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Update python.rst with multi-Scheduler LocalExecutor info #32310

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Update python.rst with multi-Scheduler LocalExecutor info #32310

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Update python.rst with multi-Scheduler LocalExecutor info
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11 changes: 11 additions & 0 deletions docs/apache-airflow/core-concepts/executor/local.rst
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Expand Up @@ -47,3 +47,14 @@ Arguably, :class:`~airflow.executors.sequential_executor.SequentialExecutor` cou
parallelism of just 1 worker, i.e. ``self.parallelism = 1``.
This option could lead to the unification of the executor implementations, running
locally, into just one :class:`~airflow.executors.local_executor.LocalExecutor` with multiple modes.

.. note::

When using the ``@task`` decorator with multiple Schedulers configured with ``executor = LocalExecutor`` in the [core] section of airflow.cfg, each scheduler will run a LocalExecutor. This means tasks would be processed in a distributed fashion across the machines running the Schedulers.
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Does this apply only when using the @task decorator? Or it is a general behaviour. If general, can we remove the leading part of the sentence which mentions about the case for @task decorator?

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My understanding is that it is general

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@pankajkoti Updated and done

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Some considerations should be taken into account:

- Restarting a Scheduler: If a scheduler is restarted, it may take some time for other schedulers to recognize the orphaned tasks and restart or fail them.

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- Sharing of instances: While a single LocalExecutor allows tasks to share the same Python process and exchange information via variables, this is not possible when using multiple Schedulers, as each Scheduler will run in its own separate Python process.
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