kyungjunleeme opened a new pull request, #54434: URL: https://github.com/apache/airflow/pull/54434
## Description Currently, when creating a task using the TaskFlow API (`@task`), the Airflow UI displays the operator name as `@task` (from `custom_operator_name`). While this is technically correct, it may be confusing for users who are new to Airflow and unfamiliar with the TaskFlow API’s internal mapping. For someone encountering Airflow for the first time, seeing `@task` in the UI provides no direct hint that it is backed by a `PythonOperator`. This can create a gap when: - Following documentation or examples that refer to `PythonOperator`. - Trying to understand DAGs that mix `@task` with other operators. - Debugging tasks and comparing behavior between `@task` and explicitly defined `PythonOperator` tasks. ## Proposed Change Change the `custom_operator_name` for the `_PythonDecoratedOperator` so that the UI displays `PythonOperator` instead of `@task`. This makes the underlying operator type explicit in the UI, improving discoverability for new users. It helps them learn that `@task` is syntactic sugar for `PythonOperator`, and provides a consistent experience when referencing official documentation or examples. ## Benefits - **Improved learnability**: New users immediately see that `@task` maps to `PythonOperator`. - **Consistency**: Aligns UI terminology with documentation and code examples. - **Easier debugging**: Users can better correlate UI information with operator behavior and parameters. I tested. <img width="3838" height="870" alt="image" src="https://github.com/user-attachments/assets/25f7dcee-b441-4c6e-8852-fdacecdedcbd"; /> <img width="3840" height="794" alt="image" src="https://github.com/user-attachments/assets/9c5b7827-466c-4424-a5d1-72460dcff838"; /> using this dag. ```python import json import pendulum from airflow.sdk import dag, task @dag( dag_id="test", schedule=None, start_date=pendulum.datetime(2025, 8, 11, tz="Asia/Seoul"), catchup=False, tags=["etl_pipeline"], ) def data_pipeline_etl(): """ ### TaskFlow API Tutorial Documentation This is a simple data pipeline example which demonstrates the use of the TaskFlow API using three simple tasks for Extract, Transform, and Load. Documentation that goes along with the Airflow TaskFlow API tutorial is located [here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html) """ @task() def extract(): """ #### Extract task A simple Extract task to get data ready for the rest of the data pipeline. In this case, getting data is simulated by reading from a hardcoded JSON string. """ data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}' order_data_dict = json.loads(data_string) return order_data_dict extract() data_pipeline_etl() ``` <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> <!-- Thank you for contributing! Please make sure that your code changes are covered with tests. And in case of new features or big changes remember to adjust the documentation. Feel free to ping committers for the review! In case of an existing issue, reference it using one of the following: closes: #ISSUE related: #ISSUE How to write a good git commit message: http://chris.beams.io/posts/git-commit/ --> <!-- Please keep an empty line above the dashes. --> --- **^ Add meaningful description above** Read the **[Pull Request Guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#pull-request-guidelines)** for more information. In case of fundamental code changes, an Airflow Improvement Proposal ([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals)) is needed. In case of a new dependency, check compliance with the [ASF 3rd Party License Policy](https://www.apache.org/legal/resolved.html#category-x). In case of backwards incompatible changes please leave a note in a newsfragment file, named `{pr_number}.significant.rst` or `{issue_number}.significant.rst`, in [airflow-core/newsfragments](https://github.com/apache/airflow/tree/main/airflow-core/newsfragments). -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
