The ultimate guide to senior engineering interviews. Strategies developed by FAANG engineers on Python concurrency, multithreading, monitors, and event loops. Prep faster for top company questions.

Concurrency in Python is one of the most complex and advanced topics brought up during senior engineering interviews. Knowledge of concurrency and multithreading can put interviewees at a considerable advantage. This course lays the foundations of advanced concurrency and multithreading and explains concepts such as Monitors, Event Loops and Deferred Callbacks in depth. It also builds simple and complete solutions to popular concurrency problems that can be asked about in interviews like the Reader-Writer Problem and the Dining Philosopher Problem. While prior knowledge of concurrency is not required to follow through with this course, familiarity with the very basic Python would be helpful.

Python Concurrency for Senior Engineering Interviews

<div class="card">
   # Sharing State   
   <div class="ans">
<p>In the previous section we saw how processes can pass objects to eachother using queues or pipes. However, if a process P1 passes an object to process P2, any changes made by P1 to the object after the pass aren't visible to P2 since P2 only receives a copy of the object. For example, consider the below program.</p>
## Using queue to pass objects between two processes
<b>

```python
from multiprocessing import Process, Queue, Semaphore
import multiprocessing


def child_process(q, sem1, sem2):
    var = q.get()
    print("Child process received var = {0} with id {1} from queue".format(str(var), id(var)))
    sem2.release()
    sem1.acquire()

    print("After changes by parent process var in child process = {0}".format(str(var)), flush=True)


if __name__ == '__main__':
    q = Queue()
    sem1 = Semaphore(0)
    sem2 = Semaphore(0)
    print("This machine has {0} CPUs".format(str(multiprocessing.cpu_count())))

    var = {"key" : "value"}
    print("Parent process puts item on queue with id " + str(id(var)))
    q.put(var)

    process = Process(target=child_process, args=(q, sem1, sem2))
    process.start()

    sem2.acquire()

    # change the var
    var["key"] = "new-value"
    print("Parent process changed the enqueued item to " + str(var), flush=True)
    sem1.release()
    process.join()
```
</b>

<p>In the above program, ignore the semaphore statements which we use to coordinate the two processes. The parent process modifies an object put on the queue but the print statements from the child process show that the change isn't visible to the child process since it has only received a copy of the object.</p>
</div>
</div>


<div>
   # Sharing State   
   <div>
<p>In the previous section we saw how processes can pass objects to eachother using queues or pipes. However, if a process P1 passes an object to process P2, any changes made by P1 to the object after the pass aren't visible to P2 since P2 only receives a copy of the object. For example, consider the below program.</p>
## Using queue to pass objects between two processes
<b>

```python
from multiprocessing import Process, Queue, Semaphore
import multiprocessing


def child_process(q, sem1, sem2):
    var = q.get()
    print("Child process received var = {0} with id {1} from queue".format(str(var), id(var)))
    sem2.release()
    sem1.acquire()

    print("After changes by parent process var in child process = {0}".format(str(var)), flush=True)


if __name__ == '__main__':
    q = Queue()
    sem1 = Semaphore(0)
    sem2 = Semaphore(0)
    print("This machine has {0} CPUs".format(str(multiprocessing.cpu_count())))

    var = {"key" : "value"}
    print("Parent process puts item on queue with id " + str(id(var)))
    q.put(var)

    process = Process(target=child_process, args=(q, sem1, sem2))
    process.start()

    sem2.acquire()

    # change the var
    var["key"] = "new-value"
    print("Parent process changed the enqueued item to " + str(var), flush=True)
    sem1.release()
    process.join()
```
</b>

<p>In the above program, ignore the semaphore statements which we use to coordinate the two processes. The parent process modifies an object put on the queue but the print statements from the child process show that the change isn't visible to the child process since it has only received a copy of the object.</p>
</div>
</div>


This lesson discusses the facilities available in the multiprocessing module to share state amongst processes.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Sharing State

Sharing State

Using queue to pass objects between two processes