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closed | apache/airflow | https://github.com/apache/airflow | 30,335 | ["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/config_templates/default_celery.py", "tests/executors/test_celery_executor.py"] | Reccomend (or set as default) to enable pool_recycle for celery workers (especially if using MySQL) | ### What do you see as an issue?
Similar to how `sql_alchemy_pool_recycle` defaults to 1800 seconds for the Airflow metastore: https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#config-database-sql-alchemy-pool-recycle
If users are using celery as their backend it provides extra stability to set `pool_recycle`. This problem is particularly acute for users who are using MySQL as backend for tasks because MySQL disconnects connections after 8 hours of being idle. While Airflow can usually force celery to retry connecting it does not always work and tasks can fail.
This is specifically reccomended by the SqlAlchemy docs:
* https://docs.sqlalchemy.org/en/14/core/pooling.html#setting-pool-recycle
* https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.pool_recycle
* https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_wait_timeout
### Solving the problem
We currently have a file which looks like this:
```python
from airflow.config_templates.default_celery import DEFAULT_CELERY_CONFIG
database_engine_options = DEFAULT_CELERY_CONFIG.get(
"database_engine_options", {}
)
# Use pool_pre_ping to detect stale db connections
# https://github.com/apache/airflow/discussions/22113
database_engine_options["pool_pre_ping"] = True
# Use pool recyle due to MySQL disconnecting sessions after 8 hours
# https://docs.sqlalchemy.org/en/14/core/pooling.html#setting-pool-recycle
# https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.pool_recycle
# https://dev.mysql.com/doc/refman/5.7/en/server-system-variables.html#sysvar_wait_timeout
database_engine_options["pool_recycle"] = 1800
DEFAULT_CELERY_CONFIG["database_engine_options"] = database_engine_options
```
And we point the env var `AIRFLOW__CELERY__CELERY_CONFIG_OPTIONS` to this object, not sure if this is best practise?
### Anything else
Maybe just change the default options to include this?
### 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/30335 | https://github.com/apache/airflow/pull/30426 | cb18d923f8253ac257c1b47e9276c39bae967666 | bc1d68a6eb01919415c399d678f491e013eb9238 | "2023-03-27T16:31:21Z" | python | "2023-06-02T14:16:25Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,324 | ["airflow/providers/cncf/kubernetes/CHANGELOG.rst", "airflow/providers/cncf/kubernetes/operators/pod.py", "airflow/providers/cncf/kubernetes/provider.yaml", "airflow/providers/cncf/kubernetes/utils/pod_manager.py", "kubernetes_tests/test_kubernetes_pod_operator.py", "tests/providers/cncf/kubernetes/decorators/test_kubernetes.py", "tests/providers/cncf/kubernetes/operators/test_pod.py", "tests/providers/cncf/kubernetes/utils/test_pod_manager.py"] | KPO deferrable needs kubernetes_conn_id while non deferrable does not | ### Apache Airflow version
2.5.2
### What happened
Not sure if this is a feature not a bug, but I can use KubernetesPodOperator fine without setting a kubernetes_conn_id.
For example:
```
start = KubernetesPodOperator(
namespace="mynamespace",
cluster_context="mycontext",
security_context={ 'runAsUser': 1000 },
name="hello",
image="busybox",
image_pull_secrets=[k8s.V1LocalObjectReference('prodregistry')],
cmds=["sh", "-cx"],
arguments=["echo Start"],
task_id="Start",
in_cluster=False,
is_delete_operator_pod=True,
config_file="/home/airflow/.kube/config",
)
```
But if I add deferrable=True to this it won't work. It seems to require an explicit kubernetes_conn_id (which we don't configure).
Is not possible to the deferrable version to work as the non deferrable one?
### What you think should happen instead
I hoped that kpo deferrable would work the same as non deferrable.
### How to reproduce
Use KPO with deferrable=True but no kubernetes_conn_id setting
### Operating System
Debian 11
### 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/30324 | https://github.com/apache/airflow/pull/28848 | a09fd0d121476964f1c9d7f12960c24517500d2c | 85b9135722c330dfe1a15e50f5f77f3d58109a52 | "2023-03-27T09:59:56Z" | python | "2023-04-08T16:26:53Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,309 | ["airflow/providers/docker/hooks/docker.py", "airflow/providers/docker/operators/docker.py", "tests/providers/docker/operators/test_docker.py"] | in DockerOperator, adding an attribute `tls_verify` to choose whether to validate the provided certificate. | ### Description
The current version of docker operator always performs TLS certificate validation. I think it would be nice to add an option to choose whether or not to validate the provided certificate.
### Use case/motivation
My work environment has several docker hosts with expired self-signed certificates. Since it is difficult to renew all certificates immediately, we are using a custom docker operator to disable certificate validation.
It would be nice if it was provided as an official feature, so I registered an issue.
### 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/30309 | https://github.com/apache/airflow/pull/30310 | 51f9910ecbf1186aff164e09d118bdf04d21dfcb | c1a685f752703eeb01f9369612af8c88c24cca09 | "2023-03-26T15:14:46Z" | python | "2023-04-14T10:17:42Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,289 | ["airflow/sensors/base.py", "tests/sensors/test_base.py"] | If the first poke of a sensor throws an exception, `timeout` does not work | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Airflow Version: 2.2.5
In `reschedule` mode, if the first poke of a sensor throws an exception, `timeout` does not work. There can be any combination of the poke returning `False` or raising an exception after that. My guess is that something is initialized in some database incorrectly, because this returns an empty list every time if the first poke raises an exception:
```
TaskReschedule.find_for_task_instance(
context["ti"], try_number=first_try_number
)
```
This happens here in the main branch: https://github.com/apache/airflow/blob/main/airflow/sensors/base.py#L174-L181
If the first poke returns `False`, I don't see this issue.
### What you think should happen instead
The timeout should be respected whether `poke` returns successfully or not.
A related issue is that if every poke raises an uncaught exception, the timeout will never be respected, since the timeout is checked only after a successful poke. Maybe both issues can be fixed at once?
### How to reproduce
Use this code. Run the dag several times, and see if the total duration including all retries is greater than the timeout.
```
import datetime
import random
from airflow import DAG
from airflow.models import TaskReschedule
from airflow.sensors.base import BaseSensorOperator
from airflow.utils.context import Context
class RandomlyFailSensor(BaseSensorOperator):
def poke(self, context: Context) -> bool:
first_try_number = context["ti"].max_tries - self.retries + 1
task_reschedules = TaskReschedule.find_for_task_instance(
context["ti"], try_number=first_try_number
)
self.log.error(f"\n\nIf this is the first attempt, or first attempt failed, "
f"this will be empty: \n\t{task_reschedules}\n\n")
if random.random() < .5:
self.log.error("\n\nIf this was the very first poke, the timeout *will not* work.\n\n")
raise Exception('Failed!')
else:
self.log.error("\n\nIf this was the very first poke, the timeout *will* work.\n\n")
return False
dag = DAG(
'sensors_test',
schedule_interval=None,
max_active_runs=1,
catchup=False,
default_args={
"owner": "me",
"depends_on_past": False,
"start_date": datetime.datetime(2018, 1, 1),
"email_on_failure": False,
"email_on_retry": False,
"execution_timeout": datetime.timedelta(minutes=10),
}
)
t_always_fail_sensor = RandomlyFailSensor(
task_id='random_fail_sensor',
mode="reschedule",
poke_interval=1,
retry_delay=datetime.timedelta(seconds=1),
timeout=15,
retries=50,
dag=dag
)
```
### Operating System
Debian 11? This Docker image: https://hub.docker.com/layers/library/python/3.8.12/images/sha256-60d1cda1542582095795c25bff869b0c615e2a913c4026ed0313ede156b60468?context=explore
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
I use an internal tool that hides the details of the deployment. If there is more info that would be helpful for debugging, let me know.
### Anything else
- This happens every time, based on the conditions I describe above.
- I'd be happy to submit a PR, but that depends on what my manager says.
- @yuqian90 might know more about this issue, since they contributed related code in [this commit](https://github.com/apache/airflow/commit/a0e6a847aa72ddb15bdc147695273fb3aec8839d#diff-62f7d8a52fefdb8e05d4f040c6d3459b4a56fe46976c24f68843dbaeb5a98487R1164).
- Impact: if the first poke throws an exception and the rest return False, the task will continue indefinitely.
### 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/30289 | https://github.com/apache/airflow/pull/30293 | 41c8e58deec2895b0a04879fcde5444b170e679e | 24887091b807527b7f32a58e85775f4daec3aa84 | "2023-03-24T20:21:45Z" | python | "2023-04-05T11:17:22Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,287 | ["airflow/providers/amazon/aws/transfers/redshift_to_s3.py", "tests/providers/amazon/aws/transfers/test_redshift_to_s3.py"] | RedshiftToS3 Operator Wrapping Query in Quotes Instead of $$ | ### Apache Airflow version
2.5.2
### What happened
When passing a select_query into the RedshiftToS3 Operator, the query will error out if it contains any single quotes because the body of the UNLOAD statement is being wrapped in single quotes.
### What you think should happen instead
Instead, it's better practice to use the double dollar sign or dollar quoting to signify the start and end of the statement to run. This removes the need to escape any special characters and avoids the statement throwing an error in the common case of using single quotes to wrap string literals.
### How to reproduce
Running the RedshiftToS3 Operator with the sql_query: `SELECT 'Single Quotes Break this Operator'` will throw the error
### Operating System
NAME="Amazon Linux" VERSION="2" ID="amzn" ID_LIKE="centos rhel fedora" VERSION_ID="2" PRETTY_NAME="Amazon Linux 2" ANSI_COLOR="0;33" CPE_NAME="cpe:2.3:o:amazon:amazon_linux:2" HOME_URL="https://amazonlinux.com//"
### Versions of Apache Airflow Providers
apache-airflow[package-extra]==2.4.3
apache-airflow-providers-amazon
### Deployment
Amazon (AWS) MWAA
### 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/30287 | https://github.com/apache/airflow/pull/35986 | e0df7441fa607645d0a379c2066ca4ab16f5cb95 | 04a781666be2955ed518780ea03bc13a1e3bd473 | "2023-03-24T18:31:54Z" | python | "2023-12-04T19:19:00Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,280 | ["airflow/www/static/css/dags.css", "airflow/www/templates/airflow/dags.html", "airflow/www/views.py", "docs/apache-airflow/core-concepts/dag-run.rst", "tests/www/views/test_views_home.py"] | Feature request - filter for dags with running status in the main page | ### Description
Feature request to filter by running dags (or by other statuses too). We have over 100 dags and we were having some performance problems. we wanted to see all the running Dags from the main page and found that we couldn't. We can see the light green circle in the runs (and that involves a lot of scrolling) but no way to filter for it.
We use SQL Server and it's job scheduling tool (SQL Agent) has this feature. The implementation for airflow shouldn't necessarily be like this but just presenting this as an example that it's a helpful feature implemented in other tools.
<img width="231" alt="image" src="https://user-images.githubusercontent.com/286903/227529646-97ac2e8e-52de-421a-8328-072f35ccdff2.png">
I'll leave implementation details for someone else.
on v2.2.5
### 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/30280 | https://github.com/apache/airflow/pull/30429 | c25251cde620481592392e5f82f9aa8a259a2f06 | dbe14c31d52a345aa82e050cc0a91ee60d9ee567 | "2023-03-24T13:11:24Z" | python | "2023-05-22T16:05:44Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,247 | ["chart/values.schema.json", "chart/values.yaml", "tests/charts/airflow_core/test_pdb_scheduler.py", "tests/charts/other/test_pdb_pgbouncer.py", "tests/charts/webserver/test_pdb_webserver.py"] | Pod Disruption Budget doesn't allow additional properties | ### Official Helm Chart version
1.8.0 (latest released)
### Apache Airflow version
2
### Kubernetes Version
>1.21
### Helm Chart configuration
```yaml
webserver:
podDisruptionBudget:
enabled: true
config:
minAvailable: 1
```
### Docker Image customizations
_No response_
### What happened
if you use the next values the chart you will not ablt to install the chart, the problem is in the schema in this line
https://github.com/apache/airflow/blob/main/chart/values.schema.json#L3320
### What you think should happen instead
_No response_
### How to reproduce
use the this values
```yaml
webserver:
podDisruptionBudget:
enabled: true
config:
minAvailable: 1
```
### 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/30247 | https://github.com/apache/airflow/pull/30603 | 3df0be0f6fe9786a5fcb85151fb83167649ee163 | 75f5f53ed0aa8df516c9d861153cab4f73318317 | "2023-03-23T04:48:42Z" | python | "2023-05-08T08:16:12Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,242 | ["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/connection.py", "airflow/secrets/metastore.py", "airflow/serialization/enums.py", "airflow/serialization/serialized_objects.py", "tests/api_internal/endpoints/test_rpc_api_endpoint.py"] | AIP-44 Migrate MetastoreBackend to Internal API | Used by Variable/Connection.
https://github.com/apache/airflow/blob/894741e311ffd642e036b80d3b1b5d53c3747cad/airflow/secrets/metastore.py#L32
| https://github.com/apache/airflow/issues/30242 | https://github.com/apache/airflow/pull/33829 | 0e4d3001397ba2005b2172ad401f9938d5d6aaf8 | 0cb875b7ec1cebb101866581166cd7b97047f941 | "2023-03-22T15:56:40Z" | python | "2023-08-29T10:24:10Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,240 | ["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/api_internal/internal_api_call.py", "airflow/serialization/enums.py", "airflow/serialization/serialized_objects.py", "tests/api_internal/endpoints/test_rpc_api_endpoint.py", "tests/api_internal/test_internal_api_call.py", "tests/serialization/test_serialized_objects.py"] | AIP-44 Implement conversion to Pydantic-ORM objects in Internal API | null | https://github.com/apache/airflow/issues/30240 | https://github.com/apache/airflow/pull/30282 | 7aca81ceaa6cb640dff9c5d7212adc4aeb078a2f | 41c8e58deec2895b0a04879fcde5444b170e679e | "2023-03-22T15:26:50Z" | python | "2023-04-05T08:54:00Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,229 | ["docs/apache-airflow/howto/operator/python.rst"] | Update Python operator how-to with @task.sensor example | ### Body
The current [how-to documentation for the `PythonSensor`](https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html#pythonsensor) does not include any references to the existing `@task.sensor` TaskFlow decorator. It would be nice to see how uses together in this doc.
### Committer
- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | https://github.com/apache/airflow/issues/30229 | https://github.com/apache/airflow/pull/30344 | 4e4e563d3fc68d1becdc1fc5ec1d1f41f6c24dd3 | 2a2ccfc27c3d40caa217ad8f6f0ba0d394ac2806 | "2023-03-22T01:19:01Z" | python | "2023-04-11T09:12:52Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,225 | ["airflow/decorators/base.py", "airflow/decorators/setup_teardown.py", "airflow/models/baseoperator.py", "airflow/utils/setup_teardown.py", "airflow/utils/task_group.py", "tests/decorators/test_setup_teardown.py", "tests/serialization/test_dag_serialization.py", "tests/utils/test_setup_teardown.py"] | Ensure setup/teardown tasks can be reused/works with task.override | Ensure that this works:
```python
@setup
def mytask():
print("I am a setup task")
with dag_maker() as dag:
mytask.override(task_id='newtask')
assert len(dag.task_group.children) == 1
setup_task = dag.task_group.children["newtask"]
assert setup_task._is_setup
```
and teardown also works | https://github.com/apache/airflow/issues/30225 | https://github.com/apache/airflow/pull/30342 | 28f73e42721bba5c5ad40bb547be9c057ca81030 | c76555930aee9692d2a839b9c7b9e2220717b8a0 | "2023-03-21T21:01:26Z" | python | "2023-03-28T18:15:07Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,220 | ["airflow/models/dag.py", "airflow/www/static/js/api/useMarkFailedTask.ts", "airflow/www/static/js/api/useMarkSuccessTask.ts", "airflow/www/static/js/api/useMarkTaskDryRun.ts", "airflow/www/static/js/dag/details/index.tsx", "airflow/www/static/js/dag/details/taskInstance/taskActions/MarkInstanceAs.tsx", "airflow/www/views.py", "tests/models/test_dag.py", "tests/www/views/test_views.py"] | set tasks as successful/failed at their task-group level. | ### Description
Ability to clear or mark task groups as success/failure and have that propagate to the tasks within that task group. Sometimes there is a need to adjust the status of tasks within a task group, which can get unwieldy depending on the number of tasks in that task group. A great quality of life upgrade, and something that seems like an intuitive feature, would be the ability to clear or change the status of all tasks at their taskgroup level through the UI.
### Use case/motivation
In the event a large number of tasks, or a whole task group in this case, need to be cleared or their status set to success/failure this would be a great improvement. For example, a manual DAG run triggered through the UI or the API that has a number of task sensors or tasks that otherwise don't matter for that DAG run - instead of setting each one as success by hand, doing so for each task group would be great.
### 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/30220 | https://github.com/apache/airflow/pull/30478 | decaaa3df2b3ef0124366033346dc21d62cff057 | 1132da19e5a7d38bef98be0b1f6c61e2c0634bf9 | "2023-03-21T18:06:34Z" | python | "2023-04-27T16:10:28Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,200 | ["chart/templates/_helpers.yaml", "chart/values.schema.json", "chart/values.yaml", "tests/charts/test_airflow_common.py"] | Support for providing SHA digest for the image in Helm chart | ### Description
This is my configuration:
```yaml
images:
airflow:
repository: <REPO>
tag: <TAG>
```
I'd like to be able to do the following:
```yaml
images:
airflow:
repository: <REPO>
digest: <SHA_DIGEST>
```
Additionally, I've tried supplying only the repository, or placing the digest as the tag, but both don't work because of [this](https://github.com/apache/airflow/blob/c44c7e1b481b7c1a0d475265835a23b0f507506c/chart/templates/_helpers.yaml#L252). The formatting is done by `repo:tag` while I need `repo@digest`.
### Use case/motivation
I'm using Terraform to deploy Airflow.
I'm using the data source of [`aws_ecr_image`](https://registry.terraform.io/providers/hashicorp/aws/latest/docs/data-sources/ecr_image) in order to pick the `latest` image.
I want to supply to the [`helm_release`](https://registry.terraform.io/providers/hashicorp/helm/latest/docs/resources/release) of Airflow the image's digest rather than `latest` as according to the docs, it's bad practice.
### 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/30200 | https://github.com/apache/airflow/pull/30214 | 78ab400d7749c683c5c122bcec0a023ded7a9603 | e95f83ef374367d7ac8e75162ebe4ae1abae487f | "2023-03-20T17:07:22Z" | python | "2023-04-10T16:37:29Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,196 | ["airflow/www/utils.py", "airflow/www/views.py"] | delete dag run times out | ### Apache Airflow version
2.5.2
### What happened
when trying to delete a dag run with many tasks (>1000) the operation times out and the dag run is not deleted.
### What you think should happen instead
_No response_
### How to reproduce
attempt to delete a dag run that contains >1000 tasks (in my case 10k) using the dagrun/list/ page results in a timeout:
code for dag (however it fails on any dag with > 1000 tasks):
```
import json
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from datetime import datetime, timedelta
from airflow.decorators import dag, task
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'retries': 0,
'retry_delay': timedelta(minutes=1),
'start_date': datetime(2023, 2, 26),
'is_delete_operator_pod': True,
'get_logs': True
}
@dag('system_test', schedule=None, default_args=default_args, catchup=False, tags=['maintenance'])
def run_test_airflow():
stress_image = 'dockerhub.prod.evogene.host/progrium/stress'
@task
def create_cmds():
commands = []
for i in range(10000):
commands.append(["stress --cpu 4 --io 1 --vm 2 --vm-bytes 6000M --timeout 60s"])
return commands
KubernetesPodOperator.partial(
image=stress_image ,
task_id=f'test_airflow',
name=f'test_airflow',
cmds=["/bin/sh", "-c"],
log_events_on_failure=True,
pod_template_file=f'/opt/airflow/dags/repo/templates/cpb_cpu_4_mem_16'
).expand(arguments=create_cmds())
run_test_airflow()
```
### Operating System
kubernetes deployment
### 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/30196 | https://github.com/apache/airflow/pull/30330 | a1b99fe5364977739b7d8f22a880eeb9d781958b | 4e4e563d3fc68d1becdc1fc5ec1d1f41f6c24dd3 | "2023-03-20T11:27:46Z" | python | "2023-04-11T07:58:08Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,169 | ["airflow/providers/google/cloud/hooks/looker.py", "tests/providers/google/cloud/hooks/test_looker.py"] | Potential issue with use of serialize in Looker SDK | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
apache-airflow-providers-common-sql==1.3.4
apache-airflow-providers-ftp==3.3.1
apache-airflow-providers-google==8.11.0
apache-airflow-providers-http==4.2.0
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-sqlite==3.3.1
### Apache Airflow version
2
### Operating System
OS X (same issue on AWS)
### Deployment
Amazon (AWS) MWAA
### Deployment details
_No response_
### What happened
I wrote a mod on top of LookerHook to access the `scheduled_plan_run_once` endpoint. The result was the following error.
```Traceback (most recent call last):
File "/usr/local/airflow/dags/utils/looker_operators_mod.py", line 125, in execute
resp = self.hook.run_scheduled_plan_once(
File "/usr/local/airflow/dags/utils/looker_hook_mod.py", line 136, in run_scheduled_plan_once
resp = sdk.scheduled_plan_run_once(plan_to_send)
File "/usr/local/lib/python3.9/site-packages/looker_sdk/sdk/api40/methods.py", line 10273, in scheduled_plan_run_once
self.post(
File "/usr/local/lib/python3.9/site-packages/looker_sdk/rtl/api_methods.py", line 171, in post
serialized = self._get_serialized(body)
File "/usr/local/lib/python3.9/site-packages/looker_sdk/rtl/api_methods.py", line 156, in _get_serialized
serialized = self.serialize(api_model=body) # type: ignore
TypeError: serialize() missing 1 required keyword-only argument: 'converter'
```
I was able to get past the error by rewriting the `get_looker_sdk` function in LookerHook to initialize with `looker_sdk.init40` instead, which resolved the serialize() issue.
### What you think should happen instead
I don't know why the serialization piece is part of the SDK initialization - would love some further context!
### How to reproduce
As far as I can tell, any call to sdk.scheduled_plan_run_once() causes this issue. I tried it with a variety of different dict plans. I only resolved it by changing how I initialized the SDK
### Anything else
n/a
### 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/30169 | https://github.com/apache/airflow/pull/34678 | 3623b77d22077b4f78863952928560833bfba2f4 | 562b98a6222912d3a3d859ca3881af3f768ba7b5 | "2023-03-17T18:50:15Z" | python | "2023-10-02T20:31:07Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,167 | ["airflow/providers/ssh/hooks/ssh.py", "airflow/providers/ssh/operators/ssh.py", "tests/providers/ssh/hooks/test_ssh.py", "tests/providers/ssh/operators/test_ssh.py"] | SSHOperator - Allow specific command timeout | ### Description
Following #29282, command timeout is set at the `SSHHook` level while it used to be able to set at the `SSHOperator` level.
I will work on a PR as soon as i can.
### Use case/motivation
Ideally, i think we could have a default value set on `SSHHook`, but with the possibility of overriding it at the `SSHOperator` level.
### Related issues
#29282
### 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/30167 | https://github.com/apache/airflow/pull/30190 | 2a42cb46af66c7d6a95a718726cb9206258a0c14 | fe727f985b1053b838433b817458517c0c0f2480 | "2023-03-17T15:56:30Z" | python | "2023-03-21T20:32:15Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,153 | ["airflow/providers/neo4j/hooks/neo4j.py", "tests/providers/neo4j/hooks/test_neo4j.py"] | Issue with Neo4j provider using some schemes | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Hi,
I've run into some issues when using the neo4j operator.
I've tried running a simple query and got an exception from the driver itself.
**Using: Airflow 2.2.2**
### What you think should happen instead
The exception stated that when using bolt+ssc URI scheme, it is not allowed to use the `encrypted` parameter which is mandatory in the hook (but actually not mandatory when using the driver standalone).
