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closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

841ed271017ff35a3124f1d1a53a5c74730fed60

84d7b5ba39b3ff1fb5b856faec8fd4e731d3f397

python

closed

apache/airflow

https://github.com/apache/airflow

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

Frequent failures of helm chart tests

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

https://github.com/apache/airflow/issues/24037

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

5d5976c08c867b8dbae8301f46e0c422d4dde1ed

779571b28b4ae1906b4a23c031d30fdc87afb93e

python

closed

apache/airflow

https://github.com/apache/airflow

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

KubernetesPodOperator/KubernetesExecutor: Failed to adopt pod 422

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

https://github.com/apache/airflow/issues/24015

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

05fb80ee9373835b2f72fad3e9976cf29aeca23b

d26dc223915c50ff58252a709bb7b33f5417dfce

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

203fe71b49da760968c26752957f765c4649423b

98b4e48fbc1262f1381e7a4ca6cce31d96e6f5e9

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

2e5737df531410d2d678d09b5d2bba5d37a06003

82f842ffc56817eb039f1c4f1e2c090e6941c6af

python

closed

apache/airflow

https://github.com/apache/airflow

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

Only autorefresh active dags on home page

### Description In https://github.com/apache/airflow/pull/22900, we added auto-refresh for the home page. Right now, we pass all dag_ids to the `last_dagruns`, `dag_stats` and `task_stats` endpoints. During auto-refresh, we should only request info for dags that are not paused. On page load, we still want to check all three endpoints for all dags in view. But for subsequent auto-refresh requests we should only check active dags. See [here](https://github.com/apache/airflow/blob/main/airflow/www/static/js/dags.js#L429) for where the homepage auto-refresh lives. ### Use case/motivation Smaller requests should make a faster home page. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23949

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

e9d19a60a017224165e835949f623f106b97e1cb

2a1472a6bef57fc57cfe4577bcbed5ba00521409

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add backfill icon to grid view dag runs

### Description In the grid view, we use a play icon to indicate manually triggered dag runs. We should do the same for a backfilled dag run. Possible icons can be found [here](https://react-icons.github.io/react-icons). Note: We use the manual run icon in both the [dag run bar component](https://github.com/apache/airflow/blob/main/airflow/www/static/js/grid/dagRuns/Bar.jsx) and in the [details panel header](https://github.com/apache/airflow/blob/main/airflow/www/static/js/grid/details/Header.jsx) ### Use case/motivation Help users quickly differentiate between manual, scheduled and backfill runs. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23945

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

6962d8a3556999af2eec459c944417ddd6d2cfb3

d470a8ef8df152eceee88b95365ff923db7cb2d7

python

closed

apache/airflow

https://github.com/apache/airflow

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

No option to set blocksize when retrieving a file in ftphook

### Apache Airflow version 2.0.0 ### What happened using ftphook, im trying to download a file in chunks but the deafult blocksize is 8192 and cannot be changed. retrieve_file code is calling conn.retrbinary(f'RETR {remote_file_name}', callback) but no blocksize is passed while this function is declared: def retrbinary(self, cmd, callback, blocksize=8192, rest=None): ### What you think should happen instead allow passing a blocksize ### How to reproduce _No response_ ### Operating System gcp ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23935

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

2f29bfefb59b0014ae9e5f641d3f6f46c4341518

64412ee867fe0918cc3b616b3fb0b72dcd88125c

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

Wrong dependecy version of requests for Databricks provider

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

https://github.com/apache/airflow/issues/23917

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

b170dc7d66a628e405a824bfbc9fb48a3b3edd63

80c3fcd097c02511463b2c4f586757af0e5f41b2

python

closed

apache/airflow

https://github.com/apache/airflow

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

`dag-processor` failed to start in docker

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

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

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

8ccff9244a6d1a936d8732721373b967e95ec404

9216489d9a25f56f7a55d032b0ebfc1bf0bf4a83

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

1bf6dded9a5dcc22238b8943028b08741e36dfe5

d788f4b90128533b1ac3a0622a8beb695b52e2c4

python

closed

apache/airflow

https://github.com/apache/airflow

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

Don’t prompt for 'breeze build-image'

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

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

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

cac7ab5c4f4239b04d7800712ee841f0e6f6ab86

90940b529340ef7f9b8c51d5c7d9b6a848617dea

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

1abdf3fd1e048f53e061cc9ad59177be7b5245ad

6fd06fa8c274b39e4ed716f8d347229e017ba8e5

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Mapping not working with op_kwargs in PythonOperator

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

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

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

281e54b442f0a02bda53ae847aae9f371306f246

5877f45d65d5aa864941efebd2040661b6f89cb1

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23826

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

389e858d934a7813c7f15ab4e46df33c5720e415

a597a76e8f893865e7380b072de612763639bfb9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Race condition between Triggerer and Scheduler

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

https://github.com/apache/airflow/issues/23824

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

94f4f81efb8c424bee8336bf6b8720821e48898a

66ffe39b0b3ae233aeb80e77eea1b2b867cc8c45

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers_manager.py"]

ModuleNotFoundExceptions not matched as optional features

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

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

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

5bfacf81c63668ea63e7cb48f4a708a67d0ac0a2

6f5749c0d04bd732b320fcbe7713f2611e3d3629

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add an AWS operator for Create RDS Database

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

https://github.com/apache/airflow/issues/23822

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

c7feb31786c7744da91d319f499d9f6015d82454

bf727525e1fd777e51cc8bc17285f6093277fdef

python

closed

apache/airflow

https://github.com/apache/airflow

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

Webserver shows wrong datetime (timezone) in log

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

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

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

5a8209e5096528b6f562efebbe71b6b9c378aaed

7de050ceeb381fb7959b65acd7008e85b430c46f

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

df388a3d5364b748993e61b522d0b68ff8b8124a

a69095fea1722e153a95ef9da93b002b82a02426

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23786

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

86cfd1244a641a8f17c9b33a34399d9be264f556

7ab5ea7853df9d99f6da3ab804ffe085378fbd8a

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23783

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

40f08900f2d1fb0d316b40dde583535a076f616b

45f4290712f5f779e57034f81dbaab5d77d5de85

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23772

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

5ab58d057abb6b1f28eb4e3fb5cec7dc9850f0b0

1cf483fa0c45e0110d99e37b4e45c72c6084aa97

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23733

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

71e4deb1b093b7ad9320eb5eb34eca8ea440a238

239a9dce5b97d45620862b42fd9018fdc9d6d505

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23727

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

c8fa9e96e29f9f8b4ff9c7db416097fb70a87c2d

0c41f437674f135fe7232a368bf9c198b0ecd2f0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add fields to CLOUD_SQL_EXPORT_VALIDATION

