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How to have multiple Python scripts interacting with each other [duplicate]

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.
multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
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):
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, to put its contents on the queue; each thread is a daemon (won't keep the process up if the 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 they 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.

Like others mentioned, CPython can use threads only for I/O waits due to GIL.
If you want to benefit from multiple cores for CPU-bound tasks, use multiprocessing:
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()

Just a note: A queue is not required for threading.
This is the simplest example I could imagine that shows 10 processes running concurrently.
import threading
from random import randint
from time import sleep
def print_number(number):
# Sleeps a random 1 to 10 seconds
rand_int_var = randint(1, 10)
sleep(rand_int_var)
print "Thread " + str(number) + " slept for " + str(rand_int_var) + " seconds"
thread_list = []
for i in range(1, 10):
# Instantiates the thread
# (i) does not make a sequence, so (i,)
t = threading.Thread(target=print_number, args=(i,))
# Sticks the thread in a list so that it remains accessible
thread_list.append(t)
# Starts threads
for thread in thread_list:
thread.start()
# This blocks the calling thread until the thread whose join() method is called is terminated.
# From http://docs.python.org/2/library/threading.html#thread-objects
for thread in thread_list:
thread.join()
# Demonstrates that the main process waited for threads to complete
print "Done"

The answer from Alex Martelli helped me. However, here is a modified version that I thought was more useful (at least to me).
Updated: works in both Python 2 and Python 3
try:
# For Python 3
import queue
from urllib.request import urlopen
except:
# For Python 2
import Queue as queue
from urllib2 import urlopen
import threading
worker_data = ['http://google.com', 'http://yahoo.com', 'http://bing.com']
# Load up a queue with your data. This will handle locking
q = queue.Queue()
for url in worker_data:
q.put(url)
# Define a worker function
def worker(url_queue):
queue_full = True
while queue_full:
try:
# Get your data off the queue, and do some work
url = url_queue.get(False)
data = urlopen(url).read()
print(len(data))
except queue.Empty:
queue_full = False
# Create as many threads as you want
thread_count = 5
for i in range(thread_count):
t = threading.Thread(target=worker, args = (q,))
t.start()

Given a function, f, thread it like this:
import threading
threading.Thread(target=f).start()
To pass arguments to f
threading.Thread(target=f, args=(a,b,c)).start()

I found this very useful: create as many threads as cores and let them execute a (large) number of tasks (in this case, calling a shell program):
import Queue
import threading
import multiprocessing
import subprocess
q = Queue.Queue()
for i in range(30): # Put 30 tasks in the queue
q.put(i)
def worker():
while True:
item = q.get()
# Execute a task: call a shell program and wait until it completes
subprocess.call("echo " + str(item), shell=True)
q.task_done()
cpus = multiprocessing.cpu_count() # Detect number of cores
print("Creating %d threads" % cpus)
for i in range(cpus):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
q.join() # Block until all tasks are done

Python 3 has the facility of launching parallel tasks. This makes our work easier.
It has thread pooling and process pooling.
The following gives an insight:
ThreadPoolExecutor Example (source)
import concurrent.futures
import urllib.request
URLS = ['http://www.foxnews.com/',
'http://www.cnn.com/',
'http://europe.wsj.com/',
'http://www.bbc.co.uk/',
'http://some-made-up-domain.com/']
# Retrieve a single page and report the URL and contents
def load_url(url, timeout):
with urllib.request.urlopen(url, timeout=timeout) as conn:
return conn.read()
# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
# Start the load operations and mark each future with its URL
future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
for future in concurrent.futures.as_completed(future_to_url):
url = future_to_url[future]
try:
data = future.result()
except Exception as exc:
print('%r generated an exception: %s' % (url, exc))
else:
print('%r page is %d bytes' % (url, len(data)))
ProcessPoolExecutor (source)
import concurrent.futures
import math
PRIMES = [
112272535095293,
112582705942171,
112272535095293,
115280095190773,
115797848077099,
1099726899285419]
def is_prime(n):
if n % 2 == 0:
return False
sqrt_n = int(math.floor(math.sqrt(n)))
for i in range(3, sqrt_n + 1, 2):
if n % i == 0:
return False
return True
def main():
with concurrent.futures.ProcessPoolExecutor() as executor:
for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
print('%d is prime: %s' % (number, prime))
if __name__ == '__main__':
main()

