| :mod:`airflow.operators.python_operator` |
| ======================================== |
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| .. py:module:: airflow.operators.python_operator |
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| Module Contents |
| --------------- |
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| .. py:class:: PythonOperator(python_callable, op_args=None, op_kwargs=None, provide_context=False, templates_dict=None, templates_exts=None, *args, **kwargs) |
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| Bases: :class:`airflow.models.BaseOperator` |
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| Executes a Python callable |
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| .. seealso:: |
| For more information on how to use this operator, take a look at the guide: |
| :ref:`howto/operator:PythonOperator` |
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| :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 provide_context: if set to true, 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. |
| :type provide_context: bool |
| :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] |
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| .. attribute:: template_fields |
| :annotation: = ['templates_dict', 'op_args', 'op_kwargs'] |
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| .. attribute:: ui_color |
| :annotation: = #ffefeb |
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| .. attribute:: shallow_copy_attrs |
| :annotation: = ['python_callable', 'op_kwargs'] |
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| .. method:: execute(self, context) |
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| .. method:: execute_callable(self) |
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| .. py:class:: BranchPythonOperator |
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| Bases: :class:`airflow.operators.python_operator.PythonOperator`, :class:`airflow.models.SkipMixin` |
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| Allows a workflow to "branch" or follow a path following the execution |
| of this task. |
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| 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. |
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| Note that using tasks with ``depends_on_past=True`` downstream from |
| ``BranchPythonOperator`` is logically unsound as ``skipped`` status |
| will invariably lead to block tasks that depend on their past successes. |
| ``skipped`` states propagates where all directly upstream tasks are |
| ``skipped``. |
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| .. method:: execute(self, context) |
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| .. py:class:: ShortCircuitOperator |
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| Bases: :class:`airflow.operators.python_operator.PythonOperator`, :class:`airflow.models.SkipMixin` |
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| Allows a workflow to continue only if a condition is met. Otherwise, the |
| workflow "short-circuits" and downstream tasks are skipped. |
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| 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. |
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| The condition is determined by the result of `python_callable`. |
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| .. method:: execute(self, context) |
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| .. py:class:: PythonVirtualenvOperator(python_callable, requirements=None, python_version=None, use_dill=False, system_site_packages=True, op_args=None, op_kwargs=None, provide_context=False, string_args=None, templates_dict=None, templates_exts=None, *args, **kwargs) |
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| Bases: :class:`airflow.operators.python_operator.PythonOperator` |
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| Allows one to run a function in a virtualenv that is created and destroyed |
| automatically (with certain caveats). |
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| 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, or op_kwargs. You can use string_args though. |
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| :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: str |
| :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_kwargs: list |
| :param op_kwargs: A dict of keyword arguments to pass to python_callable. |
| :type op_kwargs: dict |
| :param provide_context: if set to true, 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. |
| :type provide_context: bool |
| :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] |
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| .. method:: execute_callable(self) |
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| .. method:: _pass_op_args(self) |
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| .. method:: _execute_in_subprocess(self, cmd) |
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| .. method:: _write_string_args(self, filename) |
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| .. method:: _write_args(self, input_filename) |
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| .. method:: _read_result(self, output_filename) |
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| .. method:: _write_script(self, script_filename) |
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| .. method:: _generate_virtualenv_cmd(self, tmp_dir) |
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| .. method:: _generate_pip_install_cmd(self, tmp_dir) |
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| .. staticmethod:: _generate_python_cmd(tmp_dir, script_filename, input_filename, output_filename, string_args_filename) |
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| .. method:: _generate_python_code(self) |
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