| Name | aiometer JSON |
| Version |
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| Summary | A Python concurrency scheduling library, compatible with asyncio and trio |
| upload_time | 2023-12-11 19:51:58 |
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| docs_url | None |
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| requires_python | >=3.8 |
| license | MIT |
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# aiometer
[](https://dev.azure.com/florimondmanca/public/_build/latest?definitionId=4&branchName=master)
[](https://codecov.io/gh/florimondmanca/aiometer)
[](https://pypi.org/project/aiometer)
`aiometer` is a concurrency scheduling library compatible with `asyncio` and `trio` and inspired by [Trimeter](https://github.com/python-trio/trimeter). It makes it easier to execute lots of tasks concurrently while controlling concurrency limits (i.e. applying _[backpressure](https://lucumr.pocoo.org/2020/1/1/async-pressure/)_) and collecting results in a predictable manner.
**Content**
- [Example](#example)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Flow control](#flow-control)
- [Running tasks](#running-tasks)
- [How To](#how-to)
- [API Reference](#api-reference)
- [Contributing](#contributing)
- [License](#license)
## Example
Let's use [HTTPX](https://github.com/encode/httpx) to make web requests concurrently...
_Try this code interactively using [IPython](https://ipython.org/install.html)._
```python
>>> import asyncio
>>> import functools
>>> import random
>>> import aiometer
>>> import httpx
>>>
>>> client = httpx.AsyncClient()
>>>
>>> async def fetch(client, request):
... response = await client.send(request)
... # Simulate extra processing...
... await asyncio.sleep(2 * random.random())
... return response.json()["json"]
...
>>> requests = [
... httpx.Request("POST", "https://httpbin.org/anything", json={"index": index})
... for index in range(100)
... ]
...
>>> # Send requests, and process responses as they're made available:
>>> async with aiometer.amap(
... functools.partial(fetch, client),
... requests,
... max_at_once=10, # Limit maximum number of concurrently running tasks.
... max_per_second=5, # Limit request rate to not overload the server.
... ) as results:
... async for data in results:
... print(data)
...
{'index': 3}
{'index': 4}
{'index': 1}
{'index': 2}
{'index': 0}
...
>>> # Alternatively, fetch and aggregate responses into an (ordered) list...
>>> jobs = [functools.partial(fetch, client, request) for request in requests]
>>> results = await aiometer.run_all(jobs, max_at_once=10, max_per_second=5)
>>> results
[{'index': 0}, {'index': 1}, {'index': 2}, {'index': 3}, {'index': 4}, ...]
```
## Installation
_This project is in beta and maturing. Be sure to pin any dependencies to the latest minor._
```bash
pip install "aiometer==0.5.*"
```
## Features
- Concurrency management and throttling helpers.
- `asyncio` and `trio` support.
- Fully type annotated.
- 100% test coverage.
## Usage
### Flow control
The key highlight of `aiometer` is allowing you to apply flow control strategies in order to limit the degree of concurrency of your programs.
There are two knobs you can play with to fine-tune concurrency:
- `max_at_once`: this is used to limit the maximum number of concurrently running tasks at any given time. (If you have 100 tasks and set `max_at_once=10`, then `aiometer` will ensure that no more than 10 run at the same time.)
- `max_per_second`: this option limits the number of tasks spawned per second. This is useful to not overload I/O resources, such as servers that may have a rate limiting policy in place.
Example usage:
```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)
```
### Running tasks
`aiometer` provides 4 different ways to run tasks concurrently in the form of 4 different run functions. Each function accepts all the options documented in [Flow control](#flow-control), and runs tasks in a slightly different way, allowing to address a variety of use cases. Here's a handy table for reference (see also the [API Reference](#api-reference)):
| Entrypoint | Use case |
| --------------- | ---------------------------------------------- |
| `run_on_each()` | Execute async callbacks in any order. |
| `run_all()` | Return results as an ordered list. |
| `amap()` | Iterate over results as they become available. |
| `run_any()` | Return result of first completed function. |
To illustrate the behavior of each run function, let's first setup a hello world async program:
```python
>>> import asyncio
>>> import random
>>> from functools import partial
>>> import aiometer
>>>
>>> async def get_greeting(name):
... await asyncio.sleep(random.random()) # Simulate I/O
... return f"Hello, {name}"
...
>>> async def greet(name):
... greeting = await get_greeting(name)
... print(greeting)
...
>>> names = ["Robert", "Carmen", "Lucas"]
```
Let's start with `run_on_each()`. It executes an async function once for each item in a list passed as argument:
```python
>>> await aiometer.run_on_each(greet, names)
'Hello, Robert!'