I saw a lot of examples here where no real work was being performed, and they were mostly CPU-bound. Here is an example of a CPU-bound task that computes all prime numbers between 10 million and 10.05 million. I have used all four methods here:
import math
import timeit
import threading
import multiprocessing
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
def time_stuff(fn):
"""
Measure time of execution of a function
"""
def wrapper(*args, **kwargs):
t0 = timeit.default_timer()
fn(*args, **kwargs)
t1 = timeit.default_timer()
print("{} seconds".format(t1 - t0))
return wrapper
def find_primes_in(nmin, nmax):
"""
Compute a list of prime numbers between the given minimum and maximum arguments
"""
primes = []
# Loop from minimum to maximum
for current in range(nmin, nmax + 1):
# Take the square root of the current number
sqrt_n = int(math.sqrt(current))
found = False
# Check if the any number from 2 to the square root + 1 divides the current numnber under consideration
for number in range(2, sqrt_n + 1):
# If divisible we have found a factor, hence this is not a prime number, lets move to the next one
if current % number == 0:
found = True
break
# If not divisible, add this number to the list of primes that we have found so far
if not found:
primes.append(current)
# I am merely printing the length of the array containing all the primes, but feel free to do what you want
print(len(primes))
#time_stuff
def sequential_prime_finder(nmin, nmax):
"""
Use the main process and main thread to compute everything in this case
"""
find_primes_in(nmin, nmax)
#time_stuff
def threading_prime_finder(nmin, nmax):
"""
If the minimum is 1000 and the maximum is 2000 and we have four workers,
1000 - 1250 to worker 1
1250 - 1500 to worker 2
1500 - 1750 to worker 3
1750 - 2000 to worker 4
so let’s split the minimum and maximum values according to the number of workers
"""
nrange = nmax - nmin
threads = []
for i in range(8):
start = int(nmin + i * nrange/8)
end = int(nmin + (i + 1) * nrange/8)
# Start the thread with the minimum and maximum split up to compute
# Parallel computation will not work here due to the GIL since this is a CPU-bound task
t = threading.Thread(target = find_primes_in, args = (start, end))
threads.append(t)
t.start()
# Don’t forget to wait for the threads to finish
for t in threads:
t.join()
#time_stuff
def processing_prime_finder(nmin, nmax):
"""
Split the minimum, maximum interval similar to the threading method above, but use processes this time
"""
nrange = nmax - nmin
processes = []
for i in range(8):
start = int(nmin + i * nrange/8)
end = int(nmin + (i + 1) * nrange/8)
p = multiprocessing.Process(target = find_primes_in, args = (start, end))
processes.append(p)
p.start()
for p in processes:
p.join()
#time_stuff
def thread_executor_prime_finder(nmin, nmax):
"""
Split the min max interval similar to the threading method, but use a thread pool executor this time.
This method is slightly faster than using pure threading as the pools manage threads more efficiently.
This method is still slow due to the GIL limitations since we are doing a CPU-bound task.
"""
nrange = nmax - nmin
with ThreadPoolExecutor(max_workers = 8) as e:
for i in range(8):
start = int(nmin + i * nrange/8)
end = int(nmin + (i + 1) * nrange/8)
e.submit(find_primes_in, start, end)
#time_stuff
def process_executor_prime_finder(nmin, nmax):
"""
Split the min max interval similar to the threading method, but use the process pool executor.
This is the fastest method recorded so far as it manages process efficiently + overcomes GIL limitations.
RECOMMENDED METHOD FOR CPU-BOUND TASKS
"""
nrange = nmax - nmin
with ProcessPoolExecutor(max_workers = 8) as e:
for i in range(8):
start = int(nmin + i * nrange/8)
end = int(nmin + (i + 1) * nrange/8)
e.submit(find_primes_in, start, end)
def main():
nmin = int(1e7)
nmax = int(1.05e7)
print("Sequential Prime Finder Starting")
sequential_prime_finder(nmin, nmax)
print("Threading Prime Finder Starting")
threading_prime_finder(nmin, nmax)
print("Processing Prime Finder Starting")
processing_prime_finder(nmin, nmax)
print("Thread Executor Prime Finder Starting")
thread_executor_prime_finder(nmin, nmax)
print("Process Executor Finder Starting")
process_executor_prime_finder(nmin, nmax)
if __name__ == "__main__":
main()
Here are the results on my Mac OS X four-core machine
Sequential Prime Finder Starting
9.708213827005238 seconds
Threading Prime Finder Starting
9.81836523200036 seconds
Processing Prime Finder Starting
3.2467174359990167 seconds
Thread Executor Prime Finder Starting
10.228896902000997 seconds
Process Executor Finder Starting
2.656402041000547 seconds

