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Asynchronous Requests with Python requests

I tried the sample provided within the documentation of the requests library for python.

With async.map(rs), I get the response codes, but I want to get the content of each page requested. This, for example, does not work:

out = async.map(rs)
print out[0].content
Maybe the responses you're getting have empty body?
Works for me. Please post the full error you're getting.
there is no error. it just runs forever by the provided test urls.
it obviously appears when I use urls over https. http is working just fine
Most answers are outdated. In the year 2021 the current bandwagon-effect winner is: docs.aiohttp.org/en/stable

J
Jeff

Note

The below answer is not applicable to requests v0.13.0+. The asynchronous functionality was moved to grequests after this question was written. However, you could just replace requests with grequests below and it should work.

I've left this answer as is to reflect the original question which was about using requests < v0.13.0.

To do multiple tasks with async.map asynchronously you have to:

Define a function for what you want to do with each object (your task) Add that function as an event hook in your request Call async.map on a list of all the requests / actions

Example:

from requests import async
# If using requests > v0.13.0, use
# from grequests import async

urls = [
    'http://python-requests.org',
    'http://httpbin.org',
    'http://python-guide.org',
    'http://kennethreitz.com'
]

# A simple task to do to each response object
def do_something(response):
    print response.url

# A list to hold our things to do via async
async_list = []

for u in urls:
    # The "hooks = {..." part is where you define what you want to do
    # 
    # Note the lack of parentheses following do_something, this is
    # because the response will be used as the first argument automatically
    action_item = async.get(u, hooks = {'response' : do_something})

    # Add the task to our list of things to do via async
    async_list.append(action_item)

# Do our list of things to do via async
async.map(async_list)

Nice idea to have left your comment : due to compatibility issues between latest requests and grequests (lack of max_retries option in requests 1.1.0) i had to downgrade requests to retrieve async and I have found that the asynchronous functionality was moved with versions 0.13+ (pypi.python.org/pypi/requests)
from grequests import async do not work.. and this definition of dosomething work for me def do_something(response, **kwargs):, I find it from stackoverflow.com/questions/15594015/…
if the async.map call still blocks, then how is this asynchronous? Besides the requests themselves being sent asynchronously, the retrieval is still synchronous?
Replacing from requests import async by import grequests as async worked for me.
grequests now recommends requests-threads or requests-futures
C
Community

async is now an independent module : grequests.

See here : https://github.com/kennethreitz/grequests

And there: Ideal method for sending multiple HTTP requests over Python?

installation:

$ pip install grequests

usage:

build a stack:

import grequests

urls = [
    'http://www.heroku.com',
    'http://tablib.org',
    'http://httpbin.org',
    'http://python-requests.org',
    'http://kennethreitz.com'
]

rs = (grequests.get(u) for u in urls)

send the stack

grequests.map(rs)

result looks like

[<Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>]

grequests don't seem to set a limitation for concurrent requests, ie when multiple requests are sent to the same server.


With regards to the limitation on concurrent requests - you can specify a pool size when running the map()/imap(). i.e. grequests.map(rs, size=20) to have 20 concurrent grabs.
As of now this is not python3-capable (gevent fails to build v2.6 on py3.4).
I not quite understand the async part. if I let results = grequests.map(rs) the the code after this line is block, I can see the async effect?
On the github, repo, the author of grequests recommends using requests-threads or requests-futures instead.
K
Kumpelinus

I tested both requests-futures and grequests. Grequests is faster but brings monkey patching and additional problems with dependencies. requests-futures is several times slower than grequests. I decided to write my own and simply wrapped requests into ThreadPoolExecutor and it was almost as fast as grequests, but without external dependencies.

import requests
import concurrent.futures

def get_urls():
    return ["url1","url2"]

def load_url(url, timeout):
    return requests.get(url, timeout = timeout)

with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:

    future_to_url = {executor.submit(load_url, url, 10): url for url in     get_urls()}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
        try:
            data = future.result()
        except Exception as exc:
            resp_err = resp_err + 1
        else:
            resp_ok = resp_ok + 1

