Metadata-Version: 2.1
Name: dbnd-airflow
Version: 0.27.16
Summary: Machine Learning Orchestration
Home-page: https://github.com/databand-ai/dbnd
Author: Evgeny Shulman
Author-email: evgeny.shulman@databand.ai
Maintainer: Evgeny Shulman
Maintainer-email: evgeny.shulman@databand.ai
License: UNKNOWN
Project-URL: Documentation, https://databand.readme.io/
Project-URL: Bug-Tracker, https://github.com/databand-ai/dbnd/issues
Project-URL: Source-Code, https://github.com/databand-ai/dbnd
Description: # Dbnd Airflow Operator
        This plugin was written to provide an explicit way of declaratively passing messages between two airflow operators.
        
        This plugin was inspired by [AIP-31](https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-31%3A+Airflow+functional+DAG+definition).
        Essentially, this plugin connects between dbnd's implementation of tasks and pipelines to airflow operators.
        
        This implementation uses XCom communication and XCom templates to transfer said messages.
        This plugin is fully functional, however as soon as AIP-31 is implemented it will support all edge-cases.
        
        Fully tested on airflow 1.10.X.
        
        # Code Example
        Here is an example of how we achieve our goal:
        
        ```python
        import logging
        from typing import Tuple
        from datetime import timedelta, datetime
        from airflow import DAG
        from airflow.utils.dates import days_ago
        from airflow.operators.python_operator import PythonOperator
        from dbnd import task
        
        # Define arguments that we will pass to our DAG
        default_args = {
            "owner": "airflow",
            "depends_on_past": False,
            "start_date": days_ago(2),
            "retries": 1,
            "retry_delay": timedelta(seconds=10),
        }
        @task
        def my_task(p_int=3, p_str="check", p_int_with_default=0) -> str:
            logging.info("I am running")
            return "success"
        
        
        @task
        def my_multiple_outputs(p_str="some_string") -> Tuple[int, str]:
            return (1, p_str + "_extra_postfix")
        
        
        def some_python_function(input_path, output_path):
            logging.error("I am running")
            input_value = open(input_path, "r").read()
            with open(output_path, "w") as output_file:
                output_file.write(input_value)
                output_file.write("\n\n")
                output_file.write(str(datetime.now().strftime("%Y-%m-%dT%H:%M:%S")))
            return "success"
        
        # Define DAG context
        with DAG(dag_id="dbnd_operators", default_args=default_args) as dag_operators:
            # t1, t2 and t3 are examples of tasks created by instantiating operators
            # All tasks and operators created under this DAG context will be collected as a part of this DAG
            t1 = my_task(2)
            t2, t3 = my_multiple_outputs(t1)
            python_op = PythonOperator(
                task_id="some_python_function",
                python_callable=some_python_function,
                op_kwargs={"input_path": t3, "output_path": "/tmp/output.txt"},
            )
            """
            t3.op describes the operator used to execute my_multiple_outputs
            This call defines the some_python_function task's operator as dependent upon t3's operator
            """
            python_op.set_upstream(t3.op)
        ```
        
        As you can see, messages are passed explicitly between all three tasks:
        - t1, the result of the first task is passed to the next task my_multiple_outputs
        - t2 and t3 represent the results of my_multiple_outputs
        - some_python_function is wrapped with an operator
        - The new python operator is defined as dependent upon t3's execution (downstream) - explicitly.
        
        >Note: If you run a function marked with the `@task` decorator without a DAG context, and without using the dbnd
        library to run it - it will execute absolutely normally!
        
        Using this method to pass arguments between tasks not only improves developer user-experience, but also allows
        for pipeline execution support for many use-cases. It does not break currently existing DAGs.
        
        # Using dbnd_config
        Let's look at the example again, but change the default_args defined at the very top:
        ```python
        default_args = {
            "owner": "airflow",
            "depends_on_past": False,
            "start_date": days_ago(2),
            "retries": 1,
            "retry_delay": timedelta(minutes=5),
            'dbnd_config': {
                "my_task.p_int_with_default": 4
            }
        }
        ```
        
        Added a new key-value pair to the arguments called `dbnd_config`
        
        `dbnd_config` is expected to define a dictionary of configuration settings that you can pass to your tasks. For example,
        the `dbnd_config` in this code section defines that the int parameter `p_int_with_default` passed to my_task will be
        overridden and changed to `4` from the default value `0`.
        
        To see further possibilities of changing configuration settings, see our [documentation](https://dbnd.readme.io/)
        
Keywords: orchestration,data,machinelearning
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: airflow
Provides-Extra: tests
