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

<p><p>A process is a program in execution and operating systems provide different ways of creating new processes. Furthermore, each operating system has its own nuances when spawning new processes, which gets reflected in Python's APIs. The multiprocessing module offers the method <code class="mthd">set_start_method()</code> to let the developer choose the way new processes are created. There are three of them:</p>

<ul>
<li><p>fork</p></li>
<li><p>spawn</p></li>
<li><p>fork-server</p></li>
</ul>

## Example

<p>The multiprocessing module exposes APIs very similarly to the threading module to create processes. The constructor of the <code class="clazz">Process</code> class accepts a callable object that the spawned process then executes. Below is an example of creating three different processes:</p>
### Creating processes
<b>

```python
from multiprocessing import Process
from multiprocessing import current_process
import os


def process_task():
    print("{0} has pid: {1} with parent pid: {2}".format(current_process().name, os.getpid(), os.getppid()))


process = [0] * 3

for i in range(0, 3):
    process[i] = Process(target=process_task, name="process-{0}".format(i))
    process[i].start()

for i in range(0, 3):
    process[i].join()

print("{0} has pid: {1} ".format(current_process().name, os.getpid()))

```
</b>
</div>
</div>


<div>
   # Process   
   <div>

<p><p>A process is a program in execution and operating systems provide different ways of creating new processes. Furthermore, each operating system has its own nuances when spawning new processes, which gets reflected in Python's APIs. The multiprocessing module offers the method <code>set_start_method()</code> to let the developer choose the way new processes are created. There are three of them:</p>

<ul>
<li><p>fork</p></li>
<li><p>spawn</p></li>
<li><p>fork-server</p></li>
</ul>

## Example

<p>The multiprocessing module exposes APIs very similarly to the threading module to create processes. The constructor of the <code>Process</code> class accepts a callable object that the spawned process then executes. Below is an example of creating three different processes:</p>
### Creating processes
<b>

```python
from multiprocessing import Process
from multiprocessing import current_process
import os


def process_task():
    print("{0} has pid: {1} with parent pid: {2}".format(current_process().name, os.getpid(), os.getppid()))


process = [0] * 3

for i in range(0, 3):
    process[i] = Process(target=process_task, name="process-{0}".format(i))
    process[i].start()

for i in range(0, 3):
    process[i].join()

print("{0} has pid: {1} ".format(current_process().name, os.getpid()))

```
</b>
</div>
</div>


This lesson demonstrates how to create processes using the multiprocessing module.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Process

Process