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

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

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

9e25bc211f6f7bba1aff133d21fe3865dabda53d

3bf9a1df38b1ccfaf965a207d047b30452df1ba5

python

closed

apache/airflow

https://github.com/apache/airflow

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

Adding PodAnnotations for Redis Statefulset

### Description Most Airflow services come with the ability of adding annotation, a part from Redis. This feature request adds this capability into the Redis helm template as well. ### Use case/motivation Specifically for us, annotations and labels are used to integrate Airflow with external services, such as Datadog, and without it, the integration becomes a bit more complex. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23705

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

ef79a0d1c4c0a041d7ebf83b93cbb25aa3778a70

2af19f16a4d94e749bbf6c7c4704e02aac35fc11

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db_cleanup.py"]

airflow db clean - table missing exception not captured

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

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

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

252ef66438ecda87a8aac4beed1f689f14ee8bec

a80b2fcaea984813995d4a2610987a1c9068fdb5

python

closed

apache/airflow

https://github.com/apache/airflow

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

Conflicts with airflow constraints for airflow 2.3.0 python 3.9

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

https://github.com/apache/airflow/issues/23692

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

4afa8e3cecf1e4a2863715d14a45160034ad31a6

310002e44887847991b0864bbf9a921c7b11e930

python

closed

apache/airflow

https://github.com/apache/airflow

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

Data Interval wrong when manually triggering with a specific logical date

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

https://github.com/apache/airflow/issues/23689

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

026f1bb98cd05a26075bd4e4fb68f7c3860ce8db

d991d9800e883a2109b5523ae6354738e4ac5717

python

closed

apache/airflow

https://github.com/apache/airflow

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

_TaskDecorator has no __wrapped__ attribute in v2.3.0

### Apache Airflow version 2.3.0 (latest released) ### What happened I run a unit test on a task which is defined using the task decorator. In the unit test, I unwrap the task decorator with the `__wrapped__` attribute, but this no longer works in v2.3.0. It works in v2.2.5. ### What you think should happen instead I expect the wrapped function to be returned. This was what occurred in v2.2.5 When running pytest on the airflow v2.3.0 the following error is thrown: ```AttributeError: '_TaskDecorator' object has no attribute '__wrapped__'``` ### How to reproduce Here's a rough outline of the code. A module `hello.py` contains the task definition: ``` from airflow.decorators import task @task def hello_airflow(): print('hello airflow') ``` and the test contains ``` from hello import hello_airflow def test_hello_airflow(): hello_airflow.__wrapped__() ``` Then run pytest ### Operating System Rocky Linux 8.5 (Green Obsidian) ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23688

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

a8445657996f52b3ac5ce40a535d9c397c204d36

a71e4b789006b8f36cd993731a9fb7d5792fccc2

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23679

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

ce8ea6691820140a0e2d9a5dad5254bc05a5a270

888bc2e233b1672a61433929e26b82210796fd71

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23670

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

70b41e46b46e65c0446a40ab91624cb2291a5039

29afd35b9cfe141b668ce7ceccecdba60775a8ff

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/README.rst"]

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

### What do you see as an issue? I got an error when executing `./breeze build-docs -- --help` command in docs/README.rst. ```bash % ./breeze build-docs -- --help Usage: breeze build-docs [OPTIONS] Try running the '--help' flag for more information. ╭─ Error ─────────────────────────────────────────────────╮ │ Got unexpected extra argument (--help) │ ╰─────────────────────────────────────────────────────────╯ To find out more, visit https://github.com/apache/airflow/blob/main/BREEZE.rst ``` ### Solving the problem "--" in option should be removed. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23669

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

3138604b264878f27505223bd14c7814eacc1e57

3fa57168a520d8afe0c06d8a0200dd3517f43078

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add transfers operator S3 to SQL / SQL to SQL

### Description Should we add S3 to SQL to aws transfers? ### Use case/motivation 1. After process data from spark/glue(more), we need to publish data to sql 2. Synchronize data between 2 sql databases. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23666

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

e5730364b4eb5a3b30e815ca965db0f0e710edb6

efaed34213ad4416e2f4834d0cd2f60c41814507

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Crashes scheduler - Non Empty Return

### Apache Airflow version 2.3.0 (latest released) ### What happened I have a dag that looks like this. When I uncomment `py_job`(Dynamically mapped PythonOperator) it works well with `pull_messages` (Taskflow API). When I try to do the same with `DatabricksRunNowOperator` it crashes the scheduler with error Related issues #23486 ### Sample DAG ``` import json import pendulum from airflow.decorators import dag, task from airflow.operators.python import PythonOperator from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator @dag( schedule_interval=None, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, tags=['example'], ) def tutorial_taskflow_api_etl(): def random(*args, **kwargs): print ("==== kwargs inside random ====", args, kwargs) print ("I'm random") return 49 @task def pull_messages(): return [["hi"], ["hello"]] op = DatabricksRunNowOperator.partial( task_id = "new_job", job_id=42, notebook_params={"dry-run": "true"}, python_params=["douglas adams", "42"], spark_submit_params=["--class", "org.apache.spark.examples.SparkPi"] ).expand(jar_params=pull_messages()) # py_job = PythonOperator.partial( # task_id = 'py_job', # python_callable=random # ).expand(op_args= pull_messages()) tutorial_etl_dag = tutorial_taskflow_api_etl() ``` ### Error ``` [2022-05-11 11:46:30 +0000] [40] [INFO] Worker exiting (pid: 40) return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/usr/local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/usr/local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.9/site-packages/astronomer/airflow/version_check/plugin.py", line 29, in run_before fn(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 824, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 906, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/usr/local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1148, in _schedule_dag_run schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 522, in update_state info = self.task_instance_scheduling_decisions(session) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 68, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 658, in task_instance_scheduling_decisions schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis( File "/usr/local/lib/python3.9/site-packages/airflow/models/dagrun.py", line 714, in _get_ready_tis expanded_tis, _ = schedulable.task.expand_mapped_task(self.run_id, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 609, in expand_mapped_task operator.mul, self._resolve_map_lengths(run_id, session=session).values() File "/usr/local/lib/python3.9/site-packages/airflow/models/mappedoperator.py", line 595, in _resolve_map_lengths raise RuntimeError(f"Failed to populate all mapping metadata; missing: {keys}") RuntimeError: Failed to populate all mapping metadata; missing: 'jar_params' [2022-05-11 11:46:30 +0000] [31] [INFO] Shutting down: Master ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-databricks ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23642

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

5e3f652397005c5fac6c6b0099de345b5c39148d

3849ebb8d22bbc229d464c4171c9b5ff960cd089

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/trigger.py"]

