Docs update: retries & note about Dagbag error (#592)

Add retries of 2 to code samples
Add a note about the config variable to fix DagBag timeout errors

README.rst

@@ -61,7 +61,7 @@ You can render an Airflow Task Group using the ``DbtTaskGroup`` class. Here's an
     with DAG(
         dag_id="extract_dag",
         start_date=datetime(2022, 11, 27),
-        schedule="@daily",
+        schedule_interval="@daily",
     ):
         e1 = EmptyOperator(task_id="pre_dbt")
 

dev/dags/basic_cosmos_dag.py

@@ -37,5 +37,6 @@
     start_date=datetime(2023, 1, 1),
     catchup=False,
     dag_id="basic_cosmos_dag",
+    default_args={"retries": 2},
 )
 # [END local_example]

dev/dags/basic_cosmos_task_group.py

@@ -42,6 +42,7 @@ def basic_cosmos_task_group() -> None:
         ),
         operator_args={"install_deps": True},
         profile_config=profile_config,
+        default_args={"retries": 2},
     )
 
     post_dbt = EmptyOperator(task_id="post_dbt")

dev/dags/cosmos_manifest_example.py

@@ -36,5 +36,6 @@
     start_date=datetime(2023, 1, 1),
     catchup=False,
     dag_id="cosmos_manifest_example",
+    default_args={"retries": 2},
 )
 # [END local_example]

dev/dags/cosmos_profile_mapping.py

@@ -41,6 +41,7 @@ def cosmos_profile_mapping() -> None:
             ),
         ),
         operator_args={"install_deps": True},
+        default_args={"retries": 2},
     )
 
     post_dbt = EmptyOperator(task_id="post_dbt")

dev/dags/cosmos_seed_dag.py

@@ -36,11 +36,11 @@
 with DAG(
     dag_id="extract_dag",
     start_date=datetime(2022, 11, 27),
-    schedule="@daily",
+    schedule_interval="@daily",
     doc_md=__doc__,
     catchup=False,
     max_active_runs=1,
-    default_args={"owner": "01-EXTRACT"},
+    default_args={"owner": "01-EXTRACT", "retries": 2},
 ) as dag:
     with TaskGroup(group_id="drop_seeds_if_exist") as drop_seeds:
         for seed in ["raw_customers", "raw_payments", "raw_orders"]:

dev/dags/dbt_docs.py

@@ -66,6 +66,7 @@ def which_upload():
     schedule_interval="@daily",
     doc_md=__doc__,
     catchup=False,
+    default_args={"retries": 2},
 ) as dag:
     generate_dbt_docs_aws = DbtDocsS3Operator(
         task_id="generate_dbt_docs_aws",

dev/dags/example_cosmos_python_models.py

@@ -48,5 +48,6 @@
     start_date=datetime(2023, 1, 1),
     catchup=False,
     dag_id="example_cosmos_python_models",
+    default_args={"retries": 2},
 )
 # [END example_cosmos_python_models]

dev/dags/example_model_version.py

@@ -34,5 +34,6 @@
     start_date=datetime(2023, 1, 1),
     catchup=False,
     dag_id="example_model_version",
+    default_args={"retries": 2},
 )
 # [END local_example]

dev/dags/example_virtualenv.py

@@ -41,5 +41,6 @@
     start_date=datetime(2023, 1, 1),
     catchup=False,
     dag_id="example_virtualenv",
+    default_args={"retries": 2},
 )
 # [END virtualenv_example]

dev/dags/user_defined_profile.py

@@ -36,6 +36,7 @@ def user_defined_profile() -> None:
             profiles_yml_filepath=PROFILES_FILE_PATH,
         ),
         operator_args={"append_env": True, "install_deps": True},
+        default_args={"retries": 2},
     )
 
     post_dbt = EmptyOperator(task_id="post_dbt")

docs/configuration/scheduling.rst

@@ -50,7 +50,7 @@ Then, you can use Airflow's data-aware scheduling capabilities to schedule ``my_
 
     project_two = DbtDag(
         # ...
-        schedule=[get_dbt_dataset("my_conn", "project_one", "my_model")],
+        schedule_interval=[get_dbt_dataset("my_conn", "project_one", "my_model")],
         dbt_project_name="project_two",
     )
 

docs/getting_started/astro.rst

@@ -106,8 +106,12 @@ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and c
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
 
+.. note::
+   In some cases, especially in larger dbt projects, you might run into a ``DagBag import timeout`` error.
+   This error can be resolved by increasing the value of the Airflow configuration `core.dagbag_import_timeout <https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#dagbag-import-timeout>`_.
 
 Start your project
 ~~~~~~~~~~~~~~~~~~

docs/getting_started/gcc.rst

@@ -75,4 +75,10 @@ Make sure to rename the ``<your-adapter>`` value below to your adapter's Python
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in larger dbt projects, you might run into a ``DagBag import timeout`` error.
+   This error can be resolved by increasing the value of the Airflow configuration `core.dagbag_import_timeout <https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#dagbag-import-timeout>`_.

docs/getting_started/kubernetes.rst

@@ -28,7 +28,7 @@ Additional KubernetesPodOperator parameters can be added on the operator_args pa
 
 For instance,
 
-.. code-block:: text
+.. code-block:: python
 
     run_models = DbtTaskGroup(
         profile_config=ProfileConfig(

docs/getting_started/mwaa.rst

@@ -109,4 +109,10 @@ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and c
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in larger dbt projects, you might run into a ``DagBag import timeout`` error.
+   This error can be resolved by increasing the value of the Airflow configuration `core.dagbag_import_timeout <https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#dagbag-import-timeout>`_.

docs/getting_started/open-source.rst

@@ -61,4 +61,10 @@ For example, if you wanted to put your dbt project in the ``/usr/local/airflow/d
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in larger dbt projects, you might run into a ``DagBag import timeout`` error.
+   This error can be resolved by increasing the value of the Airflow configuration `core.dagbag_import_timeout <https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#dagbag-import-timeout>`_.

docs/index.rst

@@ -63,13 +63,14 @@ You can render an Airflow Task Group using the ``DbtTaskGroup`` class. Here's an
     with DAG(
         dag_id="extract_dag",
         start_date=datetime(2022, 11, 27),
-        schedule="@daily",
+        schedule_interval="@daily",
     ):
         e1 = EmptyOperator(task_id="pre_dbt")
 
         dbt_tg = DbtTaskGroup(
             project_config=ProjectConfig("jaffle_shop"),
             profile_config=profile_config,
+            default_args={"retries": 2},
         )
 
         e2 = EmptyOperator(task_id="post_dbt")