Apache Airflow Provider for Vineyard

The apache airflow provider for vineyard contains components to share intermediate data among tasks in Airflow workflows using vineyard.

Vineyard works as the XCom backend for airflow workers to allow transferring large-scale data objects between tasks that cannot be fit into the Airflow's database backend without involving external storage systems like HDFS. The Vineyard XCom backend handles object migration as well when the required inputs are not located where the task is scheduled to execute.

Table of Contents

• Requirements
• Configuration
• Usage
• Run the tests
• Deploy using Docker Compose
• Deploy on Kubernetes

Requirements

Configuration

1. Install required packages:

    pip3 install airflow-provider-vineyard
   

2. Configure Vineyard locally

   The vineyard server can be easier launched locally with the following command:

    python3 -m vineyard --socket=/tmp/vineyard.sock
   

   See also our documentation about launching vineyard.

3. Configure Airflow to use the vineyard XCom backend by specifying the environment variable

   export AIRFLOW__CORE__XCOM_BACKEND=vineyard.contrib.airflow.xcom.VineyardXCom
   

   and configure the location of the UNIX-domain IPC socket for vineyard client by

   export AIRFLOW__VINEYARD__IPC_SOCKET=/tmp/vineyard.sock
   

   or

   export VINEYARD_IPC_SOCKET=/tmp/vineyard.sock
   

   If you have deployed a distributed vineyard cluster, you can also specify the persist environment to enable the vineyard client to persist the data to the vineyard cluster.

   export AIRFLOW__VINEYARD__PERSIST=true
   

Usage

import numpy as np
import pandas as pd

from airflow.decorators import dag, task
from airflow.utils.dates import days_ago

default_args = {
    'owner': 'airflow',
}

@dag(default_args=default_args, schedule_interval=None, start_date=days_ago(2), tags=['example'])
def taskflow_etl_pandas():
    @task()
    def extract():
        order_data_dict = pd.DataFrame({
            'a': np.random.rand(100000),
            'b': np.random.rand(100000)
        })
        return order_data_dict

    @task(multiple_outputs=True)
    def transform(order_data_dict: dict):
        return {"total_order_value": order_data_dict["a"].sum()}

    @task()
    def load(total_order_value: float):
        print(f"Total order value is: {total_order_value:.2f}")

    order_data = extract()
    order_summary = transform(order_data)
    load(order_summary["total_order_value"])

taskflow_etl_pandas_dag = taskflow_etl_pandas()

The example is adapted from the documentation of Airflow, see also Tutorial on the Taskflow API.

Run the tests

Deploy using Docker Compose

We provide a reference docker-compose settings (see docker-compose.yaml) for deploying airflow with vineyard as the XCom backend on Docker Compose. For more details, please refer to the official documentation.

Before deploying the docker-compose, you need to initialize the environment as follows.

$ mkdir -p ./dags ./logs ./plugins ./config
$ echo -e "AIRFLOW_UID=$(id -u)" > .env

Then, copy the example dags to the ./dags directory.

$ cp examples_dags/v6d*.py ./dags

After that, the docker-compose containers could be deployed as

$ cd docker/
$ docker build . -f Dockerfile -t vineyardcloudnative/vineyard-airflow:2.6.3
$ docker compose up

You can see the added DAGs and run them via web ui or cli.

We have also included a diff file docker-compose.yaml.diff that shows the changed pieces that can be introduced into your own docker compose deployment.

Deploy on Kubernetes

We provide a reference settings (see values.yaml) for deploying airflow with vineyard as the XCom backend on Kubernetes, based on the official helm charts.

Next, we will show how to deploy airflow with vineyard on Kubernetes using the helm chart.

You are supposed to deploy the vineyard cluster on the kubernetes cluster first. For example, you can deploy a vineyard cluster with the vineyardctl command. Before using it, please install the vineyardctl first.

$ python3 -m vineyard.ctl deploy vineyard-deployment --create-namespace

The values.yaml mainly tweak the following settings:

• Installing vineyard dependency to the containers using pip before starting workers
• Adding a vineyardd container to the airflow pods
• Mounting the vineyardd's UNIX-domain socket and shared memory to the airflow worker pods

Note that the values.yaml may doesn't work in your environment, as airflow requires other settings like postgresql database, persistence volumes, etc. You can combine the reference values.yaml with your own specific Airflow settings.

The values.yaml for Airflow's helm chart can be used as

# add airflow helm stable repo
$ helm repo add apache-airflow https://airflow.apache.org
$ helm repo update

# deploy airflow
$ helm install -f values.yaml airflow apache-airflow/airflow --namespace airflow --create-namespace

If you want to put the vineyard example DAGs into the airflow scheduler pod and the worker pod, you can use the following command.

$ kubectl cp ./example_dags/v6d_etl.py $(kubectl get pod -lcomponent=scheduler -n airflow -o jsonpath='{.items[0].metadata.name}'):/opt/airflow/dags -c scheduler -n airflow
$ kubectl cp ./example_dags/v6d_etl.py $(kubectl get pod -lcomponent=worker -n airflow -o jsonpath='{.items[0].metadata.name}'):/opt/airflow/dags -c worker -n airflow