Triggerer process die with DB Deadlock

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

https://github.com/apache/airflow/issues/23639

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

5087f96600f6d7cc852b91079e92d00df6a50486

d86ae090350de97e385ca4aaf128235f4c21f158

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23623

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

4c9f7560355eefd57a29afee73bf04273e81a7e8

86cfd1244a641a8f17c9b33a34399d9be264f556

python

closed

apache/airflow

https://github.com/apache/airflow

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

DatabricksSubmitRunOperator and DatabricksRunNowOperator cannot define .json as template_ext

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

https://github.com/apache/airflow/issues/23622

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

84c9f4bf70cbc2f4ba19fdc5aa88791500d4daaa

acf89510cd5a18d15c1a45e674ba0bcae9293097

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add an offload option to CloudSQLExportInstanceOperator validation specification

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==5.0.0 ### Apache Airflow version 2.1.2 ### Operating System GCP Container ### Deployment Composer ### Deployment details composer-1.17.1-airflow-2.1.2 ### What happened I want to use serverless export to offload the export operation from the primary instance. https://cloud.google.com/sql/docs/mysql/import-export#serverless Used CloudSQLExportInstanceOperator with the exportContext.offload flag to perform a serverless export operation. I got the following warning: ``` {field_validator.py:266} WARNING - The field 'exportContext.offload' is in the body, but is not specified in the validation specification '[{'name': 'fileType', 'allow_empty': False}, {'name': 'uri', 'allow_empty': False}, {'name': 'databases', 'optional': True, 'type': 'list'}, {'name': 'sqlExportOptions', 'type': 'dict', 'optional': True, 'fields': [{'name': 'tables', 'optional': True, 'type': 'list'}, {'name': 'schemaOnly', 'optional': True}]}, {'name': 'csvExportOptions', 'type': 'dict', 'optional': True, 'fields': [{'name': 'selectQuery'}]}]'. This might be because you are using newer API version and new field names defined for that version. Then the warning can be safely ignored, or you might want to upgrade the operatorto the version that supports the new API version. ``` ### What you think should happen instead I think a validation specification for `exportContext.offload` should be added. ### How to reproduce Try to use `exportContext.offload`, as in the example below. ```python CloudSQLExportInstanceOperator( task_id='export_task', project_id='some_project', instance='cloud_sql_instance', body={ "exportContext": { "fileType": "csv", "uri": "gs://my-bucket/export.csv", "databases": ["some_db"], "csvExportOptions": {"selectQuery": "select * from some_table limit 10"}, "offload": True } }, ) ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

1bd75ddbe3b1e590e38d735757d99b43db1725d6

74557e41e3dcedec241ea583123d53176994cccc

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0 (latest released) ### What happened The issue started to occur after upgrading airflow from v2.2.5 to v2.3.0. The schedulers are crashing when DAG's SLA is configured. Only occurred when I used `CeleryKubernetesExecutor`. Tested on `CeleryExecutor` and it works as expected. ``` Traceback (most recent call last): File "/home/airflow/.local/bin/airflow", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main args.func(args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler _run_scheduler_job(args=args) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job job.run() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 244, in run self._execute() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 736, in _execute self._run_scheduler_loop() File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 824, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 919, in _do_scheduling self._send_dag_callbacks_to_processor(dag, callback_to_run) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1179, in _send_dag_callbacks_to_processor self._send_sla_callbacks_to_processor(dag) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1195, in _send_sla_callbacks_to_processor self.executor.send_callback(request) AttributeError: 'CeleryKubernetesExecutor' object has no attribute 'send_callback' ``` ### What you think should happen instead Work like previous version ### How to reproduce 1. Use `CeleryKubernetesExecutor` 2. Configure DAG's SLA DAG to reproduce: ``` # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Example DAG demonstrating the usage of the BashOperator.""" from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator DEFAULT_ARGS = { "sla": timedelta(hours=1), } with DAG( dag_id="example_bash_operator", default_args=DEFAULT_ARGS, schedule_interval="0 0 * * *", start_date=datetime(2021, 1, 1), catchup=False, dagrun_timeout=timedelta(minutes=60), tags=["example", "example2"], params={"example_key": "example_value"}, ) as dag: run_this_last = DummyOperator( task_id="run_this_last", ) # [START howto_operator_bash] run_this = BashOperator( task_id="run_after_loop", bash_command="echo 1", ) # [END howto_operator_bash] run_this >> run_this_last for i in range(3): task = BashOperator( task_id="runme_" + str(i), bash_command='echo "{{ task_instance_key_str }}" && sleep 1', ) task >> run_this # [START howto_operator_bash_template] also_run_this = BashOperator( task_id="also_run_this", bash_command='echo "run_id={{ run_id }} | dag_run={{ dag_run }}"', ) # [END howto_operator_bash_template] also_run_this >> run_this_last # [START howto_operator_bash_skip] this_will_skip = BashOperator( task_id="this_will_skip", bash_command='echo "hello world"; exit 99;', dag=dag, ) # [END howto_operator_bash_skip] this_will_skip >> run_this_last if __name__ == "__main__": dag.cli() ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

60a1d9d191fb8fc01893024c897df9632ad5fbf4

c5b72bf30c8b80b6c022055834fc7272a1a44526

python

closed

apache/airflow

https://github.com/apache/airflow

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

After upgrade from Airflow 2.2.4, grid disappears for some DAGs

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

https://github.com/apache/airflow/issues/23588

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

8bfad056d8ef481cc44288c5749fa5c54efadeaa

943b97850a1e82e4da22e8489c4ede958a42213d

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0 (latest released) ### What happened `task_id` with `.` e.g. `hello.world` is not rendered in grid view. ### What you think should happen instead The task should be rendered just fine in Grid view. ### How to reproduce ``` # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Example DAG demonstrating the usage of the BashOperator.""" from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator with DAG( dag_id="example_bash_operator", schedule_interval="0 0 * * *", start_date=datetime(2021, 1, 1), catchup=False, dagrun_timeout=timedelta(minutes=60), tags=["example", "example2"], params={"example_key": "example_value"}, ) as dag: run_this_last = DummyOperator( task_id="run.this.last", ) # [START howto_operator_bash] run_this = BashOperator( task_id="run.after.loop", bash_command="echo 1", ) # [END howto_operator_bash] run_this >> run_this_last for i in range(3): task = BashOperator( task_id="runme." + str(i), bash_command='echo "{{ task_instance_key_str }}" && sleep 1', ) task >> run_this # [START howto_operator_bash_template] also_run_this = BashOperator( task_id="also.run.this", bash_command='echo "run_id={{ run_id }} | dag_run={{ dag_run }}"', ) # [END howto_operator_bash_template] also_run_this >> run_this_last # [START howto_operator_bash_skip] this_will_skip = BashOperator( task_id="this.will.skip", bash_command='echo "hello world"; exit 99;', dag=dag, ) # [END howto_operator_bash_skip] this_will_skip >> run_this_last if __name__ == "__main__": dag.cli() ``` ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

