20 Multithreading in Python Interview Questions and Answers

Python is a versatile language that can be used for a variety of purposes, including web development, scientific computing, and artificial intelligence. One of the key features of Python is its support for multithreading. This allows developers to write programs that can run multiple threads concurrently.

If you’re interviewing for a position that involves Python programming, it’s likely that you’ll be asked questions about multithreading. In this article, we’ll discuss some of the most common questions about multithreading in Python and how you can answer them.

Multithreading in Python Interview Questions and Answers

Here are 20 commonly asked Multithreading in Python interview questions and answers to prepare you for your interview:

1. What is multithreading in Python?

Multithreading is a way of achieving concurrency in Python by using multiple threads to run different parts of your code simultaneously. This can be useful for tasks that are IO-bound, such as making network requests, as well as for CPU-bound tasks, such as data processing.

2. Can you explain the concept of a thread in Python?

A thread is a separate flow of execution within a program. In Python, threads can be created by using the Threading library. Threads are useful for running multiple tasks simultaneously within a program. For example, a program could have a thread that handles user input while another thread handles background tasks such as data processing or network communication.

3. How many threads can run at once in Python?

The answer to this question depends on a few factors, such as the operating system and the processor being used. Generally speaking, though, Python can handle anywhere from 1 to 32 threads at a time.

4. What are the benefits of using multithreading in Python?

Multithreading can help improve the performance of your Python program by allowing you to utilize multiple processors at the same time. This can be especially beneficial if you are working with large data sets or performing computationally intensive tasks. Additionally, multithreading can help make your program more responsive to user input by allowing it to handle multiple tasks simultaneously.

5. What’s the difference between a process and a thread?

A process is a self-contained execution environment. A thread is a unit of execution within a process.

6. Is it possible to create multiple processes from within a single thread? If yes, then how?

Yes, it is possible to create multiple processes from within a single thread. This can be done using the Python multiprocessing module.

7. Why do we need locks while writing multi-threaded code?

Locks are necessary in order to prevent race conditions, which can lead to data corruption and other undesirable outcomes. If two threads are trying to modify the same data at the same time, then they need to be locked so that only one thread can modify the data at a time. This ensures that the data remains consistent and that no corruption occurs.

8. What’s your understanding of synchronization when it comes to multithreading in Python?

Synchronization is a process of ensuring that two or more concurrent processes do not access shared data at the same time. In the context of multithreading in Python, synchronization is typically used to protect data that is shared between threads. This can be accomplished using a number of different synchronization primitives, such as locks, semaphores, and events.

9. What are some common ways of implementing locking in Python for safe concurrent access to shared resources by multiple threads?

There are a few different ways to implement locking in Python, depending on what exactly you are trying to achieve. For example, if you want to allow multiple threads to read from a shared resource but only allow one thread to write to it at a time, you could use a lock object from the threading module. This would allow multiple threads to acquire the lock for reading, but only one thread to acquire the lock for writing, ensuring that the resource is not modified while it is being read by another thread.

10. How does the Global Interpreter Lock (GIL) work in Python?

The GIL is a mechanism used in Python to ensure that only one thread is executing at a given time. This is important because Python uses reference counting to manage memory, and if two threads were to modify an object’s reference count at the same time, it could lead to unpredictable results. The GIL is implemented using a mutex, which means that only one thread can acquire it at a given time. When a thread acquires the GIL, it is said to have the “lock”, and all other threads are blocked from executing until the thread releases the lock.

11. What happens if two threads try to acquire the same lock simultaneously?

If two threads try to acquire the same lock simultaneously, the thread that acquired the lock first will proceed while the other thread will be blocked until the lock is released.

12. How does the “with” function help with multithreading in Python?

The “with” function helps with multithreading in Python by allowing code to be executed in a thread-safe manner. This means that code that would normally be executed in a single thread can be executed in multiple threads simultaneously, without running into any race conditions or other problems.

13. Are there any limitations or restrictions on the use of the Threading module in Python?

There are no real restrictions on the use of the Threading module in Python, although it is important to be aware of the fact that not all modules or libraries are thread-safe. This means that if you are using multiple threads to access data or resources that are not thread-safe, you could end up with corrupted data or undefined behavior.

14. Should all variables be declared as global while writing multi-threaded code in Python?

No, all variables do not need to be declared as global while writing multi-threaded code in Python. However, if a variable needs to be accessed and modified by multiple threads, then it should be declared as global.

15. Does Python support multiple inheritance? If not, what are its alternatives?

Python does not support multiple inheritance. However, it does provide some alternative mechanisms for achieving similar results. For example, Python’s mixin class can be used to provide functionality that is similar to multiple inheritance.

16. Which one would you prefer: a mutex or semaphore? In which situations would you choose to implement each?

There is no definitive answer to this question, as it depends on the specific needs of the application. However, in general, a mutex is used to protect a critical section of code from being accessed by multiple threads simultaneously, while a semaphore is used to control access to a shared resource.

17. What’s the best way to check that an object has been instantiated in Python?

The best way to check that an object has been instantiated in Python is to use the isinstance function. This function will take two arguments, the first being the object you want to check and the second being the class you want to check it against. If the object is an instance of the class, then isinstance will return True, otherwise it will return False.

18. When should we stop creating more threads?

In general, you should stop creating more threads when your system starts to become overloaded and unresponsive. This can happen when too many threads are trying to access the same resources, or when there are too many threads in general for the system to handle. If you are seeing performance degradation due to too many threads, then it is time to stop creating more.

19. Do you think that memory leaks are a problem when dealing with multithreading in Python?

Yes, I think that memory leaks can definitely be a problem when dealing with multithreading in Python. If you are not careful with how you manage your threads, it is easy for memory to start leaking. This can eventually lead to your program crashing.

20. Can you explain the difference between a deadlock and livelock?

A deadlock is when two threads are each waiting on the other to release a resource before they can continue. This can lead to a situation where both threads are effectively blocked indefinitely. A livelock, on the other hand, is when two threads are each trying to grab a resource that the other thread has already acquired. This can lead to a situation where both threads are busy but not making any progress.