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Table of Contents
Use queue.Queue to achieve inter-thread communication
Use multiprocessing.Queue in multiprocessing scenarios
Extend functionality using third-party library
Home Backend Development Python Tutorial Working with Queues and Message Passing in Python

Working with Queues and Message Passing in Python

Jul 06, 2025 am 01:21 AM

There are three main methods for handling queues and message passing in Python: using queue.Queue to achieve inter-thread communication, which is thread-safe. Data is added and obtained by creating queue instances and calling put() and get() methods, and task_done() is required to notify the task to be completed; multiprocessing.Queue is used in multiprocessing scenarios, which supports cross-process communication, and the underlying data is transmitted through pipelines and serialization. It is recommended to use basic or serializable types; third-party libraries such as Celery, ZeroMQ, and RQ extension functions can also be used to meet complex needs, but the deployment and maintenance costs need to be weighed. Master the built-in Queue and select external libraries according to your needs to deal with most scenarios.

Working with Queues and Message Passing in Python

Handling queues and messaging in Python is usually to enable communication between multi-threaded, multi-process tasks, or to build a producer-consumer model. The core goal is to enable data exchange between different task modules to be securely exchanged while avoiding problems such as resource competition.

Working with Queues and Message Passing in Python

Use queue.Queue to achieve inter-thread communication

queue.Queue in the Python standard library is a thread-safe queue implementation, which is very suitable for messaging in multithreaded environments. It already handles the lock mechanism internally, so you don't need to add an extra lock to pass data between multiple threads safely.

Working with Queues and Message Passing in Python

The usage is also very simple:

  • Create a queue instance: q = queue.Queue()
  • The producer calls q.put(item) to add data
  • The consumer calls q.get() to get data
  • After processing, you must call q.task_done() to notify the queue task to complete

For example, you can start multiple consumer threads to get task execution from the same queue. If the queue is empty, get() will block until a new task arrives.

Working with Queues and Message Passing in Python

It should be noted that by default, Queue is first-in-first-out (FIFO), but you can also use LifoQueue to implement last-in-first-out, or PriorityQueue to sort by priority.

Use multiprocessing.Queue in multiprocessing scenarios

When you need to pass messages between multiple processes, you can no longer use queue.Queue because normal queues cannot be shared across processes. At this time, multiprocessing.Queue should be used, which is specially designed for multiprocessing.

It is used in a similar way to the standard Queue:

  • Import and create: from multiprocessing import Queue; q = Queue()
  • Communication is achieved by sharing this queue object between processes
  • Also supports put() and get() methods

However, it should be noted that the underlying implementation of multiprocessing.Queue is used to transmit data through pipelines and serialization, so there are certain restrictions on the data types placed. It is recommended to use basic types or serializable objects.

Extend functionality using third-party library

If you need more advanced message queueing functions, such as persistence, broadcasting, delay queueing, etc., you can consider using a third-party library, such as:

  • Celery : Suitable for distributed task scheduling, combined with RabbitMQ or Redis as broker
  • ZeroMQ : Provides flexible messaging modes suitable for network communication and microservice architectures
  • Redis Queue (RQ) : A lightweight task queue that relies on Redis to store task information

These tools can help you achieve reliable messaging mechanisms in complex systems, but also increase deployment and maintenance costs. Before making a choice, you should make trade-offs based on the project size and needs.

Basically that's it. Mastering the built-in Queue type and then deciding whether to introduce external libraries according to actual needs, you can deal with most messaging scenarios.

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