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<!DOCTYPE HTML>
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=utf-8">
<script src="jquery.js"></script>
<script src="highlight.min.js"></script>
<script src="nav.js"></script>
<!-- Code Monospace Font -->
<link href='http://fonts.googleapis.com/css?family=Inconsolata' rel='stylesheet'>
<link rel="stylesheet" href="base.css">
<link rel="stylesheet" href="skeleton.css">
<link rel="stylesheet" href="layout.css">
<link rel="stylesheet" href="gevent.css">
<!-- Syntax Highlighting Theme -->
<link rel="stylesheet" href="github.min.css">
<title>Gevent Tutorial</title>
</head>
<style>
</style>
<body>
<div class="container">
<div id="sidebar" class="three columns sidebar">
<nav>
</nav>
</div>
<div class="twelve columns offset-by-three content">
<header>
<h1><span class="green">gevent</span> For the Working Python Developer</h1>
<h3 class="author">
Written by the Gevent Community
</h3>
<blockquote>
gevent is a concurrency library based around <a href="http://software.schmorp.de/pkg/libev.html">libev</a>. It provides a clean API for a variety of concurrency and network related tasks.
</blockquote>
</header>
<div class="toc">
<ul>
<li><a href="#introduction">Introduction</a><ul>
<li><a href="#contributors">Contributors</a></li>
</ul>
</li>
<li><a href="#core">Core</a><ul>
<li><a href="#greenlets">Greenlets</a></li>
<li><a href="#synchronous-asynchronous-execution">Synchronous & Asynchronous Execution</a></li>
<li><a href="#determinism">Determinism</a></li>
<li><a href="#spawning-greenlets">Spawning Greenlets</a></li>
<li><a href="#greenlet-state">Greenlet State</a></li>
<li><a href="#program-shutdown">Program Shutdown</a></li>
<li><a href="#timeouts">Timeouts</a></li>
<li><a href="#monkeypatching">Monkeypatching</a></li>
</ul>
</li>
<li><a href="#data-structures">Data Structures</a><ul>
<li><a href="#events">Events</a></li>
<li><a href="#queues">Queues</a></li>
<li><a href="#groups-and-pools">Groups and Pools</a></li>
<li><a href="#locks-and-semaphores">Locks and Semaphores</a></li>
<li><a href="#thread-locals">Thread Locals</a></li>
<li><a href="#subprocess">Subprocess</a></li>
<li><a href="#actors">Actors</a></li>
</ul>
</li>
<li><a href="#real-world-applications">Real World Applications</a><ul>
<li><a href="#gevent-zeromq">Gevent ZeroMQ</a></li>
<li><a href="#simple-servers">Simple Servers</a></li>
<li><a href="#wsgi-servers">WSGI Servers</a></li>
<li><a href="#streaming-servers">Streaming Servers</a></li>
<li><a href="#long-polling">Long Polling</a></li>
<li><a href="#websockets">Websockets</a></li>
<li><a href="#chat-server">Chat Server</a></li>
</ul>
</li>
</ul>
</div>
<h1 id="introduction">Introduction</h1>
<p>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.</p>
<h3 id="contributors">Contributors</h3>
<p>In chronological order of contribution:
<a href="http://www.stephendiehl.com">Stephen Diehl</a>
<a href="https://github.com/jerem">Jérémy Bethmont</a>
<a href="https://github.com/sww">sww</a>
<a href="https://github.com/brunoqc">Bruno Bigras</a>
<a href="https://github.com/dripton">David Ripton</a>
<a href="https://github.com/traviscline">Travis Cline</a>
<a href="https://github.com/Lothiraldan">Boris Feld</a>
<a href="https://github.com/youngsterxyf">youngsterxyf</a>
<a href="https://github.com/ehebert">Eddie Hebert</a>
<a href="http://notmyidea.org">Alexis Metaireau</a>
<a href="https://github.com/djv">Daniel Velkov</a></p>
<p>Also thanks to Denis Bilenko for writing gevent and guidance in
constructing this tutorial.</p>
<p>This is a collaborative document published under MIT license.
Have something to add? See a typo? Fork and issue a
pull request <a href="https://github.com/sdiehl/gevent-tutorial">Github</a>.
