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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 and use it to solve or speed up your applications today.

The primary pattern provided by 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 by libev. This differs from subprocceses which are new processes are spawned by the OS.


Synchronous & Asynchronous Execution

The core idea of concurrency is that a larger task can be broken down into a collection of subtasks whose operation does not depend on the other tasks and thus can be run asynchronously instead of one at a time synchronously. A switch between the two executions is known as a context swtich.

A context switch in gevent 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('Emplict context switch to foo again')

def bar(): print('Emplict context to bar') gevent.sleep(0) print('Implicit swtich switch back to bar')

gevent.joinall([ gevent.spawn(foo), gevent.spawn(bar), ]) ]]] [[[end]]]

A 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

import gevent
import urllib2
import simplejson as json

def fetch(pid):
    response = urllib2.urlopen('')
    result =
    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):

def asynchronous():
    threads = []
    for i in range(1,10):
        threads.append(gevent.spawn(fetch, i))

print 'Synchronous:'

print 'Asynchronous:'

Race Conditions

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.*

One approach to avoiding race conditions is to simply not have any global state shared between threads. To communicate threads instead pass stateless messages between each other.

Spawning Threads

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)

Initialize a new Greenlet instance running the named function


thread1 = Greenlet.spawn(foo, "Hello", 1)

Wrapper for creating and runing a new Greenlet from the named

function foo, with the passd arguments

thread2 = gevent.spawn(foo, "I live!", 2)

Lambda expressions

thread3 = gevent.spawn(lambda x: (x+1), 2)

threads = [thread1, thread2, thread3]

Block until all threads complete.

gevent.joinall(threads) ]]] [[[end]]]

In addition to using the base Greenlet class, you may also subclass Greenlet class and overload the _run method.

[[[cog from gevent import Greenlet

class MyGreenlet(Greenlet):

def __init__(self, message, n):
    self.message = message
    self.n = n

def _run(self):

g = MyGreenlet("Hi there!", 3) g.start() g.join() ]]] [[[end]]]

Greenlet State

Like any other segement of code Greenlets can fail in various ways. A greenlet may fail 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 halted
  • successful() -- Boolean, indicates whether the Greenlet has halted and not thrown an exception
  • value -- arbitrary, the value returned by the Greenlet
  • exception -- 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

Exceptions raised in the Greenlet, stay inside the Greenlet.

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

The exception raised in fail, will not propogate outside the

greenlet. A stack trace will be printed to stdout but it

will not unwind the stack of the parent.


It is possible though to raise the exception again outside

raise loser.exception

or with


]]] [[[end]]]

Program Shutdown

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():

if __name__ == '__main__':
    gevent.signal(signal.SIGQUIT, gevent.shutdown)
    thread = gevent.spawn(run_forever)


Timeouts are a constraint on the runtime of a block of code or a Greenlet.

from gevent import Timeout

seconds = 10

timeout = Timeout(seconds)

def wait():

except Timeout:
    print 'Could not complete'

Or with a context manager in a with a statement.

import gevent
from gevent import Timeout

time_to_wait = 5 # seconds

class TooLong(Exception):

with Timeout(time_to_wait, TooLong):

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]]]

Data Structures


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

def waiter():
    After 3 seconds the get call will unblock.
    a.get() # blocking
    print 'I live!'


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.

def waiter():
    After 3 seconds the get call will unblock after the setter
    puts a value into the AsyncResult.
    print a.get()



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.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.

import gevent
from gevent.queue import Queue, Empty

tasks = Queue(maxsize=3)

def worker(n):
        while True:
            task = tasks.get(timeout=1) # decrements queue size by 1
            print 'Worker %s got task %s' % (n, task)
    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):
    print 'Assigned all work in iteration 1'

    for i in xrange(10,20):
    print 'Assigned all work in iteration 2'

    gevent.spawn(worker, 'steve'),
    gevent.spawn(worker, 'john'),
    gevent.spawn(worker, 'bob'),

Groups and Pools

Locks and Semaphores


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

class Actor(gevent.Greenlet):

    def __init__(self):
        self.inbox = queue.Queue()

    def recieve(self, message):
        Define in your subclass.
        raise NotImplemented()

    def _run(self):
        self.running = True

        while self.running:
            message = self.inbox.get()

In a use case:

import gevent
from gevent.queue import Queue
from gevent import Greenlet

class Pinger(Actor):
    def recieve(self, message):
        print message

class Ponger(Actor):
    def recieve(self, message):
        print message

ping = Pinger()
pong = Ponger()


gevent.joinall([ping, pong])

Real World Applications

Holding Side Effects

In this example we hold the side effects of executing an arbitrary string,

from gevent import Greenlet

env = {}

def run_code(code, env={}):
    local = locals()
    exec(code, globals(), local)
    return local

while True:
    code = raw_input('>')

    g = Greenlet.spawn(run_code, code, env)
    g.join() # block until code executes

    # If succesfull then pass the locals to the next command
    if g.value:
        env = g.get()
        print g.exception

WSGI Servers

from gevent.pywsgi import WSGIServer

def application(environ, start_response):
    status = '200 OK'
    body = 'Hello Cruel World!'

    headers = [
        ('Content-Type', 'text/html')

    start_response(status, headers)
    return [body]

WSGIServer(('', 8000), application).serve_forever()

Performance on Gevent servers is phenomenal.

$ ab -n 10000 -c 100

Long Polling

Chat Server

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

class Room(object):

    def __init__(self):
        self.users = set()
        self.messages = []

    def backlog(self, size=25):
        return self.messages[-size:]

    def subscribe(self, user):

    def add(self, message):
        for user in self.users:
            print user

class User(object):

    def __init__(self):
        self.queue = queue.Queue()

rooms = {
    'foo': Room(),
    'bar': Room(),

users = {}

def choose_name():
    return render_template('choose.html')

def main(uid):
    return render_template('main.html',

def join(room, uid):
    user = users.get(uid, None)

    if not user:
        users[uid] = user = User()

    active_room = rooms[room]
    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):
        msg = users[uid].queue.get(timeout=10)
    except queue.Empty:
        msg = []
    return json.dumps(msg)

if __name__ == "__main__":
    http = WSGIServer(('', 5000), app)


This is a collaborative document published under MIT license. Forking on GitHub is encouraged

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