What type of exception is possible here?
requests.exceptions.Timeout
Sorry I dont understand your question. Use only single url in multiple threads? Only one case DDoS attacks ))
I don't understand why this answer got so many upvotes. The OP question was about async requests. ThreadPoolExecutor runs threads. Yes, you can make requests in multiple threads, but that will never be an async program, so I how could it be an answer for the original question?
Actually, the question was about how to load URLs in parallel. And yes thread pool executor is not the best option, it is better to use async io, but it works well in Python. And I don't understand why threads couldn't be used for async? What if you need to run CPU bound task asynchronously?
D
DragonBobZ

Unfortunately, as far as I know, the requests library is not equipped for performing asynchronous requests. You can wrap async/await syntax around requests, but that will make the underlying requests no less synchronous. If you want true async requests, you must use other tooling that provides it. One such solution is aiohttp (Python 3.5.3+). It works well in my experience using it with the Python 3.7 async/await syntax. Below I write three implementations of performing n web requests using

Purely synchronous requests (sync_requests_get_all) using the Python requests library Synchronous requests (async_requests_get_all) using the Python requests library wrapped in Python 3.7 async/await syntax and asyncio A truly asynchronous implementation (async_aiohttp_get_all) with the Python aiohttp library wrapped in Python 3.7 async/await syntax and asyncio

"""
Tested in Python 3.5.10
"""

import time
import asyncio
import requests
import aiohttp

from asgiref import sync

def timed(func):
    """
    records approximate durations of function calls
    """
    def wrapper(*args, **kwargs):
        start = time.time()
        print('{name:<30} started'.format(name=func.__name__))
        result = func(*args, **kwargs)
        duration = "{name:<30} finished in {elapsed:.2f} seconds".format(
            name=func.__name__, elapsed=time.time() - start
        )
        print(duration)
        timed.durations.append(duration)
        return result
    return wrapper

timed.durations = []


@timed
def sync_requests_get_all(urls):
    """
    performs synchronous get requests
    """
    # use session to reduce network overhead
    session = requests.Session()
    return [session.get(url).json() for url in urls]


@timed
def async_requests_get_all(urls):
    """
    asynchronous wrapper around synchronous requests
    """
    session = requests.Session()
    # wrap requests.get into an async function
    def get(url):
        return session.get(url).json()
    async_get = sync.sync_to_async(get)

    async def get_all(urls):
        return await asyncio.gather(*[
            async_get(url) for url in urls
        ])
    # call get_all as a sync function to be used in a sync context
    return sync.async_to_sync(get_all)(urls)

@timed
def async_aiohttp_get_all(urls):
    """
    performs asynchronous get requests
    """
    async def get_all(urls):
        async with aiohttp.ClientSession() as session:
            async def fetch(url):
                async with session.get(url) as response:
                    return await response.json()
            return await asyncio.gather(*[
                fetch(url) for url in urls
            ])
    # call get_all as a sync function to be used in a sync context
    return sync.async_to_sync(get_all)(urls)


if __name__ == '__main__':
    # this endpoint takes ~3 seconds to respond,
    # so a purely synchronous implementation should take
    # little more than 30 seconds and a purely asynchronous
    # implementation should take little more than 3 seconds.
    urls = ['https://postman-echo.com/delay/3']*10

    async_aiohttp_get_all(urls)
    async_requests_get_all(urls)
    sync_requests_get_all(urls)
    print('----------------------')
    [print(duration) for duration in timed.durations]

On my machine, this is the output:

async_aiohttp_get_all          started
async_aiohttp_get_all          finished in 3.20 seconds
async_requests_get_all         started
async_requests_get_all         finished in 30.61 seconds
sync_requests_get_all          started
sync_requests_get_all          finished in 30.59 seconds
----------------------
async_aiohttp_get_all          finished in 3.20 seconds
async_requests_get_all         finished in 30.61 seconds
sync_requests_get_all          finished in 30.59 seconds