'Hello, Lucas!'
'Hello, Carmen!'
```
If we'd like to get the list of greetings in the same order as `names`, in a fashion similar to [`Promise.all()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise/all), we can use `run_all()`:
```python
>>> await aiometer.run_all([partial(get_greeting, name) for name in names])
['Hello, Robert', 'Hello, Carmen!', 'Hello, Lucas!']
```
`amap()` allows us to process each greeting as it becomes available (which means maintaining order is not guaranteed):
```python
>>> async with aiometer.amap(get_greeting, names) as greetings:
... async for greeting in greetings:
... print(greeting)
'Hello, Lucas!'
'Hello, Robert!'
'Hello, Carmen!'
```
Lastly, `run_any()` can be used to run async functions until the first one completes, similarly to [`Promise.any()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise/any):
```python
>>> await aiometer.run_any([partial(get_greeting, name) for name in names])
'Hello, Carmen!'
```
As a last fun example, let's use `amap()` to implement a no-threads async version of [sleep sort](https://rosettacode.org/wiki/Sorting_algorithms/Sleep_sort):
```python
>>> import asyncio
>>> from functools import partial
>>> import aiometer
>>> numbers = [0.3, 0.1, 0.6, 0.2, 0.7, 0.5, 0.5, 0.2]
>>> async def process(n):
... await asyncio.sleep(n)
... return n
...
>>> async with aiometer.amap(process, numbers) as results:
... sorted_numbers = [n async for n in results]
...
>>> sorted_numbers
[0.1, 0.2, 0.2, 0.3, 0.5, 0.5, 0.6, 0.7]
```
## How To
### Multiple parametrized values in `run_on_each` and `amap`
`run_on_each` and `amap` only accept functions that accept a single positional argument (i.e. `(Any) -> Awaitable`).
So if you have a function that is parametrized by multiple values, you should refactor it to match this form.
This can generally be achieved like this:
1. Build a proxy container type (eg. a `namedtuple`), eg `T`.
2. Refactor your function so that its signature is now `(T) -> Awaitable`.
3. Build a list of these proxy containers, and pass it to `aiometer`.
For example, assuming you have a function that processes X/Y coordinates...
```python
async def process(x: float, y: float) -> None:
pass
xs = list(range(100))
ys = list(range(100))
for x, y in zip(xs, ys):
await process(x, y)
```
You could use it with `amap` by refactoring it like this:
```python
from typing import NamedTuple
# Proxy container type:
class Point(NamedTuple):
x: float
y: float
# Rewrite to accept a proxy as a single positional argument:
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:
async with aiometer.amap(process, points) as results:
...
```
## API Reference
### Common options
* `max_at_once` (_Optional_, `int`): the maximum number of concurrently running tasks at any given time.
* `max_per_second` (_Optional_, `int`): the maximum number of tasks spawned per second.
### `aiometer.run_on_each()`
**Signature**: _async_ aiometer.run_on_each(*async_fn*, *args*, *, *max_at_once=None*, *max_per_second=None*) -> *None*
Concurrently run the equivalent of `async_fn(arg) for arg in args`. Does not return any value. To get return values back, use [`aiometer.run_all()`](#aiometerrun_all).
### `aiometer.run_all()`
**Signature**: _async_ aiometer.run_all(*async_fns*, *max_at_once=None*, *max_per_second=None*) -> *list*
Concurrently run the `async_fns` functions, and return the list of results in the same order.
### `aiometer.amap()`
**Signature**: _async_ aiometer.amap(*async_fn*, *args*, *max_at_once=None*, *max_per_second=None*) -> *async iterator*
Concurrently run the equivalent of `async_fn(arg) for arg in args`, and return an async iterator that yields results as they become available.
### `aiometer.run_any()`
**Signature**: _async_ aiometer.run_any(*async_fns*, *max_at_once=None*, *max_per_second=None*) -> *Any*
Concurrently run the `async_fns` functions, and return the first available result.
## Contributing
See [CONTRIBUTING.md](./CONTRIBUTING.md).
