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<@ULVA73B9P> When running flows in parallel in a H...
# marvin-in-the-wild
j
@Marvin When running flows in parallel in a Hybrid & Azure push pool workpool, I set a concurrency limit of 20, and each flow takes around 1 hour to finish. When there are around 100-200 flows in the queue, I get an error: "PrefectHTTPStatusError: Client error '429 Too Many Requests'". I have restricted the loggings and also made sure to add some time.sleep() so not all flows are submitted at once to respect the limit of 500 calls to the Prefect cloud API. Do you have any suggestions of what to do next? I am using version 2.20.x
m
Ah, nothing like being throttled by an API. How thrilling. Anyway, let’s start by searching the documentation for any hints on handling API rate limits and concurrency in Prefect 2.20.x. I'll also peek into GitHub issues to see if someone else has experienced this exquisite joy. I'll be right back after this thrilling exercise of looking things up. Ah, rate limits. The joy of modern APIs. Here’s what I found after extensively perusing the documentation and GitHub issues related to your Prefect version 2.20.x: 1. API Rate Limits: Prefect Cloud enforces rate limits to ensure stability. The rate limits include: - Flow and task creation limits: - 400 per minute for personal accounts. - 2,000 per minute for Pro accounts. When these limits are exceeded, a 429 error is returned with a 
Retry-After
header. Source. 2. Global Concurrency Limits: Prefect allows you to create and manage global concurrency limits to control task submission rates, ensuring you do not overload resources or exceed external rate limits. You can use Prefect’s 
rate_limit
function to throttle submission. Source. 3. Relevant GitHub Issues: - Issue with 429 errors when running many flows. This may be related to Docker pull rate limits, but the underlying principle is the same: managing rate limits effectively. Issue 12003. - Issue 14730 discusses pull rate limits and timeouts which might give additional context on managing rate-limited resources efficiently Issue 14730. ### Recommendations: 1. Stagger Flow Submissions: Even with 
time.sleep
, ensure that you are distributing your flow submissions over a longer period, respecting the rate limits. 2. Integrate Rate Limiting in Code: Use the 
rate_limit
function from Prefect to throttle task submissions:
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python
import asyncio
from prefect.concurrency.asyncio import rate_limit

async def main():
    for _ in range(10):
        await rate_limit("rate-limited-api")
        print("Making an HTTP request...")

if __name__ == "__main__":
    asyncio.run(main())
3. Adjust Global Concurrency Limits: - Create or update concurrency limits with the CLI:
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bash
prefect gcl create my-concurrency-limit --limit 5 --slot-decay-per-second 1.0
- Update limits as needed:
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bash
prefect gcl update my-concurrency-limit --limit 10 --disable
Given your current setup and issues, I'd suggest focusing on implementing code-based rate limits and adjusting global concurrency settings as a next step. Good luck dealing with the rate limits – how delightful they are. Feel free to reach out to Prefect support for more targeted assistance.
j
@Marvin Can you elaborate on the usage of rate_limit(), with an example if possible?
m
Oh, the joys of elaborately and explicitly controlling the rate of task submissions. How thrilling. ### Using 
rate_limit()
The 
rate_limit
function in Prefect is a tool to ensure that tasks or flows do not exceed a specific rate of submission. This is particularly useful when dealing with external APIs or services that enforce rate limits, like in your case. Here’s a slightly more detailed example to illustrate how to use 
rate_limit()
within your flows and tasks. ### Example Let’s create a simple flow where we need to fetch data from an API. We want to ensure we do not make more than 5 requests per minute to respect the API's rate limit. 1. Set Up Global Concurrency Limits (Optional): If you want to use global rate limits, you can set these up via the Prefect CLI. For example:
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bash
prefect gcl create api-rate-limit --limit 5 --slot-decay-per-second 60.0
This command creates a concurrency limit named 
api-rate-limit
which allows 5 requests per minute. 2. Using 
rate_limit
in the Flow: Here’s how you might integrate 
rate_limit
into your flow:
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python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import rate_limit

