[TOC]
The structure of this tutorial assumes an intermediate level knowledge of Python but not much else. No knowledge of concurrency is expected. The goal is to give you the tools you need to get going with gevent, help you tame your existing concurrency problems and start writing asynchronous applications today.
In chronological order of contribution: Stephen Diehl Jérémy Bethmont sww Bruno Bigras David Ripton Travis Cline Boris Feld youngsterxyf Eddie Hebert Alexis Metaireau Daniel Velkov
Also thanks to Denis Bilenko for writing gevent and guidance in constructing this tutorial.
This is a collaborative document published under MIT license. Have something to add? See a typo? Fork and issue a pull request Github. Any and all contributions are welcome.
This page is also available in Japanese.
The primary pattern used in gevent is the Greenlet, a lightweight coroutine provided to Python as a C extension module. Greenlets all run inside of the OS process for the main program but are scheduled cooperatively.
Only one greenlet is ever running at any given time.
This differs from any of the real parallelism constructs provided by
multiprocessing or threading libraries which do spin processes
and POSIX threads which are scheduled by the operating system and
are truly parallel.
The core idea of concurrency is that a larger task can be broken down into a collection of subtasks whose and scheduled to run simultaneously or asynchronously, instead of one at a time or synchronously. A switch between the two subtasks is known as a context switch.
A context switch in gevent is done through yielding. In this case
example we have two contexts which yield to each other through invoking
gevent.sleep(0).
[[[cog import gevent
def foo(): print('Running in foo') gevent.sleep(0) print('Explicit context switch to foo again')
def bar(): print('Explicit context to bar') gevent.sleep(0) print('Implicit context switch back to bar')
gevent.joinall([ gevent.spawn(foo), gevent.spawn(bar), ]) ]]] [[[end]]]
It is illuminating to visualize the control flow of the program or walk through it with a debugger to see the context switches as they occur.
The real power of gevent comes when we use it for network and IO bound functions which can be cooperatively scheduled. Gevent has taken care of all the details to ensure that your network libraries will implicitly yield their greenlet contexts whenever possible. I cannot stress enough what a powerful idiom this is. But maybe an example will illustrate.
In this case the select() function is normally a blocking
call that polls on various file descriptors.
[[[cog import time import gevent from gevent import select
start = time.time() tic = lambda: 'at %1.1f seconds' % (time.time() - start)
def gr1(): # Busy waits for a second, but we don't want to stick around... print('Started Polling: ', tic()) select.select([], [], [], 2) print('Ended Polling: ', tic())
def gr2(): # Busy waits for a second, but we don't want to stick around... print('Started Polling: ', tic()) select.select([], [], [], 2) print('Ended Polling: ', tic())
def gr3(): print("Hey lets do some stuff while the greenlets poll, at", tic()) gevent.sleep(1)
gevent.joinall([ gevent.spawn(gr1), gevent.spawn(gr2), gevent.spawn(gr3), ]) ]]] [[[end]]]
Another somewhat synthetic example defines a task function
which is non-deterministic
(i.e. its output is not guaranteed to give the same result for
the same inputs). In this case the side effect of running the
function is that the task pauses its execution for a random
number of seconds.
[[[cog import gevent import random
def task(pid): """ Some non-deterministic task """ gevent.sleep(random.randint(0,2)*0.001) print('Task', pid, 'done')
def synchronous(): for i in range(1,10): task(i)
def asynchronous(): threads = [gevent.spawn(task, i) for i in xrange(10)] gevent.joinall(threads)
print('Synchronous:') synchronous()
print('Asynchronous:') asynchronous() ]]] [[[end]]]
In the synchronous case all the tasks are run sequentially, which results in the main programming blocking ( i.e. pausing the execution of the main program ) while each task executes.
The important parts of the program are the
gevent.spawn which wraps up the given function
inside of a Greenlet thread. The list of initialized greenlets
are stored in the array threads which is passed to
the gevent.joinall function which blocks the current
program to run all the given greenlets. The execution will step
forward only when all the greenlets terminate.
The important fact to notice is that the order of execution in the async case is essentially random and that the total execution time in the async case is much less than the sync case. In fact the maximum time for the synchronous case to complete is when each tasks pauses for 2 seconds resulting in a 20 seconds for the whole queue. In the async case the maximum runtime is roughly 2 seconds since none of the tasks block the execution of the others.
