ProcessPool vs ThreadPool Executors in Concurrent Futures

Lot's of content out there showing you how to run each of these, but i have not found a detailed enough description of these two important options.

My quick interpretation is that ProcessPool is intended for cpu-intensive separate processes and ThreadPool is intended for time-intensive i/o processes (which share memory, I think). So I have only ever used ProcessPool.

But I think it's important to understand the role of each of the tools available to you, just in case you may have a need in the future! So I asked ChatGPT...

Let’s break down and compare ProcessPoolExecutor and ThreadPoolExecutor, the two key classes in Python’s concurrent.futures module for running code in parallel.

🔍 What Is concurrent.futures?

concurrent.futures is a high-level Python module that makes it easy to run tasks asynchronously using:

• Threads (ThreadPoolExecutor)

• Processes (ProcessPoolExecutor)

It abstracts away the complexity of thread/process management and uses futures (objects representing the result of an asynchronous computation).

⚙️ ProcessPoolExecutor

✅ Description:

Runs functions in separate processes, meaning each task gets its own memory space and Python interpreter.

📌 Key Features:

• True parallelism (bypasses the Global Interpreter Lock, or GIL)

• Best for CPU-bound tasks (e.g. numerical computations, simulations)

• Slower to start (due to process overhead)

• No shared memory (data must be pickled to pass between processes)

📄 Example:

from concurrent.futures import ProcessPoolExecutor 
def square(x): 
	return x * x 

with ProcessPoolExecutor() as executor: 
	results = executor.map(square, [1, 2, 3, 4]) 
	print(list(results)) # [1, 4, 9, 16]

⚙️ ThreadPoolExecutor

✅ Description:

Runs functions in multiple threads within the same Python process.

📌 Key Features:

• Shares memory (lightweight)

• Best for I/O-bound tasks (e.g. file I/O, network requests)

• Cannot achieve true parallelism for CPU-bound work due to the GIL

• Lower overhead and faster startup

📄 Example:

from concurrent.futures import ThreadPoolExecutor 
def fetch_url(url): 
	import requests 
	return requests.get(url).status_code 

urls = ["https://example.com", "https://httpbin.org"]

with ThreadPoolExecutor() as executor: 
	results = executor.map(fetch_url, urls) 
	print(list(results))

🧪 When to Use Which?

Feature	ProcessPoolExecutor	ThreadPoolExecutor
Uses parallel processes	✅ Yes	❌ No (uses threads)
Can bypass Python GIL	✅ Yes	❌ No
Good for CPU-bound tasks	✅ Yes	❌ No
Good for I/O-bound tasks	⚠️ Usually overkill	✅ Yes
Startup overhead	🐢 Higher (forks processes)	⚡ Very low
Memory use	📈 Higher (each process is isolated)	📉 Lower (shared memory)
Shares data easily	❌ No (must pickle objects)	✅ Yes
Suitable for heavy compute	✅ Yes	❌ No

📝 Summary

• Use ProcessPoolExecutor for CPU-heavy tasks like numerical computation, data processing, simulations, etc.

• Use ThreadPoolExecutor for I/O-heavy tasks like web scraping, database queries, or reading files.

• Both provide a simple, clean way to run tasks in parallel using .submit(), .map(), or .as_completed().

Task Type	ThreadPoolExecutor	ProcessPoolExecutor
CPU-bound	❌ Slow because of the GIL (Global Interpreter Lock)	✅ Fast
I/O-bound	✅ Fast -- true parallelism	⚠️ Works, slower with more overhead

💡 Bonus Tip: Switch Executors Easily

Because both classes use the same API, you can switch between them with minimal code changes:

from concurrent.futures import ThreadPoolExecutor as Executor 
# or 
from concurrent.futures import ProcessPoolExecutor as Executor
with Executor() as executor: 
	futures = executor.map(task, data)