jroachgolf84 opened a new pull request, #51556:
URL: https://github.com/apache/airflow/pull/51556
#51109 outlined an issue where trying to dynamically map over an XCom return
value using a custom key. Rather than leveraging the `list` that was stored at
that key, it would dynamically-map TaskGroups using the key-value pairs in the
return value itself.
This PR addresses this by applying the same logic present in the
`_TaskDecorator` class is applied to `_TaskGroupFactory`. When attempting to
map , the following exception will be raised:
```
ValueError: cannot map over XCom with custom key '<key>' from
<Task(_PythonDecoratedOperator): t>`
```
Rather than the faulty logic outlined in #51109, the DAG will fail to parse.
This matches the same behavior as `@task`. This logic was implemented for both
`.expand()` and `.expand_kwargs()`.
To test this, the following DAG was written. The result is now a DAG Import
Error, with the stack trace below.
``` {python}
import pendulum
from airflow.decorators import dag, task, task_group
@dag(schedule=None, start_date=pendulum.datetime(2022, 1, 1))
def pipeline():
@task
def t():
return {"values": ["value_1", "value_2"]}
@task_group()
def tg(a, b):
pass
tg.partial(a=1).expand(b=t()["values"])
```
```
Traceback (most recent call last):
File
"/opt/airflow/task-sdk/src/airflow/sdk/definitions/decorators/task_group.py",
line 175, in expand_kwargs
ensure_xcomarg_return_value(expand_input.value)
File
"/opt/airflow/task-sdk/src/airflow/sdk/definitions/mappedoperator.py", line
127, in ensure_xcomarg_return_value
raise ValueError(f"cannot map over XCom with custom key {key!r} from
{operator}")
ValueError: cannot map over XCom with custom key 'targets' from
<Task(_PythonDecoratedOperator): determine_run_params>
```
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