetes-Pf-Flowschema-Uid': '1b263a21-4c75-4ef8-8147-c18780a13f0e', 'X-Kubernetes-Pf-Prioritylevel-Uid': '3cd4cda4-908c-4944-a422-5512b0fb88d6', 'Date': 'Wed, 08 Feb 2023 18:45:23 GMT', 'Content-Length': '256'}) HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"Pod in version \"v1\" cannot be handled as a Pod: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'","reason":"BadRequest","code":400} ### Operating System Google Composer Kubernetes Cluster ### Versions of Apache Airflow Providers _No response_ ### Deployment Composer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29432

https://github.com/apache/airflow/pull/29451

43443eb539058b7b4756455f76b0e883186d9250

5eefd47771a19dca838c8cce40a4bc5c555e5371

python

closed

apache/airflow

https://github.com/apache/airflow

["pyproject.toml"]

Require newer version of pypi/setuptools to remove security scan issue (CVE-2022-40897)

### Description Hi. My team is evaluating airflow, so I ran a security scan on it. It is flagging a Medium security issue with pypi/setuptools. See https://nvd.nist.gov/vuln/detail/CVE-2022-40897 for details. Is it possible to require a more recent version? Or perhaps airflow users are not vulnerable to this? ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29428

https://github.com/apache/airflow/pull/29465

9c6f83bb6f3e3b57ae0abbe9eb0582fcde265702

41dff9875bce4800495c9132b10a6c8bff900a7c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py"]

GlueJobOperator throws error after migration to newest version of Airflow

### Apache Airflow version 2.5.1 ### What happened We were using GlueJobOperator with Airflow 2.3.3 (official docker image) and it was working well, we didn't specify script file location, because it was inferred from the job name. After migration to 2.5.1 (official docker image) the operator fails if `s3_bucket` and `script_location` are not specified. That's the error I see: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/operators/glue.py", line 146, in execute glue_job_run = glue_job.initialize_job(self.script_args, self.run_job_kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 155, in initialize_job job_name = self.create_or_update_glue_job() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 300, in create_or_update_glue_job config = self.create_glue_job_config() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 97, in create_glue_job_config raise ValueError("Could not initialize glue job, error: Specify Parameter `s3_bucket`") ValueError: Could not initialize glue job, error: Specify Parameter `s3_bucket` ``` ### What you think should happen instead I was expecting that after migration the operator would work the same way. ### How to reproduce Create a dag with `GlueJobOperator` operator and do not use s3_bucket or script_location arguments ### Operating System Linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==7.1.0 ### Deployment Docker-Compose ### Deployment details `apache/airflow:2.5.1-python3.10` Docker image and official docker compose ### Anything else I believe it was commit #27893 by @romibuzi that introduced this behaviour. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29423

https://github.com/apache/airflow/pull/29659

9de301da2a44385f57be5407e80e16ee376f3d39

6c13f04365b916e938e3bea57e37fc80890b8377

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py", "tests/providers/amazon/aws/transfers/test_dynamodb_to_s3.py"]

Multiple AWS connections support in DynamoDBToS3Operator

### Description I want to add support of a separate AWS connection for DynamoDB in `DynamoDBToS3Operator` in `apache-airflow-providers-amazon` via `aws_dynamodb_conn_id` constructor argument. ### Use case/motivation Sometimes DynamoDB tables and S3 buckets live in different AWS accounts so to access both resources you need to assume a role in another account from one of them. That role can be specified in AWS connection, thus we need to support two of them in this operator. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29422

https://github.com/apache/airflow/pull/29452

8691c4f98c6cd6d96e87737158a9be0f6a04b9ad

3780b01fc46385809423bec9ef858be5be64b703

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/www/static/js/types/api-generated.ts"]

Add pagination to get_log in the rest API

### Description Right now, the `get_log` endpoint at `/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/logs/{task_try_number}` does not have any pagination and therefore we can be forced to load extremely large text blocks, which makes everything slow. (see the workaround fix we needed to do in the UI: https://github.com/apache/airflow/pull/29390) In `task_log_reader`, we do have `log_pos` and `offset` (see [here](https://github.com/apache/airflow/blob/main/airflow/utils/log/log_reader.py#L80-L83)). It would be great to expose those parameters in the REST API in order to break apart task instance logs into more manageable pieces. ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29405

https://github.com/apache/airflow/pull/30729

7d02277ae13b7d1e6cea9e6c8ff0d411100daf77

7d62cbb97e1bc225f09e3cfac440aa422087a8a7

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQuery Hook list_rows method missing page_token return value

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==7.0.0 But the problem exists in all newer versions. ### Apache Airflow version apache-airflow==2.3.2 ### Operating System Ubuntu 20.04.4 LTS ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened The `list_rows` method in the BigQuery Hook does not return the page_token value, which is necessary for paginating query results. Same problem with `get_datasets_list` method. The documentation for the `get_datasets_list` method even states that the page_token parameter can be accessed: ``` :param page_token: Token representing a cursor into the datasets. If not passed, the API will return the first page of datasets. The token marks the beginning of the iterator to be returned and the value of the ``page_token`` can be accessed at ``next_page_token`` of the :class:`~google.api_core.page_iterator.HTTPIterator`. ``` but it doesn't return HTTPIterator. Instead, it converts the `HTTPIterator` to `list[DatasetListItem]` using `list(datasets)`, making it impossible to retrieve the original `HTTPIterator` and thus impossible to obtain the `next_page_token`. ### What you think should happen instead `list_rows` \ `get_datasets_list` methods should return `Iterator` OR both the list of rows\datasets and the page_token value to allow users to retrieve multiple results pages. For backward compatibility, we can have a parameter like `return_iterator=True` or smth like that. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29396

https://github.com/apache/airflow/pull/30543

d9896fd96eb91a684a512a86924a801db53eb945

4703f9a0e589557f5176a6f466ae83fe52644cf6

python

closed

apache/airflow

https://github.com/apache/airflow

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

S3TaskHandler continuously returns "*** Falling back to local log" even if log_pos is provided when log not in s3

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==7.1.0 ### Apache Airflow version 2.5.1 ### Operating System Ubuntu 18.04 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When looking at logs in the UI for a running task when using remote s3 logging, the logs for the task are only uploaded to s3 after the task has completed. The the `S3TaskHandler` falls back to the local logs stored on the worker in that case (by falling back to the `FileTaskHandler` behavior) and prepends the line `*** Falling back to local log` to those logs. This is mostly fine, but for the new log streaming behavior, this means that `*** Falling back to local log` is returned from `/get_logs_with_metadata` on each call, even if there are no new logs. ### What you think should happen instead I'd expect the falling back message only to be included in calls with no `log_pos` in the metadata or with a `log_pos` of `0`. ### How to reproduce Start a task with `logging.remote_logging` set to `True` and `logging.remote_base_log_folder` set to `s3://something` and watch the logs while the task is running. You'll see `*** Falling back to local log` printed every few seconds. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29393

https://github.com/apache/airflow/pull/29708

13098d5c35cf056c3ef08ea98a1970ee1a3e76f8

5e006d743d1ba3781acd8e053642f2367a8e7edc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/baseoperator.py", "airflow/models/dag.py", "airflow/models/param.py"]

Cannot use TypedDict object when defining params

### Apache Airflow version 2.5.1 ### What happened Context: I am attempting to use [TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict) objects to maintain the keys used in DAG params in a single place, and check for key names across multiple DAGs that use the params. This raises an error with `mypy` as `params` expects an `Optional[Dict]`. Due to the invariance of `Dict`, this does not accept `TypedDict` objects. What happened: I passed a `TypedDict` to the `params` arg of `DAG` and got a TypeError. ### What you think should happen instead `TypedDict` objects should be accepted by `DAG`, which should accept `Optional[Mapping[str, Any]]`. Unless I'm mistaken, `params` are converted to a `ParamsDict` class and therefore the appropriate type hint is a generic `Mapping` type. ### How to reproduce Steps to reproduce ```Python from typing import TypedDict from airflow import DAG from airflow.models import Param class ParamsTypedDict(TypedDict): str_param: Param params: ParamsTypedDict = { "str_param": Param("", type="str") } with DAG( dag_id="mypy-error-dag", # The line below raises a mypy error # Argument "params" to "DAG" has incompatible type "ParamsTypedDict"; expected "Optional[Dict[Any, Any]]" [arg-type] params=params, ) as dag: pass ``` ### Operating System Amazon Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29358

https://github.com/apache/airflow/pull/29782

b6392ae5fd466fa06ca92c061a0f93272e27a26b

b069df9b0a792beca66b08d873a66d5640ddadb7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_setup_teardown.py", "airflow/models/abstractoperator.py", "airflow/models/dag.py", "tests/models/test_dag.py", "tests/models/test_dagrun.py"]

Automatically clear setup/teardown when clearing a dependent task

https://github.com/apache/airflow/issues/29329

https://github.com/apache/airflow/pull/30271

f4c4b7748655cd11d2c297de38563b2e6b840221

0c2778f348f61f3bf08b840676d681e93a60f54a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/python_kubernetes_script.py", "airflow/utils/decorators.py", "tests/decorators/test_external_python.py", "tests/decorators/test_python_virtualenv.py", "tests/providers/cncf/kubernetes/decorators/test_kubernetes.py", "tests/providers/docker/decorators/test_docker.py"]

Ensure setup/teardown work on a previously decorated function (eg task.docker)

https://github.com/apache/airflow/issues/29325

https://github.com/apache/airflow/pull/30216

3022e2ecbb647bfa0c93fbcd589d0d7431541052

df49ad179bddcdb098b3eccbf9bb6361cfbafc36

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/serialized_dag.py", "tests/models/test_serialized_dag.py"]

DAG dependencies graph not updating when deleting a DAG

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened ON Airflow 2.4.2 Dag dependencies graph show deleted DAGs that use to have dependencies to currently existing DAGs ### What you think should happen instead Deleted DAGs should not appear on DAG Dependencies ### How to reproduce Create a DAG with dependencies on other DAG, like a wait sensor. Remove new DAG ### Operating System apache/airflow:2.4.2-python3.10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29323

https://github.com/apache/airflow/pull/29407

18347d36e67894604436f3ef47d273532683b473

02a2efeae409bddcfedafe273fffc353595815cc

python

closed

apache/airflow

https://github.com/apache/airflow

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

DAG list, sorting lost when switching page

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Hi, I'm currently on Airflow 2.4.2 In /home when sorting by DAG/Owner/Next Run and going to the next page the sort resets. This feature only works if I'm looking for last or first, everything in the middle is unreachable. ### What you think should happen instead The sorting should continue over the pagination ### How to reproduce Sort by any sortable field on DagList and go to the next page ### Operating System apache/airflow:2.4.2-python3.10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29322

https://github.com/apache/airflow/pull/29756

c917c9de3db125cac1beb0a58ac81f56830fb9a5

c8cd90fa92c1597300dbbad4366c2bef49ef6390

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/experimental/get_task_instance.py", "airflow/cli/commands/task_command.py", "airflow/models/dagrun.py", "airflow/models/taskinstance.py", "airflow/serialization/pydantic/dag_run.py", "airflow/serialization/pydantic/taskinstance.py", "airflow/utils/log/logging_mixin.py", "airflow/www/views.py"]

AIP-44 Migrate TaskCommand._get_ti to Internal API

https://github.com/apache/airflow/blob/main/airflow/cli/commands/task_command.py#L145

https://github.com/apache/airflow/issues/29320

https://github.com/apache/airflow/pull/35312

ab6e623cb1a75f54fc419cee66a16e3d8ff1adc2

1e1adc569f43494aabf3712b651956636c04df7f

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQueryCreateEmptyTableOperator `exists_ok` parameter doesn't throw appropriate error when set to "False"

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers I'm using `apache-airflow-providers-google==8.2.0`, but it looks like the relevant code that's causing this to occur is still in use as of `8.8.0`. ### Apache Airflow version 2.3.2 ### Operating System Debian (from Docker image `apache/airflow:2.3.2-python3.10`) ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deployed on an EKS cluster via Helm. ### What happened The first task in one of my DAGs is to create an empty BigQuery table using the `BigQueryCreateEmptyTableOperator` as follows: ```python create_staging_table = BigQueryCreateEmptyTableOperator( task_id="create_staging_table", dataset_id="my_dataset", table_id="tmp_table", schema_fields=[ {"name": "field_1", "type": "TIMESTAMP", "mode": "NULLABLE"}, {"name": "field_2", "type": "INTEGER", "mode": "NULLABLE"}, {"name": "field_3", "type": "INTEGER", "mode": "NULLABLE"} ], exists_ok=False ) ``` Note that `exists_ok=False` explicitly here, but it is also the default value. This task exits with a `SUCCESS` status even when `my_dataset.tmp_table` already exists in a given BigQuery project. The task returns the following logs: ``` [2023-02-02, 05:52:29 UTC] {bigquery.py:875} INFO - Creating table [2023-02-02, 05:52:29 UTC] {bigquery.py:901} INFO - Table my_dataset.tmp_table already exists. [2023-02-02, 05:52:30 UTC] {taskinstance.py:1395} INFO - Marking task as SUCCESS. dag_id=my_fake_dag, task_id=create_staging_table, execution_date=20230202T044000, start_date=20230202T055229, end_date=20230202T055230 [2023-02-02, 05:52:30 UTC] {local_task_job.py:156} INFO - Task exited with return code 0 ``` ### What you think should happen instead Setting `exists_ok=False` should raise an exception and exit the task with a `FAILED` status if the table being created already exists in BigQuery. ### How to reproduce 1. Deploy Airflow 2.3.2 running Python 3.10 in some capacity 2. Ensure `apache-airflow-providers-google==8.2.0` (or 8.8.0, as I don't believe the issue has been fixed) is installed on the deployment. 3. Set up a GCP project and create a BigQuery dataset. 4. Create an empty BigQuery table with a schema. 5. Create a DAG that uses the `BigQueryCreateEmptyTableOperator` to create a new BigQuery table. 6. Run the DAG from Step 5 on the Airflow instance deployed in Step 1. 7. Observe the task's status. ### Anything else I believe the silent failure may be occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L1377), as the `except` statement results in a log output, but doesn't actually raise an exception or change a state that would make the task fail. If this is in fact the case, I'd be happy to submit a PR, but appreciate any input as to any error-handling standards/consistencies that this provider package maintains. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29301

https://github.com/apache/airflow/pull/29394

228d79c1b3e11ecfbff5a27c900f9d49a84ad365

a5adb87ab4ee537eb37ef31aba755b40f6f29a1e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/ssh/hooks/ssh.py", "airflow/providers/ssh/operators/ssh.py", "docs/apache-airflow-providers-ssh/connections/ssh.rst", "tests/providers/ssh/hooks/test_ssh.py", "tests/providers/ssh/operators/test_ssh.py"]

Ssh connection extra parameter conn_timeout doesn't work with ssh operator

### Apache Airflow Provider(s) ssh ### Versions of Apache Airflow Providers apache-airflow-providers-ssh>=3.3.0 ### Apache Airflow version 2.5.0 ### Operating System debian "11 (bullseye)" ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened I have an SSH operator task where the command can take a long time. In recent SSH provider versions(>=3.3.0) it stopped working, as I suspect it is because of #27184 . After this change looks like the timeout is 10 seconds, and after there is no output provided through SSH for 10 seconds I'm getting the following error: ``` [2023-01-26, 11:49:57 UTC] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/operators/ssh.py", line 171, in execute result = self.run_ssh_client_command(ssh_client, self.command, context=context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/operators/ssh.py", line 156, in run_ssh_client_command exit_status, agg_stdout, agg_stderr = self.ssh_hook.exec_ssh_client_command( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/hooks/ssh.py", line 521, in exec_ssh_client_command raise AirflowException("SSH command timed out") airflow.exceptions.AirflowException: SSH command timed out ``` At first I thought that this is ok, since I can just set `conn_timeout` extra parameter in my ssh connection. But then I noticed that this parameter from the connection is not used anywhere - so this doesn't work, and you have to modify your task code to set the needed value of this parameter in the SSH operator. What's more, even even with modifying task code it's not possible to achieve the previous behavior(when this parameter was not set) since now it'll be set to 10 when you pass None as value. ### What you think should happen instead I think it should be possible to pass timeout parameter through connection extra field for ssh operator (including None value, meaning no timeout). ### How to reproduce Add simple DAG with sleeping for more than 10 seconds, for example: ```python # this DAG only works for SSH provider versions <=3.2.0 from airflow.models import DAG from airflow.contrib.operators.ssh_operator import SSHOperator from airflow.utils.dates import days_ago from airflow.operators.dummy import DummyOperator args = { 'owner': 'airflow', 'start_date': days_ago(2), } dag = DAG( default_args=args, dag_id="test_ssh", max_active_runs=1, catchup=False, schedule_interval="@hourly" ) task0 = SSHOperator(ssh_conn_id='ssh_localhost', task_id="test_sleep", command=f'sleep 15s', dag=dag) task0 ``` Try configuring `ssh_localhost` connection to make the DAG work using extra conn_timeout or extra timeout (or other) parameters. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29282

https://github.com/apache/airflow/pull/29347

a21c17bc07c1eeb733eca889a02396fab401b215

fd000684d05a993ade3fef38b683ef3cdfdfc2b6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_python_decorator.py", "airflow/example_dags/example_python_operator.py", "airflow/example_dags/example_short_circuit_operator.py", "docs/apache-airflow/howto/operator/python.rst", "docs/conf.py", "docs/sphinx_design/static/custom.css", "setup.py", "tests/api_connexion/endpoints/test_task_instance_endpoint.py"]

Support tabs in docs

### What do you see as an issue? I suggest supporting tabs in the docs to improve the readability when demonstrating different ways to achieve the same things. **Motivation** We have multiple ways to achieve the same thing in Airflow, for example: - TaskFlow API & "classic" operators - CLI & REST API & API client However, our docs currently do not consistently demonstrate different ways to use Airflow. For example, https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html demonstrates TaskFlow operators in some examples and classic operators in other examples. All cases covered can be supported by both the TaskFlow & classic operators. In the case of https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html, I think a nice solution to demonstrate both approaches would be to use tabs. That way somebody who prefers the TaskFlow API can view all TaskFlow examples, and somebody who prefers the classic operators (we should give those a better name) can view only those examples. **Possible implementation** There is a package [sphinx-tabs](https://github.com/executablebooks/sphinx-tabs) for this. For the example above, having https://sphinx-tabs.readthedocs.io/en/latest/#group-tabs would be great because it enables you to view all examples of one "style" with a single click. ### Solving the problem Install https://github.com/executablebooks/sphinx-tabs with the docs. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29267

https://github.com/apache/airflow/pull/36041

f60d458dc08a5d5fbe5903fffca8f7b03009f49a

58e264c83fed1ca42486302600288230b944ab06

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/hooks/compute_ssh.py", "tests/providers/google/cloud/hooks/test_compute_ssh.py", "tests/system/providers/google/cloud/compute/example_compute_ssh.py", "tests/system/providers/google/cloud/compute/example_compute_ssh_os_login.py", "tests/system/providers/google/cloud/compute/example_compute_ssh_parallel.py"]

ComputeEngineSSHHook on parallel runs in Composer gives banner Error reading SSH protocol banner

