Multiple minor doc fixes (#14917)

apache · Mar 20, 2021 · ed872a6 · ed872a6
1 parent 4531168
commit ed872a6
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Showing 4 changed files with 4 additions and 4 deletions.
diff --git a/airflow/api_connexion/openapi/v1.yaml b/airflow/api_connexion/openapi/v1.yaml
@@ -1652,7 +1652,7 @@ components:
           type: string
           readOnly: true
           nullable: true
-          description: If the DAG is SubDAG then it is the top level DAG identifier. Otherwise, nulll.
+          description: If the DAG is SubDAG then it is the top level DAG identifier. Otherwise, null.
         is_paused:
           type: boolean
           nullable: true

diff --git a/docs/apache-airflow/backport-providers.rst b/docs/apache-airflow/backport-providers.rst
@@ -98,7 +98,7 @@ Backport providers only work when they are installed in the same namespace as th
 This is majority of cases when you simply run pip install - it installs all packages in the same folder
 (usually in ``/usr/local/lib/pythonX.Y/site-packages``). But when you install the ``apache-airflow`` and
 ``apache-airflow-backport-package-*`` using different methods (for example using ``pip install -e .`` or
-``pip install --user`` they might be installed in different namespaces.
+``pip install --user``) they might be installed in different namespaces.
 
 If that's the case, the provider packages will not be importable (the error in such case is
 ``ModuleNotFoundError: No module named 'airflow.providers'``).

diff --git a/docs/apache-airflow/dag-serialization.rst b/docs/apache-airflow/dag-serialization.rst
@@ -41,7 +41,7 @@ as :class:`~airflow.models.serialized_dag.SerializedDagModel` model.
 The Webserver now instead of having to parse the DAG file again, reads the
 serialized DAGs in JSON, de-serializes them and create the DagBag and uses it
 to show in the UI. And the Scheduler does not need the actual DAG for making Scheduling decisions,
-instead of using the DAG files, we use Serialized DAGs that contain all the information needing to
+instead of using the DAG files, we use Serialized DAGs that contain all the information needed to
 schedule the DAGs from Airflow 2.0.0 (this was done as part of :ref:`Scheduler HA <scheduler:ha>`).
 
 One of the key features that is implemented as the part of DAG Serialization is that

diff --git a/docs/apache-airflow/macros-ref.rst b/docs/apache-airflow/macros-ref.rst
@@ -62,7 +62,7 @@ Variable                                Description
 ``{{ ti }}``                            same as ``{{ task_instance }}``
 ``{{ params }}``                        a reference to the user-defined params dictionary which can be overridden by
                                         the dictionary passed through ``trigger_dag -c`` if you enabled
-                                        ``dag_run_conf_overrides_params` in ``airflow.cfg``
+                                        ``dag_run_conf_overrides_params`` in ``airflow.cfg``
 ``{{ var.value.my_var }}``              global defined variables represented as a dictionary
 ``{{ var.json.my_var.path }}``          global defined variables represented as a dictionary
                                         with deserialized JSON object, append the path to the