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Welcome!

Prefect Orion is the second-generation workflow orchestration engine from Prefect, now available as a technical preview.

Orion has been designed from the ground up to handle the dynamic, scalable workloads that the modern data stack demands. Powered by a brand-new, asynchronous rules engine, it represents an enormous amount of research, development, and dedication to a simple idea:

You should love your workflows again.

Read the docs, run the code, or host the UI. Join thousands of community members in our Slack community to share your thoughts and feedback. Thanks for being part of the mission to build a new kind of workflow system and, of course, happy engineering!

Don't Panic

Prefect Orion is under active development and may change rapidly. For production use, we recommend Prefect Core.


Hello, Orion!

Prefect is the easiest way to transform any function into a unit of work that can be observed and governed by orchestration rules.

Add workflow features like retries, distributed execution, scheduling, caching, and much more, with minimal changes to your code. Every activity is tracked and becomes visible in the Orion Dashboard.

Decorate functions to automatically retry them on failure while providing complete visibility in the Orion Dashboard.

from prefect import flow, task
from typing import List
import httpx


@task(retries=3)
def get_stars(repo: str):
    url = f"https://api.github.com/repos/{repo}"
    count = httpx.get(url).json()["stargazers_count"]
    print(f"{repo} has {count} stars!")


@flow(name="Github Stars")
def github_stars(repos: List[str]):
    for repo in repos:
        get_stars(repo)


# run the flow!
github_stars(["PrefectHQ/Prefect", "PrefectHQ/miter-design"])

Control task execution by changing a flow's executor. The tasks in this flow will automatically be submitted to run in parallel on a Dask distributed cluster.

from prefect import flow, task
from prefect.executors import DaskExecutor
from typing import List
import httpx


@task(retries=3)
def get_stars(repo: str):
    url = f"https://api.github.com/repos/{repo}"
    count = httpx.get(url).json()["stargazers_count"]
    print(f"{repo} has {count} stars!")


@flow(name="Github Stars", executor=DaskExecutor())
def github_stars(repos: List[str]):
    for repo in repos:
        get_stars(repo)


# run the flow!
if __name__ == "__main__":
    github_stars(["PrefectHQ/Prefect", "PrefectHQ/miter-design"])

Guarding __main__

When using Python multiprocessing (as Dask does), best practice is to guard global-scope calls with if __name__ == "__main__":. This avoids an infinite recursion if you run the code as a standalone script (with certain process start methods). If you run the code interactively, you don't need the guard.

With native async support, concurrent parallelism is easy. Asynchronous flows can include a mix of synchronous and asynchronous tasks, just like Python.

from prefect import flow, task
from typing import List
import httpx
import asyncio


@task(retries=3)
async def get_stars(repo: str):
    async with httpx.AsyncClient() as client:
        response = await client.get(f"https://api.github.com/repos/{repo}")
    count = response.json()["stargazers_count"]
    print(f"{repo} has {count} stars!")


@flow(name="Github Stars")
async def github_stars(repos: List[str]):
    await asyncio.gather(*[get_stars(repo) for repo in repos])


# run the flow!
asyncio.run(github_stars(["PrefectHQ/Prefect", "PrefectHQ/miter-design"]))

After running any of these flows, fire up the UI to gain insight into their execution:

prefect orion start

From here, you can continue to use Prefect interactively or set up automated deployments.

Next steps

Orion was designed to be incrementally adopted into your workflows, and our documentation is organized to support your exploration as much as possible. It is organized into four main sections whose applicability will depend on your objectives and comfort level.

Getting started

Begin by installing Orion on your machine, then follow one of our friendly tutorials to learn by example. See the Getting Started overview for more.

Concepts

Learn more about Orion's features and design by reading our in-depth concept docs. These are intended to introduce the building blocks of Orion, build up to orchestration and deployment, and finally cover some of the advanced use cases that Orion makes possible.

Frequently asked questions

Orion represents a fundamentally new way of building and orchestrating data workflows. Learn more about the project by reading our FAQ.

API reference

Orion provides a number of programmatic workflow interfaces, each of which is documented in the API Reference. This is where you can learn how a specific function works, or see the expected payload for a REST endpoint.

Join the community

Orion was made possible by the fastest-growing community of data engineers. The Prefect Slack community is a fantastic place to learn more, ask questions, or get help with workflow design. Join us and thousands of friendly data engineers to help build a new kind of workflow system.