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We are using ComputeEngineSSHHook for some of our Airflow DAGS in Cloud Composer Everything works fine when DAGs run one by one But when we start parallelism where multiple tasks are trying to connect to our GCE instance using ComputeEngineSSHHook at the same time, We experience intermittent errors like the one give below Since cloud composer by default has 3 retries, sometimes in the second or third attempt this issue gets resolved automatically but we would like to understand why this issue comes in the first place when there are multiple operators trying to generate keys and SSH into GCE instance We have tried maintaining the DAG task with banner_timeout and expire_timeout parameters but we still see this issue create_transfer_run_directory = SSHOperator( task_id="create_transfer_run_directory", ssh_hook=ComputeEngineSSHHook( instance_name=GCE_INSTANCE, zone=GCE_ZONE, use_oslogin=True, use_iap_tunnel=False, use_internal_ip=True, ), conn_timeout = 120, cmd_timeout = 120, banner_timeout = 120.0, command=f"sudo mkdir -p {transfer_run_directory}/" '{{ ti.xcom_pull(task_ids="load_config", key="transfer_id") }}', dag=dag, ) **[2023-01-31, 03:30:39 UTC] {compute_ssh.py:286} INFO - Importing SSH public key using OSLogin: user=edw-sa-gcc@pso-e2e-sql.iam.gserviceaccount.com [2023-01-31, 03:30:39 UTC] {compute_ssh.py:236} INFO - Opening remote connection to host: username=sa_115585236623848451866, hostname=10.128.0.29 [2023-01-31, 03:30:41 UTC] {transport.py:1874} ERROR - Exception (client): Error reading SSH protocol banner [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - Traceback (most recent call last): [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2271, in _check_banner [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - buf = self.packetizer.readline(timeout) [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/packet.py", line 380, in readline [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - buf += self._read_timeout(timeout) [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/packet.py", line 609, in _read_timeout [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - raise EOFError() [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - EOFError [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - During handling of the above exception, another exception occurred: [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - Traceback (most recent call last): [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2094, in run [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - self._check_banner() [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2275, in _check_banner [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - raise SSHException( [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - paramiko.ssh_exception.SSHException: Error reading SSH protocol banner [2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - [2023-01-31, 03:30:41 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 0s to retry [2023-01-31, 03:30:43 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1) [2023-01-31, 03:30:43 UTC] {transport.py:1874} INFO - Authentication (publickey) failed. [2023-01-31, 03:30:43 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 1s to retry [2023-01-31, 03:30:47 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1) [2023-01-31, 03:30:50 UTC] {transport.py:1874} INFO - Authentication (publickey) failed. [2023-01-31, 03:30:50 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 6s to retry [2023-01-31, 03:30:58 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1) [2023-01-31, 03:30:58 UTC] {transport.py:1874} INFO - Authentication (publickey) failed. [2023-01-31, 03:30:58 UTC] {taskinstance.py:1904} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/ssh/operators/ssh.py", line 157, in execute with self.get_ssh_client() as ssh_client: File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/ssh/operators/ssh.py", line 124, in get_ssh_client return self.get_hook().get_conn() File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 232, in get_conn sshclient = self._connect_to_instance(user, hostname, privkey, proxy_command) File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 245, in _connect_to_instance client.connect( File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 50, in connect return super().connect(*args, **kwargs) File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 450, in connect self._auth( File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 781, in _auth raise saved_exception File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 681, in _auth self._transport.auth_publickey(username, pkey) File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 1635, in auth_publickey return self.auth_handler.wait_for_response(my_event) File "/opt/python3.8/lib/python3.8/site-packages/paramiko/auth_handler.py", line 259, in wait_for_response raise e paramiko.ssh_exception.AuthenticationException: Authentication failed. [2023-01-31, 03:30:58 UTC] {taskinstance.py:1408} INFO - Marking task as UP_FOR_RETRY. dag_id=run_data_transfer_configs_dag, task_id=create_transfer_run_directory, execution_date=20230131T033002, start_date=20230131T033035, end_date=20230131T033058 [2023-01-31, 03:30:58 UTC] {standard_task_runner.py:92} ERROR - Failed to execute job 1418 for task create_transfer_run_directory (Authentication failed.; 21885)** ### What you think should happen instead The SSH Hook operator should be able to seamlessly SSH into the GCE instance without any intermittent authentication issues ### How to reproduce _No response_ ### Operating System Composer Kubernetes Cluster ### Versions of Apache Airflow Providers Composer Version - 2.1.3 Airflow version - 2.3.4 ### Deployment Composer ### Deployment details Kubernetes Cluster GCE Compute Engine VM (Ubuntu) ### Anything else Very random and intermittent ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29258

https://github.com/apache/airflow/pull/32365

df74553ec484ad729fcd75ccbc1f5f18e7f34dc8

0c894dbb24ad9ad90dcb10c81269ccc056789dc3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Repair functionality in DatabricksRunNowOperator

### Description The Databricks jobs 2.1 API has the ability to repair failed or skipped tasks in a Databricks workflow without having to rerun successful tasks for a given workflow run. It would be nice to be able to leverage this functionality via airflow operators. ### Use case/motivation The primary motivation is the ability to be more efficient and only have to rerun failed or skipped tasks rather than the entire workflow if only 1 out of 10 tasks fail. **Repair run API:** https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsRepairfail @alexott for visability ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29250

https://github.com/apache/airflow/pull/30786

424fc17d49afd4175826a62aa4fe7aa7c5772143

9bebf85e24e352f9194da2f98e2bc66a5e6b972e

python

closed

apache/airflow

https://github.com/apache/airflow

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

Calendar page doesn't load when using a timedelta DAG schedule

### Apache Airflow version 2.5.1 ### What happened /calendar page give a problem, here is the capture  ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 22.04.1 LTS ### Versions of Apache Airflow Providers Distributor ID: Ubuntu Description: Ubuntu 22.04.1 LTS Release: 22.04 Codename: jammy ### Deployment Other ### Deployment details Distributor ID: Ubuntu Description: Ubuntu 22.04.1 LTS Release: 22.04 Codename: jammy ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29227

https://github.com/apache/airflow/pull/29454

28126c12fbdd2cac84e0fbcf2212154085aa5ed9

f837c0105c85d777ea18c88a9578eeeeac5f57db

python

closed

apache/airflow

https://github.com/apache/airflow

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

BigQueryCreateDataTransferOperator will log AWS credentials when transferring from S3

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers [apache-airflow-providers-google 8.6.0](https://airflow.apache.org/docs/apache-airflow-providers-google/8.6.0/) ### Apache Airflow version 2.5.0 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When creating a transfer config that will move data from AWS S3, an access_key_id and secret_access_key are provided (see: https://cloud.google.com/bigquery/docs/s3-transfer). These parameters are logged and exposed as XCom return_value. ### What you think should happen instead At least the secret_access_key should be hidden or removed from the XCom return value ### How to reproduce ``` PROJECT_ID=123 TRANSFER_CONFIG={ "destination_dataset_id": destination_dataset, "display_name": display_name, "data_source_id": "amazon_s3", "schedule_options": {"disable_auto_scheduling": True}, "params": { "destination_table_name_template": destination_table, "file_format": "PARQUET", "data_path": data_path, "access_key_id": access_key_id, "secret_access_key": secret_access_key } }, gcp_bigquery_create_transfer = BigQueryCreateDataTransferOperator( transfer_config=TRANSFER_CONFIG, project_id=PROJECT_ID, task_id="gcp_bigquery_create_transfer", ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29209

https://github.com/apache/airflow/pull/29348

3dbcf99d20d47cde0debdd5faf9bd9b2ebde1718

f51742d20b2e53bcd90a19db21e4e12d2a287677

python

closed

apache/airflow

https://github.com/apache/airflow

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

TaskFlow AirflowSkipException causes downstream step to fail when multiple_outputs is true

### Apache Airflow version 2.5.1 ### What happened Most of our code is based on TaskFlow API and we have many tasks that raise AirflowSkipException (or BranchPythonOperator) on purpose to skip the next downstream task (with trigger_rule = none_failed_min_one_success). And these tasks are expecting a multiple output XCom result (local_file_path, file sizes, records count) from previous tasks and it's causing this error: `airflow.exceptions.XComNotFound: XComArg result from copy_from_data_lake_to_local_file at outbound_dag_AIR2070 with key="local_file_path" is not found!` ### What you think should happen instead Considering trigger rule "none_failed_min_one_success", we expect that upstream task should be allowed to skip and downstream tasks will still run without raising any errors caused by not found XCom results. ### How to reproduce This is an aproximate example dag based on an existing one. ```python from os import path import pendulum from airflow import DAG from airflow.decorators import task from airflow.operators.python import BranchPythonOperator DAG_ID = "testing_dag_AIR" # PGP_OPERATION = None PGP_OPERATION = "decrypt" LOCAL_FILE_PATH = "/temp/example/example.csv" with DAG( dag_id=DAG_ID, schedule='0 7-18 * * *', start_date=pendulum.datetime(2022, 12, 15, 7, 0, 0), ) as dag: @task(multiple_outputs=True, trigger_rule='none_failed_min_one_success') def copy_from_local_file_to_data_lake(local_file_path: str, dest_dir_path: str): destination_file_path = path.join(dest_dir_path, path.basename(local_file_path)) return { "destination_file_path": destination_file_path, "file_size": 100 } @task(multiple_outputs=True, trigger_rule='none_failed_min_one_success') def copy_from_data_lake_to_local_file(data_lake_file_path, local_dir_path): local_file_path = path.join(local_dir_path, path.basename(data_lake_file_path)) return { "local_file_path": local_file_path, "file_size": 100 } @task(multiple_outputs=True, task_id='get_pgp_file_info', trigger_rule='none_failed_min_one_success') def get_pgp_file_info(file_path, operation): import uuid import os src_file_name = os.path.basename(file_path) src_file_dir = os.path.dirname(file_path) run_id = str(uuid.uuid4()) if operation == "decrypt": wait_pattern = f'*{src_file_name}' else: wait_pattern = f'*{src_file_name}.pgp' target_path = 'datalake/target' return { 'src_file_path': file_path, 'src_file_dir': src_file_dir, 'target_path': target_path, 'pattern': wait_pattern, 'guid': run_id } @task(multiple_outputs=True, task_id='return_src_path', trigger_rule='none_failed_min_one_success') def return_src_path(src_file_path): return { 'file_path': src_file_path, 'file_size': 100 } @task(multiple_outputs=True, task_id='choose_result', trigger_rule='none_failed_min_one_success') def choose_result(src_file_path, src_file_size, decrypt_file_path, decrypt_file_size): import os file_path = decrypt_file_path or src_file_path file_size = decrypt_file_size or src_file_size local_dir = os.path.dirname(file_path) return { 'local_dir': local_dir, 'file_path': file_path, 'file_size': file_size, 'file_name': os.path.basename(file_path) } def switch_branch_func(pgp_operation): if pgp_operation in ["decrypt", "encrypt"]: return 'get_pgp_file_info' else: return 'return_src_path' operation = PGP_OPERATION local_file_path = LOCAL_FILE_PATH check_need_to_decrypt = BranchPythonOperator( task_id='branch_task', python_callable=switch_branch_func, op_args=(operation,)) pgp_file_info = get_pgp_file_info(local_file_path, operation) data_lake_file = copy_from_local_file_to_data_lake(pgp_file_info['src_file_path'], pgp_file_info['target_path']) decrypt_local_file = copy_from_data_lake_to_local_file( data_lake_file['destination_file_path'], pgp_file_info['src_file_dir']) src_result = return_src_path(local_file_path) result = choose_result(src_result['file_path'], src_result['file_size'], decrypt_local_file['local_file_path'], decrypt_local_file['file_size']) check_need_to_decrypt >> [pgp_file_info, src_result] pgp_file_info >> decrypt_local_file [decrypt_local_file, src_result] >> result ``` ### Operating System Windows 10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details docker-compose version: 3.7 Note: This also happens when it's deployed to one of our testing environments using official Airflow Helm Chart. ### Anything else This issue is similar to [#24338](https://github.com/apache/airflow/issues/24338), it was solved by [#25661](https://github.com/apache/airflow/pull/25661) but this case is related to multiple_outputs being set to True. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29199

https://github.com/apache/airflow/pull/32027

14eb1d3116ecef15be7be9a8f9d08757e74f981c

79eac7687cf7c6bcaa4df2b8735efaad79a7fee2

python

closed

apache/airflow

https://github.com/apache/airflow

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

SnowflakeCheckOperator - The conn_id `None` isn't defined

### Apache Airflow Provider(s) snowflake ### Versions of Apache Airflow Providers `apache-airflow-providers-snowflake==4.0.2` ### Apache Airflow version 2.5.1 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened After upgrading the _apache-airflow-providers-snowflake_ from version **3.3.0** to **4.0.2**, the SnowflakeCheckOperator tasks starts to throw the following error: ``` File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 179, in get_db_hook return self._hook File "/usr/local/lib/python3.9/functools.py", line 993, in __get__ val = self.func(instance) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 141, in _hook conn = BaseHook.get_connection(self.conn_id) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/hooks/base.py", line 72, in get_connection conn = Connection.get_connection_from_secrets(conn_id) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/connection.py", line 435, in get_connection_from_secrets raise AirflowNotFoundException(f"The conn_id `{conn_id}` isn't defined") airflow.exceptions.AirflowNotFoundException: The conn_id `None` isn't defined ``` ### What you think should happen instead _No response_ ### How to reproduce - Define a _Snowflake_ Connection with the name **snowflake_default** - Create a Task similar to this: ``` my_task = SnowflakeCheckOperator( task_id='my_task', warehouse='warehouse', database='database', schema='schema', role='role', sql='select 1 from my_table' ) ``` - Run and check the error. ### Anything else We can workaround this by adding the conn_id='snowflake_default' to the SnowflakeCheckOperator. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29198

https://github.com/apache/airflow/pull/29211

a72e28d6e1bc6ae3185b8b3971ac9de5724006e6

9b073119d401594b3575c6f7dc4a14520d8ed1d3

python

closed

apache/airflow

https://github.com/apache/airflow

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

Trigger DAG w/config raising error from task detail views

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Version: 2.4.3 (migrated from 2.2.4) Manual UI option "Trigger DAG w/config" raises an error _400 Bad Request - Invalid datetime: None_ from views "Task Instance Details", "Rendered Template", "Log" and "XCom" . Note that DAG is actually triggered , but still error response 400 is raised. ### What you think should happen instead No 400 error ### How to reproduce 1. Go to any DAG graph view 2. Select a Task > go to "Instance Details" 3. Select "Trigger DAG w/config" 4. Select Trigger 5. See error ### Operating System PRETTY_NAME="Debian GNU/Linux 11 (bullseye)" ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29197

https://github.com/apache/airflow/pull/29212

9b073119d401594b3575c6f7dc4a14520d8ed1d3

7315d6f38caa58e6b19054f3e8a20ed02df16a29

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/client/local_client.py", "airflow/cli/cli_parser.py", "airflow/cli/commands/dag_command.py", "tests/api/client/test_local_client.py", "tests/cli/commands/test_dag_command.py"]

Add `output` format to missing cli commands

### Description I have noticed that for some commands, there is an option to get the output in json or yaml (as described in this PR from 2020 https://github.com/apache/airflow/issues/12699). However, there are still some commands that do not support the `--output` argument, most notable one is the `dags trigger`. When triggering a dag, it is crucial to get the run_id that has been triggered, so the triggered dag run can be monitored by the calling party. However, the output from this command is hard to parse without resorting to (gasp!) regex: ``` [2023-01-26 11:03:41,038] {{__init__.py:42}} INFO - Loaded API auth backend: airflow.api.auth.backend.session Created <DagRun sample_dag @ 2023-01-26T11:03:41+00:00: manual__2023-01-26T11:03:41+00:00, state:queued, queued_at: 2023-01-26 11:03:41.412394+00:00. externally triggered: True> ``` As you can see, extracting the run_id `manual__2023-01-26T11:03:41+00:00` is not easy from the above output. For what I see [in the code](https://github.com/apache/airflow/blob/main/airflow/cli/cli_parser.py#L1156), the `ARG_OUTPUT` is not added to `dag_trigger` command. ### Use case/motivation At my company we want to be able to trigger dags from another airflow environment (mwaa) and be able to wait for its completion before proceeding with the calling DAG. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29178

https://github.com/apache/airflow/pull/29224

ffdc696942d96a14a5ee0279f950e3114817055c

60fc40791121b19fe379e4216529b2138162b443

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/livy/hooks/livy.py", "airflow/providers/http/hooks/http.py", "airflow/providers/http/operators/http.py", "tests/providers/http/hooks/test_http.py"]

SimpleHttpOperator not working with loginless auth_type

### Apache Airflow Provider(s) http ### Versions of Apache Airflow Providers apache-airflow-providers-http==4.1.1 ### Apache Airflow version 2.5.0 ### Operating System Ubuntu 20.04.5 LTS (Focal Fossa)" ### Deployment Virtualenv installation ### Deployment details Reproduced on a local deployment inside WSL on virtualenv - not related to specific deployment. ### What happened SimpleHttpOperator supports passing in auth_type. Hovewer, [this auth_type is only initialized if login is provided](https://github.com/astronomer/airflow-provider-sample/blob/main/sample_provider/hooks/sample_hook.py#L64-L65). In our setup we are using the Kerberos authentication. This authentication relies on kerberos sidecar with keytab, and not on user-password pair in the connection string. However, this would also be issue with any other implementation not relying on username passed in the connection string. We were trying to use some other auth providers from (`HTTPSPNEGOAuth` from [requests_gssapi](https://pypi.org/project/requests-gssapi/) and `HTTPKerberosAuth` from [requests_kerberos](https://pypi.org/project/requests-kerberos/)). We noticed that requests_kerberos is used in Airflow in some other places for Kerberos support, hence we have settled on the latter. ### What you think should happen instead A suggestion is to initialize the passed `auth_type` also if no login is present. ### How to reproduce A branch demonstrating possible fix: https://github.com/apache/airflow/commit/7d341f081f0160ed102c06b9719582cb463b538c ### Anything else The linked branch is a quick-and-dirty solution, but maybe the code could be refactored in another way? Support for **kwargs could also be useful, but I wanted to make as minimal changes as possible. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29177

https://github.com/apache/airflow/pull/29206

013490edc1046808c651c600db8f0436b40f7423

c44c7e1b481b7c1a0d475265835a23b0f507506c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/redis/provider.yaml", "docs/apache-airflow-providers-redis/index.rst", "generated/provider_dependencies.json", "tests/system/providers/redis/__init__.py", "tests/system/providers/redis/example_redis_publish.py"]

Support for Redis Time series in Airflow common packages

### Description The current Redis API version is quite old. I need to implement a DAG for Timeseries data feature. Please upgrade to version that supports this. BTW, I was able to manually update my redis worker and it now works. Can this be added to next release please. ### Use case/motivation Timeseries in Redis is a growing area needing support in Airflow ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29175

https://github.com/apache/airflow/pull/31279

df3569cf489ce8ef26f5b4d9d9c3826d3daad5f2

94cad11b439e0ab102268e9e7221b0ab9d98e0df

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/howto/docker-compose/index.rst"]

trigger process missing from Airflow docker docs

### What do you see as an issue? The section [`Fetching docker-compose.yaml`](https://github.com/apache/airflow/blob/main/docs/apache-airflow/howto/docker-compose/index.rst#fetching-docker-composeyaml) claims to talk about all the process definitions that the Dockerfile compose contain but missed to talk the `airflow-trigger` process. ### Solving the problem We need to include the `airflow-trigger` in the process list that the docker-compose file contains. ### Anything else None ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29150

https://github.com/apache/airflow/pull/29203

f8c1410a0b0e62a1c4b67389d9cfb80cc024058d

272f358fd6327468fcb04049ef675a5cf939b93e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/sensor.py"]

