Using the Airflow Airbyte Operator

Start triggering Airbyte jobs with Airflow in minutes

Airbyte allows you to trigger synchronization jobs using Airflow Operators. This tutorial explains how to configure your Airflow DAG to do so.

This tutorial is a preview. The Airbyte Operator is code complete and will be available in a couple of weeks with Airflow's next release.

1. Set up the tools

First, make sure you have Docker installed. (We'll be using the docker-compose command, so your install should contain docker-compose.)

Start Airbyte

If this is your first time using Airbyte, we suggest going through our Basic Tutorial. This tutorial will use the Connection set up in the basic tutorial.

For the purposes of this tutorial, set your Connection's sync frequency to manual. Airflow will be responsible for manually triggering the Airbyte job.

Start Airflow

If you don't have an Airflow instance, we recommend following this Quick Start Airflow Tutorial to set one up.

2. Create a DAG in Airflow to trigger your Airbyte job

Create an Airbyte connection in Airflow

Once Airflow starts, navigate to Airflow's Connections page as seen below. The Airflow UI can be accessed at http://localhost:8080/.

Airflow will use the Airbyte API to execute our actions. The Airbyte API uses HTTP, so we'll need to create a HTTP Connection. Airbyte is typically hosted at localhost:8001. Configure Airflow's HTTP connection accordingly - we've provided a screenshot example.

Don't forget to click save!

Retrieving the Airbyte Connection ID

We'll need the Airbyte Connection ID so our Airflow DAG knows which Airbyte Connection to trigger.

This ID can be seen in the URL on the connection page in the Airbyte UI. The Airbyte UI can be accessed at localhost:8000.

Creating a simple Airflow DAG to run an Airbyte Sync Job

Place the following file inside the /dags directory. Name this file dag_airbyte_example.py.

from airflow import DAG
from airflow.utils.dates import days_ago
from airflow.providers.airbyte.operator import AirbyteTriggerSyncOperator
with DAG(dag_id='trigger_airbyte_job_example',
default_args={'owner': 'airflow'},
schedule_interval='@daily',
start_date=days_ago(1)
) as dag:
money_to_json = AirbyteTriggerSyncOperator(
task_id='airbyte_money_json_example',
airbyte_conn_id='airbyte_conn_example',
connection_id='1e3b5a72-7bfd-4808-a13c-204505490110',
asynchronous=False,
timeout=3600,
wait_seconds=3
)

The Airbyte Airflow Operator accepts the following parameters:

  • airbyte_conn_id: Name of the Airflow HTTP Connection pointing at the Airbyte API. Tells Airflow where the Airbyte API is located.

  • connection_id: The ID of the Airbyte Connection to be triggered by Airflow.

  • asynchronous: Determines how the Airbyte Operator executes. When true, Airflow will monitor the Airbyte Job using an AirbyteJobSensor. Default value is false.

  • timeout: Maximum time Airflow will wait for the Airbyte job to complete. Only valid when asynchronous=False. Default value is 3600 seconds.

  • wait_seconds: The amount of time to wait between checks. Only valid when asynchronous=False. Default value is 3 seconds.

This code will produce the following simple DAG in the Airbyte UI:

Our DAG will show up in the Airflow UI shortly after we place our DAG file, and be automatically triggered shortly after.

Check Airbyte UI's Sync History tab to see if the job started syncing!

Using the asynchronous parameter

If your Airflow instance has limited resources and/or is under load, setting the asynchronous=True can help. Sensors do not occupy an Airflow worker slot, so this is helps reduce Airflow load.

from airflow import DAG
from airflow.utils.dates import days_ago
from airflow.providers.airbyte.operator import AirbyteTriggerSyncOperator
with DAG(dag_id='airbyte_trigger_job_example_async',
default_args={'owner': 'airflow'},
schedule_interval='@daily',
start_date=days_ago(1)
) as dag:
async_money_to_json = AirbyteTriggerSyncOperator(
task_id='airbyte_async_money_json_example',
airbyte_conn_id='airbyte_conn_example',
connection_id='1e3b5a72-7bfd-4808-a13c-204505490110',
asynchronous=True,
)
airbyte_sensor = AirbyteJobSensor(
task_id='airbyte_sensor_money_json_example',
airbyte_conn_id='airbyte_conn_example',
airbyte_job_id=async_money_to_json.output
)
async_money_to_json >> airbyte_sensor

That's it!

Don't be fooled by our simple example of only one Airflow task. Airbyte is a powerful data integration platform supporting many sources and destinations. The Airbyte Airflow Operator means Airbyte can now be easily used with the Airflow ecosystem - give it a shot!

We love to hear any questions or feedback on our Slack. We're still in alpha, so if you see any rough edges or want to request a connector, feel free to create an issue on our Github or thumbs up an existing issue.