A more common use case, fetching data from a server
asynchronously, the runtime of fetch() will differ between
requests given the load on the remote server.
import gevent.monkey
gevent.monkey.patch_socket()
import gevent
import urllib2
import simplejson as json
def fetch(pid):
response = urllib2.urlopen('http://json-time.appspot.com/time.json')
result = response.read()
json_result = json.loads(result)
datetime = json_result['datetime']
print 'Process ', pid, datetime
return json_result['datetime']
def synchronous():
for i in range(1,10):
fetch(i)
def asynchronous():
threads = []
for i in range(1,10):
threads.append(gevent.spawn(fetch, i))
gevent.joinall(threads)
print 'Synchronous:'
synchronous()
print 'Asynchronous:'
asynchronous()
As mentioned previously, greenlets are deterministic. Given the same configuration of greenlets and the same set of inputs and they always produce the same output. For example lets spread a task across a multiprocessing pool compared to a gevent pool.
import time
def echo(i):
time.sleep(0.001)
return i
# Non Deterministic Process Pool
from multiprocessing.pool import Pool
p = Pool(10)
run1 = [a for a in p.imap_unordered(echo, xrange(10))]
run2 = [a for a in p.imap_unordered(echo, xrange(10))]
run3 = [a for a in p.imap_unordered(echo, xrange(10))]
run4 = [a for a in p.imap_unordered(echo, xrange(10))]
print( run1 == run2 == run3 == run4 )
# Deterministic Gevent Pool
from gevent.pool import Pool
p = Pool(10)
run1 = [a for a in p.imap_unordered(echo, xrange(10))]
run2 = [a for a in p.imap_unordered(echo, xrange(10))]
run3 = [a for a in p.imap_unordered(echo, xrange(10))]
run4 = [a for a in p.imap_unordered(echo, xrange(10))]
print( run1 == run2 == run3 == run4 )
False
True
Even though gevent is normally deterministic, sources of non-determinism can creep into your program when you begin to interact with outside services such as sockets and files. Thus even though green threads are a form of "deterministic concurrency", they still can experience some of the same problems that POSIX threads and processes experience.
The perennial problem involved with concurrency is known as a race condition. Simply put is when two concurrent threads / processes depend on some shared resource but also attempt to modify this value. This results in resources whose values become time-dependent on the execution order. This is a problem, and in general one should very much try to avoid race conditions since they result program behavior which is globally non-deterministic.
The best approach to this is to simply avoid all global state all times. Global state and import-time side effects will always come back to bite you!
gevent provides a few wrappers around Greenlet initialization. Some of the most common patterns are:
[[[cog import gevent from gevent import Greenlet
def foo(message, n): """ Each thread will be passed the message, and n arguments in its initialization. """ gevent.sleep(n) print(message)
thread1 = Greenlet.spawn(foo, "Hello", 1)
thread2 = gevent.spawn(foo, "I live!", 2)
thread3 = gevent.spawn(lambda x: (x+1), 2)
threads = [thread1, thread2, thread3]
gevent.joinall(threads) ]]] [[[end]]]
In addition to using the base Greenlet class, you may also subclass
Greenlet class and override the _run method.
[[[cog import gevent from gevent import Greenlet
class MyGreenlet(Greenlet):
def __init__(self, message, n):
Greenlet.__init__(self)
self.message = message
self.n = n
def _run(self):
print(self.message)
gevent.sleep(self.n)
g = MyGreenlet("Hi there!", 3) g.start() g.join() ]]] [[[end]]]
Like any other segment of code, Greenlets can fail in various ways. A greenlet may fail to throw an exception, fail to halt or consume too many system resources.
The internal state of a greenlet is generally a time-dependent parameter. There are a number of flags on greenlets which let you monitor the state of the thread
started-- Boolean, indicates whether the Greenlet has been started.ready()-- Boolean, indicates whether the Greenlet has haltedsuccessful()-- Boolean, indicates whether the Greenlet has halted and not thrown an exceptionvalue-- arbitrary, the value returned by the Greenletexception-- exception, uncaught exception instance thrown inside the greenlet
[[[cog import gevent
def win(): return 'You win!'
def fail(): raise Exception('You fail at failing.')
winner = gevent.spawn(win) loser = gevent.spawn(fail)
print(winner.started) # True print(loser.started) # True
try: gevent.joinall([winner, loser]) except Exception as e: print('This will never be reached')
print(winner.value) # 'You win!' print(loser.value) # None
print(winner.ready()) # True print(loser.ready()) # True
print(winner.successful()) # True print(loser.successful()) # False
print(loser.exception)
]]] [[[end]]]
Greenlets that fail to yield when the main program receives a SIGQUIT may hold the program's execution longer than expected. This results in so called "zombie processes" which need to be killed from outside of the Python interpreter.