Fix access to context in functions decorated by task.sensor

### Description Hello, I am a new Airflow user. I am requesting a feature in which the airflow context (containing task instance, etc.) be available inside of functions decorated by `airflow.decorators.task.sensor`. ### Use case/motivation I have noticed that when using the `airflow.decorators.task` decorator, one can access items from the context (such as the task instance) by using `**kwargs` or keyword arguments in the decorated function. But I have discovered that the same is not true for the `airflow.decorators.task.sensor` decorator. I'm not sure if this is a bug or intentional, but it would be very useful to be able to access the context normally from functions decorated by `task.sensor`. I believe this may have been an oversight. The `DecoratedSensorOperator` class is a child class of `PythonSensor`: https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/decorators/sensor.py#L28 This `DecoratedSensorOperator` class overrides `poke`, but does not incorporate the passed in `Context` object before calling the decorated function: https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/decorators/sensor.py#L60-L61 This is in contrast to the `PythonSensor`, whose `poke` method merges the context with the existing `op_kwargs`: https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/sensors/python.py#L68-L77 This seems like an easy fix, and I'd be happy to submit a pull request. But I figured I'd start with a feature request since I'm new to the open source community. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29137

https://github.com/apache/airflow/pull/29146

0a4184e34c1d83ad25c61adc23b838e994fc43f1

2d3cc504db8cde6188c1503675a698c74404cf58

python

closed

apache/airflow

https://github.com/apache/airflow

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

[Doc] Link to examples how to use FTP provider is incorrect

### What do you see as an issue? HI. I tried to use FTP provider (https://airflow.apache.org/docs/apache-airflow-providers-ftp/stable/connections/ftp.html#howto-connection-ftp) but link to the "Example DAGs" is incorrect and Github response is 404. ### Solving the problem Please update links to Example DAGs - here and check it in other providers. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29128

https://github.com/apache/airflow/pull/29134

33ba242d7eb8661bf936a9b99a8cad4a74b29827

1fbfd312d9d7e28e66f6ba5274421a96560fb7ba

python

closed

apache/airflow

https://github.com/apache/airflow

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

Ensure teardown failure with on_failure_fail_dagrun=True fails the DagRun, and not otherwise

https://github.com/apache/airflow/issues/29125

https://github.com/apache/airflow/pull/30398

fc4166127a1d2099d358fee1ea10662838cf9cf3

db359ee2375dd7208583aee09b9eae00f1eed1f1

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow-providers-sqlite/operators.rst"]

sqlite conn id unclear

### What do you see as an issue? The sqlite conn doc here https://airflow.apache.org/docs/apache-airflow-providers-sqlite/stable/operators.html is unclear. Sqlite does not use username, password, port, schema. These need to be removed from the docs. Furthermore, it is unclear how to construct a conn string for sqlite, since the docs for constructing a conn string here https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html assume that all these fields are given. ### Solving the problem Remove unused arguments for sqlite in connection, and make it clearer how to construct a connection to sqlite ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29113

https://github.com/apache/airflow/pull/29139

d23033cff8a25e5f71d01cb513c8ec1d21bbf491

ec7674f111177c41c02e5269ad336253ed9c28b4

python

closed

apache/airflow

https://github.com/apache/airflow

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

"Operation not permitted" error when chmod on log folder

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.5.1 ### Kubernetes Version 1.24.6 ### Helm Chart configuration executor: "KubernetesExecutor" # however same issue happens with LocalExecutor logs: persistence: enabled: true size: 50Gi storageClassName: azurefile-csi ### Docker Image customizations Using airflow-2.5.1-python3.10 as a base image. Copy custom shared libraries into folder under /opt/airflow/company Copy DAGs /opt/airflow/dags ### What happened ```console After migrating from airflow 2.4.3 to 2.5.1 start getting error below. No other changes to custom image. No task is running because of this error: Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/cli/commands/task_command.py", line 384, in task_run ti.init_run_context(raw=args.raw) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/models/taskinstance.py", line 2414, in init_run_context self._set_context(self) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/logging_mixin.py", line 77, in _set_context set_context(self.log, context) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/logging_mixin.py", line 213, in set_context flag = cast(FileTaskHandler, handler).set_context(value) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 71, in set_context local_loc = self._init_file(ti) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 382, in _init_file self._prepare_log_folder(Path(full_path).parent) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/log/file_task_handler.py", line 358, in _prepare_log_folder directory.chmod(mode) File "/usr/local/lib/python3.10/pathlib.py", line 1191, in chmod self._accessor.chmod(self, mode, follow_symlinks=follow_symlinks) PermissionError: [Errno 1] Operation not permitted: '/opt/airflow/logs/dag_id=***/run_id=manual__2023-01-22T02:59:43.752407+00:00/task_id=***' ``` ### What you think should happen instead Seem like airflow attempts to set change log folder permissions and not permissioned to do it. Getting same error when executing command manually (confirmed folder path exists): chmod 511 '/opt/airflow/logs/dag_id=***/run_id=manual__2023-01-22T02:59:43.752407+00:00/task_id=***' chmod: changing permissions of '/opt/airflow/logs/dag_id=***/run_id=scheduled__2023-01-23T15:30:00+00:00/task_id=***': Operation not permitted ### How to reproduce My understanding is that this error happens before any custom code is executed. ### Anything else Error happens every time, unable to start any DAG while using airflow 2.5.1. Exactly same configuration works with 2.5.0 and 2.4.3. Same image and configuration works fine while running locally using docker-composer. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29112

https://github.com/apache/airflow/pull/30123

f5ed6ae67d0788ea2a737d781b27fbcae1e8e8af

b87cbc388bae281e553da699212ebfc6bb723eea

python

closed

apache/airflow

https://github.com/apache/airflow

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

[Google Cloud] DataprocCreateBatchOperator returns incorrect results and does not reattach

### Apache Airflow version main (development) ### What happened The provider operator for Google Cloud Dataproc Batches has two bugs: 1. The running [operator](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2123-L2124) returns successful even if the job transitions to State.CANCELLED or State.CANCELLING 2. It [attempts](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2154) to 'reattach' to a potentially running job if it AlreadyExists, but it sends the wrong type since 'result' is a Batch and needs Operation ### What you think should happen instead A new hook that polls for batch job completion. There is precedent for it in traditional dataproc with 'wait_for_job'. ### How to reproduce Use the Breeze environment and a DAG that runs DataprocCreateBatchOperator. Allow the first instance to start. Use the gcloud CLI to cancel the job. `gcloud dataproc batches cancel <batch_id> --project <project_id> --region <region>` Observe that the task completes successfully after a 3-5 minute timeout, even though the job was cancelled. Run the task again with the same batch_id. Observe the ValueError where it expects Operation but receives Batch ### Operating System Darwin 5806 21.6.0 Darwin Kernel Version 21.6.0: Mon Aug 22 20:17:10 PDT 2022; root:xnu-8020.140.49~2/RELEASE_X86_64 x86_64 ### Versions of Apache Airflow Providers Same as dev (main) version. ### Deployment Other Docker-based deployment ### Deployment details Observable in the Breeze environment, when running against real Google Infrastructure. ### Anything else Every time. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29109

https://github.com/apache/airflow/pull/29136

a770edfac493f3972c10a43e45bcd0e7cfaea65f

7e3a9fc8586d0e6d9eddbf833a75280e68050da8

python

closed

apache/airflow

https://github.com/apache/airflow

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

graph disappears during run time when using branch_task and a dynamic classic operator

### Apache Airflow version 2.5.1 ### What happened when using a dynamically generated task that gets the expand data from xcom after a branch_task the graph doesn't render. It reappears once the dag run is finished. tried with BashOperator and a KubernetesPodOperator. the developer console in the browser shows the error: `Uncaught TypeError: Cannot read properties of undefined (reading 'length') at z (graph.1c0596dfced26c638bfe.js:2:17499) at graph.1c0596dfced26c638bfe.js:2:17654 at Array.map (<anonymous>) at z (graph.1c0596dfced26c638bfe.js:2:17646) at graph.1c0596dfced26c638bfe.js:2:26602 at graph.1c0596dfced26c638bfe.js:2:26655 at graph.1c0596dfced26c638bfe.js:2:26661 at graph.1c0596dfced26c638bfe.js:2:222 at graph.1c0596dfced26c638bfe.js:2:227 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 ` grid view renders fine. ### What you think should happen instead graph should be rendered. ### How to reproduce ```@dag('branch_dynamic', schedule_interval=None, default_args=default_args, catchup=False) def branch_dynamic_flow(): @branch_task def choose_path(): return 'b' @task def a(): print('a') @task def get_args(): return ['echo 1', 'echo 2'] b = BashOperator.partial(task_id="b").expand(bash_command=get_args()) path = choose_path() path >> a() path >> b ``` ### Operating System red hat ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes | 5.1.1 | Kubernetes ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29105

https://github.com/apache/airflow/pull/29042

b2825e11852890cf0b0f4d0bcaae592311781cdf

33ba242d7eb8661bf936a9b99a8cad4a74b29827

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag/details/Dag.tsx", "airflow/www/static/js/dag/details/dagRun/index.tsx", "airflow/www/static/js/dag/details/taskInstance/Logs/LogBlock.tsx", "airflow/www/static/js/dag/details/taskInstance/index.tsx", "airflow/www/static/js/dag/grid/index.tsx", "airflow/www/static/js/utils/useOffsetHeight.tsx"]

Unnecessary scrollbars in grid view

### Apache Airflow version 2.5.0 ### What happened Compare the same DAG grid view in 2.4.3: (everything is scrolled using the "main" scrollbar of the window)  and in 2.5.0 (and 2.5.1) (left and right side of the grid have their own scrollbars):  It was much more ergonomic previously when only the main scrollbar was used. I think the relevant change was in #27560, where `maxHeight={offsetHeight}` was added to some places. Is this the intended way the grid view should look like or did happen as an accident? I tried to look around in the developer tools and it seems like removing the `max-height` from this element restores the old look: `div#react-container div div.c-1rr4qq7 div.c-k008qs div.c-19srwsc div.c-scptso div.c-l7cpmp`. Well it does for the left side of the grid view. Similar change has to be done for some other divs also.  ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29100

https://github.com/apache/airflow/pull/29367

1b18a501fe818079e535838fa4f232b03365fc75

643d736ebb32c488005b3832c2c3f226a77900b2

python

closed

apache/airflow

https://github.com/apache/airflow

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

Incorrect type annotation for `num_of_dpus` in GlueJobOperator/GlueJobHook

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==7.1.0 ### Apache Airflow version 2.2.2 ### Operating System macOS Ventura 13.1 ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened When calling GlueJobOperator and passing `create_job_kwargs={"Command": {"Name": "pythonshell"}}` I need to specify MaxCapacity and based on the code [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/glue.py#L127) that's equal to _num_of_dpus_ and that parameter is integer as stated [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/glue.py#L68) Because I want to use pythonshell, AWS Glue offers to setup ranges between 0.00625 and 1 and that can't be achieved with integer. `When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.` I was trying to pass _MaxCapacity_ in `create_job_kwargs={"Command": {"Name": "pythonshell"}, "MaxCapacity": 0.0625}` but it throws the error. ### What you think should happen instead I think that parameter _num_of_dpus_ should be type double or MaxCapacity should be allowed to setup as double if pythonshell was selected in Command -> Name. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29091

https://github.com/apache/airflow/pull/29176

e1a14ae9ee6ba819763776156a49e9df3fe80ee9

44024564cb3dd6835b0375d61e682efc1acd7d2c

python

closed

apache/airflow

https://github.com/apache/airflow

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

FTP operator has logic in __init__

### Body Similarly to SFTP (fixed in https://github.com/apache/airflow/pull/29068) the logic from __init__ should be moved to execute. The #29068 provides a blueprint for that. ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/29070

https://github.com/apache/airflow/pull/29073

8eb348911f2603feba98787d79b88bbd84bd17be

2b7071c60022b3c483406839d3c0ef734db5daad

python

closed

apache/airflow

https://github.com/apache/airflow

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

Recursively cleared external task sensors using reschedule mode instantly time out if previous run is older than sensor timeout

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Using Airflow 2.3.3, when recursively clearing downstream tasks any cleared external task sensors in other DAGs which are using reschedule mode will instantly fail with an `AirflowSensorTimeout` exception if the previous run is older than the sensor's timeout. ### What you think should happen instead The recursively cleared external task sensors should run normally, waiting for the cleared upstream task to complete, retrying up to the configured number of times and within the configured sensor timeout counting from the point in time when the sensor was cleared. ### How to reproduce 1. Load the following DAGs: ```python from datetime import datetime, timedelta, timezone from time import sleep from airflow.decorators import task from airflow.models import DAG from airflow.sensors.external_task import ExternalTaskMarker, ExternalTaskSensor from airflow.utils import timezone default_args = { 'start_date': datetime.now(timezone.utc).replace(second=0, microsecond=0), 'retries': 2, 'retry_delay': timedelta(seconds=10), } with DAG('parent_dag', schedule_interval='* * * * *', catchup=False, default_args=default_args) as parent_dag: @task(task_id='parent_task') def parent_sleep(): sleep(10) parent_task = parent_sleep() child_dag__wait_for_parent_task = ExternalTaskMarker( task_id='child_dag__wait_for_parent_task', external_dag_id='child_dag', external_task_id='wait_for_parent_task', ) parent_task >> child_dag__wait_for_parent_task with DAG('child_dag', schedule_interval='* * * * *', catchup=False, default_args=default_args) as child_dag: wait_for_parent_task = ExternalTaskSensor( task_id='wait_for_parent_task', external_dag_id='parent_dag', external_task_id='parent_task', mode='reschedule', poke_interval=15, timeout=60, ) @task(task_id='child_task') def child_sleep(): sleep(10) child_task = child_sleep() wait_for_parent_task >> child_task ``` 2. Enable the `parent_dag` and `child_dag` DAGs and wait for them to automatically run (they're scheduled to run every minute). 3. Wait for at least one additional minute (because the sensor timeout is configured to be one minute). 4. Clear the earliest `parent_dag.parent_task` task instance with the "Downstream" and "Recursive" options enabled. 5. When the cleared `child_dag.wait_for_parent_task` task tries to run it will immediately fail with an `AirflowSensorTimeout` exception. ### Operating System Debian 10.13 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else This appears to be due to a bug in `airflow.models.taskinstance.clear_task_instances()` where [it only increments the task instance's `max_tries` property if the task is found in the DAG passed in](https://github.com/apache/airflow/blob/2.3.3/airflow/models/taskinstance.py#L219-L223), but when recursively clearing tasks that won't work properly for tasks in downstream DAGs, because all task instances to be recursively cleared are passed to `clear_task_instances()` with [the DAG of the initial task being cleared](https://github.com/apache/airflow/blob/2.3.3/airflow/models/dag.py#L1905). When a cleared task instance for a sensor using reschedule mode doesn't have its `max_tries` property incremented that causes the [logic in `BaseSensorOperator.execute()`](https://github.com/apache/airflow/blob/2.3.3/airflow/sensors/base.py#L247-L264) to incorrectly choose an older `first_try_number` value, calculate the sensor run duration as the total time passed since that previous run, and fail with an `AirflowSensorTimeout` exception if that inflated run duration exceeds the sensor timeout. While I tested this in Airflow 2.3.3 because that's what my company is running, I also looked at the current `main` branch code and this appears to still be a problem in the latest version. IMO the best solution would be to change `airflow.models.taskinstance.clear_task_instances()` to make an effort to get the associated DAGs for all the task instances being cleared so their associated tasks can be read and their `max_tries` property can be incremented correctly. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29049

https://github.com/apache/airflow/pull/29065

7074167d71c93b69361d24c1121adc7419367f2a

0d2e6dce709acebdb46288faef17d322196f29a2

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/sql_to_s3.py"]

Top level code imports in AWS transfer

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.3.3 ### Operating System MacOs/Linux ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened sql_to_s3.py transfer has top level python code imports considered as Bad Practices: https://github.com/apache/airflow/blob/be31214dcf14db39b7a5f422ca272cdc13e08268/airflow/providers/amazon/aws/transfers/sql_to_s3.py#L26 According to the [official docs](https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code): ```python import numpy as np # <-- THIS IS A VERY BAD IDEA! DON'T DO THAT! ``` All imports that are not related to DAG structure and creation should be moved to callable functions, such as the `execute` method. This causes timeout errors while filling the `DagBag`: ``` File "/opt/airflow/dags/mydag.py", line 6, in <module> from airflow.providers.amazon.aws.transfers.sql_to_s3 import SqlToS3Operator File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/transfers/sql_to_s3.py", line 25, in <module> import pandas as pd File "/home/airflow/.local/lib/python3.7/site-packages/pandas/__init__.py", line 50, in <module> from pandas.core.api import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/api.py", line 48, in <module> from pandas.core.groupby import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/__init__.py", line 1, in <module> from pandas.core.groupby.generic import ( File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 73, in <module> from pandas.core.frame import DataFrame File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/frame.py", line 193, in <module> from pandas.core.series import Series File "/home/airflow/.local/lib/python3.7/site-packages/pandas/core/series.py", line 141, in <module> import pandas.plotting File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 724, in exec_module File "<frozen importlib._bootstrap_external>", line 859, in get_code File "<frozen importlib._bootstrap_external>", line 917, in get_data File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/timeout.py", line 68, in handle_timeout raise AirflowTaskTimeout(self.error_message) airflow.exceptions.AirflowTaskTimeout: DagBag import timeout for /opt/airflow/dags/mydag.py after 30.0s. Please take a look at these docs to improve your DAG import time: * https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#top-level-python-code * https://airflow.apache.org/docs/apache-airflow/2.3.3/best-practices.html#reducing-dag-complexity, PID: 7 ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29036

https://github.com/apache/airflow/pull/29045

af0bbe62a5fc26bac189acd9039f5bbc83c2d429

62825678b3100b0e0ea3b4e14419d259a36ba074

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py"]

Metrics dagrun.duration.failed.<dag_id> not updated when the dag run failed due to timeout

### Apache Airflow version 2.5.0 ### What happened When the dag was set with `dagrun_timeout` [parameter](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG) and the dag run failed due to time out reason, the metrics `dagrun.duration.failed.<dag_id>` was not triggered. ### What you think should happen instead According to the [doc](https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html#timers), the metrics `dagrun.duration.failed.<dag_id>` should capture `Milliseconds taken for a DagRun to reach failed state`. Then it should capture all kinds of dag failure including the failure caused by dag level time out. ### How to reproduce set `dagrun_timeout` [parameter](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG) (e.g. `dagrun_timeout=timedelta(seconds=5)`), then set up a BashOperator task run longer than dagrun_timeout. (e.g., `bash_command='sleep 120'`,). Then check the metrics, dagrun.duration.failed.<dag_id> can not capture this failed dag run due to timeout reason. ### Operating System Ubuntu 22.04.1 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==7.1.0 apache-airflow-providers-common-sql==1.3.3 apache-airflow-providers-ftp==3.3.0 apache-airflow-providers-http==4.1.1 apache-airflow-providers-imap==3.1.1 apache-airflow-providers-postgres==5.4.0 apache-airflow-providers-sqlite==3.3.1 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else According to the [doc](https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html#timers), the metrics `dagrun.duration.failed.<dag_id>` should capture `Milliseconds taken for a DagRun to reach failed state`. However, if the dag run was failed due to the dag run level timeout, the metric can not capture the failed dag run. I deep dive to the airflow code and figured out the reason. The timer `dagrun.duration.failed.{self.dag_id}` was triggered in the method _emit_duration_stats_for_finished_state. [code](https://github.com/apache/airflow/blob/2.5.0/airflow/models/dagrun.py#L880-L894) ``` def _emit_duration_stats_for_finished_state(self): if self.state == State.RUNNING: return if self.start_date is None: self.log.warning("Failed to record duration of %s: start_date is not set.", self) return if self.end_date is None: self.log.warning("Failed to record duration of %s: end_date is not set.", self) return duration = self.end_date - self.start_date if self.state == State.SUCCESS: Stats.timing(f"dagrun.duration.success.{self.dag_id}", duration) elif self.state == State.FAILED: Stats.timing(f"dagrun.duration.failed.{self.dag_id}", duration) ``` The function `_emit_duration_stats_for_finished_state` was only called in the update_state() method for class DagRun(). [code](https://github.com/apache/airflow/blob/2.5.0/airflow/models/dagrun.py#L650-L677) If the update_state() method was not call, then `_emit_duration_stats_for_finished_state` will not used. ``` if self._state == DagRunState.FAILED or self._state == DagRunState.SUCCESS: msg = ( "DagRun Finished: dag_id=%s, execution_date=%s, run_id=%s, " "run_start_date=%s, run_end_date=%s, run_duration=%s, " "state=%s, external_trigger=%s, run_type=%s, " "data_interval_start=%s, data_interval_end=%s, dag_hash=%s" ) self.log.info( msg, self.dag_id, self.execution_date, self.run_id, self.start_date, self.end_date, (self.end_date - self.start_date).total_seconds() if self.start_date and self.end_date else None, self._state, self.external_trigger, self.run_type, self.data_interval_start, self.data_interval_end, self.dag_hash, ) session.flush() self._emit_true_scheduling_delay_stats_for_finished_state(finished_tis) self._emit_duration_stats_for_finished_state() ``` When a dag run was timed out, in the scheduler job, it will only call set_state(). [code](https://github.com/apache/airflow/blob/2.5.0/airflow/jobs/scheduler_job.py#L1280-L1312) ``` if ( dag_run.start_date and dag.dagrun_timeout and dag_run.start_date < timezone.utcnow() - dag.dagrun_timeout ): dag_run.set_state(DagRunState.FAILED) unfinished_task_instances = ( session.query(TI) .filter(TI.dag_id == dag_run.dag_id) .filter(TI.run_id == dag_run.run_id) .filter(TI.state.in_(State.unfinished)) ) for task_instance in unfinished_task_instances: task_instance.state = TaskInstanceState.SKIPPED session.merge(task_instance) session.flush() self.log.info("Run %s of %s has timed-out", dag_run.run_id, dag_run.dag_id) active_runs = dag.get_num_active_runs(only_running=False, session=session) # Work out if we should allow creating a new DagRun now? if self._should_update_dag_next_dagruns(dag, dag_model, active_runs): dag_model.calculate_dagrun_date_fields(dag, dag.get_run_data_interval(dag_run)) callback_to_execute = DagCallbackRequest( full_filepath=dag.fileloc, dag_id=dag.dag_id, run_id=dag_run.run_id, is_failure_callback=True, processor_subdir=dag_model.processor_subdir, msg="timed_out", ) dag_run.notify_dagrun_state_changed() return callback_to_execute ``` From the above code, we can see that when the DAG run was timed out, it will call the set_state() method only. Here update_state() method was not called and that is why the metrics dagrun.duration.failed.{self.dag_id} was not set up accordingly. Please fix this bug to let the timer `dagrun.duration.failed.<dag_id>` can capture the failed dag run due to dag level timed out. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29013

https://github.com/apache/airflow/pull/29076

9dedf81fa18e57755aa7d317f08f0ea8b6c7b287

ca9a59b3e8c08286c8efd5ca23a509f9178a3cc9

python

closed

apache/airflow

https://github.com/apache/airflow

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

KubernetesPodOperator xcom push failure

### Apache Airflow version 2.5.0 ### What happened Kubernetes pod operator failed to push xcom value. After upgrading airflow from 2.2.4 to 2.5.0 (and apache-airflow-providers-cncf-kubernetes from 3.0.2 to 5.0.0) pushing of xcom values from kubernetes pod operator stopped working. ### What you think should happen instead Example of log before the upgrade ``` [2023-01-17 06:57:54,357] {pod_launcher.py:313} INFO - Running command... cat /airflow/xcom/return.json [2023-01-17 06:57:54,398] {pod_launcher.py:313} INFO - Running command... kill -s SIGINT 1 [2023-01-17 06:57:55,012] {pod_launcher.py:186} INFO - ["No non-accuracy metrics changed more than 10.0% between 2023-01-15 and 2023-01-16\n"] ``` and after the upgrade ``` [2023-01-18T07:12:32.784+0900] {pod_manager.py:368} INFO - Checking if xcom sidecar container is started. [2023-01-18T07:12:32.804+0900] {pod_manager.py:372} INFO - The xcom sidecar container is started. [2023-01-18T07:12:32.845+0900] {pod_manager.py:407} INFO - Running command... if [ -s /airflow/xcom/return.json ]; then cat /airflow/xcom/return.json; else echo __airflow_xcom_result_empty__; fi [2023-01-18T07:12:32.895+0900] {pod_manager.py:407} INFO - Running command... kill -s SIGINT 1 [2023-01-18T07:12:33.405+0900] {kubernetes_pod.py:431} INFO - Result file is empty. ``` Looking at other timestamps in the log file, it appears that the xcom sidecar is run before the pod finishes, instead of waiting until the end. ### How to reproduce I used `airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator` with `get_logs=False`. ### Operating System Debian GNU/Linux 11 (bullseye) (based on official airflow image) ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes 3.0.2 and 5.0.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Deployed on GKE using the official helm chart ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/29002

https://github.com/apache/airflow/pull/29052

4a9e1e8a1fcf76c0bd9e2c501b0da0466223f6ac

1e81a98cc69344a35c50b00e2d25a6d48a9bded2

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Mapping skips tasks before upstream has started