The exception:
neo4j.exceptions.ConfigurationError: The config settings "encrypted", "trust", "trusted_certificates", and "ssl_context" can only be used with the URI schemes ['bolt', 'neo4j']. Use the other URI schemes ['bolt+ssc', 'bolt+s', 'neo4j+ssc', 'neo4j+s'] for setting encryption settings.
In my opinion:
if there's a URI scheme with bolt+ssc, and a GraphDatabase.driver was chosen in the connection settings, it should not be used with the `encrypted` parameter.
I did edit the hook myself and tried this, worked great for me.
### How to reproduce
install the neo4j provider (I used v3.1.0)
Create a neo4j connection in the UI.
Add your host, user/login, password and extras.
In the extras:
{
"encrypted": false,
"neo4j_scheme": false,
"certs_self_signed": true
}
### Operating System
Linux
### Versions of Apache Airflow Providers
pyairtable==1.0.0
tableauserverclient==0.17.0
apache-airflow-providers-mysql==2.1.1
apache-airflow-providers-salesforce==3.3.0
apache-airflow-providers-slack==4.1.0
apache-airflow-providers-tableau==2.1.2
apache-airflow-providers-postgres==2.3.0
apache-airflow-providers-jdbc==2.0.1
apache-airflow-providers-neo4j==3.1.0
mysql-connector-python==8.0.27
slackclient>=1.0.0,<2.0.0
boto3==1.20.26
cached-property==1.5.2
### Deployment
Amazon (AWS) MWAA
### 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/30153 | https://github.com/apache/airflow/pull/30418 | 93a5422c5677a42b3329c329d65ff2b38b1348c2 | cd458426c66aca201e43506c950ee68c2f6c3a0a | "2023-03-16T19:47:42Z" | python | "2023-04-21T22:01:31Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,124 | ["airflow/models/taskinstance.py", "airflow/utils/state.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py", "tests/models/test_cleartasks.py", "tests/models/test_dagrun.py"] | DagRun's start_date updated when user clears task of the running Dagrun | ### Apache Airflow version
2.5.1
### What happened
DagRun state and start_date are reset if somebody is clearing a task of the running DagRun.
### What you think should happen instead
I think we should not reset DagRun `state` and `start_date` in it's in the running or queued states because it doesn't make any sense for me. `state` and `start_date` of the DgRun should remain the same in case somebody's clearing a task of the running DagRun
### How to reproduce
Let's say we have a Dag with 2 tasks in it - short one and the long one:
```
dag = DAG(
'dummy-dag',
schedule_interval='@once',
catchup=False,
)
DagContext.push_context_managed_dag(dag)
bash_success = BashOperator(
task_id='bash-success',
bash_command='echo "Start and finish"; exit 0',
retries=0,
)
date_ind_success = BashOperator(
task_id='bash-long-success',
bash_command='echo "Start and finish"; sleep 300; exit 0',
)
```
Let's day we have a running Dagrun of this DAG. First task finishes in a second and the long one is still running. We have a start_date and duration set and the Dagrun is still running. It runs for example for a 30 secs (pic 1 and 2)
<img width="486" alt="image" src="https://user-images.githubusercontent.com/23456894/225335210-c2223ad1-771b-459d-b8ed-8f0aacb9b890.png">
<img width="492" alt="image" src="https://user-images.githubusercontent.com/23456894/225335272-ad737aef-2051-4e27-ae36-38c76d720c95.png">
Then we are clearing the short task. It causes clear of the Dagrun state (to `queued`) and clears `start_date` like we have a new Dagrun (pic 3 and 4)
<img width="407" alt="image" src="https://user-images.githubusercontent.com/23456894/225335397-6c7e0df7-a26a-46ed-8eaa-56ff928fc01a.png">
<img width="498" alt="image" src="https://user-images.githubusercontent.com/23456894/225335491-4d6a860a-e923-4878-b212-a6ccb4b590a3.png">
### Operating System
Unix/MacOS
### 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/30124 | https://github.com/apache/airflow/pull/30125 | 0133f6806dbfb60b84b5bea4ce0daf073c246d52 | 070ecbd87c5ac067418b2814f554555da0a4f30c | "2023-03-15T14:26:30Z" | python | "2023-04-26T15:27:48Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,089 | ["airflow/www/views.py", "tests/www/views/test_views_rendered.py"] | Connection password values appearing unmasked in the "Task Instance Details" -> "Environment" field | ### Apache Airflow version
Airflow 2.5.1
### What happened
Connection password values appearing in the "Task Instance Details" -> "Task Attributes" -> environment field.
We are setting environment variables for the docker_operator with values from the password field in a connection.
The values from the password field are masked in the "Rendered Template" section and in the logs but it's showing the values in the "environment" field under Task Instance Details.
### What you think should happen instead
These password values should be masked like they are in the "Rendered Template" and logs.
### How to reproduce
Via this DAG, can run off any image.
Create a connection called "DATABASE_CONFIG" with a password in the password field.
Run this DAg and then check its Task Instance Details.
DAG Code:
```
from airflow import DAG
from docker.types import Mount
from airflow.providers.docker.operators.docker import DockerOperator
from datetime import timedelta
from airflow.models import Variable
from airflow.hooks.base_hook import BaseHook
import pendulum
import json
# Amount of times to retry job on failure
retries = 0
environment_config = {
"DB_WRITE_PASSWORD": BaseHook.get_connection("DATABASE_CONFIG").password,
}
# Setup default args for the job
default_args = {
"owner": "airflow",
"start_date": pendulum.datetime(2023, 1, 1, tz="Australia/Sydney"),
"retries": retries,
}
# Create the DAG
dag = DAG(
"test_dag", # DAG ID
default_args=default_args,
schedule_interval="* * * * *",
catchup=False,
)
# # Create the DAG object
with dag as dag:
docker_task = DockerOperator(
task_id="task",
image="<image>",
execution_timeout=timedelta(minutes=2),
environment=environment_config,
command="<command>",
api_version="auto",
docker_url="tcp://docker.for.mac.localhost:2375",
)
```
Rendered Template is good:
In "Task Instance Details"
### Operating System
centOS Linux and MAC
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
Running on a docker via the airflow docker-compose
### 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/30089 | https://github.com/apache/airflow/pull/31125 | db359ee2375dd7208583aee09b9eae00f1eed1f1 | ffe3a68f9ada2d9d35333d6a32eac2b6ac9c70d6 | "2023-03-14T04:35:49Z" | python | "2023-05-08T14:59:58Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,075 | ["airflow/api_connexion/openapi/v1.yaml"] | Unable to set DagRun state in create Dagrun endpoint ("Property is read-only - 'state'") | ### Apache Airflow version
main (development)
### What happened
While working on another change I noticed that the example [POST from the API docs](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/post_dag_run) actually leads to a request Error:
```
curl -X POST -H "Cookie: session=xxxx" localhost:8080/api/v1/dags/data_warehouse_dag_5by1a2rogu/dagRuns -d '{"dag_run_id":"string2","logical_date":"2019-08-24T14:15:24Z","execution_date":"2019-08-24T14:15:24Z","conf":{},"state":"queued","note":"strings"}' -H 'Content-Type: application/json'
{
"detail": "Property is read-only - 'state'",
"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"
}
```
I believe that this comes from the DagRunSchema marking this field as dump_only:
https://github.com/apache/airflow/blob/478fd826522b6192af6b86105cfa0686583e34c2/airflow/api_connexion/schemas/dag_run_schema.py#L69
So either -
1) The documentation / API spec is incorrect and this field cannot be set in the request
2) The marshmallow schema is incorrect and this field is incorrectly marked as `dump_only`
I think that its the former, as there's [even a test to ensure that this field can't be set in a request](https://github.com/apache/airflow/blob/751a995df55419068f11ebabe483dba3302916ed/tests/api_connexion/endpoints/test_dag_run_endpoint.py#L1247-L1257) - I can look into this and fix it soon.
### What you think should happen instead
The API should accept requested which follow examples from the documentation.
### How to reproduce
Spin up breeze and POST a create dagrun request which attempts to set the DagRun state.
### Operating System
Breeze
### Versions of Apache Airflow Providers
_No response_
### Deployment
Other
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/30075 | https://github.com/apache/airflow/pull/30149 | f01140141f1fe51b6ee1eba5b02ab7516a67c9c7 | e01c14661a4ec4bee3a2066ac1323fbd8a4386f1 | "2023-03-13T17:28:20Z" | python | "2023-03-21T18:26:51Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,073 | ["airflow/models/taskinstance.py", "tests/ti_deps/deps/test_trigger_rule_dep.py"] | Task group expand fails on empty list at get_relevant_upstream_map_indexes | ### Apache Airflow version
2.5.1
### What happened
Expanding of task group fails when the list is empty and there is a task which references mapped index in xcom pull of that group.
throws below error
Traceback (most recent call last):
File "/opt/bitnami/airflow/venv/bin/airflow", line 8, in <module>
sys.exit(main())
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/__main__.py", line 39, in main
args.func(args)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 52, in command
return func(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/cli.py", line 108, in wrapper
return f(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 73, in scheduler
_run_scheduler_job(args=args)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 43, in _run_scheduler_job
job.run()
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 258, in run
self._execute()
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 759, in _execute
self._run_scheduler_loop()
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 885, in _run_scheduler_loop
num_queued_tis = self._do_scheduling(session)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 964, in _do_scheduling
callback_tuples = self._schedule_all_dag_runs(guard, dag_runs, session)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/retries.py", line 78, in wrapped_function
for attempt in run_with_db_retries(max_retries=retries, logger=logger, **retry_kwargs):
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 384, in __iter__
do = self.iter(retry_state=retry_state)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 351, in iter
return fut.result()
File "/opt/bitnami/python/lib/python3.9/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/opt/bitnami/python/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/retries.py", line 87, in wrapped_function
return func(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1253, in _schedule_all_dag_runs
callback_to_run = self._schedule_dag_run(dag_run, session)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1322, in _schedule_dag_run
schedulable_tis, callback_to_run = dag_run.update_state(session=session, execute_callbacks=False)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/session.py", line 72, in wrapper
return func(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/dagrun.py", line 563, in update_state
info = self.task_instance_scheduling_decisions(session)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/session.py", line 72, in wrapper
return func(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/dagrun.py", line 710, in task_instance_scheduling_decisions
schedulable_tis, changed_tis, expansion_happened = self._get_ready_tis(
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/dagrun.py", line 793, in _get_ready_tis
if not schedulable.are_dependencies_met(session=session, dep_context=dep_context):
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/utils/session.py", line 72, in wrapper
return func(*args, **kwargs)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1070, in are_dependencies_met
for dep_status in self.get_failed_dep_statuses(dep_context=dep_context, session=session):
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1091, in get_failed_dep_statuses
for dep_status in dep.get_dep_statuses(self, session, dep_context):
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/ti_deps/deps/base_ti_dep.py", line 107, in get_dep_statuses
yield from self._get_dep_statuses(ti, session, cxt)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/ti_deps/deps/trigger_rule_dep.py", line 93, in _get_dep_statuses
yield from self._evaluate_trigger_rule(ti=ti, dep_context=dep_context, session=session)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/ti_deps/deps/trigger_rule_dep.py", line 219, in _evaluate_trigger_rule
.filter(or_(*_iter_upstream_conditions()))
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/ti_deps/deps/trigger_rule_dep.py", line 191, in _iter_upstream_conditions
map_indexes = _get_relevant_upstream_map_indexes(upstream_id)
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/ti_deps/deps/trigger_rule_dep.py", line 138, in _get_relevant_upstream_map_indexes
return ti.get_relevant_upstream_map_indexes(
File "/opt/bitnami/airflow/venv/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2652, in get_relevant_upstream_map_indexes
ancestor_map_index = self.map_index * ancestor_ti_count // ti_count
### What you think should happen instead
In case of empty list all the task group should be skipped
### How to reproduce
from airflow.operators.bash import BashOperator
from airflow.operators.python import get_current_context
import pendulum
from airflow.decorators import dag, task, task_group
from airflow.operators.empty import EmptyOperator
@dag(dag_id="test", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule=None, catchup=False,
render_template_as_native_obj=True
)
def testdag():
task1 =EmptyOperator(task_id="get_attribute_can_json_mapping")
@task
def lkp_schema_output_mapping(**context):
return 1
@task
def task2(**context):
return 2
@task
def task3(table_list, **context):
return []
[task2(), task1,
group2.expand(file_name=task3(table_list=task2()))]
@task_group(
group_id="group2"
)
def group2(file_name):
@task
def get_table_name(name):
return "testing"
table_name = get_table_name(file_name)
run_this = BashOperator(
task_id="run_this",
bash_command="echo {{task_instance.xcom_pull(task_ids='copy_to_staging.get_table_name',"
"map_indexes=task_instance.map_index)}}",
)
table_name >> run_this
dag = testdag()
if __name__ == "__main__":
dag.test()
### Operating System
Debian GNU/Linux 11
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==7.1.0
apache-airflow-providers-apache-cassandra==3.1.0
apache-airflow-providers-apache-drill==2.3.1
apache-airflow-providers-apache-druid==3.3.1
apache-airflow-providers-apache-hdfs==3.2.0
apache-airflow-providers-apache-hive==5.1.1
apache-airflow-providers-apache-pinot==4.0.1
apache-airflow-providers-arangodb==2.1.0
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cloudant==3.1.0
apache-airflow-providers-cncf-kubernetes==5.1.1
apache-airflow-providers-common-sql==1.3.3
apache-airflow-providers-databricks==4.0.0
apache-airflow-providers-docker==3.4.0
apache-airflow-providers-elasticsearch==4.3.3
apache-airflow-providers-exasol==4.1.3
apache-airflow-providers-ftp==3.3.0
apache-airflow-providers-google==8.8.0
apache-airflow-providers-grpc==3.1.0
apache-airflow-providers-hashicorp==3.2.0
apache-airflow-providers-http==4.1.1
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-influxdb==2.1.0
apache-airflow-providers-microsoft-azure==5.1.0
apache-airflow-providers-microsoft-mssql==3.3.2
apache-airflow-providers-mongo==3.1.1
apache-airflow-providers-mysql==4.0.0
apache-airflow-providers-neo4j==3.2.1
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-presto==4.2.1
apache-airflow-providers-redis==3.1.0
apache-airflow-providers-sendgrid==3.1.0
apache-airflow-providers-sftp==4.2.1
apache-airflow-providers-slack==7.2.0
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.4.0
apache-airflow-providers-trino==4.3.1
apache-airflow-providers-vertica==3.3.1
### Deployment
Other
### Deployment details
_No response_
### Anything else
I have manually changed below in the taskinstance.py(get_relevant_upstream_map_indexes method) and it ran fine. Please check if you can implement the same
if ti_count is None or ti_count == 0:
return None
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/30073 | https://github.com/apache/airflow/pull/30084 | 66b5f90f4536329ba1fe0e54e3f15ec98c1e2730 | 8d22828e2519a356e9e38c78c3efee1d13b45675 | "2023-03-13T16:55:34Z" | python | "2023-03-15T22:58:24Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,071 | ["chart/templates/cleanup/cleanup-cronjob.yaml", "chart/templates/statsd/statsd-deployment.yaml", "chart/values.schema.json", "chart/values.yaml", "tests/charts/test_cleanup_pods.py", "tests/charts/test_statsd.py"] | Helm Chart: allow setting annotations for resource controllers (CronJob, Deployment) | ### Description
The Helm Chart allows setting annotations for the pods created by the `CronJob` [but not the CronJob controller itself](https://github.com/apache/airflow/blob/helm-chart/1.8.0/chart/templates/cleanup/cleanup-cronjob.yaml).
The values file should offer an option to provide custom annotations for the `CronJob` controller, similarly to how the DB migrations job exposes `.Values.migrateDatabaseJob.jobAnnotations`
In the same fashion, other `Deployment` templates expose custom annotations, but [statsd deployment doesn't](https://github.com/apache/airflow/blob/helm-chart/1.8.0/chart/templates/statsd/statsd-deployment.yaml).
### Use case/motivation
Other tools e.g. ArgoCD may require the use of annotations, for example:
* [ArgoCD Sync Options](https://argo-cd.readthedocs.io/en/stable/user-guide/sync-options)
* [ArgoCD Sync Phases and Waves](https://argo-cd.readthedocs.io/en/stable/user-guide/sync-waves/)
Example use case:
_Set the cleanup CronJob to be synced after the webserver and scheduler deployments have been synced with ArgoCD_
### Related issues
https://github.com/apache/airflow/issues/25446 originally mentioned the issue regarding the StatsD deployment, but the accepted fix was https://github.com/apache/airflow/pull/25732 which allows setting annotations for the pod template, not the `Deployment` itself
### 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/30071 | https://github.com/apache/airflow/pull/30126 | c1aa4b9500f417e6669a79fbf59c11ae6e6993a2 | 8b634ffa6aa5a83e1f87f1a62bfa07e78147f5c5 | "2023-03-13T12:13:38Z" | python | "2023-03-16T19:09:20Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,042 | ["airflow/www/utils.py", "airflow/www/views.py"] | Search/filter by note in List Dag Run | ### Description
Going to Airflow web UI, Browse>DAG Run displays the list of runs, but there is no way to search or filter based on the text in the "Note" column.
### Use case/motivation
It is possible to do a free text search for the "Run Id" field. The Note field may contain pieces of information that may be relevant to find, or to filter on the basis of these notes.
### Related issues
Sorting by Note in List Dag Run fails:
https://github.com/apache/airflow/issues/30041
### 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/30042 | https://github.com/apache/airflow/pull/31455 | f00c131cbf5b2c19c817d1a1945326b80f8c79e7 | 5794393c95156097095e6fbf76d7faeb6ec08072 | "2023-03-11T14:16:02Z" | python | "2023-05-25T18:17:15Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,023 | ["docs/apache-airflow/best-practices.rst"] | Variable with template is ambiguous, especially for new users | ### What do you see as an issue?
In the doc below, it states `Make sure to use variable with template in operator, not in the top level code.`
https://github.com/apache/airflow/blob/main/docs/apache-airflow/best-practices.rst
It then gives this example as a Good Example.
**Good Example**
```
bash_use_variable_good = BashOperator(
task_id="bash_use_variable_good",
bash_command="echo variable foo=${foo_env}",
env={"foo_env": "{{ var.value.get('foo') }}"},
)
```
example below, since `{{ var.value.get('foo') }}` is in the top level code (since the `__init__` method is run every time the dag file is parsed.
This can be ambiguous for users, especially new users, to understand the true difference between templated and non-templated variables.
The difference between the two examples below isn't that one of them is using top-level code and the other isn't, it's that one is jinja templated and the other isn't. There is a great opportunity here to showcase the utility of jinja templating.
```
bash_use_variable_bad_3 = BashOperator(
task_id="bash_use_variable_bad_3",
bash_command="echo variable foo=${foo_env}",
env={"foo_env": Variable.get("foo")}, # DON'T DO THAT
)
```
and
```
bash_use_variable_good = BashOperator(
task_id="bash_use_variable_good",
bash_command="echo variable foo=${foo_env}",
env={"foo_env": "{{ var.value.get('foo') }}"},
)
```
### Solving the problem
Replacing `Make sure to use variable with template in operator, not in the top level code.` with a sentence that is more in line with the examples following it will not only show alignment but also highlight the benefits of jinja templating in top level code.
Perhaps:
```
In top-level code, variables using jinja templates do not produce a request until runtime, whereas, `Variable.get()` produces a request every time the dag file is parsed by the scheduler. This will lead to suboptimal performance for the scheduler and can cause the dag file to timeout before it is fully parsed.
```
### 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/30023 | https://github.com/apache/airflow/pull/30040 | 8d22828e2519a356e9e38c78c3efee1d13b45675 | f1e40cf799c5ae73ec6f7991efe604f2088d8622 | "2023-03-10T14:13:32Z" | python | "2023-03-16T00:06:35Z" |
closed | apache/airflow | https://github.com/apache/airflow | 30,010 | ["airflow/providers/snowflake/CHANGELOG.rst", "airflow/providers/snowflake/operators/snowflake.py"] | SnowflakeOperator default autocommit flipped to False | ### Apache Airflow Provider(s)
snowflake
### Versions of Apache Airflow Providers
This started with apache-airflow-providers-snowflake==4.0.0 and is still an issue with 4.0.4
### Apache Airflow version
2.5.1
### Operating System
Debian GNU/Linux 11 (bullseye)
### Deployment
Astronomer
### Deployment details
This is affecting both local and hosted deployments
### What happened
We are testing out several updated packages, and one thing that broke was the SnowflakeOperator when it was executing a stored procedure. The specific error points to autocommit being set to False:
`Stored procedure execution error: Scoped transaction started in stored procedure is incomplete and it was rolled back.`
Whereas this used to work in version 3.2.0:
```
copy_data_snowflake = SnowflakeOperator(
task_id=f'copy_{table_name}_snowflake',
sql=query,
)
```
In order for it to work now, we have to specify autocommit=True:
```
copy_data_snowflake = SnowflakeOperator(
task_id=f'copy_{table_name}_snowflake',
sql=query,
autocommit=True,
)
```
[The code](https://github.com/apache/airflow/blob/599c587e26d5e0b8fa0a0967f3dc4fa92d257ed0/airflow/providers/snowflake/operators/snowflake.py#L45) still indicates that the default is True, but I believe [this commit](https://github.com/apache/airflow/commit/ecd4d6654ff8e0da4a7b8f29fd23c37c9c219076#diff-e9f45fcabfaa0f3ed0c604e3bf2215fed1c9d3746e9c684b89717f9cd75f1754L98) broke it.
### What you think should happen instead
The default for autocommit should revert to the previous behavior, matching the documentation.
### How to reproduce
In Snowflake:
```
CREATE OR REPLACE TABLE PUBLIC.FOO (BAR VARCHAR);
CREATE OR REPLACE PROCEDURE PUBLIC.FOO()
RETURNS VARCHAR
LANGUAGE SQL
AS $$
INSERT INTO PUBLIC.FOO VALUES('bar');
$$
;
```
In Airflow, this fails:
```
copy_data_snowflake = SnowflakeOperator(
task_id='call_foo',
sql="call public.foo()",
)
```
But this succeeds:
```
copy_data_snowflake = SnowflakeOperator(
task_id='call_foo',
sql="call public.foo()",
autocommit=True,
)
```
### Anything else
It looks like this may be an issue with stored procedures specifically. If I instead do this:
```
copy_data_snowflake = SnowflakeOperator(
task_id='call_foo',
sql="INSERT INTO PUBLIC.FOO VALUES('bar');",
)
```
The logs show that although autocommit is confusingly set to False, a `COMMIT` statement is executed:
```
[2023-03-09, 18:43:09 CST] {cursor.py:727} INFO - query: [ALTER SESSION SET autocommit=False]
[2023-03-09, 18:43:09 CST] {cursor.py:740} INFO - query execution done
[2023-03-09, 18:43:09 CST] {cursor.py:878} INFO - Number of results in first chunk: 1
[2023-03-09, 18:43:09 CST] {sql.py:375} INFO - Running statement: INSERT INTO PUBLIC.FOO VALUES('bar');, parameters: None
[2023-03-09, 18:43:09 CST] {cursor.py:727} INFO - query: [INSERT INTO PUBLIC.FOO VALUES('bar');]
[2023-03-09, 18:43:09 CST] {cursor.py:740} INFO - query execution done
[2023-03-09, 18:43:09 CST] {sql.py:384} INFO - Rows affected: 1
[2023-03-09, 18:43:09 CST] {snowflake.py:380} INFO - Rows affected: 1
[2023-03-09, 18:43:09 CST] {snowflake.py:381} INFO - Snowflake query id: 01aad76b-0606-feb5-0000-26b511d0ba02
[2023-03-09, 18:43:09 CST] {cursor.py:727} INFO - query: [COMMIT]
```
### 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/30010 | https://github.com/apache/airflow/pull/30020 | 26c6a1c11bcd463d1923bbd9622cbe0682bc9e8a | b9c231ceb0f3053a27744b80e95f08ac0684fe38 | "2023-03-10T01:05:10Z" | python | "2023-03-10T17:47:46Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,980 | ["airflow/providers/microsoft/azure/hooks/data_lake.py"] | ADLS Gen2 Hook incorrectly forms account URL when using Active Directory authentication method (Azure Data Lake Storage V2) | ### Apache Airflow Provider(s)
microsoft-azure
### Versions of Apache Airflow Providers
apache-airflow-providers-microsoft-azure 5.2.1
### Apache Airflow version
2.5.1
### Operating System
Ubuntu 18.04
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
When attempting to use Azure Active Directory application to connect to Azure Data Lake Storage Gen2 hook, the generated account URL sent to the DataLakeServiceClient is incorrect.