Any and all contributions are welcome.</p>
<p>This page is also <a href="http://methane.github.com/gevent-tutorial-ja">available in Japanese</a>.</p>
<h1 id="core">Core</h1>
<h2 id="greenlets">Greenlets</h2>
<p>The primary pattern used in gevent is the <strong>Greenlet</strong>, 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. </p>
<blockquote>
<p>Only one greenlet is ever running at any given time.</p>
</blockquote>
<p>This differs from any of the real parallelism constructs provided by
<code>multiprocessing</code> or <code>threading</code> libraries which do spin processes
and POSIX threads which are scheduled by the operating system and
are truly parallel.</p>
<h2 id="synchronous-asynchronous-execution">Synchronous & Asynchronous Execution</h2>
<p>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 <em>asynchronously</em>, instead of one at a time or <em>synchronously</em>. A
switch between the two subtasks is known as a <em>context switch</em>.</p>
<p>A context switch in gevent is done through <em>yielding</em>. In this case
example we have two contexts which yield to each other through invoking
<code>gevent.sleep(0)</code>.</p>
<pre><code class="python">
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),
])
</pre>
<p></code>
<pre><code class="python">
Running in foo
Explicit context to bar
Explicit context switch to foo again
Implicit context switch back to bar
</pre></code></p>
<p>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.</p>
<p><img alt="Greenlet Control Flow" src="flow.gif" /></p>
<p>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.</p>
<p>In this case the <code>select()</code> function is normally a blocking
call that polls on various file descriptors.</p>
<pre><code class="python">
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),
])
</pre>
<p></code>
<pre><code class="python">
Started Polling: at 0.0 seconds
Started Polling: at 0.0 seconds
Hey lets do some stuff while the greenlets poll, at at 0.0 seconds
Ended Polling: at 2.0 seconds
Ended Polling: at 2.0 seconds
</pre></code></p>
<p>Another somewhat synthetic example defines a <code>task</code> function
which is <em>non-deterministic</em>
(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.</p>
<pre><code class="python">
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()
</pre>
<p></code>
<pre><code class="python">
Synchronous:
Task 1 done
Task 2 done
Task 3 done
Task 4 done
Task 5 done
Task 6 done
Task 7 done
Task 8 done
Task 9 done
Asynchronous:
Task 0 done
Task 9 done
Task 1 done
Task 4 done
Task 6 done
Task 5 done
Task 8 done
Task 3 done
Task 2 done
Task 7 done
</pre></code></p>
<p>In the synchronous case all the tasks are run sequentially,
which results in the main programming <em>blocking</em> (
i.e. pausing the execution of the main program )
while each task executes.</p>
<p>The important parts of the program are the
<code>gevent.spawn</code> which wraps up the given function
inside of a Greenlet thread. The list of initialized greenlets
are stored in the array <code>threads</code> which is passed to
the <code>gevent.joinall</code> function which blocks the current
program to run all the given greenlets. The execution will step
forward only when all the greenlets terminate.</p>
<p>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.</p>
<p>A more common use case, fetching data from a server
asynchronously, the runtime of <code>fetch()</code> will differ between
requests given the load on the remote server.</p>
<pre><code class="python">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()
</code>
</pre>
<h2 id="determinism">Determinism</h2>
<p>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.</p>
<pre>
<code class="python">
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 )
</code>
</pre>
<pre>
<code class="python">False
True</code>
</pre>
<p>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.</p>
<p>The perennial problem involved with concurrency is known as a
<em>race condition</em>. 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.</p>
<p>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!</p>
<h2 id="spawning-greenlets">Spawning Greenlets</h2>
<p>gevent provides a few wrappers around Greenlet initialization.
Some of the most common patterns are:</p>
<pre><code class="python">
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
# foo
thread1 = Greenlet.spawn(foo, "Hello", 1)
# Wrapper for creating and runing a new Greenlet from the named
# function foo, with the passed 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)
</pre>
<p></code>
<pre><code class="python">
Hello
I live!
</pre></code></p>
<p>In addition to using the base Greenlet class, you may also subclass
Greenlet class and override the <code>_run</code> method.</p>
<pre><code class="python">
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()
</pre>
<p></code>
<pre><code class="python">
Hi there!
</pre></code></p>
<h2 id="greenlet-state">Greenlet State</h2>
<p>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.</p></p>
<p>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</p>
<ul>
<li><code>started</code> -- Boolean, indicates whether the Greenlet has been started. </li></li>
<li><code>ready()</code> -- Boolean, indicates whether the Greenlet has halted</li></li>
<li><code>successful()</code> -- Boolean, indicates whether the Greenlet has halted and not thrown an exception</li></li>
<li><code>value</code> -- arbitrary, the value returned by the Greenlet</li></li>
<li><code>exception</code> -- exception, uncaught exception instance thrown inside the greenlet</li></li>
</ul>
<pre><code class="python">
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.
print(loser.exception)
# It is possible though to raise the exception again outside
# raise loser.exception
# or with
# loser.get()
</pre>
<p></code>
<pre><code class="python">
True
True
You win!
None
True
True
True
False
You fail at failing.