### Apache Airflow version 2.5.0 ### What happened In some cases we are seeing dynamic mapped task being skipped before upstream tasks have started & the dynamic count for the task can be calculated. We see this both locally in a with the `LocalExecutor` & on our cluster with the `KubernetesExecutor`. To trigger the issue we need multiple dynamic tasks merging into a upstream task, see the images below for example. If there is no merging the tasks run as expected. The tasks also need to not know the number of dynamic tasks that will be created on DAG start, for example by chaining in an other dynamic task output.   If the DAG, task, or upstream tasks are cleared the skipped task runs as expected. The issue exists both on airflow 2.4.x & 2.5.0. Happy to help debug this further & answer any questions! ### What you think should happen instead The tasks should run after upstream tasks are done. ### How to reproduce The following code is able to reproduce the issue on our side: ```python from datetime import datetime from airflow import DAG from airflow.decorators import task from airflow.utils.task_group import TaskGroup from airflow.operators.empty import EmptyOperator # Only one chained tasks results in only 1 of the `skipped_tasks` skipping. # Add in extra tasks results in both `skipped_tasks` skipping, but # no earlier tasks are ever skipped. CHAIN_TASKS = 1 @task() def add(x, y): return x, y with DAG( dag_id="test_skip", schedule=None, start_date=datetime(2023, 1, 13), ) as dag: init = EmptyOperator(task_id="init_task") final = EmptyOperator(task_id="final") for i in range(2): with TaskGroup(f"task_group_{i}") as tg: chain_task = [i] for j in range(CHAIN_TASKS): chain_task = add.partial(x=j).expand(y=chain_task) skipped_task = ( add.override(task_id="skipped").partial(x=i).expand(y=chain_task) ) # Task isn't skipped if final (merging task) is removed. init >> tg >> final ``` ### Operating System MacOS ### Versions of Apache Airflow Providers This can be reproduced without any extra providers installed. ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28973

https://github.com/apache/airflow/pull/30641

8cfc0f6332c45ca750bc2317ea1e283aaf2ac5bd

5f2628d36cb8481ee21bd79ac184fd8fdce3e47d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add a way to skip Docker Operator task

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow 2.3.3 Raising the `AirflowSkipException` in the source code, using the `DockerOperator`, is supposed to mark the task as skipped, according to the [docs](https://airflow.apache.org/docs/apache-airflow/stable/concepts/tasks.html#special-exceptions). However, what happens is that the task is marked as failed with the logs showing `ERROR - Task failed with exception`. ### What you think should happen instead Tasks should be marked as skipped, not failed. ### How to reproduce Raise the `AirflowSkipException` in the python source code, while using the `DockerOperator`. ### Operating System Ubuntu 20.04.5 LTS (GNU/Linux 5.4.0-125-generic x86_64) ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28951

https://github.com/apache/airflow/pull/28996

bc5cecc0db27cb8684c238b36ad12c7217d0c3ca

3a7bfce6017207218889b66976dbee1ed84292dc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/decorators/kubernetes.py", "airflow/providers/cncf/kubernetes/python_kubernetes_script.jinja2", "tests/providers/cncf/kubernetes/decorators/test_kubernetes.py"]

@task.kubernetes TaskFlow decorator fails with IndexError and is unable to receive input

### Apache Airflow Provider(s) cncf-kubernetes ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes 5.0.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened When passing arguments (either args or kwargs) to a @task.kubernetes decorated function, the following exception occurs: Task Logs: ``` [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:621} INFO - Building pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 with labels: {'dag_id': 'test_k8s_input_1673647477', 'task_id': 'k8s_with_input', 'run_id': 'backfill__2023-01-01T0000000000-c16e0472d', 'kubernetes_pod_operator': 'True', 'try_number': '1'} [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:404} INFO - Found matching pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 with labels {'airflow_kpo_in_cluster': 'True', 'airflow_version': '2.5.0', 'dag_id': 'test_k8s_input_1673647477', 'kubernetes_pod_operator': 'True', 'run_id': 'backfill__2023-01-01T0000000000-c16e0472d', 'task_id': 'k8s_with_input', 'try_number': '1'} [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:405} INFO - `try_number` of task_instance: 1 [2023-01-13, 22:05:40 UTC] {kubernetes_pod.py:406} INFO - `try_number` of pod: 1 [2023-01-13, 22:05:40 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:41 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:42 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:43 UTC] {pod_manager.py:189} WARNING - Pod not yet started: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - + python -c 'import base64, os;x = os.environ["__PYTHON_SCRIPT"];f = open("/tmp/script.py", "w"); f.write(x); f.close()' [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - + python /tmp/script.py [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - Traceback (most recent call last): [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - File "/tmp/script.py", line 14, in <module> [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - with open(sys.argv[1], "rb") as file: [2023-01-13, 22:05:44 UTC] {pod_manager.py:237} INFO - IndexError: list index out of range [2023-01-13, 22:05:44 UTC] {kubernetes_pod.py:499} INFO - Deleting pod: k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 [2023-01-13, 22:05:44 UTC] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/decorators/kubernetes.py", line 104, in execute return super().execute(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/decorators/base.py", line 217, in execute return_value = super().execute(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 465, in execute self.cleanup( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 489, in cleanup raise AirflowException( airflow.exceptions.AirflowException: Pod k8s-airflow-pod-5c285c340fdf4e-81721f4662e247e793f497ada2f1ce55 returned a failure: ``` ### What you think should happen instead K8's decorator should properly receive input. The [python command invoked here](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/cncf/kubernetes/decorators/kubernetes.py#L75) does not pass input. Contrast this with the [docker version of the decorator](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/docker/decorators/docker.py#L105) which does properly pass pickled input. ### How to reproduce Create a dag: ```py import os from airflow import DAG from airflow.decorators import task DEFAULT_TASK_ARGS = { "owner": "gcp-data-platform", "start_date": "2022-12-16", "retries": 0, } @task.kubernetes( image="python:3.8-slim-buster", namespace=os.getenv("AIRFLOW__KUBERNETES_EXECUTOR__NAMESPACE"), in_cluster=False, ) def k8s_with_input(val: str) -> str: import datetime print(f"Got val: {val}") return val with DAG( schedule_interval="@daily", max_active_runs=1, max_active_tasks=5, catchup=False, dag_id="test_oom_dag", default_args=DEFAULT_TASK_ARGS, ) as dag: output = k8s_with_input.override(task_id="k8s_with_input")("a") ``` Run and observe failure: <img width="907" alt="image" src="https://user-images.githubusercontent.com/9200263/212427952-15466317-4e61-4b71-9971-2cdedba4f7ba.png"> Task logs above. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28933

https://github.com/apache/airflow/pull/28942

73c8e7df0be8b254e3727890b51ca0f76308e6b5

9a5c3e0ac0b682d7f2c51727a56e06d68bc9f6be

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/auth/backend/kerberos_auth.py", "docs/apache-airflow/administration-and-deployment/security/api.rst"]

Airflow API kerberos authentication error

### Apache Airflow version 2.5.0 ### What happened Configured AUTH_DB authentication for web server and Kerberos authentication for API. Web server works well. Try to get any API endpoint and get an error 500. I see Kerberos authentication step is done, but authorization step fails. 'User' object (now it is just a string) doesn't have such parameter. Request error ``` янв 13 13:54:14 nginx-test airflow[238738]: [2023-01-13 13:54:14,923] {app.py:1741} ERROR - Exception on /api/v1/dags [GET] янв 13 13:54:14 nginx-test airflow[238738]: Traceback (most recent call last): янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 2525, in wsgi_app янв 13 13:54:14 nginx-test airflow[238738]: response = self.full_dispatch_request() янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1822, in full_dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: rv = self.handle_user_exception(e) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1820, in full_dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: rv = self.dispatch_request() янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/flask/app.py", line 1796, in dispatch_request янв 13 13:54:14 nginx-test airflow[238738]: return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/decorator.py", line 68, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/uri_parsing.py", line 149, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/validation.py", line 399, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: return function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/response.py", line 112, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: response = function(request) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/connexion/decorators/parameter.py", line 120, in wrapper янв 13 13:54:14 nginx-test airflow[238738]: return function(**kwargs) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/api_connexion/security.py", line 50, in decorated янв 13 13:54:14 nginx-test airflow[238738]: if appbuilder.sm.check_authorization(permissions, kwargs.get("dag_id")): янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/www/security.py", line 715, in check_authorization янв 13 13:54:14 nginx-test airflow[238738]: can_access_all_dags = self.has_access(*perm) янв 13 13:54:14 nginx-test airflow[238738]: File "/usr/local/lib/python3.8/dist-packages/airflow/www/security.py", line 419, in has_access янв 13 13:54:14 nginx-test airflow[238738]: if (action_name, resource_name) in user.perms: янв 13 13:54:14 nginx-test airflow[238738]: AttributeError: 'str' object has no attribute 'perms' янв 13 13:54:14 nginx-test airflow[238738]: 127.0.0.1 - - [13/Jan/2023:13:54:14 +0300] "GET /api/v1/dags HTTP/1.1" 500 1561 "-" "curl/7.68.0" ``` Starting airflow-webserver log (no errors) ``` янв 13 13:38:51 nginx-test airflow[238502]: ____________ _____________ янв 13 13:38:51 nginx-test airflow[238502]: ____ |__( )_________ __/__ /________ __ янв 13 13:38:51 nginx-test airflow[238502]: ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / янв 13 13:38:51 nginx-test airflow[238502]: ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / янв 13 13:38:51 nginx-test airflow[238502]: _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ янв 13 13:38:51 nginx-test airflow[238502]: Running the Gunicorn Server with: янв 13 13:38:51 nginx-test airflow[238502]: Workers: 4 sync янв 13 13:38:51 nginx-test airflow[238502]: Host: 0.0.0.0:10000 янв 13 13:38:51 nginx-test airflow[238502]: Timeout: 120 янв 13 13:38:51 nginx-test airflow[238502]: Logfiles: - - янв 13 13:38:51 nginx-test airflow[238502]: Access Logformat: янв 13 13:38:51 nginx-test airflow[238502]: ================================================================= янв 13 13:38:51 nginx-test airflow[238502]: [2023-01-13 13:38:51,209] {webserver_command.py:431} INFO - Received signal: 15. Closing gunicorn. янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238525 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238523 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238526 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [WARNING] Worker with pid 238524 was terminated due to signal 15 янв 13 13:38:51 nginx-test airflow[238519]: [2023-01-13 13:38:51 +0300] [238519] [INFO] Shutting down: Master янв 13 13:38:52 nginx-test systemd[1]: airflow-webserver.service: Succeeded. янв 13 13:38:52 nginx-test systemd[1]: Stopped Airflow webserver daemon. янв 13 13:38:52 nginx-test systemd[1]: Started Airflow webserver daemon. янв 13 13:38:54 nginx-test airflow[238732]: /usr/local/lib/python3.8/dist-packages/airflow/api/auth/backend/kerberos_auth.py:50 DeprecationWarning: '_request_ctx_stack' is dep> янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,393] {kerberos_auth.py:78} INFO - Kerberos: hostname nginx-test.mycompany янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,393] {kerberos_auth.py:88} INFO - Kerberos init: airflow nginx-test.mycompany янв 13 13:38:54 nginx-test airflow[238732]: [2023-01-13 13:38:54,394] {kerberos_auth.py:93} INFO - Kerberos API: server is airflow/nginx-test.mycompany@MYCOMPANY> янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Starting gunicorn 20.1.0 янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Listening at: http://0.0.0.0:10000 (238732) янв 13 13:38:56 nginx-test airflow[238732]: [2023-01-13 13:38:56 +0300] [238732] [INFO] Using worker: sync янв 13 13:38:56 nginx-test airflow[238735]: [2023-01-13 13:38:56 +0300] [238735] [INFO] Booting worker with pid: 238735 янв 13 13:38:57 nginx-test airflow[238736]: [2023-01-13 13:38:57 +0300] [238736] [INFO] Booting worker with pid: 238736 янв 13 13:38:57 nginx-test airflow[238737]: [2023-01-13 13:38:57 +0300] [238737] [INFO] Booting worker with pid: 238737 янв 13 13:38:57 nginx-test airflow[238738]: [2023-01-13 13:38:57 +0300] [238738] [INFO] Booting worker with pid: 238738 ``` I tried to skip rights check, commenting problem lines and returning True from has_access function and if I remember it right in one more function from security.py. And I got it working. But it has been just a hack to check where is the problem. ### What you think should happen instead It should return right json answer with code 200. ### How to reproduce 1. webserver_config.py: default 2. airflow.cfg changed lines: ``` [core] security = kerberos [api] auth_backends = airflow.api.auth.backend.kerberos_auth,airflow.api.auth.backend.session [kerberos] ccache = /tmp/airflow_krb5_ccache principal = airflow/nginx-test.mycompany reinit_frequency = 3600 kinit_path = kinit keytab = /root/airflow/airflow2.keytab forwardable = True include_ip = True [webserver] base_url = http://localhost:10000 web_server_port = 10000 ``` 3. Create keytab file with airflow principal 4. Log in as domain user, make request (for example): curl --verbose --negotiate -u : http://nginx-test.mycompany:10000/api/v1/dags ### Operating System Ubuntu. VERSION="20.04.5 LTS (Focal Fossa)" ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28919

https://github.com/apache/airflow/pull/29054

80dbfbc7ad8f63db8565baefa282bc01146803fe

135aef30be3f9b8b36556f3ff5e0d184b0f74f22

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/start.rst"]

quick start fails: DagRun for example_bash_operator with run_id or execution_date of '2015-01-01' not found

### Apache Airflow version 2.5.0 ### What happened I follow the [quick start guide](https://airflow.apache.org/docs/apache-airflow/stable/start.html) When I execute `airflow tasks run example_bash_operator runme_0 2015-01-01` I got the following error: ``` [2023-01-13 15:50:42,493] {dagbag.py:538} INFO - Filling up the DagBag from /root/airflow/dags [2023-01-13 15:50:42,761] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): prepare_email>, send_email already registered for DAG: example_dag_decorator [2023-01-13 15:50:42,761] {taskmixin.py:205} WARNING - Dependency <Task(EmailOperator): send_email>, prepare_email already registered for DAG: example_dag_decorator [2023-01-13 15:50:42,830] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_entry_group>, delete_entry_group already registered for DAG: example_complex [2023-01-13 15:50:42,830] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_entry_group>, create_entry_group already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_entry_gcs>, delete_entry already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_entry>, create_entry_gcs already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): create_tag>, delete_tag already registered for DAG: example_complex [2023-01-13 15:50:42,831] {taskmixin.py:205} WARNING - Dependency <Task(BashOperator): delete_tag>, create_tag already registered for DAG: example_complex [2023-01-13 15:50:42,852] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,852] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,853] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,853] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,854] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,854] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): print_the_context>, log_sql_query already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {taskmixin.py:205} WARNING - Dependency <Task(_PythonDecoratedOperator): log_sql_query>, print_the_context already registered for DAG: example_python_operator [2023-01-13 15:50:42,855] {example_python_operator.py:90} WARNING - The virtalenv_python example task requires virtualenv, please install it. [2023-01-13 15:50:43,608] {tutorial_taskflow_api_virtualenv.py:29} WARNING - The tutorial_taskflow_api_virtualenv example DAG requires virtualenv, please install it. /root/miniconda3/lib/python3.7/site-packages/airflow/models/dag.py:3524 RemovedInAirflow3Warning: Param `schedule_interval` is deprecated and will be removed in a future release. Please use `schedule` instead. Traceback (most recent call last): File "/root/miniconda3/bin/airflow", line 8, in <module> sys.exit(main()) File "/root/miniconda3/lib/python3.7/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 384, in task_run ti, _ = _get_ti(task, args.map_index, exec_date_or_run_id=args.execution_date_or_run_id, pool=args.pool) File "/root/miniconda3/lib/python3.7/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 163, in _get_ti session=session, File "/root/miniconda3/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 118, in _get_dag_run ) from None airflow.exceptions.DagRunNotFound: DagRun for example_bash_operator with run_id or execution_date of '2023-11-01' not found ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 18.04.3 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28912

https://github.com/apache/airflow/pull/28949

c57c23dce39992eafcf86dc08a1938d7d407803f

a4f6f3d6fe614457ff95ac803fd15e9f0bd38d27

python

closed

apache/airflow

https://github.com/apache/airflow

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

Misnamed param in EcsRunTaskOperator

### What do you see as an issue? In the `EcsRunTaskOperator`, one of the params in the docstring is `region_name`, but it should be `region`: https://github.com/apache/airflow/blob/2.5.0/airflow/providers/amazon/aws/operators/ecs.py#L281 ### Solving the problem _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28910

https://github.com/apache/airflow/pull/29562

eb46eeb33d58436aa5860f2f0031fad3dea3ce3b

cadab59e8df90588b07cf8d9ee3ce13f9a79f656

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/pgbouncer/pgbouncer-deployment.yaml", "chart/values.schema.json", "chart/values.yaml"]

