1. Use multiprocessing when you need to do many heavy calculations and you can split them up.
2. Use asyncio or threading when you're performing I/O operations -- communicating with external resources or reading/writing from/to files.
3. Multiprocessing and asyncio can be used together, but a good rule of thumb is to fork a process before you thread/use asyncio instead of the other way around -- threads are relatively cheap compared to processes.

When you're writing new code, use asyncio. If you need to interface with older libraries or those that don't support asyncio, you might be better off with threading.

This is because when we use the "await" syntax, we essentially tell our program "hold on, I'll be right back," but our program keeps track of how long it takes us to finish what we're doing. Once we're done, our program will know, and will pick back up as soon as it's able. Threading in Python allows asynchronicity, but our program could theoretically skip around different threads that may not yet be ready, wasting time if there are threads ready to continue running.

A thread is a way of allowing your computer to break up a single process/program into many lightweight pieces that execute in parallel. Somewhat confusingly, Python's standard implementation of threading limits threads to only being able to execute one at a time due to something called the Global Interpreter Lock (GIL). The GIL is necessary because CPython's (Python's default implementation) memory management is not thread-safe. Because of this limitation, threading in Python is concurrent, but not parallel. To get around this, Python has a separate multiprocessing module not limited by the GIL that spins up separate processes, enabling parallel execution of your code. Using the multiprocessing module is nearly identical to using the threading module.

Asynchronous nature of threading: as one function waits, another one begins, and so on.

Reducing the performance hit of copying data between processes:

Option #1: Just use threads

Processes have overhead, threads do not. And while it’s true that generic Python code won’t parallelize well when using multiple threads, that’s not necessarily true for your Python code. For example, NumPy releases the GIL for many of its operations, which means you can use multiple CPU cores even with threads.

``` # numpy_gil.py import numpy as np from time import time from multiprocessing.pool import ThreadPool

arr = np.ones((1024, 1024, 1024))

start = time() for i in range(10): arr.sum() print("Sequential:", time() - start)

expected = arr.sum()

start = time() with ThreadPool(4) as pool: result = pool.map(np.sum, [arr] * 10) assert result == [expected] * 10 print("4 threads:", time() - start) ```

When run, we see that NumPy uses multiple cores just fine when using threads, at least for this operation:

$ python numpy_gil.py Sequential: 4.253053188323975 4 threads: 1.3854241371154785

Pandas is built on NumPy, so many numeric operations will likely release the GIL as well. However, anything involving strings, or Python objects in general, will not. So another approach is to use a library like Polars which is designed from the ground-up for parallelism, to the point where you don’t have to think about it at all, it has an internal thread pool.

Option #2: Live with it

If you’re stuck with using processes, you might just decide to live with the overhead of pickling. In particular, if you minimize how much data gets passed and forth between processes, and the computation in each process is significant enough, the cost of copying and serializing data might not significantly impact your program’s runtime. Spending a few seconds on pickling doesn’t really matter if your subsequent computation takes 10 minutes.

Option #3: Write the data to disk

Instead of passing data directly, you can write the data to disk, and then pass the path to this file: * to the subprocess (as an argument) * to parent process (as the return value of the function running in the worker process).

The recipient process can then parse the file.

``` import pandas as pd import multiprocessing as mp from pathlib import Path from tempfile import mkdtemp from time import time

def noop(df: pd.DataFrame): # real code would process the dataframe here pass

def noop_from_path(path: Path): df = pd.read_parquet(path, engine="fastparquet") # real code would process the dataframe here pass

def main(): df = pd.DataFrame({"column": list(range(10_000_000))})

with mp.get_context("spawn").Pool(1) as pool:
    # Pass the DataFrame to the worker process
    # directly, via pickling:
    start = time()
    pool.apply(noop, (df,))
    print("Pickling-based:", time() - start)

    # Write the DataFrame to a file, pass the path to
    # the file to the worker process:
    start = time()
    path = Path(mkdtemp()) / "temp.parquet"
    df.to_parquet(
        path,
        engine="fastparquet",
        # Run faster by skipping compression:
        compression="uncompressed",
    )
    pool.apply(noop_from_path, (path,))
    print("Parquet-based:", time() - start)

if name == "main": main() `` **Option #4:multiprocessing.shared_memory`**

Because processes sometimes do want to share memory, operating systems typically provide facilities for explicitly creating shared memory between processes. Python wraps this facilities in the multiprocessing.shared_memory module.