"asnyc" is this a typo, or on purpose?
definitely a typo
Your async_aiohttp_get_all() is a nice solution. I came up with something similar, but had an extra async def fetch_all(urls): return await asyncio.gather(*[fetch(url) for url in urls]) outside of it, which had my solution creating separate aiohttp.ClientSession() instances for each URL whereas by embedding a local function, you're able to reuse the same session... much more Pythonic IMO. Can you remind me of the benefit of using sync.async_to_sync() with the existence of get_all() vs. asyncio.run() without get_all()?
awesomely done, definitely async_aiohttp working better than all!
Is it just me or does this pure aiohttp version use asgiref.sync.async_to_sync to run it? is there a way to do this without including the extra module?
A
Androbin

maybe requests-futures is another choice.

from requests_futures.sessions import FuturesSession

session = FuturesSession()
# first request is started in background
future_one = session.get('http://httpbin.org/get')
# second requests is started immediately
future_two = session.get('http://httpbin.org/get?foo=bar')
# wait for the first request to complete, if it hasn't already
response_one = future_one.result()
print('response one status: {0}'.format(response_one.status_code))
print(response_one.content)
# wait for the second request to complete, if it hasn't already
response_two = future_two.result()
print('response two status: {0}'.format(response_two.status_code))
print(response_two.content)

It is also recommended in the office document. If you don't want involve gevent, it's a good one.


One of the easiest solutions. Number of concurrent requests can be increased by defining max_workers parameter
It'd be nice to see an example of this scaled so we're not using one variable name per item to loop over.
having one thread per request is a hell waste of resources! it is not possible to do for example 500 requests simultaneously, it will kill your cpu. this should never be considered a good solution.
@CorneliuMaftuleac good point. Regarding the thread usage, you definitely need to care about it and the library provide an option to enable the threading pool or processing pool. ThreadPoolExecutor(max_workers=10)
@Dreampuf processing pool I believe is even worse?
a
arshbot

I have a lot of issues with most of the answers posted - they either use deprecated libraries that have been ported over with limited features, or provide a solution with too much magic on the execution of the request, making it difficult to error handle. If they do not fall into one of the above categories, they're 3rd party libraries or deprecated.

Some of the solutions works alright purely in http requests, but the solutions fall short for any other kind of request, which is ludicrous. A highly customized solution is not necessary here.

Simply using the python built-in library asyncio is sufficient enough to perform asynchronous requests of any type, as well as providing enough fluidity for complex and usecase specific error handling.

import asyncio

loop = asyncio.get_event_loop()

def do_thing(params):
    async def get_rpc_info_and_do_chores(id):
        # do things
        response = perform_grpc_call(id)
        do_chores(response)

    async def get_httpapi_info_and_do_chores(id):
        # do things
        response = requests.get(URL)
        do_chores(response)

    async_tasks = []
    for element in list(params.list_of_things):
       async_tasks.append(loop.create_task(get_chan_info_and_do_chores(id)))
       async_tasks.append(loop.create_task(get_httpapi_info_and_do_chores(ch_id)))

    loop.run_until_complete(asyncio.gather(*async_tasks))

How it works is simple. You're creating a series of tasks you'd like to occur asynchronously, and then asking a loop to execute those tasks and exit upon completion. No extra libraries subject to lack of maintenance, no lack of functionality required.


If I understand correctly, this will block the event loop while doing the GRPC and HTTP call? So if these calls take seconds to complete, your entire event loop will block for seconds? To avoid this, you need to use GRPC or HTTP libraries that are async. Then you can for example do await response = requests.get(URL). No?
Unfortunately, when trying this out, I found that making a wrapper around requests is barely faster (and in some cases slower) than just calling a list of URLs synchronously. E.g, requesting an endpoint that takes 3 seconds to respond 10 times using the strategy above takes about 30 seconds. If you want true async performance, you need to use something like aiohttp.
@arshbot Yes, if your chores are asynchronous, then you will see speed-ups, despite waiting on synchronous calls to requests.get. But the question is how to perform asynchronous requests with the python requests library. This answer does not do that, so my criticism stands.
I think this should be bumped. Using async event loops seems enough to fire asynchronous requests. No need to install external dependencies.
@iedmrc sadly, this is not the case. For a task to be non-blocking it has to be implemented using the newer async tools in Python, and this is not the case with the requests library. If you just use stick requests tasks in an async event loop, those would still be blocking. That being said, you can (as suggested in other responses) use things like gevent or threads with requests, but certainly not asyncio.
U
Uri