A common pattern is to listen SIGQUIT events on the main program
and to invoke gevent.shutdown before exit.
import gevent
import signal
def run_forever():
gevent.sleep(1000)
if __name__ == '__main__':
gevent.signal(signal.SIGQUIT, gevent.shutdown)
thread = gevent.spawn(run_forever)
thread.join()
Timeouts are a constraint on the runtime of a block of code or a Greenlet.
import gevent
from gevent import Timeout
seconds = 10
timeout = Timeout(seconds)
timeout.start()
def wait():
gevent.sleep(10)
try:
gevent.spawn(wait).join()
except Timeout:
print 'Could not complete'
Or with a context manager in a with statement.
import gevent
from gevent import Timeout
time_to_wait = 5 # seconds
class TooLong(Exception):
pass
with Timeout(time_to_wait, TooLong):
gevent.sleep(10)
In addition, gevent also provides timeout arguments for a variety of Greenlet and data stucture related calls. For example:
[[[cog import gevent from gevent import Timeout
def wait(): gevent.sleep(2)
timer = Timeout(1).start() thread1 = gevent.spawn(wait)
try: thread1.join(timeout=timer) except Timeout: print('Thread 1 timed out')
timer = Timeout.start_new(1) thread2 = gevent.spawn(wait)
try: thread2.get(timeout=timer) except Timeout: print('Thread 2 timed out')
try: gevent.with_timeout(1, wait) except Timeout: print('Thread 3 timed out')
]]] [[[end]]]
Alas we come to dark corners of Gevent. I've avoided mentioning
monkey patching up until now to try and motivate the powerful
coroutine patterns but the time has come to discuss the dark arts
of monkey-patching. If you noticed above we invoked the commnad
monkey.patch_socket(). This is a purely side-effectful command to
modify the standard library's socket library
import socket
print( socket.socket )
print "After monkey patch"
from gevent import monkey
monkey.patch_socket()
print( socket.socket )
import select
print select.select
monkey.patch_select()
print "After monkey patch"
print( select.select )
class 'socket.socket'
After monkey patch
class 'gevent.socket.socket'
built-in function select
After monkey patch
function select at 0x1924de8
Python's runtime allows for most objects to be modified at runtime
including modules, classes, and even functions. This is generally an
astoudingly bad idea since it creates an "implicit side-effect" that is
most often extremely difficult to debug if problems occur, nevertheless
in extreme situations where a library needs to alter the fundamental
behavior of Python itself monkey patches can be used. In this case gevent
is capable of patching most of the blocking system calls in the standard
library including those in socket, ssl, threading and
select modules to instead behave cooperatively.
For example, the Redis python bindings normally uses regular tcp
sockets to communicate with the redis-server instance. Simply
by invoking gevent.monkey.patch_all() we can make the redis
bindings schedule requests cooperatively and work with the rest
of our gevent stack.
This lets us integrate libraries that would not normally work with gevent without ever writing a single line of code. While monkey-patching is still evil, in this case it is a "usefull evil".
Events are a form of asynchronous communication between Greenlets.
import gevent
from gevent.event import AsyncResult
a = AsyncResult()
def setter():
"""
After 3 seconds set wake all threads waiting on the value of
a.
"""
gevent.sleep(3)
a.set()
def waiter():
"""
After 3 seconds the get call will unblock.
"""
a.get() # blocking
print 'I live!'
gevent.joinall([
gevent.spawn(setter),
gevent.spawn(waiter),
])
A extension of the Event object is the AsyncResult which allows you to send a value along with the wakeup call. This is sometimes called a future or a deferred, since it holds a reference to a future value that can be set on an arbitrary time schedule.
import gevent
from gevent.event import AsyncResult
a = AsyncResult()
def setter():
"""
After 3 seconds set the result of a.
