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prefect-databricks

PyPI

Welcome!

Prefect integrations for 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. For more information about how to use Prefect, please refer to the Prefect documentation.

Installation

Install prefect-databricks with pip:

pip install prefect-databricks

A list of available blocks in prefect-databricks and their setup instructions can be found here.

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()

Use with_options to customize options on any existing task or flow

custom_example_execute_endpoint_flow = example_execute_endpoint_flow.with_options(
    name="My custom flow name",
    retries=2,
    retry_delay_seconds=10,
)

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}.

For more tips on how to use tasks and flows in a Collection, check out Using Collections!

Blocks Catalog

Below is a list of Blocks available for registration in prefect-databricks.

To register blocks in this module to view and edit them on Prefect Cloud:

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

Credentials Module

DatabricksCredentials

To load the DatabricksCredentials:

from prefect import flow
from prefect_databricks.credentials import DatabricksCredentials

@flow
def my_flow():
    my_block = DatabricksCredentials.load("MY_BLOCK_NAME")

my_flow()

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.

Feel free to star or watch prefect-databricks for updates too!

Contributing

If you'd like to help contribute to fix an issue or add a feature to prefect-databricks, please propose changes through a pull request from a fork of the repository.

Here are the steps: 1. Fork the repository 2. Clone the forked repository 3. Install the repository and its dependencies:

pip install -e ".[dev]"
4. Make desired changes 5. Add tests 6. Insert an entry to CHANGELOG.md 7. Install pre-commit to perform quality checks prior to commit:
pre-commit install
8. git commit, git push, and create a pull request