It substitutes in the Client ID (`login` field) where the storage account name should be.
### What you think should happen instead
The `host` field on the connection form should be used to store the storage account name and should be used to fill the account URL for both Active Directory and Key-based authentication.
### How to reproduce
1. Create an "Azure Data Lake Storage V2" connection (adls) and put the AAD application Client ID into `login` field, Client secret into `password` field and Tenant ID into `tenant_id` field.
2. Attempt to perform any operations with the `AzureDataLakeStorageV2Hook` hook.
3. Notice how it fails, and that the URL in the logs is incorrectly `https://{client_id}.dfs.core.windows.net/...`, when it should be `https://{storage_account}.dfs.core.windows.net/...`
This can be fixed by:
1. Making your own copy of the hook.
2. Entering the storage account name into the `host` field (currently labelled "Account Name (Active Directory Auth)").
3. Editing the `get_conn` method to substitute `conn.host` into the `account_url` (instead of `conn.login`).
### 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/29980 | https://github.com/apache/airflow/pull/29981 | def1f89e702d401f67a94f34a01f6a4806ea92e6 | 008f52444a84ceaa2de7c2166b8f253f55ca8c21 | "2023-03-08T15:42:36Z" | python | "2023-03-10T12:11:28Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,967 | ["chart/dockerfiles/pgbouncer-exporter/build_and_push.sh", "chart/dockerfiles/pgbouncer/build_and_push.sh", "chart/newsfragments/30054.significant.rst"] | Build our supporting images for chart in multi-platform versions | ### Body
The supporting images of ours are built using one platform only but they could be multiplatform.
The scripts to build those should be updated to support multi-platform builds.
### Committer
- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | https://github.com/apache/airflow/issues/29967 | https://github.com/apache/airflow/pull/30054 | 5a3be7256b2a848524d3635d7907b6829a583101 | 39cfc67cad56afa3b2434bc8e60bcd0676d41fc1 | "2023-03-08T00:22:45Z" | python | "2023-03-15T22:19:52Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,960 | ["airflow/providers/amazon/aws/hooks/glue.py", "airflow/providers/amazon/aws/operators/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py"] | GlueJobOperator failing with Invalid type for parameter RoleName after updating provider version. | ### Apache Airflow Provider(s)
amazon
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon = "7.3.0"
### Apache Airflow version
2.5.1
### Operating System
Debian GNU/Linux
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
After updating the provider version to 7.3.0 from 6.0.0, our glue jobs started failing. We currently use the GlueJobOperator to run existing Glue jobs that we manage in Terraform. The full traceback is below:
```
Traceback (most recent call last):
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/amazon/aws/operators/glue.py", line 150, in execute
glue_job_run = glue_job.initialize_job(self.script_args, self.run_job_kwargs)
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 165, in initialize_job
job_name = self.create_or_update_glue_job()
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 325, in create_or_update_glue_job
config = self.create_glue_job_config()
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 108, in create_glue_job_config
execution_role = self.get_iam_execution_role()
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 143, in get_iam_execution_role
glue_execution_role = iam_client.get_role(RoleName=self.role_name)
File "/home/airflow/.local/lib/python3.9/site-packages/botocore/client.py", line 530, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/airflow/.local/lib/python3.9/site-packages/botocore/client.py", line 919, in _make_api_call
request_dict = self._convert_to_request_dict(
File "/home/airflow/.local/lib/python3.9/site-packages/botocore/client.py", line 990, in _convert_to_request_dict
request_dict = self._serializer.serialize_to_request(
File "/home/airflow/.local/lib/python3.9/site-packages/botocore/validate.py", line 381, in serialize_to_request
raise ParamValidationError(report=report.generate_report())
botocore.exceptions.ParamValidationError: Parameter validation failed:
Invalid type for parameter RoleName, value: None, type: <class 'NoneType'>, valid types: <class 'str'>
```
### What you think should happen instead
The operator creates a new job run for a glue job without additional configuration.
### How to reproduce
Create a DAG with a GlueJobOperator without using `iam_role_name`. Example:
```python
task = GlueJobOperator(task_id="glue-task", job_name=<glue-job-name>)
```
### 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/29960 | https://github.com/apache/airflow/pull/30162 | fe727f985b1053b838433b817458517c0c0f2480 | 46d9a0c294ea72574a79f0fb567eb9dc97cf96c1 | "2023-03-07T16:44:40Z" | python | "2023-03-21T20:50:19Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,959 | ["airflow/jobs/local_task_job_runner.py", "airflow/jobs/scheduler_job_runner.py", "airflow/models/dagrun.py", "airflow/models/taskinstance.py", "airflow/serialization/pydantic/job.py"] | expand dynamic mapped tasks in batches | ### Description
expanding tasks in batches to allow mapped tasks spawn more than 1024 processes.
### Use case/motivation
Maximum length of a list is limited to 1024 by `max_map_length (AIRFLOW__CORE__MAX_MAP_LENGTH)`.
during scheduling of the new tasks, an UPDATE query is ran that tries to set all the new tasks at once. Increasing `max_map_length` more than 4K makes airflow scheduler completely unresponsive.
Also, Postgres throws `stack depth limit exceeded` error which can be fixed by updating to a newer version and setting `max_stack_depth` higher. But it doesn't really matter because airflow scheduler freezes up.
As a workaround, I split the dag runs into subdag runs which works but it would be much nicer if we didn't have to worry about exceeding `max_map_length`.
### Related issues
It was discussed here:
[Increasing 'max_map_length' leads to SQL 'max_stack_depth' error with 5000 dags to be spawned #28478](https://github.com/apache/airflow/discussions/28478)
### 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/29959 | https://github.com/apache/airflow/pull/30372 | 5f2628d36cb8481ee21bd79ac184fd8fdce3e47d | ed39b6fab7a241e2bddc49044c272c5f225d6692 | "2023-03-07T16:12:04Z" | python | "2023-04-22T19:10:56Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,958 | ["airflow/providers/google/cloud/transfers/bigquery_to_gcs.py", "airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", "tests/providers/google/cloud/transfers/test_gcs_to_bigquery.py"] | GCSToBigQueryOperator does not respect the destination project ID | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
apache-airflow-providers-google==8.10.0
### Apache Airflow version
2.3.4
### Operating System
Ubuntu 18.04.6 LTS
### Deployment
Google Cloud Composer
### Deployment details
Google Cloud Composer 2.1.2
### What happened
[`GCSToBigQueryOperator`](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L58) does not respect the BigQuery project ID specified in [`destination_project_dataset_table`](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L74-L77) argument. Instead, it prioritizes the project ID defined in the [Airflow connection](https://i.imgur.com/1tTIlQF.png).
### What you think should happen instead
The project ID specified via [`destination_project_dataset_table`](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L74-L77) should be respected.
**Use case:** Suppose our Composer environment and service account (SA) live in `project-A`, and we want to transfer data into foreign projects `B`, `C`, and `D`. We don't have credentials (and thus don't have Airflow connections defined) for projects `B`, `C`, and `D`. Instead, all transfers are executed by our singular SA in `project-A`. (Assume this SA has cross-project IAM policies). Thus, we want to use a _single_ SA and _single_ [Airflow connection](https://i.imgur.com/1tTIlQF.png) (i.e. `gcp_conn_id=google_cloud_default`) to send data into 3+ destination projects. I imagine this is a fairly common setup for sending data across GCP projects.
**Root cause:** I've been studying the source code, and I believe the bug is caused by [line 309](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L309). Experimentally, I have verified that `hook.project_id` traces back to the [Airflow connection's project ID](https://i.imgur.com/1tTIlQF.png). If no destination project ID is explicitly specified, then it makes sense to _fall back_ on the connection's project. However, if the destination project is explicitly provided, surely the operator should honor that. I think this bug can be fixed by amending line 309 as follows:
```python
project=passed_in_project or hook.project_id
```
This pattern is used successfully in many other areas of the repo: [example](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/operators/gcs.py#L154).
### How to reproduce
Admittedly, this bug is difficult to reproduce, because it requires two GCP projects, i.e. a service account in `project-A`, and inbound GCS files and a destination BigQuery table in `project-B`. Also, you need an Airflow server with a `google_cloud_default` connection that points to `project-A` like [this](https://i.imgur.com/1tTIlQF.png). Assuming all that exists, the bug can be reproduced via the following Airflow DAG:
```python
from airflow import DAG
from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator
from datetime import datetime
GCS_BUCKET='my_bucket'
GCS_PREFIX='path/to/*.json'
BQ_PROJECT='project-B'
BQ_DATASET='my_dataset'
BQ_TABLE='my_table'
SERVICE_ACCOUNT='my_account@project-A.iam.gserviceaccount.com'
with DAG(
dag_id='my_dag',
start_date=datetime(2023, 1, 1),
schedule_interval=None,
) as dag:
task = GCSToBigQueryOperator(
task_id='gcs_to_bigquery',
bucket=GCS_BUCKET,
source_objects=GCS_PREFIX,
source_format='NEWLINE_DELIMITED_JSON',
destination_project_dataset_table='{}.{}.{}'.format(BQ_PROJECT, BQ_DATASET, BQ_TABLE),
impersonation_chain=SERVICE_ACCOUNT,
)
```
Stack trace:
```
Traceback (most recent call last):
File "/opt/python3.8/lib/python3.8/site-packages/airflow/executors/debug_executor.py", line 79, in _run_task
ti.run(job_id=ti.job_id, **params)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/session.py", line 71, in wrapper
return func(*args, session=session, **kwargs)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1797, in run
self._run_raw_task(
File "/opt/python3.8/lib/python3.8/site-packages/airflow/utils/session.py", line 68, in wrapper
return func(*args, **kwargs)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1464, in _run_raw_task
self._execute_task_with_callbacks(context, test_mode)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1612, in _execute_task_with_callbacks
result = self._execute_task(context, task_orig)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1673, in _execute_task
result = execute_callable(context=context)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 387, in execute
job = self._submit_job(self.hook, job_id)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py", line 307, in _submit_job
return hook.insert_job(
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/common/hooks/base_google.py", line 468, in inner_wrapper
return func(self, *args, **kwargs)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/bigquery.py", line 1549, in insert_job
job._begin()
File "/opt/python3.8/lib/python3.8/site-packages/google/cloud/bigquery/job/base.py", line 510, in _begin
api_response = client._call_api(
File "/opt/python3.8/lib/python3.8/site-packages/google/cloud/bigquery/client.py", line 782, in _call_api
return call()
File "/opt/python3.8/lib/python3.8/site-packages/google/api_core/retry.py", line 283, in retry_wrapped_func
return retry_target(
File "/opt/python3.8/lib/python3.8/site-packages/google/api_core/retry.py", line 190, in retry_target
return target()
File "/opt/python3.8/lib/python3.8/site-packages/google/cloud/_http/__init__.py", line 494, in api_request
raise exceptions.from_http_response(response)
google.api_core.exceptions.Forbidden: 403 POST https://bigquery.googleapis.com/bigquery/v2/projects/{project-A}/jobs?prettyPrint=false: Access Denied: Project {project-A}: User does not have bigquery.jobs.create permission in project {project-A}.
```
From the stack trace, notice the operator is (incorrectly) attempting to insert into `project-A` rather than `project-B`.
### Anything else
Perhaps out-of-scope, but the inverse direction also suffers from this same problem, i.e. [BigQueryToGcsOperator](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/bigquery_to_gcs.py#L38) and [line 192](https://github.com/apache/airflow/blob/3374fdfcbddb630b4fc70ceedd5aed673e6c0a0d/airflow/providers/google/cloud/transfers/bigquery_to_gcs.py#L192).
### 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/29958 | https://github.com/apache/airflow/pull/30053 | 732fcd789ddecd5251d391a8d9b72f130bafb046 | af4627fec988995537de7fa172875497608ef710 | "2023-03-07T16:07:36Z" | python | "2023-03-20T08:34:19Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,957 | ["chart/templates/scheduler/scheduler-deployment.yaml", "chart/templates/webserver/webserver-deployment.yaml", "chart/values.schema.json", "chart/values.yaml", "tests/charts/test_scheduler.py", "tests/charts/test_webserver.py"] | hostAliases for scheduler and webserver | ### Description
I am not sure why this PR was not merged (https://github.com/apache/airflow/pull/23558) but I think it would be great to add hostAliases not just to the workers, but the scheduler and webserver too.
### Use case/motivation
Be able to modify /etc/hosts in webserver and scheduler.
### 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/29957 | https://github.com/apache/airflow/pull/30051 | 5c15b23023be59a87355c41ab23a46315cca21a5 | f07d300c4c78fa1b2becb4653db8d25b011ea273 | "2023-03-07T15:25:15Z" | python | "2023-03-12T14:22:05Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,939 | ["airflow/providers/amazon/aws/links/emr.py", "airflow/providers/amazon/aws/operators/emr.py", "airflow/providers/amazon/aws/sensors/emr.py", "tests/providers/amazon/aws/operators/test_emr_add_steps.py", "tests/providers/amazon/aws/operators/test_emr_create_job_flow.py", "tests/providers/amazon/aws/operators/test_emr_modify_cluster.py", "tests/providers/amazon/aws/operators/test_emr_terminate_job_flow.py", "tests/providers/amazon/aws/sensors/test_emr_job_flow.py", "tests/providers/amazon/aws/sensors/test_emr_step.py"] | AWS EMR Operators: Add Log URI in task logs to speed up debugging | ### Description
Airflow is widely used to launch, interact and submit jobs on AWS EMR Clusters. Existing EMR operators do not provide links to the EMR logs (Job Flow/Step logs), as a result in case of failures the users need to switch to EMR Console or go to AWS S3 console to locate the logs for EMR Jobs and Steps using the job_flow_id available in the EMR Operators and in Xcom.
It will be really convenient and help with debugging if the EMR log links are present in Operator Task logs, it will obviate the need to switch to AWS S3 or AWS EMR consoles from Airflow and lookup the logs using job_flow_ids. It will be a nice improvement for the developer experience.
LogUri for Cluster is available in [DescribeCluster](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr/client/describe_cluster.html)
LogFile path for Steps in case of failure is available in [ListSteps](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr/client/list_steps.html)
### Use case/motivation
Ability to go to EMR logs directly from Airflow EMR Task logs.
### Related issues
N/A
### 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/29939 | https://github.com/apache/airflow/pull/31032 | 6c92efbe8b99e172fe3b585114e1924c0bb2f26b | 2d5166f9829835bdfd6479aa789c8a27147288d6 | "2023-03-06T18:03:55Z" | python | "2023-05-03T23:18:02Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,912 | ["airflow/providers/google/cloud/transfers/bigquery_to_gcs.py", "tests/providers/google/cloud/transfers/test_bigquery_to_gcs.py"] | BigQueryToGCSOperator does not wait for completion | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
apache-airflow-providers-google==7.0.0
### Apache Airflow version
2.3.2
### Operating System
Debian GNU/Linux
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
[Deferrable mode for BigQueryToGCSOperator #27683](https://github.com/apache/airflow/pull/27683) changed the functionality of the `BigQueryToGCSOperator` so that it no longer waits for the completion of the operation. This is because the `nowait=True` parameter is now [being set](https://github.com/apache/airflow/pull/27683/files#diff-23c5b2e773487f9c28b75b511dbf7269eda1366f16dec84a349d95fa033ffb3eR191).
### What you think should happen instead
This is unexpected behavior. Any downstream tasks of the `BigQueryToGCSOperator` that expect the CSVs to have been written by the time they are called may result in errors (and have done so in our own operations).
The property should at least be configurable.
### How to reproduce
1. Leverage the `BigQueryToGcsOperator` in your DAG.
2. Have it write a large table to a CSV somewhere in GCS
3. Notice that the task completes almost immediately but the CSVs may not exist in GCS until later.
### 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/29912 | https://github.com/apache/airflow/pull/29925 | 30b2e6c185305a56f9fd43683f1176f01fe4e3f6 | 464ab1b7caa78637975008fcbb049d5b52a8b005 | "2023-03-03T23:29:15Z" | python | "2023-03-05T10:40:38Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,903 | ["airflow/models/baseoperator.py", "tests/models/test_mappedoperator.py"] | Task-level retries overrides from the DAG-level default args are not respected when using `partial` | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
When running a DAG that is structured like:
```
@dag{dag_id="my_dag", default_args={"retries":0"}}
def dag():
op = MyOperator.partial(task_id="my_task", retries=3).expand(...)
```
The following test fails:
```
def test_retries(self) -> None:
dag_bag = DagBag(dag_folder=DAG_FOLDER, include_examples=False)
dag = dag_bag.dags["my_dag"]
for task in dag.tasks:
if "my_task" in task.task_id:
self.assertEqual(3, task.retries) # fails - this is 0
```
When printing out `task.partial_kwargs`, and looking at how the default args and partial args are merged, it seems like the default args are always taking precedence, even though in the `partial` global function, the `retries` do get set later on with the task-level parameter value. This doesn't seem to be respected though.
### What you think should happen instead
_No response_
### How to reproduce
If you run my above unit test for a test DAG, on version 2.4.3, it should show up as a test failure.
### Operating System
OS Ventura
### Versions of Apache Airflow Providers
_No response_
### Deployment
Google Cloud Composer
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29903 | https://github.com/apache/airflow/pull/29913 | 57c09e59ee9273ff64cd4a85b020a4df9b1d9eca | f01051a75e217d5f20394b8c890425915383101f | "2023-03-03T19:22:23Z" | python | "2023-04-14T12:16:11Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,900 | ["airflow/models/dag.py", "airflow/timetables/base.py", "airflow/timetables/simple.py", "docs/apache-airflow/core-concepts/dag-run.rst", "tests/models/test_dag.py", "tests/timetables/test_continuous_timetable.py"] | Add continues scheduling option | ### Body
There are some use cases where users want to trigger new DAG run as soon as one finished. This is a request I've seen several times with some variations (for example like this [Stackoverflow question](https://stackoverflow.com/q/75623153/14624409)) but the basic request is the same.
The workaround users do to get such functionality is place `TriggerDagRunOperator` as last task of their DAG invoking the same DAG:
```
from datetime import datetime
from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
with DAG(
dag_id="example",
start_date=datetime(2023, 1, 1,),
catchup=False,
schedule=None,
) as dag:
task = EmptyOperator(task_id="first")
trigger = TriggerDagRunOperator(
task_id="trigger",
trigger_dag_id="example",
)
task >> trigger
```
As you can see this works nicely:
My suggestion is to add first class support for this use case, so the above example will be changed to:
```
from datetime import datetime
from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
with DAG(
dag_id="example",
start_date=datetime(2023, 1, 1,),
catchup=False,
schedule="@continues",
) as dag:
task = EmptyOperator(task_id="first")
```
I guess it won't exactly be `"@continues"` but more likely new [ScheduleArg](https://github.com/apache/airflow/blob/8b8552f5c4111fe0732067d7af06aa5285498a79/airflow/models/dag.py#L127) type but I show it like that just for simplification of the idea.
### Committer
- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | https://github.com/apache/airflow/issues/29900 | https://github.com/apache/airflow/pull/29909 | 70680ded7a4056882008b019f5d1a8f559a301cd | c1aa4b9500f417e6669a79fbf59c11ae6e6993a2 | "2023-03-03T12:30:33Z" | python | "2023-03-16T19:08:45Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,875 | ["airflow/cli/cli_parser.py", "airflow/cli/commands/connection_command.py", "docs/apache-airflow/howto/connection.rst", "tests/cli/commands/test_connection_command.py"] | Airflow Connection Testing Using Airflow CLI | ### Description
Airflow Connection testing using airflow CLI would be very useful , where users can quick add test function to test connection in their applications. It will benefit CLI user to create and test new connections right from instance and reduce time on troubleshooting any connection issue.
### Use case/motivation
airflow connection testing using airflow CLI , similar function as we have in Airflow CLI.
example: airflow connection test "hello_id"
### Related issues
N/A
### 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/29875 | https://github.com/apache/airflow/pull/29892 | a3d59c8c759582c27f5a234ffd4c33a9daeb22a9 | d2e5b097e6251e31fb4c9bb5bf16dc9c77b56f75 | "2023-03-02T14:13:55Z" | python | "2023-03-09T09:26:10Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,858 | ["airflow/www/package.json", "airflow/www/static/js/api/index.ts", "airflow/www/static/js/api/useDag.ts", "airflow/www/static/js/api/useDagCode.ts", "airflow/www/static/js/dag/details/dagCode/CodeBlock.tsx", "airflow/www/static/js/dag/details/dagCode/index.tsx", "airflow/www/static/js/dag/details/index.tsx", "airflow/www/templates/airflow/dag.html", "airflow/www/yarn.lock"] | Migrate DAG Code page to Grid Details | - [ ] Use REST API to render DAG Code in the grid view as a tab when a user has no runs/tasks selected
- [ ] Redirect all urls to new code
- [ ] delete the old code view | https://github.com/apache/airflow/issues/29858 | https://github.com/apache/airflow/pull/31113 | 3363004450355582712272924fac551dc1f7bd56 | 4beb89965c4ee05498734aa86af2df7ee27e9a51 | "2023-03-02T00:38:49Z" | python | "2023-05-17T16:27:06Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,843 | ["airflow/models/taskinstance.py", "tests/www/views/test_views.py"] | The "Try Number" filter under task instances search is comparing integer with non-integer object | ### Apache Airflow version
2.5.1
### What happened
The `Try Number` filter is comparing the given integer with an instance of a "property" object
* screenshots
* text version
```
Something bad has happened.
Airflow is used by many users, and it is very likely that others had similar problems and you can easily find
a solution to your problem.
Consider following these steps:
* gather the relevant information (detailed logs with errors, reproduction steps, details of your deployment)
* find similar issues using:
* [GitHub Discussions](https://github.com/apache/airflow/discussions)
* [GitHub Issues](https://github.com/apache/airflow/issues)
* [Stack Overflow](https://stackoverflow.com/questions/tagged/airflow)
* the usual search engine you use on a daily basis
* if you run Airflow on a Managed Service, consider opening an issue using the service support channels
* if you tried and have difficulty with diagnosing and fixing the problem yourself, consider creating a [bug report](https://github.com/apache/airflow/issues/new/choose).
Make sure however, to include all relevant details and results of your investigation so far.
Python version: 3.8.16
Airflow version: 2.5.1
Node: kip-airflow-8b665fdd7-lcg6q
-------------------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 2525, in wsgi_app
response = self.full_dispatch_request()
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1822, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1820, in full_dispatch_request
rv = self.dispatch_request()
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1796, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/security/decorators.py", line 133, in wraps
return f(self, *args, **kwargs)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/views.py", line 554, in list
widgets = self._list()
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/baseviews.py", line 1164, in _list
widgets = self._get_list_widget(
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/baseviews.py", line 1063, in _get_list_widget
count, lst = self.datamodel.query(
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/sqla/interface.py", line 461, in query
count = self.query_count(query, filters, select_columns)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/sqla/interface.py", line 382, in query_count
return self._apply_inner_all(
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/sqla/interface.py", line 368, in _apply_inner_all
query = self.apply_filters(query, inner_filters)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/sqla/interface.py", line 223, in apply_filters
return filters.apply_all(query)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/filters.py", line 300, in apply_all
query = flt.apply(query, value)
File "/usr/local/airflow/.local/lib/python3.8/site-packages/flask_appbuilder/models/sqla/filters.py", line 169, in apply
return query.filter(field > value)
TypeError: '>' not supported between instances of 'property' and 'int'
```
### What you think should happen instead
The "Try Number" search should compare integer with integer
### How to reproduce
1. Go to "Browse" -> "Task Instances"
2. "Search" -> "Add Filter" -> choose "Dag Id" and "Try Number"
3. Choose "Greater than" in the drop-down and enter an integer
4. Click "Search"
### Operating System
Debian GNU/Linux 10 (buster)
### Versions of Apache Airflow Providers
_No response_
### Deployment
Other Docker-based deployment
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29843 | https://github.com/apache/airflow/pull/29850 | 00a2c793c7985f8165c2bef9106fc81ee66e07bb | a3c9902bc606f0c067a45f09e9d3d152058918e9 | "2023-03-01T17:45:26Z" | python | "2023-03-10T12:01:15Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,841 | ["setup.cfg"] | high memory leak, cannot start even webserver | ### Apache Airflow version
2.5.1
### What happened
I'd used airflow 2.3.1 and everything was fine.
