airflow-spark-on-k8s-job-builder 0.6.0

A library to limit the amount of unnecessary boilerplate code required when launching Spark Jobs against k8s Spark-Operator in Airflow

Airflow Spark-on-k8s Job Builder

Trying to avoid excessive boilerplate code duplication for building a DAG that will submit a Spark job to Spark-Operator in a Kubernetes cluster.

Using this library

This library was only tested together with Airflow deployments versions 2.7.2 and 2.10.3, and on AWS EKS.

airflow version 2.7.2

In version 2.7.2 we had to use the SparkSensor in order to follow progress and infer when a spark job was successfully completed. For that we pass use_sensor=True to the SparkK8sJobBuilder constructor.

Here is an example of how to use this library:

from airflow import DAG
from datetime import datetime, timedelta

from airflow_spark_on_k8s_job_builder.core import SparkK8sJobBuilder

default_args = {
    "owner": "data-engineering-team",
    "retries": 3,
    "email_on_retry": False,
    "retry_delay": timedelta(minutes=2),
}

DAG_ID = "spark_pi"
TASK_ID = "spark-pi-submit"
TASK_NAMESPACE = "airflow"

builder = SparkK8sJobBuilder(
    task_id=TASK_ID,
    job_name="test-spark-on-k8s",
    main_class="org.apache.spark.examples.SparkPi",
    main_application_file="local:///opt/spark/examples/jars/spark-examples_2.12-3.4.1.jar",
    service_account=TASK_NAMESPACE,
    namespace=TASK_NAMESPACE,
    docker_img="apache/spark",
    docker_img_tag="3.4.1-scala2.12-java11-ubuntu",
    use_sensor=True,
)

dag_params = builder.build_dag_params(
    extra_params={
        "env": "dev",
    },
)

with (DAG(
        DAG_ID,
        default_args=default_args,
        description="An example spark k8s task to exemplify how one can build "
                    "spark airflow coordinates apps on top of k8s",
        params=dag_params,
        schedule_interval=timedelta(days=1),
        catchup=False,
        doc_md=__doc__,
        start_date=datetime(2024, 11, 21),
        dagrun_timeout=timedelta(hours=3),
        max_active_runs=1,
        template_searchpath=[
            # we need this to instruct airflow where it can find the spark yaml file
            "/home/airflow/.local/lib/python3.9/site-packages/airflow_spark_on_k8s_job_builder/",
            "/opt/airflow/dags/",
        ],
        tags=["tutorials", "hello-world"],
) as dag):
    spark_tasks = builder \
        .set_dag(dag) \
        .set_executor_cores(2) \
        .set_executor_instances(2) \
        .set_executor_memory("4g") \
        .build()
    spark_tasks[0] >> spark_tasks[1]

airflow version 2.10.3

In version 2.10.3 the sensor was not needed anymore, but we had to implement a workaround for SparkKubernetesOperator to be able to understand that spark job was successfully completed. To use that fix, you can either call .setup_xcom_sidecar_container() method on the builder object, or, alternatively, instantiate the builder and pass the option update_xcom_sidecar_container=True (directly in SparkK8sJobBuilder constructor).

Here is an example of how to use this library:

from datetime import datetime, timedelta

from airflow import DAG

from airflow_spark_on_k8s_job_builder.core import SparkK8sJobBuilder

default_args = {
    "owner": "data-engineering-team",
    "retries": 3,
    "email_on_retry": False,
    "retry_delay": timedelta(minutes=2),
}

DAG_ID = "spark_pi"
TASK_ID = "spark-pi-submit"
TASK_NAMESPACE = "airflow"

builder = SparkK8sJobBuilder(
    task_id=TASK_ID,
    job_name="test-spark-on-k8s",
    main_class="org.apache.spark.examples.SparkPi",
    main_application_file="local:///opt/spark/examples/jars/spark-examples_2.12-3.4.1.jar",
    service_account=TASK_NAMESPACE,
    namespace=TASK_NAMESPACE,
    docker_img="apache/spark",
    docker_img_tag="3.4.1-scala2.12-java11-ubuntu",
    use_sensor=False,
)

dag_params = builder.build_dag_params(
    extra_params={
        "env": "dev",
    },
)

with (DAG(
        DAG_ID,
        default_args=default_args,
        description="An example spark k8s task to exemplify how one can build "
                    "spark airflow coordinates apps on top of k8s",
        params=dag_params,
        schedule_interval=timedelta(days=1),
        catchup=False,
        doc_md=__doc__,
        start_date=datetime(2024, 11, 21),
        dagrun_timeout=timedelta(hours=3),
        max_active_runs=1,
        template_searchpath=[
            # we need this to instruct airflow where it can find the spark yaml file
            "/home/airflow/.local/lib/python3.9/site-packages/airflow_spark_on_k8s_job_builder/",
            "/opt/airflow/dags/",
        ],
        tags=["tutorials", "hello-world"],
) as dag):
    builder \
        .set_dag(dag) \
        .set_executor_cores(2) \
        .set_executor_instances(2) \
        .set_executor_memory("4g") \
        .setup_xcom_sidecar_container() \
        .build()

Note that the library also contains the yaml template for the spark job, which is used by the SparkK8sJobBuilder to build the DAG. So depending on where you install it, you'll need to reference that same path in the template_searchpath option.

Development

This project uses python 3.9 for development, and pip-compile for dependency management.

The following setup is a suggestion using pyenv to manage both python version, and python virtual environment.

# install current project python version
pyenv install $(cat .python-version)
# confirm your pyenv is using this python version
pyenv which python
pyenv which pip

# create a virtualenv
pyenv virtualenv $(cat .python-version) airflowsparkk8sbuilder

# activate the local virtualenv
pyenv activate airflowsparkk8sbuilder

# make sure pip is up to date
pip install --upgrade pip

# install pip-tools for pip-compile and pip-sync features
pip install pip-tools

# install wheel
pip install wheel

# run pip-sync to install pip-compile generated requirements and dev requirements
pip-sync requirements.txt requirements-dev.txt

Adding Dependencies

The requirements.txt and requirements-dev.txt files are generated using pip-compile and should not be edited manually. To add new dependencies, simply add them to the respective requirements.in or requirements-dev.in files and update the .txt files by running:

pip-compile requirements.in --output-file requirements.txt
pip-compile requirements-dev.in --output-file requirements-dev.txt

To make sure your environment is up-to-date with the latest changes you added, run pip-sync command:

pip-sync requirements.txt requirements-dev.txt

Note: The dev requirements are constrained by any dependencies in the requirements file.

Releasing

Releasing to test pypi

Update the library's version in setup.py. This should build your app in ./dist directory.

Then:

# activate venv
pyenv activate airflowsparkk8sbuilder
# clean up previous builds
python setup.py clean --all && rm -rf dist && rm -rf airflow_spark_on_k8s_job_builder.egg-info
# build a package
python -m build
# upload to test pypi
twine upload --repository testpypi dist/*