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Futured provides a consistent interface for concurrent functional programming in Python. It wraps any callable to return a `concurrent.futures.Future`, wraps any async coroutine to return an `asyncio.Future`, and provides concurrent iterators and context managers for futures.
## Usage
### threaded, processed
Transform any callable into one which runs in a thread or process pool, and returns a future.
```python
from futured import threaded, processed
import httpx
fetch = threaded(httpx.Client().get)
fetch(url) # return Future
fs = (fetch(url + path) for path in paths)
threaded.results(fs) # generate results from futures
threaded.results(fs, timeout=...) # generate results as completed
fetch.map(urls) # generate results in order
fetch.map(urls, timeout=...) # generate results as completed
fetch.mapzip(urls) # generate (url, result) pairs as completed
```
Thread and process pool executors may be used as context managers, customized with options, and reused with different callables.
```python
threaded(max_workers=...)(func, ...)
processed(max_workers=...)(func, ...)
```
`futured` classes have a `waiting` context manager which collects results from tasks. Futures can be registered at creation, or appended to the list of tasks.
```python
with threaded.waiting(*fs) as tasks:
tasks.append(future)
tasks # list of completed results
```
`futured` classes provide a `tasks` interface which generalizes `futures.as_completed` and `futures.wait`, while allowing the set of tasks to be modified, e.g., for retries.
```python
threaded.tasks(fs, timeout=...) # mutable set of running tasks which iterate as completed
```
### asynced
The same interface works for `asyncio`.
```python
from futured import asynced
import httpx
fetch = asynced(httpx.AsyncClient().get)
fetch(url) # return coroutine
asynced.results(fs) # generate results from futures
asynced.results(fs, timeout=...) # generate results as completed
fetch.map(urls) # generate results in order
fetch.map(urls, timeout=...) # generate results as completed
fetch.mapzip(urls) # generate (url, result) pairs as completed
```
`asynced` provides utilities for calling coroutines from a synchronous context. `waiting` is similar to [trio's nursery](https://trio.readthedocs.io/en/latest/reference-core.html#nurseries-and-spawning), but returns results from a synchronous `with` block.
```python
asynced.run(async_func, ...) # call and run until complete
asynced.run(async_gen, ...) # call and run synchronous iterator
with asynced.waiting(*fs) as tasks: # concurrent coroutines completed in a block
asynced.tasks(fs, timeout=...) # mutable set of running tasks which iterate as completed
```
### decorators
Naturally `futured` wrappers can be used as decorators, but arguments can also be partially bound.
```python
@threaded
def slow():
...
fetch = threaded(httpx.Client().get, url)
fetch(params=...)
```
Methods are supported, as well as a `decorated` utility for automatically subclassing.
```python
from futured import decorated
FutureClient = decorated(httpx.Client, request=threaded)
# equivalent to
class FutureClient(httpx.Client):
request = threaded(httpx.Client.request)
```
### command
`command` wraps `subprocess.Popen` to provide a `Future` compatible interface.
```python
from futured import futured, command
command('ls').result() # return stdout or raises stderr
command('ls').pipe('wc') # pipes into next command, or | ('wc',... )
for line in command('ls'): # iterable lines
command.coroutine('ls') # return coroutine
futured(command, 'ls') # supports `map` interface
asynced(command.coroutine, 'ls') # supports `map` interface with timeout
```
### forked
`forked` allows iteration in separate child processes.
```python
from futured import forked
for value in forked(values, max_workers=...):
# in a child process
# in parent after children have exited
```
## Installation
```console
% pip install futured
```
## Tests
100% branch coverage.
