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">
   # Pool   
   <div class="ans">

<p>The <code class="clazz">Pool</code> object consists of a group of processes that can receive tasks for execution. The concept is very similar to a <b>thread pool</b>. Creating and tearing down threads is expensive so most programming language frameworks provide a notion of a pool of threads. Once a thread is done executing a task, it is returned back to the pool rather than being terminated. The process pool concept is similar where the processes are kept alive till there are tasks to be executed. Let's dive into an example to see how the <code class="clazz">Pool</code> class works. Consider the code below:</p>
## Example of using Pool class
<b>

```python
def init(main_id):
    print("pool process with id {0} received a task from main process with id {1}".format(os.getpid(), main_id))


def square(x):
    return x * x


if __name__ == '__main__':
    main_process_id = os.getpid()

    pool = Pool(processes=1,
                initializer=init,
                initargs=(main_process_id,),
                maxtasksperchild=1)

    result = pool.apply(square, (3,))
    print(result)

```
</b>

<p>The above program submits an integer to the pool to get the square of its value. If you run the below program you'll see the output showing PIDs of two different processes. One is the main process and the other is the pool process that actually does the computation.</p>
</div>
</div>


<div>
   # Pool   
   <div>

<p>The <code>Pool</code> object consists of a group of processes that can receive tasks for execution. The concept is very similar to a <b>thread pool</b>. Creating and tearing down threads is expensive so most programming language frameworks provide a notion of a pool of threads. Once a thread is done executing a task, it is returned back to the pool rather than being terminated. The process pool concept is similar where the processes are kept alive till there are tasks to be executed. Let's dive into an example to see how the <code>Pool</code> class works. Consider the code below:</p>
## Example of using Pool class
<b>

```python
def init(main_id):
    print("pool process with id {0} received a task from main process with id {1}".format(os.getpid(), main_id))


def square(x):
    return x * x


if __name__ == '__main__':
    main_process_id = os.getpid()

    pool = Pool(processes=1,
                initializer=init,
                initargs=(main_process_id,),
                maxtasksperchild=1)

    result = pool.apply(square, (3,))
    print(result)

```
</b>

<p>The above program submits an integer to the pool to get the square of its value. If you run the below program you'll see the output showing PIDs of two different processes. One is the main process and the other is the pool process that actually does the computation.</p>
</div>
</div>


This lesson discusses the various APIs and their working for both thread and process pools.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Pool

Pool