028087b5a6e94fd98542d0e681d947979eb1011f

afdfece9372fed83602d50e2eaa365597b7d0101

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

The xmltodict 0.13.0 breaks some emr tests

### Apache Airflow version main (development) ### What happened The xmltodict 0.13.0 breaks some EMR tests (this is happening in `main` currently: Example: https://github.com/apache/airflow/runs/6343826225?check_suite_focus=true#step:9:13417 ``` tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_create_job_flow_extra_args: ValueError: Malformatted input tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_create_job_flow_uses_the_emr_config_to_create_a_cluster: ValueError: Malformatted input tests/providers/amazon/aws/hooks/test_emr.py::TestEmrHook::test_get_cluster_id_by_name: ValueError: Malformatted input ``` Downgrading to 0.12.0 fixes the problem. ### What you think should happen instead The tests should work ### How to reproduce * Run Breeze * Run `pytest tests/providers/amazon/aws/hooks/test_emr.py` -> observe it to succeed * Run `pip install xmltodict==0.13.0` -> observe it being upgraded from 0.12.0 * Run `pytest tests/providers/amazon/aws/hooks/test_emr.py` -> observe it to fail with `Malformed input` error ### Operating System Any ### Versions of Apache Airflow Providers Latest from main ### Deployment Other ### Deployment details CI ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23576

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

614b2329c1603ef1e2199044e2cc9e4b7332c2e0

eec85d397ef0ecbbe5fd679cf5790adae2ad9c9f

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0 (latest released) ### What happened Dag processor failed to start if `[core] sql_alchemy_conn` not defined ``` airflow-local-airflow-dag-processor-1 | [2022-05-08 16:42:35,835] {configuration.py:494} WARNING - section/key [core/sql_alchemy_conn] not found in config airflow-local-airflow-dag-processor-1 | Traceback (most recent call last): airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/bin/airflow", line 8, in <module> airflow-local-airflow-dag-processor-1 | sys.exit(main()) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 38, in main airflow-local-airflow-dag-processor-1 | args.func(args) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 51, in command airflow-local-airflow-dag-processor-1 | return func(*args, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 99, in wrapper airflow-local-airflow-dag-processor-1 | return f(*args, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/dag_processor_command.py", line 53, in dag_processor airflow-local-airflow-dag-processor-1 | sql_conn: str = conf.get('core', 'sql_alchemy_conn').lower() airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/configuration.py", line 486, in get airflow-local-airflow-dag-processor-1 | return self._get_option_from_default_config(section, key, **kwargs) airflow-local-airflow-dag-processor-1 | File "/home/airflow/.local/lib/python3.9/site-packages/airflow/configuration.py", line 496, in _get_option_from_default_config airflow-local-airflow-dag-processor-1 | raise AirflowConfigException(f"section/key [{section}/{key}] not found in config") airflow-local-airflow-dag-processor-1 | airflow.exceptions.AirflowConfigException: section/key [core/sql_alchemy_conn] not found in config ``` ### What you think should happen instead Since https://github.com/apache/airflow/pull/22284 `sql_alchemy_conn` moved to `[database]` section `dag-processor` should use this configuration ### How to reproduce Run `airflow dag-processor` without defined `[core] sql_alchemy_conn` https://github.com/apache/airflow/blob/6e5955831672c71bfc0424dd50c8e72f6fd5b2a7/airflow/cli/commands/dag_processor_command.py#L52-L53 ### Operating System Arch Linux ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-docker==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-grpc==2.0.4 apache-airflow-providers-hashicorp==2.2.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-odbc==2.0.4 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-sendgrid==2.0.4 apache-airflow-providers-sftp==2.6.0 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-snowflake==2.6.0 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

827bfda59b7a0db6ada697ccd01c739d37430b9a

9837e6d813744e3c5861c32e87b3aeb496d0f88d

python

closed

apache/airflow

https://github.com/apache/airflow

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

templates_dict, op_args, op_kwargs no longer rendered in PythonVirtualenvOperator

### Apache Airflow version 2.3.0 (latest released) ### What happened Templated strings in templates_dict, op_args, op_kwargs of PythonVirtualenvOperator are no longer rendered. ### What you think should happen instead All templated strings in templates_dict, op_args and op_kwargs must be rendered, i.e. these 3 arguments must be template_fields of PythonVirtualenvOperator, as it was in Airflow 2.2.3 ### How to reproduce _No response_ ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else This is due to template_fields class variable being set in PythonVirtualenvOperator `template_fields: Sequence[str] = ('requirements',)` that overrode class variable of PythonOperator `template_fields = ('templates_dict', 'op_args', 'op_kwargs')`. I read in some discussion that wanted to make requirements a template field for PythonVirtualenvOperator, but we must specify all template fields of parent class as well. `template_fields: Sequence[str] = ('templates_dict', 'op_args', 'op_kwargs', 'requirements',)` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23557

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

7132be2f11db24161940f57613874b4af86369c7

1657bd2827a3299a91ae0abbbfe4f6b80bd4cdc0

python

closed

apache/airflow

https://github.com/apache/airflow

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

Dynamic Task Mapping is Immutable within a Run

### Apache Airflow version 2.3.0 (latest released) ### What happened Looks like mapped tasks are immutable, even when the source XCOM that created them changes. This is a problem for things like Late Arriving Data and Data Reprocessing ### What you think should happen instead Mapped tasks should change in response to a change of input ### How to reproduce Here is a writeup and MVP DAG demonstrating the issue https://gist.github.com/fritz-astronomer/d159d0e29d57458af5b95c0f253a3361 ### Operating System docker/debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Astronomer ### Deployment details _No response_ ### Anything else Can look into a fix - but may not be able to submit a full PR ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23550

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

ad297c91777277e2b76dd7b7f0e3e3fc5c32e07c

b692517ce3aafb276e9d23570e9734c30a5f3d1f

python

closed

apache/airflow

https://github.com/apache/airflow

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

Gantt Chart Broken After Deleting a Task

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

https://github.com/apache/airflow/issues/23546

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

e09e4635b0dc50cbd3a18f8be02ce9b2e2f3d742

4b731f440734b7a0da1bbc8595702aaa1110ad8d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow .airflowignore not handling soft link properly.

