Docs update: retries note about Dagbag error.

dev/dags/basic_cosmos_dag.py

@@ -34,5 +34,6 @@
     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

@@ -25,7 +25,7 @@
 
 
 @dag(
-    schedule_interval="@daily",
+    schedule="@daily",
     start_date=datetime(2023, 1, 1),
     catchup=False,
 )
@@ -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

@@ -32,9 +32,10 @@
     render_config=RenderConfig(load_method=LoadMode.DBT_MANIFEST, select=["path:models/customers.sql"]),
     operator_args={"install_deps": True},
     # normal dag parameters
-    schedule_interval="@daily",
+    schedule="@daily",
     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

@@ -18,7 +18,7 @@
 
 
 @dag(
-    schedule_interval="@daily",
+    schedule="@daily",
     start_date=datetime(2023, 1, 1),
     catchup=False,
 )
@@ -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

@@ -40,7 +40,7 @@
     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

@@ -63,9 +63,10 @@ def which_upload():
 with DAG(
     dag_id="docs_dag",
     start_date=datetime(2023, 1, 1),
-    schedule_interval="@daily",
+    schedule="@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

@@ -44,9 +44,10 @@
         "append_env": True,
     },
     # normal dag parameters
-    schedule_interval="@daily",
+    schedule="@daily",
     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

@@ -30,9 +30,10 @@
     profile_config=profile_config,
     operator_args={"install_deps": True},
     # normal dag parameters
-    schedule_interval="@daily",
+    schedule="@daily",
     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

@@ -37,9 +37,10 @@
         "install_deps": True,
     },
     # normal dag parameters
-    schedule_interval="@daily",
+    schedule="@daily",
     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

@@ -16,7 +16,7 @@
 
 
 @dag(
-    schedule_interval="@daily",
+    schedule="@daily",
     start_date=datetime(2023, 1, 1),
     catchup=False,
 )
@@ -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

@@ -17,7 +17,7 @@ To schedule a dbt project on a time-based schedule, you can use Airflow's schedu
     jaffle_shop = DbtDag(
         # ...
         start_date=datetime(2023, 1, 1),
-        schedule_interval="@daily",
+        schedule="@daily",
     )
 
 
@@ -45,7 +45,7 @@ Then, you can use Airflow's data-aware scheduling capabilities to schedule ``my_
     project_one = DbtDag(
         # ...
         start_date=datetime(2023, 1, 1),
-        schedule_interval="@daily",
+        schedule="@daily",
     )
 
     project_two = DbtDag(

docs/getting_started/astro.rst

@@ -102,12 +102,17 @@ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and c
             dbt_executable_path=f"{os.environ['AIRFLOW_HOME']}/dbt_venv/bin/dbt",
         ),
         # normal dag parameters
-        schedule_interval="@daily",
+        schedule="@daily",
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
 
+.. note::
+   In some cases, especially in projects containing a large number of DAGs, you might run into an error saying ``<> is not a valid ResourceType``.
+   The recommended solution is to use the Cosmos manifest parsing method `dbt_manifest <https://astronomer.github.io/astronomer-cosmos/configuration/parsing-methods.html#dbt-manifest>`_.
+   Alternatively, the 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

@@ -71,8 +71,15 @@ Make sure to rename the ``<your-adapter>`` value below to your adapter's Python
             "py_requirements": ["<your-adapter>"],
         },
         # normal dag parameters
-        schedule_interval="@daily",
+        schedule="@daily",
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in projects containing a large number of DAGs, you might run into an error saying ``<> is not a valid ResourceType``.
+   The recommended solution is to use the Cosmos manifest parsing method `dbt_manifest <https://astronomer.github.io/astronomer-cosmos/configuration/parsing-methods.html#dbt-manifest>`_.
+   Alternatively, the 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

@@ -105,8 +105,15 @@ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and c
         ),
         profile_config=profile_config,
         # normal dag parameters
-        schedule_interval="@daily",
+        schedule="@daily",
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in projects containing a large number of DAGs, you might run into an error saying ``<> is not a valid ResourceType``.
+   The recommended solution is to use the Cosmos manifest parsing method `dbt_manifest <https://astronomer.github.io/astronomer-cosmos/configuration/parsing-methods.html#dbt-manifest>`_.
+   Alternatively, the 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

@@ -57,8 +57,15 @@ For example, if you wanted to put your dbt project in the ``/usr/local/airflow/d
             dbt_executable_path=f"{os.environ['AIRFLOW_HOME']}/dbt_venv/bin/dbt",
         ),
         # normal dag parameters
-        schedule_interval="@daily",
+        schedule="@daily",
         start_date=datetime(2023, 1, 1),
         catchup=False,
         dag_id="my_cosmos_dag",
+        default_args={"retries": 2},
     )
+
+
+.. note::
+   In some cases, especially in projects containing a large number of DAGs, you might run into an error saying ``<> is not a valid ResourceType``.
+   The recommended solution is to use the Cosmos manifest parsing method `dbt_manifest <https://astronomer.github.io/astronomer-cosmos/configuration/parsing-methods.html#dbt-manifest>`_.
+   Alternatively, the 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

@@ -70,6 +70,7 @@ You can render an Airflow Task Group using the ``DbtTaskGroup`` class. Here's an
         dbt_tg = DbtTaskGroup(
             project_config=ProjectConfig("jaffle_shop"),
             profile_config=profile_config,
+            default_args={"retries": 2},
         )
 
         e2 = EmptyOperator(task_id="post_dbt")