Process vs Thread in Python

Hi there, I posted some time ago, and now I’m continuing because it helps keep important things organized and makes it easier to review and remember information.

I want to dive into the topic of processes and threads in Python because an HR representative asked me about it, and I wasn’t prepared to answer…

What’s differences between thread and process in Python?

Definition #

• Process: A process is an independent execution unit that contains its own memory space. Each Python program runs in its own process, and processes are managed by the operating system.
• Thread: A thread is a smaller unit of execution that runs within the context of a process. All threads within a process share the same memory space.

Memory #

• Process: Processes do not share memory by default. Each process has its own memory space, and inter-process communication (IPC) is required to exchange data between processes (e.g., using pipes, sockets, or shared memory).
• Thread: Threads within the same process share the same memory space, which makes communication between threads easier but increases the risk of race conditions and other synchronization issues.

Concurrency #

• Process: Python’s multiprocessing module can create multiple processes, bypassing the Global Interpreter Lock (GIL), allowing true parallel execution of Python code on multi-core CPUs.
• Thread: Python’s threading module creates threads within a single process. However, due to the GIL, only one thread executes Python bytecode at a time in CPython. Threads are better suited for I/O-bound tasks rather than CPU-bound tasks.

Performance #

• Process: Suitable for CPU-bound tasks because processes can run on multiple CPU cores in parallel, effectively bypassing the GIL.
• Thread: Suitable for I/O-bound tasks, such as reading/writing files, network requests, or database queries, since threads can efficiently wait for I/O operations to complete.

Overheads #

• Process: Processes have higher overhead because creating a process involves duplicating the memory space and context.
• Thread: Threads have lower overhead because they share the memory space of the process.

Crash Isolation #

• Process: A crash in one process does not affect other processes.
• Thread: A crash in one thread can bring down the entire process.

Use Cases #

• Process: CPU-intensive tasks (e.g., data processing, machine learning). Tasks requiring isolated memory spaces.
• Thread: Tasks that benefit from shared memory and faster communication within a process. I/O-bound tasks (e.g., web scraping, handling web requests).

Summary Table #

Feature	Process	Thread
Memory	Separate memory space	Shared memory space
GIL Constraint	No	Yes (in CPython)
Overhead	High	Low
Communication	Via IPC mechanisms	Shared memory
Crash Isolation	Isolated	Shared
Best for	CPU-bound tasks	I/O-bound tasks