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Fixup some punctuation and grammar (#29342)
After noticing one punctuation mistake around "however", I figured there must be more. So I fixed them, and some other stuff adjacent to them.
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‎docs/apache-airflow-providers-google/operators/cloud/dataflow.rst‎

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@@ -146,7 +146,7 @@ See: `Configuring PipelineOptions for execution on the Cloud Dataflow service <h
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Asynchronous execution
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""""""""""""""""""""""
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Dataflow batch jobs are by default asynchronous - however this is dependent on the application code (contained in the JAR
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Dataflow batch jobs are by default asynchronous; however, this is dependent on the application code (contained in the JAR
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or Python file) and how it is written. In order for the Dataflow job to execute asynchronously, ensure the
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pipeline objects are not being waited upon (not calling ``waitUntilFinish`` or ``wait_until_finish`` on the
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``PipelineResult`` in your application code).

‎docs/apache-airflow-providers-http/operators.rst‎

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@@ -49,7 +49,7 @@ the response text back.
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For historical reasons, configuring ``HTTPS`` connectivity via HTTP operator is, well, difficult and
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counter-intuitive. The Operator defaults to ``http`` protocol and you can change the schema used by the
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operator via ``scheme`` connection attribute. However this field was originally added to connection for
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operator via ``scheme`` connection attribute. However, this field was originally added to connection for
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database type of URIs, where database schemes are set traditionally as first component of URI ``path``.
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Therefore if you want to configure as ``https`` connection via URI, you need to pass ``https`` scheme
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to the SimpleHttpOperator. AS stupid as it looks, your connection URI will look like this:
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``https://your_host:443/my_endpoint`` you need to set the endpoint parameter to ``my_endpoint``.
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Alternatively, if you want, you could also percent-encode the host including the ``https://`` prefix,
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and as long it contains ``://`` (percent-encoded ``%3a%2f%2f``), the first component of the path will
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not be used as scheme. Your URI definition might then look like: ``http://https%3a%2f%2fyour_host:443/``
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In this case however the ``path`` will not be used at all - you still need to use ``endpoint``
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not be used as scheme. Your URI definition might then look like ``http://https%3a%2f%2fyour_host:443/``.
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In this case, however, the ``path`` will not be used at all - you still need to use ``endpoint``
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parameter in the task if wish to make a request with specific path. As counter-intuitive as it is, this
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is historically the way how the operator/hook works and it's not easy to change without breaking
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backwards compatibility because there are other operators build on top of the ``SimpleHttpOperator`` that

‎docs/apache-airflow-providers/howto/create-update-providers.rst‎

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@@ -246,7 +246,7 @@ In the ``airflow/providers/<NEW_PROVIDER>/provider.yaml`` add information of you
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.. note:: Defining your own connection types
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You only need to add ``connection-types`` in case you have some hooks that have customized UI behavior. However
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You only need to add ``connection-types`` in case you have some hooks that have customized UI behavior. However,
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it is only supported for Airflow 2.2.0. If your providers are also targeting Airflow below 2.2.0 you should
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provide the deprecated ``hook-class-names`` array. The ``connection-types`` array allows for optimization
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of importing of individual connections and while Airflow 2.2.0 is able to handle both definition, the
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This feature is available in Airflow 2.3+.
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Some providers might provide optional features, which are only available when some packages or libraries
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are installed. Such features will typically result in ``ImportErrors`` however those import errors
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are installed. Such features will typically result in ``ImportErrors``; however, those import errors
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should be silently ignored rather than pollute the logs of Airflow with false warnings. False warnings
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are a very bad pattern, as they tend to turn into blind spots, so avoiding false warnings is encouraged.
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However until Airflow 2.3, Airflow had no mechanism to selectively ignore "known" ImportErrors. So
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However, until Airflow 2.3, Airflow had no mechanism to selectively ignore "known" ImportErrors. So
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Airflow 2.1 and 2.2 silently ignored all ImportErrors coming from providers with actually lead to
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ignoring even important import errors - without giving the clue to Airflow users that there is something
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missing in provider dependencies.

‎docs/apache-airflow-providers/index.rst‎

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@@ -83,8 +83,8 @@ Logging
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'''''''
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The providers can add additional task logging capabilities. By default ``Apache Airflow`` saves logs for
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tasks locally and make them available to Airflow UI via internal http server, however via providers
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you can add extra logging capabilities, where Airflow Logs can be written to a remote service and
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tasks locally and make them available to Airflow UI via internal http server. However, providers
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can add extra logging capabilities, where Airflow Logs can be written to a remote service and
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retrieved from those services.
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You can see all task loggers available via community-managed providers in

‎docs/apache-airflow/administration-and-deployment/lineage.rst‎

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Inlets can be a (list of) upstream task ids or statically defined as an attr annotated object
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as is, for example, the ``File`` object. Outlets can only be attr annotated object. Both are rendered
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at run time. However the outlets of a task in case they are inlets to another task will not be re-rendered
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at run time. However, the outlets of a task in case they are inlets to another task will not be re-rendered
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for the downstream task.
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.. note:: Operators can add inlets and outlets automatically if the operator supports it.