Pgbouncer metrics exporter restarts

### Official Helm Chart version 1.6.0 ### Apache Airflow version 2.4.2 ### Kubernetes Version 1.21 ### Helm Chart configuration Nothing really specific ### Docker Image customizations _No response_ ### What happened From time to time we have pg_bouncer metrics exporter that fails its healthcheck. When it fails its healtchecks three times in a row, pgbouncer stop being reachable and drops all the ongoing connection. Is it possible to make the pgbouncer healtcheck configurable at least the timeout parameter of one second that seems really short? ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28891

https://github.com/apache/airflow/pull/29752

d0fba865aed1fc21d82f0a61cddb1fa0bd4b7d0a

44f89c6db115d91aba91955fde42475d1a276628

python

closed

apache/airflow

https://github.com/apache/airflow

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

`webserver.instance_name` shows markup text in `<title>` tag

### Apache Airflow version 2.5.0 ### What happened https://github.com/apache/airflow/pull/20888 enables the use of markup to style the `webserver.instance_name`. However, if the instance name has HTML code, this will also be reflected in the `<title>` tag, as shown in the screenshot below.  This is not a pretty behaviour. ### What you think should happen instead Ideally, if `webserver. instance_name_has_markup = True`, then the text inside the `<title>` should be stripped of HTML code. For example: - Set `webserver.instance_name` to some text with markup, like `<b style="color: red">title</b>` - Set `webserver.Instance_name_has_markup` to `true` This is how the `<title>` tag should look like: ```html <title>DAGs - title</title> ``` Instead of: ``` <title>DAGs - &lt;b style=&#34;color: red&#34;&gt;title&lt;b&gt;</title> ``` ### How to reproduce - Airflow version 2.3+, which is [when this change has been introduced](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#instance-name-has-markup) - Set `webserver.instance_name` to some text with markup, like `<b style="color: red">title</b>` - Set `webserver.Instance_name_has_markup` to `true` ### Operating System Doesn't matter ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28888

https://github.com/apache/airflow/pull/28894

696b91fafe4a557f179098e0609eb9d9dcb73f72

971e3226dc3ca43900f0b79c42afffb14c59d691

python

closed

apache/airflow

https://github.com/apache/airflow

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

Azure Blob storage exposes crendentials in UI

### Apache Airflow version Other Airflow 2 version (please specify below) 2.3.3 ### What happened Azure Blob Storage exposes credentials in the UI <img width="1249" alt="Screenshot 2023-01-12 at 14 00 05" src="https://user-images.githubusercontent.com/35199552/212072943-adca75c4-2226-4251-9446-e8f18fb22081.png"> ### What you think should happen instead _No response_ ### How to reproduce Create an Azure Blob storage connection. then click on the edit button on the connection. ### Operating System debain ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28884

https://github.com/apache/airflow/pull/28914

6f4544cfbdfa3cabb3faaeea60a651206cd84e67

3decb189f786781bb0dfb3420a508a4a2a22bd8b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/callModal.js", "airflow/www/templates/airflow/dag.html", "airflow/www/views.py"]

Graph UI: Add Filter Downstream & Filter DownStream & Upstream

### Description Currently Airflow has a `Filter Upstream` View/option inside the graph view. (As documented [here](https://docs.astronomer.io/learn/airflow-ui#graph-view) under `Filter Upstream`) <img width="682" alt="image" src="https://user-images.githubusercontent.com/9246654/211711759-670a1180-7f90-4ecd-84b0-2f3b290ff477.png"> It would be great if there were also the options 1. `Filter Downstream` & 2. `Filter Downstream & Upstream` ### Use case/motivation Sometimes it is useful to view downstream tasks & down & upstream tasks when reviewing dags. This feature would make it as easy to view those as it is to view upstream today. ### Related issues I found nothing with a quick search ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28847

https://github.com/apache/airflow/pull/29226

624520db47f736af820b4bc834a5080111adfc96

a8b2de9205dd805ee42cf6b0e15e7e2805752abb

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py", "airflow/providers/amazon/aws/waiters/README.md", "airflow/providers/amazon/aws/waiters/dynamodb.json", "docs/apache-airflow-providers-amazon/transfer/dynamodb_to_s3.rst", "tests/providers/amazon/aws/transfers/test_dynamodb_to_s3.py", "tests/providers/amazon/aws/waiters/test_custom_waiters.py", "tests/system/providers/amazon/aws/example_dynamodb_to_s3.py"]

Export DynamoDB table to S3 with PITR

### Description Airflow provides the Amazon DynamoDB to Amazon S3 below. https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/transfer/dynamodb_to_s3.html Most of Data Engineer build their "export DDB data to s3" pipeline using "within the point in time recovery window". https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb.html#DynamoDB.Client.export_table_to_point_in_time I appreciate if airflow has this function as a native function. ### Use case/motivation My daily batch job exports its data with pitr option. All of tasks is written by apache-airflow-providers-amazon except "export_table_to_point_in_time" task. "export_table_to_point_in_time" task only used the python operator. I expect I can unify the task as apache-airflow-providers-amazon library. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28830

https://github.com/apache/airflow/pull/31142

71c26276bcd3ddd5377d620e6b8baef30b72eaa0

cd3fa33e82922e01888d609ed9c24b9c2dadfa27

python

closed

apache/airflow

https://github.com/apache/airflow

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

Bad request when triggering dag run with `note` in payload

### Apache Airflow version 2.5.0 ### What happened Specifying a `note` in the payload (as mentioned [in the doc](https://airflow.apache.org/docs/apache-airflow/2.5.0/stable-rest-api-ref.html#operation/post_dag_run)) when triggering a new dag run yield a 400 bad request (Git Version: .release:2.5.0+fa2bec042995004f45b914dd1d66b466ccced410) ### What you think should happen instead As far as I understand the documentation, I should be able to set a note for this dag run, and it is not the case. ### How to reproduce This is a local airflow, using default credentials and default setup when following [this guide](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#) DAG: <details> ``` import airflow from airflow import DAG import logging from airflow.operators.python import PythonOperator from airflow.operators.dummy import DummyOperator from datetime import timedelta logger = logging.getLogger("airflow.task") default_args = { "owner": "airflow", "depends_on_past": False, "retries": 0, "retry_delay": timedelta(minutes=5), } def log_body(**context): logger.info(f"Body: {context['dag_run'].conf}") with DAG( "my-validator", default_args=default_args, schedule_interval=None, start_date=airflow.utils.dates.days_ago(0), catchup=False ) as dag: ( PythonOperator( task_id="abcde", python_callable=log_body, provide_context=True ) >> DummyOperator( task_id="todo" ) ) ``` </details> Request: <details> ``` curl --location --request POST '0.0.0.0:8080/api/v1/dags/my-validator/dagRuns' \ --header 'Authorization: Basic YWlyZmxvdzphaXJmbG93' \ --header 'Content-Type: application/json' \ --data-raw '{ "conf": { "key":"value" }, "note": "test" }' ``` </details> Response: <details> ``` { "detail": "{'note': ['Unknown field.']}", "status": 400, "title": "Bad Request", "type": "https://airflow.apache.org/docs/apache-airflow/2.5.0/stable-rest-api-ref.html#section/Errors/BadRequest" } ``` </details> Removing the `note` key, returns 200... with a null `note`! <details> ``` { "conf": { "key": "value" }, "dag_id": "my-validator", "dag_run_id": "manual__2023-01-10T10:45:26.102802+00:00", "data_interval_end": "2023-01-10T10:45:26.102802+00:00", "data_interval_start": "2023-01-10T10:45:26.102802+00:00", "end_date": null, "execution_date": "2023-01-10T10:45:26.102802+00:00", "external_trigger": true, "last_scheduling_decision": null, "logical_date": "2023-01-10T10:45:26.102802+00:00", "note": null, "run_type": "manual", "start_date": null, "state": "queued" } ``` </details> ### Operating System Ubuntu 20.04.5 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else Everytime. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28825

https://github.com/apache/airflow/pull/29228

e626131563efb536f325a35c78585b74d4482ea3

b94f36bf563f5c8372086cec63b74eadef638ef8

python

closed

apache/airflow

https://github.com/apache/airflow

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

DatabricksSubmitRunOperator Get failed for Multi Task Databricks Job Run

### Apache Airflow Provider(s) databricks ### Versions of Apache Airflow Providers As we are running DatabricksSubmitRunOperator to run multi task databricks job as I am using airflow providers with mostly all flavours of versions, but when the databricks job get failed, DatabricksSubmitRunOperator gives below error its because this operator running get-output API, hence taking job run id instead of taking task run id Error ```console File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 355, in _do_api_call for attempt in self._get_retry_object(): File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/tenacity/__init__.py", line 382, in __iter__ do = self.iter(retry_state=retry_state) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/tenacity/__init__.py", line 349, in iter return fut.result() File "/usr/lib/python3.8/concurrent/futures/_base.py", line 437, in result return self.__get_result() File "/usr/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 365, in _do_api_call response.raise_for_status() File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/requests/models.py", line 960, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/operators/databricks.py", line 375, in execute _handle_databricks_operator_execution(self, hook, self.log, context) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/operators/databricks.py", line 90, in _handle_databricks_operator_execution run_output = hook.get_run_output(operator.run_id) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks.py", line 280, in get_run_output run_output = self._do_api_call(OUTPUT_RUNS_JOB_ENDPOINT, json) File "/home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/providers/databricks/hooks/databricks_base.py", line 371, in _do_api_call raise AirflowException( airflow.exceptions.AirflowException: Response: b'{"error_code":"INVALID_PARAMETER_VALUE","message":"Retrieving the output of runs with multiple tasks is not supported. Please retrieve the output of each individual task run instead."}', Status Code: 400 [2023-01-10, 05:15:12 IST] {taskinstance.py} INFO - Marking task as FAILED. dag_id=experiment_metrics_store_experiment_4, task_id=, execution_date=20230109T180804, start_date=20230109T180810, end_date=20230109T181512 [2023-01-10, 05:15:13 IST] {warnings.py} WARNING - /home/ubuntu/.venv/airflow/lib/python3.8/site-packages/airflow/utils/email.py:119: PendingDeprecationWarning: Fetching SMTP credentials from configuration variables will be deprecated in a future release. Please set credentials using a connection instead. send_mime_email(e_from=mail_from, e_to=recipients, mime_msg=msg, conn_id=conn_id, dryrun=dryrun) ``` ### Apache Airflow version 2.3.2 ### Operating System macos ### Deployment Other ### Deployment details _No response_ ### What happened _No response_ ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28812

https://github.com/apache/airflow/pull/25427

87a0bd969b5bdb06c6e93236432eff6d28747e59

679a85325a73fac814c805c8c34d752ae7a94312

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/sql_to_gcs.py", "tests/providers/google/cloud/transfers/test_sql_to_gcs.py"]

BaseSQLToGCSOperator no longer returns at least one file even if empty

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==8.6.0 ### Apache Airflow version 2.5.0 ### Operating System Debian GNU/Linux ### Deployment Astronomer ### Deployment details _No response_ ### What happened PR `Expose SQL to GCS Metadata (https://github.com/apache/airflow/pull/24382)` made a breaking change [here](https://github.com/apache/airflow/blob/3eee33ac8cb74cfbb08bce9090e9c601cf98da44/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L286) that results in no files being returned when there are no data rows (empty table) rather than a single empty file as in the past. ### What you think should happen instead I would like to preserve the original behavior of having at least one file returned even if it is empty. Or to make that behavior optional via a new parameter. The original behavior can be implemented with the following code change: FROM: ``` if file_to_upload["file_row_count"] > 0: yield file_to_upload ``` TO: ``` if file_no == 0 or file_to_upload["file_row_count"] > 0: yield file_to_upload ``` ### How to reproduce Create a DAG that uses BaseSQLToGCSOperator with a SQL command that references an empty SQL table or returns no rows. The `execute` method will not write any files. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28806

https://github.com/apache/airflow/pull/28959

7f2b065ccd01071cff8f298b944d81f3ff3384b5

5350be2194250366536db7f78b88dc8e49c9620e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/datasets/manager.py", "airflow/jobs/scheduler_job.py", "docs/apache-airflow/administration-and-deployment/logging-monitoring/metrics.rst"]

statsd metric for dataset count

### Description A count of datasets that are currently registered/declared in an Airflow deployment. ### Use case/motivation Would be nice to see how deployments are adopting datasets. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28803

https://github.com/apache/airflow/pull/28907

5d84b59554c93fd22e92b46a1061b40b899a8dec

7689592c244111b24bc52e7428c5a3bb80a4c2d6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/cli_parser.py", "setup.cfg"]

Add colors in help outputs of Airfow CLI commands

### Body Folowing up after https://github.com/apache/airflow/pull/22613#issuecomment-1374530689 - seems that there is a new [rich-argparse](https://github.com/hamdanal/rich-argparse) project that might give us the option without rewriting Airflow's argument parsing to click (click has a number of possible performance issues that might impact airlfow's speed of CLI command parsing) Seems this might be rather easy thing to do (just adding the formatter class for argparse). Would be nice if someone implements it and tests (also for performance of CLI). ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28789

https://github.com/apache/airflow/pull/29116

c310fb9255ba458b2842315f65f59758b76df9d5

fdac67b3a5350ab4af79fd98612592511ca5f3fc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/manager.py"]

AIP-44 Migrate DagFileProcessorManager.clear_nonexistent_import_errors to Internal API

https://github.com/apache/airflow/blob/main/airflow/dag_processing/manager.py#L773

https://github.com/apache/airflow/issues/28785

https://github.com/apache/airflow/pull/28976

ca9a59b3e8c08286c8efd5ca23a509f9178a3cc9

09b3a29972430e5749d772359692fe4a9d528e48

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "scripts/docker/clean-logs.sh"]

Script "clean-logs.sh" has an unexpected burst behavior

### Apache Airflow version 2.5.0 ### What happened I've noticed that my Airflow Scheduler logs are full of the following message: Trimming airflow logs to 15 days. Trimming airflow logs to 15 days. ... My deployment uses the Helm chart, so it's probably specific to the Docker related assets. This script has a loop where every 900 seconds it will delete old log files. However, on every activation, the part where it prints the log message and deletes the file is burst a few hundred times in less than a second. The cycle repeats. ### What you think should happen instead It should only print one log message (and delete files once) on every cycle. ### How to reproduce Just comment out the lines regarding the actual file deletion and run it in any bash shell. It should get triggered after 15 minutes. ### Operating System openSUSE Tumbleweed ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28778

https://github.com/apache/airflow/pull/28780

207f65b542a8aa212f04a9d252762643cfd67a74

4b1a36f833b77d3f0bec78958d1fb9f360b7b11b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/json.py", "airflow/www/utils.py", "airflow/www/views.py", "tests/www/test_utils.py"]

DAG Run List UI Breaks when a non-JSON serializable value is added to dag_run.conf

### Apache Airflow version 2.5.0 ### What happened When accessing `dag_run.conf` via a task's context, I was able to add a value that is non-JSON serializable. When I tried to access the Dag Run List UI (`/dagrun/list/`) or the Dag's Grid View, I was met with these error messages respectively: **Dag Run List UI** ``` Ooops! Something bad has happened. Airflow is used by many users, and it is very likely that others had similar problems and you can easily find a solution to your problem. Consider following these steps: * gather the relevant information (detailed logs with errors, reproduction steps, details of your deployment) * find similar issues using: * [GitHub Discussions](https://github.com/apache/airflow/discussions) * [GitHub Issues](https://github.com/apache/airflow/issues) * [Stack Overflow](https://stackoverflow.com/questions/tagged/airflow) * the usual search engine you use on a daily basis * if you run Airflow on a Managed Service, consider opening an issue using the service support channels * if you tried and have difficulty with diagnosing and fixing the problem yourself, consider creating a [bug report](https://github.com/apache/airflow/issues/new/choose). Make sure however, to include all relevant details and results of your investigation so far. ``` **Grid View** ``` Auto-refresh Error <!DOCTYPE html> <html lang="en"> <head> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> </head> <body> <div class="container"> <h1> Ooops! </h1> <div> <pre> Something bad has happened. Airflow is used by many users, and it is very likely that others had similar problems and you can easily find a solution to your problem. Consider following these steps: * gather the relevant information (detailed logs ``` I was able to push the same value to XCom with `AIRFLOW__CORE__ENABLE_XCOM_PICKLING=True`, and the XCom List UI (`/xcom/list/`) did **not** throw an error. In the postgres instance I am using for the Airflow DB, both `dag_run.conf` & `xcom.value` have `BYTEA` types. ### What you think should happen instead Since we are able to add (and commit) a non-JSON serializable value into a Dag Run's conf, the UI should not break when trying to load this value. We could also ensure that one DAG Run's conf does not break the List UI for all Dag Runs (across all DAGs), and the DAG's Grid View. ### How to reproduce - Set `AIRFLOW__CORE__ENABLE_XCOM_PICKLING=True` - Trigger this DAG: ``` import datetime from airflow.decorators import dag, task from airflow.models.xcom import XCom @dag( schedule_interval=None, start_date=datetime.datetime(2023, 1, 1), ) def ui_issue(): @task() def update_conf(**kwargs): dag_conf = kwargs["dag_run"].conf dag_conf["non_json_serializable_value"] = b"1234" print(dag_conf) @task() def push_to_xcom(**kwargs): dag_conf = kwargs["dag_run"].conf print(dag_conf) XCom.set(key="dag_conf", value=dag_conf, dag_id=kwargs["ti"].dag_id, task_id=kwargs["ti"].task_id, run_id=kwargs["ti"].run_id) return update_conf() >> push_to_xcom() dag = ui_issue() ``` - View both the Dag Runs and XCom lists in the UI. - The DAG Run List UI should break, and the XCom List UI should show a value of `{'non_json_serializable_value': b'1234'}` for `ui_issue.push_to_xcom`. ### Operating System Debian Bullseye ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else The XCom List UI was able to render this value. We could extend this capability to the DAG Run List UI. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28772

https://github.com/apache/airflow/pull/28777

82c5a5f343d2310822f7bb0d316efa0abe9d4a21

8069b500e8487675df0472b4a5df9081dcfa9d6c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/connection_command.py", "tests/cli/commands/test_connection_command.py"]

Cannot create connection without defining host using CLI

### Apache Airflow version 2.5.0 ### What happened In order to send logs to s3 bucket after finishing the task, I added a connection to airflow using cli. ```airflow connections add connection_id_1 --conn-uri aws://s3/?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` Then I got a logging warning saying: [2023-01-06T13:28:39.585+0000] {logging_mixin.py:137} WARNING - <string>:8 DeprecationWarning: Host s3 specified in the connection is not used. Please, set it on extra['endpoint_url'] instead Instead I was trying to remove the host from the `conn-uri` I provided but every attempt to create a connection failed (list of my attempts below): ```airflow connections add connection_id_1 --conn-uri aws://?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ```airflow connections add connection_id_1 --conn-uri aws:///?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ### What you think should happen instead I believe there are 2 options: 1. Allow to create connection without defining host or 2. Remove the warning log ### How to reproduce Create an S3 connection using CLI: ```airflow connections add connection_id_1 --conn-uri aws://s3/?region_name=eu-west-1&endpoint_url=https%3A%2F%2Fs3.eu-west-1.amazonaws.com``` ### Operating System Linux - official airflow image from docker hub apache/airflow:slim-2.5.0 ### Versions of Apache Airflow Providers ``` apache-airflow-providers-cncf-kubernetes | 5.0.0 | Kubernetes apache-airflow-providers-common-sql | 1.3.1 | Common SQL Provider apache-airflow-providers-databricks | 4.0.0 | Databricks apache-airflow-providers-ftp | 3.2.0 | File Transfer Protocol (FTP) apache-airflow-providers-hashicorp | 3.2.0 | Hashicorp including Hashicorp Vault apache-airflow-providers-http | 4.1.0 | Hypertext Transfer Protocol (HTTP) apache-airflow-providers-imap | 3.1.0 | Internet Message Access Protocol (IMAP) apache-airflow-providers-postgres | 5.3.1 | PostgreSQL apache-airflow-providers-sqlite | 3.3.1 | SQLite ``` ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else This log message is printed every second minute so it is pretty annoying. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28766

https://github.com/apache/airflow/pull/28922

c5ee4b8a3a2266ef98b379ee28ed68ff1b59ac5f

d8b84ce0e6d36850cd61b1ce37840c80aaec0116

python

closed

apache/airflow

https://github.com/apache/airflow

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

All Airflow Configurations set via Environment Variable are masked when `expose_config` is set as `non-sensitive-only`

