Apply global concurrency and rate limits

Global concurrency limits allow you to manage execution efficiently, controlling how many tasks, flows, or other operations can run simultaneously. They are ideal for optimizing resource usage, preventing bottlenecks, and customizing task execution.

Rate Limits ensure system stability by governing the frequency of requests or operations. They are suitable for preventing overuse, ensuring fairness, and handling errors gracefully.

When selecting between global concurrency and rate limits, consider your primary goal:

• Choose global concurrency limits for resource optimization and task management.

• Choose rate limits to maintain system stability and fair access to services.

The core difference between a rate limit and a concurrency limit is the way slots are released. With a rate limit, slots are released at a controlled rate determined by slot_decay_per_second. With a concurrency limit, slots are released when the concurrency manager exits.

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Manage global concurrency and rate limits

You can create, read, edit, and delete concurrency limits through the Prefect UI, CLI, or Python SDK.

When creating a concurrency limit, you can specify the following parameters:

• Name: The name of the concurrency limit. This name is also how you’ll reference the concurrency limit in your code. Special characters, such as /, %, &, >, <, are not allowed.
• Concurrency Limit: The maximum number of slots that can be occupied on this concurrency limit.
• Slot Decay Per Second: Controls the rate at which slots are released when the concurrency limit is used as a rate limit. You must configure this value when using the rate_limit function.
• Active: Whether or not the concurrency limit is in an active state.

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Active vs. inactive limits

Global concurrency limits can be in an active or inactive state:

• Active: In this state, slots can be occupied, and code execution is blocked when slots are unable to be acquired.
• Inactive: In this state, slots are not occupied, and code execution is not blocked. Concurrency enforcement occurs only when you activate the limit.

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Slot decay

To implement rate limiting, you can configure “slot decay”, which determines how rapidly used slots are freed up for new tasks.

When you set up a concurrency limit with slot decay:

1. Each time a slot is occupied, it becomes unavailable for other tasks to use.
2. The slot eventually becomes available again over time, based on the slot decay rate (with slot_decay_per_second).
3. This creates a “rate limiting” effect, limiting how often slots can be used.

To configure slot decay, set the slot_decay_per_second parameter when creating or updating a concurrency limit. This value determines how quickly slots refresh:

• A higher value (for example, 5.0) means slots refresh quickly. Tasks can run more frequently, but with short pauses between them.
• A lower value (for example, 0.1) means slots refresh slowly. Tasks run less frequently, with longer pauses between them.

For example:

• With a decay rate of 5.0, you could run a task roughly every 0.2 seconds.
• With a decay rate of 0.1, you’d wait about 10 seconds between task runs.

Choose a decay rate that balances your required frequency of task execution with the acceptable limit of overall system load. This allows you to fine-tune your workflow’s performance and resource usage.

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Through the UI

You can manage global concurrency limits in the Concurrency section of the Prefect UI.

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Through the CLI

You can manage global concurrency limits through the Prefect CLI.

To create a new concurrency limit, use the prefect gcl create command. You must specify a --limit argument, and can optionally specify a --slot-decay-per-second and --disable argument.

prefect gcl create my-concurrency-limit --limit 5 --slot-decay-per-second 1.0

Inspect the details of a concurrency limit using the prefect gcl inspect command:

prefect gcl inspect my-concurrency-limit

To update a concurrency limit, use the prefect gcl update command. You can update the --limit, --slot-decay-per-second, --enable, and --disable arguments:

prefect gcl update my-concurrency-limit --limit 10

prefect gcl update my-concurrency-limit --disable

To delete a concurrency limit, use the prefect gcl delete command:

prefect gcl delete my-concurrency-limit
Are you sure you want to delete global concurrency limit 'my-concurrency-limit'? [y/N]: y
Deleted global concurrency limit with name 'my-concurrency-limit'.

See all available commands and options by running prefect gcl --help.