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

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

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

7ab5ea7853df9d99f6da3ab804ffe085378fbd8a

8494fc7036c33683af06a0e57474b8a6157fda05

python

closed

apache/airflow

https://github.com/apache/airflow

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

Provide resources attribute in KubernetesPodOperator to be templated

### Description Make resources in KubernetesPodOperator as templated. We need to modify this during several runs and it needs code change for each run. ### Use case/motivation For running CPU and memory intensive workloads, we want to continuously optimise the "limt_cpu" and "limit_memory" parameters. Hence, we want to provide these parameters as a part of the pipeline definition. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23529

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

aefadb8c5b9272613d5806b054a1b46edf29d82e

47a2b9ee7f1ff2cc1cc1aa1c3d1b523c88ba29fb

python

closed

apache/airflow

https://github.com/apache/airflow

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

Cassandra container 3.0.26 fails to start on CI

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

https://github.com/apache/airflow/issues/23523

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

953b85d8a911301c040a3467ab2a1ba2b6d37cd7

22a564296be1aee62d738105859bd94003ad9afc

python

closed

apache/airflow

https://github.com/apache/airflow

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

Json files from S3 downloading as text files

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

https://github.com/apache/airflow/issues/23514

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

d544e8fbeb362e76e14d7615d354a299445e5b5a

777b57f0c6a8ca16df2b96fd17c26eab56b3f268

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

9ab1a6a3e70b32a3cddddf0adede5d2f3f7e29ea

8f99c793ec4289f7fc28d890b6c2887f0951e09b

python

closed

apache/airflow

https://github.com/apache/airflow

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

Tasks stuck indefinitely when following container logs

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

https://github.com/apache/airflow/issues/23497

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

97006910a384579c9f0601a72410223f9b6a0830

6d2face107f24b7e7dce4b98ae3def1178e1fc4c

python

closed

apache/airflow

https://github.com/apache/airflow

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

Grid View - Multilevel taskgroup shows white text on the UI

### Apache Airflow version 2.3.0 (latest released) ### What happened Blank text if there are nested Task Groups . Nested TaskGroup - Graph view:  Nested TaskGroup - Grid view:  ### What you think should happen instead We should see the text as up task group level. ### How to reproduce ### deploy below DAG: ``` from airflow import DAG from airflow.operators.dummy import DummyOperator from airflow.utils.dates import datetime from airflow.utils.task_group import TaskGroup with DAG(dag_id="grid_view_dag", start_date=datetime(2022, 5, 3, 0, 00), schedule_interval=None, concurrency=2, max_active_runs=2) as dag: parent_task_group = None for i in range(0, 10): with TaskGroup(group_id=f"tg_level_{i}", parent_group=parent_task_group) as tg: t = DummyOperator(task_id=f"task_level_{i}") parent_task_group = tg ``` ### got to grid view and expand the nodes:  #### you can see the text after text selection:  ### Operating System N/A ### Versions of Apache Airflow Providers N/A ### Deployment Docker-Compose ### Deployment details reproducible using the following docker-compose file: https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23476

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

d9902958448b9d6e013f90f14d2d066f3121dcd5

14befe3ad6a03f27e20357e9d4e69f99d19a06d1

python

closed

apache/airflow

https://github.com/apache/airflow

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

Could not get DAG access permission after upgrade to 2.3.0

### Apache Airflow version 2.3.0 (latest released) ### What happened I upgraded my airflow instance from version 2.1.3 to 2.3.0 but got issue that there are no permission for new DAGs. **The issue only happens in DAG which has dag_id contains dot symbol.** ### What you think should happen instead There should be 3 new permissions for a DAG. ### How to reproduce + Create a new DAG with id, lets say: `dag.id_1` + Go to the UI -> Security -> List Role + Edit any Role + Try to insert permissions of new DAG above to chosen role. -> Could not get any permission for created DAG above. There are 3 DAG permissions named `can_read_DAG:dag`, `can_edit_DAG:dag`, `can_delete_DAG:dag` There should be 3 new permissions: `can_read_DAG:dag.id_1`, `can_edit_DAG:dag.id_1`, `can_delete_DAG:dag.id_1` ### Operating System Kubernetes ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23473

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

ae3e68af3c42a53214e8264ecc5121049c3beaf3

cc35fcaf89eeff3d89e18088c2e68f01f8baad56

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add Postgres 14 support

### Description _No response_ ### Use case/motivation Using Postgres 14 as backend ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23460

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

9ab9cd47cff5292c3ad602762ae3e371c992ea92

6169e0a69875fb5080e8d70cfd9d5e650a9d13ba

python

closed

apache/airflow

https://github.com/apache/airflow

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

External DAG processor not working

### Apache Airflow version 2.3.0 (latest released) ### What happened Running a standalone Dag Processor instance with `airflow dag-processor` throws the following exception: ``` Standalone DagProcessor is not supported when using sqlite. ``` ### What you think should happen instead The `airflow dag-processor` should start without an exception in case of Postgres database ### How to reproduce The error is in the following line: https://github.com/apache/airflow/blob/6f146e721c81e9304bf7c0af66fc3d203d902dab/airflow/cli/commands/dag_processor_command.py#L53 It should be ```python sql_conn: str = conf.get('database', 'sql_alchemy_conn').lower() ``` due to the change in the configuration file done in https://github.com/apache/airflow/pull/22284 ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

827bfda59b7a0db6ada697ccd01c739d37430b9a

9837e6d813744e3c5861c32e87b3aeb496d0f88d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Empty `expand()` crashes the scheduler

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

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

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

c9b21b8026c595878ee4cc934209fc1fc2ca2396

9214018153dd193be6b1147629f73b23d8195cce

python

closed

apache/airflow

https://github.com/apache/airflow

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

Mapping over multiple parameters results in 1 task fewer than expected

### Apache Airflow version 2.3.0 (latest released) ### What happened While testing the [example](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html#mapping-over-multiple-parameters) given for `Mapping over multiple parameters` I noticed only 5 tasks are being mapped rather than the expected 6. task example from the doc: ``` @task def add(x: int, y: int): return x + y added_values = add.expand(x=[2, 4, 8], y=[5, 10]) ``` The doc mentions: ``` # This results in the add function being called with # add(x=2, y=5) # add(x=2, y=10) # add(x=4, y=5) # add(x=4, y=10) # add(x=8, y=5) # add(x=8, y=10) ``` But when I create a DAG with the example, only 5 tasks are mapped instead of 6:  ### What you think should happen instead A task should be mapped for all 6 possible outcomes, rather than only 5 ### How to reproduce Create a DAG using the example provided [here](Mapping over multiple parameters) and check the number of mapped instances:  ### Operating System macOS 11.5.2 ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-databricks==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-snowflake==2.6.0 apache-airflow-providers-sqlite==2.1.3 ### Deployment Astronomer ### Deployment details Localhost instance of Astronomer Runtime 5.0.0 ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

0fde90d92ae306f37041831f5514e9421eee676b

3fb8e0b0b4e8810bedece873949871a94dd7387a

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add a queue DAG run endpoint to REST API

