Python库 | gusty-0.4.0.tar.gz

# gusty [](https://pypi.org/project/gusty/) [](https://pypi.org/project/gusty/)  [](https://codecov.io/github/chriscardillo/gusty?branch=master) [](https://github.com/psf/black) gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's `create_dag` function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is provide a list of `dependencies` or `external_dependencies` inside of a task file, and gusty will automatically set each task's dependencies and create external task sensors for any external dependencies listed. gusty works with both Airflow 1.x and Airflow 2.x, and has even more features, all of which aim to make the creation, management, and iteration of DAGs more fluid, so that you can intuitively design your DAG and build your tasks. ## What's in gusty? ### Four Ways to Make Tasks gusty will turn every file in a DAG directory into a task. gusty supports four different file types, which offer convenient ways to specify an operator and operator parameters for task creation. | File Type | How It Works | | --------- | -------------------------------------------------------------------------------------------------------------- | | .yml | Declare an `operator` and pass in any operator parameters using YAML | | .py | Simply define a function named `python_callable` and gusty will automatically turn it into a `PythonOperator` | | .ipynb | Put a YAML block at the top of your notebook and specify an `operator` that renders your Jupyter Notebook | | .Rmd | Use the YAML block at the top of your notebook and specify an `operator` that renders your R Markdown Document | Here is quick example of a YAML task file, which might be called something like `hello_world.yml`: ```yml operator: airflow.operators.bash.BashOperator bash_command: echo hello world ``` The resulting task would be a `BashOperator` with the task id `hello_world`. Here is the same approach using a Python file instead, named `hello_world.py`, which gusty will automatically turn into a `PythonOperator`: ```py def python_callable(): phrase = "hello world" print(phrase) ``` ### Easy Dependencies #### Declarative Dependencies Every task file type supports `dependencies` and `external_dependencies` parameters, which gusty will use to automatically assign dependencies between tasks and create external task sensors for any external dependencies listed for a given task. For .yml, .ipynb, and .Rmd task file types, dependencies and external_dependencies would be defined using YAML syntax: ```yml operator: airflow.operators.bash.BashOperator bash_command: echo hello world dependencies: - same_dag_task external_dependencies: - another_dag: another_task - a_whole_dag: all ``` For external dependencies, the keyword `all` can be used when the task should wait on an entire external DAG to run successfully. For a .py task file type, we can define these dependencies simply as variables: ```py dependencies = [ "same_dag_task" ] external_dependencies = [ {"another_dag": "another_task"}, {"a_whole_dag": "all"} ] def python_callable(): phrase = "hello world" print(phrase) ``` #### Dynamic Dependencies gusty can also detect and generate dependencies through a task object's `dependencies` attribute. This means you can also **dynamically** set dependencies. One popular example of this option would be if your operator runs SQL, you can parse that SQL for table names, and attach a list of those table names to the operator's `dependencies` attribute. If those table names listed in the `dependencies` attribute are also task ids in the DAG, gusty will be able to automatically set these dependencies for you! ### DAG and TaskGroup Control Both DAG and TaskGroup objects are created automatically simply by being directories and subfolders, respectively. The directory path you provide to gusty's `create_dag` function will become your DAG (and DAG name), and any subfolder in that DAG by default will be turned into a TaskGroup. gusty offers a few compatible methods for configuring DAGs and Task Groups that we'll cover below. #### Metadata A special file name in any directory or subfolder is `METADATA.yml`, which gusty will use to determine how to configure that DAG or TaskGroup object. Here is an example of a `METADATA.yml` file you might place in a DAG directory: ```yml description: "An example of a DAG created using METADATA.yml" schedule_interval: "1 0 * * *" default_args: owner: airflow depends_on_past: False start_date: !days_ago 1 email: airflow@example.com email_on_failure: False email_on_retry: False retries: 1 retry_delay: !timedelta 'minutes: 5' ``` And here is an example of a `METADATA`.yml file you might place in a TaskGroup subfolder: ```yml tooltip: "This is a task group tooltip" prefix_group_id: True dependencies: - hello_world ``` As seen in the above example, gusty will also accept `dependencies` and `external_dependencies` in a TaskGroup's `METADATA.yml`. This means gusty can wire up your TaskGroup dependencies as well! Note that gusty disables the TaskGroup `prefix_group_id` argument by default, as it's one of gusty's few opinions that tasks should explicitly named unless you say otherwise. gusty also offers a `suffix_group_id` argument for Task Groups! #### create_dag While `METADATA.yml` will always be the primary source of truth for a DAG or TaskGroup's configuration, gusty's `create_dag` function also accepts any parameters that can be passed to Airflow's DAG class, as well as a dictionary of `task_group_defaults` to set default behavior for any TaskGroup created by gusty. Here's an example using `create_dag`, where instead of metadata we use `create_dag` arguments: ```py import airflow from datetime import timedelta from airflow.utils.dates import days_ago from gusty import create_dag dag = create_dag( '/usr/local/airflow/dags/hello_world', description="A dag created without any metadata", schedule_interval="1 0 * * *", default_args={ "owner": "airflow", "depends_on_past": False, "start_date": days_ago(1), "email": "airflow@example.com", "email_on_failure": False, "email_on_retry": False, "retries": 1, "retry_delay": timedelta(minutes=5), }, task_group_defaults={ "tooltip": "This is a task group tooltip", "prefix_group_id": True } ) ``` You might notice that `task_group_defaults` does not include dependencies. For Task Groups, dependencies must be set using TaskGroup-specific metadata. Default arguments in `create_dag` and a DAG or TaskGroup's `METADATA.yml` can be mixed and matched. `METADATA.yml` will always override defaults set in `create_dag`. #### DAG-level Features gusty features additional helpful arguments at the DAG-level to help you design your DAGs with ease: - **`root_tasks`** - A list of task ids which should represent the roots of a DAG. For example, an HTTP sensor might have to succeed before any downstream tasks in the DAG run. - **`leaf_tasks`** - A list of task ids which should represent the leaves of a DAG. For example, at the end of the DAG run, you might save a repo