Using the blazing new concurrent.futures module
def sqr(val):
import time
time.sleep(0.1)
return val * val
def process_result(result):
print(result)
def process_these_asap(tasks):
import concurrent.futures
with concurrent.futures.ProcessPoolExecutor() as executor:
futures = []
for task in tasks:
futures.append(executor.submit(sqr, task))
for future in concurrent.futures.as_completed(futures):
process_result(future.result())
# Or instead of all this just do:
# results = executor.map(sqr, tasks)
# list(map(process_result, results))
def main():
tasks = list(range(10))
print('Processing {} tasks'.format(len(tasks)))
process_these_asap(tasks)
print('Done')
return 0
if __name__ == '__main__':
import sys
sys.exit(main())
The executor approach might seem familiar to all those who have gotten their hands dirty with Java before.
Also on a side note: To keep the universe sane, don't forget to close your pools/executors if you don't use with context (which is so awesome that it does it for you)

For me, the perfect example for threading is monitoring asynchronous events. Look at this code.
# thread_test.py
import threading
import time
class Monitor(threading.Thread):
def __init__(self, mon):
threading.Thread.__init__(self)
self.mon = mon
def run(self):
while True:
if self.mon[0] == 2:
print "Mon = 2"
self.mon[0] = 3;
You can play with this code by opening an IPython session and doing something like:
>>> from thread_test import Monitor
>>> a = [0]
>>> mon = Monitor(a)
>>> mon.start()
>>> a[0] = 2
Mon = 2
>>>a[0] = 2
Mon = 2
Wait a few minutes
>>> a[0] = 2
Mon = 2

Most documentation and tutorials use Python's Threading and Queue module, and they could seem overwhelming for beginners.
Perhaps consider the concurrent.futures.ThreadPoolExecutor module of Python 3.
Combined with with clause and list comprehension it could be a real charm.
from concurrent.futures import ThreadPoolExecutor, as_completed
def get_url(url):
# Your actual program here. Using threading.Lock() if necessary
return ""
# List of URLs to fetch
urls = ["url1", "url2"]
with ThreadPoolExecutor(max_workers = 5) as executor:
# Create threads
futures = {executor.submit(get_url, url) for url in urls}
# as_completed() gives you the threads once finished
for f in as_completed(futures):
# Get the results
rs = f.result()

With borrowing from this post we know about choosing between the multithreading, multiprocessing, and async/asyncio and their usage.
Python 3 has a new built-in library in order to make concurrency and parallelism — concurrent.futures
So I'll demonstrate through an experiment to run four tasks (i.e. .sleep() method) by Threading-Pool:
from concurrent.futures import ThreadPoolExecutor, as_completed
from time import sleep, time
def concurrent(max_worker):
futures = []
tic = time()
with ThreadPoolExecutor(max_workers=max_worker) as executor:
futures.append(executor.submit(sleep, 2)) # Two seconds sleep
futures.append(executor.submit(sleep, 1))
futures.append(executor.submit(sleep, 7))
futures.append(executor.submit(sleep, 3))
for future in as_completed(futures):
if future.result() is not None:
print(future.result())
print(f'Total elapsed time by {max_worker} workers:', time()-tic)
concurrent(5)
concurrent(4)
concurrent(3)
concurrent(2)
concurrent(1)
Output:
Total elapsed time by 5 workers: 7.007831811904907
Total elapsed time by 4 workers: 7.007944107055664
Total elapsed time by 3 workers: 7.003149509429932
Total elapsed time by 2 workers: 8.004627466201782
Total elapsed time by 1 workers: 13.013478994369507
[NOTE]:
As you can see in the above results, the best case was 3 workers for those four tasks.
If you have a process task instead of I/O bound or blocking (multiprocessing instead of threading) you can change the ThreadPoolExecutor to ProcessPoolExecutor.