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Using the concurrency context manager

The concurrencycontext manager allows control over the maximum number of concurrent operations. Select either the synchronous (sync) or asynchronous (async) version, depending on your use case. Here’s how to use it:

Sync

from prefect import flow, task
from prefect.concurrency.sync import concurrency


@task
def process_data(x, y):
    with concurrency("database", occupy=1):
        return x + y


@flow
def my_flow():
    for x, y in [(1, 2), (2, 3), (3, 4), (4, 5)]:
        process_data.submit(x, y)


if __name__ == "__main__":
    my_flow()

Async

import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency


@task
async def process_data(x, y):
    async with concurrency("database", occupy=1):
        return x + y


@flow
def my_flow():
    for x, y in [(1, 2), (2, 3), (3, 4), (4, 5)]:
        process_data.submit(x, y)


if __name__ == "__main__":
    asyncio.run(my_flow())

1. The code imports the necessary modules and the concurrency context manager. Use the prefect.concurrency.sync module for sync usage and the prefect.concurrency.asyncio module for async usage.
2. It defines a process_data task, taking x and y as input arguments. Inside this task, the concurrency context manager controls concurrency, using the database concurrency limit and occupying one slot. If another task attempts to run with the same limit and no slots are available, that task is blocked until a slot becomes available.
3. A flow named my_flow is defined. Within this flow, it iterates through a list of tuples, each containing pairs of x and y values. For each pair, the process_data task is submitted with the corresponding x and y values for processing.

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Using rate_limit

The Rate Limit feature provides control over the frequency of requests or operations, ensuring responsible usage and system stability. Depending on your requirements, you can use rate_limit to govern both synchronous (sync) and asynchronous (async) operations. Here’s how to make the most of it:

Sync

from prefect import flow, task
from prefect.concurrency.sync import rate_limit


@task
def make_http_request():
    rate_limit("rate-limited-api")
    print("Making an HTTP request...")


@flow
def my_flow():
    for _ in range(10):
        make_http_request.submit()


if __name__ == "__main__":
    my_flow()

Async

import asyncio

from prefect import flow, task
from prefect.concurrency.asyncio import rate_limit


@task
async def make_http_request():
    await rate_limit("rate-limited-api")
    print("Making an HTTP request...")


@flow
def my_flow():
    for _ in range(10):
        make_http_request.submit()


if __name__ == "__main__":
    asyncio.run(my_flow())

1. The code imports the necessary modules and the rate_limit function. Use the prefect.concurrency.sync module for sync usage and the prefect.concurrency.asyncio module for async usage.
2. It defines a make_http_request task. Inside this task, the rate_limit function ensures that the requests are made at a controlled pace.
3. A flow named my_flow is defined. Within this flow the make_http_request task is submitted 10 times.

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Use concurrency and rate_limit outside of a flow

Useconcurrency and rate_limit outside of a flow to control concurrency and rate limits for any operation.

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())

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Use cases

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Throttling task submission

Throttling task submission helps avoid overloading resources, complying with external rate limits, or ensuring a steady, controlled flow of work.

In this scenario the rate_limit function throttles the submission of tasks. The rate limit acts as a bottleneck, ensuring that tasks are submitted at a controlled rate, governed by the slot_decay_per_second setting on the associated concurrency limit.

from prefect import flow, task
from prefect.concurrency.sync import rate_limit


@task
def my_task(i):
    return i


@flow
def my_flow():
    for _ in range(100):
        rate_limit("slow-my-flow", occupy=1)
        my_task.submit(1)


if __name__ == "__main__":
    my_flow()

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Manage database connections

Manage the maximum number of concurrent database connections to avoid exhausting database resources.

This scenario uses a concurrency limit named database. It has a maximum concurrency limit that matches the maximum number of database connections. The concurrency context manager controls the number of database connections allowed at any one time.

from prefect import flow, task, concurrency
from myproject import db

@task
def database_query(query):
    # Here we request a single slot on the 'database' concurrency limit. This
    # will block in the case that all of the database connections are in use
    # ensuring that we never exceed the maximum number of database connections.
    with concurrency("database", occupy=1):
        result = db.execute(query)
        return result

@flow
def my_flow():
    queries = ["SELECT * FROM table1", "SELECT * FROM table2", "SELECT * FROM table3"]

    for query in queries:
        database_query.submit(query)

if __name__ == "__main__":
    my_flow()

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Parallel data processing

Limit the maximum number of parallel processing tasks.

This scenario limits the number of process_data tasks to five at any one time. The concurrency context manager requests five slots on the data-processing concurrency limit. This blocks until five slots are free and then submits five more tasks, ensuring that the maximum number of parallel processing tasks is never exceeded.

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]
            )

    print(processed_data)


if __name__ == "__main__":
    asyncio.run(main())

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Other ways to limit concurrency

In addition to global concurrency limits, Prefect provides several other ways to limit concurrency for fine-grained control.

Unlike global concurrency limits, which are a more general way to control concurrency for any Python-based operation, the following concurrency limit options are specific to Prefect objects:

• Work pool flow run concurrency limits
• Work queue flow run concurrency limits
• Deployment flow run concurrency limits
• Task run concurrency limits