"""
gevent.sleep(3)
a.set('Hello!')
def waiter():
"""
After 3 seconds the get call will unblock after the setter
puts a value into the AsyncResult.
"""
print a.get()
gevent.joinall([
gevent.spawn(setter),
gevent.spawn(waiter),
])
Queues are ordered sets of data that have the usual put / get
operations but are written in a way such that they can be safely
manipulated across Greenlets.
For example if one Greenlet grabs an item off of the queue, the same item will not grabbed by another Greenlet executing simultaneously.
[[[cog import gevent from gevent.queue import Queue
tasks = Queue()
def worker(n): while not tasks.empty(): task = tasks.get() print('Worker %s got task %s' % (n, task)) gevent.sleep(0)
print('Quitting time!')
def boss(): for i in xrange(1,25): tasks.put_nowait(i)
gevent.spawn(boss).join()
gevent.joinall([ gevent.spawn(worker, 'steve'), gevent.spawn(worker, 'john'), gevent.spawn(worker, 'nancy'), ]) ]]] [[[end]]]
Queues can also block on either put or get as the need arises.
Each of the put and get operations has a non-blocking
counterpart, put_nowait and
get_nowait which will not block, but instead raise
either gevent.queue.Empty or
gevent.queue.Full in the operation is not possible.
In this example we have the boss running simultaneously to the
workers and have a restriction on the Queue that it can contain no
more than three elements. This restriction means that the put
operation will block until there is space on the queue.
Conversely the get operation will block if there are
no elements on the queue to fetch, it also takes a timeout
argument to allow for the queue to exit with the exception
gevent.queue.Empty if no work can found within the
time frame of the Timeout.
[[[cog import gevent from gevent.queue import Queue, Empty
tasks = Queue(maxsize=3)
def worker(n): try: while True: task = tasks.get(timeout=1) # decrements queue size by 1 print('Worker %s got task %s' % (n, task)) gevent.sleep(0) except Empty: print('Quitting time!')
def boss(): """ Boss will wait to hand out work until a individual worker is free since the maxsize of the task queue is 3. """
for i in xrange(1,10):
tasks.put(i)
print('Assigned all work in iteration 1')
for i in xrange(10,20):
tasks.put(i)
print('Assigned all work in iteration 2')
gevent.joinall([ gevent.spawn(boss), gevent.spawn(worker, 'steve'), gevent.spawn(worker, 'john'), gevent.spawn(worker, 'bob'), ]) ]]] [[[end]]]
A group is a collection of running greenlets which are managed
and scheduled together as group. It also doubles as parallel
dispatcher that mirrors the Python multiprocessing library.
[[[cog import gevent from gevent.pool import Group
def talk(msg): for i in xrange(3): print(msg)
g1 = gevent.spawn(talk, 'bar') g2 = gevent.spawn(talk, 'foo') g3 = gevent.spawn(talk, 'fizz')
group = Group() group.add(g1) group.add(g2) group.join()
group.add(g3) group.join() ]]] [[[end]]]
This is very usefull for managing groups of asynchronous tasks that.
As mentioned above Group also provides an API for dispatching jobs to grouped greenlets and collecting their results in various ways.
[[[cog import gevent from gevent import getcurrent from gevent.pool import Group
group = Group()
def hello_from(n): print('Size of group', len(group)) print('Hello from Greenlet %s' % id(getcurrent()))
group.map(hello_from, xrange(3))
def intensive(n): gevent.sleep(3 - n) return 'task', n
print('Ordered')
ogroup = Group() for i in ogroup.imap(intensive, xrange(3)): print(i)
print('Unordered')
igroup = Group() for i in igroup.imap_unordered(intensive, xrange(3)): print(i)
]]] [[[end]]]
A pool is a structure designed for handling dynamic numbers of greenlets which need to be concurrency-limited. This is often desirable in cases where one wants to do many network or IO bound tasks in parallel.