I would like to contribute with a simple example and the explanations I've found useful when I had to tackle this problem myself.
In this answer you will find some information about Python's GIL (global interpreter lock) and a simple day-to-day example written using multiprocessing.dummy plus some simple benchmarks.
Global Interpreter Lock (GIL)
Python doesn't allow multi-threading in the truest sense of the word. It has a multi-threading package, but if you want to multi-thread to speed your code up, then it's usually not a good idea to use it.
Python has a construct called the global interpreter lock (GIL).
The GIL makes sure that only one of your 'threads' can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread.
This happens very quickly so to the human eye it may seem like your threads are executing in parallel, but they are really just taking turns using the same CPU core.
All this GIL passing adds overhead to execution. This means that if you want to make your code run faster then using the threading
package often isn't a good idea.
There are reasons to use Python's threading package. If you want to run some things simultaneously, and efficiency is not a concern,
then it's totally fine and convenient. Or if you are running code that needs to wait for something (like some I/O) then it could make a lot of sense. But the threading library won't let you use extra CPU cores.
Multi-threading can be outsourced to the operating system (by doing multi-processing), and some external application that calls your Python code (for example, Spark or Hadoop), or some code that your Python code calls (for example: you could have your Python code call a C function that does the expensive multi-threaded stuff).
Why This Matters
Because lots of people spend a lot of time trying to find bottlenecks in their fancy Python multi-threaded code before they learn what the GIL is.
Once this information is clear, here's my code:
#!/bin/python
from multiprocessing.dummy import Pool
from subprocess import PIPE,Popen
import time
import os
# In the variable pool_size we define the "parallelness".
# For CPU-bound tasks, it doesn't make sense to create more Pool processes
# than you have cores to run them on.
#
# On the other hand, if you are using I/O-bound tasks, it may make sense
# to create a quite a few more Pool processes than cores, since the processes
# will probably spend most their time blocked (waiting for I/O to complete).
pool_size = 8
def do_ping(ip):
if os.name == 'nt':
print ("Using Windows Ping to " + ip)
proc = Popen(['ping', ip], stdout=PIPE)
return proc.communicate()[0]
else:
print ("Using Linux / Unix Ping to " + ip)
proc = Popen(['ping', ip, '-c', '4'], stdout=PIPE)
return proc.communicate()[0]
os.system('cls' if os.name=='nt' else 'clear')
print ("Running using threads\n")
start_time = time.time()
pool = Pool(pool_size)
website_names = ["www.google.com","www.facebook.com","www.pinterest.com","www.microsoft.com"]
result = {}
for website_name in website_names:
result[website_name] = pool.apply_async(do_ping, args=(website_name,))
pool.close()
pool.join()
print ("\n--- Execution took {} seconds ---".format((time.time() - start_time)))
# Now we do the same without threading, just to compare time
print ("\nRunning NOT using threads\n")
start_time = time.time()
for website_name in website_names:
do_ping(website_name)
print ("\n--- Execution took {} seconds ---".format((time.time() - start_time)))
# Here's one way to print the final output from the threads
output = {}
for key, value in result.items():
output[key] = value.get()
print ("\nOutput aggregated in a Dictionary:")
print (output)
print ("\n")
print ("\nPretty printed output: ")
for key, value in output.items():
print (key + "\n")
print (value)

Here is the very simple example of CSV import using threading. (Library inclusion may differ for different purpose.)
Helper Functions:
from threading import Thread
from project import app
import csv
def import_handler(csv_file_name):
thr = Thread(target=dump_async_csv_data, args=[csv_file_name])
thr.start()
def dump_async_csv_data(csv_file_name):
with app.app_context():
with open(csv_file_name) as File:
reader = csv.DictReader(File)
for row in reader:
# DB operation/query
Driver Function:
import_handler(csv_file_name)

Here is multi threading with a simple example which will be helpful. You can run it and understand easily how multi threading is working in Python. I used a lock for preventing access to other threads until the previous threads finished their work. By the use of this line of code,
tLock = threading.BoundedSemaphore(value=4)
you can allow a number of processes at a time and keep hold to the rest of the threads which will run later or after finished previous processes.
import threading
import time
#tLock = threading.Lock()
tLock = threading.BoundedSemaphore(value=4)
def timer(name, delay, repeat):
print "\r\nTimer: ", name, " Started"
tLock.acquire()
print "\r\n", name, " has the acquired the lock"
while repeat > 0:
time.sleep(delay)
print "\r\n", name, ": ", str(time.ctime(time.time()))
repeat -= 1
print "\r\n", name, " is releaseing the lock"
tLock.release()
print "\r\nTimer: ", name, " Completed"
def Main():
t1 = threading.Thread(target=timer, args=("Timer1", 2, 5))
t2 = threading.Thread(target=timer, args=("Timer2", 3, 5))
t3 = threading.Thread(target=timer, args=("Timer3", 4, 5))
t4 = threading.Thread(target=timer, args=("Timer4", 5, 5))
t5 = threading.Thread(target=timer, args=("Timer5", 0.1, 5))
t1.start()
t2.start()
t3.start()
t4.start()
t5.start()
print "\r\nMain Complete"
if __name__ == "__main__":
Main()

