Asynchronous Python HTTP for Humans.
.. image:: https://travis-ci.org/ross/requests-futures.svg?branch=master :target: https://travis-ci.org/ross/requests-futures
Small add-on for the python requests_ http library. Makes use of python 3.2's
concurrent.futures_ or the backport_ for prior versions of python.
The additional API and changes are minimal and strives to avoid surprises.
The following synchronous code:
.. code-block:: python
from requests import Session
session = Session()
# first requests starts and blocks until finished
response_one = session.get('http://httpbin.org/get')
# second request starts once first is finished
response_two = session.get('http://httpbin.org/get?foo=bar')
# both requests are complete
print('response one status: {0}'.format(response_one.status_code))
print(response_one.content)
print('response two status: {0}'.format(response_two.status_code))
print(response_two.content)
Can be translated to make use of futures, and thus be asynchronous by creating a FuturesSession and catching the returned Future in place of Response. The Response can be retrieved by calling the result method on the Future:
.. code-block:: python
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)
By default a ThreadPoolExecutor is created with 8 workers. If you would like to adjust that value or share a executor across multiple sessions you can provide one to the FuturesSession constructor.
.. code-block:: python
from concurrent.futures import ThreadPoolExecutor
from requests_futures.sessions import FuturesSession
session = FuturesSession(executor=ThreadPoolExecutor(max_workers=10))
# ...
As a shortcut in case of just increasing workers number you can pass
max_workers straight to the FuturesSession constructor:
.. code-block:: python
from requests_futures.sessions import FuturesSession
session = FuturesSession(max_workers=10)
FutureSession will use an existing session object if supplied:
.. code-block:: python
from requests import session
from requests_futures.sessions import FuturesSession
my_session = session()
future_session = FuturesSession(session=my_session)
That's it. The api of requests.Session is preserved without any modifications beyond returning a Future rather than Response. As with all futures exceptions are shifted (thrown) to the future.result() call so try/except blocks should be moved there.
The most common piece of information needed is the URL of the request. This can
be accessed without any extra steps using the request property of the
response object.
.. code-block:: python
from concurrent.futures import as_completed
from pprint import pprint
from requests_futures.sessions import FuturesSession
session = FuturesSession()
futures=[session.get(f'http://httpbin.org/get?{i}') for i in range(3)]
for future in as_completed(futures):
resp = future.result()
pprint({
'url': resp.request.url,
'content': resp.json(),
})
There are situations in which you may want to tie additional information to a request/response. There are a number of ways to go about this, the simplest is to attach additional information to the future object itself.
.. code-block:: python
from concurrent.futures import as_completed
from pprint import pprint
from requests_futures.sessions import FuturesSession
session = FuturesSession()
futures=[]
for i in range(3):
future = session.get('http://httpbin.org/get')
future.i = i
futures.append(future)
for future in as_completed(futures):
resp = future.result()
pprint({
'i': future.i,
'content': resp.json(),
})
If you know that you won't be needing any additional responses from futures that haven't yet resolved, it's a good idea to cancel those requests. You can do this by using the session as a context manager:
.. code-block:: python
from requests_futures.sessions import FuturesSession
with FuturesSession(max_workers=1) as session:
future = session.get('https://httpbin.org/get')
future2 = session.get('https://httpbin.org/delay/10')
future3 = session.get('https://httpbin.org/delay/10')
response = future.result()
In this example, the second or third request will be skipped, saving time and resources that would otherwise be wasted.
Without preserving the requests order:
.. code-block:: python
from concurrent.futures import as_completed
from requests_futures.sessions import FuturesSession
with FuturesSession() as session:
futures = [session.get('https://httpbin.org/delay/{}'.format(i % 3)) for i in range(10)]
for future in as_completed(futures):
resp = future.result()
print(resp.json()['url'])
Additional processing can be done in the background using requests's hooks_ functionality. This can be useful for shifting work out of the foreground, for a simple example take json parsing.
