VIPIN SOMA
08/20/2024, 1:55 PMMarvin
08/20/2024, 1:56 PMconcurrency
context manager. Here's an example for limiting the number of parallel tasks:
import asyncio
from prefect.concurrency.sync import concurrency
async def process_data(data):
print(f"Processing: {data}")
await asyncio.sleep(1)
return f"Processed: {data}"
async def main():
data_items = list(range(100))
processed_data = []
while data_items:
with concurrency("data-processing", occupy=5):
chunk = [data_items.pop() for _ in range(5)]
processed_data += await asyncio.gather(*[process_data(item) for item in chunk])
asyncio.run(main())
For more details, refer to the documentation on global-concurrency-limits.
2. Using Async Flows:
You can create and run asynchronous flows for concurrent execution. Here's a basic example:
import asyncio
from prefect import flow
async def some_async_function():
await asyncio.sleep(1)
return "I'm an async function"
@flow(log_prints=True)
async def my_async_workflow():
print("I can call any python code here, including prefect tasks or flows")
await some_async_function()
if __name__ == "__main__":
asyncio.run(my_async_workflow())
You can find this example here.
These methods will help you run Prefect flows in parallel effectively.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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