None of the previous solutions actually used multiple cores on my GNU/Linux server (where I don't have administrator rights). They just ran on a single core.
I used the lower level os.fork interface to spawn multiple processes. This is the code that worked for me:
from os import fork
values = ['different', 'values', 'for', 'threads']
for i in range(len(values)):
p = fork()
if p == 0:
my_function(values[i])
break

As a python3 version of the second anwser:
import queue as Queue
import threading
import urllib.request
# Called by each thread
def get_url(q, url):
q.put(urllib.request.urlopen(url).read())
theurls = ["http://google.com", "http://yahoo.com", "http://www.python.org","https://wiki.python.org/moin/"]
q = Queue.Queue()
def thread_func():
for u in theurls:
t = threading.Thread(target=get_url, args = (q,u))
t.daemon = True
t.start()
s = q.get()
def non_thread_func():
for u in theurls:
get_url(q,u)
s = q.get()
And you can test it:
start = time.time()
thread_func()
end = time.time()
print(end - start)
start = time.time()
non_thread_func()
end = time.time()
print(end - start)
non_thread_func() should cost 4 times the time spent than thread_func()

import threading
import requests
def send():
r = requests.get('https://www.stackoverlow.com')
thread = []
t = threading.Thread(target=send())
thread.append(t)
t.start()

It's very easy to understand. Here are the two simple ways to do threading.
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading
def a(a=1, b=2):
print(a)
time.sleep(5)
print(b)
return a+b
def b(**kwargs):
if "a" in kwargs:
print("am b")
else:
print("nothing")
to_do=[]
executor = ThreadPoolExecutor(max_workers=4)
ex1=executor.submit(a)
to_do.append(ex1)
ex2=executor.submit(b, **{"a":1})
to_do.append(ex2)
for future in as_completed(to_do):
print("Future {} and Future Return is {}\n".format(future, future.result()))
print("threading")
to_do=[]
to_do.append(threading.Thread(target=a))
to_do.append(threading.Thread(target=b, kwargs={"a":1}))
for threads in to_do:
threads.start()
for threads in to_do:
threads.join()

This code below can run 10 threads concurrently printing the numbers from 0 to 99:
from threading import Thread
def test():
for i in range(0, 100):
print(i)
thread_list = []
for _ in range(0, 10):
thread = Thread(target=test)
thread_list.append(thread)
for thread in thread_list:
thread.start()
for thread in thread_list:
thread.join()
And, this code below is the shorthand for loop version of the above code running 10 threads concurrently printing the numbers from 0 to 99:
from threading import Thread
def test():
[print(i) for i in range(0, 100)]
thread_list = [Thread(target=test) for _ in range(0, 10)]
[thread.start() for thread in thread_list]
[thread.join() for thread in thread_list]
This is the result below:
...
99
83
97
84
98
99
85
86
87
88
...

The easiest way of using threading/multiprocessing is to use more high level libraries like autothread.
import autothread
from time import sleep as heavyworkload
#autothread.multithreaded() # <-- This is all you need to add
def example(x: int, y: int):
heavyworkload(1)
return x*y
Now, you can feed your functions lists of ints. Autothread will handle everything for you and just give you the results computed in parallel.
result = example([1, 2, 3, 4, 5], 10)

Timeouts for multiprocessing?

