Lee-W commented on issue #34206:
URL: https://github.com/apache/airflow/issues/34206#issuecomment-2342600710
how about using it with dynamic task mapping?
```python
from __future__ import annotations
from pendulum import datetime
from airflow.datasets import Dataset, DatasetAlias
from airflow.datasets.metadata import Metadata
from airflow.decorators import dag, task
my_alias_name = "alias-dataset"
@dag(
dag_display_name="TEST Alias upstream",
start_date=datetime(2024, 8, 1),
schedule=None,
catchup=False,
tags=["Test"],
)
def dataset_alias_dynamic_test():
@task
def upstream_task():
return ["a", "b"]
@task(outlets=[DatasetAlias(my_alias_name)])
def use_metadata(name):
yield Metadata(
Dataset(name),
alias=my_alias_name,
extra={}, # extra is NOT optional
)
use_metadata.expand(name=upstream_task())
dataset_alias_dynamic_test()
@dag(start_date=datetime(2024, 8, 1),
schedule=[DatasetAlias(my_alias_name)], catchup=False, tags=["Test"])
def downstream_alias():
@task
def t1():
return 0
t1()
downstream_alias()
```
change `upstream_task` to the task that returns successful tables
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