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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2021/11/22 12:41:47 UTC
[GitHub] [airflow] cstenkamp opened a new issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
cstenkamp opened a new issue #19743:
URL: https://github.com/apache/airflow/issues/19743
### Apache Airflow version
2.1.4
### Operating System
Ubuntu 20.04.3 (airflow in docker-compose)
### Versions of Apache Airflow Providers
_No response_
### Deployment
Docker-Compose
### Deployment details
Docker-Compose stack based on Docker-Image `apache/airflow:2.1.4`
### What happened
When running a DAG using the DebugExecutor (see below), `catchup=False` is ignored and airflow starts backfilling the task.
### What you expected to happen
With `catchup=False`, Airflow shouldn't schedule all previous tasks but only the newest one, regardless of the executor
### How to reproduce
```
from datetime import datetime
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
default_args = {
"start_date": datetime(2021, 11, 10),
"catchup": False,
}
dag_args = dict(
dag_id="accuracy_checker",
schedule_interval=None,
default_args=default_args,
catchup=False,
max_active_runs=1,
)
with DAG(**dag_args) as dag:
start_pipeline = DummyOperator(
task_id="start_pipeline",
dag=dag,
)
finish_pipeline = DummyOperator(
task_id="finish_pipeline",
dag=dag,
)
start_pipeline >> finish_pipeline
if __name__ == "__main__":
from airflow.utils.state import State
dag.clear(dag_run_state=State.NONE)
dag.run()
```
### Anything else
-
### 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)
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[GitHub] [airflow] ashb commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ashb commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980050943
@CSammy if you aren't using that executor (it's unlikely unless you or someone else explicitly configured it) then your issue is separate to this one.
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[GitHub] [airflow] github-actions[bot] commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-1006176252
This issue has been closed because it has not received response from the issue author.
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[GitHub] [airflow] cstenkamp commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
cstenkamp commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980076768
@ashb But for me the issue still persists.
I am only using the DebugExecutor when I explicitly want to debug my code. In my `airflow.cfg` I specified the CeleryExecutor, and only when I want to be able to explicitly set Breakpoints into my code in the dag I overwrite the respective environment variable and execute the python-file directly - thus I only use the DebugExecutor when I invoke this dag using `dag.run()`.
As a workaround for now I also change the start_date to something like `datetime(2021, 11, 10) if os.getenv("AIRFLOW__CORE__EXECUTOR") !== "DebugExecutor" else datetime.now()-timedelta(minutes=32)`, however I don't think that's the best way to solve this problem
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[GitHub] [airflow] CSammy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-979984290
I can confirm as well, using the official Helm chart with Docker tag `2.2.2-python3.9`.
When setting a DAG to
```python
schedule_interval="0 0 * * 6",
start_date=datetime.datetime(2021, 11, 1),
catchup=False,
```
and running it manually for the first time, it starts runs for 2021-11-13 and 2021-11-20.
I would've expected it to start a single run for today.
One possible workaround for the moment is to use
```python
start_date=airflow.utils.dates.days_ago(1),
```
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[GitHub] [airflow] potiuk edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
potiuk edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980044945
Or maybe add a warning in the UI as we do with "Sequential Executor".
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[GitHub] [airflow] CSammy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980078753
Executing `airflow info` on the `scheduler` pod throws the following error:
`airflow command error: argument subcommand: invalid choice: 'info'`
I'm using the standard Helm chart in version 1.3.0, Docker tag pinned to `2.2.2-python3.9`. Configuration is adjusted only in minor details. Below is the complete DAG I'm using. It results in the following output in the UI (screenshot is including the button I'm using to run the DAG).
