determine_kwargs ( context ) ¶ execute_callable ( ) ¶Ĭalls the python callable with the given arguments.
Refer to get_template_context for more context. This is the main method to derive when creating an operator.Ĭontext is the same dictionary used as when rendering jinja templates. Template_fields : Sequence = ('templates_dict', 'op_args', 'op_kwargs') ¶ template_fields_renderers ¶ BLUE = '#ffefeb' ¶ ui_color ¶ shallow_copy_attrs : Sequence = ('python_callable', 'op_kwargs') ¶ execute ( context ) ¶ Such as transmission a large amount of XCom to TaskAPI. It can be set to False to prevent log output of return value when you return huge data Defaults to True, which allows return value log output. Show_return_value_in_logs ( bool) – a bool value whether to show return_value Processing templated fields, for examples Templates_exts ( Sequence | None) – a list of file extensions to resolve while In your callable’s context after the template has been applied. _init_ and execute takes place and are made available Will get templated by the Airflow engine sometime between Templates_dict ( dict | None) – a dictionary where the values are templates that Op_args ( Collection | None) – a list of positional arguments that will get unpacked when Op_kwargs ( Mapping | None) – a dictionary of keyword arguments that will get unpacked Python_callable ( Callable) – A reference to an object that is callable PythonOperator ( *, python_callable, op_args = None, op_kwargs = None, templates_dict = None, templates_exts = None, show_return_value_in_logs = True, ** kwargs ) ¶īases: ĭef my_python_callable ( ** kwargs ): ti = kwargs next_ds = kwargs Parameters Dict will unroll to xcom values with keys as keys.Ĭlass. Multiple_outputs ( bool | None) – if set, function return value will be Op_args – a list of positional arguments that will get unpacked when Op_kwargs – a dictionary of keyword arguments that will get unpacked Python_callable ( Callable | None) – A reference to an object that is callable Please use the following instead:įrom corators import my_task() Parameters task ( python_callable = None, multiple_outputs = None, ** kwargs ) ¶Ĭalls and allows users to turn a python function intoĪn Airflow task. Retrieve the execution context dictionary without altering user method's signature.Ī.
Using Public Interface to extend Airflow capabilities.ExternalPythonOperator.execute_callable().PythonVirtualenvOperator.execute_callable().PythonVirtualenvOperator.template_fields.PythonOperator.template_fields_renderers.Using the Public Interface for DAG Authors.Users can run Python, Scala, and Java in interactive mode for exploration, development, and testing. For example, initiate the session through the UI, start interacting with it in the web-based shell, then drop into your local terminal for a spark-shell experience.īoth CLI and web based interactive shell sessions are now supported. Since there is no one size fits all approach to development, CDE interactive sessions give data engineers flexible end-points to start developing Spark applications from anywhere - in a web-based terminal, local CLI, favorite IDE, and even via JDBC from third-party tools.ĬDE exposes sessions as first-class entities via the APIs, as well as the UI and CLI, allowing users to navigate seamlessly across interfaces. Cloudera Data Engineering (CDE) 1.19 introduces interactive Spark sessions for development workflows to take advantage of autoscaling compute and orchestration capabilities that's hybrid and multi-cloud ready.
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