Most popular interview questions on Python Concurrency

Understanding concurrency is a key skill for Python developers, especially for those who are developing high-performance or network-based applications. Here’s a list of popular interview questions and answers related to Python concurrency:

1. What is Concurrency in Python?

Answer:
Concurrency in Python refers to the ability of a system to handle multiple tasks at the same time but not necessarily simultaneously. It allows multiple IO-bound or CPU-bound tasks to be executed more efficiently.

2. What is the Global Interpreter Lock (GIL) in Python?

Answer:
The Global Interpreter Lock (GIL) is a mutex that protects access to Python objects in the CPython interpreter. This means that even in a multi-threaded environment, only one thread can execute Python bytecodes at a time.

3. What is the difference between multi-threading and multi-processing?

Answer:
Multi-threading involves multiple threads of a single process, sharing the same memory space. Multi-processing involves multiple processes with their own memory space.

# Multi-threading
from threading import Thread

def print_numbers():
    for i in range(10):
        print(i)

thread = Thread(target=print_numbers)
thread.start()

# Multi-processing
from multiprocessing import Process

def print_numbers():
    for i in range(10):
        print(i)

process = Process(target=print_numbers)
process.start()

4. What is the asyncio library?

Answer:
asyncio is a library in Python that provides an event loop and allows for asynchronous programming using async and await syntax.

import asyncio

async def main():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

asyncio.run(main())

5. How do you use a ThreadPoolExecutor?

Answer:
You can use ThreadPoolExecutor from the concurrent.futures module to execute functions in a pool of threads.

from concurrent.futures import ThreadPoolExecutor

def print_numbers(n):
    for i in range(n):
        print(i)

with ThreadPoolExecutor() as executor:
    executor.submit(print_numbers, 10)

6. What is the queue module in Python?

Answer:
The queue module provides a way to create FIFO, LIFO, and priority queues, which are useful in multi-threading environments.

from queue import Queue

q = Queue()
q.put(1)
q.put(2)
print(q.get())  # Output: 1

7. How do you handle exceptions in threads?

Answer:
You can use the concurrent.futures.ThreadPoolExecutor and its result() method to catch exceptions.

from concurrent.futures import ThreadPoolExecutor

def raise_exception():
    raise ValueError("An error occurred")

with ThreadPoolExecutor() as executor:
    future = executor.submit(raise_exception)
    try:
        future.result()
    except ValueError as e:
        print(e)

8. What are semaphores?

Answer:
Semaphores are synchronization primitives that are used to control access to a shared resource.

from threading import Semaphore

sem = Semaphore()

with sem:
    print("Critical section")

9. What is a deadlock?

Answer:
Deadlock is a situation in concurrent programming where two or more tasks are unable to proceed because they are each waiting for the other to release a lock.

10. How do you run a coroutine function in Python?

Answer:
Coroutine functions are defined using async def and are run using an event loop provided by libraries like asyncio.

import asyncio

async def hello_world():
    print("Hello, world!")

asyncio.run(hello_world())

This should give you a good starting point for understanding Python concurrency.