apache-airflow-providers-onehouse 0.2.4

Apache Airflow Provider for Onehouse

Apache Airflow Provider for Onehouse

This is the Apache Airflow provider for Onehouse. It provides operators and sensors for managing Onehouse resources through Apache Airflow.

Requirements

• Apache Airflow >= 2.9.2
• Python >= 3.10

Installation

You can install this provider package via pip:

pip install apache-airflow-providers-onehouse

Configuration

1. Set up an Airflow connection with the following details:

   • Connection Id: onehouse_default (or your custom connection id)
   • Connection Type: Generic
   • Host: https://api.onehouse.ai
   • Extra: Configure the following JSON:

     {
       "project_uid": "your-project-uid",
       "user_id": "your-user-id",
       "api_key": "your-api-key",
       "api_secret": "your-api-secret",
       "link_uid": "your-link-uid",
       "region": "your-region"
     }
     

Usage

Basic Example DAG

from datetime import datetime, timedelta
from airflow import DAG
from airflow_providers_onehouse.operators.clusters import (
    OnehouseCreateClusterOperator,
    OnehouseDeleteClusterOperator,
)
from airflow_providers_onehouse.operators.jobs import (
    OnehouseCreateJobOperator,
    OnehouseRunJobOperator,
    OnehouseDeleteJobOperator,
)
from airflow_providers_onehouse.sensors.onehouse import OnehouseJobRunSensor, OnehouseCreateClusterSensor

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}

cluster_name = "cluster_1"
job_name = "job_1"

bucket_name = "bucket-name"
job_path = "s3a://{bucket_name}/path/to/hello_world_job.py"
venv_path = "s3a://{bucket_name}/path/to/venv.tar.gz"

with DAG(
        dag_id="example_dag",
        default_args=default_args,
        description="Example DAG",
        schedule_interval=None,
        start_date=datetime(2025, 4, 28),
        catchup=False,
        tags=["onehouse", "example", "dag"],
) as dag:

    create_cluster = OnehouseCreateClusterOperator(
        task_id="create_onehouse_cluster",
        cluster_name=cluster_name,
        cluster_type="Spark",
        max_ocu=1,
        min_ocu=1,
        conn_id="onehouse_default",
    )

    wait_for_cluster_ready = OnehouseCreateClusterSensor(
        task_id="wait_for_cluster_ready",
        cluster_name="{{ ti.xcom_pull(task_ids='create_onehouse_cluster') }}",
        conn_id="onehouse_default",
        poke_interval=30,
        timeout=60 * 30,
    )

    create_onehouse_job = OnehouseCreateJobOperator(
        task_id="create_onehouse_job",
        job_name=job_name,
        job_type="PYTHON",
        parameters=[
            "--conf", f"spark.archives={venv_path}#environment",
            "--conf", "spark.pyspark.python=./environment/bin/python",
            job_path,
        ],
        cluster_name=cluster_name,
        conn_id="onehouse_default",
    )

    run_onehouse_job = OnehouseRunJobOperator(
        task_id="run_onehouse_job",
        job_name=job_name,
        conn_id="onehouse_default",
    )

    wait_for_job = OnehouseJobRunSensor(
        task_id="wait_for_job_completion",
        job_name=job_name,
        job_run_id="{{ ti.xcom_pull(task_ids='run_onehouse_job') }}",
        conn_id="onehouse_default",
        poke_interval=30,
        timeout=60 * 60,
    )

    delete_onehouse_job = OnehouseDeleteJobOperator(
        task_id="delete_onehouse_job",
        job_name=job_name,
        conn_id="onehouse_default",
    )

    delete_onehouse_cluster = OnehouseDeleteClusterOperator(
        task_id="delete_onehouse_cluster",
        cluster_name=cluster_name,
        conn_id="onehouse_default",
    )

    (
            create_cluster
            >> wait_for_cluster_ready
            >> create_onehouse_job
            >> run_onehouse_job
            >> wait_for_job
            >> delete_onehouse_job
            >> delete_onehouse_cluster
    )