‎docs/apache-airflow/administration-and-deployment/logging-monitoring/errors.rst‎

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``SUBPROCESS_`` prefix added. This happens also for all other subprocesses.
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This behaviour can be disabled by setting ``default_integrations`` sentry configuration parameter to
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``False`` which disables ``StdlibIntegration``. This however also disables other default integrations
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and you need to enable them manually if you want to get them enabled,
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see `Sentry Default Integrations <https://docs.sentry.io/platforms/python/guides/wsgi/configuration/integrations/default-integrations/>`_
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``False`` which disables ``StdlibIntegration``. However, this also disables other default integrations,
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so you need to enable them manually if you want them to remain enabled
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(see `Sentry Default Integrations <https://docs.sentry.io/platforms/python/guides/wsgi/configuration/integrations/default-integrations/>`_).
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.. code-block:: ini
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‎docs/apache-airflow/administration-and-deployment/scheduler.rst‎

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@@ -111,7 +111,7 @@ outline of the scheduling loop is:
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- Select schedulable TaskInstances, and whilst respecting Pool limits and other concurrency limits, enqueue
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them for execution
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This does however place some requirements on the Database.
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This does, however, place some requirements on the Database.
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.. _scheduler:ha:db_requirements:
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which dramatically decreases performance. Note that Airflow Scheduler in versions prior to ``2.1.4``
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generated a lot of ``Page Cache`` memory used by log files (when the log files were not removed).
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This was generally harmless, as the memory is just cache and could be reclaimed at any time by the system,
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however in version ``2.1.4`` and beyond, writing logs will not generate excessive ``Page Cache`` memory.
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however, in version ``2.1.4`` and beyond, writing logs will not generate excessive ``Page Cache`` memory.
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Regardless - make sure when you look at memory usage, pay attention to the kind of memory you are observing.
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Usually you should look at ``working memory``(names might vary depending on your deployment) rather
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than ``total memory used``.
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"""""""""""""""""""""""""""""""
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The following config settings can be used to control aspects of the Scheduler.
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However you can also look at other non-performance-related scheduler configuration parameters available at
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:doc:`../configurations-ref` in ``[scheduler]`` section.
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However, you can also look at other non-performance-related scheduler configuration parameters available at
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:doc:`../configurations-ref` in the ``[scheduler]`` section.
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- :ref:`config:scheduler__max_dagruns_to_create_per_loop`
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‎docs/apache-airflow/administration-and-deployment/security/secrets/secrets-backend/index.rst‎

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The default implementation of Secret backend requires use of an Airflow-specific format of storing
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secrets for connections. Currently most community provided implementations require the connections to
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be stored as JSON or the Airflow Connection URI format (see
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:doc:`apache-airflow-providers:core-extensions/secrets-backends`). However some organizations may need to store the credentials (passwords/tokens etc) in some other way, for example if the same credentials store needs to be used for multiple data platforms, or if you are using a service with a built-in mechanism of rotating the credentials that does not work with the Airflow-specific format.
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:doc:`apache-airflow-providers:core-extensions/secrets-backends`). However, some organizations may need to store the credentials (passwords/tokens etc) in some other way. For example, if the same credentials store needs to be used for multiple data platforms, or if you are using a service with a built-in mechanism of rotating the credentials that does not work with the Airflow-specific format.
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In this case you will need to roll your own secret backend as described in the previous chapter,
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possibly extending an existing secrets backend and adapting it to the scheme used by your organization.

‎docs/apache-airflow/administration-and-deployment/security/webserver.rst‎

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--role Admin \
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--email spiderman@superhero.org
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It is however possible to switch on authentication by either using one of the supplied
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backends or creating your own.
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To deactivate the authentication and allow users to be identified as Anonymous, the following entry
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in ``$AIRFLOW_HOME/webserver_config.py`` needs to be set with the desired role that the Anonymous
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user will have by default:

‎docs/apache-airflow/authoring-and-scheduling/dynamic-task-mapping.rst‎

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# add(x=8, y=5)
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# add(x=8, y=10)
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This would result in the add task being called 6 times. Please note however that the order of expansion is not guaranteed.
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This would result in the add task being called 6 times. Please note, however, that the order of expansion is not guaranteed.
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Mapping with non-TaskFlow operators
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===================================
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If you wish to not have a large mapped task consume all available runner slots you can use the ``max_active_tis_per_dag`` setting on the task to restrict how many can be running at the same time.
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Note however that this applies to all copies of that task against all active DagRuns, not just to this one specific DagRun.
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Note, however, that this applies to all copies of that task against all active DagRuns, not just to this one specific DagRun.
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.. code-block:: python
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