covaliov opened a new issue #19869:
URL: https://github.com/apache/airflow/issues/19869
### Apache Airflow version
2.2.2 (latest released)
### Operating System
Darwin Kernel Version 21.1.0 RELEASE_ARM64_T8101 arm64
### Versions of Apache Airflow Providers
_No response_
### Deployment
Virtualenv installation
### Deployment details
python_version | 3.9.7 (default, Sep 16 2021, 23:53:23) [Clang 12.0.0 ]
### What happened
The following error is displayed in Web UI:
```
Broken DAG: [<EDITED>/scripts/airflow/dags/master/sample_dag/sample_dag.py]
Traceback (most recent call last):
File
"<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py",
line 271, in serialize_to_json
serialized_object[key] = _encode_timetable(value)
File
"<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py",
line 152, in _encode_timetable
raise _TimetableNotRegistered(importable_string)
airflow.serialization.serialized_objects._TimetableNotRegistered: Timetable
class 'workday.AfterWorkdayTimetable' is not registered
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File
"<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py",
line 937, in to_dict
json_dict = {"__version": cls.SERIALIZER_VERSION, "dag":
cls.serialize_dag(var)}
File
"<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py",
line 849, in serialize_dag
raise SerializationError(f'Failed to serialize DAG {dag.dag_id!r}: {e}')
airflow.exceptions.SerializationError: Failed to serialize DAG
'learning_example_workday_timetable': Timetable class
'workday.AfterWorkdayTimetable' is not registered
```
### What you expected to happen
For the custom timetable to be implemented and used by DAG.
### How to reproduce
Following instructions from [Custom DAG Scheduling with
Timetables](https://airflow.apache.org/docs/apache-airflow/stable/howto/timetable.html)
with following new DAG to implement:
```python
import datetime
from airflow import DAG
from airflow import plugins_manager
from airflow.operators.dummy import DummyOperator
from workday import AfterWorkdayTimetable
with DAG(
dag_id="learning_example_workday_timetable",
start_date=datetime.datetime(2021,11,20),
timetable=AfterWorkdayTimetable(),
tags=["example","learning","timetable"],
) as dag:
DummyOperator(task_id="run_this")
```
### Anything else
I have tried digging through the code and believe the issue is in this line:
https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L149
Perhaps the [Custom DAG Scheduling with
Timetables](https://airflow.apache.org/docs/apache-airflow/stable/howto/timetable.html)
expects an `__eq__` implemented in the `AfterWorkdayTimetable` class but it
would appear that the `AfterWorkdayTimetable` class imported through the DAG
and the `AfterWorkdayTimetable` class imported through `plugin_manager` have
different `id()`'s:
https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L129
The only way I could get it to import successfully was via the following
sequence of import statements since
[_get_registered_timetable](https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L124)
uses a lazy import:
```python
import datetime
from airflow import DAG
from airflow import plugins_manager
from airflow.operators.dummy import DummyOperator
plugins_manager.initialize_timetables_plugins()
from workday import AfterWorkdayTimetable
```
I also had the webserver and scheduler restarted and confirmed the plugin is
seen via cli:
```bash
airflow plugins
✔ dev-airflow
19:01:43
name | source
=========================+===========================
workday_timetable_plugin | $PLUGINS_FOLDER/workday.py
```
### 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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