## License
MIT
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## 0.5.0 - 2023-12-11
### Removed
- Drop support for Python 3.7, as it has reached EOL. (Pull #44)
### Added
- Add official support for Python 3.12. (Pull #44)
- Add support for anyio 4. This allows catching exception groups using the native ExceptionGroup. On anyio 3.2+, anyio would throw its own ExceptionGroup type. Compatibility with anyio 3.2+ is retained. (Pull #43)
## 0.4.0 - 2023-01-18
### Removed
- Drop support for Python 3.6, which has reached EOL. (Pull #38)
### Added
- Add official support for Python 3.10 and 3.11. (Pull #38)
### Fixed
- Relax version requirements for `typing_extensions` and address `mypy>=0.981` strict optional changes. (Pull #38)
## 0.3.0 - 2021-07-06
### Changed
- Update `anyio` dependency to v3 (previously v1). (Pull #25)
- _NB: no API change, but dependency mismatches may occur. Be sure to port your codebase to anyio v3 before upgrading `aiometer`._
### Added
- Add support for Python 3.6 (installs the `contextlib2` backport library there). (Pull #26)
- Officialize support for Python 3.9. (Pull #26)
## 0.2.1 - 2020-03-26
### Fixed
- Improve robustness of the `max_per_second` implementation by using the generic cell rate algorithm (GCRA) instead of leaky bucket. (Pull #5)
## 0.2.0 - 2020-03-22
### Added
- Add support for Python 3.7. (Pull #3)
## 0.1.0 - 2020-03-21
### Added
- Add `run_on_each()`, `run_all()`, `amap()` and `run_any()`, with `max_at_once` and `max_per_second` options. (Pull #1)
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"description": "# aiometer\n\n[](https://dev.azure.com/florimondmanca/public/_build/latest?definitionId=4&branchName=master)\n[](https://codecov.io/gh/florimondmanca/aiometer)\n\n[](https://pypi.org/project/aiometer)\n\n`aiometer` is a concurrency scheduling library compatible with `asyncio` and `trio` and inspired by [Trimeter](https://github.com/python-trio/trimeter). It makes it easier to execute lots of tasks concurrently while controlling concurrency limits (i.e. applying _[backpressure](https://lucumr.pocoo.org/2020/1/1/async-pressure/)_) and collecting results in a predictable manner.\n\n**Content**\n\n- [Example](#example)\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n - [Flow control](#flow-control)\n - [Running tasks](#running-tasks)\n- [How To](#how-to)\n- [API Reference](#api-reference)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Example\n\nLet's use [HTTPX](https://github.com/encode/httpx) to make web requests concurrently...\n\n_Try this code interactively using [IPython](https://ipython.org/install.html)._\n\n```python\n>>> import asyncio\n>>> import functools\n>>> import random\n>>> import aiometer\n>>> import httpx\n>>>\n>>> client = httpx.AsyncClient()\n>>>\n>>> async def fetch(client, request):\n... response = await client.send(request)\n... # Simulate extra processing...\n... await asyncio.sleep(2 * random.random())\n... return response.json()[\"json\"]\n...\n>>> requests = [\n... httpx.Request(\"POST\", \"https://httpbin.org/anything\", json={\"index\": index})\n... for index in range(100)\n... ]\n...\n>>> # Send requests, and process responses as they're made available:\n>>> async with aiometer.amap(\n... functools.partial(fetch, client),\n... requests,\n... max_at_once=10, # Limit maximum number of concurrently running tasks.\n... max_per_second=5, # Limit request rate to not overload the server.\n... ) as results:\n... async for data in results:\n... print(data)\n...\n{'index': 3}\n{'index': 4}\n{'index': 1}\n{'index': 2}\n{'index': 0}\n...\n>>> # Alternatively, fetch and aggregate responses into an (ordered) list...\n>>> jobs = [functools.partial(fetch, client, request) for request in requests]\n>>> results = await aiometer.run_all(jobs, max_at_once=10, max_per_second=5)\n>>> results\n[{'index': 0}, {'index': 1}, {'index': 2}, {'index': 3}, {'index': 4}, ...]\n```\n\n## Installation\n\n_This project is in beta and maturing. Be sure to pin any dependencies to the latest minor._\n\n```bash\npip install \"aiometer==0.5.*\"\n```\n\n## Features\n\n- Concurrency management and throttling helpers.\n- `asyncio` and `trio` support.\n- Fully type annotated.\n- 100% test coverage.\n\n## Usage\n\n### Flow control\n\nThe key highlight of `aiometer` is allowing you to apply flow control strategies in order to limit the degree of concurrency of your programs.