Effortless Concurrency Management with aiometer

Concurrency management is a critical aspect of building efficient and high-performing applications that can handle multiple tasks simultaneously. In today’s fast-paced world, developers need powerful tools that simplify the execution of tasks concurrently while maintaining control over concurrency limits. That’s where aiometer, a concurrency scheduling library compatible with asyncio and trio, comes into play.

Features

aiometer offers several features that make it an excellent choice for managing concurrency in your Python applications. Some of its features include:

1. Concurrency management and throttling helpers: aiometer allows you to control the degree of concurrency in your programs by limiting the maximum number of concurrently running tasks and the number of tasks spawned per second.

2. asyncio and trio support: Whether you’re working with asyncio or trio, aiometer is compatible with both, making it versatile and adaptable to your preferred asynchronous programming framework.

3. Fully type annotated: aiometer is fully type annotated, providing better code readability and enhancing the developer experience.

4. 100% test coverage: With rigorous testing in place, you can rely on aiometer’s stability and robustness in handling concurrent tasks.

Installation

Getting started with aiometer is a breeze. To install the library, you can use pip and run the following command:

bash
pip install "aiometer==0.5.*"

Please note that aiometer is currently in beta and maturing. Make sure to pin any dependencies to the latest minor version for compatibility.

Usage

One of the key highlights of aiometer is its ability to apply flow control strategies to limit the degree of concurrency in your programs. There are two knobs you can adjust to fine-tune concurrency:

• max_at_once: This option limits the maximum number of concurrently running tasks at any given time.
• max_per_second: This setting controls the number of tasks spawned per second, preventing resource overload.

Here’s an example of how you can use aiometer to control concurrency:

“`python
import asyncio
import aiometer

async def make_query(query):
await asyncio.sleep(0.05) # Simulate a database request.

queries = [‘SELECT * from authors’] * 1000

Allow at most 5 queries to run concurrently at any given time:

await aiometer.run_on_each(make_query, queries, max_at_once=5)

Make at most 10 queries per second:

await aiometer.run_on_each(make_query, queries, max_per_second=10)

Run at most 10 concurrent jobs, spawning new ones at least every 5 seconds:

async def job(id):
await asyncio.sleep(10) # A very long task.

await aiometer.run_on_each(job, range(100), max_at_once=10, max_per_second=0.2)
“`

aiometer provides four different run functions to execute tasks concurrently, each with its own use case:

1. run_on_each(): Executes async callbacks in any order.
2. run_all(): Returns results as an ordered list.
3. amap(): Iterates over results as they become available.
4. run_any(): Returns the result of the first completed function.

By using the appropriate run function, you can address a variety of concurrency use cases in your applications.

How To

If your function is parametrized by multiple values, such as X/Y coordinates, you can still use aiometer’s run_on_each() and amap() functions by refactoring your function to match the required form. Here’s how:

1. Build a proxy container type, such as a namedtuple, that encapsulates the multiple values.
2. Refactor your function to accept the proxy container as a single positional argument.
3. Build a list of proxy containers based on your original values, and pass it to aiometer.

Here’s an example of how you can use multiple parametrized values with run_on_each() and amap():

“`python
from typing import NamedTuple
import aiometer

Proxy container type:

class Point(NamedTuple):
x: float
y: float

async def process(point: Point) -> None:
x = point.x
y = point.y
…

xs = list(range(100))
ys = list(range(100))

Build a list of proxy containers:

points = [Point(x, y) for x, y in zip(x, y)]

Use it with aiometer:

async with aiometer.amap(process, points) as results:
…
“`

By following these steps, you can seamlessly adapt your existing functions to work with aiometer’s concurrency management functions.

Conclusion

aiometer is a powerful concurrency scheduling library that simplifies the execution of concurrent tasks while providing fine-grained control over concurrency limits. Whether you’re developing applications with asyncio or trio, aiometer offers a versatile and efficient solution to manage concurrency. By leveraging its flow control strategies and utilizing the appropriate run functions, you can optimize the performance of your applications and ensure efficient resource utilization.

With its ease of installation, comprehensive documentation, and active community, aiometer is a valuable addition to any Python developer’s toolbox. Give it a try and embrace the future of concurrent programming!

Author: Dr. Emily Techscribe

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