Introduction
Multiprocessing vs Threading in Python: What to Use When? If you’ve ever tried to speed up your Python application, you’ve probably come across two common techniques: threading and multiprocessing. At first glance, they might seem interchangeable — both are about doing “more at once”, right? Well, not quite.
Threading and multiprocessing are two very different strategies for running tasks concurrently, and choosing the wrong one can backfire. Especially with Python’s Global Interpreter Lock (GIL) — a quirky little mechanism that often confuses developers when they’re trying to “go parallel”.
In this post, we’ll go beyond textbook definitions and unpack real-world insights to help you decide when to use threading and when to use multiprocessing, even if you’re not a Python expert.
Table of Contents
What is Threading in Python?
Threading allows a program to run multiple threads (smaller units of a process) at the same time. Threads share the same memory space, which means they can communicate and exchange data quickly. That’s both a strength and a weakness.
In practical terms, threading is best suited for I/O-bound tasks — like reading and writing files, making network requests, or waiting for user input.
Let’s say you’re building a desktop app that fetches weather data while allowing the user to scroll through old reports. Threading is perfect here — one thread handles the network request while the other keeps the UI responsive.
However, Python’s GIL makes it tricky. Even if you spin up 10 threads, only one thread can execute Python bytecode at a time. This is fine for I/O-bound operations (where threads are mostly waiting), but terrible for CPU-bound tasks like mathematical computations, image processing, or data crunching.
What is Multiprocessing in Python?
Multiprocessing is all about running separate processes, each with its own Python interpreter and memory space. These processes don’t share memory, which means you avoid GIL issues entirely.
This model is ideal for CPU-bound tasks, where you need to maximize the use of multiple cores. Think of things like video rendering, training a machine learning model, or running complex simulations. Multiprocessing lets you take full advantage of multi-core CPUs.
But it comes with a cost — since each process is isolated, sharing data is slower and more complex. You’ll often use queues or pipes to communicate between processes, which adds overhead.
When Should You Use Threading?
You’ll want to lean toward threading when:
- The task involves waiting — for a file, a web request, or user input.
- You’re dealing with lightweight background operations.
- Your app is I/O-bound and doesn’t need multiple cores.
- You care about low memory usage — threads are lighter than processes.
Examples:
- A chatbot that listens for user commands while querying an API.
- A GUI application that loads images in the background.
- A script that scrapes multiple web pages simultaneously.
For more on I/O-bound performance patterns in Python, this article on Real Python does a good job breaking it down with examples and diagrams.
When Should You Use Multiprocessing?
Multiprocessing is your go-to when:
- You’re working on CPU-heavy calculations.
- The task can be split into independent subtasks.
- You need to utilize multiple cores effectively.
- You’re hitting performance ceilings due to the GIL.
Examples:
- Analyzing a large dataset across multiple processes.
- Running concurrent simulations or game engines.
- Training different ML models in parallel.
An insightful breakdown of Python’s multiprocessing strategies can be found in this detailed blog by Towards Data Science: Click here to read.
Threading vs Multiprocessing: Real-World Analogy
Think of threading like roommates sharing a flat — they can talk easily (shared memory), but they might bump into each other (GIL). Multiprocessing is like neighbors in separate houses — no collisions, but you have to walk over and knock if you want to talk (inter-process communication).
Performance Trade-offs
Here’s where things get a bit nuanced.
- Threading is faster to start and uses less memory.
- Multiprocessing has better performance for computation, but it’s heavier to manage.
- If your app needs to process hundreds of web requests per minute, threading is better.
- But if you’re processing thousands of images to detect objects, multiprocessing wins.
A 2022 benchmark showed that for simple data analysis, multiprocessing could reduce execution time by up to 80%, compared to threading. Of course, actual results vary depending on hardware and task complexity.
Gotchas to Watch Out For
For Threading:
- Be careful of race conditions — when threads try to modify the same variable.
- Debugging threaded apps can get messy.
- The GIL can unexpectedly throttle performance.
For Multiprocessing:
- Communication between processes isn’t simple.
- More memory usage.
- Harder to debug — errors in subprocesses don’t always show up clearly.
Which One Should You Use for Web Apps?
If you’re using frameworks like Django or Flask, threading is often the default choice. Many WSGI servers (like Gunicorn) support threaded workers to handle multiple users. But for CPU-heavy endpoints (like PDF generation or video encoding), offloading those tasks to a multiprocessing background worker (via Celery, for example) is smart.
Which One Should You Use for Data Science?
For data preprocessing, multiprocessing is a huge help — especially with tools like pandas, NumPy, or joblib. Threading, on the other hand, might be useful for loading data from multiple sources simultaneously.
Fun fact: Libraries like scikit-learn often use multiprocessing under the hood when you pass n_jobs=-1 to utilize all cores.
Final Thoughts: It’s Not Always Either/Or
You don’t have to choose just one. Many high-performance applications use both threading and multiprocessing together.
For example:
- Use threading to download 1,000 files simultaneously.
- Then use multiprocessing to process those files in parallel.
This hybrid approach can deliver significant performance improvements without overloading your system.
Conclusion
So, what should you use — threading or multiprocessing? Well, it depends.
If your app is waiting more than it’s working, threading is your friend. If it’s crunching numbers and pushing your CPU to its limits, multiprocessing is the way to go.
It’s less about which is “better” and more about which is right for the job.
Still not sure what fits your use case? A general rule of thumb:
- Waiting? Use threads.
- Working? Use processes.
And if you’re somewhere in between? Test both. Benchmark your performance. There’s no substitute for real-world testing.
For a hands-on visual breakdown without diving into code, check out this overview from GeeksforGeeks.
Have You Tried This in Your Own Projects?
If you’ve used threading or multiprocessing in real-world Python projects, share your experience. What worked, what didn’t, and what surprised you?
Let’s make this less theoretical and more practical — your story could help another developer make the right choice.
Find more Python content at: https://allinsightlab.com/category/software-development