An opinionated implementation of exclusively using airflow DockerOperators for all Operators
Project description
airflow-docker
Description
An opinionated implementation of exclusively using airflow DockerOperators for all Operators.
Default Operator
from airflow_docker.operator import Operator
task = Operator(
image='some-image:latest',
...
)
Default Sensor
from airflow_docker.operator import Sensor
sensor = Sensor(
image='some-image:latest',
...
)
Task Code
from airflow_docker_helper import client
client.sensor(True)
Branch Operator
Dag Task
from airflow_docker.operator import BranchOperator
branching_task = BranchOperator(
image='some-image:latest',
...
)
Task Code
from airflow_docker_helper import client
client.branch_to_tasks(['task1', 'task2'])
Short Circuit Operator
Dag Task
from airflow_docker.operator import ShortCircuitOperator
short_circuit = ShortCircuitOperator(
image='some-image:latest',
...
)
Task Code
from airflow_docker_helper import client
client.short_circuit() # This task will short circuit if this function gets called
Context Usage
Dag Task
from airflow_docker.operator import Operator
task = Operator(
image='some-image:latest',
provide_context=True,
...
)
Task Code
from airflow_docker_helper import client
context = client.context()
Configuration
The following operator defaults can be set under the airflowdocker
namespace:
- force_pull (boolean true/false)
- auto_remove (boolean true/false)
- network_mode
For example, to set force_pull
to False by default set the following environment variable like so:
export AIRFLOW__AIRFLOWDOCKER__FORCE_PULL=false
Plugin
This package works as an airflow plugin as well. When installed and running airflow, dags can import like so
from airflow.{type, like "operators", "sensors"}.{name specificed inside the plugin class} import *
i.e.
from airflow.operators.airflow_docker import Operator
Project details
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