</pre></code></p>
<h2 id="program-shutdown">Program Shutdown</h2>
<p>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.</p>
<p>A common pattern is to listen SIGQUIT events on the main program
and to invoke <code>gevent.shutdown</code> before exit.</p>
<pre>
<code class="python">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()
</code>
</pre>
<h2 id="timeouts">Timeouts</h2>
<p>Timeouts are a constraint on the runtime of a block of code or a
Greenlet.</p>
<pre>
<code class="python">
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'
</code>
</pre>
<p>Or with a context manager in a <code>with</code> statement.</p>
<pre>
<code class="python">import gevent
from gevent import Timeout
time_to_wait = 5 # seconds
class TooLong(Exception):
pass
with Timeout(time_to_wait, TooLong):
gevent.sleep(10)
</code>
</pre>
<p>In addition, gevent also provides timeout arguments for a
variety of Greenlet and data stucture related calls. For example:</p>
<pre><code class="python">
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')
</pre>
<p></code>
<pre><code class="python">
Thread 1 timed out
Thread 2 timed out
Thread 3 timed out
</pre></code></p>
<h2 id="monkeypatching">Monkeypatching</h2>
<p>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
<code>monkey.patch_socket()</code>. This is a purely side-effectful command to
modify the standard library's socket library</p>
<pre>
<code class="python">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 )
</code>
</pre>
<pre>
<code class="python">class 'socket.socket'
After monkey patch
class 'gevent.socket.socket'
After monkey patch
built-in function select
function select at 0x1924de8
</code>
</pre>
<p>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 <code>socket</code>, <code>ssl</code>, <code>threading</code> and
<code>select</code> modules to instead behave cooperatively.</p>
<p>For example, the Redis python bindings normally uses regular tcp
sockets to communicate with the <code>redis-server</code> instance. Simply
by invoking <code>gevent.monkey.patch_all()</code> we can make the redis
bindings schedule requests cooperatively and work with the rest
of our gevent stack.</p>
<p>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".</p>
<h1 id="data-structures">Data Structures</h1>
<h2 id="events">Events</h2>
<p>Events are a form of asynchronous communication between
Greenlets.</p>
<pre>
<code class="python">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),
])
</code>
</pre>
<p>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.</p>
<pre>
<code class="python">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),
])
</code>
</pre>
<h2 id="queues">Queues</h2>
<p>Queues are ordered sets of data that have the usual <code>put</code> / <code>get</code>
operations but are written in a way such that they can be safely
manipulated across Greenlets.</p>
<p>For example if one Greenlet grabs an item off of the queue, the
same item will not grabbed by another Greenlet executing
simultaneously.</p>
<pre><code class="python">
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'),
])
</pre>
<p></code>
<pre><code class="python">
Worker steve got task 1
Worker john got task 2
Worker nancy got task 3
Worker steve got task 4
Worker nancy got task 5
Worker john got task 6
Worker steve got task 7
Worker john got task 8
Worker nancy got task 9
Worker steve got task 10
Worker nancy got task 11
Worker john got task 12
Worker steve got task 13
Worker john got task 14
Worker nancy got task 15
Worker steve got task 16
Worker nancy got task 17
Worker john got task 18
Worker steve got task 19
Worker john got task 20
Worker nancy got task 21
Worker steve got task 22
Worker nancy got task 23
Worker john got task 24
Quitting time!
Quitting time!
Quitting time!
</pre></code></p>
<p>Queues can also block on either <code>put</code> or <code>get</code> as the need arises. </p>
<p>Each of the <code>put</code> and <code>get</code> operations has a non-blocking
counterpart, <code>put_nowait</code> and
<code>get_nowait</code> which will not block, but instead raise
either <code>gevent.queue.Empty</code> or
<code>gevent.queue.Full</code> in the operation is not possible.</p>
<p>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 <code>put</code>
operation will block until there is space on the queue.
Conversely the <code>get</code> 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
<code>gevent.queue.Empty</code> if no work can found within the
time frame of the Timeout.</p>
<pre><code class="python">
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'),
])
</pre>
<p></code>
<pre><code class="python">
Worker steve got task 1
Worker john got task 2
Worker bob got task 3
Worker steve got task 4
Worker bob got task 5
Worker john got task 6
Assigned all work in iteration 1
Worker steve got task 7
Worker john got task 8
Worker bob got task 9
Worker steve got task 10
Worker bob got task 11
Worker john got task 12
Worker steve got task 13
Worker john got task 14
Worker bob got task 15
Worker steve got task 16
Worker bob got task 17
Worker john got task 18
Assigned all work in iteration 2
Worker steve got task 19
Quitting time!
Quitting time!
Quitting time!
</pre></code></p>
<h2 id="groups-and-pools">Groups and Pools</h2>
<p>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 <code>multiprocessing</code> library.</p>
<pre><code class="python">
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()
</pre>
<p></code>
<pre><code class="python">
bar
bar
bar
foo
foo
foo
fizz
fizz
fizz
</pre></code></p>
<p>This is very usefull for managing groups of asynchronous tasks
that.</p>
<p>As mentioned above Group also provides an API for dispatching
jobs to grouped greenlets and collecting their results in various
ways.</p>
<pre><code class="python">
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)
</pre>
<p></code>
<pre><code class="python">
Size of group 3
Hello from Greenlet 42121488
Size of group 3
Hello from Greenlet 42119248
Size of group 3
Hello from Greenlet 42120208
Ordered
('task', 0)
('task', 1)
('task', 2)
Unordered
('task', 2)
('task', 1)
('task', 0)
</pre></code></p>
<p>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.</p>
<pre><code class="python">
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))
</pre>
<p></code>
<pre><code class="python">
Size of pool 2
Size of pool 2
Size of pool 1
</pre></code></p>
<p>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.</p>
<pre>
<code class="python">from gevent.pool import Pool
class SocketPool(object):
def __init__(self):
self.pool = Pool(1000)
self.pool.start()