| :py:mod:`airflow.operators.python` |
| ================================== |
| |
| .. py:module:: airflow.operators.python |
| |
| |
| Module Contents |
| --------------- |
| |
| Classes |
| ~~~~~~~ |
| |
| .. autoapisummary:: |
| |
| airflow.operators.python.PythonOperator |
| airflow.operators.python.BranchPythonOperator |
| airflow.operators.python.ShortCircuitOperator |
| airflow.operators.python.PythonVirtualenvOperator |
| |
| |
| |
| Functions |
| ~~~~~~~~~ |
| |
| .. autoapisummary:: |
| |
| airflow.operators.python.task |
| airflow.operators.python.get_current_context |
| |
| |
| |
| .. py:function:: task(python_callable: Optional[Callable] = None, multiple_outputs: Optional[bool] = None, **kwargs) |
| |
| Deprecated function that calls @task.python and allows users to turn a python function into |
| an Airflow task. Please use the following instead: |
| |
| from airflow.decorators import task |
| |
| @task |
| def my_task() |
| |
| :param python_callable: A reference to an object that is callable |
| :type python_callable: python callable |
| :param op_kwargs: a dictionary of keyword arguments that will get unpacked |
| in your function (templated) |
| :type op_kwargs: dict |
| :param op_args: a list of positional arguments that will get unpacked when |
| calling your callable (templated) |
| :type op_args: list |
| :param multiple_outputs: if set, function return value will be |
| unrolled to multiple XCom values. Dict will unroll to xcom values with keys as keys. |
| Defaults to False. |
| :type multiple_outputs: bool |
| :return: |
| |
| |
| .. py:class:: PythonOperator(*, python_callable: Callable, op_args: Optional[Collection[Any]] = None, op_kwargs: Optional[Mapping[str, Any]] = None, templates_dict: Optional[Dict] = None, templates_exts: Optional[List[str]] = None, **kwargs) |
| |
| Bases: :py:obj:`airflow.models.BaseOperator` |
| |
| Executes a Python callable |
| |
| .. seealso:: |
| For more information on how to use this operator, take a look at the guide: |
| :ref:`howto/operator:PythonOperator` |
| |
| When running your callable, Airflow will pass a set of keyword arguments that can be used in your |
| function. This set of kwargs correspond exactly to what you can use in your jinja templates. |
| For this to work, you need to define ``**kwargs`` in your function header, or you can add directly the |
| keyword arguments you would like to get - for example with the below code your callable will get |
| the values of ``ti`` and ``next_ds`` context variables. |
| |
| With explicit arguments: |
| |
| .. code-block:: python |
| |
| def my_python_callable(ti, next_ds): |
| pass |
| |
| With kwargs: |
| |
| .. code-block:: python |
| |
| def my_python_callable(**kwargs): |
| ti = kwargs["ti"] |
| next_ds = kwargs["next_ds"] |
| |
| |
| :param python_callable: A reference to an object that is callable |
| :type python_callable: python callable |
| :param op_kwargs: a dictionary of keyword arguments that will get unpacked |
| in your function |
| :type op_kwargs: dict (templated) |
| :param op_args: a list of positional arguments that will get unpacked when |
| calling your callable |
| :type op_args: list (templated) |
| :param templates_dict: a dictionary where the values are templates that |
| will get templated by the Airflow engine sometime between |
| ``__init__`` and ``execute`` takes place and are made available |
| in your callable's context after the template has been applied. (templated) |
| :type templates_dict: dict[str] |
| :param templates_exts: a list of file extensions to resolve while |
| processing templated fields, for examples ``['.sql', '.hql']`` |
| :type templates_exts: list[str] |
| |
| .. py:attribute:: template_fields |
| :annotation: = ['templates_dict', 'op_args', 'op_kwargs'] |
| |
| |
| |
| .. py:attribute:: template_fields_renderers |
| |
| |
| |
| |
| .. py:attribute:: BLUE |
| :annotation: = #ffefeb |
| |
| |
| |
| .. py:attribute:: ui_color |
| |
| |
| |
| |
| .. py:attribute:: shallow_copy_attrs |
| :annotation: = ['python_callable', 'op_kwargs'] |
| |
| |
| |
| .. py:method:: execute(self, context: Dict) |
| |
| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
| |
| Refer to get_template_context for more context. |
| |
| |
| .. py:method:: determine_kwargs(self, context: Mapping[str, Any]) -> Mapping[str, Any] |
| |
| |
| .. py:method:: execute_callable(self) |
| |
| Calls the python callable with the given arguments. |
| |
| :return: the return value of the call. |
| :rtype: any |
| |
| |
| |
| .. py:class:: BranchPythonOperator(*, python_callable: Callable, op_args: Optional[Collection[Any]] = None, op_kwargs: Optional[Mapping[str, Any]] = None, templates_dict: Optional[Dict] = None, templates_exts: Optional[List[str]] = None, **kwargs) |
| |
| Bases: :py:obj:`PythonOperator`, :py:obj:`airflow.models.skipmixin.SkipMixin` |
| |
| Allows a workflow to "branch" or follow a path following the execution |
| of this task. |
| |
| It derives the PythonOperator and expects a Python function that returns |
| a single task_id or list of task_ids to follow. The task_id(s) returned |
| should point to a task directly downstream from {self}. All other "branches" |
| or directly downstream tasks are marked with a state of ``skipped`` so that |
| these paths can't move forward. The ``skipped`` states are propagated |
| downstream to allow for the DAG state to fill up and the DAG run's state |
| to be inferred. |
| |
| .. py:method:: execute(self, context: Dict) |
| |
| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
| |
| Refer to get_template_context for more context. |
| |
| |
| |
| .. py:class:: ShortCircuitOperator(*, python_callable: Callable, op_args: Optional[Collection[Any]] = None, op_kwargs: Optional[Mapping[str, Any]] = None, templates_dict: Optional[Dict] = None, templates_exts: Optional[List[str]] = None, **kwargs) |
