Hey everyone! I recently faced a DSA challenge in a Python interview where I had to do the following: 1. Explain the time complexity of my solution, 2. Optimize it if I could, and 3. Manage race conditions in a multi-core, multi-threaded environment. The catch? No built-in libraries like `threading` or `concurrent.futures` were allowed! I need to implement synchronization primitives completely from scratch, including things like Mutexes, Semaphores, Condition Variables, Atomic Variables, Spin Locks, Peterson's Solution, and the Bakery Algorithm. I'm trying to brush up on concurrency concepts and would appreciate any insights or examples from anyone who has tackled anything like this in Python. Even just one of those implementations would be super helpful. Thanks!
4 Answers
Honestly, these interview requirements seem a little overboard to me. It's like they're trying to see if you can build your own computer from scratch! This kind of low-level problem doesn't seem to fit Python's strengths.
Can we just talk about how some people are clearly using LLMs to ask questions now? It's wild how lazy some folks are getting!
You might want to check out Peterson's Algorithm or Lamport's Bakery to create locks. Even though Python uses the GIL which makes variable assignments atomic, this task still feels more like busy-work. I've got a feeling even top coders would struggle with this one without a week of preparation for chaos testing.
You know, the problem doesn't explicitly require you to implement threading mechanisms. You could potentially approach it by avoiding mutation and using a functional programming style instead. It might simplify things a bit!
Seriously! Next they'll ask us to write an OS on our own, right?