[[[cog import gevent from gevent import getcurrent from gevent.pool import Pool
pool = Pool(2)
def hello_from(n): print('Size of pool', len(pool))
pool.map(hello_from, xrange(3)) ]]] [[[end]]]
Often when building gevent driven services one will center the entire service around a pool structure. An example might be a class which polls on various sockets.
from gevent.pool import Pool
class SocketPool(object):
def __init__(self):
self.pool = Pool(1000)
self.pool.start()
def listen(self, socket):
while True:
socket.recv()
def add_handler(self, socket):
if self.pool.full():
raise Exception("At maximum pool size")
else:
self.pool.spawn(self.listen, socket)
def shutdown(self):
self.pool.kill()
A semaphore is a low level synchronization primitive that allows
greenlets to coordinate and limit concurrent access or execution. A
semaphore exposes two methods, acquire and release The
difference between the number of times and a semaphore has been
acquired and released is called the bound of the semaphore. If a
semaphore bound reaches 0 it will block until another greenlet
releases its acquisition.
[[[cog from gevent import sleep from gevent.pool import Pool from gevent.coros import BoundedSemaphore
sem = BoundedSemaphore(2)
def worker1(n): sem.acquire() print('Worker %i acquired semaphore' % n) sleep(0) sem.release() print('Worker %i released semaphore' % n)
def worker2(n): with sem: print('Worker %i acquired semaphore' % n) sleep(0) print('Worker %i released semaphore' % n)
pool = Pool() pool.map(worker1, xrange(0,2)) pool.map(worker2, xrange(3,6)) ]]] [[[end]]]
A semaphore with bound of 1 is known as a Lock. it provides exclusive execution to one greenlet. They are often used to ensure that resources are only in use at one time in the context of a program.
Gevnet also allows you to specify data which is local the
greenlet context. Internally this is implemented as a global
lookup which addresses a private namespace keyed by the
greenlet's getcurrent() value.
[[[cog import gevent from gevent.local import local
stash = local()
def f1(): stash.x = 1 print(stash.x)
def f2(): stash.y = 2 print(stash.y)
try:
stash.x
except AttributeError:
print("x is not local to f2")
g1 = gevent.spawn(f1) g2 = gevent.spawn(f2)
gevent.joinall([g1, g2]) ]]] [[[end]]]
Many web framework thats integrate with gevent store HTTP session objects inside of gevent thread locals. For example using the Werkzeug utility library and its proxy object we can create Flask style request objects.
from gevent.local import local
from werkzeug.local import LocalProxy
from werkzeug.wrappers import Request
from contextlib import contextmanager
from gevent.wsgi import WSGIServer
_requests = local()
request = LocalProxy(lambda: _requests.request)
@contextmanager
def sessionmanager(environ):
_requests.request = Request(environ)
yield
_requests.request = None
def logic():
return "Hello " + request.remote_addr
def application(environ, start_response):
status = '200 OK'
with sessionmanager(environ):
body = logic()
headers = [
('Content-Type', 'text/html')
]
start_response(status, headers)
return [body]
WSGIServer(('', 8000), application).serve_forever()
Flask's system is more a bit sophisticated than this example, but the
idea of using thread locals as local session storage is nontheless the
same.
As of Gevent 1.0, support has been added for cooperative waiting
on subprocess.
import gevent
from gevent.subprocess import Popen, PIPE
# Uses a green pipe which is cooperative
sub = Popen(['uname'], stdout=PIPE)
read_output = gevent.spawn(sub.stdout.read)
output = read_output.join()
print(output.value)
Linux
Many people also want to use gevent and multiprocessing together. This
can be done as most multiprocessing objects expose the underlying file
descriptors.
[[[cog
import gevent
from multiprocessing import Process, Pipe
from gevent.socket import wait_read, wait_write
a, b = Pipe()
c, d = Pipe()
def relay():
for i in xrange(10):
msg = b.recv()
c.send(msg + " in " + str(i))
def put_msg():
for i in xrange(10):
wait_write(a.fileno())
a.send('hi')
def get_msg():
for i in xrange(10):
wait_read(d.fileno())
print(d.recv())
if name == 'main':
proc = Process(target=relay)
proc.start()
g1 = gevent.spawn(get_msg)
g2 = gevent.spawn(put_msg)
gevent.joinall([g1, g2], timeout=1)
]]]
[[[end]]]
The actor model is a higher level concurrency model popularized
by the language Erlang. In short the main idea is that you have a
collection of independent Actors which have an inbox from which
they receive messages from other Actors. The main loop inside the
Actor iterates through its messages and takes action according to
its desired behavior.