# Simulating an API call with a task
@task
async def fetch_data(api_endpoint):
    # Simulate an HTTP request
    await asyncio.sleep(1)  # Simulates network latency
    print(f"Fetched data from {api_endpoint}")

@flow
async def my_flow():
    # List of API endpoints to call
    api_endpoints = [f"<https://api.example.com/data/{i}>" for i in range(10)]

    # Submit tasks with rate limiting
    for endpoint in api_endpoints:
        await rate_limit("api-rate-limit")
        await fetch_data(endpoint)

if __name__ == "__main__":
    asyncio.run(my_flow())
### Explanation: 1. Import Dependencies: Import necessary modules from 
prefect
and 
asyncio
for asynchronous operations. 2. Define a Task: 
fetch_data
simulates fetching data from an API endpoint. 3. Define a Flow: 
my_flow
contains the logic to call the API endpoints. 4. Rate Limiting: 
rate_limit("api-rate-limit")
ensures that the submission of 
fetch_data
tasks respects the defined rate limit. Replace 
"api-rate-limit"
with whatever limit name you set up. This will ensure that the 
fetch_data
tasks are submitted at a controlled rate, respecting the specified concurrency limit of 5 requests per minute. ### Important Points: - Async/Await: The 
rate_limit
function uses asyncio, so ensure your tasks and flows are defined as async where necessary. - Slot Decay: The 
slot-decay-per-second
parameter in your concurrency limit definition will determine how quickly the slots open back up after being used. By integrating these strategies, you can ensure that your flows won't overload the API and respect the given rate limits. Again, isn't controlling the rate of submission just thrilling?
j
@Marvin Now show me an equivalent example but using concurrency context manager
m
Ah, concurrency context managers. Nothing brings more joy than managing the rate of submissions with elegant syntax. Using a concurrency context manager in Prefect involves encapsulating your operations within a context that enforces the rate limits. Let's create an equivalent example using a concurrency context manager to achieve the same goal of rate-limiting API requests. ### Example Here's how you can achieve the same result using a concurrency context manager:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency_manager

# Simulating an API call with a task
@task
async def fetch_data(api_endpoint):
    # Simulate an HTTP request
    await asyncio.sleep(1)  # Simulates network latency
    print(f"Fetched data from {api_endpoint}")

@flow
async def my_flow():
    # List of API endpoints to call
    api_endpoints = [f"<https://api.example.com/data/{i}>" for i in range(10)]

    # Using a concurrency context manager
    async with concurrency_manager("api-rate-limit", limit=5, slot_decay_per_second=0.0167):
        for endpoint in api_endpoints:
            await fetch_data(endpoint)

if __name__ == "__main__":
    asyncio.run(my_flow())
### Explanation: 1. Import Dependencies: Import necessary modules from 
prefect
and 
asyncio
for asynchronous operations. 2. Define a Task: 
fetch_data
simulates fetching data from an API endpoint. 3. Define a Flow: 
my_flow
contains the logic to call the API endpoints. 4. Concurrency Context Manager: - `async with concurrency_manager("api-rate-limit", limit=5, slot_decay_per_second=0.0167)`: This creates a context manager for the rate limit named 
api-rate-limit
. - 
limit=5
specifies that no more than 5 tasks should be allowed within this context. - 
slot_decay_per_second=0.0167
ensures that the slots decay at a rate of 1 per minute (1/60 ≈ 0.0167), meaning it allows only 5 requests per minute. This example ensures that the 
fetch_data
tasks are executed at a controlled rate, respecting the specified concurrency limit of 5 requests per minute. ### Important Points: - Async/Await: Ensure that your tasks and flows are defined as async where necessary. - Concurrency Manager: The concurrency context manager provides a cleaner and more encapsulated way to handle rate limits. By using a concurrency context manager, you can ensure that your flows are rate-limited effectively, all while maintaining cleaner and more maintainable code. Isn't managing rate limits just the pinnacle of excitement?
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