airflow-yeedu-operator 2.7.0

Submission and monitoring of jobs and notebooks using the Yeedu API in Apache Airflow.

Airflow Yeedu Operator

Note: This version of airflow-yeedu-operator is compatible only with Apache Airflow 2.x. Apache Airflow 3.x is not supported.

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Installation

To install the Yeedu Operator in your Airflow environment, run:

pip3 install airflow-yeedu-operator

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Overview

The YeeduOperator enables Airflow users to submit and monitor Spark jobs and notebooks in Yeedu. It provides a smooth interface to:

• Submit notebooks and jobs to Yeedu
• Monitor job progress and completion
• Handle failures and capture logs in Airflow UI

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Prerequisites

• Apache Airflow 2.x environment
• Valid credentials to interact with the Yeedu API.
• Yeedu Authentication (LDAP, AAD, or SSO)
• Valid certificate for SSL if applicable

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Airflow Connection Setup

Step 1: Create Airflow Connection

1. In the Airflow UI, go to Admin > Connections
2. Click the + Add Connection button to create a new connection

Fill in the following fields:

Field	Value / Example
Conn Id	yeedu_connection
Conn Type	HTTP
Login	Your LDAP/AAD username (if applicable)
Password	Your password (if applicable)
Extra	JSON with SSL options (see below)

Extra JSON Field

{
    "YEEDU_AIRFLOW_VERIFY_SSL": "true",
    "YEEDU_SSL_CERT_FILE": "/path/to/cert/file"
}

Replace /path/to/cert/file with the actual path to your certificate file.

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SSO Token Setup (Only for SSO auth)

If your Yeedu authentication method is SSO, follow these steps:

1. Go to Admin > Variables
2. Click + Add Variable
3. Enter:
   • Key: e.g., yeedu_sso_token
   • Value: your Yeedu login token

You will refer to this variable in your DAG using token_variable_name.

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Example DAG

DAG Definition

from datetime import datetime, timedelta
from airflow import DAG
from yeedu.operators.yeedu import YeeduOperator

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'yeedu_job_execution',
    default_args=default_args,
    description='DAG to execute jobs using Yeedu API',
    schedule_interval='@once',
    catchup=False,
)

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Task Configuration

LDAP / AAD Authentication

Use Login and Password in the Airflow connection:

submit_job_task = YeeduOperator(
    task_id='LDAP_TASK',
    job_url='https://hostname:{restapi_port}/tenant/tenant_id/workspace/workspace_id/spark/notebook/notebook_id', 
    #Replace with your Job/Notebook Url
    connection_id='yeedu_connection', # Replace with your Connection Id
    dag=dag,
)

Copy the Job/Notebook URL from the Yeedu UI and replace the port in the URL with the actual restapi_port value before using it in the DAG.

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SSO Authentication

Use a token stored in Airflow Variables:

submit_job_task = YeeduOperator(
    task_id='SSO_TASK',
    job_url='https://hostname:{restapi_port}/tenant/tenant_id/workspace/workspace_id/spark/notebook/notebook_id', 
    #Replace with your Job/Notebook Url
    connection_id='yeedu_connection',
    token_variable_name='yeedu_sso_token',  # Replace with your variable key
    dag=dag,
)

You must skip Login and Password in the Airflow connection for SSO.

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Execution Steps

1. Save your DAG file in the dags/ folder of your Airflow installation.
2. Ensure the connection and (if needed) token variable are configured correctly.
3. Trigger the DAG manually or let it run on the scheduled interval.
4. Monitor the Airflow UI/Yeedu UI for execution progress and logs.

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Screenshot (Connection Example)

Screenshot (Variable Example)

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