However, unlike threads, where the same memory address space allows trivially sharing Python objects, in this case you’re mostly limited to sharing arrays. And as we’ve seen, NumPy releases the GIL for expensive operations, which means you can just use threads, which is much simpler. Still, in case you ever need it, it’s worth knowing this module exists.

Note: The module also includes ShareableList, which is a bit like a Python list but limited to int, float, bool, small str and bytes, and None. But this doesn’t help you cheaply share an arbitrary Python object.

A bad option for Linux: the "fork" context

You may have noticed we did multiprocessing.get_context("spawn").Pool() to create a process pool. This is because Python has multiple implementations of multiprocessing on some OSes. "spawn" is the only option on Windows, the only non-broken option on macOS, and available on Linux. When using "spawn", a completely new process is created, so you always have to copy data across.

On Linux, the default is "fork": the new child process has a complete copy of the memory of the parent process at the time of the child process’ creation. This means any objects in the parent (arrays, giant dicts, whatever) that were created before the child process was created, and were stored somewhere helpful like a module, are accessible to the child. Which means you don’t need to pickle/unpickle to access them.

Sounds useful, right? There’s only one problem: the "fork" context is super-broken, which is why it will stop being the default in Python 3.14.

Consider the following program:

``` import threading import sys from multiprocessing import Process

def thread1(): for i in range(1000): print("hello", file=sys.stderr)

threading.Thread(target=thread1).start()

def foo(): pass

Process(target=foo).start() ```

On my computer, this program consistently deadlocks: it freezes and never exits. Any time you have threads in the parent process, the "fork" context can cause in potential deadlocks, or even corrupted memory, in the child process.

You might think that you’re fine because you don’t start any threads. But many Python libraries start a thread pool on import, for example NumPy. If you’re using NumPy, Pandas, or any other library that depends on NumPy, you are running a threaded program, and therefore at risk of deadlocks, segfaults, or data corruption when using the "fork" multiprocessing context. For more details see this article on why multiprocessing’s default is broken on Linux.

You’re just shooting yourself in the foot if you take this approach.

Multiple threads let you run code in parallel, potentially on multiple CPUs. On Python, however, the global interpreter lock makes this parallelism harder to achieve.

Multiple processes also let you run code in parallel—so what’s the difference between threads and processes?

All the threads inside a single process share the same memory address space. If thread 1 in a process stores some memory at address 0x7f0cd1a88810, thread 2 can access the same memory at the same address. That means passing objects between threads is cheap: you just need to get the pointer to the memory address from one thread to the other. A memory address is 8 bytes: this is not a lot of data to move around.

In contrast, processes do not share the same memory space. There are some shared memory facilities provided by the operating system, typically, and we’ll get to that later. But by default, no memory is shared. That means you can’t just share the address of your data across processes: you have to copy the data.

```python from concurrent.futures import ThreadPoolExecutor, as_completed

with ThreadPoolExecutor(max_workers=5) as executor: executor.submit(do_something_blockint) ```

The Python standard library has a few options for concurrent programming, depending on some factors:

• Is the task you’re completing IO-bound (e.g. reading from a network, writing to disk)
• Does the task require CPU-heavy work, e.g. computation
• Can the tasks be broken into small chunks or are they large pieces of work?

Here are the models:

• Threads are fast to create, you can share any Python objects between them and have a small overhead. Their drawback is that Python threads are bound to the GIL of the process, so if the workload is CPU-intensive then you won’t see any performance gains. Threading is very useful for background, polling tasks like a function that waits and listens for a message on a queue.
• Coroutines are extremely fast to create, you can share any Python objects between them and have a miniscule overhead. Coroutines are ideal for IO-based activity that has an underlying API that supports async/await.
• Multiprocessing is a Python wrapper that creates Python processes and links them together. These processes are slow to start, so the workload that you give them needs to be large enough to see the benefit of parallelising the workload. However, they are truly parallel since each one has it’s own GIL.
• Sub interpreters have the parallelism of multiprocessing, but with a much faster startup time.

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