### Description Add a POST endpoint to queue a dag run like we currently do [here](https://github.com/apache/airflow/issues/23419). Url format: `api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/queue` ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23420

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

1220c1a7a9698cdb15289d7066b29c209aaba6aa

4485393562ea4151a42f1be47bea11638b236001

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add a DAG Run clear endpoint to REST API

### Description Add a POST endpoint to clear a dag run like we currently do [here](https://github.com/apache/airflow/blob/main/airflow/www/views.py#L2087). Url format: `api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/clear` ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23419

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

f352ee63a5d09546a7997ba8f2f8702a1ddb4af7

b83cc9b5e2c7e2516b0881861bbc0f8589cb531d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add more fields to DAG Run API endpoints

### Description There are a few fields that would be useful to include in the REST API for getting a DAG run or list of DAG runs: `data_interval_start` `data_interval_end` `last_scheduling_decision` `run_type` as (backfill, manual and scheduled) ### Use case/motivation We use this information in the Grid view as part of `tree_data`. If we added these extra fields to the REST APi we could remove all dag run info from tree_data. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23415

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

22b49d334ef0008be7bd3d8481b55b8ab5d71c80

6178491a117924155963586b246d2bf54be5320f

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0 (latest released) ### What happened When running `airflow db clean`, if a to-be-cleaned dag is also tagged, a foreign key constraint in dag_tag is violated. Full error: ``` sqlalchemy.exc.IntegrityError: (psycopg2.errors.ForeignKeyViolation) update or delete on table "dag" violates foreign key constraint "dag_tag_dag_id_fkey" on table "dag_tag" DETAIL: Key (dag_id)=(some-dag-id-here) is still referenced from table "dag_tag". ``` ### What you think should happen instead _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-mssql==2.1.3 apache-airflow-providers-oracle==2.2.3 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-samba==3.0.4 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23414

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

e2401329345dcc5effa933b92ca969b8779755e4

8ccff9244a6d1a936d8732721373b967e95ec404

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0 (latest released) ### What happened A PythonSensor that works on versions <2.3.0 in mode reschedule is now marking the task as `UP_FOR_RETRY` instead. Log says: ``` [2022-05-02, 15:48:23 UTC] {python.py:66} INFO - Poking callable: <function test at 0x7fd56286bc10> [2022-05-02, 15:48:23 UTC] {taskinstance.py:1853} INFO - Rescheduling task, marking task as UP_FOR_RESCHEDULE [2022-05-02, 15:48:23 UTC] {local_task_job.py:156} INFO - Task exited with return code 0 [2022-05-02, 15:48:23 UTC] {local_task_job.py:273} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` But it directly marks it as `UP_FOR_RETRY` and then follows `retry_delay` and `retries` ### What you think should happen instead It should mark the task as `UP_FOR_RESCHEDULE` and reschedule it according to the `poke_interval` ### How to reproduce ``` from datetime import datetime, timedelta from airflow import DAG from airflow.sensors.python import PythonSensor def test(): return False default_args = { "owner": "airflow", "depends_on_past": False, "start_date": datetime(2022, 5, 2), "email_on_failure": False, "email_on_retry": False, "retries": 1, "retry_delay": timedelta(minutes=1), } dag = DAG("dag_csdepkrr_development_v001", default_args=default_args, catchup=False, max_active_runs=1, schedule_interval=None) t1 = PythonSensor(task_id="PythonSensor", python_callable=test, poke_interval=30, mode='reschedule', dag=dag) ``` ### Operating System Latest Docker image ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==3.3.0 apache-airflow-providers-celery==2.1.4 apache-airflow-providers-cncf-kubernetes==4.0.1 apache-airflow-providers-docker==2.6.0 apache-airflow-providers-elasticsearch==3.0.3 apache-airflow-providers-ftp==2.1.2 apache-airflow-providers-google==6.8.0 apache-airflow-providers-grpc==2.0.4 apache-airflow-providers-hashicorp==2.2.0 apache-airflow-providers-http==2.1.2 apache-airflow-providers-imap==2.2.3 apache-airflow-providers-microsoft-azure==3.8.0 apache-airflow-providers-mysql==2.2.3 apache-airflow-providers-odbc==2.0.4 apache-airflow-providers-oracle==2.2.3 apache-airflow-providers-postgres==4.1.0 apache-airflow-providers-redis==2.0.4 apache-airflow-providers-sendgrid==2.0.4 apache-airflow-providers-sftp==2.5.2 apache-airflow-providers-slack==4.2.3 apache-airflow-providers-sqlite==2.1.3 apache-airflow-providers-ssh==2.4.3 ``` ### Deployment Docker-Compose ### Deployment details Latest Docker compose from the documentation ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23411

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

d3b08802861b006fc902f895802f460a72d504b0

f9e2a3051cd3a5b6fcf33bca4c929d220cf5661e

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py"]

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

### Apache Airflow version 2.3.0 (latest released) ### What happened We used to configure the Database using the AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD Environment variable. Now the config option moved from CORE to DATABASE. However, we intended to keep backward compatibility as stated in the [Release Notes](AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD). Upon 2.3.0 update however, the _CMD suffixed variables are no longer recognized for database configuration in Core - I think due to a missing entry here: https://github.com/apache/airflow/blob/8622808aa79531bcaa5099d26fbaf54b4afe931a/airflow/configuration.py#L135 ### What you think should happen instead We should only get a deprecation warning but the Database should be configured correctly. ### How to reproduce Configure Airflow using an external Database using the AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD environment variable. Notice that Airflow falls back to SQLight. ### Operating System kubernetes ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23408

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

6178491a117924155963586b246d2bf54be5320f

0cdd401cda61006a42afba243f1ad813315934d4

python

closed

apache/airflow

https://github.com/apache/airflow

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

Airflow kubernetes pod operator fetch xcom fails

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

https://github.com/apache/airflow/issues/23396

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

b0406f58f0c51db46d2da7c7c84a0b5c3d4f09ae

faae9faae396610086d5ea18d61c356a78a3d365

python

closed

apache/airflow

https://github.com/apache/airflow

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

Scheduler crashes with psycopg2.errors.DeadlockDetected exception

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

https://github.com/apache/airflow/issues/23361

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

741c20770230c83a95f74fe7ad7cc9f95329f2cc

be2b53eaaf6fc136db8f3fa3edd797a6c529409a

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23356

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

49cfb6498eed0acfc336a24fd827b69156d5e5bb

640d4f9636d3867d66af2478bca15272811329da

python

closed

apache/airflow

https://github.com/apache/airflow

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

Silent DAG import error by making owner a list

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

https://github.com/apache/airflow/issues/23343

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

9a0080c20bb2c4a9c0f6ccf1ece79bde895688ac

c4887bcb162aab9f381e49cecc2f212600c493de

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSTransformOperator: provide Jinja templating in source and destination object names