```console
% pytest [--cov]
```
## Changes
1.4
* Python >=3.8 required
1.3
* Python >=3.7 required
* Python 3.10 event loop changes
* Streams replaced with tasks
1.2
* Python >=3.6 required
1.1
* Stream completed futures from a pending pool
1.0
* Executed functions are context managers
* `starmap` supported
0.3
* `forked` has optional maximum number of workers
* `waiting` context manager
* `command` pipes (`|`)
* `distributed.Client` support
0.2
* `command.coroutine` creates asyncio subprocesses
* `futured.mapzip` generates results zipped with arguments
* `asynced.run` supports asynchronous iterators
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"description": "[](https://pypi.org/project/futured/)\n\n[](https://pepy.tech/project/futured)\n\n[](https://github.com/coady/futured/actions)\n[](https://codecov.io/github/coady/futured)\n[](https://github.com/coady/futured/security/code-scanning)\n[](https://pypi.org/project/black/)\n[](http://mypy-lang.org/)\n\nFutured provides a consistent interface for concurrent functional programming in Python. It wraps any callable to return a `concurrent.futures.Future`, wraps any async coroutine to return an `asyncio.Future`, and provides concurrent iterators and context managers for futures.\n\n## Usage\n### threaded, processed\nTransform any callable into one which runs in a thread or process pool, and returns a future.\n\n```python\nfrom futured import threaded, processed\nimport httpx\n\nfetch = threaded(httpx.Client().get)\nfetch(url) # return Future\n\nfs = (fetch(url + path) for path in paths)\nthreaded.results(fs) # generate results from futures\nthreaded.results(fs, timeout=...) # generate results as completed\n\nfetch.map(urls) # generate results in order\nfetch.map(urls, timeout=...) # generate results as completed\nfetch.mapzip(urls) # generate (url, result) pairs as completed\n```\n\nThread and process pool executors may be used as context managers, customized with options, and reused with different callables.\n\n```python\nthreaded(max_workers=...)(func, ...)\nprocessed(max_workers=...)(func, ...)\n```\n\n`futured` classes have a `waiting` context manager which collects results from tasks. Futures can be registered at creation, or appended to the list of tasks.\n\n```python\nwith threaded.waiting(*fs) as tasks:\n tasks.append(future)\ntasks # list of completed results\n```\n\n`futured` classes provide a `tasks` interface which generalizes `futures.as_completed` and `futures.wait`, while allowing the set of tasks to be modified, e.g., for retries.\n\n```python\nthreaded.tasks(fs, timeout=...) # mutable set of running tasks which iterate as completed\n```\n\n### asynced\nThe same interface works for `asyncio`.\n\n```python\nfrom futured import asynced\nimport httpx\n\nfetch = asynced(httpx.AsyncClient().get)\nfetch(url) # return coroutine\n\nasynced.results(fs) # generate results from futures\nasynced.results(fs, timeout=...) # generate results as completed\n\nfetch.map(urls) # generate results in order\nfetch.map(urls, timeout=...) # generate results as completed\nfetch.mapzip(urls) # generate (url, result) pairs as completed\n```\n\n`asynced` provides utilities for calling coroutines from a synchronous context. `waiting` is similar to [trio's nursery](https://trio.readthedocs.io/en/latest/reference-core.html#nurseries-and-spawning), but returns results from a synchronous `with` block.\n\n```python\nasynced.run(async_func, ...) # call and run until complete\nasynced.run(async_gen, ...) # call and run synchronous iterator\nwith asynced.waiting(*fs) as tasks: # concurrent coroutines completed in a block\nasynced.tasks(fs, timeout=...) # mutable set of running tasks which iterate as completed\n```\n\n### decorators\nNaturally `futured` wrappers can be used as decorators, but arguments can also be partially bound.\n\n```python\n@threaded\ndef slow():\n ...\n\nfetch = threaded(httpx.Client().get, url)\nfetch(params=...)\n```\n\nMethods are supported, as well as a `decorated` utility for automatically subclassing.\n\n```python\nfrom futured import decorated\n\nFutureClient = decorated(httpx.Client, request=threaded)\n\n # equivalent to\nclass FutureClient(httpx.Client):\n request = threaded(httpx.Client.request)\n```\n\n### command\n`command` wraps `subprocess.Popen` to provide a `Future` compatible interface.\n\n```python\nfrom futured import futured, command\n\ncommand('ls').result() # return stdout or raises stderr\ncommand('ls').pipe('wc') # pipes into next command, or | ('wc',... )\nfor line in command('ls'): # iterable lines\ncommand.coroutine('ls') # return coroutine\n\nfutured(command, 'ls') # supports `map` interface\nasynced(command.coroutine, 'ls') # supports `map` interface with timeout\n```\n\n### forked\n`forked` allows iteration in separate child processes.\n\n```python\nfrom futured import forked\n\nfor value in forked(values, max_workers=...):\n # in a child process\n # in parent after children have exited\n```\n\n## Installation\n```console\n% pip install futured\n```\n\n## Tests\n100% branch coverage.\n\n```console\n% pytest [--cov]\n```\n\n## Changes\n1.4\n\n* Python >=3.8 required\n\n1.3\n\n* Python >=3.7 required\n* Python 3.10 event loop changes\n* Streams replaced with tasks\n\n1.2\n\n* Python >=3.6 required\n\n1.1\n\n* Stream completed futures from a pending pool\n\n1.0\n\n* Executed functions are context managers\n* `starmap` supported\n\n0.3\n\n* `forked` has optional maximum number of workers\n* `waiting` context manager\n* `command` pipes (`|`)\n* `distributed.Client` support\n\n0.2\n\n* `command.coroutine` creates asyncio subprocesses\n* `futured.mapzip` generates results zipped with arguments\n* `asynced.run` supports asynchronous iterators\n",
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