| |
| Bases: :py:obj:`PythonOperator`, :py:obj:`airflow.models.skipmixin.SkipMixin` |
| |
| Allows a workflow to continue only if a condition is met. Otherwise, the |
| workflow "short-circuits" and downstream tasks are skipped. |
| |
| The ShortCircuitOperator is derived from the PythonOperator. It evaluates a |
| condition and short-circuits the workflow if the condition is False. Any |
| downstream tasks are marked with a state of "skipped". If the condition is |
| True, downstream tasks proceed as normal. |
| |
| The condition is determined by the result of `python_callable`. |
| |
| .. py:method:: execute(self, context: Dict) |
| |
| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
| |
| Refer to get_template_context for more context. |
| |
| |
| |
| .. py:class:: PythonVirtualenvOperator(*, python_callable: Callable, requirements: Optional[Iterable[str]] = None, python_version: Optional[Union[str, int, float]] = None, use_dill: bool = False, system_site_packages: bool = True, op_args: Optional[Collection[Any]] = None, op_kwargs: Optional[Mapping[str, Any]] = None, string_args: Optional[Iterable[str]] = None, templates_dict: Optional[Dict] = None, templates_exts: Optional[List[str]] = None, **kwargs) |
| |
| Bases: :py:obj:`PythonOperator` |
| |
| Allows one to run a function in a virtualenv that is created and destroyed |
| automatically (with certain caveats). |
| |
| The function must be defined using def, and not be |
| part of a class. All imports must happen inside the function |
| and no variables outside of the scope may be referenced. A global scope |
| variable named virtualenv_string_args will be available (populated by |
| string_args). In addition, one can pass stuff through op_args and op_kwargs, and one |
| can use a return value. |
| Note that if your virtualenv runs in a different Python major version than Airflow, |
| you cannot use return values, op_args, op_kwargs, or use any macros that are being provided to |
| Airflow through plugins. You can use string_args though. |
| |
| .. seealso:: |
| For more information on how to use this operator, take a look at the guide: |
| :ref:`howto/operator:PythonVirtualenvOperator` |
| |
| :param python_callable: A python function with no references to outside variables, |
| defined with def, which will be run in a virtualenv |
| :type python_callable: function |
| :param requirements: A list of requirements as specified in a pip install command |
| :type requirements: list[str] |
| :param python_version: The Python version to run the virtualenv with. Note that |
| both 2 and 2.7 are acceptable forms. |
| :type python_version: Optional[Union[str, int, float]] |
| :param use_dill: Whether to use dill to serialize |
| the args and result (pickle is default). This allow more complex types |
| but requires you to include dill in your requirements. |
| :type use_dill: bool |
| :param system_site_packages: Whether to include |
| system_site_packages in your virtualenv. |
| See virtualenv documentation for more information. |
| :type system_site_packages: bool |
| :param op_args: A list of positional arguments to pass to python_callable. |
| :type op_args: list |
| :param op_kwargs: A dict of keyword arguments to pass to python_callable. |
| :type op_kwargs: dict |
| :param string_args: Strings that are present in the global var virtualenv_string_args, |
| available to python_callable at runtime as a list[str]. Note that args are split |
| by newline. |
| :type string_args: list[str] |
| :param templates_dict: a dictionary where the values are templates that |
| will get templated by the Airflow engine sometime between |
| ``__init__`` and ``execute`` takes place and are made available |
| in your callable's context after the template has been applied |
| :type templates_dict: dict of str |
| :param templates_exts: a list of file extensions to resolve while |
| processing templated fields, for examples ``['.sql', '.hql']`` |
| :type templates_exts: list[str] |
| |
| .. py:attribute:: BASE_SERIALIZABLE_CONTEXT_KEYS |
| |
| |
| |
| |
| .. py:attribute:: PENDULUM_SERIALIZABLE_CONTEXT_KEYS |
| |
| |
| |
| |
| .. py:attribute:: AIRFLOW_SERIALIZABLE_CONTEXT_KEYS |
| |
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| |
| |
| .. py:method:: execute(self, context: airflow.utils.context.Context) |
| |
| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
| |
| Refer to get_template_context for more context. |
| |
| |
| .. py:method:: determine_kwargs(self, context: Mapping[str, Any]) -> Mapping[str, Any] |
| |
| |
| .. py:method:: execute_callable(self) |
| |
| Calls the python callable with the given arguments. |
| |
| :return: the return value of the call. |
| :rtype: any |
| |
| |
| .. py:method:: get_python_source(self) |
| |
| Returns the source of self.python_callable |
| @return: |
| |
| |
| .. py:method:: __deepcopy__(self, memo) |
| |
| Hack sorting double chained task lists by task_id to avoid hitting |
| max_depth on deepcopy operations. |
| |
| |
| |
| .. py:function:: get_current_context() -> airflow.utils.context.Context |
| |
| Obtain the execution context for the currently executing operator without |
| altering user method's signature. |
| This is the simplest method of retrieving the execution context dictionary. |
| |
| **Old style:** |
| |
| .. code:: python |
| |
| def my_task(**context): |
| ti = context["ti"] |
| |
| **New style:** |
| |
| .. code:: python |
| |
| from airflow.operators.python import get_current_context |
| |
| |
| def my_task(): |
| context = get_current_context() |
| ti = context["ti"] |
| |
| Current context will only have value if this method was called after an operator |
| was starting to execute. |
| |
| |