You can use httpx for that.

import httpx

async def get_async(url):
    async with httpx.AsyncClient() as client:
        return await client.get(url)

urls = ["http://google.com", "http://wikipedia.org"]

# Note that you need an async context to use `await`.
await asyncio.gather(*map(get_async, urls))

if you want a functional syntax, the gamla lib wraps this into get_async.

Then you can do


await gamla.map(gamla.get_async(10))(["http://google.com", "http://wikipedia.org"])

The 10 is the timeout in seconds.

(disclaimer: I am its author)


And respx for mocking/testing :)
Hi @Uri, I am getting below error in trying the code you mentioned in this answer. await asyncio.gather(*map(get_async, urls)) ^ SyntaxError: invalid syntax Please guide
Note that you need an async context to use await.
M
Monkey Boson

I know this has been closed for a while, but I thought it might be useful to promote another async solution built on the requests library.

list_of_requests = ['http://moop.com', 'http://doop.com', ...]

from simple_requests import Requests
for response in Requests().swarm(list_of_requests):
    print response.content

The docs are here: http://pythonhosted.org/simple-requests/


@YSY Feel free to post an issue: github.com/ctheiss/simple-requests/issues; I literally use this library thousands of times a day.
Boston, how do you handle 404/500 errors? what about https urls? will appreciate a snipping that supports thousands of urls. can you please paste an example? thanks
@YSY By default 404/500 errors raise an exception. This behaviour can be overridden (see pythonhosted.org/simple-requests/…). HTTPS urls are tricky due to the reliance on gevent, which currently has an outstanding bug on this (github.com/gevent/gevent/issues/477). There is a shim in the ticket you can run, but it will still throw warnings for SNI servers (but it will work). As for snipping, I'm afraid all my usages are at my company and closed. But I assure you we execute thousands of requests over tens of jobs.
Library looks sleek with respect to interaction. Is Python3+ usable? Sorry could not see any mention.
@Jethro absolutely right, the library would need a total re-write since the underlying technologies are quite different in Python 3. For right now, the library is "complete" but only works for Python 2.
T
Tom Christie

If you want to use asyncio, then requests-async provides async/await functionality for requests - https://github.com/encode/requests-async


confirmed, works great. On the project page it says this work has been overtaken by the following project github.com/encode/httpx
v
vaskrneup

DISCLAMER: Following code creates different threads for each function.

This might be useful for some of the cases as it is simpler to use. But know that it is not async but gives illusion of async using multiple threads, even though decorator suggests that.

You can use the following decorator to give a callback once the execution of function is completed, the callback must handle the processing of data returned by the function.

Please note that after the function is decorated it will return a Future object.

import asyncio

## Decorator implementation of async runner !!
def run_async(callback, loop=None):
    if loop is None:
        loop = asyncio.get_event_loop()

    def inner(func):
        def wrapper(*args, **kwargs):
            def __exec():
                out = func(*args, **kwargs)
                callback(out)
                return out

            return loop.run_in_executor(None, __exec)

        return wrapper

    return inner

Example of implementation:

urls = ["https://google.com", "https://facebook.com", "https://apple.com", "https://netflix.com"]
loaded_urls = []  # OPTIONAL, used for showing realtime, which urls are loaded !!


def _callback(resp):
    print(resp.url)
    print(resp)
    loaded_urls.append((resp.url, resp))  # OPTIONAL, used for showing realtime, which urls are loaded !!