DAG file:
```python
import datetime
import os
from airflow import DAG
from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
default_args = {
"namespace": os.environ["K8S_NAMESPACE"],
# K8s service account linked to the GCP service account
"service_account_name": "airflow2-dag-default",
"image_pull_policy": "Always",
"get_logs": True,
# -- DAG-SPECIFIC
"image": "google/cloud-sdk:slim",
"env_vars": {"GCP_PROJECT": os.environ["GCP_PROJECT"]},
}
with DAG(
dag_id="debug_dag",
# Saturday midnight
schedule_interval="0 0 * * 6",
start_date=datetime.datetime(2021, 11, 1),
catchup=False,
tags=["debug dag for catchup tests"],
default_args=default_args,
) as dag:
gcp_test_task = KubernetesPodOperator(
# Task name in Airflow 2 UI
task_id="gcp-test-task",
# Pod name
name="task-gcp-test-task",
cmds=["sleep", "infinity"],
)
gcp_test_task
```
My impression is that not the DebugExecutor is at fault. I believe the `catchup` parameter is not effective at the moment, for some reason. I'm happy to open a ticket for "`catchup` is always `True` no matter what you specify with the default Executor`" if that matches your guidelines for tickets better here, but I felt like it wouldn't make much sense to open a second ticket for what I perceive to be the same issue just with a different executor.
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[GitHub] [airflow] github-actions[bot] closed issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
github-actions[bot] closed issue #19743:
URL: https://github.com/apache/airflow/issues/19743
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[GitHub] [airflow] ephraimbuddy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ephraimbuddy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-975542040
Can you test with 2.2.2, there are changes in how catchup works since 2.1.4
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[GitHub] [airflow] ashb edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ashb edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980037083
Running with the scheduler with the DebugExecutor anywhere outside of the `airflow dags test` command is not recommended or supported.
Oh I see, you have `dag.run()` which is also a valid way of using this executor, sorry.
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[GitHub] [airflow] CSammy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980172293
I have created a separate ticket (https://github.com/apache/airflow/issues/19844) due your observation, @cstenkamp .
If they end up being caused by the same underlying issue, it's still possible to close it.
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[GitHub] [airflow] CSammy edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980078753
Executing `airflow info` on the `scheduler` pod throws the following error:
`airflow command error: argument subcommand: invalid choice: 'info'`
I'm using the standard Helm chart in version 1.3.0, Docker tag pinned to `2.2.2-python3.9`. Configuration is adjusted only in minor details. Below is the complete DAG I'm using. It results in the following output in the UI (screenshot is including the button I'm using to run the DAG).
DAG file:
```python
import datetime
import os
from airflow import DAG
from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
default_args = {
"namespace": os.environ["K8S_NAMESPACE"],
# K8s service account linked to the GCP service account
"service_account_name": "airflow2-dag-default",
"image_pull_policy": "Always",
"get_logs": True,
# -- DAG-SPECIFIC
"image": "google/cloud-sdk:slim",
}
with DAG(
dag_id="debug_dag",
# Saturday midnight
schedule_interval="0 0 * * 6",
start_date=datetime.datetime(2021, 11, 1),
catchup=False,
tags=["debug dag for catchup tests"],
default_args=default_args,
) as dag:
gcp_test_task = KubernetesPodOperator(
# Task name in Airflow 2 UI
task_id="gcp-test-task",
# Pod name
name="task-gcp-test-task",
cmds=["sleep", "infinity"],
)
gcp_test_task
```
My impression is that not the DebugExecutor is at fault. I believe the `catchup` parameter is not effective at the moment, for some reason. I'm happy to open a ticket for "`catchup` is always `True` no matter what you specify with the default Executor`" if that matches your guidelines for tickets better here, but I felt like it wouldn't make much sense to open a second ticket for what I perceive to be the same issue just with a different executor.
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[GitHub] [airflow] cstenkamp commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
cstenkamp commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980106039
@CSammy for me, only when I run it using the DebugExecutor it tries to backfill. If I schedule it normally (using the UI or using `airflow dags test`), `catchup=True` is not ignored and it only schedules the one latest task
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[GitHub] [airflow] potiuk commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
potiuk commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980047516
The `airflow info` command should tell you what you use.
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[GitHub] [airflow] CSammy edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-979984290
I can confirm as well, using the official Helm chart with Docker tag `2.2.2-python3.9`.
When setting a DAG to
```python
schedule_interval="0 0 * * 6",
start_date=datetime.datetime(2021, 11, 1),
catchup=False,
```
and running it manually for the first time, it starts runs for 2021-11-13 and 2021-11-20.