### Apache Airflow version 2.5.0 ### What happened In [Airflow 2.4.0](https://github.com/apache/airflow/blob/main/RELEASE_NOTES.rst#airflow-240-2022-09-19), a new feature was added that added an option to mask sensitive data in UI configuration page ([PR](https://github.com/apache/airflow/pull/25346)). I have set `AIRFLOW__WEBSERVER__EXPOSE_CONFIG` as `NON-SENSITIVE-ONLY`. The feature is working partially as the `airflow.cfg` file display only has [sensitive configurations](https://github.com/apache/airflow/blob/2.5.0/airflow/configuration.py#L149-L160) marked as `< hidden >`. However, the `Running Configuration` table below the file display has all configuration set via environment variables marked as `< hidden >` which I believe is unintended. I did not change `airflow.cfg` so the value here is displaying the default value of `False` as expected.  The value for `expose_config` I expect to be shown as `NON-SENSITIVE-ONLY` but it shown as `< hidden >`.  ### What you think should happen instead As mentioned previously, the value for `expose_config` I expect to be shown as `NON-SENSITIVE-ONLY`. Only the [sensitive variables](https://github.com/apache/airflow/blob/2.5.0/airflow/configuration.py#L149-L160) should be set as `< hidden >`. ### How to reproduce Set an Airflow configuration through the environment variable and check on the Configuration page. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28756

https://github.com/apache/airflow/pull/28802

9a7f07491e603123182adfd5706fbae524e33c0d

0a8d0ab56689c341e65a36c0287c9d635bae1242

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/operators/cloud_base.py", "tests/providers/google/cloud/operators/test_cloud_base.py"]

KubernetesExecutor leaves failed pods due to deepcopy issue with Google providers

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened With Airflow 2.3 and 2.4 there appears to be a bug in the KubernetesExecutor when used in conjunction with the Google airflow providers. This bug does not affect Airflow 2.2 due to the pip version requirements. The bug specifically presents itself when using nearly any Google provider operator. During the pod lifecycle, all is well until the executor in the pod starts to clean up following a successful run. Airflow itself still see's the task marked as a success, but in Kubernetes, while the task is finishing up after reporting status, it actually crashes and puts the pod into a Failed state silently: ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 103, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 382, in task_run _run_task_by_selected_method(args, dag, ti) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 189, in _run_task_by_selected_method _run_task_by_local_task_job(args, ti) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/task_command.py", line 247, in _run_task_by_local_task_job run_job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 247, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 137, in _execute self.handle_task_exit(return_code) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 168, in handle_task_exit self._run_mini_scheduler_on_child_tasks() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/local_task_job.py", line 253, in _run_mini_scheduler_on_child_tasks partial_dag = task.dag.partial_subset( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2188, in partial_subset dag.task_dict = { File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2189, in <dictcomp> t.task_id: _deepcopy_task(t) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 2186, in _deepcopy_task return copy.deepcopy(t, memo) File "/usr/local/lib/python3.9/copy.py", line 153, in deepcopy y = copier(memo) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/baseoperator.py", line 1163, in __deepcopy__ setattr(result, k, copy.deepcopy(v, memo)) File "/usr/local/lib/python3.9/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/usr/local/lib/python3.9/copy.py", line 264, in _reconstruct y = func(*args) File "/usr/local/lib/python3.9/enum.py", line 384, in __call__ return cls.__new__(cls, value) File "/usr/local/lib/python3.9/enum.py", line 702, in __new__ raise ve_exc ValueError: <object object at 0x7f570181a3c0> is not a valid _MethodDefault ``` Based on a quick look, it appears to be related to the default argument that Google is using in its operators which happens to be an Enum, and fails during a deepcopy at the end of the task. Example operator that is affected: https://github.com/apache/airflow/blob/403ed7163f3431deb7fc21108e1743385e139907/airflow/providers/google/cloud/hooks/dataproc.py#L753 Reference to the Google Python API core which has the Enum causing the problem: https://github.com/googleapis/python-api-core/blob/main/google/api_core/gapic_v1/method.py#L31 ### What you think should happen instead Kubernetes pods should succeed, be marked as `Completed`, and then be gracefully terminated. ### How to reproduce Use any `apache-airflow-providers-google` >= 7.0.0 which includes `google-api-core` >= 2.2.2. Run a DAG with a task which uses any of the Google operators which have `_MethodDefault` as a default argument. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.0.0 apache-airflow-providers-apache-hive==5.0.0 apache-airflow-providers-celery==3.0.0 apache-airflow-providers-cncf-kubernetes==4.4.0 apache-airflow-providers-common-sql==1.3.1 apache-airflow-providers-docker==3.2.0 apache-airflow-providers-elasticsearch==4.2.1 apache-airflow-providers-ftp==3.1.0 apache-airflow-providers-google==8.4.0 apache-airflow-providers-grpc==3.0.0 apache-airflow-providers-hashicorp==3.1.0 apache-airflow-providers-http==4.0.0 apache-airflow-providers-imap==3.0.0 apache-airflow-providers-microsoft-azure==4.3.0 apache-airflow-providers-mysql==3.2.1 apache-airflow-providers-odbc==3.1.2 apache-airflow-providers-postgres==5.2.2 apache-airflow-providers-presto==4.2.0 apache-airflow-providers-redis==3.0.0 apache-airflow-providers-sendgrid==3.0.0 apache-airflow-providers-sftp==4.1.0 apache-airflow-providers-slack==6.0.0 apache-airflow-providers-sqlite==3.2.1 apache-airflow-providers-ssh==3.2.0 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28751

https://github.com/apache/airflow/pull/29518

ec31648be4c2fc4d4a7ef2bd23be342ca1150956

5a632f78eb6e3dcd9dc808e73b74581806653a89

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/utils.py", "tests/test_utils/www.py", "tests/www/views/conftest.py", "tests/www/views/test_views_home.py"]

UIAlert returns AttributeError: 'NoneType' object has no attribute 'roles' when specifying AUTH_ROLE_PUBLIC

### Apache Airflow version 2.5.0 ### What happened When adding a [role-based UIAlert following these docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/customize-ui.html#add-custom-alert-messages-on-the-dashboard), I received the below stacktrace: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.9/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/views.py", line 780, in index dashboard_alerts = [ File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/views.py", line 781, in <listcomp> fm for fm in settings.DASHBOARD_UIALERTS if fm.should_show(get_airflow_app().appbuilder.sm) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/www/utils.py", line 820, in should_show user_roles = {r.name for r in securitymanager.current_user.roles} AttributeError: 'NoneType' object has no attribute 'roles' ``` On further inspection, I realized this is happening because my webserver_config.py has this specification: ```py # Uncomment and set to desired role to enable access without authentication AUTH_ROLE_PUBLIC = 'Viewer' ``` When we set AUTH_ROLE_PUBLIC to a role like Viewer, [this line](https://github.com/apache/airflow/blob/ad7f8e09f8e6e87df2665abdedb22b3e8a469b49/airflow/www/utils.py#L828) returns an exception because `securitymanager.current_user` is None. Relevant code snippet: ```py def should_show(self, securitymanager) -> bool:Open an interactive python shell in this frame """Determine if the user should see the message based on their role membership""" if self.roles: user_roles = {r.name for r in securitymanager.current_user.roles} if not user_roles.intersection(set(self.roles)): return False return True ``` ### What you think should happen instead If we detect that the securitymanager.current_user is None, we should not attempt to get its `roles` attribute. Instead, we can check to see if the AUTH_ROLE_PUBLIC is set in webserver_config.py which will tell us if a public role is being used. If it is, we can assume that because the current_user is None, the current_user's role is the public role. In code, this might look like this: ```py def should_show(self, securitymanager) -> bool: """Determine if the user should see the message based on their role membership""" if self.roles: user_roles = set() if hasattr(securitymanager.current_user, "roles"): user_roles = {r.name for r in securitymanager.current_user.roles} elif "AUTH_ROLE_PUBLIC" in securitymanager.appbuilder.get_app.config: # Give anonymous user public role user_roles = set([securitymanager.appbuilder.get_app.config["AUTH_ROLE_PUBLIC"]]) if not user_roles.intersection(set(self.roles)): return False return True ``` Expected result on the webpage: <img width="1440" alt="image" src="https://user-images.githubusercontent.com/9200263/210823778-4c619b75-40a3-4caa-9a2c-073651da7f0d.png"> ### How to reproduce Start breeze: ``` breeze --python 3.7 --backend postgres start-airflow ``` After the webserver, triggerer, and scheduler are started, modify webserver_config.py to uncomment AUTH_ROLE_PUBLIC and add airflow_local_settings.py: ```bash cd $AIRFLOW_HOME # Uncomment AUTH_ROLE_PUBLIC vi webserver_config.py mkdir -p config # Add sample airflow_local_settings.py below vi config/airflow_local_settings.py ``` ```py from airflow.www.utils import UIAlert DASHBOARD_UIALERTS = [ UIAlert("Role based alert", category="warning", roles=["Viewer"]), ] ``` Restart the webserver and navigate to airflow. You should see this page: <img width="1440" alt="image" src="https://user-images.githubusercontent.com/9200263/210820838-e74ffc23-7b6b-42dc-85f1-29ab8b0ee3d5.png"> ### Operating System Debian 11 ### Versions of Apache Airflow Providers 2.5.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details Locally ### Anything else This problem only occurs if you add a role based UIAlert and are using AUTH_ROLE_PUBLIC ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28746

https://github.com/apache/airflow/pull/28781

1e9c8e52fda95a0a30b3ae298d5d3adc1971ed45

f17e2ba48b59525655a92e04684db664a672918f

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/logs-persistent-volume-claim.yaml", "chart/values.schema.json", "chart/values.yaml"]

annotations in logs pvc

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.5.0 ### Kubernetes Version v1.22.8+d48376b ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened When creating the dags pvc, it is possible to inject annotations to the object. ### What you think should happen instead There should be the possibility to inject annotations to the logs pvc as well. ### How to reproduce _No response_ ### Anything else We are using annotations on pvc to disable the creation of backup snapshots provided by our company platform. (OpenShift) ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28745

https://github.com/apache/airflow/pull/29270

6ef5ba9104f5a658b003f8ade274f19d7ec1b6a9

5835b08e8bc3e11f4f98745266d10bbae510b258

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/common/sql/hooks/sql.py", "airflow/providers/exasol/hooks/exasol.py", "airflow/providers/exasol/operators/exasol.py", "tests/providers/exasol/hooks/test_sql.py", "tests/providers/exasol/operators/test_exasol.py", "tests/providers/exasol/operators/test_exasol_sql.py"]

AttributeError: 'ExaStatement' object has no attribute 'description'

### Apache Airflow Provider(s) exasol ### Versions of Apache Airflow Providers apache-airflow-providers-common-sql==1.3.1 apache-airflow-providers-exasol==4.1.1 ### Apache Airflow version 2.5.0 ### Operating System Rocky Linux 8.7 (like RHEL 8.7) ### Deployment Other Docker-based deployment ### Deployment details - Docker Images built using Python 3.9 and recommended constraints https://raw.githubusercontent.com/apache/airflow/constraints-2.5.0/constraints-3.9.txt - Deployment to AWS ECS ### What happened After upgrading from Airflow 2.4.3 to 2.5.0, the ExasolOperator stopped working even when executing simple SQL Statements. See log snippet below for details. It looks like the Exasol Hook fails due to a missing attribute. It seems likely the issue was introduced in a refactoring of the Exasol Hook to use common DBApiHook https://github.com/apache/airflow/pull/28009/commits ### What you think should happen instead _No response_ ### How to reproduce Any execution of ExasolOperator in Airflow built with the mentioned constraints should show the issue. ### Anything else ```console [2023-01-04, 15:31:33 CET] {exasol.py:176} INFO - Running statement: EXECUTE SCRIPT mohn_fw.update_select_to_date_for_area('CORE'), parameters: None [2023-01-04, 15:31:33 CET] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 255, in execute output = hook.run( File "/usr/local/lib/python3.9/site-packages/airflow/providers/exasol/hooks/exasol.py", line 178, in run result = handler(cur) File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/hooks/sql.py", line 62, in fetch_all_handler if cursor.description is not None: AttributeError: 'ExaStatement' object has no attribute 'description' [2023-01-04, 15:31:33 CET] {taskinstance.py:1322} INFO - Marking task as UP_FOR_RETRY. dag_id=MOH_DWH_DAILY_CORE, task_id=update_select_to_date_for_area, execution_date=20221225T210000, start_date=20230104T143132, end_date=20230104T143133 [2023-01-04, 15:31:33 CET] {standard_task_runner.py:100} ERROR - Failed to execute job 46137 for task update_select_to_date_for_area ('ExaStatement' object has no attribute 'description'; 7245) ``` ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28731

https://github.com/apache/airflow/pull/28744

c0b2fcff24184aa0c5beb9c0d06ce7d67b5c5b7e

9a7f07491e603123182adfd5706fbae524e33c0d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/utils/waiter.py", "tests/providers/amazon/aws/utils/test_waiter.py"]

Fix custom waiter function in AWS provider package

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.5.0 ### Operating System MacOS ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened Discussed in #28294 ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28691

https://github.com/apache/airflow/pull/28753

2b92c3c74d3259ebac714f157c525836f0af50f0

ce188e509389737b3c0bdc282abea2425281c2b7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/batch.py", "tests/providers/amazon/aws/operators/test_batch.py"]

Improve AWS Batch hook and operator

### Description AWS Batch hook and operator do not support the boto3 parameter shareIdentifier, which is required to submit jobs to specific types of queues. ### Use case/motivation I wish that AWS Batch hook and operator support the submit of jobs to queues that require shareIdentifier parameter. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28680

https://github.com/apache/airflow/pull/30829

bd542fdf51ad9550e5c4348f11e70b5a6c9adb48

612676b975a2ff26541bb2581fbdf2befc6c3de9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/telegram/CHANGELOG.rst", "airflow/providers/telegram/hooks/telegram.py", "airflow/providers/telegram/provider.yaml", "docs/spelling_wordlist.txt", "generated/provider_dependencies.json", "tests/providers/telegram/hooks/test_telegram.py"]

Support telegram-bot v20+

### Body Currently our telegram integration uses Telegram v13 telegram-bot library. On 1st of Jan 2023 a new, backwards incompatible version of Telegram-bot has been released : https://pypi.org/project/python-telegram-bot/20.0/#history and at least as reported by MyPy and our test suite test failures, Telegram 20 needs some changes to work: Here is a transition guide that might be helpful. Transition guide is here: https://github.com/python-telegram-bot/python-telegram-bot/wiki/Transition-guide-to-Version-20.0 In the meantime we limit telegram to < 20.0.0 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28670

https://github.com/apache/airflow/pull/28953

68412e166414cbf6228385e1e118ec0939857496

644cea14fff74d34f823b5c52c9dbf5bad33bd52

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/apache/beam/operators/beam.py"]

BeamRunGoPipelineOperator: temp dir with Go file from GCS is removed before starting the pipeline

### Apache Airflow Provider(s) apache-beam ### Versions of Apache Airflow Providers apache-airflow-providers-apache-beam==4.1.0 apache-airflow-providers-google==8.6.0 ### Apache Airflow version 2.5.0 ### Operating System macOS 13.1 ### Deployment Virtualenv installation ### Deployment details _No response_ ### What happened When using the `BeamRunGoPipelineOperator` with a `go_file` on GCS, the object is downloaded to a temporary directory, however the directory with the file has already been removed by the time it is needed, i.e. when executing `go mod init` and starting the pipeline. ### What you think should happen instead The `BeamRunGoPipelineOperator.execute` method enters into a `tempfile.TemporaryDirectory` context manager using [with](https://github.com/apache/airflow/blob/2.5.0/airflow/providers/apache/beam/operators/beam.py#L588) when downloading the `go_file` from GCS to the local filesystem. On completion of the context, this temporary directory is removed. `BeamHook.start_go_pipeline`, which uses the file, is called outside of the context however, which means the file no longer exists when `go mod init` is called. A suggested solution is to use the `enter_context` method of the existing `ExitStack` to also enter into the TemporaryDirectory context manager. This allows the go_file to still exist when it is time to initialize the go module and start the pipeline: ```python with ExitStack() as exit_stack: if self.go_file.lower().startswith("gs://"): gcs_hook = GCSHook(self.gcp_conn_id, self.delegate_to) tmp_dir = exit_stack.enter_context(tempfile.TemporaryDirectory(prefix="apache-beam-go")) tmp_gcs_file = exit_stack.enter_context( gcs_hook.provide_file(object_url=self.go_file, dir=tmp_dir) ) self.go_file = tmp_gcs_file.name self.should_init_go_module = True ``` ### How to reproduce The problem can be reproduced by creating a DAG which uses the `BeamRunGoPipelineOperator` and passing a `go_file` with a GS URI: ```python import pendulum from airflow import DAG from airflow.providers.apache.beam.operators.beam import BeamRunGoPipelineOperator with DAG( dag_id="beam_go_dag", start_date=pendulum.today("UTC"), ) as dag: BeamRunGoPipelineOperator( task_id="beam_go_pipeline", go_file="gs://my-bucket/main.go" ) ``` ### Anything else Relevant logs: ``` [2023-01-01T12:41:06.155+0100] {taskinstance.py:1303} INFO - Executing <Task(BeamRunGoPipelineOperator): beam_go_pipeline> on 2023-01-01 00:00:00+00:00 [2023-01-01T12:41:06.411+0100] {taskinstance.py:1510} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=airflow AIRFLOW_CTX_DAG_ID=beam_go_dag AIRFLOW_CTX_TASK_ID=beam_go_pipeline AIRFLOW_CTX_EXECUTION_DATE=2023-01-01T00:00:00+00:00 AIRFLOW_CTX_TRY_NUMBER=1 AIRFLOW_CTX_DAG_RUN_ID=backfill__2023-01-01T00:00:00+00:00 [2023-01-01T12:41:06.430+0100] {base.py:73} INFO - Using connection ID 'google_cloud_default' for task execution. [2023-01-01T12:41:06.441+0100] {credentials_provider.py:323} INFO - Getting connection using `google.auth.default()` since no key file is defined for hook. [2023-01-01T12:41:08.701+0100] {gcs.py:323} INFO - File downloaded to /var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4/tmp6j9g5090main.go [2023-01-01T12:41:08.704+0100] {process_utils.py:179} INFO - Executing cmd: go mod init main [2023-01-01T12:41:08.712+0100] {taskinstance.py:1782} ERROR - Task failed with exception Traceback (most recent call last): File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/cloud/hooks/gcs.py", line 402, in provide_file yield tmp_file File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/operators/beam.py", line 621, in execute self.beam_hook.start_go_pipeline( File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/hooks/beam.py", line 339, in start_go_pipeline init_module("main", working_directory) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/go_module_utils.py", line 37, in init_module execute_in_subprocess(go_mod_init_cmd, cwd=go_module_path) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/utils/process_utils.py", line 168, in execute_in_subprocess execute_in_subprocess_with_kwargs(cmd, cwd=cwd) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/utils/process_utils.py", line 180, in execute_in_subprocess_with_kwargs with subprocess.Popen( File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/subprocess.py", line 969, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/subprocess.py", line 1845, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/apache/beam/operators/beam.py", line 584, in execute with ExitStack() as exit_stack: File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 576, in __exit__ raise exc_details[1] File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 561, in __exit__ if cb(*exc_details): File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/contextlib.py", line 153, in __exit__ self.gen.throw(typ, value, traceback) File "/Users/johannaojeling/repo/johannaojeling/airflow/airflow/providers/google/cloud/hooks/gcs.py", line 399, in provide_file with NamedTemporaryFile(suffix=file_name, dir=dir) as tmp_file: File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 502, in __exit__ self.close() File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 509, in close self._closer.close() File "/Users/johannaojeling/.pyenv/versions/3.10.6/lib/python3.10/tempfile.py", line 446, in close unlink(self.name) FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/1_/7h5npt456j5f063tq7ngyxdw0000gn/T/apache-beam-gosmk3lv_4/tmp6j9g5090main.go' [2023-01-01T12:41:08.829+0100] {taskinstance.py:1321} INFO - Marking task as FAILED. dag_id=beam_go_dag, task_id=beam_go_pipeline, execution_date=20230101T000000, start_date=20230101T114106, end_date=20230101T114108 [...] ``` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28662

https://github.com/apache/airflow/pull/28664

675af73ceb5bc8b03d46a7cd903a73f9b8faba6f

8da678ccd2e5a30f9c2d22c7526b7a238c185d2f

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/jobs/test_local_task_job.py"]

