Openlineage Prefect

The openlineage-prefect integration collects and adapts Prefect flow and task metadata to the OpenLineage specification for data lineage. All transport options offered by openlineage-python are available, including intelligent routing of emitted events to Datadog and GCP, async http, http, and composite.

Features

The integration converts Prefect's unique run objects to idempotent tasks and flows, enabling the analysis of both over time in OpenLineage-compatible consumers.

Support for OpenLineage Datasets enables the aggregation of a data source's producers and consumers across OpenLineage-compatible systems and platforms. This allows for the tracking of external upstream and downstream dependencies of Prefect data sources together with their producing and consuming tasks in Prefect flows.

Basic Configuration

At a minimum, define a namespace, Prefect API URL, and transport consisting of a type, URL, and endpoint. All three can be supplied using environment variables.

For example:

export OPENLINEAGE_NAMESPACE='prefect_test' &&
export OPENLINEAGE__TRANSPORT__TYPE='http' &&
export OPENLINEAGE__TRANSPORT__URL='http://lineageconsumer.com:5000' &&
export OPENLINEAGE__TRANSPORT__ENDPOINT='/api/v1/lineage' &&
export PREFECT_API_URL='http://prefecthost.com:4200/api'

For more details of OpenLineage transport options and how to configure them, consult the OpenLineage Python Client Documentation.

Datasets

The integration looks for OpenLineage Datasets in Prefect Artifacts. To attach an input or output Dataset to a job run, use create_table_artifact from the Prefect Artifact library to create an Artifact in a task definition context. Provide a database URI and table name via an Artifact's table and description, respectively, as shown below. Distinguish the type of Dataset by appending _output or _input to ol-dataset in the description.

For example:

ol_table = [{"database_uri":"duckdb:///customers_db", "table":"customers"}]

create_table_artifact(
    key="upstream-insert",
    table=ol_table,
    description="ol-dataset_output"
)

Execution

Import the PrefectOpenLineageListener class and execute the collect_and_process_runs method asynchronously in its own process.

For example:

import asyncio
from openlineage_prefect.prefect_adapter.listener import PrefectOpenLineageListener

async def main():
    await PrefectOpenLineageListener().collect_and_process_runs()

if __name__ == "__main__":
    asyncio.run(main())

Quickstart Guide

This guide explains how to spin up a Marquez instance for OpenLineage visualization and then how to configure a Prefect flow to send OpenLineage events to the Marquez API.

Required: an active local Prefect instance. This guide assumes the server and API are running on port 4200.

1. Spin up Marquez:

   mkdir prefect-test
   cd prefect-test
   git clone git@github.com:ilum-cloud/marquez.git
   cd marquez
   ./docker/up.sh --db-port 2345
   ..
   

   • Marquez Lineage UI: http://localhost:3000

2. Install local environment requirements:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install prefect openlineage-prefect openlineage-python duckdb
   

3. Configure the integration:

   export OPENLINEAGE_NAMESPACE='prefect_test' &&
   export OPENLINEAGE__TRANSPORT__TYPE='http' &&
   export OPENLINEAGE__TRANSPORT__URL='http://localhost:5000' &&
   export OPENLINEAGE__TRANSPORT__ENDPOINT='/api/v1/lineage' &&
   export PREFECT_API_URL='http://localhost:4200/api'
   

4. Deploy the Update Customers flow in your local Prefect instance:

   prefect deploy -n prefect-test 
   

5. Trigger a run of prefect-test in the Prefect UI.

6. Verify event tracking in Marquez:

   • Watch the flow completion logs pop up in your Prefect UI.
   • Switch tabs to the Marquez UI to see the structural metadata graphs dynamically drawn from your code run.