I've searched StackOverflow and although I've found many questions on this, I haven't found an answer that fits for my situation/not a strong python programmer to adapt their answer to fit my need.
I've looked here to no avail:
kill a function after a certain time in windows
Python: kill or terminate subprocess when timeout
signal.alarm replacement in Windows [Python]
I am using multiprocessing to run multiple SAP windows at once to pull reports. The is set up to run on a schedule every 5 minutes. Every once in a while, one of the reports gets stalled due to the GUI interface and never ends. I don't get an error or exception, it just stalls forever. What I would like is to have a timeout function that during this part of the code that is executed in SAP, if it takes longer than 4 minutes, it times out, closes SAP, skips the rest of the code, and waits for next scheduled report time.
I am using Windows Python 2.7
import multiprocessing
from multiprocessing import Manager, Process
import time
import datetime
### OPEN SAP ###
def start_SAP():
print 'opening SAP program'
### REPORTS IN SAP ###
def report_1(q, lock):
while True: # logic to get shared queue
if not q.empty():
lock.acquire()
k = q.get()
time.sleep(1)
lock.release()
break
else:
time.sleep(1)
print 'running report 1'
def report_2(q, lock):
while True: # logic to get shared queue
if not q.empty():
lock.acquire()
k = q.get()
time.sleep(1)
lock.release()
break
else:
time.sleep(1)
print 'running report 2'
def report_3(q, lock):
while True: # logic to get shared queue
if not q.empty():
lock.acquire()
k = q.get()
time.sleep(1)
lock.release()
break
else:
time.sleep(1)
time.sleep(60000) #mimicking the stall for report 3 that takes longer than allotted time
print 'running report 3'
def report_N(q, lock):
while True: # logic to get shared queue
if not q.empty():
lock.acquire()
k = q.get()
time.sleep(1)
lock.release()
break
else:
time.sleep(1)
print 'running report N'
### CLOSES SAP ###
def close_SAP():
print 'closes SAP'
def format_file():
print 'formatting files'
def multi_daily_pull():
lock = multiprocessing.Lock() # creating a lock in multiprocessing
shared_list = range(6) # creating a shared list for all functions to use
q = multiprocessing.Queue() # creating an empty queue in mulitprocessing
for n in shared_list: # putting list into the queue
q.put(n)
print 'Starting process at ', time.strftime('%m/%d/%Y %H:%M:%S')
print 'Starting SAP Pulls at ', time.strftime('%m/%d/%Y %H:%M:%S')
StartSAP = Process(target=start_SAP)
StartSAP.start()
StartSAP.join()
report1= Process(target=report_1, args=(q, lock))
report2= Process(target=report_2, args=(q, lock))
report3= Process(target=report_3, args=(q, lock))
reportN= Process(target=report_N, args=(q, lock))
report1.start()
report2.start()
report3.start()
reportN.start()
report1.join()
report2.join()
report3.join()
reportN.join()
EndSAP = Process(target=close_SAP)
EndSAP.start()
EndSAP.join()
formatfile = Process(target=format_file)
formatfile .start()
formatfile .join()
if __name__ == '__main__':
multi_daily_pull()

One way to do what you want would be to use the optional timeout argument that the Process.join() method accepts. This will make it only block the calling thread at most that length of time.
I also set the daemon attribute of each Process instance so your main thread will be able to terminate even if one of the processes it started is still "running" (or has hung up).
One final point, you don't need a multiprocessing.Lock to control access a multiprocessing.Queue, because they handle that aspect of things automatically, so I removed it. You may still want to have one for some other reason, such as controlling access to stdout so printing to it from the various processes doesn't overlap and mess up what is output to the screen.
import multiprocessing
from multiprocessing import Process
import time
import datetime
def start_SAP():
print 'opening SAP program'
### REPORTS IN SAP ###
def report_1(q):
while True: # logic to get shared queue
if q.empty():
time.sleep(1)
else:
k = q.get()
time.sleep(1)
break
print 'report 1 finished'
def report_2(q):
while True: # logic to get shared queue
if q.empty():
time.sleep(1)
else:
k = q.get()
time.sleep(1)
break
print 'report 2 finished'
def report_3(q):
while True: # logic to get shared queue
if q.empty():
time.sleep(1)
else:
k = q.get()
time.sleep(60000) # Take longer than allotted time
break
print 'report 3 finished'
def report_N(q):
while True: # logic to get shared queue
if q.empty():
time.sleep(1)
else:
k = q.get()
time.sleep(1)
break
print 'report N finished'
def close_SAP():
print 'closing SAP'
def format_file():
print 'formatting files'
def multi_daily_pull():
shared_list = range(6) # creating a shared list for all functions to use
q = multiprocessing.Queue() # creating an empty queue in mulitprocessing
for n in shared_list: # putting list into the queue
q.put(n)
print 'Starting process at ', time.strftime('%m/%d/%Y %H:%M:%S')
print 'Starting SAP Pulls at ', time.strftime('%m/%d/%Y %H:%M:%S')
StartSAP = Process(target=start_SAP)
StartSAP.start()
StartSAP.join()
report1 = Process(target=report_1, args=(q,))
report1.daemon = True
report2 = Process(target=report_2, args=(q,))
report2.daemon = True
report3 = Process(target=report_3, args=(q,))
report3.daemon = True
reportN = Process(target=report_N, args=(q,))
reportN.daemon = True
report1.start()
report2.start()
report3.start()
reportN.start()
report1.join(30)
report2.join(30)
report3.join(30)
reportN.join(30)
EndSAP = Process(target=close_SAP)
EndSAP.start()
EndSAP.join()
formatfile = Process(target=format_file)
formatfile .start()
formatfile .join()
if __name__ == '__main__':
multi_daily_pull()

