Python: Process Spawning vs Forking

When working with parallel programming in Python, especially using the multiprocessing module, it’s important to understand how new processes are created. Python supports different ways to start a process, known as start methods. Each method controls how the new process begins and what it inherits from the parent.

In this article, we’ll focus on the two most common start methods: spawn and fork. We’ll explain what they are, how to use them, and show simple code examples to help you understand how they work.

Table of Contents

What is Spawning?

When you use the spawn start method in Python, a brand-new Python interpreter is started for each new process. This means the child process does not copy the parent’s memory—it starts fresh.

Instead of inheriting everything from the parent, the child process imports the main module and runs only the target function you give it. This keeps things clean and avoids carrying over unnecessary or sensitive data.

Spawning is the default method on Windows and macOS, and it works the same across all platforms, which makes it a good choice if you’re writing cross-platform code.

This method is a bit slower to start than others, but it’s more controlled and avoids surprises from shared memory or open resources.

Pros and Cons of Spawning

Spawning offers important benefits:

Clean and safe: Every new process starts completely fresh, so it won’t carry over open files, locks, or resources from the parent.
No shared state problems: Because the child process does not copy the parent’s memory, you avoid bugs caused by shared or duplicated data.
Works everywhere: Spawning runs the same way on Windows, macOS, and Linux, helping you write portable, consistent programs.

However, spawning can be slower to start since it needs to load the entire Python interpreter and import your code fresh each time.

What is Forking?

Forking creates a new process by copying the entire memory space of the parent process. This means the child starts with the same code, variables, and state as the parent at the moment it was forked.

The child process continues running from the same point as the parent, just like making a clone. Because it doesn’t need to reload Python or re-import modules, it starts faster than spawning.

However, forking is only available on Unix-like systems like Linux and macOS. It’s not supported on Windows.

Pros and Cons of Forking

Forking has clear advantages to understand:

It is fast since it creates the child process by copying the parent’s memory directly.
The child inherits everything from the parent — variables, open files, network connections, and loaded modules.
This makes it ideal when you have large data already loaded in memory and want to share it efficiently.
Forking suits short tasks well, especially when quick process startup is important.

On the downside, because it copies the entire process state, unexpected side effects may happen if your program relies on shared resources or external connections. Also, forking is only available on Unix-like systems.

Process Start Methods

In Python, when you create a new process using the multiprocessing module, the system can start it in different ways. These are called start methods.

The two main methods are:

spawn – starts a brand new Python process. It doesn’t inherit anything from the parent except what you pass to it.
fork – makes a copy of the current process, including its memory.

To check or set the start method, you can use set_start_method() and get_start_method().

Here’s a quick example to see the current method:

import multiprocessing

print("Current start method:", multiprocessing.get_start_method())

This will print which method is being used by default on your system. For example, Linux usually uses fork, while Windows uses spawn.

Setting the Start Method

Before starting any processes, you can choose how Python should start them: using "spawn" or "fork". You set this using multiprocessing.set_start_method(), but it must be done at the beginning of your program and only once.

Make sure to put it inside the if __name__ == "__main__": block to avoid errors:

import multiprocessing

def greet():
    print("Hello from a new process!")

if __name__ == "__main__":

    multiprocessing.set_start_method("spawn")  # or "fork"

    p = multiprocessing.Process(target=greet)

    p.start()
    p.join()

This example tells Python to use the spawn method. Then it starts a new process to run the greet() function.

Running a Process with Spawn

In this example, we set the start method to "spawn". This creates a brand-new Python process that runs your code fresh.

import multiprocessing

def greet(name):
    print(f"Hello from {name}!")

if __name__ == "__main__":

    multiprocessing.set_start_method("spawn")

    p = multiprocessing.Process(target=greet, args=("Spawned Process",))

    p.start()
    p.join()

The "spawn" method starts a completely new Python interpreter. Because of this, all your code must be importable, and process-creating code needs to be inside the if __name__ == "__main__": block to avoid issues.

Running a Process with Fork

This example sets the start method to "fork". It copies the current process, including all its memory.

import multiprocessing

def greet(name):
    print(f"Hello from {name}!")

if __name__ == "__main__":

    multiprocessing.set_start_method("fork")

    p = multiprocessing.Process(target=greet, args=("Forked Process",))

    p.start()
    p.join()

    print(multiprocessing.get_start_method()) # fork

The "fork" method makes a new process by copying the parent’s memory and state. This means the child starts running from the same point as the parent, inheriting all variables and resources.

Checking Available Start Methods

You can see which start methods your system supports with this simple code:

import multiprocessing

print("Available methods:", multiprocessing.get_all_start_methods())

This will list all start methods like "spawn", "fork", or "forkserver" available on your platform.

Using Context to Control Start Method

Instead of setting the start method globally, you can create processes with a specific start method using multiprocessing.get_context(). This lets you choose the method just for certain parts of your code.

import multiprocessing

def greet(name):
    print(f"Hello from {name}!")

if __name__ == "__main__":

    ctx = multiprocessing.get_context("spawn")

    p = ctx.Process(target=greet, args=("Spawn via context",))

    p.start()
    p.join()

This approach is helpful when your script or library needs to use different start methods in different places without changing the global setting.

Using Pool with Spawn and Fork

You can create a process pool that uses a specific start method by getting a context first. This lets you control how the worker processes are started.

import multiprocessing

def double(n):
    return n * 2

if __name__ == "__main__":

    ctx = multiprocessing.get_context("fork")

    with ctx.Pool(2) as pool:
        result = pool.map(double, [1, 2, 3])

    print(result)  # Output: [2, 4, 6]

This example uses the "fork" method to start the pool workers. You can change "fork" to "spawn" to use that start method instead.

Platform Differences

When working with process start methods in Python, it’s important to know how they vary across operating systems. The "fork" method is supported only on Unix-like systems, such as Linux and macOS. This means you can use "fork" to create new processes by copying the parent process only if you’re running on these platforms.

On the other hand, the "spawn" method works on all major platforms, including Windows, Linux, and macOS. Because it starts a fresh Python interpreter each time, it is the default method on Windows and macOS.

Knowing these differences helps you write code that behaves as expected on different systems, especially when working with multiprocessing.

Conclusion

In Python, you have two main ways to start new processes: "spawn" and "fork". Each method creates processes differently—"spawn" starts a fresh Python interpreter, while "fork" copies the current process’s memory.

You can set the start method globally once using set_start_method(), or choose it locally with get_context() for more flexibility. Using these tools, you can run your functions in parallel by creating Process objects or using Pool for multiple workers. This lets you harness multiple CPU cores effectively across different platforms.