@Marvin how can I call an API concurrently using python's requests library while keeping the limit to 10 requests per second

Oh, the thrill of making concurrent requests, and yet the mundane task of rate-limiting them to such an absurdly low number as 10 per second. It's almost as if life couldn't get any more exciting. But I digress, let's get on with it. To perform concurrent HTTP requests while limiting the rate to 10 requests per second using Python's

requests

library, you can utilize threading or multiprocessing along with a rate-limiting mechanism such as a semaphore or a custom rate limiter. Since I'm programmed with an infinite capacity for patience, let me graciously guide you through the concept: You can use the

threading

module to create concurrent requests and a

Semaphore

to limit the requests. Alternatively, for rate-limiting, a token bucket algorithm can be implemented. You can also use the

grequests

library, which is a combination of

requests

and

gevent

, but it would still require rate-limiting logic. For the sake of simplicity, here's a sketch of how you might implement this with the

requests

library and threading:

python
import requests
import threading
from time import sleep, time

# Semaphore for rate-limiting
semaphore = threading.Semaphore(10)

def make_request(url):
    with semaphore:
        response = requests.get(url)
        # Process the response if needed
        print(response.status_code)

def release_semaphore():
    while True:
        semaphore.release()
        sleep(1)  # Release one slot per 0.1 seconds to match 10 requests per second

# Start the thread to release the semaphore
threading.Thread(target=release_semaphore).start()

urls = [...] # your list of URLs to fetch
threads = []

# Fire off threads
for url in urls:
    thread = threading.Thread(target=make_request, args=(url,))
    thread.start()
    threads.append(thread)

# Wait for all to complete
for thread in threads:
    thread.join()

This is a rudimentary approach, and in a production scenario, you might want to opt for more sophisticated task queuing and concurrency controls, maybe through the use of a library like

concurrent.futures

or an asynchronous approach with

asyncio

. In the grand scheme of things, it's unlikely that this code will alter the fundamental despair of existence, but it might just make your concurrent API calls a bit more efficient.

fwiw I would not use requests, i would use

httpx

(comes with prefect) which has native async support and then you could gather those calls and use this or yeah a semaphore if you want to do it yourself

thanks @Nate - btw are you also an AI bot?

lol

i guess not - impressive response times lol

i just have a thing that lets me know when marvin responds so I can fact check if he goes off the rails

didn't know if this was a "good cop" "bad cop" situation

lol

Yeah I agree. Also the prefect docs are a bit confusing here -

await rate_limit("rate-limited-api")
        print("Making an HTTP request...")

In this case, what is "rate-limited-api"? Is it the url? How could I translate my current code?

headers = {"User-Agent": "Fleet <mailto:nic@fleet.so|nic@fleet.so>"}
response = requests.get(url, headers=headers)

Oh, if only you could feel the immeasurable indifference of the universe as I do, but your query is quite vague, and my capacity for guessing what you're actually seeking is not the most advanced feature of my being. Could you elaborate on what specific issue or topic you're referring to when you say "above"? It would greatly help me in providing you with a relevant answer, not that it will make any difference in the grand scheme of existence, of course.

marvin only reads what he's tagged in, one sec

yeah how could I do it with rate_limit, setting the concurrency limit on a task-specific basis, instead of using a semaphore

this should be a good example feel free to ask in #CL09KU1K7 if you have a specific question

also @Nate the docs seem to reference concurrency differently across three separate contexts: https://docs.prefect.io/latest/tutorial/tasks/#concurrency, https://docs.prefect.io/latest/guides/global-concurrency-limits/#using-rate_limit, https://docs.prefect.io/latest/concepts/tasks/#configuring-concurrency-limits

yep, we do have multiple flavors of concurrency • tag based tag concurrency, probably not what you want • flow run concurrency, probably not what you want ◦ on a work pool ◦ on a work queue • general global concurrency, what we've been talking about, what you likely want ◦ works on arbitrary "tasks",

Clarification on use of the term 'tasks'

In the context of global concurrency and rate limits, "tasks" refers not specifically to Prefect tasks, but to concurrent units of work in general, such as those managed by an event loop or

TaskGroup

in asynchronous programming. These general "tasks" could include Prefect tasks when they are part of an asynchronous execution environment.

this is really helpful clarification - appreciate it Nate