Then I decided to move to airflow 2.5.1.
I can't start even webserver, airflow on my laptop consumes the entire memory (32Gb) and OOM killer comes.
I investigated a bit. So it starts with airflow 2.3.4. Only using official docker image (apache/airflow:2.3.4) and only on linux laptop, mac is ok.
Memory leak starts when source code tries to import for example `airflow.cli.commands.webserver_command` module using `airflow.utils.module_loading.import_string`.
I dived deeply and found that it happens when "import daemon" is performed.
You can reproduce it with this command: `docker run --rm --entrypoint="" apache/airflow:2.3.4 /bin/bash -c "python -c 'import daemon'"`. Once again, reproducec only on linux (my kernel is 6.1.12).
That's weird considering `daemon` hasn't been changed since 2018.
### What you think should happen instead
_No response_
### How to reproduce
docker run --rm --entrypoint="" apache/airflow:2.3.4 /bin/bash -c "python -c 'import daemon'"
### Operating System
Arch Linux (kernel 6.1.12)
### 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/29841 | https://github.com/apache/airflow/pull/29916 | 864ff2e3ce185dfa3df0509a4bd3c6b5169e907f | c8cc49af2d011f048ebea8a6559ddd5fca00f378 | "2023-03-01T15:36:01Z" | python | "2023-03-04T15:27:20Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,839 | ["airflow/api_connexion/endpoints/dag_run_endpoint.py", "tests/api_connexion/endpoints/test_dag_run_endpoint.py"] | Calling endpoint dags/{dag_id}/dagRuns for removed DAG returns "500 Internal Server Error" instead of "404 Not Found" | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Apache Airflow version: 2.4.0
I remove DAG from storage then trigger it:
curl -X POST 'http://localhost:8080/api/dags/<DAG_ID>/dag_runs' --header 'Content-Type: application/json' --data '{"dag_run_id":"my_id"}'
it returns:
```
Traceback (most recent call last):
File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 2525, in wsgi_app
response = self.full_dispatch_request()
File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1822, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1820, in full_dispatch_request
rv = self.dispatch_request()
File "/home/airflow/.local/lib/python3.8/site-packages/flask/app.py", line 1796, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/decorator.py", line 68, in wrapper
response = function(request)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/uri_parsing.py", line 149, in wrapper
response = function(request)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/validation.py", line 196, in wrapper
response = function(request)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/validation.py", line 399, in wrapper
return function(request)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/response.py", line 112, in wrapper
response = function(request)
File "/home/airflow/.local/lib/python3.8/site-packages/connexion/decorators/parameter.py", line 120, in wrapper
return function(**kwargs)
File "/home/airflow/.local/lib/python3.8/site-packages/airflow/api_connexion/security.py", line 51, in decorated
return func(*args, **kwargs)
File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 75, in wrapper
return func(*args, session=session, **kwargs)
File "/home/airflow/.local/lib/python3.8/site-packages/airflow/api_connexion/endpoints/dag_run_endpoint.py", line 310, in post_dag_run
dag_run = dag.create_dagrun(
AttributeError: 'NoneType' object has no attribute 'create_dagrun'
```
### What you think should happen instead
should response with 404 "A specified resource is not found."
### How to reproduce
- remove existing DAG file from storage
- create a new DAG run using API endpoint /api/dags/<DAG_ID>/dag_runs for that deleted DAG
### Operating System
18.04.1 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/29839 | https://github.com/apache/airflow/pull/29860 | fcd3c0149f17b364dfb94c0523d23e3145976bbe | 751a995df55419068f11ebabe483dba3302916ed | "2023-03-01T13:51:58Z" | python | "2023-03-03T14:40:07Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,836 | ["airflow/www/forms.py", "airflow/www/validators.py", "tests/www/test_validators.py", "tests/www/views/test_views_connection.py"] | Restrict allowed characters in connection ids | ### Description
I bumped into a bug where a connection id was suffixed with a whitespace e.g. "myconn ". When referencing the connection id "myconn" (without whitespace), you get a connection not found error.
To avoid such human errors, I suggest restricting the characters allowed for connection ids.
Some suggestions:
- There's an `airflow.utils.helpers.validate_key` function for validating the DAG id. Probably a good idea to reuse this.
- I believe variable ids are also not validated, would be good to check those too.
### Use case/motivation
_No response_
### 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/29836 | https://github.com/apache/airflow/pull/31140 | 85482e86f5f93015487938acfb0cca368059e7e3 | 5cb8ef80a0bd84651fb660c552563766d8ec0ea1 | "2023-03-01T11:58:40Z" | python | "2023-05-12T10:25:37Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,819 | ["airflow/serialization/serialized_objects.py", "tests/serialization/test_dag_serialization.py"] | DAG fails serialization if template_field contains execution_timeout | ### Apache Airflow version
2.5.1
### What happened
If an Operator specifies a template_field with `execution_timeout` then the DAG will serialize correctly but throw an error during deserialization. This causes the entire scheduler to crash and breaks the application.
### What you think should happen instead
The scheduler should never go down because of some code someone wrote, this should probably throw an error during serialization.
### How to reproduce
Define an operator like this
```
class ExecutionTimeoutOperator(BaseOperator):
template_fields = ("execution_timeout", )
def __init__(self, execution_timeout: timedelta, **kwargs):
super().__init__(**kwargs)
self.execution_timeout = execution_timeout
```
then make a dag like this
```
dag = DAG(
"serialize_with_default",
schedule_interval="0 12 * * *",
start_date=datetime(2023, 2, 28),
catchup=False,
default_args={
"execution_timeout": timedelta(days=4),
},
)
with dag:
execution = ExecutionTimeoutOperator(task_id="execution", execution_timeout=timedelta(hours=1))
```
that will break the scheduler, you can force the stack trace by doing this
```
from airflow.models import DagBag
db = DagBag('dags/', read_dags_from_db=True)
db.get_dag('serialize_with_default')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 75, in wrapper
return func(*args, session=session, **kwargs)
File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 190, in get_dag
self._add_dag_from_db(dag_id=dag_id, session=session)
File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 265, in _add_dag_from_db
dag = row.dag
File "/usr/local/lib/python3.9/site-packages/airflow/models/serialized_dag.py", line 218, in dag
dag = SerializedDAG.from_dict(self.data)
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1287, in from_dict
return cls.deserialize_dag(serialized_obj["dag"])
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1194, in deserialize_dag
v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v}
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 1194, in <dictcomp>
v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v}
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 955, in deserialize_operator
cls.populate_operator(op, encoded_op)
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 864, in populate_operator
v = cls._deserialize_timedelta(v)
File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 513, in _deserialize_timedelta
return datetime.timedelta(seconds=seconds)
TypeError: unsupported type for timedelta seconds component: str
```
### Operating System
Mac 13.1 (22C65)
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==5.1.0
apache-airflow-providers-apache-hdfs==3.2.0
apache-airflow-providers-apache-hive==5.1.1
apache-airflow-providers-apache-spark==4.0.0
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cncf-kubernetes==5.1.1
apache-airflow-providers-common-sql==1.3.3
apache-airflow-providers-datadog==3.1.0
apache-airflow-providers-ftp==3.3.0
apache-airflow-providers-http==4.1.1
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-jdbc==3.3.0
apache-airflow-providers-jenkins==3.2.0
apache-airflow-providers-mysql==4.0.0
apache-airflow-providers-pagerduty==3.1.0
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-presto==4.2.1
apache-airflow-providers-slack==7.2.0
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.4.0
### Deployment
Docker-Compose
### Deployment details
I could repro this with docker-compose and in a helm backed deployment so I don't think it's really related to the deployment details
### Anything else
In the serialization code there are two pieces of logic that are in
direct conflict with each other. The first dictates how template fields
are serialized, from the code
```
# Store all template_fields as they are if there are JSON Serializable
# If not, store them as strings
```
and the second special cases a few names of arguments that need to be
deserialized in a specific way
```
elif k in {"retry_delay", "execution_timeout", "sla", "max_retry_delay"}:
v = cls._deserialize_timedelta(v)
```
so during serialization airflow sees that execution_timeout is a
template field, serializes it as a string, then during deserialization
it is a special name that forces the deserialization as timedelta and BOOM!
### 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/29819 | https://github.com/apache/airflow/pull/29821 | 6d2face107f24b7e7dce4b98ae3def1178e1fc4c | 7963360b8d43a15791a6b7d4335f482fce1d82d2 | "2023-02-28T18:48:13Z" | python | "2023-03-04T18:19:09Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,803 | ["airflow/utils/db.py"] | Run DAG in isolated session | ### Apache Airflow version
2.5.1
### What happened
Trying the new `airflow.models.DAG.test` function to run e2e tests on a DAG in a `pytest` fashion I find there's no way to force to write to a different db other than the configured one.
This should create an alchemy session for an inmemory db, initialise the db and then use it for the test
```python
@fixture(scope="session")
def airflow_db():
# in-memory database
engine = create_engine(f"sqlite://")
with Session(engine) as db_session:
initdb(session=db_session, load_connections=False)
yield db_session
def test_dag_runs_default(airflow_db):
dag.test(session=airflow_db)
```
However `initdb` never receives the `engine` from `settings` that has been initialised before. It uses the engine **from `settings` instead of the engine from the session**.
https://github.com/apache/airflow/blob/main/airflow/utils/db.py#L694-L695
```python
with create_global_lock(session=session, lock=DBLocks.MIGRATIONS):
Base.metadata.create_all(settings.engine)
Model.metadata.create_all(settings.engine)
```
Then `_create_flask_session_tbl()` reads again the database from the config (which might be the same as when settings was initialised or not) and creates all Airflow tables in a database different from the provided in the session again.
### What you think should happen instead
The sql alchemy base, models and airflow tables should be created in the database provided by the session.
In case the session is injected then, this will match the config. But if a session is provided, it should use this session instead
### How to reproduce
This inits the db specified in the config (defaults to `${HOME}/airflow/airflow.db`), then the test tries to use the in-memory one and breaks
```python
@fixture(scope="session")
def airflow_db():
# in-memory database
engine = create_engine(f"sqlite://")
with Session(engine) as db_session:
initdb(session=db_session, load_connections=False)
yield db_session
def test_dag_runs_default(airflow_db):
dag.test(session=airflow_db)
```
### Operating System
MacOs
### 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/29803 | https://github.com/apache/airflow/pull/29804 | 7ce3b66237fbdb1605cf1f7cec06f0b823c455a1 | 0975560dfa48f43b340c4db9c03658a11ae7c666 | "2023-02-28T13:56:11Z" | python | "2023-04-10T08:06:23Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,781 | ["airflow/providers/sftp/hooks/sftp.py", "airflow/providers/sftp/sensors/sftp.py", "tests/providers/sftp/hooks/test_sftp.py", "tests/providers/sftp/sensors/test_sftp.py"] | newer_than and file_pattern don't work well together in SFTPSensor | ### Apache Airflow Provider(s)
sftp
### Versions of Apache Airflow Providers
4.2.3
### Apache Airflow version
2.5.1
### Operating System
macOS Ventura 13.2.1
### Deployment
Astronomer
### Deployment details
_No response_
### What happened
I wanted to use `file_pattern` and `newer_than` in `SFTPSensor` to find only the files that landed in SFTP after the data interval of the prior successful DAG run (`{{ prev_data_interval_end_success }}`).
I have four text files (`file.txt`, `file1.txt`, `file2.txt` and `file3.txt`) but only `file3.txt` has the last modification date after the data interval of the prior successful DAG run. I use the following file pattern: `"*.txt"`.
The moment the first file (`file.txt`) was matched and the modification date did not meet the requirement, the task changed the status to `up_for_reschedule`.
### What you think should happen instead
The other files matching the pattern should be checked as well.
### How to reproduce
```python
import pendulum
from airflow import DAG
from airflow.providers.sftp.sensors.sftp import SFTPSensor
with DAG(
dag_id="sftp_test",
start_date=pendulum.datetime(2023, 2, 1, tz="UTC"),
schedule="@once",
render_template_as_native_obj=True,
):
wait_for_file = SFTPSensor(
task_id="wait_for_file",
sftp_conn_id="sftp_default",
path="/upload/",
file_pattern="*.txt",
newer_than="{{ prev_data_interval_end_success }}",
)
```
### 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/29781 | https://github.com/apache/airflow/pull/29794 | 60d98a1bc2d54787fcaad5edac36ecfa484fb42b | 9357c81828626754c990c3e8192880511a510544 | "2023-02-27T12:25:27Z" | python | "2023-02-28T05:45:59Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,759 | ["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py"] | Improve code in `KubernetesPodOperator._render_nested_template_fields` | ### Apache Airflow Provider(s)
cncf-kubernetes
### Versions of Apache Airflow Providers
apache-airflow-providers-cncf-kubernetes==5.2.1
### Apache Airflow version
2.5.1
### Operating System
Arch Linux
### Deployment
Other
### Deployment details
_No response_
### What happened
Not really showing a failure in operation, but the code in the [`KubernetesPodOperator._render_nested_template_fields`](https://github.com/apache/airflow/blob/d26dc223915c50ff58252a709bb7b33f5417dfce/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py#L373-L403) function could be improved.
The current code is formed by 6 conditionals checking the type of the `content` variable. Even when the 1st of the succeed, the other 5 conditionals are still checked, which is inefficient because the function could end right there, saving time and resources.
### What you think should happen instead
The conditionals flow could be fixed with a simple map, using a dictionary to immediately get the value or fallback to the default one.
### How to reproduce
There is no bug _per se_ to reproduce. It's just making the code cleaner and more efficient, avoiding to keep computing conditionals even when the condition has been resolved.
### 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/29759 | https://github.com/apache/airflow/pull/29760 | 9357c81828626754c990c3e8192880511a510544 | 1e536eb43de4408612bf7bb7d9d2114470c6f43a | "2023-02-25T09:33:25Z" | python | "2023-02-28T05:46:37Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,754 | ["airflow/example_dags/example_dynamic_task_mapping_with_no_taskflow_operators.py", "docs/apache-airflow/authoring-and-scheduling/dynamic-task-mapping.rst", "tests/serialization/test_dag_serialization.py", "tests/www/views/test_views_acl.py"] | Add classic operator example for dynamic task mapping "reduce" task | ### What do you see as an issue?
The [documentation for Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html#simple-mapping
) does not include an example of a "reduce" task (e.g. `sum_it` in the [examples](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html#simple-mapping)) using the classic (or non-TaskFlow) operators. It only includes an example that uses the TaskFlow operators.
When I attempted to write a "reduce" task using classic operators for my DAG, I found that there wasn't an obvious approach.
### Solving the problem
We should add an example of a "reduce" task that uses the classic (non-TaskFlow) operators.
For example, for the given `sum_it` example:
```
"""Example DAG demonstrating the usage of dynamic task mapping reduce using classic
operators.
"""
from __future__ import annotations
from datetime import datetime
from airflow import DAG
from airflow.decorators import task
from airflow.operators.python import PythonOperator
def add_one(x: int):
return x + 1
def sum_it(values):
total = sum(values)
print(f"Total was {total}")
with DAG(dag_id="example_dynamic_task_mapping_reduce", start_date=datetime(2022, 3, 4)):
add_one_task = PythonOperator.partial(
task_id="add_one",
python_callable=add_one,
).expand(
op_kwargs=[
{"x": 1},
{"x": 2},
{"x": 3},
]
)
sum_it_task = PythonOperator(
task_id="sum_it",
python_callable=sum_it,
op_kwargs={"values": add_one_task.output},
)
```
### 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/29754 | https://github.com/apache/airflow/pull/29762 | c9607d44de5a3c9674a923a601fc444ff957ac7e | 4d4c2b9d8b5de4bf03524acf01a298c162e1d9e4 | "2023-02-24T23:35:25Z" | python | "2023-05-31T05:47:46Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,746 | ["airflow/providers/databricks/operators/databricks.py", "tests/providers/databricks/operators/test_databricks.py"] | DatabricksSubmitRunOperator does not support passing output of another task to `base_parameters` | ### Apache Airflow Provider(s)
databricks
### Versions of Apache Airflow Providers
apache-airflow-providers-databricks==4.0.0
### Apache Airflow version
2.4.3
### Operating System
MAC OS
### Deployment
Virtualenv installation
### Deployment details
The issue is consistent across multiple Airflow deployments (locally on Docker Compose, remotely on MWAA in AWS, locally using virualenv)
### What happened
Passing `base_parameters` key into `notebook_task` parameter for `DatabricksSubmitRunOperator` as output of a previous task (TaskFlow paradigm) does not work.
After inspection of `DatabricksSubmitRunOperator.init` it seems that the problem relies on the fact that it uses `utils.databricks.normalise_json_content` to validate input parameters and, given that the input parameter is of type `PlainXComArg`, it fails to parse.
The workaround I found is to call it using `partial` and `expand`, which is a bit hacky and much less legible
### What you think should happen instead
`DatabricksSubmitRunOperator` should accept `PlainXComArg` arguments on init and eventually validate on `execute`, prior to submitting job run.
### How to reproduce
This DAG fails to parse:
```python3
with DAG(
"dag_erroring",
start_date=days_ago(1),
params={"param_1": "", "param_2": ""},
) as dag:
@task
def from_dag_params_to_notebook_params(**context):
# Transform/Validate DAG input parameters to sth expected by Notebook
notebook_param_1 = context["dag_run"].conf["param_1"] + "abcd"
notebook_param_2 = context["dag_run"].conf["param_2"] + "efgh"
return {"some_param": notebook_param_1, "some_other_param": notebook_param_2}
DatabricksSubmitRunOperator(
task_id="my_notebook_task",
new_cluster={
"cluster_name": "single-node-cluster",
"spark_version": "7.6.x-scala2.12",
"node_type_id": "i3.xlarge",
"num_workers": 0,
"spark_conf": {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "[*, 4]",
},
"custom_tags": {"ResourceClass": "SingleNode"},
},
notebook_task={
"notebook_path": "some/path/to/a/notebook",
"base_parameters": from_dag_params_to_notebook_params(),
},
libraries=[],
databricks_retry_limit=3,
timeout_seconds=86400,
polling_period_seconds=20,
)
```
This one does not:
```python3
with DAG(
"dag_parsing_fine",
start_date=days_ago(1),
params={"param_1": "", "param_2": ""},
) as dag:
@task
def from_dag_params_to_notebook_params(**context):
# Transform/Validate DAG input parameters to sth expected by Notebook
notebook_param_1 = context["dag_run"].conf["param_1"] + "abcd"
notebook_param_2 = context["dag_run"].conf["param_2"] + "efgh"
return [{"notebook_path": "some/path/to/a/notebook", "base_parameters":{"some_param": notebook_param_1, "some_other_param": notebook_param_2}}]
DatabricksSubmitRunOperator.partial(
task_id="my_notebook_task",
new_cluster={
"cluster_name": "single-node-cluster",
"spark_version": "7.6.x-scala2.12",
"node_type_id": "i3.xlarge",
"num_workers": 0,
"spark_conf": {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "[*, 4]",
},
"custom_tags": {"ResourceClass": "SingleNode"},
},
libraries=[],
databricks_retry_limit=3,
timeout_seconds=86400,
polling_period_seconds=20,
).expand(notebook_task=from_dag_params_to_notebook_params())
```
### 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/29746 | https://github.com/apache/airflow/pull/29840 | c95184e8bc0f974ea8d2d51cbe3ca67e5f4516ac | c405ecb63e352c7a29dd39f6f249ba121bae7413 | "2023-02-24T15:50:14Z" | python | "2023-03-07T15:03:17Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,733 | ["airflow/providers/databricks/hooks/databricks.py", "airflow/providers/databricks/operators/databricks.py", "airflow/providers/databricks/provider.yaml", "docs/apache-airflow-providers-databricks/operators/jobs_create.rst", "tests/providers/databricks/hooks/test_databricks.py", "tests/providers/databricks/operators/test_databricks.py", "tests/system/providers/databricks/example_databricks.py"] | Databricks create/reset then run-now | ### Description
Allow an Airflow DAG to define a Databricks job with the `api/2.1/jobs/create` (or `api/2.1/jobs/reset`) endpoint then run that same job with the `api/2.1/jobs/run-now` endpoint. This would give similar capabilities as the DatabricksSubmitRun operator, but the `api/2.1/jobs/create` endpoint supports additional parameters that the `api/2.1/jobs/runs/submit` doesn't (e.g. `job_clusters`, `email_notifications`, etc.).
### Use case/motivation
Create and run a Databricks job all in the Airflow DAG. Currently, DatabricksSubmitRun operator uses the `api/2.1/jobs/runs/submit` endpoint which doesn't support all features and creates runs that aren't tied to a job in the Databricks UI. Also, DatabricksRunNow operator requires you to define the job either directly in the Databricks UI or through a separate CI/CD pipeline causing the headache of having to change code in multiple places.
### 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/29733 | https://github.com/apache/airflow/pull/35156 | da2fdbb7609f7c0e8dd1d1fd9efaec31bb937fe8 | a8784e3c352aafec697d3778eafcbbd455b7ba1d | "2023-02-23T21:01:27Z" | python | "2023-10-27T18:52:26Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,712 | ["airflow/providers/amazon/aws/hooks/emr.py", "tests/providers/amazon/aws/hooks/test_emr.py"] | EMRHook.get_cluster_id_by_name() doesn't use pagination | ### Apache Airflow version
2.5.1
### What happened
When using EMRHook.get_cluster_id_by_name or any any operator that depends on it (e.g. EMRAddStepsOperator), if the results of the ListClusters API call is paginated (e.g. if your account has more than 50 clusters in the current region), and the desired cluster is in the 2nd page of results, None will be returned instead of the cluster ID.
### What you think should happen instead
Boto's pagination API should be used and the cluster ID should be returned.
### How to reproduce
Use `EmrAddStepsOperator` with the `job_flow_name` parameter on an `aws_conn_id` with more than 50 EMR clusters in the current region.