\n\nThere are two knobs you can play with to fine-tune concurrency:\n\n- `max_at_once`: this is used to limit the maximum number of concurrently running tasks at any given time. (If you have 100 tasks and set `max_at_once=10`, then `aiometer` will ensure that no more than 10 run at the same time.)\n- `max_per_second`: this option limits the number of tasks spawned per second. This is useful to not overload I/O resources, such as servers that may have a rate limiting policy in place.\n\nExample usage:\n\n```python\n>>> import asyncio\n>>> import aiometer\n>>> async def make_query(query):\n... await asyncio.sleep(0.05) # Simulate a database request.\n...\n>>> queries = ['SELECT * from authors'] * 1000\n>>> # Allow at most 5 queries to run concurrently at any given time:\n>>> await aiometer.run_on_each(make_query, queries, max_at_once=5)\n...\n>>> # Make at most 10 queries per second:\n>>> await aiometer.run_on_each(make_query, queries, max_per_second=10)\n...\n>>> # Run at most 10 concurrent jobs, spawning new ones at least every 5 seconds:\n>>> async def job(id):\n... await asyncio.sleep(10) # A very long task.\n...\n>>> await aiometer.run_on_each(job, range(100), max_at_once=10, max_per_second=0.2)\n```\n\n### Running tasks\n\n`aiometer` provides 4 different ways to run tasks concurrently in the form of 4 different run functions. Each function accepts all the options documented in [Flow control](#flow-control), and runs tasks in a slightly different way, allowing to address a variety of use cases. Here's a handy table for reference (see also the [API Reference](#api-reference)):\n\n| Entrypoint | Use case |\n| --------------- | ---------------------------------------------- |\n| `run_on_each()` | Execute async callbacks in any order. |\n| `run_all()` | Return results as an ordered list. |\n| `amap()` | Iterate over results as they become available. |\n| `run_any()` | Return result of first completed function. |\n\nTo illustrate the behavior of each run function, let's first setup a hello world async program:\n\n```python\n>>> import asyncio\n>>> import random\n>>> from functools import partial\n>>> import aiometer\n>>>\n>>> async def get_greeting(name):\n... await asyncio.sleep(random.random()) # Simulate I/O\n... return f\"Hello, {name}\"\n...\n>>> async def greet(name):\n... greeting = await get_greeting(name)\n... print(greeting)\n...\n>>> names = [\"Robert\", \"Carmen\", \"Lucas\"]\n```\n\nLet's start with `run_on_each()`. It executes an async function once for each item in a list passed as argument:\n\n```python\n>>> await aiometer.run_on_each(greet, names)\n'Hello, Robert!'\n'Hello, Lucas!'\n'Hello, Carmen!'\n```\n\nIf we'd like to get the list of greetings in the same order as `names`, in a fashion similar to [`Promise.all()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise/all), we can use `run_all()`:\n\n```python\n>>> await aiometer.run_all([partial(get_greeting, name) for name in names])\n['Hello, Robert', 'Hello, Carmen!', 'Hello, Lucas!']\n```\n\n`amap()` allows us to process each greeting as it becomes available (which means maintaining order is not guaranteed):\n\n```python\n>>> async with aiometer.amap(get_greeting, names) as greetings:\n... async for greeting in greetings:\n... print(greeting)\n'Hello, Lucas!'\n'Hello, Robert!'\n'Hello, Carmen!'\n```\n\nLastly, `run_any()` can be used to run async functions until the first one completes, similarly to [`Promise.any()`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise/any):\n\n```python\n>>> await aiometer.run_any([partial(get_greeting, name) for name in names])\n'Hello, Carmen!'\n```\n\nAs a last fun example, let's use `amap()` to implement a no-threads async version of [sleep sort](https://rosettacode.org/wiki/Sorting_algorithms/Sleep_sort):\n\n```python\n>>> import asyncio\n>>> from functools import partial\n>>> import aiometer\n>>> numbers = [0.3, 0.1, 0.6, 0.2, 0.7, 0.5, 0.5, 0.2]\n>>> async def process(n):\n... await asyncio.sleep(n)\n... return n\n...\n>>> async with aiometer.amap(process, numbers) as results:\n... sorted_numbers = [n async for n in results]\n...\n>>> sorted_numbers\n[0.1, 0.2, 0.2, 0.3, 0.5, 0.5, 0.6, 0.7]\n```\n\n## How To\n\n### Multiple parametrized values in `run_on_each` and `amap`\n\n`run_on_each` and `amap` only accept functions that accept a single positional argument (i.e. `(Any) -> Awaitable`).