Pipeline management is crucial for efficient data operations within a company. Many engineering teams rely on tools like Airflow to help them organize workflows, including ETL processes, reporting pipelines, or machine learning projects.
Airflow offers rich extensibility, allowing developers to arrange workloads into a sequence of tasks. These tasks are then declared within a DAG context in a dag.py file, specifying task dependencies.
As a workflow grows in complexity, the increasing intricacy of task relations can lead to confusion and disrupt the DAG structure. This complexity often results in decreased code maintainability, particularly in collaborative scenarios.
airfly tries to alleviate such pain points and streamline the development life cycle. It operates under the assumption that all tasks are managed within a certain Python module. Developers define task dependencies while creating task objects. During deployment, airfly can resolve the dependency tree and automatically generate the dag.py for you.
airfly helps you build complex dags
dag.pyautomation: focus on your task, let airfly handle the rest.- No need to install Airflow: keep your environment lean.
- support task group: a nice feature from Airflow 2.0+
- support duck typing: flexible class inheritance.
Download airfly from PyPI
$ pip install airfly
$ airfly --help
Usage: airfly [OPTIONS]
Options:
--version Show version and exit.
-n, --name TEXT Assign to DAG id.
-m, --modname TEXT Name of the module to search tasks for building
the task dependency tree and using it to
generate the airflow DAG file.
-p, --path TEXT Insert into "sys.path" to include certain
modules, multi-value is allowed.
-e, --exclude-pattern TEXT Exclude the tasks from the dependency tree if
their __qualname__ get matched with this regex
pattern.
-i, --includes TEXT Paths of python files, the code within will be
included in the output DAG file, multi-value is
allowed.
-d, --dag-params TEXT Parameters to construct DAG object, defined by a
dictionary in a python file. Pass this option
with <python-file>:<variable> form, the
<variable> should be the dictionary which will
be passed to DAG as keyword arguments.
-t, --task-class TEXT Target task class to search, default:
'airfly.model.v1.AirFly'
-g, --task-group BOOLEAN Whether to enable TaskGroup, default: True
--help Show this message and exit.
airfly assumes the tasks are populated in a Python module(or a package, e.g., man_dag in the below example), the dependencies are declared by assigning upstream or downstream attributes to each task. A task holds some attributes corresponding to an airflow operator, when airfly walks through the entire module, all tasks are discovered and collected, the dependency tree and the DAG context are auto-built, with some ast helpers, airfly can wrap the information, convert it into python code, and finally save them to dag.py.
main_dag
├── __init__.py
├── mod_a.py
│ ├── task_a1
│ └── task_a2
│ └── upstream: task_a1
├── mod_b.py
│ └── task_b1
│ └── downstream: task_a1, task_a2
├── sub_dag
│ ├── __init__.py
│ ├── mod_c.py
: :Declare a task as following(see demo):
# in demo.py
from airfly.model import AirFly
class print_date(AirFly):
op_class = "BashOperator"
op_params = dict(bash_command="date")
# during dag generation,
# this class will be converted to airflow operator
print_date._to_ast(print_date).show()
# examples_tutorial_demo_print_date = BashOperator(
# task_id='examples.tutorial.demo.print_date',
# bash_command='date',
# task_group=group_examples_tutorial_demo
# )op_class (str): specifies the airflow operator to this task.op_params: keyword arguments which will be passed to the airflow operator(op_class), a parameter (i.e., value in the dictionary) could be one of the primitive types, a function or a class.
You can also define the attributes by property:
from airfly.model import AirFly
class print_date(AirFly):
@property
def op_class(self):
return "BashOperator"
@property
def op_params(self):
return dict(bash_command="date")
print_date._to_ast(print_date).show()
# examples_tutorial_demo_print_date = BashOperator(
# task_id='examples.tutorial.demo.print_date',
# bash_command='date',
# task_group=group_examples_tutorial_demo
# )By default, the class name(print_date) maps to task_id to the applied operator after dag generation. You can change this behavior by overriding _get_taskid as a classmethod, you have to make sure the task id is globally unique:
from airfly.model import AirFly
class print_date(AirFly):
@classmethod
def _get_taskid(cls):
# customize the task id
return f"my_task_{cls.__qualname__}"
op_class = "BashOperator"
op_params = dict(bash_command="date")
print_date._to_ast(print_date).show()
