How to use threading in Python?
I am trying to understand threading in Python. I've looked at the documentation and examples, but quite frankly, many examples are overly sophisticated and I'm having trouble understanding them.
How do you clearly show tasks being divided for multi-threading?
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with python with map and pool .
The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks . I'll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results = []
for item in my_array:
results.append(my_function(item))
Description
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Implementation
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
import urllib2
from multiprocessing.dummy import Pool as ThreadPool
urls = [
'http://www.python.org',
'http://www.python.org/about/',
'http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html',
'http://www.python.org/doc/',
'http://www.python.org/download/',
'http://www.python.org/getit/',
'http://www.python.org/community/',
'https://wiki.python.org/moin/',
]
# make the Pool of workers
pool = ThreadPool(4)
# open the urls in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)
# close the pool and wait for the work to finish
pool.close()
pool.join()
And the timing results:
Single thread: 14.4 seconds
4 Pool: 3.1 seconds
8 Pool: 1.4 seconds
13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later): (source):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround.
(Thanks to user136036 for the helpful comment)
Here's a simple example: you need to try a few alternative URLs and return the contents of the first one to respond.
import Queue
import threading
import urllib2
# called by each thread
def get_url(q, url):
q.put(urllib2.urlopen(url).read())
theurls = ["http://google.com", "http://yahoo.com"]
q = Queue.Queue()
for u in theurls:
t = threading.Thread(target=get_url, args = (q,u))
t.daemon = True
t.start()
s = q.get()
print s
This is a case where threading is used as a simple optimization: each subthread is waiting for a URL to resolve and respond, in order to put its contents on the queue; each thread is a daemon (won't keep the process up if main thread ends -- that's more common than not); the main thread starts all subthreads, does a get on the queue to wait until one of them has done a put , then emits the results and terminates (which takes down any subthreads that might still be running, since they're daemon threads).
Proper use of threads in Python is invariably connected to I/O operations (since CPython doesn't use multiple cores to run CPU-bound tasks anyway, the only reason for threading is not blocking the process while there's a wait for some I/O). Queues are almost invariably the best way to farm out work to threads and/or collect the work's results, by the way, and they're intrinsically threadsafe so they save you from worrying about locks, conditions, events, semaphores, and other inter-thread coordination/communication concepts.
NOTE: For actual parallelization in Python, you should use the multiprocessing module to fork multiple processes that execute in parallel (due to the global interpreter lock, Python threads provide interleaving but are in fact executed serially, not in parallel, and are only useful when interleaving I/O operations).
However, if you are merely looking for interleaving (or are doing I/O operations that can be parallelized despite the global interpreter lock), then the threading module is the place to start. As a really simple example, let's consider the problem of summing a large range by summing subranges in parallel:
import threading
class SummingThread(threading.Thread):
def __init__(self,low,high):
super(SummingThread, self).__init__()
self.low=low
self.high=high
self.total=0
def run(self):
for i in range(self.low,self.high):
self.total+=i
thread1 = SummingThread(0,500000)
thread2 = SummingThread(500000,1000000)
thread1.start() # This actually causes the thread to run
thread2.start()
thread1.join() # This waits until the thread has completed
thread2.join()
# At this point, both threads have completed
result = thread1.total + thread2.total
print result
Note that the above is a very stupid example, as it does absolutely no I/O and will be executed serially albeit interleaved (with the added overhead of context switching) in CPython due to the global interpreter lock.
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