### Description Provide an option to receive the source_object and destination_object via Jinja params. ### Use case/motivation Usecase: Need to execute a DAG to fetch a video from GCS bucket based on paramater and then transform it and store it back. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23327

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

505af06303d8160c71f6a7abe4792746f640083d

c82b3b94660a38360f61d47676ed180a0d32c189

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23315

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

d3028e1e9036a3c67ec4477eee6cd203c12f7f5c

59e93106d55881163a93dac4a5289df1ba6e1db5

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

3977e1798d8294ba628b5f330f43702c1a5c79fc

48915bd149bd8b58853880d63b8c6415688479ec

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCP Composer v1.18.6 and 2.0.10 incompatible with CloudSqlProxyRunner

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

https://github.com/apache/airflow/issues/23292

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

0c9c1cf94acc6fb315a9bc6f5bf1fbd4e4b4c923

1f3260354988b304cf31d5e1d945ce91798bed48

python

closed

apache/airflow

https://github.com/apache/airflow

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

Cycle incorrectly detected in DAGs when using Labels within Task Groups

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

https://github.com/apache/airflow/issues/23285

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

726b27f86cf964924e5ee7b29a30aefe24dac45a

3182303ce50bda6d5d27a6ef4e19450fb4e47eea

python

closed

apache/airflow

https://github.com/apache/airflow

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

Get DAG tasks in REST API does not include is_mapped

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened The rest API endpoint for get [/dags/{dag_id}/tasks](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/get_tasks) does not include `is_mapped`. Example: `consumer` is mapped but I have no way to tell that from the API response: <img width="306" alt="Screen Shot 2022-04-27 at 11 35 54 AM" src="https://user-images.githubusercontent.com/4600967/165556420-f8ade6e6-e904-4be0-a759-5281ddc04cba.png"> <img width="672" alt="Screen Shot 2022-04-27 at 11 35 25 AM" src="https://user-images.githubusercontent.com/4600967/165556310-742ec23d-f5a8-4cae-bea1-d00fd6c6916f.png"> ### What you think should happen instead Someone should be able to know if a task from get /tasks is mapped or not. ### How to reproduce call get /tasks on a dag with mapped tasks. see there is no way to determine if it is mapped from the response body. ### Operating System Mac OSX ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23284

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

98ec8c6990347fda60cbad33db915dc21497b1f0

f3d80c2a0dce93b908d7c9de30c9cba673eb20d5

python

closed

apache/airflow

https://github.com/apache/airflow

["breeze-legacy"]

Breeze-legacy missing flag_build_docker_images

### Apache Airflow version main (development) ### What happened Running `./breeze-legacy` warns about a potential issue: ```shell ❯ ./breeze-legacy --help Good version of docker 20.10.13. ./breeze-legacy: line 1434: breeze::flag_build_docker_images: command not found ... ``` And sure enough, `flag_build_docker_images` is referenced but not defined anywhere: ```shell ❯ ag flag_build_docker_images breeze-legacy 1433:$(breeze::flag_build_docker_images) ``` And I believe that completely breaks `breeze-legacy`: ```shell ❯ ./breeze-legacy Good version of docker 20.10.13. ERROR: Allowed platform: [ ]. Passed: 'linux/amd64' Switch to supported value with --platform flag. ERROR: The previous step completed with error. Please take a look at output above ``` ### What you think should happen instead Breeze-legacy should still work. Bash functions should be defined if they are still in use. ### How to reproduce Pull `main` branch. Run `./breeze-legacy`. ### Operating System macOS 11.6.4 Big Sur (Intel) ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23272

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

1e87f51d163a8db7821d3a146c358879aff7ec0e

aee40f82ccec7651abe388d6a2cbac35f5f4c895

python

closed

apache/airflow

https://github.com/apache/airflow

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

wasb hook not using AZURE_CLIENT_ID environment variable as client_id for ManagedIdentityCredential

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

https://github.com/apache/airflow/issues/23266

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

fcfaa8307ac410283f1270a0df9e557570e5ffd3

8f181c10344bd319ac5f6aeb102ee3c06e1f1637

python

closed

apache/airflow

https://github.com/apache/airflow

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

Pool option does not work in backfill command

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

https://github.com/apache/airflow/issues/23249

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

511d0ee256b819690ccf0f6b30d12340b1dd7f0a

3970ea386d5e0a371143ad1e69b897fd1262842d

python

closed

apache/airflow

https://github.com/apache/airflow

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

Add api call for changing task instance status

### Description In the UI you can change the status of a task instance, but there is no API call available for the same feature. It would be nice to have an api call for this as well. ### Use case/motivation I found a solution on stack-overflow on [How to add manual tasks in an Apache Airflow Dag]. There is a suggestion to set a task on failed and change it manually to succeed when the task is done. Our project has many manual tasks. This suggestions seems like a good option, but there is no api call yet to call instead of change all status manually. I would like to use an api call for this instead. You can change the status of on a dag run so it also seems natural to have something similar for task instances. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23246

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

5c37b503f118b8ad2585dff9949dd8fdb96689ed

1e6f1d54c54e5dc50078216e23ba01560ebb133c

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

3221ed5968423ea7a0dc7e1a4b51084351c2d56b

eceb4cc5888a7cf86a9250fff001fede2d6aba0f

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23174

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

e08b59da48743ff0d0ce145d1bc06bb7b5f86e68

1bf6dded9a5dcc22238b8943028b08741e36dfe5

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened Have a DAG with mapped tasks. Mark at least two mapped tasks as failed. Mark one of the failures as success. See the other task(s) switch to `no_status` and rerun.  ### What you think should happen instead Marking a single mapped task as a success probably shouldn't affect other failed mapped tasks. ### How to reproduce _No response_ ### Operating System OSX ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23171

