[GitHub] [airflow] josh-fell commented on a diff in pull request #25657: Update code examples from "classic" operators to TaskFlow decorators (faq, best practices, index)

[GitBox <gi...@apache.org>]

josh-fell commented on code in PR #25657:
URL: https://github.com/apache/airflow/pull/25657#discussion_r946065430


##########
docs/apache-airflow/best-practices.rst:
##########
@@ -135,26 +135,21 @@ Bad example:
       catchup=False,
       tags=["example"],
   ) as dag:
-
+      @task()
       def print_array():

Review Comment:
   The example should show calling `print_array()` as well. Otherwise the task _technically_ doesn't get added to the DAG. Any users, I suspect especially beginners, could copy/paste this code and not understand why the `print_array` task doesn't appear in the DAG.



##########
docs/apache-airflow/best-practices.rst:
##########
@@ -163,7 +158,7 @@ Good example:
       catchup=False,
       tags=["example"],
   ) as dag:
-
+      @task()
       def print_array():

Review Comment:
   Same here. Let's make sure the code snippet actually calls `print_array()`.



##########
docs/apache-airflow/index.rst:
##########
@@ -40,24 +40,27 @@ Take a look at the following snippet of code:
     from datetime import datetime
 
     from airflow import DAG
+    from airflow.decorators import task
     from airflow.operators.bash import BashOperator
-    from airflow.operators.python import PythonOperator
 
     # A DAG represents a workflow, a collection of tasks
     with DAG(dag_id="demo", start_date=datetime(2022, 1, 1), schedule="0 0 * * *") as dag:
 
         # Tasks are represented as operators
         hello = BashOperator(task_id="hello", bash_command="echo hello")
-        airflow = PythonOperator(task_id="airflow", python_callable=lambda: print("airflow"))
+
+        @task()
+        def airflow():
+            print("airflow")
 
         # Set dependencies between tasks
-        hello >> airflow
+        hello >> airflow()
 
 
 Here you see:
 
 - A DAG named "demo", starting on Jan 1st 2022 and running once a day. A DAG is Airflow's representation of a workflow.
-- Two tasks, a BashOperator running a Bash script and a PythonOperator running a Python script
+- Two tasks, a BashOperator running a Bash script and a python function defined using the ``@task`` decorator

Review Comment:
   ```suggestion
   - Two tasks, a BashOperator running a Bash script and a Python function defined using the ``@task`` decorator
   ```
   Small nit here.



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