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queue

The Python queue module provides reliable thread-safe implementations of the queue data structure. It is commonly used for task scheduling and managing work between multiple threads.

Here’s a quick example:

Language: Python
>>> import queue >>> tasks = queue.Queue() >>> tasks.put("task1") >>> tasks.put("task2") >>> tasks.get() 'task1' >>> tasks.get() 'task2'

Key Features

  • Provides thread-safe FIFO, LIFO, and priority queues
  • Supports blocking and non-blocking operations
  • Allows a maximum size to be set for queues
  • Includes a lightweight SimpleQueue without task tracking
  • Supports graceful shutdown of queues with Queue.shutdown() (Python 3.13+)

Frequently Used Classes and Functions

Object Type Description
queue.Queue Class Provides a first-in, first-out (FIFO) queue
queue.Queue.put() Method Adds an item to the queue
queue.Queue.get() Method Removes and returns an item from the queue
queue.Queue.qsize() Method Returns the approximate size of the queue
queue.Queue.empty() Method Checks whether the queue is empty
queue.Queue.full() Method Checks whether the queue is full
queue.LifoQueue Class Provides a last-in, first-out (LIFO) queue
queue.PriorityQueue Class Provides a queue where entries are retrieved by priority
queue.SimpleQueue Class Provides an unbounded FIFO queue without task tracking

Examples

Using a LIFO queue:

Language: Python
>>> from queue import LifoQueue >>> q = LifoQueue() >>> q.put("task1") >>> q.put("task2") >>> q.get() 'task2'

Using a priority queue:

Language: Python
>>> from queue import PriorityQueue >>> q = PriorityQueue() >>> q.put((2, "task1")) >>> q.put((1, "task2")) >>> q.get() (1, 'task2')

Common Use Cases

  • Managing task scheduling in multi-threaded applications
  • Implementing producer-consumer patterns
  • Prioritizing tasks using priority queues

Real-World Example

Consider a scenario where multiple worker threads need to process tasks concurrently. In this situation, you can use the queue module to manage these tasks efficiently:

Language: Python Filename: workers.py
import logging import queue import threading import time logging.basicConfig(level=logging.INFO, format="%(threadName)s %(message)s") def worker(tasks): while True: task = tasks.get() if task is None: tasks.task_done() break logging.info(f"processing {task}") time.sleep(1) tasks.task_done() tasks = queue.Queue() for task in ["task1", "task2", "task3", "task4", "task5"]: tasks.put(task) num_workers = 2 for _ in range(num_workers): tasks.put(None) threads = [ threading.Thread(target=worker, args=(tasks,)) for _ in range(num_workers) ] for thread in threads: thread.start() tasks.join() for thread in threads: thread.join() # Output: # Thread-1 (worker) processing task1 # Thread-2 (worker) processing task2 # Thread-1 (worker) processing task3 # Thread-2 (worker) processing task4 # Thread-1 (worker) processing task5

In this example, you use the queue module to distribute tasks among two worker threads, ensuring that each task is processed exactly once.

Tutorial

Python Stacks, Queues, and Priority Queues in Practice

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.

intermediate algorithms data-structures

For additional information on related topics, take a look at the following resources:

By Leodanis Pozo Ramos • Updated May 22, 2026
Python Standard Library Share Feedback