Queue doesn't process all elements when there are many threads

I have noticed that when I have many threads pulling elements from a queue, there are less elements processed than the number that I put into the queue. This is sporadic but seems to happen somewhere around half the time when I run the following code.
#!/bin/env python
from threading import Thread
import httplib, sys
from Queue import Queue
import time
import random
concurrent = 500
num_jobs = 500
results = {}
def doWork():
while True:
result = None
try:
result = curl(q.get())
except Exception as e:
print "Error when trying to get from queue: {0}".format(str(e))
if results.has_key(result):
results[result] += 1
else:
results[result] = 1
try:
q.task_done()
except:
print "Called task_done when all tasks were done"
def curl(ourl):
result = 'all good'
try:
time.sleep(random.random() * 2)
except Exception as e:
result = "error: %s" % str(e)
except:
result = str(sys.exc_info()[0])
finally:
return result or "None"
print "\nRunning {0} jobs on {1} threads...".format(num_jobs, concurrent)
q = Queue()
for i in range(concurrent):
t = Thread(target=doWork)
t.daemon = True
t.start()
for x in range(num_jobs):
q.put("something")
try:
q.join()
except KeyboardInterrupt:
sys.exit(1)
total_responses = 0
for result in results:
num_responses = results[result]
print "{0}: {1} time(s)".format(result, num_responses)
total_responses += num_responses
print "Number of elements processed: {0}".format(total_responses)

Tim Peters hit the nail on the head in the comments. The issue is that the tracking of results is threaded and isn't protected by any sort of mutex. That allows something like this to happen:
thread A gets result: "all good"
thread A checks results[result]
thread A sees no such key
thread A suspends # <-- before counting its result
thread B gets result: "all good"
thread B checks results[result]
thread B sees no such key
thread B sets results['all good'] = 1
thread C ...
thread C sets results['all good'] = 2
thread D ...
thread A resumes # <-- and remembers it needs to count its result still
thread A sets results['all good'] = 1 # resetting previous work!
A more typical workflow might have a results queue that the main thread is listening on.
workq = queue.Queue()
resultsq = queue.Queue()
make_work(into=workq)
do_work(from=workq, respond_on=resultsq)
# do_work would do respond_on.put_nowait(result) instead of
# return result
results = {}
while True:
try:
result = resultsq.get()
except queue.Empty:
break # maybe? You'd probably want to retry a few times
results.setdefault(result, 0) += 1

How to kill Finished threads in python

My multi-threading script raising this error :
thread.error : can't start new thread
when it reached 460 threads :
threading.active_count() = 460
I assume the old threads keeps stack up, since the script didn't kill them. This is my code:
import threading
import Queue
import time
import os
import csv
def main(worker):
#Do Work
print worker
return
def threader():
while True:
worker = q.get()
main(worker)
q.task_done()
def main_threader(workers):
global q
global city
q = Queue.Queue()
for x in range(20):
t = threading.Thread(target=threader)
t.daemon = True
print "\n\nthreading.active_count() = " + str(threading.active_count()) + "\n\n"
t.start()
for worker in workers:
q.put(worker)
q.join()
How do I kill the old threads when their job is done? (Is return not enough?)

Your threader function never exits, so your threads never die. Since you're just processing one fixed set of work and never adding items after you start working, you could set the threads up to exit when the queue is empty.
See the following altered version of your code and the comments I added:
def threader(q):
# let the thread die when all work is done
while not q.empty():
worker = q.get()
main(worker)
q.task_done()
def main_threader(workers):
# you don't want global variables
#global q
#global city
q = Queue.Queue()
# make sure you fill the queue *before* starting the worker threads
for worker in workers:
q.put(worker)
for x in range(20):
t = threading.Thread(target=threader, args=[q])
t.daemon = True
print "\n\nthreading.active_count() = " + str(threading.active_count()) + "\n\n"
t.start()
q.join()
Notice that I removed global q, and instead I pass q to the thread function. You don't want threads created by a previous call to end up sharing a q with new threads (edit although q.join() prevents this anyway, it's still better to avoid globals).

How to close Threads in Python?