Gevent does not have a primitive Actor type, but we can define
one very simply using a Queue inside of a subclassed Greenlet.
import gevent
from gevent.queue import Queue
class Actor(gevent.Greenlet):
def __init__(self):
self.inbox = Queue()
Greenlet.__init__(self)
def receive(self, message):
"""
Define in your subclass.
"""
raise NotImplemented()
def _run(self):
self.running = True
while self.running:
message = self.inbox.get()
self.receive(message)
In a use case:
import gevent
from gevent.queue import Queue
from gevent import Greenlet
class Pinger(Actor):
def receive(self, message):
print message
pong.inbox.put('ping')
gevent.sleep(0)
class Ponger(Actor):
def receive(self, message):
print message
ping.inbox.put('pong')
gevent.sleep(0)
ping = Pinger()
pong = Ponger()
ping.start()
pong.start()
ping.inbox.put('start')
gevent.joinall([ping, pong])
ZeroMQ is described by its authors as
"a socket library that acts as a concurrency framework". It is a
very powerful messaging layer for building concurrent and
distributed applications.
ZeroMQ provides a variety of socket primitives, the simplest of
which being a Request-Response socket pair. A socket has two
methods of interest send and recv, both of which are
normally blocking operations. But this is remedied by a briliant
library by Travis Cline which
uses gevent.socket to poll ZeroMQ sockets in a non-blocking
manner. You can install gevent-zeromq from PyPi via: pip install gevent-zeromq
[[[cog
import gevent
from gevent_zeromq import zmq
context = zmq.Context()
def server():
server_socket = context.socket(zmq.REQ)
server_socket.bind("tcp://127.0.0.1:5000")
for request in range(1,10):
server_socket.send("Hello")
print('Switched to Server for ', request)
# Implicit context switch occurs here
server_socket.recv()
def client():
client_socket = context.socket(zmq.REP)
client_socket.connect("tcp://127.0.0.1:5000")
for request in range(1,10):
client_socket.recv()
print('Switched to Client for ', request)
# Implicit context switch occurs here
client_socket.send("World")
publisher = gevent.spawn(server)
client = gevent.spawn(client)
gevent.joinall([publisher, client])
]]]
[[[end]]]
# On Unix: Access with ``$ nc 127.0.0.1 5000``
# On Window: Access with ``$ telnet 127.0.0.1 5000``
from gevent.server import StreamServer
def handle(socket, address):
socket.send("Hello from a telnet!\n")
for i in range(5):
socket.send(str(i) + '\n')
socket.close()
server = StreamServer(('127.0.0.1', 5000), handle)
server.serve_forever()
Gevent provides two WSGI servers for serving content over HTTP.
Henceforth called wsgi and pywsgi:
- gevent.wsgi.WSGIServer
- gevent.pywsgi.WSGIServer
In earlier versions of gevent before 1.0.x, gevent used libevent
instead of libev. Libevent included a fast HTTP server which was
used by gevent's wsgi server.
In gevent 1.0.x there is no http server included. Instead
gevent.wsgi is now an alias for the pure Python server in
gevent.pywsgi.
If you are using gevent 1.0.x, this section does not apply
For those familiar with streaming HTTP services, the core idea is
that in the headers we do not specify a length of the content. We
instead hold the connection open and flush chunks down the pipe,
prefixing each with a hex digit indicating the length of the
chunk. The stream is closed when a size zero chunk is sent.
HTTP/1.1 200 OK
Content-Type: text/plain
Transfer-Encoding: chunked
8
<p>Hello
9
World</p>
0
The above HTTP connection could not be created in wsgi
because streaming is not supported. It would instead have to
buffered.
from gevent.wsgi import WSGIServer
def application(environ, start_response):
status = '200 OK'
body = '<p>Hello World</p>'
headers = [
('Content-Type', 'text/html')
]
start_response(status, headers)
return [body]
WSGIServer(('', 8000), application).serve_forever()
Using pywsgi we can however write our handler as a generator and
yield the result chunk by chunk.
from gevent.pywsgi import WSGIServer
def application(environ, start_response):
status = '200 OK'
headers = [
('Content-Type', 'text/html')
]
start_response(status, headers)
yield "<p>Hello"
yield "World</p>"
WSGIServer(('', 8000), application).serve_forever()
But regardless, performance on Gevent servers is phenomenal
compared to other Python servers. libev is a very vetted technology
and its derivative servers are known to perform well at scale.
