prefect-databricks

Welcome!

Prefect integrations interacting with Databricks

The tasks within this collection were created by a code generator using the service's OpenAPI spec.

The service's REST API documentation can be found here.

Getting Started

Python setup

Requires an installation of Python 3.7+.

We recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.

These tasks are designed to work with Prefect 2.0. For more information about how to use Prefect, please refer to the Prefect documentation.

Installation

Install prefect-databricks with pip:

pip install prefect-databricks

Then, register to view the block on Prefect Cloud:

prefect block register -m prefect_databricks.credentials

Note, to use the load method on Blocks, you must already have a block document saved through code or saved through the UI.

Lists jobs on the Databricks instance

from prefect import flow
from prefect_databricks import DatabricksCredentials
from prefect_databricks.jobs import jobs_list


@flow
def example_execute_endpoint_flow():
    databricks_credentials = DatabricksCredentials.load("my-block")
    jobs = jobs_list(
        databricks_credentials,
        limit=5
    )
    return jobs

example_execute_endpoint_flow()

Launch a new cluster and run a Databricks notebook

Notebook named example.ipynb on Databricks which accepts a name parameter:

name = dbutils.widgets.get("name")
message = f"Don't worry {name}, I got your request! Welcome to prefect-databricks!"
print(message)

Prefect flow that launches a new cluster to run example.ipynb:

from prefect import flow
from prefect_databricks import DatabricksCredentials
from prefect_databricks.jobs import jobs_runs_submit
from prefect_databricks.models.jobs import (
    AutoScale,
    AwsAttributes,
    JobTaskSettings,
    NotebookTask,
    NewCluster,
)


@flow
def jobs_runs_submit_flow(notebook_path, **base_parameters):
    databricks_credentials = DatabricksCredentials.load("my-block")

    # specify new cluster settings
    aws_attributes = AwsAttributes(
        availability="SPOT",
        zone_id="us-west-2a",
        ebs_volume_type="GENERAL_PURPOSE_SSD",
        ebs_volume_count=3,
        ebs_volume_size=100,
    )
    auto_scale = AutoScale(min_workers=1, max_workers=2)
    new_cluster = NewCluster(
        aws_attributes=aws_attributes,
        autoscale=auto_scale,
        node_type_id="m4.large",
        spark_version="10.4.x-scala2.12",
        spark_conf={"spark.speculation": True},
    )

    # specify notebook to use and parameters to pass
    notebook_task = NotebookTask(
        notebook_path=notebook_path,
        base_parameters=base_parameters,
    )

    # compile job task settings
    job_task_settings = JobTaskSettings(
        new_cluster=new_cluster,
        notebook_task=notebook_task,
        task_key="prefect-task"
    )

    run = jobs_runs_submit(
        databricks_credentials=databricks_credentials,
        run_name="prefect-job",
        tasks=[job_task_settings]
    )

    return run


jobs_runs_submit_flow("/Users/username@gmail.com/example.ipynb", name="Marvin")

Note, instead of using the built-in models, you may also input valid JSON. For example, AutoScale(min_workers=1, max_workers=2) is equivalent to {"min_workers": 1, "max_workers": 2}.

Resources

If you encounter any bugs while using prefect-databricks, feel free to open an issue in the prefect-databricks repository.

If you have any questions or issues while using prefect-databricks, you can find help in either the Prefect Discourse forum or the Prefect Slack community.

Development

If you'd like to install a version of prefect-databricks for development, clone the repository and perform an editable install with pip:

git clone https://github.com/PrefectHQ/prefect-databricks.git

cd prefect-databricks/

pip install -e ".[dev]"

# Install linting pre-commit hooks
pre-commit install