I would've expected it to start a single run for today.
The DAG uses `KubernetesPodOperator` exclusively.
One possible workaround for the moment is to use
```python
start_date=airflow.utils.dates.days_ago(1),
```
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[GitHub] [airflow] abhi-quinyx commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
abhi-quinyx commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-979905015
I can confirm that similar issue exists in 2.2.2
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[GitHub] [airflow] ashb commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ashb commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980044013
@CSammy then my previous comment stands: using DebugExecutor with a scheduler is not really supported.
@potiuk Should we disallow running `scheduler` with the DebugExecutor do you think?
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[GitHub] [airflow] boring-cyborg[bot] commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
boring-cyborg[bot] commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-975482984
Thanks for opening your first issue here! Be sure to follow the issue template!
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[GitHub] [airflow] CSammy edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980078753
~~Executing `airflow info` on the `scheduler` pod throws the following error:
`airflow command error: argument subcommand: invalid choice: 'info'`~~
My mistake. It says `CeleryExecutor`.
I'm using the standard Helm chart in version 1.3.0, Docker tag pinned to `2.2.2-python3.9`. Configuration is adjusted only in minor details. Below is the complete DAG I'm using. It results in the following output in the UI (screenshot is including the button I'm using to run the DAG).
DAG file:
```python
import datetime
import os
from airflow import DAG
from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
default_args = {
"namespace": os.environ["K8S_NAMESPACE"],
# K8s service account linked to the GCP service account
"service_account_name": "airflow2-dag-default",
"image_pull_policy": "Always",
"get_logs": True,
# -- DAG-SPECIFIC
"image": "google/cloud-sdk:slim",
}
with DAG(
dag_id="debug_dag",
# Saturday midnight
schedule_interval="0 0 * * 6",
start_date=datetime.datetime(2021, 11, 1),
catchup=False,
tags=["debug dag for catchup tests"],
default_args=default_args,
) as dag:
gcp_test_task = KubernetesPodOperator(
# Task name in Airflow 2 UI
task_id="gcp-test-task",
# Pod name
name="task-gcp-test-task",
cmds=["sleep", "infinity"],
)
gcp_test_task
```
My impression is that not the DebugExecutor is at fault. I believe the `catchup` parameter is not effective at the moment, for some reason. I'm happy to open a ticket for "`catchup` is always `True` no matter what you specify with the default Executor`" if that matches your guidelines for tickets better here, but I felt like it wouldn't make much sense to open a second ticket for what I perceive to be the same issue just with a different executor.
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[GitHub] [airflow] CSammy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980042843
To be honest I'm not too familiar with the CLI or code side of Airflow itself. I'm pressing the "run" button of the DAG in the UI.
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[GitHub] [airflow] github-actions[bot] commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-1002819762
This issue has been automatically marked as stale because it has been open for 30 days with no response from the author. It will be closed in next 7 days if no further activity occurs from the issue author.
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[GitHub] [airflow] ashb commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ashb commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980037083
Running with the scheduler with the DebugExecutor anywhere outside of the `airflow dags test` command is not recommended or supported.
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[GitHub] [airflow] ashb commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
ashb commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980038777
@CSammy are you running a scheduler with the DebugExecutor, or using `dag.run()`/airflow dags test`?
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[GitHub] [airflow] CSammy commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980045703
Please be aware that I'm using a completely normal DAG with KubernetesPodOperators. How do I find out if I'm using a DebugExecutor?
I have the impression this issue may not be related to the DebugExecutor.
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[GitHub] [airflow] potiuk commented on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
potiuk commented on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980044945
Or maybe add a warning as we do with "Sequential Executor".
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[GitHub] [airflow] CSammy edited a comment on issue #19743: `catchup=False` doesn't stop airflow from backfilling if executed using the DebugExecutor
Posted by GitBox <gi...@apache.org>.
CSammy edited a comment on issue #19743:
URL: https://github.com/apache/airflow/issues/19743#issuecomment-980172293
I have created a separate ticket (https://github.com/apache/airflow/issues/19844) due to your observation, @cstenkamp .
If they end up being caused by the same underlying issue, it's still possible to close it.
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