# my_task_print_date = BashOperator(
# task_id='my_task_print_date',
# bash_command='date',
# task_group=group_my_task_print_date
# )Specifying task dependency with upstream or downstream.
# in demo.py
from textwrap import dedent
templated_command = dedent(
"""
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
)
class templated(AirFly):
op_class = "BashOperator"
op_params = dict(depends_on_past=False,
bash_command=templated_command,
params={"my_param": "Parameter I passed in"})
class sleep(AirFly):
op_class = "BashOperator"
op_params = dict(depends_on_past=False,
bash_command="sleep 5",
retries=3)
upstream = print_date
@property # property also works
def downstream(self):
return (templated,)upstream/downstream: return a task class or a iterable such as list or tuple.
Generate the dag by the command:
$ airfly --name demo_dag --modname demo > dag.pyOutput in dag.py:
# This file is auto-generated by airfly 1.0.0
from airflow.models import DAG
from airflow.utils.task_group import TaskGroup
with DAG("demo_dag") as dag:
from airflow.operators.bash import BashOperator
group_demo = TaskGroup(group_id="demo", prefix_group_id=False)
demo_print_date = BashOperator(
task_id="demo.print_date", bash_command="date", task_group=group_demo
)
demo_sleep = BashOperator(
task_id="demo.sleep",
depends_on_past=False,
bash_command="sleep 5",
retries=3,
task_group=group_demo,
)
demo_templated = BashOperator(
task_id="demo.templated",
depends_on_past=False,
bash_command='\n{% for i in range(5) %}\n echo "{{ ds }}"\n echo "{{ macros.ds_add(ds, 7)}}"\n echo "{{ params.my_param }}"\n{% endfor %}\n',
params={"my_param": "Parameter I passed in"},
task_group=group_demo,
)
demo_print_date >> demo_sleep
demo_sleep >> demo_templatedMake sure the demo module is in the current environment so that airfly can find it.
If it's not the case, you can use --path/-p to add the location of the module into sys.path, e.g.,
.
├── folder
│ └── subfolder
│ └── demo.py # Assume this is the target module
:
$ airfly --name demo_dag --path folder/subfolder --modname demo > dag.pyThe target module can be a package as well, e.g.,
.
├── folder
│ └── subfolder
│ └── demo # Assume this is the target package
│ ├── __init__.py
│ ├── module_a.py
: :
$ airfly --name demo_dag --path folder/subfolder --modname demo > dag.pyManage the DAG arguments in a python file(see demo), then pass them to airfly.
# in params.py
from datetime import timedelta
from airflow.utils.dates import days_ago
default_args = {
"owner": "airflow",
"depends_on_past": False,
"email": ["airflow@example.com"],
"email_on_failure": False,
"email_on_retry": False,
"retries": 1,
"retry_delay": timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
# 'wait_for_downstream': False,
# 'dag': dag,
# 'sla': timedelta(hours=2),
# 'execution_timeout': timedelta(seconds=300),
# 'on_failure_callback': some_function,
# 'on_success_callback': some_other_function,
# 'on_retry_callback': another_function,
# 'sla_miss_callback': yet_another_function,
# 'trigger_rule': 'all_success'
}
dag_kwargs = dict(
default_args=default_args,
description="A simple tutorial DAG",
schedule_interval=timedelta(days=1),
start_date=days_ago(2),
tags=["example"],
)Inject the arguments by passing --dag-params option, with the format of <python-file>:<variable>:
$ airfly --name demo_dag --modname demo --dag-params params.py:dag_kwargs > dag.py
Output in dag.py:
# This file is auto-generated by airfly 1.0.0
from datetime import timedelta
from airflow.models import DAG
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup
# >>>>>>>>>> Include from 'params.py'
default_args = {
"owner": "airflow",
"depends_on_past": False,
"email": ["airflow@example.com"],
"email_on_failure": False,
"email_on_retry": False,
"retries": 1,
"retry_delay": timedelta(minutes=5),
}
dag_kwargs = dict(
default_args=default_args,
description="A simple tutorial DAG",
schedule_interval=timedelta(days=1),
start_date=days_ago(2),
tags=["example"],
)
# <<<<<<<<<< End of code insertion
with DAG("demo_dag", **dag_kwargs) as dag:
from airflow.operators.bash import BashOperator
group_demo = TaskGroup(group_id="demo", prefix_group_id=False)
demo_print_date = BashOperator(
task_id="demo.print_date", bash_command="date", task_group=group_demo
)
demo_sleep = BashOperator(
task_id="demo.sleep",
depends_on_past=False,
bash_command="sleep 5",
retries=3,
task_group=group_demo,
)
demo_templated = BashOperator(
task_id="demo.templated",
depends_on_past=False,
bash_command='\n{% for i in range(5) %}\n echo "{{ ds }}"\n echo "{{ macros.ds_add(ds, 7)}}"\n echo "{{ params.my_param }}"\n{% endfor %}\n',
params={"my_param": "Parameter I passed in"},
task_group=group_demo,
)
demo_print_date >> demo_sleep
demo_sleep >> demo_templatedairfly wraps required information including variables and imports into output python script, and pass the specified value to DAG object.