### Operating System
Linux
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==7.2.1
### 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/29712 | https://github.com/apache/airflow/pull/29732 | 607068f4f0d259b638743db5b101660da1b43d11 | 9662fd8cc05f69f51ca94b495b14f907aed0d936 | "2023-02-23T00:39:37Z" | python | "2023-05-01T18:45:02Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,702 | ["airflow/api_connexion/endpoints/connection_endpoint.py", "airflow/api_connexion/endpoints/update_mask.py", "airflow/api_connexion/endpoints/variable_endpoint.py", "tests/api_connexion/endpoints/test_update_mask.py", "tests/api_connexion/endpoints/test_variable_endpoint.py"] | Updating Variables Description via PATCH in Airflow API is Clearing the existing description field of the variable and unable to update description field | ### Apache Airflow version
2.5.1
### What happened
When i made these patch requests to update description of the variable via axios
### 1) Trying to modify new value and description
```javascript
let payload ={ key : "example_variable", value : "new_value", description: "new_Description" }
axios.patch("https://localhost:8080/api/v1/variables/example_variable" , payload ,
{
auth : {
username : "username",
password : "password"
},
headers: {
"Content-Type" : "application/json",
}
});
```
following response received and In the airflow , Existing Variable's ```Description``` is cleared and set to ```None```
```html
response body : {
"description" : "new_Description",
"key": "example_variable",
"value" : "new_value"
}
```
### 2) Trying to update Description with update_mask
```javascript
let payload ={ key : "example_variable", value : "value", description: "new_Description" }
axios.patch("https://localhost:8080/api/v1/variables/example_variable?update_mask=description" , payload ,
{
auth : {
username : "username",
password : "password"
},
headers: {
"Content-Type" : "application/json",
}
});
```
following response received
```html
response body : {
"detail" : null,
"status": 400,
"detail" : "No field to update",
"type" : "https://airflow.apache.org/docs/apache-airflow/2.5.0/stable-rest-api-ref.html#section/Errors/BadRequest"
}
```
### What you think should happen instead
The filed "description" is ignored both while setting the Variable (L113) and ```update_mask``` (L107-111).
https://github.com/apache/airflow/blob/1768872a0085ba423d0a34fe6cc4e1e109f3adeb/airflow/api_connexion/endpoints/variable_endpoint.py#L97-L115
Also in Variable setter its set to ```None``` if input doesn't contain description field
https://github.com/apache/airflow/blob/1768872a0085ba423d0a34fe6cc4e1e109f3adeb/airflow/models/variable.py#L156-L165
### How to reproduce
## PATCH in Airflow REST API
### API call
"https://localhost:8080/api/v1/variables/example_variable?update_mask=description"
### payload
{ key : "example_variable", value : "value", description: "new_Description" }
### headers
"Content-Type" : "application/json"
OR
### API call
"https://localhost:8080/api/v1/variables/example_variable
### payload
{ key : "example_variable", value : "new_value", description: "new_Description" }
### headers
"Content-Type" : "application/json"
### Operating System
Ubuntu 22.04.1 LTS
### Versions of Apache Airflow Providers
_No response_
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### Anything else
Possible Solution to update Description field can be like
https://github.com/apache/airflow/blob/1768872a0085ba423d0a34fe6cc4e1e109f3adeb/airflow/api_connexion/endpoints/connection_endpoint.py#L134-L145
### 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/29702 | https://github.com/apache/airflow/pull/29711 | 3f6b5574c61ef9765d077bdd08ccdaba14013e4a | de8e07dc6fea620541e0daa67131e8fe21dbd5fe | "2023-02-22T19:21:40Z" | python | "2023-03-18T21:03:41Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,687 | ["airflow/models/renderedtifields.py"] | Deadlock when airflow try to update 'k8s_pod_yaml' in 'rendered_task_instance_fields' table | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
**Airflow 2.4.2**
We run into a problem, where HttpSensor has an error because of deadlock. We are running 3 different dags with 12 max_active_runs, that call api and check for response if it should reshedule it or go to next task. All these sensors have 1 minutes poke interval, so 36 of them are running at the same time. Sometimes (like once in 20 runs) we get following deadlock error:
`Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1803, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) MySQLdb.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 "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1457, in _run_raw_task self._execute_task_with_callbacks(context, test_mode) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1579, in _execute_task_with_callbacks RenderedTaskInstanceFields.write(rtif) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 75, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.7/contextlib.py", line 119, in __exit__ next(self.gen) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 36, in create_session session.commit() File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 1428, in commit self._transaction.commit(_to_root=self.future) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 829, in commit self._prepare_impl() File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl self.session.flush() File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3345, in flush self._flush(objects) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3485, in _flush transaction.rollback(_capture_exception=True) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/util/langhelpers.py", line 72, in __exit__ with_traceback=exc_tb, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ raise exception File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 3445, in _flush flush_context.execute() File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute rec.execute(self) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 633, in execute uow, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 241, in save_obj update, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 1001, in _emit_update_statements statement, multiparams, execution_options=execution_options File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1614, in _execute_20 return meth(self, args_10style, kwargs_10style, execution_options) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/sql/elements.py", line 326, in _execute_on_connection self, multiparams, params, execution_options File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1491, in _execute_clauseelement cache_hit=cache_hit, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1846, in _execute_context e, statement, parameters, cursor, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 2027, in _handle_dbapi_exception sqlalchemy_exception, with_traceback=exc_info[2], from_=e File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 207, in raise_ raise exception File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1803, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 719, in do_execute cursor.execute(statement, parameters) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/cursors.py", line 206, in execute res = self._query(query) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/cursors.py", line 319, in _query db.query(q) File "/home/airflow/.local/lib/python3.7/site-packages/MySQLdb/connections.py", line 254, in query _mysql.connection.query(self, query) sqlalchemy.exc.OperationalError: (MySQLdb.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') [SQL: UPDATE rendered_task_instance_fields SET k8s_pod_yaml=%s WHERE rendered_task_instance_fields.dag_id = %s AND rendered_task_instance_fields.task_id = %s AND rendered_task_instance_fields.run_id = %s AND rendered_task_instance_fields.map_index = %s] [parameters: ('{"metadata": {"annotations": {"dag_id": "bidder-joiner", "task_id": "capitest", "try_number": "1", "run_id": "scheduled__2023-02-15T14:15:00+00:00"}, ... (511 characters truncated) ... e": "AIRFLOW_IS_K8S_EXECUTOR_POD", "value": "True"}], "image": "artifactorymaster.outbrain.com:5005/datainfra/airflow:8cbd2a3d8c", "name": "base"}]}}', 'bidder-joiner', 'capitest', 'scheduled__2023-02-15T14:15:00+00:00', -1)] (Background on this error at: https://sqlalche.me/e/14/e3q8)
`
`Failed to execute job 3966 for task capitest ((MySQLdb.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction') [SQL: UPDATE rendered_task_instance_fields SET k8s_pod_yaml=%s WHERE rendered_task_instance_fields.dag_id = %s AND rendered_task_instance_fields.task_id = %s AND rendered_task_instance_fields.run_id = %s AND rendered_task_instance_fields.map_index = %s] [parameters: ('{"metadata": {"annotations": {"dag_id": "bidder-joiner", "task_id": "capitest", "try_number": "1", "run_id": "scheduled__2023-02-15T14:15:00+00:00"}, ... (511 characters truncated) ... e": "AIRFLOW_IS_K8S_EXECUTOR_POD", "value": "True"}], "image": "artifactorymaster.outbrain.com:5005/datainfra/airflow:8cbd2a3d8c", "name": "base"}]}}', 'bidder-joiner', 'capitest', 'scheduled__2023-02-15T14:15:00+00:00', -1)] (Background on this error at: https://sqlalche.me/e/14/e3q8); 68)
`
I checked MySql logs and deadlock is caused by query:
```
DELETE FROM rendered_task_instance_fields WHERE rendered_task_instance_fields.dag_id = 'bidder-joiner-raw_data_2nd_pass_delay' AND rendered_task_instance_fields.task_id = 'is_data_ready' AND ((rendered_task_instance_fields.dag_id, rendered_task_instance_fields.task_id, rendered_task_instance_fields.run_id) NOT IN (SELECT subq2.dag_id, subq2.task_id, subq2.run_id
FROM (SELECT subq1.dag_id AS dag_id, subq1.task_id AS task_id, subq1.run_id AS run_id
FROM (SELECT DISTINCT rendered_task_instance_fields.dag_id AS dag_id, rendered_task_instance_fields.task_id AS task_id, rendered_task_instance_fields.run_id AS run_id, dag_run.execution_date AS execution_date
FROM rendered_task_instance_fields INNER JOIN dag_run ON rendered_task_instance_fields.dag_id = dag_run.dag_id AND rendered_task_instance_fields.run_id = dag_run.run_id
WHERE rendered_task_instance_fields.dag_id = 'bidder-joiner-raw_data
```
### What you think should happen instead
I found similar issue open on github (https://github.com/apache/airflow/issues/25765) so I think it should be resolved in the same way - adding @retry_db_transaction annotation to function that is executing this query
### How to reproduce
Create 3 dags with 12 max_active_runs that use HttpSensor at the same time, same poke interval and mode reschedule.
### Operating System
Ubuntu 20
### Versions of Apache Airflow Providers
apache-airflow-providers-common-sql>=1.2.0
mysql-connector-python>=8.0.11
mysqlclient>=1.3.6
apache-airflow-providers-mysql==3.2.1
apache-airflow-providers-http==4.0.0
apache-airflow-providers-slack==6.0.0
apache-airflow-providers-apache-spark==3.0.0
### 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/29687 | https://github.com/apache/airflow/pull/32341 | e53320d62030a53c6ffe896434bcf0fc85803f31 | c8a3c112a7bae345d37bb8b90d68c8d6ff2ef8fc | "2023-02-22T09:00:28Z" | python | "2023-07-05T11:28:16Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,679 | ["tests/cli/commands/test_internal_api_command.py"] | Fix Quarantined `test_cli_internal_api_background` | ### Body
Recently, [this test](https://github.com/apache/airflow/blob/9de301da2a44385f57be5407e80e16ee376f3d39/tests/cli/commands/test_internal_api_command.py#L134-L137) began to failed with timeout error and it has affected all tests in single CI run. As temporary solution this test was marked as `quarantined`.
We should figure out why it happen and try to resolve it.
### Committer
- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | https://github.com/apache/airflow/issues/29679 | https://github.com/apache/airflow/pull/29688 | f99d27e5bde8e76fdb504fa213b9eb898c4bc903 | 946bded31af480d03cb2d45a3f8cdd0a9c32838d | "2023-02-21T21:47:56Z" | python | "2023-02-23T07:08:08Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,677 | ["airflow/providers/amazon/aws/operators/lambda_function.py", "docs/apache-airflow-providers-amazon/operators/lambda.rst", "tests/always/test_project_structure.py", "tests/providers/amazon/aws/operators/test_lambda_function.py", "tests/system/providers/amazon/aws/example_lambda.py"] | Rename AWS lambda related resources | ### Apache Airflow Provider(s)
amazon
### Versions of Apache Airflow Providers
_No response_
### Apache Airflow version
2.5.0
### Operating System
MacOS
### Deployment
Virtualenv installation
### Deployment details
AWS Lambda in Amazon provider package do not follow the convention #20296. Hook, operators and sensors related to AWS lambda need to be renamed to follow this convention. Here are the proposed changes in order to fix it:
- Rename `airflow/providers/amazon/aws/operators/lambda_function.py` to `airflow/providers/amazon/aws/operators/lambda.py`
- Rename `airflow/providers/amazon/aws/sensors/lambda_function.py` to `airflow/providers/amazon/aws/sensors/lambda.py`
- Rename `airflow/providers/amazon/aws/hooks/lambda_function.py` to `airflow/providers/amazon/aws/hooks/lambda.py`
- Rename `AwsLambdaInvokeFunctionOperator` to `LambdaInvokeFunctionOperator`
Since all these changes are breaking changes, it will have to be done following the deprecation pattern:
- Copy/paste the files with the new name
- Update the existing hook, operators and sensors to inherit from these new classes
- Deprecate these classes by sending deprecation warnings. See an example [here](airflow/providers/amazon/aws/operators/aws_lambda.py)
### What happened
_No response_
### What you think should happen instead
_No response_
### How to reproduce
N/A
### 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/29677 | https://github.com/apache/airflow/pull/29749 | b2ecaf9d2c6ccb94ae97728a2d54d31bd351f11e | 38b901ec3f07e6e65880b11cc432fb8ad6243629 | "2023-02-21T19:36:46Z" | python | "2023-02-24T21:40:54Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,671 | ["tests/providers/openlineage/extractors/test_default_extractor.py"] | Adapt OpenLineage default extractor to properly accept all OL implementation | ### Body
Adapt default extractor to accept any valid type returned from Operators `get_openlineage_facets_*` method.
This needs to ensure compatibility with operators made with external extractors for current openlineage-airflow integration.
### Committer
- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project. | https://github.com/apache/airflow/issues/29671 | https://github.com/apache/airflow/pull/31381 | 89bed231db4807826441930661d79520250f3075 | 4e73e47d546bf3fd230f93056d01e12f92274433 | "2023-02-21T18:43:14Z" | python | "2023-06-13T19:09:28Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,666 | ["airflow/providers/hashicorp/_internal_client/vault_client.py", "airflow/providers/hashicorp/secrets/vault.py", "tests/providers/hashicorp/_internal_client/test_vault_client.py", "tests/providers/hashicorp/secrets/test_vault.py"] | Multiple Mount Points for Hashicorp Vault Back-end | ### Description
Support mounting to multiple namespaces with the Hashicorp Vault Secrets Back-end
### Use case/motivation
As a data engineer I wish to utilize secrets stored in multiple mount paths (to support connecting to multiple namespaces) without having to mount to a higher up namespace.
### 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/29666 | https://github.com/apache/airflow/pull/29734 | d0783744fcae40b0b6b2e208a555ea5fd9124dfb | dff425bc3d92697bb447010aa9f3b56519a59f1e | "2023-02-21T16:44:08Z" | python | "2023-02-24T09:48:01Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,663 | ["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/stats.py", "tests/core/test_stats.py"] | Option to Disable High Cardinality Metrics on Statsd | ### Description
With recent PRs enabling tags-support on Statsd metrics, we gained a deeper understanding into the issue of publishing high cardinality metrics. Through this issue, I hope to facilitate the discussion in categorizing metric cardinality of Airflow specific events and tags, and finding a way to disable high cardinality metrics and including it into 2.6.0 release
In the world of Observability & Metrics, cardinality is broadly defined as the following:
`number of unique metric names * number of unique application tag pairs`
This means that events with _unbounded_ number of tag-pairs (key value pair of tags) as well as events with _unbounded_ number of unique metric names will incur expensive storage requirements on the metrics backend.
Let's take a look at the following metric:
`local_task_job.task_exit.<job_id>.<dag_id>.<task_id>.<return_code>`
Here, we have 4 different variable/tag-like attributes embedded into the metric name that I think we can categorize into 3 levels of cardinality.
1. High cardinality / Unbounded metric
2. Medium cardinality / semi-bounded metric
3. Low cardinality / categorically-bounded metric
### High Cardinality / Unbounded Metric
Example tag: <job_id>
This category of metrics are strictly unbounded, and incorporates a monotonically increasing attribute like <job_id> or <run_id>. To demonstrate just how explosive the growth of these metrics can be, let's take an example. In an Airflow instance with 1000 daily jobs, with a metric retention period of 10 days, we are increasing the cardinality of our metrics by 10,000 on just one single metric just by adding this tag alone. If we add this tag to a few other metrics, that could easily result in an explosion of metric cardinality. As a benchmark,[ DataDog's Enterprise level pricing plan only has 200 custom metrics per host included](https://www.datadoghq.com/pricing/), and anything beyond that needs to be added at a premium. These metrics should be avoided at all costs.
### Medium Cardinality / semi-bounded metric
Example tag: <dag_id>, <task_id>
This category of metrics are semi-bounded. They are not bounded by a pre-defined category of enums, but they are bounded by the number of dags or tasks there are within an Airflow infrastructure. This means that although these metrics can lead to increasing levels of cardinality in an Airflow cluster with increasing number of dags, cardinality will still be temporarily bounded. I.e. a given cluster will maintain its level of cardinality over time.
### Low Cardinality / categorically-bounded metric
Example tag: <return_code>
This category of metrics is strictly bounded by a category of enums. <return_code> and <task_state> are good examples of attributes with low cardinality. Ideally, we would only want to publish metrics with this level of cardinality.
Using above definition of High Cardinality, I've identified the following metrics as examples that fall under this criteria.
https://github.com/apache/airflow/blob/main/airflow/jobs/local_task_job.py#L292
https://github.com/apache/airflow/blob/main/airflow/dag_processing/processor.py#L444
https://github.com/apache/airflow/blob/main/airflow/jobs/scheduler_job.py#L691
https://github.com/apache/airflow/blob/main/airflow/jobs/scheduler_job.py#L1584
https://github.com/apache/airflow/blob/main/airflow/models/dag.py#L1331
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1258
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1577
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1847
I would like to propose that we need to provide the option to disable 'Unbounded metrics' with 2.6.0 release. In order to ensure backward compatibility, we could leave the default behavior to publish all metrics, but implement a single Boolean flag to disable these high cardinality metrics.
### Use case/motivation
_No response_
### Related issues
https://github.com/apache/airflow/pull/28961
https://github.com/apache/airflow/pull/29093
### 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/29663 | https://github.com/apache/airflow/pull/29881 | 464ab1b7caa78637975008fcbb049d5b52a8b005 | 86cd79ffa76d4e4d4abe3fe829d7797852a713a5 | "2023-02-21T16:12:58Z" | python | "2023-03-06T06:20:05Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,662 | ["airflow/www/decorators.py"] | Audit Log is unclear when using Azure AD login | ### Apache Airflow version
2.5.1
### What happened
We're using an Azure OAUTH based login in our Airflow implementation, and everything works great. This is more of a visual problem than an actual bug.
In the Audit logs, the `owner` key is mapped to the username, which in most cases is airflow. But, in situations where we manually pause a DAG or enable it, it is mapped to our generated username, which doesn't really tell one who it is unless they were to look up that string in the users list. Example:
It would be nice if it were possible to include the user's first and last name alongside the username. I could probably give this one a go myself, if I could get a hint on where to look.
I've found the dag_audit_log.html template, but not sure where to change log.owner.
### What you think should happen instead
It would be good to get a representation such as username (FirstName LastName).
### How to reproduce
N/A
### Operating System
Debian GNU/Linux 11
### Versions of Apache Airflow Providers
_No response_
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
Deployed with Helm chart v1.7.0, and Azure OAUTH for login.
### 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/29662 | https://github.com/apache/airflow/pull/30185 | 0b3b6704cb12a3b8f22da79d80b3db85528418b7 | a03f6ccb153f9b95f624d5bc3346f315ca3f0211 | "2023-02-21T15:10:30Z" | python | "2023-05-17T20:15:55Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,621 | ["chart/templates/dags-persistent-volume-claim.yaml", "chart/values.yaml"] | Fix adding annotations for dag persistence PVC | ### Official Helm Chart version
1.8.0 (latest released)
### Apache Airflow version
2.5.0
### Kubernetes Version
v1.25.4
### Helm Chart configuration
The dags persistence section doesn't have a default value for annotations and the usage looks like:
```
annotations:
{{- if .Values.dags.persistence.annotations}}
{{- toYaml .Values.dags.persistence.annotations | nindent 4 }}
{{- end }}
```
### Docker Image customizations
_No response_
### What happened
As per the review comments here: https://github.com/apache/airflow/pull/29270#pullrequestreview-1304890651, due to this design, the upgrades might suffer. Fix them to be helm upgrade friendly
### What you think should happen instead
The design should be written in an helm upgrade friendly way, refer to this suggestion https://github.com/apache/airflow/pull/29270#pullrequestreview-1304890651
### How to reproduce
-
### 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/29621 | https://github.com/apache/airflow/pull/29622 | 5835b08e8bc3e11f4f98745266d10bbae510b258 | 901774718c5d7ff7f5ddc6f916701d281bb60a4b | "2023-02-20T03:20:25Z" | python | "2023-02-20T22:58:03Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,593 | ["airflow/providers/common/sql/operators/sql.py", "tests/providers/common/sql/operators/test_sql.py"] | Cannot disable XCom push in SnowflakeOperator | ### Apache Airflow Provider(s)
snowflake
### Versions of Apache Airflow Providers
4.0.3
### Apache Airflow version
2.5.0
### Operating System
docker/linux
### Deployment
Astronomer
### Deployment details
Normal Astro CLI
### What happened
```
>>> SnowflakeOperator(
...,
do_xcom_push=False
).execute()
ERROR - Task failed with exception
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 272, in execute
return self._process_output([output], hook.descriptions)[-1]
File "/usr/local/lib/python3.9/site-packages/airflow/providers/snowflake/operators/snowflake.py", line 118, in _process_output
for row in result_list:
TypeError: 'NoneType' object is not iterable
```
### What you think should happen instead
XCom's should be able to be turned off
### How to reproduce
1)
```
astro dev init
```
2) `dags/snowflake_test.py`
```
import os
from datetime import datetime
from airflow import DAG
from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
os.environ["AIRFLOW_CONN_SNOWFLAKE"] = "snowflake://......."
with DAG('snowflake_test', schedule=None, start_date=datetime(2023, 1, 1)):
SnowflakeOperator(
task_id='snowflake_test',
snowflake_conn_id="snowflake",
sql="select 1;",
do_xcom_push=False
)
```
3)
```
astro run -d dags/snowflake_test.py snowflake_test
```
```
Loading DAGs...
Running snowflake_test... [2023-02-17 18:45:33,537] {connection.py:280} INFO - Snowflake Connector for Python Version: 2.9.0, Python Version: 3.9.16, Platform: Linux-5.15.49-linuxkit-aarch64-with-glibc2.31
...
[2023-02-17 18:45:34,608] {cursor.py:727} INFO - query: [select 1]
[2023-02-17 18:45:34,698] {cursor.py:740} INFO - query execution done
...
[2023-02-17 18:45:34,785] {connection.py:581} INFO - closed
[2023-02-17 18:45:34,841] {connection.py:584} INFO - No async queries seem to be running, deleting session
FAILED
'NoneType' object is not iterable
```
### 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/29593 | https://github.com/apache/airflow/pull/29599 | 2bc1081ea6ca569b4e7fc538bfc827d74e8493ae | 19f1e7c27b85e297497842c73f13533767ebd6ba | "2023-02-17T16:27:19Z" | python | "2023-02-22T09:33:08Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,585 | ["airflow/providers/docker/decorators/docker.py", "tests/providers/docker/decorators/test_docker.py"] | template_fields not working in the decorator `task.docker` | ### Apache Airflow Provider(s)
docker
### Versions of Apache Airflow Providers
apache-airflow-providers-docker 3.4.0
### Apache Airflow version
2.5.1
### Operating System
Linux
### Deployment
Docker-Compose
### Deployment details
_No response_
### What happened
The templated fields are not working under `task.docker`
```python
@task.docker(image="python:3.9-slim-bullseye", container_name='python_{{macros.datetime.now() | ts_nodash}}', multiple_outputs=True)
def transform(order_data_dict: dict):
"""
#### Transform task
A simple Transform task which takes in the collection of order data and
computes the total order value.
"""
total_order_value = 0
for value in order_data_dict.values():
total_order_value += value
return {"total_order_value": total_order_value}
```
Will throws error with un-templated `container_name`
`Bad Request ("Invalid container name (python_{macros.datetime.now() | ts_nodash}), only [a-zA-Z0-9][a-zA-Z0-9_.-] are allowed")`
### What you think should happen instead
All these fields should work with docker operator:
https://airflow.apache.org/docs/apache-airflow-providers-docker/stable/_api/airflow/providers/docker/operators/docker/index.html
```
template_fields: Sequence[str]= ('image', 'command', 'environment', 'env_file', 'container_name')
```
### How to reproduce
with the example 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/29585 | https://github.com/apache/airflow/pull/29586 | 792416d4ad495f1e5562e6170f73f4d8f1fa2eff | 7bd87e75def1855d8f5b91e9ab1ffbbf416709ec | "2023-02-17T09:32:11Z" | python | "2023-02-17T17:51:57Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,578 | ["airflow/jobs/scheduler_job_runner.py", "docs/apache-airflow/administration-and-deployment/logging-monitoring/metrics.rst", "newsfragments/30374.significant.rst"] | scheduler.tasks.running metric is always 0 | ### Apache Airflow version
2.5.1
### What happened
I'd expect the `scheduler.tasks.running` metric to represent the number of running tasks, but it is always zero. It appears that #10956 broke this when it removed [the line that increments `num_tasks_in_executor`](https://github.com/apache/airflow/pull/10956/files#diff-bde85feb359b12bdd358aed4106ef4fccbd8fa9915e16b9abb7502912a1c1ab3L1363). Right now that variable is set to 0, never incremented, and the emitted as a gauge.
### What you think should happen instead
`scheduler.tasks.running` should either represent the number of tasks running or be removed altogether.
