Python
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queue โ A synchronized queue class โ Python 3.14.4 documentation
February 23, 2026 - Source code: Lib/queue.py The queue module implements multi-producer, multi-consumer queues. It is especially useful in threaded programming when information must be exchanged safely between multip...
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Python
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18.5.8. Queues โ Python 3.5.10 documentation
December 18, 2020 - Unlike the standard library queue, you can reliably know this Queueโs size with qsize(), since your single-threaded asyncio application wonโt be interrupted between calling qsize() and doing an operation on the Queue.
Faucet
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queue โ Python documentation
If maxsize is <= 0, the queue size is infinite. ''' def __init__(self, maxsize=0): self.maxsize = maxsize self._init(maxsize) # mutex must be held whenever the queue is mutating. All methods # that acquire mutex must release it before returning. mutex # is shared between the three conditions, ...
Readthedocs
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8.10. Queue โ A synchronized queue class โ Python 2.7.6 documentation
Constructor for a FIFO queue. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed.
Real Python
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Python Stacks, Queues, and Priority Queues in Practice โ Real Python
December 1, 2023 - In this tutorial, you'll take a deep dive into the theory and practice of queues in programming. Along the way, you'll get to know the different types of queues, implement them, and then learn about the higher-level queues in Python's standard library. Be prepared to do a lot of coding.
Python
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Search โ Python 3.9.24 documentation
Please activate JavaScript to enable the search functionality ยท From here you can search these documents. Enter your search words into the box below and click "search". Note that the search function will automatically search for all of the words. Pages containing fewer words won't appear in ...
W3Schools
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Queues with Python
Queues are often mentioned together with Stacks, which is a similar data structure described on the previous page. For Python lists (and arrays), a Queue can look and behave like this:
Python documentation
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5. Data Structures โ Python 3.14.4 documentation
>>> from collections import deque >>> queue = deque(["Eric", "John", "Michael"]) >>> queue.append("Terry") # Terry arrives >>> queue.append("Graham") # Graham arrives >>> queue.popleft() # The first to arrive now leaves 'Eric' >>> queue.popleft() # The second to arrive now leaves 'John' >>> queue # Remaining queue in order of arrival deque(['Michael', 'Terry', 'Graham'])
Top answer 1 of 2
23
The for loop is launching a number of worker threads to perform the function defined by "worker". Here is working code that should run on your system in python 2.7.
import Queue
import threading
# input queue to be processed by many threads
q_in = Queue.Queue(maxsize=0)
# output queue to be processed by one thread
q_out = Queue.Queue(maxsize=0)
# number of worker threads to complete the processing
num_worker_threads = 10
# process that each worker thread will execute until the Queue is empty
def worker():
while True:
# get item from queue, do work on it, let queue know processing is done for one item
item = q_in.get()
q_out.put(do_work(item))
q_in.task_done()
# squares a number and returns the number and its square
def do_work(item):
return (item,item*item)
# another queued thread we will use to print output
def printer():
while True:
# get an item processed by worker threads and print the result. Let queue know item has been processed
item = q_out.get()
print "%d squared is : %d" % item
q_out.task_done()
# launch all of our queued processes
def main():
# Launches a number of worker threads to perform operations using the queue of inputs
for i in range(num_worker_threads):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
# launches a single "printer" thread to output the result (makes things neater)
t = threading.Thread(target=printer)
t.daemon = True
t.start()
# put items on the input queue (numbers to be squared)
for item in range(10):
q_in.put(item)
# wait for two queues to be emptied (and workers to close)
q_in.join() # block until all tasks are done
q_out.join()
print "Processing Complete"
main()
Python 3 version per @handle
import queue
import threading
# input queue to be processed by many threads
q_in = queue.Queue(maxsize=0)
# output queue to be processed by one thread
q_out = queue.Queue(maxsize=0)
# number of worker threads to complete the processing
num_worker_threads = 10
# process that each worker thread will execute until the Queue is empty
def worker():
while True:
# get item from queue, do work on it, let queue know processing is done for one item
item = q_in.get()
q_out.put(do_work(item))
q_in.task_done()
# squares a number and returns the number and its square
def do_work(item):
return (item,item*item)
# another queued thread we will use to print output
def printer():
while True:
# get an item processed by worker threads and print the result. Let queue know item has been processed
item = q_out.get()
print("{0[0]} squared is : {0[1]}".format(item) )
q_out.task_done()
# launch all of our queued processes
def main():
# Launches a number of worker threads to perform operations using the queue of inputs
for i in range(num_worker_threads):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
# launches a single "printer" thread to output the result (makes things neater)
t = threading.Thread(target=printer)
t.daemon = True
t.start()
# put items on the input queue (numbers to be squared)
for item in range(10):
q_in.put(item)
# wait for two queues to be emptied (and workers to close)
q_in.join() # block until all tasks are done
q_out.join()
print( "Processing Complete" )
main()
2 of 2
3
You can think of the number of worker threads as the number of bank tellers at a bank. So people (your items) stand in line (your queue) to be processed by a bank teller (your worker thread). Queues are actually an easy and well understood mechanism to manage complexities in threads.
I have adjusted your code a bit to show how it works.
import queue
import time
from threading import Thread
def do_work(item):
print("processing", item)
def source():
item = 1
while True:
print("starting", item)
yield item
time.sleep(0.2)
item += 1
def worker():
while True:
item = q.get()
do_work(item)
q.task_done()
q = queue.Queue(maxsize=0)
def main():
for i in range(2):
t = Thread(target=worker)
t.daemon = True
t.start()
for item in source():
q.put(item)
q.join() # block until all tasks are done
main()
Simplilearn
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Queue in Python: Working With Queue Data Structure in Python
March 5, 2026 - A queue is a built-in module of python used in threaded programming. It stores items sequentially in a FIFO manner. Learn all about the queue in python now!
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