To benchmark, try Apache Benchmark ab or see this
Benchmark of Python WSGI Servers
for comparison with other servers.
$ ab -n 10000 -c 100 http://127.0.0.1:8000/
import gevent
from gevent.queue import Queue, Empty
from gevent.pywsgi import WSGIServer
import simplejson as json
data_source = Queue()
def producer():
while True:
data_source.put_nowait('Hello World')
gevent.sleep(1)
def ajax_endpoint(environ, start_response):
status = '200 OK'
headers = [
('Content-Type', 'application/json')
]
start_response(status, headers)
while True:
try:
datum = data_source.get(timeout=5)
yield json.dumps(datum) + '\n'
except Empty:
pass
gevent.spawn(producer)
WSGIServer(('', 8000), ajax_endpoint).serve_forever()
Websocket example which requires gevent-websocket.
# Simple gevent-websocket server
import json
import random
from gevent import pywsgi, sleep
from geventwebsocket.handler import WebSocketHandler
class WebSocketApp(object):
'''Send random data to the websocket'''
def __call__(self, environ, start_response):
ws = environ['wsgi.websocket']
x = 0
while True:
data = json.dumps({'x': x, 'y': random.randint(1, 5)})
ws.send(data)
x += 1
sleep(0.5)
server = pywsgi.WSGIServer(("", 10000), WebSocketApp(),
handler_class=WebSocketHandler)
server.serve_forever()
HTML Page:
<html>
<head>
<title>Minimal websocket application</title>
<script type="text/javascript" src="jquery.min.js"></script>
<script type="text/javascript">
$(function() {
// Open up a connection to our server
var ws = new WebSocket("ws://localhost:10000/");
// What do we do when we get a message?
ws.onmessage = function(evt) {
$("#placeholder").append('<p>' + evt.data + '</p>')
}
// Just update our conn_status field with the connection status
ws.onopen = function(evt) {
$('#conn_status').html('<b>Connected</b>');
}
ws.onerror = function(evt) {
$('#conn_status').html('<b>Error</b>');
}
ws.onclose = function(evt) {
$('#conn_status').html('<b>Closed</b>');
}
});
</script>
</head>
<body>
<h1>WebSocket Example</h1>
<div id="conn_status">Not Connected</div>
<div id="placeholder" style="width:600px;height:300px;"></div>
</body>
</html>
The final motivating example, a realtime chat room. This example
requires Flask ( but not neccesarily so, you could use Django,
Pyramid, etc ). The corresponding Javascript and HTML files can
be found here.
# Micro gevent chatroom.
# ----------------------
from flask import Flask, render_template, request
from gevent import queue
from gevent.pywsgi import WSGIServer
import simplejson as json
app = Flask(__name__)
app.debug = True
rooms = {
'topic1': Room(),
'topic2': Room(),
}
users = {}
class Room(object):
def __init__(self):
self.users = set()
self.messages = []
def backlog(self, size=25):
return self.messages[-size:]
def subscribe(self, user):
self.users.add(user)
def add(self, message):
for user in self.users:
print user
user.queue.put_nowait(message)
self.messages.append(message)
class User(object):
def __init__(self):
self.queue = queue.Queue()
@app.route('/')
def choose_name():
return render_template('choose.html')
@app.route('/<uid>')
def main(uid):
return render_template('main.html',
uid=uid,
rooms=rooms.keys()
)
@app.route('/<room>/<uid>')
def join(room, uid):
user = users.get(uid, None)
if not user:
users[uid] = user = User()
active_room = rooms[room]
active_room.subscribe(user)
print 'subscribe', active_room, user
messages = active_room.backlog()
return render_template('room.html',
room=room, uid=uid, messages=messages)
@app.route("/put/<room>/<uid>", methods=["POST"])
def put(room, uid):
user = users[uid]
room = rooms[room]
message = request.form['message']
room.add(':'.join([uid, message]))
return ''
@app.route("/poll/<uid>", methods=["POST"])
def poll(uid):
try:
msg = users[uid].queue.get(timeout=10)
except queue.Empty:
msg = []
return json.dumps(msg)
if __name__ == "__main__":
http = WSGIServer(('', 5000), app)
http.serve_forever()