### How to reproduce
_No response_
### Operating System
Ubuntu 18.04
### 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/29578 | https://github.com/apache/airflow/pull/30374 | d8af20f064b8d8abc9da1f560b2d7e1ac7dd1cc1 | cce9b2217b86a88daaea25766d0724862577cc6c | "2023-02-16T17:59:47Z" | python | "2023-04-13T11:04:12Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,576 | ["airflow/triggers/temporal.py", "tests/triggers/test_temporal.py"] | DateTimeSensorAsync breaks if target_time is timezone-aware | ### Apache Airflow version
2.5.1
### What happened
`DateTimeSensorAsync` fails with the following error if `target_time` is aware:
```
[2022-06-29, 05:09:11 CDT] {taskinstance.py:1889} ERROR - Task failed with exception
Traceback (most recent call last):a
File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/sensors/time_sensor.py", line 60, in execute
trigger=DateTimeTrigger(moment=self.target_datetime),
File "/opt/conda/envs/production/lib/python3.9/site-packages/airflow/triggers/temporal.py", line 42, in __init__
raise ValueError(f"The passed datetime must be using Pendulum's UTC, not {moment.tzinfo!r}")
ValueError: The passed datetime must be using Pendulum's UTC, not Timezone('America/Chicago')
```
### What you think should happen instead
Given the fact that `DateTimeSensor` correctly handles timezones, this seems like a bug. `DateTimeSensorAsync` should be a drop-in replacement for `DateTimeSensor`, and therefore should have the same timezone behavior.
### How to reproduce
```
#!/usr/bin/env python3
import datetime
from airflow.decorators import dag
from airflow.sensors.date_time import DateTimeSensor, DateTimeSensorAsync
import pendulum
@dag(
start_date=datetime.datetime(2022, 6, 29),
schedule='@daily',
)
def datetime_sensor_dag():
naive_time1 = datetime.datetime(2023, 2, 16, 0, 1)
aware_time1 = datetime.datetime(2023, 2, 16, 0, 1).replace(tzinfo=pendulum.local_timezone())
naive_time2 = pendulum.datetime(2023, 2, 16, 23, 59)
aware_time2 = pendulum.datetime(2023, 2, 16, 23, 59).replace(tzinfo=pendulum.local_timezone())
DateTimeSensor(task_id='naive_time1', target_time=naive_time1, mode='reschedule')
DateTimeSensor(task_id='naive_time2', target_time=naive_time2, mode='reschedule')
DateTimeSensor(task_id='aware_time1', target_time=aware_time1, mode='reschedule')
DateTimeSensor(task_id='aware_time2', target_time=aware_time2, mode='reschedule')
DateTimeSensorAsync(task_id='async_naive_time1', target_time=naive_time1)
DateTimeSensorAsync(task_id='async_naive_time2', target_time=naive_time2)
DateTimeSensorAsync(task_id='async_aware_time1', target_time=aware_time1) # fails
DateTimeSensorAsync(task_id='async_aware_time2', target_time=aware_time2) # fails
datetime_sensor_dag()
```
This can also happen if the `target_time` is naive and `core.default_timezone = system`.
### Operating System
CentOS Stream 8
### Versions of Apache Airflow Providers
N/A
### Deployment
Other
### Deployment details
Standalone
### Anything else
This appears to be nearly identical to #24736. Probably worth checking other time-related sensors as well.
### 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/29576 | https://github.com/apache/airflow/pull/29606 | fd000684d05a993ade3fef38b683ef3cdfdfc2b6 | 79c07e3fc5d580aea271ff3f0887291ae9e4473f | "2023-02-16T16:03:25Z" | python | "2023-02-19T20:27:44Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,552 | ["airflow/providers/google/suite/hooks/drive.py", "airflow/providers/google/suite/transfers/local_to_drive.py", "tests/providers/google/suite/hooks/test_drive.py", "tests/providers/google/suite/transfers/test_local_to_drive.py"] | Google provider doesn't let uploading file to a shared drive | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
```
apache-airflow-providers-google: version 8.9.0
```
### Apache Airflow version
2.5.1
### Operating System
Linux - official airflow image from docker hub apache/airflow:slim-2.5.1
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
Not sure if it's a bug or a feature.
Originally I have used `LocalFilesystemToGoogleDriveOperator` to try uploading a file into a shared Google Drive without success.
Provider didn't find a directory with a given name so it created a new one without browsing `shared drives`. Method call that doesn't allow to upload it is here:
https://github.com/apache/airflow/blob/main/airflow/providers/google/suite/hooks/drive.py#L223
### What you think should happen instead
It would be nice if there was an optional parameter to provide a `drive_id` into which user would like to upload a file. With the same directory check behaviour that already exists but extended to `shared drives`
### How to reproduce
1. Create a directory on the shared google drive
2. Fill `LocalFilesystemToGoogleDriveOperator` constructor with the arguments.
3. Execute the function
### Anything else
I am willing to submit a PR but I would need to know more details, your thoughts, expectations of the implementation to make as little iteration on it possible.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29552 | https://github.com/apache/airflow/pull/29477 | 0222f7d91cee80cc1a464f277f99e69e845c52db | f37772adfdfdee8763147e0563897e4d5d5657c8 | "2023-02-15T08:52:31Z" | python | "2023-02-18T19:29:35Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,538 | ["airflow/providers/google/CHANGELOG.rst", "airflow/providers/google/marketing_platform/hooks/campaign_manager.py", "airflow/providers/google/marketing_platform/operators/campaign_manager.py", "airflow/providers/google/marketing_platform/sensors/campaign_manager.py", "airflow/providers/google/provider.yaml", "docs/apache-airflow-providers-google/operators/marketing_platform/campaign_manager.rst", "tests/providers/google/marketing_platform/hooks/test_campaign_manager.py", "tests/providers/google/marketing_platform/operators/test_campaign_manager.py", "tests/providers/google/marketing_platform/sensors/test_campaign_manager.py", "tests/system/providers/google/marketing_platform/example_campaign_manager.py"] | GoogleCampaignManagerReportSensor not working correctly on API Version V4 | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Hello,
My organization has been running Airflow 2.3.4 and we have run into a problem in regard to the Google Campaign Manager Report Sensor. The purpose of this sensor is to check if a report has finished processing and is ready to be downloaded. If we use API Version: v3.5 it works flawlessly. Unfortunately, if we use API Version v4, the sensor malfunctions. It always succeeds regardless of whether the report is ready to download or not. This causes the job to fail downstream because it makes it impossible to download a file that it is not ready.
At first this doesn't seem like a big problem in just using v3.5. However, Google announced that they are going to only let you use API version v4 starting in a week. Is there a way we can get this resolved 😭?
Thanks!
### What you think should happen instead
_No response_
### How to reproduce
_No response_
### Operating System
linux?
### Versions of Apache Airflow Providers
_No response_
### Deployment
Composer
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29538 | https://github.com/apache/airflow/pull/30598 | 5b42aa3b8d0ec069683e22c2cb3b8e8e6e5fee1c | da2749cae56d6e0da322695b3286acd9393052c8 | "2023-02-14T15:13:33Z" | python | "2023-04-15T13:34:31Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,537 | ["airflow/cli/commands/config_command.py", "tests/cli/commands/test_config_command.py"] | Docker image fails to start if celery config section is not defined | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Using Airflow `2.3.4`
We removed any config values we did not explicitly set from `airflow.cfg`. This was to make future upgrades less involved, as we could only compare configuration values we explicitly set, rather than all permutations of versions. This has been [recommended in slack](https://apache-airflow.slack.com/archives/CCQB40SQJ/p1668441275427859?thread_ts=1668394200.637899&cid=CCQB40SQJ) as an approach.
e.g. we set `AIRFLOW__CELERY__BROKER_URL` as an environment variable - we do not set this in `airflow.cfg`, so we removed the `[celery]` section from the Airflow configuration.
We set `AIRFLOW__CORE__EXECUTOR=CeleryExecutor`, so we are using the Celery executor.
Upon starting the Airflow scheduler, it exited with code `1`, and this message:
```
The section [celery] is not found in config.
```
Upon adding back in an empty
```
[celery]
```
section to `airflow.cfg`, this error went away. I have verified that it still picks up `AIRFLOW__CELERY__BROKER_URL` correctly.
### What you think should happen instead
I'd expect Airflow to take defaults as listed [here](https://airflow.apache.org/docs/apache-airflow/2.3.4/howto/set-config.html), I wouldn't expect the presence of configuration sections to cause errors.
### How to reproduce
1. Setup a docker image for the Airflow `scheduler` with `apache/airflow:slim-2.3.4)-python3.10` and the following configuration in `airflow.cfg` - with no `[celery]` section:
```
[core]
# The executor class that airflow should use. Choices include
# ``SequentialExecutor``, ``LocalExecutor``, ``CeleryExecutor``, ``DaskExecutor``,
# ``KubernetesExecutor``, ``CeleryKubernetesExecutor`` or the
# full import path to the class when using a custom executor.
executor = CeleryExecutor
[logging]
[metrics]
[secrets]
[cli]
[debug]
[api]
[lineage]
[atlas]
[operators]
[hive]
[webserver]
[email]
[smtp]
[sentry]
[celery_kubernetes_executor]
[celery_broker_transport_options]
[dask]
[scheduler]
[triggerer]
[kerberos]
[github_enterprise]
[elasticsearch]
[elasticsearch_configs]
[kubernetes]
[smart_sensor]
```
2. Run the `scheduler` command, also setting `AIRFLOW__CELERY__BROKER_URL` to point to a Celery redis broker.
3. Observe that the scheduler exits.
### Operating System
Ubuntu 20.04.5 LTS (Focal Fossa)
### Versions of Apache Airflow Providers
_No response_
### Deployment
Other Docker-based deployment
### Deployment details
AWS ECS
Docker `apache/airflow:slim-2.3.4)-python3.10`
Separate:
- Webserver
- Triggerer
- Scheduler
- Celery worker
- Celery flower
services
### Anything else
This seems to occur due to this `get-value` check in the Airflow image entrypoint: https://github.com/apache/airflow/blob/28126c12fbdd2cac84e0fbcf2212154085aa5ed9/scripts/docker/entrypoint_prod.sh#L203-L212
### 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/29537 | https://github.com/apache/airflow/pull/29541 | 84b13e067f7b0c71086a42957bb5cf1d6dc86d1d | 06d45f0f2c8a71c211e22cf3792cc873f770e692 | "2023-02-14T14:58:55Z" | python | "2023-02-15T01:41:37Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,532 | ["airflow/api_internal/endpoints/rpc_api_endpoint.py", "airflow/models/dag.py", "airflow/models/dagwarning.py"] | AIP-44 Migrate DagWarning.purge_inactive_dag_warnings to Internal API | Used in https://github.com/mhenc/airflow/blob/master/airflow/dag_processing/manager.py#L613
should be straighforward | https://github.com/apache/airflow/issues/29532 | https://github.com/apache/airflow/pull/29534 | 289ae47f43674ae10b6a9948665a59274826e2a5 | 50b30e5b92808e91ad9b6b05189f560d58dd8152 | "2023-02-14T13:13:04Z" | python | "2023-02-15T00:13:44Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,531 | ["airflow/ti_deps/deps/prev_dagrun_dep.py", "tests/ti_deps/deps/test_prev_dagrun_dep.py"] | Dynamic task mapping does not always create mapped tasks | ### Apache Airflow version
2.5.1
### What happened
Same problem as https://github.com/apache/airflow/issues/28296, but seems to happen nondeterministically, and still happens when ignoring `depends_on_past=True`.
I've got a task that retrieves some filenames, which then creates dynamically mapped tasks to move the files, one per task.
I'm using a similar task across multiple DAGs. However, task mapping fails on some DAG runs: it inconsistently happens per DAG run, and some DAGs do not seem to be affected at all. These seem to be the DAGs where no task was ever mapped, so that the mapped task instance ended up in a Skipped state.
What happens is that multiple files will be found, but only a single dynamically mapped task will be created. This task never starts and has map_index of -1. It can be found under the "List instances, all runs" menu, but says "No Data found." under the "Mapped Tasks" tab.
When I press the "Run" button when the mapped task is selected, the following error appears:
```
Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped!
```
The previous task _has_ run however. No errors appeared in my Airflow logs.
When I try to run the task with **Ignore All Deps** enabled, I get the error:
```
Could not queue task instance for execution, dependencies not met: Previous Dagrun State: depends_on_past is true for this task's DAG, but the previous task instance has not run yet., Task has been mapped: The task has yet to be mapped!
```
This last bit is a contradiction, the task cannot be mapped and not mapped simultaneously.
If the amount of mapped tasks is 0 while in this erroneous state, the mapped tasks will not be marked as skipped as expected.
### What you think should happen instead
The mapped tasks should not get stuck with "no status".
The mapped tasks should be created and ran successfully, or in the case of a 0-length list output of the upstream task they should be skipped.
### How to reproduce
Run the below DAG, if it runs successfully clear several tasks out of order. This may not immediately reproduce the bug, but after some task clearing, for me it always ends up in the faulty state described above.
```
from airflow import DAG
from airflow.decorators import task
import datetime as dt
from airflow.operators.python import PythonOperator
import random
@task
def get_filenames_kwargs():
return [
{"file_name": i}
for i in range(random.randint(0, 2))
]
def print_filename(file_name):
print(file_name)
with DAG(
dag_id="dtm_test_2",
start_date=dt.datetime(2023, 2, 10),
default_args={
"owner": "airflow",
"depends_on_past": True,
},
schedule="@daily",
) as dag:
get_filenames_task = get_filenames_kwargs.override(task_id="get_filenames_task")()
print_filename_task = PythonOperator.partial(
task_id="print_filename_task",
python_callable=print_filename,
).expand(op_kwargs=get_filenames_task)
```
### Operating System
Amazon Linux v2
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29531 | https://github.com/apache/airflow/pull/32397 | 685328e3572043fba6db432edcaacf8d06cf88d0 | 73bc49adb17957e5bb8dee357c04534c6b41f9dd | "2023-02-14T12:47:12Z" | python | "2023-07-23T23:53:52Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,515 | ["airflow/www/templates/airflow/task.html"] | Hide non-used docs attributes from Task Instance Detail | ### Description
Inside a BashOperator, I added a markdown snippet of documentation for the "Task Instance Details" of my Airflow nodes.
Now I can see my markdown, defined by the attribute "doc_md", but also
Attribute: bash_command
Attribute: doc
Attribute: doc_json
Attribute: doc_rst
Attribute: doc_yaml
I think it would look better if only the chosen type of docs would be shown in the Task Instance detail, instead of leaving the names of other attributes without anything added to them.
### Use case/motivation
I would like to see only the type of doc attribute that I chose to add to my task instance detail and hide all the others docs type.
### Related issues
_No response_
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29515 | https://github.com/apache/airflow/pull/29545 | 655ffb835eb4c5343c3f2b4d37b352248f2768ef | f2f6099c5a2f3613dce0cc434a95a9479d748cf5 | "2023-02-13T22:10:31Z" | python | "2023-02-16T14:17:49Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,435 | ["airflow/decorators/base.py", "tests/decorators/test_python.py"] | TaskFlow API `multiple_outputs` inferral causes import errors when using TYPE_CHECKING | ### Apache Airflow version
2.5.1
### What happened
When using the TaskFlow API, I like to generally keep a good practice of adding type annotations in the TaskFlow functions so others reading the DAG and task code have better context around inputs/outputs, keep imports solely used for typing behind `typing.TYPE_CHECKING`, and utilize PEP 563 for forwarding annotation evaluations. Unfortunately, when using ~PEP 563 _and_ `TYPE_CHECKING`~ just TYPE_CHECKING, DAG import errors occur with a "NameError: <name> is not defined." exception.
### What you think should happen instead
Users should be free to use ~PEP 563 and~ `TYPE_CHECKING` when using the TaskFlow API and not hit DAG import errors along the way.
### How to reproduce
Using a straightforward use case of transforming a DataFrame, let's assume this toy example:
```py
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from pendulum import datetime
from airflow.decorators import dag, task
if TYPE_CHECKING:
from pandas import DataFrame
@dag(start_date=datetime(2023, 1, 1), schedule=None)
def multiple_outputs():
@task()
def transform(df: DataFrame) -> dict[str, Any]:
...
transform()
multiple_outputs()
```
Add this DAG to your DAGS_FOLDER and the following import error should be observed:
<img width="641" alt="image" src="https://user-images.githubusercontent.com/48934154/217713685-ec29d5cc-4a48-4049-8dfa-56cbd76cddc3.png">
### Operating System
Debian GNU/Linux
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==6.2.0
apache-airflow-providers-apache-hive==5.1.1
apache-airflow-providers-apache-livy==3.2.0
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cncf-kubernetes==5.1.1
apache-airflow-providers-common-sql==1.3.3
apache-airflow-providers-databricks==4.0.0
apache-airflow-providers-dbt-cloud==2.3.1
apache-airflow-providers-elasticsearch==4.3.3
apache-airflow-providers-ftp==3.3.0
apache-airflow-providers-google==8.8.0
apache-airflow-providers-http==4.1.1
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-microsoft-azure==5.1.0
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-redis==3.1.0
apache-airflow-providers-sftp==4.2.1
apache-airflow-providers-snowflake==4.0.2
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.4.0
astronomer-providers==1.14.0
### Deployment
Astronomer
### Deployment details
OOTB local Airflow install with LocalExecutor built with the Astro CLI.
### Anything else
- This behavior/error was not observed using Airflow 2.4.3.
- As a workaround, `multiple_outputs` can be explicitly set on the TaskFlow function to skip the inferral.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29435 | https://github.com/apache/airflow/pull/29445 | f9e9d23457cba5d3e18b5bdb7b65ecc63735b65b | b1306065054b98a63c6d3ab17c84d42c2d52809a | "2023-02-09T03:55:48Z" | python | "2023-02-12T07:45:26Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,432 | ["airflow/models/mappedoperator.py", "tests/models/test_mappedoperator.py", "tests/test_utils/mock_operators.py"] | Jinja templating doesn't work with container_resources when using dymanic task mapping with Kubernetes Pod Operator | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Google Cloud Composer Version - 2.1.5
Airflow Version - 2.4.3
We are trying to use dynamic task mapping with Kubernetes Pod Operator. Our use-case is to return the pod's CPU and memory requirements from a function which is included as a macro in DAG
Without dynamic task mapping it works perfectly, but when used with the dynamic task mapping, it is unable to recognize the macro.
container_resources is a templated field as per the [docs](https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/_api/airflow/providers/cncf/kubernetes/operators/kubernetes_pod/index.html#airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator), the feature was introduced in this [PR](https://github.com/apache/airflow/pull/27457).
We also tried the toggling the boolean `render_template_as_native_obj`, but still no luck.
Providing below a trimmed version of our DAG to help reproduce the issue. (function to return cpu and memory is trivial here just to show example)
### What you think should happen instead
It should have worked similar with or without dynamic task mapping.
### How to reproduce
Deployed the following DAG in Google Cloud Composer.
```
import datetime
import os
from airflow import models
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import (
KubernetesPodOperator,
)
from kubernetes.client import models as k8s_models
dvt_image = os.environ.get("DVT_IMAGE")
default_dag_args = {"start_date": datetime.datetime(2022, 1, 1)}
def pod_mem():
return "4000M"
def pod_cpu():
return "1000m"
with models.DAG(
"sample_dag",
schedule_interval=None,
default_args=default_dag_args,
render_template_as_native_obj=True,
user_defined_macros={
"pod_mem": pod_mem,
"pod_cpu": pod_cpu,
},
) as dag:
task_1 = KubernetesPodOperator(
task_id="task_1",
name="task_1",
namespace="default",
image=dvt_image,
cmds=["bash", "-cx"],
arguments=["echo hello"],
service_account_name="sa-k8s",
container_resources=k8s_models.V1ResourceRequirements(
limits={
"memory": "{{ pod_mem() }}",
"cpu": "{{ pod_cpu() }}",
}
),
startup_timeout_seconds=1800,
get_logs=True,
image_pull_policy="Always",
config_file="/home/airflow/composer_kube_config",
dag=dag,
)
task_2 = KubernetesPodOperator.partial(
task_id="task_2",
name="task_2",
namespace="default",
image=dvt_image,
cmds=["bash", "-cx"],
service_account_name="sa-k8s",
container_resources=k8s_models.V1ResourceRequirements(
limits={
"memory": "{{ pod_mem() }}",
"cpu": "{{ pod_cpu() }}",
}
),
startup_timeout_seconds=1800,
get_logs=True,
image_pull_policy="Always",
config_file="/home/airflow/composer_kube_config",
dag=dag,
).expand(arguments=[["echo hello"]])
task_1 >> task_2
```
task_1 (without dynamic task mapping) completes successfully, while task_2(with dynamic task mapping) fails.
Looking at the error logs, it failed while rendering the Pod spec since the calls to pod_cpu() and pod_mem() are unresolved.
Here is the traceback:
Exception when attempting to create Namespaced Pod: { "apiVersion": "v1", "kind": "Pod", "metadata": { "annotations": {}, "labels": { "dag_id": "sample_dag", "task_id": "task_2", "run_id": "manual__2023-02-08T183926.890852Z-eee90e4ee", "kubernetes_pod_operator": "True", "map_index": "0", "try_number": "2", "airflow_version": "2.4.3-composer", "airflow_kpo_in_cluster": "False" }, "name": "task-2-46f76eb0432d42ae9a331a6fc53835b3", "namespace": "default" }, "spec": { "affinity": {}, "containers": [ { "args": [ "echo hello" ], "command": [ "bash", "-cx" ], "env": [], "envFrom": [], "image": "us.gcr.io/ams-e2e-testing/edw-dvt-tool", "imagePullPolicy": "Always", "name": "base", "ports": [], "resources": { "limits": { "memory": "{{ pod_mem() }}", "cpu": "{{ pod_cpu() }}" } }, "volumeMounts": [] } ], "hostNetwork": false, "imagePullSecrets": [], "initContainers": [], "nodeSelector": {}, "restartPolicy": "Never", "securityContext": {}, "serviceAccountName": "sa-k8s", "tolerations": [], "volumes": [] } }
Traceback (most recent call last):
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/cncf/kubernetes/utils/pod_manager.py", line 143, in run_pod_async
resp = self._client.create_namespaced_pod(
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 7356, in create_namespaced_pod
return self.create_namespaced_pod_with_http_info(namespace, body, **kwargs) # noqa: E501
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api/core_v1_api.py", line 7455, in create_namespaced_pod_with_http_info
return self.api_client.call_api(
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 348, in call_api
return self.__call_api(resource_path, method,
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 180, in __call_api
response_data = self.request(
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/api_client.py", line 391, in request
return self.rest_client.POST(url,
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/rest.py", line 275, in POST
return self.request("POST", url,
File "/opt/python3.8/lib/python3.8/site-packages/kubernetes/client/rest.py", line 234, in request
raise ApiException(http_resp=r)
kubernetes.client.exceptions.ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'Audit-Id': '1ef20c0b-6980-4173-b9cc-9af5b4792e86', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Kubernetes-Pf-Flowschema-Uid': '1b263a21-4c75-4ef8-8147-c18780a13f0e', 'X-Kubernetes-Pf-Prioritylevel-Uid': '3cd4cda4-908c-4944-a422-5512b0fb88d6', 'Date': 'Wed, 08 Feb 2023 18:45:23 GMT', 'Content-Length': '256'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"Pod in version \"v1\" cannot be handled as a Pod: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'","reason":"BadRequest","code":400}
### Operating System
Google Composer Kubernetes Cluster
### Versions of Apache Airflow Providers
_No response_
### Deployment
Composer
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29432 | https://github.com/apache/airflow/pull/29451 | 43443eb539058b7b4756455f76b0e883186d9250 | 5eefd47771a19dca838c8cce40a4bc5c555e5371 | "2023-02-08T19:01:33Z" | python | "2023-02-13T08:48:47Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,428 | ["pyproject.toml"] | Require newer version of pypi/setuptools to remove security scan issue (CVE-2022-40897) | ### Description
Hi. My team is evaluating airflow, so I ran a security scan on it. It is flagging a Medium security issue with pypi/setuptools. See https://nvd.nist.gov/vuln/detail/CVE-2022-40897 for details. Is it possible to require a more recent version? Or perhaps airflow users are not vulnerable to this?
