What is Multithreading and why do we need Multithreading?

What is Multithreading and why do we need Multithreading?

Multithreading is a technique that helps us to run multiple tasks in parallel. It means we can run multiple pieces of code in parallel.

To perform multithreading in Python we have a module named threading.

Why we need multithreading in Python?

As we know, Python is a scripting language and follows the bottom-up approach. It means Python executes the code line by line from top to bottom. Or Python's behavior is, it does not execute the next line till it not completes the execution of the first line.

The problem when we are not using multithreading.

For example, we have 1000 lines of code and within the code, there is a piece of code at line 300, which takes a long time to execute. According to Python behavior now the next part or next lines of code wait for the completness of their upper code. By this, the next complete code goes to the waiting state.

Example: 

What is the problem when we do not use multithreading.

print("heyy! the eagle eye...")

print("let's learn multithreading")

for i in range(1, 1000):
    print("i am taking time, this is my work..eeeeeeee")

print("i want to execute.")

print("i am important code.")

print("let me execute now! please")

Output:

heyy! the eagle eye...
let's learn multithreading
i am taking time, this is my work..eeeeeeee

# this code execute 1000 times

i am taking time, this is my work..eeeeeeee
i am taking time, this is my work..eeeeeeee
i want to execute.
i am important code.
let me execute now! please

Let's learn some concepts using questions and answers.

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25+ interview questions about multithreading

Question 1. What is multithreading?

Answer: Multithreading is a programming technique where multiple threads (smaller units of execution) run within the same process simultaneously. Threads share the process's memory and resources, allowing concurrent execution of tasks.

 

Question 2. Why do we need multithreading?

Answer: Multithreading improves program efficiency by allowing multiple tasks to run concurrently, especially useful in I/O-bound and GUI applications. It increases responsiveness and better utilizes CPU resources.

 

Question 3. When should we use multithreading?

Answer: Use multithreading when:

• Performing I/O-bound tasks (e.g., reading/writing files).
• Running a GUI application where the interface should remain responsive.
• Handling multiple requests (e.g., in web servers).

 

Question 4. What is a process in multithreading?

Answer: A process is an independent program in execution with its own memory and resources. Multithreading occurs within a single process, where multiple threads share the same memory.

 

Question 5. What is a thread?

Answer: A thread is a lightweight unit of execution within a process. Threads in the same process share memory and resources, making communication between them faster.

 

Question 6. What is the difference between a process and a thread?

Answer:

Process: Independent, has its own memory and resources, more overhead.

Thread: Runs within a process, shares memory and resources with other threads, less overhead.

 

Question 7. What is the Global Interpreter Lock (GIL) in Python?

Answer: The GIL is a mutex in CPython that ensures only one thread executes Python bytecode at a time, even in multithreaded programs. It simplifies memory management but limits performance in CPU-bound tasks.

 

Question 8. What is concurrent programming?

Answer: Concurrent programming is the ability to run multiple tasks (threads or processes) in overlapping time periods. Multithreading is one way to achieve concurrency.

 

Question 9. What are the advantages of multithreading?

Answer: Efficient CPU utilization.

• Faster task completion in I/O-bound scenarios.
• Improved application responsiveness (e.g., GUIs).
• Simpler resource sharing between threads.

 

Question 10. What are the disadvantages of multithreading?

Answer:

• Difficult to debug and test.
• Potential for deadlocks and race conditions.
• Limited performance gains in Python due to the GIL for CPU-bound tasks.

 

Question 11. What are some use cases of multithreading?

Answer:

• Running multiple tasks (e.g., downloading files simultaneously).
• Maintaining responsive GUIs.
• Handling multiple requests in a server application.

 

Question 12. What is the difference between parallelism and concurrency?

Answer:

• Concurrency: Multiple tasks progress independently but not necessarily at the same time.
• Parallelism: Multiple tasks execute simultaneously on multiple CPUs or cores.

 

Question 13. What is a race condition in multithreading?

Answer: A race condition occurs when multiple threads access shared data simultaneously, and the outcome depends on the thread execution order, leading to unpredictable results.

 

Question 14. What is a deadlock?

Answer: A deadlock occurs when two or more threads are waiting for each other to release resources, preventing them from proceeding, leading to a halt in execution.

 

Question 15. How does multithreading work in Python with the GIL?

Answer: Due to the GIL, only one thread executes Python bytecode at a time in CPython. This limits true parallelism for CPU-bound tasks but works well for I/O-bound tasks.

 

Question 16. Can multithreading improve performance in Python?

Answer: Yes, it improves performance for I/O-bound tasks (e.g., file operations, network requests) but has limited benefits for CPU-bound tasks due to the GIL.

 

Question 17. What are daemon threads?

Answer: Daemon threads are background threads that automatically stop when the main program exits. They are often used for tasks like logging or monitoring.

 

Question 18.  What is thread synchronization?

Answer: Thread synchronization is a technique to control the execution order of threads, ensuring safe access to shared resources. This is achieved using locks, semaphores, or barriers.

 

Question 19. What is the difference between multithreading and multiprocessing?

Answer:

• Multithreading: Multiple threads in one process share memory.
• Multiprocessing: Multiple processes with separate memory spaces run independently.

 

Question 20. What are the challenges of multithreading?

Answer:

• Debugging and testing issues due to non-deterministic execution.
• Handling shared resources safely.
• Deadlocks, race conditions, and starvation.

 

Question 21. What is a thread pool?

Answer: A thread pool is a collection of pre-instantiated threads that can be reused for executing tasks, reducing the overhead of thread creation and destruction.

 

Question 22. What is the difference between user threads and kernel threads?

Answer:

• User threads: Managed by the application and the threading library.
• Kernel threads: Managed by the operating system.

 

Question 23. Why is multithreading more efficient for I/O-bound tasks?

Answer: I/O operations (e.g., reading files, making network requests) spend time waiting for external resources. Threads can perform other tasks while waiting, improving efficiency.

 

Question 24. How is resource sharing handled in multithreading?

Answer: Resources are shared between threads of the same process. However, to avoid conflicts, synchronization mechanisms like locks are used.

 

Question 25. What is context switching in multithreading?

Answer: Context switching is the process of saving the state of a thread and restoring the state of another thread. It allows multitasking but adds overhead.