I have some issue with too many Threads unfinished.
I think that queue command .join() just close queue and not the threads using it.
In my script I need to check 280k domains and for each domain get list of his MX records and obtain an IPv6 address of servers if it has it.
I used threads and thanks for them the script its many times faster. But there is a problem, although there is join() for the queue, number of alive threads is growing till an error occur that informs that cant create any new thread (limitation of OS?).
How can I terminate/close/stop/reset threads after each For loop when I am retrieving new domain from database?
Thread Class definition...
class MX_getAAAA_thread(threading.Thread):
def __init__(self,queue,id_domain):
threading.Thread.__init__(self)
self.queue = queue
self.id_domain = id_domain
def run(self):
while True:
self.mx = self.queue.get()
res = dns.resolver.Resolver()
res.lifetime = 1.5
res.timeout = 0.5
try:
answers = res.query(self.mx,'AAAA')
ip_mx = str(answers[0])
except:
ip_mx = "N/A"
lock.acquire()
sql = "INSERT INTO mx (id_domain,mx,ip_mx) VALUES (" + str(id_domain) + ",'" + str(self.mx) + "','" + str(ip_mx) + "')"
try:
cursor.execute(sql)
db.commit()
except:
db.rollback()
print "MX" , '>>' , ip_mx, ' :: ', str(self.mx)
lock.release()
self.queue.task_done()
Thread class in use...
(The main For-loop is not here, this is just part of his body)
try:
answers = resolver.query(domain, 'MX')
qMX = Queue.Queue()
for i in range(len(answers)):
t = MX_getAAAA_thread(qMX,id_domain)
t.setDaemon(True)
threads.append(t)
t.start()
for mx in answers:
qMX.put(mx.exchange)
qMX.join()
except NoAnswer as e:
print "MX - Error: No Answer"
except Timeout as etime:
print "MX - Error: dns.exception.Timeout"
print "end of script"
I tried to:
for thread in threads:
thread.join()
after the queue was done, but thread.join() never stops waiting, despite fact that there is no need to wait, because when queue.join() executes there is nothing to do for threads.

What I often do when my thread involves an infinite loop like this, is to change the condition to something I can control from the outside. For example like this:
def run(self):
self.keepRunning = True
while self.keepRunning:
# do stuff
That way, I can change the keepRunning property from the outside and set it to false to gracefully terminate the thread the next time it checks the loop condition.
Btw. as you seem to spawn exactly one thread for each item you put into the queue, you don’t even need to have the threads loop at all, although I would argue that you should always enforce a maximum limit of threads that can be created in this way (i.e. for i in range(min(len(answers), MAX_THREAD_COUNT)):)
Alternative
In your case, instead of terminating the threads in each for-loop iteration, you could just reuse the threads. From what I gather from your thread’s source, all that makes a thread unique to an iteration is the id_domain property you set on its creation. You could however just provide that as well with your queue, so the threads are completely independent and you can reuse them.
This could look like this:
qMX = Queue.Queue()
threads = []
for i in range(MAX_THREAD_COUNT):
t = MX_getAAAA_thread(qMX)
t.daemon = True
threads.append(t)
t.start()
for id_domain in enumerateIdDomains():
answers = resolver.query(id_domain, 'MX')
for mx in answers:
qMX.put((id_domain, mx.exchange)) # insert a tuple
qMX.join()
for thread in threads:
thread.keepRunning = False
Of course, you would need to change your thread a bit then:
class MX_getAAAA_thread(threading.Thread):
def __init__(self, queue):
threading.Thread.__init__(self)
self.queue = queue
def run(self):
self.keepRunning = True
while self.keepRunning:
id_domain, mx = self.queue.get()
# do stuff

I do not see why you need a Queue in the first place.
After all in your design every thread just processes one task.
You should be able to pass that task to the thread on creation.
This way you do not need a Queue and you get rid of the while-loop:
class MX_getAAAA_thread(threading.Thread):
def __init__(self, id_domain, mx):
threading.Thread.__init__(self)
self.id_domain = id_domain
self.mx = mx
Then you can rid of the while-loop inside the run-method:
def run(self):
res = dns.resolver.Resolver()
res.lifetime = 1.5
res.timeout = 0.5
try:
answers = res.query(self.mx,'AAAA')
ip_mx = str(answers[0])
except:
ip_mx = "N/A"
with lock:
sql = "INSERT INTO mx (id_domain,mx,ip_mx) VALUES (" + str(id_domain) + ",'" + str(self.mx) + "','" + str(ip_mx) + "')"
try:
cursor.execute(sql)
db.commit()
except:
db.rollback()
print "MX" , '>>' , ip_mx, ' :: ', str(self.mx)
Create one thread for each task
for mx in answers:
t = MX_getAAAA_thread(qMX, id_domain, mx)
t.setDaemon(True)
threads.append(t)
t.start()
and join them
for thread in threads:
thread.join()

Joining the threads will do the trick, but the joins in your case are blocking indefinitely because your threads aren't ever exiting your run loop. You need to exit the run method so that the threads can be joined.