By passing --exclude-pattern to match any unwanted objects with their __qualname__. then filter them out.
$ airfly --name demo_dag --modname demo --exclude-pattern templated > dag.py
Output in dag.py:
# This file is auto-generated by airfly 1.0.0
from airflow.models import DAG
from airflow.utils.task_group import TaskGroup
with DAG("demo_dag") as dag:
from airflow.operators.bash import BashOperator
group_demo = TaskGroup(group_id="demo", prefix_group_id=False)
demo_print_date = BashOperator(
task_id="demo.print_date", bash_command="date", task_group=group_demo
)
demo_sleep = BashOperator(
task_id="demo.sleep",
depends_on_past=False,
bash_command="sleep 5",
retries=3,
task_group=group_demo,
)
demo_print_date >> demo_sleepThe templated task is gone.
airfly defines TaskGroup in the DAG context and assigns task_group to each operator for you.
It maps the module hierarchy to the nested group structure,
so the tasks in the same python module will be grouped closer.
If you don't like this feature, pass --task-group/-g with False to disable it.
In fact, there's no need to inherite from AirFly, you can have your own task class definition, as long as it provides certain attributes, airfly can still work for you.
# my_task_model.py
from typing import Any, Dict, Iterable, Type, Union
TaskClass = Type["MyTask"]
class MyTask:
# airfly assumes these attributes exist
op_class: str = "BashOperator"
op_params: Dict[str, Any] = None
op_module: str = None
upstream: Union[TaskClass, Iterable[TaskClass]] = None
downstream: Union[TaskClass, Iterable[TaskClass]] = None
# other stuffs
# in demo2.py
from textwrap import dedent
from my_task_model import MyTask
class print_date(MyTask):
op_params = dict(bash_command="date")
templated_command = dedent(
"""
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
)
class templated(MyTask):
op_params = dict(
depends_on_past=False,
bash_command=templated_command,
params={"my_param": "Parameter I passed in"},
)
class sleep(MyTask):
op_params = dict(depends_on_past=False, bash_command="sleep 5", retries=3)
upstream = print_date
downstream = (templated,)Pass the task definition with --task-class
$ airfly --name demo_dag --modname demo2 --task-class my_task_model.MyTask > dag.py
Output in dag.py:
# This file is auto-generated by airfly 1.0.0
from airflow.models import DAG
from airflow.utils.task_group import TaskGroup
with DAG("demo_dag") as dag:
from airflow.operators.bash import BashOperator
group_demo2 = TaskGroup(group_id="demo2", prefix_group_id=False)
demo2_print_date = BashOperator(
task_id="demo2.print_date", bash_command="date", task_group=group_demo2
)
demo2_sleep = BashOperator(
task_id="demo2.sleep",
depends_on_past=False,
bash_command="sleep 5",
retries=3,
task_group=group_demo2,
)
demo2_templated = BashOperator(
task_id="demo2.templated",
depends_on_past=False,
bash_command='\n{% for i in range(5) %}\n echo "{{ ds }}"\n echo "{{ macros.ds_add(ds, 7)}}"\n echo "{{ params.my_param }}"\n{% endfor %}\n',
params={"my_param": "Parameter I passed in"},
task_group=group_demo2,
)
demo2_print_date >> demo2_sleep
demo2_sleep >> demo2_templatedPlease explore more examples here.