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

d262a72ca7ab75df336b93cefa338e7ba3f90ebb

26a9ec65816e3ec7542d63ab4a2a494931a06c9b

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23168

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

b33cd10941dd10d461023df5c2d3014f5dcbb7ac

b45240ad21ca750106931ba2b882b3238ef2b37d

python

closed

apache/airflow

https://github.com/apache/airflow

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

GCSToGCSOperator ignores replace parameter when there is no wildcard

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

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

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

03718194f4fa509f16fcaf3d41ff186dbae5d427

82c244f9c7f24735ee952951bcb5add45422d186

python

closed

apache/airflow

https://github.com/apache/airflow

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

docker container still running while dag run failed

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

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

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

5d5d62e41e93fe9845c96ab894047422761023d8

237d2225d6b92a5012a025ece93cd062382470ed

python

closed

apache/airflow

https://github.com/apache/airflow

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

location is missing in BigQueryDataTransferServiceTransferRunSensor

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

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

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

692a0899430f86d160577c3dd0f52644c4ffad37

967140e6c3bd0f359393e018bf27b7f2310a2fd9

python

closed

apache/airflow

https://github.com/apache/airflow

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

Task stuck in "scheduled" when running in backfill job

### Apache Airflow version 2.2.4 ### What happened We are running airflow 2.2.4 with KubernetesExecutor. I have created a dag to run airflow backfill command with SubprocessHook. What was observed is that when I started to backfill a few days' dagruns the backfill would get stuck with some dag runs having tasks staying in the "scheduled" state and never getting running. We are using the default pool and the pool is totoally free when the tasks got stuck. I could find some logs saying: `TaskInstance: <TaskInstance: test_dag_2.task_1 backfill__2022-03-29T00:00:00+00:00 [queued]> found in queued state but was not launched, rescheduling` and nothing else in the log. ### What you think should happen instead The tasks stuck in "scheduled" should start running when there is free slot in the pool. ### How to reproduce Airflow 2.2.4 with python 3.8.13, KubernetesExecutor running in AWS EKS. One backfill command example is: `airflow dags backfill test_dag_2 -s 2022-03-01 -e 2022-03-10 --rerun-failed-tasks` The test_dag_2 dag is like: ``` import time from datetime import timedelta import pendulum from airflow import DAG from airflow.decorators import task from airflow.models.dag import dag from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator from airflow.operators.python import PythonOperator default_args = { 'owner': 'airflow', 'depends_on_past': False, 'email': ['airflow@example.com'], 'email_on_failure': True, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } def get_execution_date(**kwargs): ds = kwargs['ds'] print(ds) with DAG( 'test_dag_2', default_args=default_args, description='Testing dag', start_date=pendulum.datetime(2022, 4, 2, tz='UTC'), schedule_interval="@daily", catchup=True, max_active_runs=1, ) as dag: t1 = BashOperator( task_id='task_1', depends_on_past=False, bash_command='sleep 30' ) t2 = PythonOperator( task_id='get_execution_date', python_callable=get_execution_date ) t1 >> t2 ``` ### Operating System Debian GNU/Linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==3.0.0 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-cncf-kubernetes==3.0.2 apache-airflow-providers-docker==2.4.1 apache-airflow-providers-elasticsearch==2.2.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==6.4.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.1 apache-airflow-providers-http==2.0.3 apache-airflow-providers-imap==2.2.0 apache-airflow-providers-microsoft-azure==3.6.0 apache-airflow-providers-microsoft-mssql==2.1.0 apache-airflow-providers-odbc==2.0.1 apache-airflow-providers-postgres==3.0.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.4.1 apache-airflow-providers-slack==4.2.0 apache-airflow-providers-snowflake==2.5.0 apache-airflow-providers-sqlite==2.1.0 apache-airflow-providers-ssh==2.4.0 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

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

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

49cfb6498eed0acfc336a24fd827b69156d5e5bb

640d4f9636d3867d66af2478bca15272811329da

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23114

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

923f1ef30e8f4c0df2845817b8f96373991ad3ce

5c97e5be484ff572070b0ad320c5936bc028be93

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23107

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

1e8ac47589967f2a7284faeab0f65b01bfd8202d

91b82763c5c17e8ab021f2d4f2a5681ea90adf6b

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23092

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

8b1ebdacd8ddbe841a74830f750ed8f5e6f38f0a

d233c12c30f9a7f3da63348f3f028104cb14c76b

python

closed

apache/airflow

https://github.com/apache/airflow

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

Breeze: Running integration tests in Breeze

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

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

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

83784d9e7b79d2400307454ccafdacddaee16769

7ba4e35a9d1b65b4c1a318ba4abdf521f98421a2

python

closed

apache/airflow

https://github.com/apache/airflow

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

Breeze: Add running unit tests with Breeze

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

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

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

83784d9e7b79d2400307454ccafdacddaee16769

7ba4e35a9d1b65b4c1a318ba4abdf521f98421a2

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23068

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

511ea702d5f732582d018dad79754b54d5e53f9d

9e2531fa4d9890f002d184121e018e3face5586b

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

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

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

b24650c0cc156ceb5ef5791f1647d4d37a529920

5164cdbe98ad63754d969b4b300a7a0167565e33

python

closed

apache/airflow

https://github.com/apache/airflow

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

Graph view: Nodes arrows are cut

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

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

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

749e53def43055225a2e5d09596af7821d91b4ac

028087b5a6e94fd98542d0e681d947979eb1011f

python

closed

apache/airflow

https://github.com/apache/airflow

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

PostgresToGCSOperator does not allow nested JSON

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

https://github.com/apache/airflow/issues/23040

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

ca3fbbbe14203774a16ddd23e82cfe652b22eb4a

766726f2e3a282fcd2662f5dc6e9926dc38a6540

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23033

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

4fa718e4db2daeb89085ea20e8b3ce0c895e415c

8dedd2ac13a6cdc0c363446985f492e0f702f639

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

### Apache Airflow version 2.3.0b1 (pre-release) ### What happened I ran: ``` $ airflow tasks states-for-dag-run taskmap_xcom_pull 'manual__2022-04-14T13:27:04.958420+00:00' dag_id | execution_date | task_id | state | start_date | end_date ==================+==================================+===========+=========+==================================+================================= taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | foo | success | 2022-04-14T13:27:05.343134+00:00 | 2022-04-14T13:27:05.598641+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | bar | success | 2022-04-14T13:27:06.256684+00:00 | 2022-04-14T13:27:06.462664+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.480364+00:00 | 2022-04-14T13:27:07.713226+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.512084+00:00 | 2022-04-14T13:27:07.768716+00:00 taskmap_xcom_pull | 2022-04-14T13:27:04.958420+00:00 | identity | success | 2022-04-14T13:27:07.546097+00:00 | 2022-04-14T13:27:07.782719+00:00 ``` ...targeting a dagrun for which `identity` had three expanded tasks. All three showed up, but the output didn't show me enough to know which one was which. ### What you think should happen instead There should be a `map_index` column so that I know which one is which. ### How to reproduce Run a dag with expanded tasks, then try to view their states via the cli ### Operating System debian (docker) ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/23028

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

10c9cb5318fd8a9e41a7b4338e5052c8feece7ae

b24650c0cc156ceb5ef5791f1647d4d37a529920

python

closed

apache/airflow

https://github.com/apache/airflow

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

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

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

https://github.com/apache/airflow/issues/23018

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

b8bbfd4b318108b4fdadc78cd46fd1735da243ae

197cff3194e855b9207c3c0da8ae093a0d5dda55

python