airflow-provider-vineyard 0.5.3

Vineyard provider for apache-airflow

Apache Airflow Provider for Vineyard

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

Vineyard works as a 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 is not located on where the task is scheduled to execute.

Table of Contents

• Requirements
• Configuration and Usage
• Run the tests
• Deploy on Kubernetes

Requirements

The following packages are needed to run Airflow on Vineyard,

• airflow >= 2.1.0
• vineyard >= 0.2.12

Configuration and Usage

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 UNIX-domain IPC socket for vineyard client by

    export AIRFLOW__VINEYARD__IPC_SOCKET=/tmp/vineyard.sock
   

   or

    export VINEYARD_IPC_SOCKET=/tmp/vineyard.sock
   

4. Launching your airflow scheduler and workers, and run the following DAG as example,

    ```python
    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()
    ```
   

In above example, task :code:extract and task :code:transform shares a :code:pandas.DataFrame as the intermediate data, which is impossible as it cannot be pickled and when the data is large, it cannot be fit into the table in backend databases of Airflow.

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

Run the tests

1. Start your vineyardd with the following command,

    python3 -m vineyard
   

2. Set airflow to use the vineyard XCom backend, and run tests with pytest,

    export AIRFLOW__CORE__XCOM_BACKEND=vineyard.contrib.airflow.xcom.VineyardXCom
   
    pytest -s -vvv python/vineyard/contrib/airflow/tests/test_python_dag.py
    pytest -s -vvv python/vineyard/contrib/airflow/tests/test_pandas_dag.py
   

The pandas test suite is not possible to run with the default XCom backend, vineyard enables airflow to exchange complex and big data without modify the DAG and tasks!

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.

Deploying vineyard requires etcd, to ease to deploy process, you first need to setup a standalone etcd cluster. A test etcd cluster with only one instance can be deployed by

$ kubectl create -f etcd.yaml

The values.yaml mainly tweaks the following settings:

• Installing vineyard dependency to the containers using pip before start 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, presistance 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 $RELEASE_NAME apache-airflow/airflow --namespace $NAMESPACE