Working with asyncio

Hatchet’s Python SDK, similarly to other popular libraries like FastAPI, Langchain, etc., makes heavy use of asyncio, and recommends that you do as well!

However, as is the case in FastAPI, when using asyncio in Hatchet, you need to be careful to not have any blocking logic in the functions you define as tasks, as this will block the asyncio event loop and prevent additional work from executing until the blocking operation has completed.

For example, this is async-safe:

import asyncio
 
async def my_task() -> int:
    await asyncio.sleep(5)
 
    return 42

But this is not:

async def my_task() -> int:
    time.sleep(5)
 
    return 42

In the second case, your worker will not be able to process any other work that’s defined as async until the five-second sleep has finished.

Using asyncio.to_thread and loop.run_in_executor

To avoid problems caused by blocking code, you can run your blocking code in an executor with asyncio.to_thread or, more verbosely, loop.run_in_executor. The two examples below are async-safe and will no longer block the event loop.

import asyncio
 
async def my_task() -> int:
    await asyncio.to_thread(time.sleep, 5)
 
    return 42

import asyncio
 
async def my_task() -> int:
    loop = asyncio.get_event_loop()
 
    await loop.run_in_executor(None, time.sleep, 5)
 
    return 42

More Resources for working with asyncio

If you’re looking for more info on developing with AsyncIO more broadly, we highly recommend the following resources:

• Python’s Documentation on Developing with AsyncIO
• Tusamma’s Medium post about How AsyncIO works
• Zac Hatfield-Dodds’s PyCon 2023 talk on Async: scaling structured concurrency with static and dynamic analysis