.. code-block:: python
from pprint import pprint
from requests_futures.sessions import FuturesSession
session = FuturesSession()
def response_hook(resp, *args, **kwargs):
# parse the json storing the result on the response object
resp.data = resp.json()
future = session.get('http://httpbin.org/get', hooks={
'response': response_hook,
})
# do some other stuff, send some more requests while this one works
response = future.result()
print('response status {0}'.format(response.status_code))
# data will have been attached to the response object in the background
pprint(response.data)
Hooks can also be applied to the session.
.. code-block:: python
from pprint import pprint
from requests_futures.sessions import FuturesSession
def response_hook(resp, *args, **kwargs):
# parse the json storing the result on the response object
resp.data = resp.json()
session = FuturesSession()
session.hooks['response'] = response_hook
future = session.get('http://httpbin.org/get')
# do some other stuff, send some more requests while this one works
response = future.result()
print('response status {0}'.format(response.status_code))
# data will have been attached to the response object in the background
pprint(response.data) pprint(response.data)
A more advanced example that adds an elapsed property to all requests.
.. code-block:: python
from pprint import pprint
from requests_futures.sessions import FuturesSession
from time import time
class ElapsedFuturesSession(FuturesSession):
def request(self, method, url, hooks=None, *args, **kwargs):
start = time()
if hooks is None:
hooks = {}
def timing(r, *args, **kwargs):
r.elapsed = time() - start
try:
if isinstance(hooks['response'], (list, tuple)):
# needs to be first so we don't time other hooks execution
hooks['response'].insert(0, timing)
else:
hooks['response'] = [timing, hooks['response']]
except KeyError:
hooks['response'] = timing
return super(ElapsedFuturesSession, self) \
.request(method, url, hooks=hooks, *args, **kwargs)
session = ElapsedFuturesSession()
future = session.get('http://httpbin.org/get')
# do some other stuff, send some more requests while this one works
response = future.result()
print('response status {0}'.format(response.status_code))
print('response elapsed {0}'.format(response.elapsed))
Similarly to ThreadPoolExecutor, it is possible to use an instance of
ProcessPoolExecutor. As the name suggest, the requests will be executed
concurrently in separate processes rather than threads.
.. code-block:: python
from concurrent.futures import ProcessPoolExecutor
from requests_futures.sessions import FuturesSession
session = FuturesSession(executor=ProcessPoolExecutor(max_workers=10))
# ... use as before
.. HINT::
Using the ProcessPoolExecutor is useful, in cases where memory
usage per request is very high (large response) and cycling the interpreter
is required to release memory back to OS.
A base requirement of using ProcessPoolExecutor is that the Session.request,
FutureSession all be pickle-able.
This means that only Python 3.5 is fully supported, while Python versions
3.4 and above REQUIRE an existing requests.Session instance to be passed
when initializing FutureSession. Python 2.X and < 3.4 are currently not
supported.
.. code-block:: python
# Using python 3.4
from concurrent.futures import ProcessPoolExecutor
from requests import Session
from requests_futures.sessions import FuturesSession
session = FuturesSession(executor=ProcessPoolExecutor(max_workers=10),
session=Session())
# ... use as before
In case pickling fails, an exception is raised pointing to this documentation.
.. code-block:: python
# Using python 2.7
from concurrent.futures import ProcessPoolExecutor
from requests import Session
from requests_futures.sessions import FuturesSession
session = FuturesSession(executor=ProcessPoolExecutor(max_workers=10),
session=Session())
Traceback (most recent call last):
...
RuntimeError: Cannot pickle function. Refer to documentation: https://github.com/ross/requests-futures/#using-processpoolexecutor
.. IMPORTANT::
FuturesSession it must be importable (module global)pip install requests-futures
.. _requests: https://github.com/kennethreitz/requests
.. _concurrent.futures: http://docs.python.org/dev/library/concurrent.futures.html
.. _backport: https://pypi.python.org/pypi/futures
.. _hooks: http://docs.python-requests.org/en/master/user/advanced/#event-hooks