Source code for airflow.policies

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from __future__ import annotations

from typing import TYPE_CHECKING

import pluggy

local_settings_hookspec = pluggy.HookspecMarker("airflow.policy")
hookimpl = pluggy.HookimplMarker("airflow.policy")

__all__: list[str] = ["hookimpl"]

    from airflow.models.baseoperator import BaseOperator
    from airflow.models.dag import DAG
    from airflow.models.taskinstance import TaskInstance

[docs]def task_policy(task: BaseOperator) -> None: """ This policy setting allows altering tasks after they are loaded in the DagBag. It allows administrator to rewire some task's parameters. Alternatively you can raise ``AirflowClusterPolicyViolation`` exception to stop DAG from being executed. Here are a few examples of how this can be useful: * You could enforce a specific queue (say the ``spark`` queue) for tasks using the ``SparkOperator`` to make sure that these tasks get wired to the right workers * You could enforce a task timeout policy, making sure that no tasks run for more than 48 hours :param task: task to be mutated """
[docs]def dag_policy(dag: DAG) -> None: """ This policy setting allows altering DAGs after they are loaded in the DagBag. It allows administrator to rewire some DAG's parameters. Alternatively you can raise ``AirflowClusterPolicyViolation`` exception to stop DAG from being executed. Here are a few examples of how this can be useful: * You could enforce default user for DAGs * Check if every DAG has configured tags :param dag: dag to be mutated """
[docs]def task_instance_mutation_hook(task_instance: TaskInstance) -> None: """ This setting allows altering task instances before being queued by the Airflow scheduler. This could be used, for instance, to modify the task instance during retries. :param task_instance: task instance to be mutated """
[docs]def pod_mutation_hook(pod) -> None: """ Mutate pod before scheduling. This setting allows altering ``kubernetes.client.models.V1Pod`` object before they are passed to the Kubernetes client for scheduling. This could be used, for instance, to add sidecar or init containers to every worker pod launched by KubernetesExecutor or KubernetesPodOperator. """
[docs]def get_airflow_context_vars(context) -> dict[str, str]: # type: ignore[empty-body] """ This setting allows getting the airflow context vars, which are key value pairs. They are then injected to default airflow context vars, which in the end are available as environment variables when running tasks dag_id, task_id, execution_date, dag_run_id, try_number are reserved keys. :param context: The context for the task_instance of interest. """
@local_settings_hookspec(firstresult=True) def get_dagbag_import_timeout(dag_file_path: str) -> int | float: # type: ignore[empty-body] """ This setting allows for dynamic control of the DAG file parsing timeout based on the DAG file path. It is useful when there are a few DAG files requiring longer parsing times, while others do not. You can control them separately instead of having one value for all DAG files. If the return value is less than or equal to 0, it means no timeout during the DAG parsing. """ class DefaultPolicy: """:meta private:""" # Default implementations of the policy functions @staticmethod @hookimpl def get_dagbag_import_timeout(dag_file_path: str): from airflow.configuration import conf return conf.getfloat("core", "DAGBAG_IMPORT_TIMEOUT") @staticmethod @hookimpl def get_airflow_context_vars(context): return {} def make_plugin_from_local_settings(pm: pluggy.PluginManager, module, names: list[str]): """ Turn the functions from airflow_local_settings module into a custom/local plugin, so that plugin-registered functions can co-operate with pluggy/setuptool entrypoint plugins of the same methods. Airflow local settings will be "win" (i.e. they have the final say) as they are the last plugin registered. :meta private: """ import inspect import textwrap import attr hook_methods = set() def _make_shim_fn(name, desired_sig, target): # Functions defined in airflow_local_settings are called by positional parameters, so the names don't # have to match what we define in the "template" policy. # # However Pluggy validates the names match (and will raise an error if they don't!) # # To maintain compat, if we detect the names don't match, we will wrap it with a dynamically created # shim function that looks somewhat like this: # # def dag_policy_name_mismatch_shim(dag): # airflow_local_settings.dag_policy(dag) # codestr = textwrap.dedent( f""" def {name}_name_mismatch_shim{str(desired_sig)}: return __target({' ,'.join(desired_sig.parameters)}) """ ) code = compile(codestr, "<policy-shim>", "single") scope = {"__target": target} exec(code, scope, scope) return scope[f"{name}_name_mismatch_shim"] @attr.define(frozen=True) class AirflowLocalSettingsPolicy: hook_methods: tuple[str, ...] __name__ = "AirflowLocalSettingsPolicy" def __dir__(self): return self.hook_methods for name in names: if not hasattr(pm.hook, name): continue policy = getattr(module, name) if not policy: continue local_sig = inspect.signature(policy) policy_sig = inspect.signature(globals()[name]) # We only care if names/order/number of parameters match, not type hints if local_sig.parameters.keys() != policy_sig.parameters.keys(): policy = _make_shim_fn(name, policy_sig, target=policy) setattr(AirflowLocalSettingsPolicy, name, staticmethod(hookimpl(policy, specname=name))) hook_methods.add(name) if hook_methods: pm.register(AirflowLocalSettingsPolicy(hook_methods=tuple(hook_methods))) return hook_methods

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