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 #