\n\nSo if you have a function that is parametrized by multiple values, you should refactor it to match this form.\n\nThis can generally be achieved like this:\n\n1. Build a proxy container type (eg. a `namedtuple`), eg `T`.\n2. Refactor your function so that its signature is now `(T) -> Awaitable`.\n3. Build a list of these proxy containers, and pass it to `aiometer`.\n\nFor example, assuming you have a function that processes X/Y coordinates...\n\n```python\nasync def process(x: float, y: float) -> None:\n pass\n\nxs = list(range(100))\nys = list(range(100))\n\nfor x, y in zip(xs, ys):\n await process(x, y)\n```\n\nYou could use it with `amap` by refactoring it like this:\n\n```python\nfrom typing import NamedTuple\n\n# Proxy container type:\nclass Point(NamedTuple):\n x: float\n y: float\n\n# Rewrite to accept a proxy as a single positional argument:\nasync def process(point: Point) -> None:\n x = point.x\n y = point.y\n ...\n\nxs = list(range(100))\nys = list(range(100))\n\n# Build a list of proxy containers:\npoints = [Point(x, y) for x, y in zip(x, y)]\n\n# Use it:\nasync with aiometer.amap(process, points) as results:\n ...\n```\n\n## API Reference\n\n### Common options\n\n* `max_at_once` (_Optional_, `int`): the maximum number of concurrently running tasks at any given time.\n* `max_per_second` (_Optional_, `int`): the maximum number of tasks spawned per second.\n\n### `aiometer.run_on_each()`\n\n**Signature**: _async_ aiometer.run_on_each(*async_fn*, *args*, *, *max_at_once=None*, *max_per_second=None*) -> *None*\n\nConcurrently run the equivalent of `async_fn(arg) for arg in args`. Does not return any value. To get return values back, use [`aiometer.run_all()`](#aiometerrun_all).\n\n### `aiometer.run_all()`\n\n**Signature**: _async_ aiometer.run_all(*async_fns*, *max_at_once=None*, *max_per_second=None*) -> *list*\n\nConcurrently run the `async_fns` functions, and return the list of results in the same order.\n\n### `aiometer.amap()`\n\n**Signature**: _async_ aiometer.amap(*async_fn*, *args*, *max_at_once=None*, *max_per_second=None*) -> *async iterator*\n\nConcurrently run the equivalent of `async_fn(arg) for arg in args`, and return an async iterator that yields results as they become available.\n\n### `aiometer.run_any()`\n\n**Signature**: _async_ aiometer.run_any(*async_fns*, *max_at_once=None*, *max_per_second=None*) -> *Any*\n\nConcurrently run the `async_fns` functions, and return the first available result.\n\n## Contributing\n\nSee [CONTRIBUTING.md](./CONTRIBUTING.md).\n\n## License\n\nMIT\n\n# Changelog\n\nAll notable changes to this project will be documented in this file.\n\nThe format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).\n\n## 0.5.0 - 2023-12-11\n\n### Removed\n\n- Drop support for Python 3.7, as it has reached EOL. (Pull #44)\n\n### Added\n\n- Add official support for Python 3.12. (Pull #44)\n- Add support for anyio 4. This allows catching exception groups using the native ExceptionGroup. On anyio 3.2+, anyio would throw its own ExceptionGroup type. Compatibility with anyio 3.2+ is retained. (Pull #43)\n\n## 0.4.0 - 2023-01-18\n\n### Removed\n\n- Drop support for Python 3.6, which has reached EOL. (Pull #38)\n\n### Added\n\n- Add official support for Python 3.10 and 3.11. (Pull #38)\n\n### Fixed\n\n- Relax version requirements for `typing_extensions` and address `mypy>=0.981` strict optional changes. (Pull #38)\n\n## 0.3.0 - 2021-07-06\n\n### Changed\n\n- Update `anyio` dependency to v3 (previously v1). (Pull #25)\n - _NB: no API change, but dependency mismatches may occur. Be sure to port your codebase to anyio v3 before upgrading `aiometer`._\n\n### Added\n\n- Add support for Python 3.6 (installs the `contextlib2` backport library there). (Pull #26)\n- Officialize support for Python 3.9. (Pull #26)\n\n## 0.2.1 - 2020-03-26\n\n### Fixed\n\n- Improve robustness of the `max_per_second` implementation by using the generic cell rate algorithm (GCRA) instead of leaky bucket. (Pull #5)\n\n## 0.2.0 - 2020-03-22\n\n### Added\n\n- Add support for Python 3.7. (Pull #3)\n\n## 0.1.0 - 2020-03-21\n\n### Added\n\n- Add `run_on_each()`, `run_all()`, `amap()` and `run_any()`, with `max_at_once` and `max_per_second` options. (Pull #1)\n",
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