Fix Quarantine tests

### Body We have several tests marked in the code base with `@pytest.mark.quarantined` It means that the tests are flaky and if fail in CI it does not fail the build. The goal is to fix the tests and make them stable. This task is to gather all of them under the same issue instead of dedicated issue per test. - [x] [TestImpersonation](https://github.com/apache/airflow/blob/bfcae349b88fd959e32bfacd027a5be976fe2132/tests/core/test_impersonation_tests.py#L117) - [x] [TestImpersonationWithCustomPythonPath](https://github.com/apache/airflow/blob/bfcae349b88fd959e32bfacd027a5be976fe2132/tests/core/test_impersonation_tests.py#L181) - [x] [test_exception_propagation](https://github.com/apache/airflow/blob/76f81cd4a7433b7eeddb863b2ae6ee59176cf816/tests/jobs/test_local_task_job.py#L772) - [x] [test_localtaskjob_maintain_heart_rate](https://github.com/apache/airflow/blob/76f81cd4a7433b7eeddb863b2ae6ee59176cf816/tests/jobs/test_local_task_job.py#L402) - [x] [test_exception_propagation](https://github.com/apache/airflow/blob/4d0fd8ef6adc35f683c7561f05688a65fd7451f4/tests/executors/test_celery_executor.py#L103) - [x] [test_process_sigterm_works_with_retries](https://github.com/apache/airflow/blob/65010fda091242870a410c65478eae362899763b/tests/jobs/test_local_task_job.py#L770) ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28658

https://github.com/apache/airflow/pull/29087

90ce88bf34b2337f89eed67e41092f53bf24e9c1

a6e21bc6ce428eadf44f62b05aeea7bbd3447a7b

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/helm-chart/index.rst"]

version 2.4.1 migration job "run-airflow-migrations" run once only when deploy via helm or flux/kustomization

### Official Helm Chart version 1.7.0 (latest released) ### Apache Airflow version 2.4.1 ### Kubernetes Version v4.5.4 ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened manually copied from [the Q & A 27992 migration job](https://github.com/apache/airflow/discussions/27992) (the button create issue from discussion did not work) I found my migration job would not restart for the 2nd time (the 1st time run was when the default airflow is deployed onto Kubernetes and it had no issues), and then i started to apply changes to the values.yaml file such as **make the database to be azure postgresql**; but then it would not take the values into effect, see screen shots; of course my debug skills on kubernetes are not high, so i would need extra help if extra info is needed.    ``` database: sql_alchemy_conn_secret: airflow-postgres-redis sql_alchemy_connect_args: { "keepalives": 1, "keepalives_idle": 30, "keepalives_interval": 5, "keepalives_count": 5, } postgresql: enabled: false pgbouncer: enabled: false # Airflow database & redis config data: metadataSecretName: airflow-postgres-redis ``` check again the pod for waiting for the migration:  and below was the 1st success at the initial installation (which did not use external db) ``` kubectl describe job airflow-airflow-run-airflow-migrations Name: airflow-airflow-run-airflow-migrations Namespace: airflow Selector: controller-uid=efdc3c7b-5172-4841-abcf-17e055fa6e2e Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=run-airflow-migrations helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Tue, 27 Dec 2022 14:21:50 +0100 Completed At: Tue, 27 Dec 2022 14:22:29 +0100 Duration: 39s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=run-airflow-migrations controller-uid=efdc3c7b-5172-4841-abcf-17e055fa6e2e job-name=airflow-airflow-run-airflow-migrations release=airflow-airflow tier=airflow Service Account: airflow-airflow-migrate-database-job Containers: run-airflow-migrations: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow db upgrade Environment: PYTHONUNBUFFERED: 1 AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-airflow-airflow-metadata'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Anything else my further experiment/try tells me the jobs were only run once. more independent tests could be done with a bit help, such as what kind of changes will trigger migration job to run. See below helm release history: the 1st installation worked; and i could not make the 3rd release to succeed even though the values are 100% correct; so **the bug/issue short description is: helmRelease in combination with `flux` have issues with db migration jobs (only run once <can be successful>) which makes it a stopper for further upgrade** ``` REVISION UPDATED STATUS CHART APP VERSION DESCRIPTION 1 Wed Dec 28 02:22:42 2022 superseded airflow-1.7.0 2.4.1 Install complete 2 Wed Dec 28 02:43:25 2022 deployed airflow-1.7.0 2.4.1 Upgrade complete ``` see below equivalent values , even tried to disable the db migration did not make flux to work with it. ``` createUserJob: useHelmHooks: false migrateDatabaseJob: useHelmHooks: false config: webserver: expose_config: 'non-sensitive-only' postgresql: enabled: false pgbouncer: enabled: true # The maximum number of connections to PgBouncer maxClientConn: 100 # The maximum number of server connections to the metadata database from PgBouncer metadataPoolSize: 10 # The maximum number of server connections to the result backend database from PgBouncer resultBackendPoolSize: 5 # Airflow database & redis config data: metadataSecretName: airflow-postgres-redis # to generate strong secret: python3 -c 'import secrets; print(secrets.token_hex(16))' webserverSecretKeySecretName: airflow-webserver-secret ``` and see below 2 jobs ``` $ kubectl describe job -n airflow Name: airflow-airflow-create-user Namespace: airflow Selector: controller-uid=8b09e28b-ba3a-4cee-b20f-693a3aa15363 Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=create-user-job helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Wed, 28 Dec 2022 03:22:46 +0100 Completed At: Wed, 28 Dec 2022 03:24:32 +0100 Duration: 106s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=create-user-job controller-uid=8b09e28b-ba3a-4cee-b20f-693a3aa15363 job-name=airflow-airflow-create-user release=airflow-airflow tier=airflow Service Account: airflow-airflow-create-user-job Containers: create-user: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow users create "$@" -- -r Admin -u admin -e admin@example.com -f admin -l user -p admin Environment: AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> Name: airflow-airflow-run-airflow-migrations Namespace: airflow Selector: controller-uid=5da8c81f-7920-4eaf-9d7a-58a48c740bdc Labels: app.kubernetes.io/managed-by=Helm chart=airflow-1.7.0 component=run-airflow-migrations helm.toolkit.fluxcd.io/name=airflow helm.toolkit.fluxcd.io/namespace=airflow heritage=Helm release=airflow-airflow tier=airflow Annotations: batch.kubernetes.io/job-tracking: meta.helm.sh/release-name: airflow-airflow meta.helm.sh/release-namespace: airflow Parallelism: 1 Completions: 1 Completion Mode: NonIndexed Start Time: Wed, 28 Dec 2022 03:22:46 +0100 Completed At: Wed, 28 Dec 2022 03:23:07 +0100 Duration: 21s Pods Statuses: 0 Active (0 Ready) / 1 Succeeded / 0 Failed Pod Template: Labels: component=run-airflow-migrations controller-uid=5da8c81f-7920-4eaf-9d7a-58a48c740bdc job-name=airflow-airflow-run-airflow-migrations release=airflow-airflow tier=airflow Service Account: airflow-airflow-migrate-database-job Containers: run-airflow-migrations: Image: apache/airflow:2.4.1 Port: <none> Host Port: <none> Args: bash -c exec \ airflow db upgrade Environment: PYTHONUNBUFFERED: 1 AIRFLOW__CORE__FERNET_KEY: <set to the key 'fernet-key' in secret 'airflow-airflow-fernet-key'> Optional: false AIRFLOW__CORE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW_CONN_AIRFLOW_DB: <set to the key 'connection' in secret 'airflow-postgres-redis'> Optional: false AIRFLOW__WEBSERVER__SECRET_KEY: <set to the key 'webserver-secret-key' in secret 'airflow-airflow-webserver-secret-key'> Optional: false AIRFLOW__CELERY__BROKER_URL: <set to the key 'connection' in secret 'airflow-airflow-broker-url'> Optional: false Mounts: /opt/airflow/airflow.cfg from config (ro,path="airflow.cfg") Volumes: config: Type: ConfigMap (a volume populated by a ConfigMap) Name: airflow-airflow-airflow-config Optional: false Events: <none> ``` ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28637

https://github.com/apache/airflow/pull/29078

30ad26e705f50442f05dd579990372196323fc86

6c479437b1aedf74d029463bda56b42950278287

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/processor.py", "airflow/models/dagbag.py", "tests/models/test_dagbag.py"]

AIP-44 Migrate Dagbag.sync_to_db to internal API.

This method is used in DagFileProcessor.process_file - it may be easier to migrate all it's internal calls instead of the whole method.

https://github.com/apache/airflow/issues/28615

https://github.com/apache/airflow/pull/29188

05242e95bbfbaf153e4ae971fc0d0a5314d5bdb8

5c15b23023be59a87355c41ab23a46315cca21a5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/api_internal/internal_api_call.py", "airflow/models/dag.py", "tests/api_internal/test_internal_api_call.py"]

AIP-44 Migrate DagModel.get_paused_dag_ids to Internal API

https://github.com/apache/airflow/issues/28614

https://github.com/apache/airflow/pull/28693

f114c67c03a9b4257cc98bb8a970c6aed8d0c673

ad738198545431c1d10619f8e924d082bf6a3c75

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/trigger.py"]

AIP-44 Migrate Trigger class to Internal API

https://github.com/apache/airflow/issues/28613

https://github.com/apache/airflow/pull/29099

69babdcf7449c95fea7fe3b9055c677b92a74298

ee0a56a2caef0ccfb42406afe57b9d2169c13a01

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/xcom.py"]

AIP-44 Migrate XCom get*/clear* to Internal API

https://github.com/apache/airflow/issues/28612

https://github.com/apache/airflow/pull/29083

9bc48747ddbd609c2bd3baa54a5d0472e9fdcbe4

a1ffb26e5bcf4547e3b9e494cf7ccd24af30c2e6

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "STATIC_CODE_CHECKS.rst", "airflow/cli/commands/info_command.py", "scripts/ci/pre_commit/pre_commit_check_provider_yaml_files.py", "scripts/in_container/run_provider_yaml_files_check.py"]

Add pre-commit/test to verify extra links refer to existed classes

### Body We had an issue where extra link class (`AIPlatformConsoleLink`) was removed in [PR](https://github.com/apache/airflow/pull/26836) without removing the class from the `provider.yaml` extra links this resulted in web server exception as shown in https://github.com/apache/airflow/pull/28449 **The Task:** Add validation that classes of extra-links in provider.yaml are importable ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/28510

https://github.com/apache/airflow/pull/28516

7ccbe4e7eaa529641052779a89e34d54c5a20f72

e47c472e632effbfe3ddc784788a956c4ca44122

python

closed

apache/airflow

https://github.com/apache/airflow

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

Issues with Custom Menu Items on Smaller Windows

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened We take advantage of the custom menu items with flask appbuilder offer a variety of dropdown menus with custom DAG filters. We've notice two things: 1. When you have too many dropdown menu items in a single category, several menu items are unreachable when using the Airflow UI on a small screen: <img width="335" alt="Screenshot 2022-12-19 at 6 34 24 PM" src="https://user-images.githubusercontent.com/40223998/208548419-f9d1ff57-6cad-4a40-bc58-dbf20148a92a.png"> 2. When you have too many menu categories, multiple rows of dropdown menus are displayed, but cover some other components. <img width="1077" alt="Screenshot 2022-12-19 at 6 32 05 PM" src="https://user-images.githubusercontent.com/40223998/208548222-44e50717-9040-4899-be06-d503a8c0f69a.png"> ### What you think should happen instead 1. When you have too many dropdown menu items in a single category, there should be a scrollbar. 2. When you have too many menu categories, multiple rows of dropdown menus are displayed, the menu shouldn't cover the dag import errors or any part of the UI ### How to reproduce 1. Add a bunch of menu items under the same category in a custom plugin and resize your window smaller 2. Add a large number of menu item categories in a custom plugin and resize your window smaller. ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers 2.4.3 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else I'm happy to make a PR for this. I just don't have the frontend context. If someone can point me in the right direction that'd be great ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28483

https://github.com/apache/airflow/pull/28561

ea3be1a602b3e109169c6e90e555a418e2649f9a

2aa52f4ce78e1be7f34b0995d40be996b4826f26

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/CHANGELOG.rst", "airflow/providers/amazon/aws/transfers/sql_to_s3.py", "airflow/providers/amazon/provider.yaml", "airflow/providers/apache/hive/hooks/hive.py", "generated/provider_dependencies.json"]

Make pandas an optional dependency for amazon provider

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version latest ### Operating System any ### Deployment Other ### Deployment details _No response_ ### What happened First of all, apologies if this is not the right section to post a GH issue. I looked for provider specific feature requests but couldnt find such section. We use the aws provider at my company to interact from airflow with AWS services. We are using poetry for building the testing environment to test our dags. However the build times are quite long, and the reason is building pandas, which is a [dependency ](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/provider.yaml#L62) of the amazon provider. By checking the provider's code, it seems pandas is used in a small minority of functions inside the provider: ``` ./aws/transfers/hive_to_dynamodb.py:93: data = hive.get_pandas_df(self.sql, schema=self.schema) ``` and ``` ./aws/transfers/sql_to_s3.py:159: data_df = sql_hook.get_pandas_df(sql=self.query, parameters=self.parameters) ``` Forcing every AWS Airflow user that do not use hive or want to turn sql into an s3 file to install pandas is a bit cumbersome. ### What you think should happen instead given how heavy the package is and how little is used in the amazon provider, pandas should be an optional dependency. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28468

https://github.com/apache/airflow/pull/28505

bc7feda66ed7bb2f2940fa90ef26ff90dd7a8c80

d9ae90fc6478133767e29774920ed797175146bc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/jenkins/hooks/jenkins.py", "docs/apache-airflow-providers-jenkins/connections.rst", "tests/providers/jenkins/hooks/test_jenkins.py"]

Airflow 2.2.4 Jenkins Connection - unable to set as the hook expects to be

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Hello team, I am trying to use the `JenkinsJobTriggerOperator` version v3.1.0 on an Airflow instance version 2.2.4 Checking the documentation regards how to set up the connection and the hook in order to use `https` instead of the default `http`, I see https://airflow.apache.org/docs/apache-airflow-providers-jenkins/3.1.0/connections.html ``` Extras (optional) Specify whether you want to use http or https scheme by entering true to use https or false for http in extras. Default is http. ``` Unfortunately from the Airflow UI when trying to specify the connection and especially the `Extras` options it accepts a JSON-like object, so whatever you put differently to a dictionary the code fails to update the extra options for that connection. Checking in more details what the [Jenkins hook](https://airflow.apache.org/docs/apache-airflow-providers-jenkins/3.1.0/_modules/airflow/providers/jenkins/hooks/jenkins.html#JenkinsHook.conn_name_attr) does: ``` self.connection = connection connection_prefix = "http" # connection.extra contains info about using https (true) or http (false) if to_boolean(connection.extra): connection_prefix = "https" url = f"{connection_prefix}://{connection.host}:{connection.port}" ``` where the `connection.extra` cannot be a simple true/false string! ### What you think should happen instead Either we should get the `http` or `https` from the `Schema` Or we should update the [JenkinsHook](https://airflow.apache.org/docs/apache-airflow-providers-jenkins/stable/_modules/airflow/providers/jenkins/hooks/jenkins.html#JenkinsHook.default_conn_name) to read the provided dictionary for http value: `if to_boolean(connection.extra.https)` ### How to reproduce _No response_ ### Operating System macos Monterey 12.6.2 ### Versions of Apache Airflow Providers ``` pip freeze | grep apache-airflow-providers apache-airflow-providers-celery==2.1.0 apache-airflow-providers-common-sql==1.3.1 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.3 apache-airflow-providers-imap==2.2.0 apache-airflow-providers-jenkins==3.1.0 apache-airflow-providers-sqlite==2.1.0 ``` ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28465

https://github.com/apache/airflow/pull/30301

f7d5b165fcb8983bd82a852dcc5088b4b7d26a91

1f8bf783b89d440ecb3e6db536c63ff324d9fc62

python

closed

apache/airflow

https://github.com/apache/airflow

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

TaskInstances do not succeed when using enable_logging=True option in DockerSwarmOperator

### Apache Airflow Provider(s) docker ### Versions of Apache Airflow Providers apache-airflow-providers-celery==3.1.0 apache-airflow-providers-docker==3.3.0 ### Apache Airflow version 2.5.0 ### Operating System centos 7 ### Deployment Other Docker-based deployment ### Deployment details Running an a docker-swarm cluster deployed locally. ### What happened Same issue as https://github.com/apache/airflow/issues/13675 With logging_enabled=True the DAG never completes and stays in running. When using DockerSwarmOperator together with the default enable_logging=True option, tasks do not succeed and stay in state running. When checking the docker service logs I can clearly see that the container ran and ended successfully. Airflow however does not recognize that the container finished and keeps the tasks in state running. ### What you think should happen instead DAG should complete. ### How to reproduce Docker-compose deployment: ```console curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.5.0/docker-compose.yaml' docker compose up airflow-init docker compose up -d ``` DAG code: ```python from airflow import DAG from docker.types import Mount, SecretReference from airflow.providers.docker.operators.docker_swarm import DockerSwarmOperator from datetime import timedelta from airflow.utils.dates import days_ago from airflow.models import Variable # Setup default args for the job default_args = { 'owner': 'airflow', 'start_date': days_ago(2), 'retries': 0 } # Create the DAG dag = DAG( 'test_dag', # DAG ID default_args=default_args, schedule_interval='0 0 * * *', catchup=False ) # # Create the DAG object with dag as dag: docker_swarm_task = DockerSwarmOperator( task_id="job_run", image="<any image>", execution_timeout=timedelta(minutes=5), command="<specific code>", api_version='auto', tty=True, enable_logging=True ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28452

https://github.com/apache/airflow/pull/35677

3bb5978e63f3be21a5bb7ae89e7e3ce9d06a4ab8

882108862dcaf08e7f5da519b3d186048d4ec7f9

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSToBigQueryOperator fails when schema_object is specified without schema_fields

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow 2.5.0 apache-airflow-providers-apache-beam 4.1.0 apache-airflow-providers-cncf-kubernetes 5.0.0 apache-airflow-providers-google 8.6.0 apache-airflow-providers-grpc 3.1.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details KubernetesExecutor ### What happened GCSToBigQueryOperator allows multiple ways to specify schema of the BigQuery table: 1. Setting autodetect == True 1. Setting schema_fields directly with autodetect == False 1. Setting a schema_object and optionally a schema_object_bucket with autodetect == False This third method seems to be broken in the latest provider version (8.6.0) and will always result in this error: ``` [2022-12-16, 21:06:18 UTC] {taskinstance.py:1772} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 395, in execute self.configuration = self._check_schema_fields(self.configuration) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 524, in _check_schema_fields raise RuntimeError( RuntimeError: Table schema was not found. Set autodetect=True to automatically set schema fields from source objects or pass schema_fields explicitly ``` The reason for this is because [this block](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L318-L320) where `if self.schema_object and self.source_format != "DATASTORE_BACKUP":`. fails to set self.schema_fields. It only sets the local variable, schema_fields. When self._check_schema_fields is subsequently called [here](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L395), we enter the [first block](https://github.com/apache/airflow/blob/25bdbc8e6768712bad6043618242eec9c6632618/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L523-L528) because autodetect is false and schema_fields is not set. ### What you think should happen instead No error should be raised if autodetect is set to False and a valid schema_object is provided ### How to reproduce 1. Create a simple BigQuery table with a single column col1: ```sql CREATE TABLE `my-project.my_dataset.test_gcs_to_bigquery` (col1 INT); ``` 2. Upload a json blob for this object to a bucket (e.g., data/schemas/table.json) 3. Upload a simple CSV for the source file to load to a bucket (e.g., data/source/file.csv) 4. Run the following command: ```py gcs_to_biquery = GCSToBigQueryOperator( task_id="gcs_to_bigquery", destination_project_dataset_table="my-project.my_dataset.test_gcs_to_bigquery", bucket="my_bucket_name", create_disposition="CREATE_IF_NEEDED", write_disposition="WRITE_TRUNCATE", source_objects=["data/source/file.csv"], source_format="CSV", autodetect=False, schema_object="data/schemas/table.json", ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28441

https://github.com/apache/airflow/pull/28444

032a542feeb617d1f92580b97fa0ad3cdca09d63

9eacf607be109eb6ab80f7e27d234a17fb128ae0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Webserver reports "ImportError: Module "airflow.providers.google.cloud.operators.mlengine" does not define a "AIPlatformConsoleLink" attribute/class"