# Must provide a callback function, callback func will be executed after the func completes execution
# Callback function will accept the value returned by the function.
@run_async(_callback)
def get(url):
    return requests.get(url)


for url in urls:
    get(url)

If you wish to see which url are loaded in real-time then, you can add the following code at the end as well:

while True:
    print(loaded_urls)
    if len(loaded_urls) == len(urls):
        break

This works but it generates a new thread for each request, which seems to defeat the purpose of using asyncio.
@rtaft Thank you for the suggestion, I have corrected my words.
D
Demitri
from threading import Thread

threads=list()

for requestURI in requests:
    t = Thread(target=self.openURL, args=(requestURI,))
    t.start()
    threads.append(t)

for thread in threads:
    thread.join()

...

def openURL(self, requestURI):
    o = urllib2.urlopen(requestURI, timeout = 600)
    o...

this is "normal" requests in threads. is not bad example buy is off-topic.
L
Louis Maddox

I second the suggestion above to use HTTPX, but I often use it in a different way so am adding my answer.

I personally use asyncio.run (introduced in Python 3.7) rather than asyncio.gather and also prefer the aiostream approach, which can be used in combination with asyncio and httpx.

As in this example I just posted, this style is helpful for processing a set of URLs asynchronously even despite the (common) occurrence of errors. I particularly like how that style clarifies where the response processing occurs and for ease of error handling (which I find async calls tend to give more of).

It's easier to post a simple example of just firing off a bunch of requests asynchronously, but often you also want to handle the response content (compute something with it, perhaps with reference to the original object that the URL you requested was to do with).

The core of that approach looks like:

async with httpx.AsyncClient(timeout=timeout) as session:
    ws = stream.repeat(session)
    xs = stream.zip(ws, stream.iterate(urls))
    ys = stream.starmap(xs, fetch, ordered=False, task_limit=20)
    process = partial(process_thing, things=things, pbar=pbar, verbose=verbose)
    zs = stream.map(ys, process)
    return await zs

where:

process_thing is an async response content handling function

things is the input list (which the urls generator of URL strings came from), e.g. a list of objects/dictionaries

pbar is a progress bar (e.g. tqdm.tqdm) [optional but useful]

All of that goes in an async function async_fetch_urlset which is then run by calling a synchronous 'top-level' function named e.g. fetch_things which runs the coroutine [this is what's returned by an async function] and manages the event loop:

def fetch_things(urls, things, pbar=None, verbose=False):
    return asyncio.run(async_fetch_urlset(urls, things, pbar, verbose))

Since a list passed as input (here it's things) can be modified in-place, you can effectively get output back (as we're used to from synchronous function calls)


D
David Watson

I have been using python requests for async calls against github's gist API for some time.

For an example, see the code here:

https://github.com/davidthewatson/flasgist/blob/master/views.py#L60-72

This style of python may not be the clearest example, but I can assure you that the code works. Let me know if this is confusing to you and I will document it.


S
Sam

I have also tried some things using the asynchronous methods in python, how ever I have had much better luck using twisted for asynchronous programming. It has fewer problems and is well documented. Here is a link of something simmilar to what you are trying in twisted.

http://pythonquirks.blogspot.com/2011/04/twisted-asynchronous-http-request.html


Twisted is old fashioned. Use HTTPX instead.
E
Eliav Louski

Non of the answers above helped me because they assume that you have a predefined list of requests, while in my case i need to be able to listen to requests and respond asynchronously (in similar way to how it works in nodejs).

def handle_finished_request(r, **kwargs):
    print(r)


# while True:
def main():
    while True:
        address = listen_to_new_msg()  # based on your server

        # schedule async requests and run 'handle_finished_request' on response
        req = grequests.get(address, timeout=1, hooks=dict(response=handle_finished_request))
        job = grequests.send(req)  # does not block! for more info see https://stackoverflow.com/a/16016635/10577976


main()

the handle_finished_request callback would be called when a response is received. note: for some reason timeout (or no response) does not trigger error here

This simple loop can trigger async requests similarly to how it would work in nodejs server