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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:
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Python queue Module
The queue module provides synchronized queue classes for multi-producer, multi-consumer scenarios. Use it to safely pass work between threads using FIFO, LIFO, or priority ordering.
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Python QUEUEs | Queue implementation example - YouTube
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Working with Queues in Python - An introductory guide - YouTube
Python QUEUEs | Queue implementation example
Python Queue
Python
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Queues โ Python 3.14.4 documentation
February 22, 2026 - If maxsize is less than or equal to zero, the queue size is infinite. If it is an integer greater than 0, then await put() blocks when the queue reaches maxsize until an item is removed by get(). Unlike the standard library threading queue, the size of the queue is always known and can be returned by calling the qsize() method.
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DSA Queues
But to explicitly create a data structure for queues, with basic operations, we should create a queue class instead. This way of creating queues in Python is also more similar to how queues can be created in other programming languages like C and Java.
Python Module of the Week
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Queue โ A thread-safe FIFO implementation - Python Module of the Week
Now available for Python 3! Buy the book! ... The Queue module provides a FIFO implementation suitable for multi-threaded programming. It can be used to pass messages or other data between producer and consumer threads safely. Locking is handled for the caller, so it is simple to have as many ...
RQ
python-rq.org
RQ: Simple job queues for Python
RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. It is backed by Redis/Valkey and is designed to have a low barrier to entry.
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4.12. Implementing a Queue in Python โ Problem Solving with Algorithms and Data Structures
It is again appropriate to create a new class for the implementation of the abstract data type queue. As before, we will use the power and simplicity of the list collection to build the internal representation of the queue ยท We need to decide which end of the list to use as the rear and which ...
Python
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queue โ A synchronized queue class โ Python v3.0.1 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.
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queue โ A synchronized queue class โ Python 3.9.2 documentation
Source code: Lib/queue.py ยท The queue module implements multi-producer, multi-consumer queues. It is especially useful in threaded programming when information must be exchanged safely between multiple threads. The Queue class in this module implements all the required locking semantics
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()
Python
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18.5.8. Queues โ Python 3.5.10 documentation
December 18, 2020 - If it is an integer greater than 0, then yield from put() will block when the queue reaches maxsize, until an item is removed by get(). Unlike the standard library queue, you can reliably know this Queueโs size with qsize(), since your single-threaded asyncio application wonโt be interrupted ...
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Stacks and Queues in Python
August 28, 2023 - Again, here we use the append and pop operations of the list to simulate the core operations of a queue. Python has a deque (pronounced 'deck') library that provides a sequence with efficient methods to work as a stack or a queue.