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow 2.5.0 apache-airflow-providers-apache-beam 4.1.0 apache-airflow-providers-cncf-kubernetes 5.0.0 apache-airflow-providers-google 8.6.0 apache-airflow-providers-grpc 3.1.0 ### Apache Airflow version 2.5.0 ### Operating System Debian 11 ### Deployment Official Apache Airflow Helm Chart ### Deployment details KubernetesExecutor ### What happened We are seeing this stacktrace on our webserver when a task is clicked: ``` 10.253.8.251 - - [15/Dec/2022:18:32:58 +0000] "GET /object/next_run_datasets/recs_ranking_purchase_ranker_dag HTTP/1.1" 200 2 "https://web.airflow.etsy-syseng-gke-prod.etsycloud.com/dags/recs_ranking_purchase_ranker_dag/code" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36" raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute/class') File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 38, in import_string imported_class = import_string(class_name) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers_manager.py", line 275, in _sanity_check Traceback (most recent call last): During handling of the above exception, another exception occurred: AttributeError: module 'airflow.providers.google.cloud.operators.mlengine' has no attribute 'AIPlatformConsoleLink' return getattr(module, class_name) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/module_loading.py", line 36, in import_string Traceback (most recent call last): [2022-12-15 18:32:58,068] {providers_manager.py:243} WARNING - Exception when importing 'airflow.providers.google.cloud.operators.mlengine.AIPlatformConsoleLink' from 'apache-airflow-providers-google' package ImportError: Module "airflow.providers.google.cloud.operators.mlengine" does not define a "AIPlatformConsoleLink" attribute/class ``` ### What you think should happen instead These errors should now appear. ### How to reproduce Start webserver anew, navigate to a dag, click on a task, and tail webserver logs ### Anything else [This YAML file](https://github.com/apache/airflow/blob/providers-google/8.6.0/airflow/providers/google/provider.yaml#L968) is being utilized as config which then results in the import error here: https://github.com/apache/airflow/blob/providers-google/8.6.0/airflow/providers_manager.py#L885-L891 ``` extra-links: - airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleLink - airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleIndexableLink - airflow.providers.google.cloud.operators.mlengine.AIPlatformConsoleLink ``` We should remove this from extra-links as it was removed as of apache-airflow-providers-google 8.5.0 ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28393

https://github.com/apache/airflow/pull/28449

b213f4fd2627bb2a2a4c96fe2845471db430aa5d

7950fb9711384f8ac4609fc19f319edb17e296ef

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "airflow/executors/kubernetes_executor.py", "airflow/www/views.py"]

Manual task trigger fails for kubernetes executor with psycopg2 InvalidTextRepresentation error

### Apache Airflow version main (development) ### What happened Manual task trigger fails for kubernetes executor with the following error. Manual trigger of dag works without any issue. ``` [2022-12-15 20:05:38,442] {app.py:1741} ERROR - Exception on /run [POST] Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1900, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute cursor.execute(statement, parameters) psycopg2.errors.InvalidTextRepresentation: invalid input syntax for integer: "manual" LINE 3: ...ate = 'queued' AND task_instance.queued_by_job_id = 'manual' ^ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 2525, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1822, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1820, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.10/site-packages/flask/app.py", line 1796, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/auth.py", line 47, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/decorators.py", line 125, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/www/views.py", line 1896, in run executor.start() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 586, in start self.clear_not_launched_queued_tasks() File "/home/airflow/.local/lib/python3.10/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.10/site-packages/airflow/executors/kubernetes_executor.py", line 510, in clear_not_launched_queued_tasks queued_tis: list[TaskInstance] = query.all() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2773, in all return self._iter().all() File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py", line 2916, in _iter result = self.session.execute( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/session.py", line 1714, in execute result = conn._execute_20(statement, params or {}, execution_options) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1705, in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/sql/elements.py", line 334, in _execute_on_connection return connection._execute_clauseelement( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1572, in _execute_clauseelement ret = self._execute_context( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1943, in _execute_context self._handle_dbapi_exception( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 2124, in _handle_dbapi_exception util.raise_( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/util/compat.py", line 211, in raise_ raise exception File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1900, in _execute_context self.dialect.do_execute( File "/home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.DataError: (psycopg2.errors.InvalidTextRepresentation) invalid input syntax for integer: "manual" LINE 3: ...ate = 'queued' AND task_instance.queued_by_job_id = 'manual' ``` ^ ### What you think should happen instead should be able to trigger the task manually from the UI ### How to reproduce deploy the main branch with kubernetes executor and postgres db. ### Operating System ubuntu 20 ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details Python version: 3.10.9 Airflow version: 2.6.0.dev0 helm.sh/chart=postgresql-10.5.3 ### Anything else the issue is caused due to this check: https://github.com/apache/airflow/blob/b263dbcb0f84fd9029591d1447a7c843cb970f15/airflow/executors/kubernetes_executor.py#L505-L507 in `celery_executor` there is a similar check, but i believe it is not called at the ti executor time. and also since it is in a try/catch the exception is not visible. https://github.com/apache/airflow/blob/b263dbcb0f84fd9029591d1447a7c843cb970f15/airflow/executors/celery_executor.py#L394-L412 ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28391

https://github.com/apache/airflow/pull/28394

be0e35321f0bbd7d21c75096cad45dbe20c2359a

9510043546d1ac8ac56b67bafa537e4b940d68a4

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile.ci", "airflow/www/extensions/init_views.py", "airflow/www/package.json", "airflow/www/templates/swagger-ui/index.j2", "airflow/www/webpack.config.js", "airflow/www/yarn.lock", "setup.cfg"]

CVE-2019-17495 for swagger-ui

### Apache Airflow version 2.5.0 ### What happened this issue https://github.com/apache/airflow/issues/18383 still isn't closed. It seems like the underlying swagger-ui bundle has been abandoned by its maintainer, and we should instead point swagger UI bundle to this version which is kept up-to-date https://github.com/bartsanchez/swagger_ui_bundle edit : it seems like this might not be coming from the swagger_ui_bundle any more but instead perhaps from connexion. I'm not familiar with python dependencies, so forgive me if I'm mis-reporting this. There are CVE scanner tools that notifies https://github.com/advisories/GHSA-c427-hjc3-wrfw using the apache/airflow:2.1.4 The python deps include swagger-ui-2.2.10 and swagger-ui-3.30.0 as part of the bundle. It is already included at ~/.local/lib/python3.6/site-packages/swagger_ui_bundle swagger-ui-2.2.10 swagger-ui-3.30.0 ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System any ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28381

https://github.com/apache/airflow/pull/28788

35a8ffc55af220b16ea345d770f80f698dcae3fb

35ad16dc0f6b764322b1eb289709e493fbbb0ae0

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/default_webserver_config.py"]

CSRF token should be expire with session

### Apache Airflow version 2.5.0 ### What happened In the default configuration, the CSRF token [expires in one hour](https://pythonhosted.org/Flask-WTF/config.html#forms-and-csrf). This setting leads to frequent errors in the UI – for no good reason. ### What you think should happen instead A short expiration date for the CSRF token is not the right value in my view and I [agree with this answer](https://security.stackexchange.com/a/56520/22108) that the CSRF token should basically never expire, instead pegging itself to the current session. That is, the CSRF token should last as long as the current session. The easiest way to accomplish this is by generating the CSRF token from the session id. ### How to reproduce _No response_ ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28356

https://github.com/apache/airflow/pull/28730

04306f18b0643dfed3ed97863bbcf24dc50a8973

543e9a592e6b9dc81467c55169725e192fe95e89

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/kubernetes_executor.py"]

Scheduler pod hang when K8s API call fail

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Airflow version: `2.3.4` I have deployed airflow with the official Helm in K8s with `KubernetesExecutor`. Sometimes the scheduler hang when calling K8s API. The log: ``` bash ERROR - Exception when executing Executor.end Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 752, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 842, in _run_scheduler_loop self.executor.heartbeat() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/base_executor.py", line 171, in heartbeat self.sync() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 649, in sync next_event = self.event_scheduler.run(blocking=False) File "/usr/local/lib/python3.8/sched.py", line 151, in run action(*argument, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/event_scheduler.py", line 36, in repeat action(*args, **kwargs) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 673, in _check_worker_pods_pending_timeout for pod in pending_pods().items: File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 15697, in list_namespaced_pod return self.list_namespaced_pod_with_http_info(namespace, **kwargs) # noqa: E501 File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 15812, in list_namespaced_pod_with_http_info return self.api_client.call_api( File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 348, in call_api return self.__call_api(resource_path, method, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 180, in __call_api response_data = self.request( File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 373, in request return self.rest_client.GET(url, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/rest.py", line 240, in GET return self.request("GET", url, File "/home/airflow/.local/lib/python3.8/site-packages/kubernetes/client/rest.py", line 213, in request r = self.pool_manager.request(method, url, File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/request.py", line 74, in request return self.request_encode_url( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/request.py", line 96, in request_encode_url return self.urlopen(method, url, **extra_kw) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/poolmanager.py", line 376, in urlopen response = conn.urlopen(method, u.request_uri, **kw) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 815, in urlopen return self.urlopen( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/home/airflow/.local/lib/python3.8/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 182, in _exit_gracefully sys.exit(os.EX_OK) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.8/site-packages/airflow/jobs/scheduler_job.py", line 773, in _execute self.executor.end() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 823, in end self._flush_task_queue() File "/home/airflow/.local/lib/python3.8/site-packages/airflow/executors/kubernetes_executor.py", line 776, in _flush_task_queue self.log.debug('Executor shutting down, task_queue approximate size=%d', self.task_queue.qsize()) File "<string>", line 2, in qsize File "/usr/local/lib/python3.8/multiprocessing/managers.py", line 835, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.8/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) ConnectionResetError: [Errno 104] Connection reset by peer ``` Then the executor process was killed and the pod was still running. But the scheduler does not work. After restarting, the scheduler worked usually. ### What you think should happen instead When the error occurs, the executor needs to auto restart or the scheduler should be killed. ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28328

https://github.com/apache/airflow/pull/28685

57a889de357b269ae104b721e2a4bb78b929cea9

a3de721e2f084913e853aff39d04adc00f0b82ea

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/ti_deps/deps/prev_dagrun_dep.py", "tests/models/test_dagrun.py"]

Dynamic task mapping does not correctly handle depends_on_past

### Apache Airflow version Other Airflow 2 version (please specify below) ### What happened Using Airflow 2.4.2. I've got a task that retrieves some filenames, which then creates dynamically mapped tasks to move the files, one per task. I'm using a similar task across multiple DAGs. However, task mapping fails on some DAG runs: it inconsistently happens per DAG run, and some DAGs do not seem to be affected at all. These seem to be the DAGs where no task was ever mapped, so that the mapped task instance ended up in a Skipped state. What happens is that multiple files will be found, but only a single dynamically mapped task will be created. This task never starts and has map_index of -1. It can be found under the "List instances, all runs" menu, but says "No Data found." under the "Mapped Tasks" tab. When I press the "Run" button when the mapped task is selected, the following error appears: ``` Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped! ``` The previous task *has* run however. No errors appeared in my Airflow logs. ### What you think should happen instead The appropriate amount of task instances should be created, they should correctly resolve the ```depends_on_past``` check and then proceed to run correctly. ### How to reproduce This DAG reliably reproduces the error for me. The first set of mapped tasks succeeds, the subsequent ones do not. ```python from airflow import DAG from airflow.decorators import task import datetime as dt from airflow.operators.python import PythonOperator @task def get_filenames_kwargs(): return [ {"file_name": i} for i in range(10) ] def print_filename(file_name): print(file_name) with DAG( dag_id="dtm_test", start_date=dt.datetime(2022, 12, 10), default_args={ "owner": "airflow", "depends_on_past": True, }, schedule="@daily", ) as dag: get_filenames_task = get_filenames_kwargs.override(task_id="get_filenames_task")() print_filename_task = PythonOperator.partial( task_id="print_filename_task", python_callable=print_filename, ).expand(op_kwargs=get_filenames_task) # Perhaps redundant get_filenames_task >> print_filename_task ``` ### Operating System Amazon Linux 2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28296

https://github.com/apache/airflow/pull/28379

a62840806c37ef87e4112c0138d2cdfd980f1681

8aac56656d29009dbca24a5948c2a2097043f4f3

python

closed

apache/airflow

https://github.com/apache/airflow

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

S3KeySensor 'bucket_key' instantiates as a nested list when rendered as a templated_field

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==6.2.0 ### Apache Airflow version 2.5.0 ### Operating System Red Hat Enterprise Linux Server 7.6 (Maipo) ### Deployment Virtualenv installation ### Deployment details Simple virtualenv deployment ### What happened bucket_key is a template_field in S3KeySensor, which means that is expected to be rendered as a template field. The supported types for the attribute are both 'str' and 'list'. There is also a [conditional operation in the __init__ function](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L89) of the class that relies on the type of the input data, that converts the attribute to a list of strings. If a list of str is passed in through Jinja template, **self.bucket_key** is available as a _**doubly-nested list of strings**_, rather than a list of strings. This is because the input value of **bucket_key** can only be a string type that represents the template-string when used as a template_field. These template_fields are then converted to their corresponding values when instantiated as a task_instance. Example log from __init__ function: ` scheduler | DEBUG | type: <class 'list'> | val: ["{{ ti.xcom_pull(task_ids='t1') }}"]` Example log from poke function: `poke | DEBUG | type: <class 'list'> | val: [["s3://test_bucket/test_key1", "s3://test_bucket/test_key2"]]` This leads to the poke function throwing an [exception](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/s3.py#L172) as each individual key needs to be a string value to parse the url, but is being passed as a list (since self.bucket_key is a nested list). ### What you think should happen instead Instead of putting the input value of **bucket_key** in a list, we should store the value as-is upon initialization of the class, and just conditionally check the type of the attribute within the poke function. [def \_\_init\_\_](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L89) `self.bucket_key = bucket_key` (which willstore the input values correctly as a str or a list when the task instance is created and the template fields are rendered) [def poke](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/sensors/s3.py#L127) ``` def poke(self, context: Context): if isinstance(self.bucket_key, str): return self._check_key(key) else: return all(self._check_key(key) for key in self.bucket_key) ``` ### How to reproduce 1. Use a template field as the bucket_key attribute in S3KeySensor 2. Pass a list of strings as the rendered template input value for the bucket_key attribute in the S3KeySensor task. (e.g. as an XCOM or Variable pulled value) Example: ``` with DAG( ... render_template_as_native_obj=True, ) as dag: @task(task_id="get_list_of_str", do_xcom_push=True) def get_list_of_str(): return ["s3://test_bucket/test_key1", "s3://test_bucket/test_key1"] t = get_list_of_str() op = S3KeySensor(task_id="s3_key_sensor", bucket_key="{{ ti.xcom_pull(task_ids='get_list_of_str') }}") t >> op ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28272

https://github.com/apache/airflow/pull/28340

9d9b15989a02042a9041ff86bc7e304bb06caa15

381160c0f63a15957a631da9db875f98bb8e9d64

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/variable.py"]

AIP-44 Migrate Variable to Internal API

Link: https://github.com/apache/airflow/blob/main/airflow/models/variable.py Methods to migrate: - val - set - delete - update Note that get_variable_from_secrets shouls still be executed locally. It may be better to first close https://github.com/apache/airflow/issues/28267

https://github.com/apache/airflow/issues/28271

https://github.com/apache/airflow/pull/28795

9c3cd3803f0c4c83b1f8220525e1ac42dd676549

bea49094be3e9d84243383017ca7d21dda62f329

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/manager.py", "tests/api_internal/endpoints/test_rpc_api_endpoint.py", "tests/api_internal/test_internal_api_call.py", "tests/dag_processing/test_manager.py"]

AIP-44 Migrate DagFileProcessorManager._deactivate_stale_dags to Internal API

https://github.com/apache/airflow/issues/28270

https://github.com/apache/airflow/pull/28476

c18dbe963ad87c03d49e95dfe189b765cc18fbec

29a26a810ee8250c30f8ba0d6a72bc796872359c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/dag_processing/processor.py", "airflow/utils/log/logging_mixin.py", "tests/dag_processing/test_processor.py"]

AIP-44 Migrate DagFileProcessor.manage_slas to Internal API

https://github.com/apache/airflow/issues/28268

https://github.com/apache/airflow/pull/28502

7e2493e3c8b2dbeb378dba4e40110ab1e4ad24da

0359a42a3975d0d7891a39abe4395bdd6f210718

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_internal/internal_api_call.py", "airflow/cli/commands/internal_api_command.py", "airflow/cli/commands/scheduler_command.py", "airflow/www/app.py", "tests/api_internal/test_internal_api_call.py"]

AIP-44 Provide information to internal_api_call decorator about the running component

Scheduler/Webserver should never use Internal API, so calling any method decorated with internal_api_call should still execute them locally

https://github.com/apache/airflow/issues/28267

https://github.com/apache/airflow/pull/28783

50b30e5b92808e91ad9b6b05189f560d58dd8152

6046aef56b12331b2bb39221d1935b2932f44e93

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "airflow/cli/cli_parser.py", "airflow/cli/commands/internal_api_command.py", "airflow/www/extensions/init_views.py", "tests/cli/commands/test_internal_api_command.py"]

AIP-44 Implement standalone internal-api component

https://github.com/apache/airflow/pull/27892 added Internal API as part of Webserver. We need to introduce `airlfow internal-api` CLI command that starts Internal API as a independent component.

https://github.com/apache/airflow/issues/28266

https://github.com/apache/airflow/pull/28425

760c52949ac41ffa7a2357aa1af0cdca163ddac8

367e8f135c2354310b67b3469317f15cec68dafa

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/role_command.py", "airflow/www/extensions/init_appbuilder.py"]

Airflow CLI to list roles is slow

### Apache Airflow version 2.5.0 ### What happened We're currently running a suboptimal setup where database connectivity is laggy, 125ms roundtrip. This has interesting consequences. For example, `airflow roles list` is really slow. Turns out that it's doing a lot of individual queries. ### What you think should happen instead Ideally, listing roles should be a single (perhaps complex) query. ### How to reproduce We're using py-spy to sample program execution: ```bash $ py-spy record -o spy.svg -i --rate 250 --nonblocking airflow roles list ``` Now, to see the bad behavior, the database should incur significant latency. ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28242

https://github.com/apache/airflow/pull/28244

2f5c77b0baa0ab26d2c51fa010850653ded80a46

e24733662e95ad082e786d4855066cd4d36015c9

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/sqlalchemy.py", "tests/utils/test_sqlalchemy.py"]

Scheduler error: 'V1PodSpec' object has no attribute '_ephemeral_containers'

### Apache Airflow version 2.5.0 ### What happened After upgrade 2.2.5 -> 2.5.0 scheduler failing with error: ``` AttributeError: 'V1PodSpec' object has no attribute '_ephemeral_containers' ``` tried with no luck: ``` airflow dags reserialize ``` Full Traceback: ```verilog Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 39, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 52, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 108, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 73, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 43, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 247, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 759, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 889, in _run_scheduler_loop num_finished_events = self._process_executor_events(session=session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 705, in _process_executor_events self.executor.send_callback(request) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/executors/celery_kubernetes_executor.py", line 213, in send_callback self.callback_sink.send(request) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/callbacks/database_callback_sink.py", line 34, in send db_callback = DbCallbackRequest(callback=callback, priority_weight=10) File "<string>", line 4, in __init__ File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/state.py", line 480, in _initialize_instance manager.dispatch.init_failure(self, args, kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__ compat.raise_( File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ raise exception File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/state.py", line 477, in _initialize_instance return manager.original_init(*mixed[1:], **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/db_callback_request.py", line 46, in __init__ self.callback_data = callback.to_json() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/callbacks/callback_requests.py", line 91, in to_json val = BaseSerialization.serialize(self.__dict__, strict=True) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 450, in serialize return cls._encode(cls.serialize(var.__dict__, strict=strict), type_=DAT.SIMPLE_TASK_INSTANCE) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in serialize {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 407, in <dictcomp> {str(k): cls.serialize(v, strict=strict) for k, v in var.items()}, type_=DAT.DICT File "/home/airflow/.local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 412, in serialize json_pod = PodGenerator.serialize_pod(var) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/kubernetes/pod_generator.py", line 411, in serialize_pod return api_client.sanitize_for_serialization(pod) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 241, in sanitize_for_serialization return {key: self.sanitize_for_serialization(val) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 241, in <dictcomp> return {key: self.sanitize_for_serialization(val) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 237, in sanitize_for_serialization obj_dict = {obj.attribute_map[attr]: getattr(obj, attr) File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/api_client.py", line 239, in <dictcomp> if getattr(obj, attr) is not None} File "/home/airflow/.local/lib/python3.9/site-packages/kubernetes/client/models/v1_pod_spec.py", line 397, in ephemeral_containers return self._ephemeral_containers AttributeError: 'V1PodSpec' object has no attribute '_ephemeral_containers' ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details AWS EKS ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/28227

https://github.com/apache/airflow/pull/28454

dc06bb0e26a0af7f861187e84ce27dbe973b731c

27f07b0bf5ed088c4186296668a36dc89da25617

python