### Use case/motivation
_No response_
### Related issues
_No response_
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29428 | https://github.com/apache/airflow/pull/29465 | 9c6f83bb6f3e3b57ae0abbe9eb0582fcde265702 | 41dff9875bce4800495c9132b10a6c8bff900a7c | "2023-02-08T15:11:54Z" | python | "2023-02-11T16:03:14Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,423 | ["airflow/providers/amazon/aws/hooks/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py"] | GlueJobOperator throws error after migration to newest version of Airflow | ### Apache Airflow version
2.5.1
### What happened
We were using GlueJobOperator with Airflow 2.3.3 (official docker image) and it was working well, we didn't specify script file location, because it was inferred from the job name. After migration to 2.5.1 (official docker image) the operator fails if `s3_bucket` and `script_location` are not specified. That's the error I see:
```
Traceback (most recent call last):
File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/operators/glue.py", line 146, in execute
glue_job_run = glue_job.initialize_job(self.script_args, self.run_job_kwargs)
File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 155, in initialize_job
job_name = self.create_or_update_glue_job()
File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 300, in create_or_update_glue_job
config = self.create_glue_job_config()
File "/home/airflow/.local/lib/python3.10/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 97, in create_glue_job_config
raise ValueError("Could not initialize glue job, error: Specify Parameter `s3_bucket`")
ValueError: Could not initialize glue job, error: Specify Parameter `s3_bucket`
```
### What you think should happen instead
I was expecting that after migration the operator would work the same way.
### How to reproduce
Create a dag with `GlueJobOperator` operator and do not use s3_bucket or script_location arguments
### Operating System
Linux
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==7.1.0
### Deployment
Docker-Compose
### Deployment details
`apache/airflow:2.5.1-python3.10` Docker image and official docker compose
### Anything else
I believe it was commit #27893 by @romibuzi that introduced this behaviour.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29423 | https://github.com/apache/airflow/pull/29659 | 9de301da2a44385f57be5407e80e16ee376f3d39 | 6c13f04365b916e938e3bea57e37fc80890b8377 | "2023-02-08T09:09:12Z" | python | "2023-02-22T00:00:18Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,422 | ["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py", "tests/providers/amazon/aws/transfers/test_dynamodb_to_s3.py"] | Multiple AWS connections support in DynamoDBToS3Operator | ### Description
I want to add support of a separate AWS connection for DynamoDB in `DynamoDBToS3Operator` in `apache-airflow-providers-amazon` via `aws_dynamodb_conn_id` constructor argument.
### Use case/motivation
Sometimes DynamoDB tables and S3 buckets live in different AWS accounts so to access both resources you need to assume a role in another account from one of them.
That role can be specified in AWS connection, thus we need to support two of them in this operator.
### Related issues
_No response_
### Are you willing to submit a PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29422 | https://github.com/apache/airflow/pull/29452 | 8691c4f98c6cd6d96e87737158a9be0f6a04b9ad | 3780b01fc46385809423bec9ef858be5be64b703 | "2023-02-08T08:58:26Z" | python | "2023-03-09T22:02:18Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,405 | ["airflow/api_connexion/openapi/v1.yaml", "airflow/www/static/js/types/api-generated.ts"] | Add pagination to get_log in the rest API | ### Description
Right now, the `get_log` endpoint at `/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/logs/{task_try_number}` does not have any pagination and therefore we can be forced to load extremely large text blocks, which makes everything slow. (see the workaround fix we needed to do in the UI: https://github.com/apache/airflow/pull/29390)
In `task_log_reader`, we do have `log_pos` and `offset` (see [here](https://github.com/apache/airflow/blob/main/airflow/utils/log/log_reader.py#L80-L83)). It would be great to expose those parameters in the REST API in order to break apart task instance logs into more manageable pieces.
### Use case/motivation
_No response_
### Related issues
_No response_
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29405 | https://github.com/apache/airflow/pull/30729 | 7d02277ae13b7d1e6cea9e6c8ff0d411100daf77 | 7d62cbb97e1bc225f09e3cfac440aa422087a8a7 | "2023-02-07T16:10:57Z" | python | "2023-04-22T20:49:40Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,396 | ["airflow/providers/google/cloud/hooks/bigquery.py", "tests/providers/google/cloud/hooks/test_bigquery.py"] | BigQuery Hook list_rows method missing page_token return value | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
apache-airflow-providers-google==7.0.0
But the problem exists in all newer versions.
### Apache Airflow version
apache-airflow==2.3.2
### Operating System
Ubuntu 20.04.4 LTS
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
The `list_rows` method in the BigQuery Hook does not return the page_token value, which is necessary for paginating query results. Same problem with `get_datasets_list` method.
The documentation for the `get_datasets_list` method even states that the page_token parameter can be accessed:
```
:param page_token: Token representing a cursor into the datasets. If not passed,
the API will return the first page of datasets. The token marks the beginning of the
iterator to be returned and the value of the ``page_token`` can be accessed at
``next_page_token`` of the :class:`~google.api_core.page_iterator.HTTPIterator`.
```
but it doesn't return HTTPIterator. Instead, it converts the `HTTPIterator` to `list[DatasetListItem]` using `list(datasets)`, making it impossible to retrieve the original `HTTPIterator` and thus impossible to obtain the `next_page_token`.
### What you think should happen instead
`list_rows` \ `get_datasets_list` methods should return `Iterator` OR both the list of rows\datasets and the page_token value to allow users to retrieve multiple results pages. For backward compatibility, we can have a parameter like `return_iterator=True` or smth like that.
### How to reproduce
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29396 | https://github.com/apache/airflow/pull/30543 | d9896fd96eb91a684a512a86924a801db53eb945 | 4703f9a0e589557f5176a6f466ae83fe52644cf6 | "2023-02-07T02:26:41Z" | python | "2023-04-08T17:01:57Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,393 | ["airflow/providers/amazon/aws/log/s3_task_handler.py", "tests/providers/amazon/aws/log/test_s3_task_handler.py"] | S3TaskHandler continuously returns "*** Falling back to local log" even if log_pos is provided when log not in s3 | ### Apache Airflow Provider(s)
amazon
### Versions of Apache Airflow Providers
apache-airflow-providers-amazon==7.1.0
### Apache Airflow version
2.5.1
### Operating System
Ubuntu 18.04
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
When looking at logs in the UI for a running task when using remote s3 logging, the logs for the task are only uploaded to s3 after the task has completed. The the `S3TaskHandler` falls back to the local logs stored on the worker in that case (by falling back to the `FileTaskHandler` behavior) and prepends the line `*** Falling back to local log` to those logs.
This is mostly fine, but for the new log streaming behavior, this means that `*** Falling back to local log` is returned from `/get_logs_with_metadata` on each call, even if there are no new logs.
### What you think should happen instead
I'd expect the falling back message only to be included in calls with no `log_pos` in the metadata or with a `log_pos` of `0`.
### How to reproduce
Start a task with `logging.remote_logging` set to `True` and `logging.remote_base_log_folder` set to `s3://something` and watch the logs while the task is running. You'll see `*** Falling back to local log` printed every few seconds.
### Anything else
_No response_
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29393 | https://github.com/apache/airflow/pull/29708 | 13098d5c35cf056c3ef08ea98a1970ee1a3e76f8 | 5e006d743d1ba3781acd8e053642f2367a8e7edc | "2023-02-06T20:33:08Z" | python | "2023-02-23T21:25:39Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,358 | ["airflow/models/baseoperator.py", "airflow/models/dag.py", "airflow/models/param.py"] | Cannot use TypedDict object when defining params | ### Apache Airflow version
2.5.1
### What happened
Context: I am attempting to use [TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict) objects to maintain the keys used in DAG params in a single place, and check for key names across multiple DAGs that use the params.
This raises an error with `mypy` as `params` expects an `Optional[Dict]`. Due to the invariance of `Dict`, this does not accept `TypedDict` objects.
What happened: I passed a `TypedDict` to the `params` arg of `DAG` and got a TypeError.
### What you think should happen instead
`TypedDict` objects should be accepted by `DAG`, which should accept `Optional[Mapping[str, Any]]`.
Unless I'm mistaken, `params` are converted to a `ParamsDict` class and therefore the appropriate type hint is a generic `Mapping` type.
### How to reproduce
Steps to reproduce
```Python
from typing import TypedDict
from airflow import DAG
from airflow.models import Param
class ParamsTypedDict(TypedDict):
str_param: Param
params: ParamsTypedDict = {
"str_param": Param("", type="str")
}
with DAG(
dag_id="mypy-error-dag",
# The line below raises a mypy error
# Argument "params" to "DAG" has incompatible type "ParamsTypedDict"; expected "Optional[Dict[Any, Any]]" [arg-type]
params=params,
) as dag:
pass
```
### Operating System
Amazon Linux
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29358 | https://github.com/apache/airflow/pull/29782 | b6392ae5fd466fa06ca92c061a0f93272e27a26b | b069df9b0a792beca66b08d873a66d5640ddadb7 | "2023-02-03T14:40:04Z" | python | "2023-03-07T21:25:15Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,329 | ["airflow/example_dags/example_setup_teardown.py", "airflow/models/abstractoperator.py", "airflow/models/dag.py", "tests/models/test_dag.py", "tests/models/test_dagrun.py"] | Automatically clear setup/teardown when clearing a dependent task | null | https://github.com/apache/airflow/issues/29329 | https://github.com/apache/airflow/pull/30271 | f4c4b7748655cd11d2c297de38563b2e6b840221 | 0c2778f348f61f3bf08b840676d681e93a60f54a | "2023-02-02T15:44:26Z" | python | "2023-06-21T13:34:18Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,325 | ["airflow/providers/cncf/kubernetes/python_kubernetes_script.py", "airflow/utils/decorators.py", "tests/decorators/test_external_python.py", "tests/decorators/test_python_virtualenv.py", "tests/providers/cncf/kubernetes/decorators/test_kubernetes.py", "tests/providers/docker/decorators/test_docker.py"] | Ensure setup/teardown work on a previously decorated function (eg task.docker) | null | https://github.com/apache/airflow/issues/29325 | https://github.com/apache/airflow/pull/30216 | 3022e2ecbb647bfa0c93fbcd589d0d7431541052 | df49ad179bddcdb098b3eccbf9bb6361cfbafc36 | "2023-02-02T15:43:06Z" | python | "2023-03-24T17:01:34Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,323 | ["airflow/models/serialized_dag.py", "tests/models/test_serialized_dag.py"] | DAG dependencies graph not updating when deleting a DAG | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
ON Airflow 2.4.2 Dag dependencies graph show deleted DAGs that use to have dependencies to currently existing DAGs
### What you think should happen instead
Deleted DAGs should not appear on DAG Dependencies
### How to reproduce
Create a DAG with dependencies on other DAG, like a wait sensor.
Remove new DAG
### Operating System
apache/airflow:2.4.2-python3.10
### Versions of Apache Airflow Providers
_No response_
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29323 | https://github.com/apache/airflow/pull/29407 | 18347d36e67894604436f3ef47d273532683b473 | 02a2efeae409bddcfedafe273fffc353595815cc | "2023-02-02T15:22:37Z" | python | "2023-02-13T19:25:49Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,322 | ["airflow/www/utils.py", "airflow/www/views.py", "tests/www/test_utils.py"] | DAG list, sorting lost when switching page | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Hi, I'm currently on Airflow 2.4.2
In /home when sorting by DAG/Owner/Next Run and going to the next page the sort resets.
This feature only works if I'm looking for last or first, everything in the middle is unreachable.
### What you think should happen instead
The sorting should continue over the pagination
### How to reproduce
Sort by any sortable field on DagList and go to the next page
### Operating System
apache/airflow:2.4.2-python3.10
### Versions of Apache Airflow Providers
_No response_
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29322 | https://github.com/apache/airflow/pull/29756 | c917c9de3db125cac1beb0a58ac81f56830fb9a5 | c8cd90fa92c1597300dbbad4366c2bef49ef6390 | "2023-02-02T15:19:51Z" | python | "2023-03-02T14:59:43Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,320 | ["airflow/api/common/experimental/get_task_instance.py", "airflow/cli/commands/task_command.py", "airflow/models/dagrun.py", "airflow/models/taskinstance.py", "airflow/serialization/pydantic/dag_run.py", "airflow/serialization/pydantic/taskinstance.py", "airflow/utils/log/logging_mixin.py", "airflow/www/views.py"] | AIP-44 Migrate TaskCommand._get_ti to Internal API | https://github.com/apache/airflow/blob/main/airflow/cli/commands/task_command.py#L145 | https://github.com/apache/airflow/issues/29320 | https://github.com/apache/airflow/pull/35312 | ab6e623cb1a75f54fc419cee66a16e3d8ff1adc2 | 1e1adc569f43494aabf3712b651956636c04df7f | "2023-02-02T15:10:45Z" | python | "2023-11-08T15:53:52Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,301 | ["airflow/providers/google/cloud/operators/bigquery.py", "tests/providers/google/cloud/operators/test_bigquery.py"] | BigQueryCreateEmptyTableOperator `exists_ok` parameter doesn't throw appropriate error when set to "False" | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
I'm using `apache-airflow-providers-google==8.2.0`, but it looks like the relevant code that's causing this to occur is still in use as of `8.8.0`.
### Apache Airflow version
2.3.2
### Operating System
Debian (from Docker image `apache/airflow:2.3.2-python3.10`)
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
Deployed on an EKS cluster via Helm.
### What happened
The first task in one of my DAGs is to create an empty BigQuery table using the `BigQueryCreateEmptyTableOperator` as follows:
```python
create_staging_table = BigQueryCreateEmptyTableOperator(
task_id="create_staging_table",
dataset_id="my_dataset",
table_id="tmp_table",
schema_fields=[
{"name": "field_1", "type": "TIMESTAMP", "mode": "NULLABLE"},
{"name": "field_2", "type": "INTEGER", "mode": "NULLABLE"},
{"name": "field_3", "type": "INTEGER", "mode": "NULLABLE"}
],
exists_ok=False
)
```
Note that `exists_ok=False` explicitly here, but it is also the default value.
This task exits with a `SUCCESS` status even when `my_dataset.tmp_table` already exists in a given BigQuery project. The task returns the following logs:
```
[2023-02-02, 05:52:29 UTC] {bigquery.py:875} INFO - Creating table
[2023-02-02, 05:52:29 UTC] {bigquery.py:901} INFO - Table my_dataset.tmp_table already exists.
[2023-02-02, 05:52:30 UTC] {taskinstance.py:1395} INFO - Marking task as SUCCESS. dag_id=my_fake_dag, task_id=create_staging_table, execution_date=20230202T044000, start_date=20230202T055229, end_date=20230202T055230
[2023-02-02, 05:52:30 UTC] {local_task_job.py:156} INFO - Task exited with return code 0
```
### What you think should happen instead
Setting `exists_ok=False` should raise an exception and exit the task with a `FAILED` status if the table being created already exists in BigQuery.
### How to reproduce
1. Deploy Airflow 2.3.2 running Python 3.10 in some capacity
2. Ensure `apache-airflow-providers-google==8.2.0` (or 8.8.0, as I don't believe the issue has been fixed) is installed on the deployment.
3. Set up a GCP project and create a BigQuery dataset.
4. Create an empty BigQuery table with a schema.
5. Create a DAG that uses the `BigQueryCreateEmptyTableOperator` to create a new BigQuery table.
6. Run the DAG from Step 5 on the Airflow instance deployed in Step 1.
7. Observe the task's status.
### Anything else
I believe the silent failure may be occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L1377), as the `except` statement results in a log output, but doesn't actually raise an exception or change a state that would make the task fail.
If this is in fact the case, I'd be happy to submit a PR, but appreciate any input as to any error-handling standards/consistencies that this provider package maintains.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29301 | https://github.com/apache/airflow/pull/29394 | 228d79c1b3e11ecfbff5a27c900f9d49a84ad365 | a5adb87ab4ee537eb37ef31aba755b40f6f29a1e | "2023-02-02T06:30:16Z" | python | "2023-02-26T19:09:08Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,282 | ["airflow/providers/ssh/hooks/ssh.py", "airflow/providers/ssh/operators/ssh.py", "docs/apache-airflow-providers-ssh/connections/ssh.rst", "tests/providers/ssh/hooks/test_ssh.py", "tests/providers/ssh/operators/test_ssh.py"] | Ssh connection extra parameter conn_timeout doesn't work with ssh operator | ### Apache Airflow Provider(s)
ssh
### Versions of Apache Airflow Providers
apache-airflow-providers-ssh>=3.3.0
### Apache Airflow version
2.5.0
### Operating System
debian "11 (bullseye)"
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
I have an SSH operator task where the command can take a long time. In recent SSH provider versions(>=3.3.0) it stopped working, as I suspect it is because of #27184 . After this change looks like the timeout is 10 seconds, and after there is no output provided through SSH for 10 seconds I'm getting the following error:
```
[2023-01-26, 11:49:57 UTC] {taskinstance.py:1772} ERROR - Task failed with exception
Traceback (most recent call last):
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/operators/ssh.py", line 171, in execute
result = self.run_ssh_client_command(ssh_client, self.command, context=context)
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/operators/ssh.py", line 156, in run_ssh_client_command
exit_status, agg_stdout, agg_stderr = self.ssh_hook.exec_ssh_client_command(
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/ssh/hooks/ssh.py", line 521, in exec_ssh_client_command
raise AirflowException("SSH command timed out")
airflow.exceptions.AirflowException: SSH command timed out
```
At first I thought that this is ok, since I can just set `conn_timeout` extra parameter in my ssh connection. But then I noticed that this parameter from the connection is not used anywhere - so this doesn't work, and you have to modify your task code to set the needed value of this parameter in the SSH operator. What's more, even even with modifying task code it's not possible to achieve the previous behavior(when this parameter was not set) since now it'll be set to 10 when you pass None as value.
### What you think should happen instead
I think it should be possible to pass timeout parameter through connection extra field for ssh operator (including None value, meaning no timeout).
### How to reproduce
Add simple DAG with sleeping for more than 10 seconds, for example:
```python
# this DAG only works for SSH provider versions <=3.2.0
from airflow.models import DAG
from airflow.contrib.operators.ssh_operator import SSHOperator
from airflow.utils.dates import days_ago
from airflow.operators.dummy import DummyOperator
args = {
'owner': 'airflow',
'start_date': days_ago(2),
}
dag = DAG(
default_args=args,
dag_id="test_ssh",
max_active_runs=1,
catchup=False,
schedule_interval="@hourly"
)
task0 = SSHOperator(ssh_conn_id='ssh_localhost',
task_id="test_sleep",
command=f'sleep 15s',
dag=dag)
task0
```
Try configuring `ssh_localhost` connection to make the DAG work using extra conn_timeout or extra timeout (or other) parameters.
### Anything else
_No response_
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29282 | https://github.com/apache/airflow/pull/29347 | a21c17bc07c1eeb733eca889a02396fab401b215 | fd000684d05a993ade3fef38b683ef3cdfdfc2b6 | "2023-02-01T08:52:03Z" | python | "2023-02-19T18:51:51Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,267 | ["airflow/example_dags/example_python_decorator.py", "airflow/example_dags/example_python_operator.py", "airflow/example_dags/example_short_circuit_operator.py", "docs/apache-airflow/howto/operator/python.rst", "docs/conf.py", "docs/sphinx_design/static/custom.css", "setup.py", "tests/api_connexion/endpoints/test_task_instance_endpoint.py"] | Support tabs in docs | ### What do you see as an issue?
I suggest supporting tabs in the docs to improve the readability when demonstrating different ways to achieve the same things.
**Motivation**
We have multiple ways to achieve the same thing in Airflow, for example:
- TaskFlow API & "classic" operators
- CLI & REST API & API client
However, our docs currently do not consistently demonstrate different ways to use Airflow. For example, https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html demonstrates TaskFlow operators in some examples and classic operators in other examples. All cases covered can be supported by both the TaskFlow & classic operators.
In the case of https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html, I think a nice solution to demonstrate both approaches would be to use tabs. That way somebody who prefers the TaskFlow API can view all TaskFlow examples, and somebody who prefers the classic operators (we should give those a better name) can view only those examples.
**Possible implementation**
There is a package [sphinx-tabs](https://github.com/executablebooks/sphinx-tabs) for this. For the example above, having https://sphinx-tabs.readthedocs.io/en/latest/#group-tabs would be great because it enables you to view all examples of one "style" with a single click.
### Solving the problem
Install https://github.com/executablebooks/sphinx-tabs with the docs.
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29267 | https://github.com/apache/airflow/pull/36041 | f60d458dc08a5d5fbe5903fffca8f7b03009f49a | 58e264c83fed1ca42486302600288230b944ab06 | "2023-01-31T14:23:42Z" | python | "2023-12-06T08:44:18Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,258 | ["airflow/providers/google/cloud/hooks/compute_ssh.py", "tests/providers/google/cloud/hooks/test_compute_ssh.py", "tests/system/providers/google/cloud/compute/example_compute_ssh.py", "tests/system/providers/google/cloud/compute/example_compute_ssh_os_login.py", "tests/system/providers/google/cloud/compute/example_compute_ssh_parallel.py"] | ComputeEngineSSHHook on parallel runs in Composer gives banner Error reading SSH protocol banner | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
We are using ComputeEngineSSHHook for some of our Airflow DAGS in Cloud Composer
Everything works fine when DAGs run one by one
But when we start parallelism where multiple tasks are trying to connect to our GCE instance using ComputeEngineSSHHook at the same time,
We experience intermittent errors like the one give below
Since cloud composer by default has 3 retries, sometimes in the second or third attempt this issue gets resolved automatically but we would like to understand why this issue comes in the first place when there are multiple operators trying to generate keys and SSH into GCE instance
We have tried maintaining the DAG task with banner_timeout and expire_timeout parameters but we still see this issue
create_transfer_run_directory = SSHOperator(
task_id="create_transfer_run_directory",
ssh_hook=ComputeEngineSSHHook(
instance_name=GCE_INSTANCE,
zone=GCE_ZONE,
use_oslogin=True,
use_iap_tunnel=False,
use_internal_ip=True,
),
conn_timeout = 120,
cmd_timeout = 120,
banner_timeout = 120.0,
command=f"sudo mkdir -p {transfer_run_directory}/"
'{{ ti.xcom_pull(task_ids="load_config", key="transfer_id") }}',
dag=dag,
)
**[2023-01-31, 03:30:39 UTC] {compute_ssh.py:286} INFO - Importing SSH public key using OSLogin: user=edw-sa-gcc@pso-e2e-sql.iam.gserviceaccount.com
[2023-01-31, 03:30:39 UTC] {compute_ssh.py:236} INFO - Opening remote connection to host: username=sa_115585236623848451866, hostname=10.128.0.29
[2023-01-31, 03:30:41 UTC] {transport.py:1874} ERROR - Exception (client): Error reading SSH protocol banner
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - Traceback (most recent call last):
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2271, in _check_banner
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - buf = self.packetizer.readline(timeout)
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/packet.py", line 380, in readline
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - buf += self._read_timeout(timeout)
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/packet.py", line 609, in _read_timeout
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - raise EOFError()
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - EOFError
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR -
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - During handling of the above exception, another exception occurred:
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR -
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - Traceback (most recent call last):
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2094, in run
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - self._check_banner()
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 2275, in _check_banner
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - raise SSHException(
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR - paramiko.ssh_exception.SSHException: Error reading SSH protocol banner
[2023-01-31, 03:30:41 UTC] {transport.py:1872} ERROR -
[2023-01-31, 03:30:41 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 0s to retry
[2023-01-31, 03:30:43 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1)
[2023-01-31, 03:30:43 UTC] {transport.py:1874} INFO - Authentication (publickey) failed.
[2023-01-31, 03:30:43 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 1s to retry
[2023-01-31, 03:30:47 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1)
[2023-01-31, 03:30:50 UTC] {transport.py:1874} INFO - Authentication (publickey) failed.
