@Marvin If I have a lot of ETL functionality in a class that is also used outside of my prefect setup - what is some good patterns to reuse that in prefect and decorate it for the prefect usage, but not necessarily all else? And If I decorate the "main version" with prefect decorators, will that interfere when used outside of prefect?

✅ thought for 361.8 seconds

quick and dirty approach would be

Foo(...).run_as_flow()

where

class Foo:
   ...
   def run_as_flow(self, **kwargs):
      from prefect import flow
      return flow(self.run)(**kwargs)

Great question. Short answer: you can safely reuse your existing class in Prefect without tightly coupling to it. In Prefect 3.x, a

@task

-decorated function still behaves like a normal Python function when called outside a flow, so it won’t interfere with non-Prefect usage as long as you don’t rely on Prefect-only context (e.g.,

get_run_logger()

, retries) during those calls. Good patterns to consider - Keep business logic pure; wrap with thin Prefect adapters - Keep your ETL class free of Prefect imports and orchestration concerns. - Create small wrapper functions (or methods) that call into your class and decorate those with

@task

. This localizes Prefect to your orchestration layer and keeps your core code portable and testable.

# core.py (no Prefect imports)
class ETL:
    def __init__(self, cfg):
        self.cfg = cfg

    def extract(self, src):
        ...

    def transform(self, data):
        ...

    def load(self, data, dest):
        ...

# orchestrate.py
from prefect import task, flow

from core import ETL

@task
def extract_t(cfg, src):
    etl = ETL(cfg)
    return etl.extract(src)

@task
def transform_t(data):
    etl = ETL(cfg=None)  # or pass cfg if needed
    return etl.transform(data)

@task
def load_t(cfg, data, dest):
    etl = ETL(cfg)
    etl.load(data, dest)

@flow
def etl_flow(cfg, src, dest):
    raw = extract_t.submit(cfg, src)
    clean = transform_t.submit(raw)
    load_t.submit(cfg, clean, dest)

- Decorating methods directly (works, with caveats) - You can put

@task

on instance methods; Prefect will bind

self

correctly. - Be mindful that passing a live instance to a remote worker means that instance will be serialized (pickled). If

self

holds non-serializable or heavy state (DB conns, clients), prefer passing config and constructing the object inside the task instead.

from prefect import task

class ETL:
    def __init__(self, cfg):
        self.cfg = cfg

    @task
    def extract(self, src):
        return ...  # uses self.cfg

# Outside a flow this is fine – runs like a normal function
etl = ETL(cfg=...)
rows = etl.extract(src)  # synchronous call, no orchestration

- Conditional/no-op decoration if you want zero coupling - If you want one code path that can run with or without Prefect:

import os
from prefect import task

ORCHESTRATE = os.getenv("PREFECT_ORCHESTRATE", "1") == "1"
def maybe(deco):
    return deco if ORCHESTRATE else (lambda f: f)

@maybe(task(retries=2))
def transform(data):
    ...

- Use the .fn attribute and with_options for reuse and testing -

my_task.fn(...)

calls the undecorated function (great for unit tests). -

my_task.with_options(name="X", retries=3)

gives you a reconfigured task without redecorating. - Logging pattern that works in and out of Prefect - Use Prefect run logger when available; otherwise fall back to stdlib logging.

from prefect import get_run_logger
import logging

def loggable(msg, **kwargs):
    try:
        logger = get_run_logger()
        <http://logger.info|logger.info>(msg, **kwargs)
    except Exception:
        logging.getLogger(__name__).info(msg)

- Result and parameter serialization - By default, Prefect pickles task inputs/outputs. Passing large, stateful objects (e.g., class instances) increases payload size and can fail if they’re not pickleable. Prefer passing small, serializable config and constructing resources inside the task. - If you need JSON-only I/O, set a serializer on the task and return JSON-serializable objects.

from prefect.serializers import JSONSerializer
from prefect import task

@task(result_serializer=JSONSerializer())
def summarize(data: dict) -> dict:
    ...

- Caching with bound methods - If you decorate instance methods,

self

becomes part of the task inputs and can affect cache keys. If you want cache keys to ignore

self

, either switch to the wrapper pattern or provide a custom

cache_key_fn

that excludes it. - Async support

yea honestly marvin's first suggestion is what i'd recommend i.e. keep your functionality separate, wrap it in prefect only when you actually need to

thanks nate