[2023-01-31, 03:30:50 UTC] {compute_ssh.py:258} INFO - Failed to connect. Waiting 6s to retry
[2023-01-31, 03:30:58 UTC] {transport.py:1874} INFO - Connected (version 2.0, client OpenSSH_8.9p1)
[2023-01-31, 03:30:58 UTC] {transport.py:1874} INFO - Authentication (publickey) failed.
[2023-01-31, 03:30:58 UTC] {taskinstance.py:1904} ERROR - Task failed with exception
Traceback (most recent call last):
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/ssh/operators/ssh.py", line 157, in execute
with self.get_ssh_client() as ssh_client:
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/ssh/operators/ssh.py", line 124, in get_ssh_client
return self.get_hook().get_conn()
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 232, in get_conn
sshclient = self._connect_to_instance(user, hostname, privkey, proxy_command)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 245, in _connect_to_instance
client.connect(
File "/opt/python3.8/lib/python3.8/site-packages/airflow/providers/google/cloud/hooks/compute_ssh.py", line 50, in connect
return super().connect(*args, **kwargs)
File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 450, in connect
self._auth(
File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 781, in _auth
raise saved_exception
File "/opt/python3.8/lib/python3.8/site-packages/paramiko/client.py", line 681, in _auth
self._transport.auth_publickey(username, pkey)
File "/opt/python3.8/lib/python3.8/site-packages/paramiko/transport.py", line 1635, in auth_publickey
return self.auth_handler.wait_for_response(my_event)
File "/opt/python3.8/lib/python3.8/site-packages/paramiko/auth_handler.py", line 259, in wait_for_response
raise e
paramiko.ssh_exception.AuthenticationException: Authentication failed.
[2023-01-31, 03:30:58 UTC] {taskinstance.py:1408} INFO - Marking task as UP_FOR_RETRY. dag_id=run_data_transfer_configs_dag, task_id=create_transfer_run_directory, execution_date=20230131T033002, start_date=20230131T033035, end_date=20230131T033058
[2023-01-31, 03:30:58 UTC] {standard_task_runner.py:92} ERROR - Failed to execute job 1418 for task create_transfer_run_directory (Authentication failed.; 21885)**
### What you think should happen instead
The SSH Hook operator should be able to seamlessly SSH into the GCE instance without any intermittent authentication issues
### How to reproduce
_No response_
### Operating System
Composer Kubernetes Cluster
### Versions of Apache Airflow Providers
Composer Version - 2.1.3
Airflow version - 2.3.4
### Deployment
Composer
### Deployment details
Kubernetes Cluster
GCE Compute Engine VM (Ubuntu)
### Anything else
Very random and intermittent
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29258 | https://github.com/apache/airflow/pull/32365 | df74553ec484ad729fcd75ccbc1f5f18e7f34dc8 | 0c894dbb24ad9ad90dcb10c81269ccc056789dc3 | "2023-01-31T03:43:49Z" | python | "2023-08-02T09:16:03Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,250 | ["airflow/providers/databricks/hooks/databricks.py", "tests/providers/databricks/hooks/test_databricks.py"] | Repair functionality in DatabricksRunNowOperator | ### Description
The Databricks jobs 2.1 API has the ability to repair failed or skipped tasks in a Databricks workflow without having to rerun successful tasks for a given workflow run. It would be nice to be able to leverage this functionality via airflow operators.
### Use case/motivation
The primary motivation is the ability to be more efficient and only have to rerun failed or skipped tasks rather than the entire workflow if only 1 out of 10 tasks fail.
**Repair run API:**
https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsRepairfail
@alexott for visability
### Related issues
_No response_
### Are you willing to submit a PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29250 | https://github.com/apache/airflow/pull/30786 | 424fc17d49afd4175826a62aa4fe7aa7c5772143 | 9bebf85e24e352f9194da2f98e2bc66a5e6b972e | "2023-01-30T21:24:49Z" | python | "2023-04-22T21:21:14Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,227 | ["airflow/www/views.py", "tests/www/views/test_views_tasks.py"] | Calendar page doesn't load when using a timedelta DAG schedule | ### Apache Airflow version
2.5.1
### What happened
/calendar page give a problem, here is the capture
### What you think should happen instead
_No response_
### How to reproduce
_No response_
### Operating System
Ubuntu 22.04.1 LTS
### Versions of Apache Airflow Providers
Distributor ID: Ubuntu
Description: Ubuntu 22.04.1 LTS
Release: 22.04
Codename: jammy
### Deployment
Other
### Deployment details
Distributor ID: Ubuntu
Description: Ubuntu 22.04.1 LTS
Release: 22.04
Codename: jammy
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29227 | https://github.com/apache/airflow/pull/29454 | 28126c12fbdd2cac84e0fbcf2212154085aa5ed9 | f837c0105c85d777ea18c88a9578eeeeac5f57db | "2023-01-30T01:32:44Z" | python | "2023-02-14T17:06:09Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,209 | ["airflow/providers/google/cloud/operators/bigquery_dts.py", "tests/providers/google/cloud/operators/test_bigquery_dts.py"] | BigQueryCreateDataTransferOperator will log AWS credentials when transferring from S3 | ### Apache Airflow Provider(s)
google
### Versions of Apache Airflow Providers
[apache-airflow-providers-google 8.6.0](https://airflow.apache.org/docs/apache-airflow-providers-google/8.6.0/)
### Apache Airflow version
2.5.0
### Operating System
Debian GNU/Linux 11 (bullseye)
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
When creating a transfer config that will move data from AWS S3, an access_key_id and secret_access_key are provided (see: https://cloud.google.com/bigquery/docs/s3-transfer).
These parameters are logged and exposed as XCom return_value.
### What you think should happen instead
At least the secret_access_key should be hidden or removed from the XCom return value
### How to reproduce
```
PROJECT_ID=123
TRANSFER_CONFIG={
"destination_dataset_id": destination_dataset,
"display_name": display_name,
"data_source_id": "amazon_s3",
"schedule_options": {"disable_auto_scheduling": True},
"params": {
"destination_table_name_template": destination_table,
"file_format": "PARQUET",
"data_path": data_path,
"access_key_id": access_key_id,
"secret_access_key": secret_access_key
}
},
gcp_bigquery_create_transfer = BigQueryCreateDataTransferOperator(
transfer_config=TRANSFER_CONFIG,
project_id=PROJECT_ID,
task_id="gcp_bigquery_create_transfer",
)
```
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29209 | https://github.com/apache/airflow/pull/29348 | 3dbcf99d20d47cde0debdd5faf9bd9b2ebde1718 | f51742d20b2e53bcd90a19db21e4e12d2a287677 | "2023-01-28T19:58:00Z" | python | "2023-02-20T23:06:50Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,199 | ["airflow/models/xcom_arg.py", "tests/decorators/test_python.py"] | TaskFlow AirflowSkipException causes downstream step to fail when multiple_outputs is true | ### Apache Airflow version
2.5.1
### What happened
Most of our code is based on TaskFlow API and we have many tasks that raise AirflowSkipException (or BranchPythonOperator) on purpose to skip the next downstream task (with trigger_rule = none_failed_min_one_success).
And these tasks are expecting a multiple output XCom result (local_file_path, file sizes, records count) from previous tasks and it's causing this error:
`airflow.exceptions.XComNotFound: XComArg result from copy_from_data_lake_to_local_file at outbound_dag_AIR2070 with key="local_file_path" is not found!`
### What you think should happen instead
Considering trigger rule "none_failed_min_one_success", we expect that upstream task should be allowed to skip and downstream tasks will still run without raising any errors caused by not found XCom results.
### How to reproduce
This is an aproximate example dag based on an existing one.
```python
from os import path
import pendulum
from airflow import DAG
from airflow.decorators import task
from airflow.operators.python import BranchPythonOperator
DAG_ID = "testing_dag_AIR"
# PGP_OPERATION = None
PGP_OPERATION = "decrypt"
LOCAL_FILE_PATH = "/temp/example/example.csv"
with DAG(
dag_id=DAG_ID,
schedule='0 7-18 * * *',
start_date=pendulum.datetime(2022, 12, 15, 7, 0, 0),
) as dag:
@task(multiple_outputs=True, trigger_rule='none_failed_min_one_success')
def copy_from_local_file_to_data_lake(local_file_path: str, dest_dir_path: str):
destination_file_path = path.join(dest_dir_path, path.basename(local_file_path))
return {
"destination_file_path": destination_file_path,
"file_size": 100
}
@task(multiple_outputs=True, trigger_rule='none_failed_min_one_success')
def copy_from_data_lake_to_local_file(data_lake_file_path, local_dir_path):
local_file_path = path.join(local_dir_path, path.basename(data_lake_file_path))
return {
"local_file_path": local_file_path,
"file_size": 100
}
@task(multiple_outputs=True, task_id='get_pgp_file_info', trigger_rule='none_failed_min_one_success')
def get_pgp_file_info(file_path, operation):
import uuid
import os
src_file_name = os.path.basename(file_path)
src_file_dir = os.path.dirname(file_path)
run_id = str(uuid.uuid4())
if operation == "decrypt":
wait_pattern = f'*{src_file_name}'
else:
wait_pattern = f'*{src_file_name}.pgp'
target_path = 'datalake/target'
return {
'src_file_path': file_path,
'src_file_dir': src_file_dir,
'target_path': target_path,
'pattern': wait_pattern,
'guid': run_id
}
@task(multiple_outputs=True, task_id='return_src_path', trigger_rule='none_failed_min_one_success')
def return_src_path(src_file_path):
return {
'file_path': src_file_path,
'file_size': 100
}
@task(multiple_outputs=True, task_id='choose_result', trigger_rule='none_failed_min_one_success')
def choose_result(src_file_path, src_file_size, decrypt_file_path, decrypt_file_size):
import os
file_path = decrypt_file_path or src_file_path
file_size = decrypt_file_size or src_file_size
local_dir = os.path.dirname(file_path)
return {
'local_dir': local_dir,
'file_path': file_path,
'file_size': file_size,
'file_name': os.path.basename(file_path)
}
def switch_branch_func(pgp_operation):
if pgp_operation in ["decrypt", "encrypt"]:
return 'get_pgp_file_info'
else:
return 'return_src_path'
operation = PGP_OPERATION
local_file_path = LOCAL_FILE_PATH
check_need_to_decrypt = BranchPythonOperator(
task_id='branch_task',
python_callable=switch_branch_func,
op_args=(operation,))
pgp_file_info = get_pgp_file_info(local_file_path, operation)
data_lake_file = copy_from_local_file_to_data_lake(pgp_file_info['src_file_path'], pgp_file_info['target_path'])
decrypt_local_file = copy_from_data_lake_to_local_file(
data_lake_file['destination_file_path'], pgp_file_info['src_file_dir'])
src_result = return_src_path(local_file_path)
result = choose_result(src_result['file_path'], src_result['file_size'],
decrypt_local_file['local_file_path'], decrypt_local_file['file_size'])
check_need_to_decrypt >> [pgp_file_info, src_result]
pgp_file_info >> decrypt_local_file
[decrypt_local_file, src_result] >> result
```
### Operating System
Windows 10
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
docker-compose version: 3.7
Note: This also happens when it's deployed to one of our testing environments using official Airflow Helm Chart.
### Anything else
This issue is similar to [#24338](https://github.com/apache/airflow/issues/24338), it was solved by [#25661](https://github.com/apache/airflow/pull/25661) but this case is related to multiple_outputs being set to True.
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29199 | https://github.com/apache/airflow/pull/32027 | 14eb1d3116ecef15be7be9a8f9d08757e74f981c | 79eac7687cf7c6bcaa4df2b8735efaad79a7fee2 | "2023-01-27T18:27:43Z" | python | "2023-06-21T09:55:57Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,198 | ["airflow/providers/snowflake/operators/snowflake.py"] | SnowflakeCheckOperator - The conn_id `None` isn't defined | ### Apache Airflow Provider(s)
snowflake
### Versions of Apache Airflow Providers
`apache-airflow-providers-snowflake==4.0.2`
### Apache Airflow version
2.5.1
### Operating System
Debian GNU/Linux 11 (bullseye)
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
After upgrading the _apache-airflow-providers-snowflake_ from version **3.3.0** to **4.0.2**, the SnowflakeCheckOperator tasks starts to throw the following error:
```
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 179, in get_db_hook
return self._hook
File "/usr/local/lib/python3.9/functools.py", line 993, in __get__
val = self.func(instance)
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/common/sql/operators/sql.py", line 141, in _hook
conn = BaseHook.get_connection(self.conn_id)
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/hooks/base.py", line 72, in get_connection
conn = Connection.get_connection_from_secrets(conn_id)
File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/connection.py", line 435, in get_connection_from_secrets
raise AirflowNotFoundException(f"The conn_id `{conn_id}` isn't defined")
airflow.exceptions.AirflowNotFoundException: The conn_id `None` isn't defined
```
### What you think should happen instead
_No response_
### How to reproduce
- Define a _Snowflake_ Connection with the name **snowflake_default**
- Create a Task similar to this:
```
my_task = SnowflakeCheckOperator(
task_id='my_task',
warehouse='warehouse',
database='database',
schema='schema',
role='role',
sql='select 1 from my_table'
)
```
- Run and check the error.
### Anything else
We can workaround this by adding the conn_id='snowflake_default' to the SnowflakeCheckOperator.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29198 | https://github.com/apache/airflow/pull/29211 | a72e28d6e1bc6ae3185b8b3971ac9de5724006e6 | 9b073119d401594b3575c6f7dc4a14520d8ed1d3 | "2023-01-27T18:24:51Z" | python | "2023-01-29T08:54:39Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,197 | ["airflow/www/templates/airflow/dag.html"] | Trigger DAG w/config raising error from task detail views | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Version: 2.4.3 (migrated from 2.2.4)
Manual UI option "Trigger DAG w/config" raises an error _400 Bad Request - Invalid datetime: None_ from views "Task Instance Details", "Rendered Template", "Log" and "XCom" . Note that DAG is actually triggered , but still error response 400 is raised.
### What you think should happen instead
No 400 error
### How to reproduce
1. Go to any DAG graph view
2. Select a Task > go to "Instance Details"
3. Select "Trigger DAG w/config"
4. Select Trigger
5. See error
### Operating System
PRETTY_NAME="Debian GNU/Linux 11 (bullseye)"
### Versions of Apache Airflow Providers
_No response_
### Deployment
Other 3rd-party Helm chart
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29197 | https://github.com/apache/airflow/pull/29212 | 9b073119d401594b3575c6f7dc4a14520d8ed1d3 | 7315d6f38caa58e6b19054f3e8a20ed02df16a29 | "2023-01-27T18:07:01Z" | python | "2023-01-29T08:56:35Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,178 | ["airflow/api/client/local_client.py", "airflow/cli/cli_parser.py", "airflow/cli/commands/dag_command.py", "tests/api/client/test_local_client.py", "tests/cli/commands/test_dag_command.py"] | Add `output` format to missing cli commands | ### Description
I have noticed that for some commands, there is an option to get the output in json or yaml (as described in this PR from 2020 https://github.com/apache/airflow/issues/12699).
However, there are still some commands that do not support the `--output` argument, most notable one is the `dags trigger`.
When triggering a dag, it is crucial to get the run_id that has been triggered, so the triggered dag run can be monitored by the calling party. However, the output from this command is hard to parse without resorting to (gasp!) regex:
```
[2023-01-26 11:03:41,038] {{__init__.py:42}} INFO - Loaded API auth backend: airflow.api.auth.backend.session
Created <DagRun sample_dag @ 2023-01-26T11:03:41+00:00: manual__2023-01-26T11:03:41+00:00, state:queued, queued_at: 2023-01-26 11:03:41.412394+00:00. externally triggered: True>
```
As you can see, extracting the run_id `manual__2023-01-26T11:03:41+00:00` is not easy from the above output.
For what I see [in the code](https://github.com/apache/airflow/blob/main/airflow/cli/cli_parser.py#L1156), the `ARG_OUTPUT` is not added to `dag_trigger` command.
### Use case/motivation
At my company we want to be able to trigger dags from another airflow environment (mwaa) and be able to wait for its completion before proceeding with the calling DAG.
### Related issues
_No response_
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29178 | https://github.com/apache/airflow/pull/29224 | ffdc696942d96a14a5ee0279f950e3114817055c | 60fc40791121b19fe379e4216529b2138162b443 | "2023-01-26T11:05:20Z" | python | "2023-02-19T15:15:56Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,177 | ["airflow/providers/apache/livy/hooks/livy.py", "airflow/providers/http/hooks/http.py", "airflow/providers/http/operators/http.py", "tests/providers/http/hooks/test_http.py"] | SimpleHttpOperator not working with loginless auth_type | ### Apache Airflow Provider(s)
http
### Versions of Apache Airflow Providers
apache-airflow-providers-http==4.1.1
### Apache Airflow version
2.5.0
### Operating System
Ubuntu 20.04.5 LTS (Focal Fossa)"
### Deployment
Virtualenv installation
### Deployment details
Reproduced on a local deployment inside WSL on virtualenv - not related to specific deployment.
### What happened
SimpleHttpOperator supports passing in auth_type. Hovewer, [this auth_type is only initialized if login is provided](https://github.com/astronomer/airflow-provider-sample/blob/main/sample_provider/hooks/sample_hook.py#L64-L65).
In our setup we are using the Kerberos authentication. This authentication relies on kerberos sidecar with keytab, and not on user-password pair in the connection string. However, this would also be issue with any other implementation not relying on username passed in the connection string.
We were trying to use some other auth providers from (`HTTPSPNEGOAuth` from [requests_gssapi](https://pypi.org/project/requests-gssapi/) and `HTTPKerberosAuth` from [requests_kerberos](https://pypi.org/project/requests-kerberos/)). We noticed that requests_kerberos is used in Airflow in some other places for Kerberos support, hence we have settled on the latter.
### What you think should happen instead
A suggestion is to initialize the passed `auth_type` also if no login is present.
### How to reproduce
A branch demonstrating possible fix:
https://github.com/apache/airflow/commit/7d341f081f0160ed102c06b9719582cb463b538c
### Anything else
The linked branch is a quick-and-dirty solution, but maybe the code could be refactored in another way? Support for **kwargs could also be useful, but I wanted to make as minimal changes as possible.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29177 | https://github.com/apache/airflow/pull/29206 | 013490edc1046808c651c600db8f0436b40f7423 | c44c7e1b481b7c1a0d475265835a23b0f507506c | "2023-01-26T08:28:39Z" | python | "2023-03-20T13:52:02Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,175 | ["airflow/providers/redis/provider.yaml", "docs/apache-airflow-providers-redis/index.rst", "generated/provider_dependencies.json", "tests/system/providers/redis/__init__.py", "tests/system/providers/redis/example_redis_publish.py"] | Support for Redis Time series in Airflow common packages | ### Description
The current Redis API version is quite old. I need to implement a DAG for Timeseries data feature. Please upgrade to version that supports this. BTW, I was able to manually update my redis worker and it now works. Can this be added to next release please.
### Use case/motivation
Timeseries in Redis is a growing area needing support in Airflow
### Related issues
_No response_
### Are you willing to submit a PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29175 | https://github.com/apache/airflow/pull/31279 | df3569cf489ce8ef26f5b4d9d9c3826d3daad5f2 | 94cad11b439e0ab102268e9e7221b0ab9d98e0df | "2023-01-26T03:42:51Z" | python | "2023-05-16T13:11:18Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,150 | ["docs/apache-airflow/howto/docker-compose/index.rst"] | trigger process missing from Airflow docker docs | ### What do you see as an issue?
The section [`Fetching docker-compose.yaml`](https://github.com/apache/airflow/blob/main/docs/apache-airflow/howto/docker-compose/index.rst#fetching-docker-composeyaml) claims to talk about all the process definitions that the Dockerfile compose contain but missed to talk the `airflow-trigger` process.
### Solving the problem
We need to include the `airflow-trigger` in the process list that the docker-compose file contains.
### Anything else
None
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29150 | https://github.com/apache/airflow/pull/29203 | f8c1410a0b0e62a1c4b67389d9cfb80cc024058d | 272f358fd6327468fcb04049ef675a5cf939b93e | "2023-01-25T08:50:27Z" | python | "2023-01-30T09:52:43Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,137 | ["airflow/decorators/sensor.py"] | Fix access to context in functions decorated by task.sensor | ### Description
Hello, I am a new Airflow user. I am requesting a feature in which the airflow context (containing task instance, etc.) be available inside of functions decorated by `airflow.decorators.task.sensor`.
### Use case/motivation
I have noticed that when using the `airflow.decorators.task` decorator, one can access items from the context (such as the task instance) by using `**kwargs` or keyword arguments in the decorated function. But I have discovered that the same is not true for the `airflow.decorators.task.sensor` decorator. I'm not sure if this is a bug or intentional, but it would be very useful to be able to access the context normally from functions decorated by `task.sensor`.
I believe this may have been an oversight. The `DecoratedSensorOperator` class is a child class of `PythonSensor`:
https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/decorators/sensor.py#L28
This `DecoratedSensorOperator` class overrides `poke`, but does not incorporate the passed in `Context` object before calling the decorated function:
https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/decorators/sensor.py#L60-L61
This is in contrast to the `PythonSensor`, whose `poke` method merges the context with the existing `op_kwargs`:
https://github.com/apache/airflow/blob/1fbfd312d9d7e28e66f6ba5274421a96560fb7ba/airflow/sensors/python.py#L68-L77
This seems like an easy fix, and I'd be happy to submit a pull request. But I figured I'd start with a feature request since I'm new to the open source community.
### Related issues
_No response_
### Are you willing to submit a PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29137 | https://github.com/apache/airflow/pull/29146 | 0a4184e34c1d83ad25c61adc23b838e994fc43f1 | 2d3cc504db8cde6188c1503675a698c74404cf58 | "2023-01-24T20:19:59Z" | python | "2023-02-20T00:20:08Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,128 | ["docs/apache-airflow-providers-ftp/index.rst"] | [Doc] Link to examples how to use FTP provider is incorrect | ### What do you see as an issue?
HI.
I tried to use FTP provider (https://airflow.apache.org/docs/apache-airflow-providers-ftp/stable/connections/ftp.html#howto-connection-ftp) but link to the "Example DAGs" is incorrect and Github response is 404.
### Solving the problem
Please update links to Example DAGs - here and check it in other providers.
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29128 | https://github.com/apache/airflow/pull/29134 | 33ba242d7eb8661bf936a9b99a8cad4a74b29827 | 1fbfd312d9d7e28e66f6ba5274421a96560fb7ba | "2023-01-24T12:07:45Z" | python | "2023-01-24T19:24:26Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,125 | ["airflow/models/dag.py", "airflow/models/dagrun.py", "tests/models/test_dag.py", "tests/models/test_dagrun.py"] | Ensure teardown failure with on_failure_fail_dagrun=True fails the DagRun, and not otherwise | null | https://github.com/apache/airflow/issues/29125 | https://github.com/apache/airflow/pull/30398 | fc4166127a1d2099d358fee1ea10662838cf9cf3 | db359ee2375dd7208583aee09b9eae00f1eed1f1 | "2023-01-24T11:08:45Z" | python | "2023-05-08T10:58:30Z" |
closed | apache/airflow | https://github.com/apache/airflow | 29,113 | ["docs/apache-airflow-providers-sqlite/operators.rst"] | sqlite conn id unclear | ### What do you see as an issue?
The sqlite conn doc here https://airflow.apache.org/docs/apache-airflow-providers-sqlite/stable/operators.html is unclear.
Sqlite does not use username, password, port, schema. These need to be removed from the docs. Furthermore, it is unclear how to construct a conn string for sqlite, since the docs for constructing a conn string here https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html assume that all these fields are given.
### Solving the problem
Remove unused arguments for sqlite in connection, and make it clearer how to construct a connection to sqlite
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| https://github.com/apache/airflow/issues/29113 | https://github.com/apache/airflow/pull/29139 | d23033cff8a25e5f71d01cb513c8ec1d21bbf491 | ec7674f111177c41c02e5269ad336253ed9c28b4 | "2023-01-23T17:44:59Z" | python | "2023-05-01T20:34:12Z" |