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">
   # Global Interpreter Lock   
   <div class="ans">
<p>In one of the previous sections we discussed I/O and CPU bound programs. In this lesson, we'll understand why only I/O bound Python programs can leverage multithreading to speed up execution time. But first, a quick refresher of how Python works is necessary to understand the acronym GIL or global interpreter lock.</p>

## How Python works
<p>Python is an interpreted language, that is, there is no static time compiling as that happens in the case of Java, C or C++. The program that interprets user code is called the <i>Interpreter</i>. <span class="hi-yl">An interpreter is a program that executes other programs.</span> At a higher level when we run a Python program (.py file), the Python interpreter compiles the source code into byte code. The generated byte code is a lower-level platform-independent representation that can be understood by the Python Virtual Machine (PVM). In the next step, the byte code is routed to the PVM for execution. Note that PVM isn't a separate component. Rather, it is just a loop in the Python interpreter that is responsible for executing byte code line by line. The PVM is really a part of the interpreter.</p>


## Python Interpreter
<p>The Python interpreter as explained is responsible for executing a program, <span class="hi-yl">but it can only execute a single thread at a time. This is the falling of the reference implementation of Python - CPython, called so because it is written in the C language. So if your machine has one, ten, or a hundred processors, the Python interpreter is only able to run a single thread at a time using a single processor.</span> Two threads on a machine with two available processors can't be executed in parallel each running on a single CPU.</p>

<p>This design has direct consequences on the performance of CPU bound programs since they don't experience any speed-up in the presence of additional processors. In fact, they may run slower because of the additional housekeeping overhead required for running multiple threads.</p>

## Why is that so?
<p>One may wonder what was the design decision behind restricting the interpreter to run a single thread. <span class="hi-yl">The answer lies in how memory management works in Python - reference counter.</span></p>
</div>
</div>


<div>
   # Global Interpreter Lock   
   <div>
<p>In one of the previous sections we discussed I/O and CPU bound programs. In this lesson, we'll understand why only I/O bound Python programs can leverage multithreading to speed up execution time. But first, a quick refresher of how Python works is necessary to understand the acronym GIL or global interpreter lock.</p>

## How Python works
<p>Python is an interpreted language, that is, there is no static time compiling as that happens in the case of Java, C or C++. The program that interprets user code is called the <i>Interpreter</i>. <span>An interpreter is a program that executes other programs.</span> At a higher level when we run a Python program (.py file), the Python interpreter compiles the source code into byte code. The generated byte code is a lower-level platform-independent representation that can be understood by the Python Virtual Machine (PVM). In the next step, the byte code is routed to the PVM for execution. Note that PVM isn't a separate component. Rather, it is just a loop in the Python interpreter that is responsible for executing byte code line by line. The PVM is really a part of the interpreter.</p>


## Python Interpreter
<p>The Python interpreter as explained is responsible for executing a program, <span>but it can only execute a single thread at a time. This is the falling of the reference implementation of Python - CPython, called so because it is written in the C language. So if your machine has one, ten, or a hundred processors, the Python interpreter is only able to run a single thread at a time using a single processor.</span> Two threads on a machine with two available processors can't be executed in parallel each running on a single CPU.</p>

<p>This design has direct consequences on the performance of CPU bound programs since they don't experience any speed-up in the presence of additional processors. In fact, they may run slower because of the additional housekeeping overhead required for running multiple threads.</p>

## Why is that so?
<p>One may wonder what was the design decision behind restricting the interpreter to run a single thread. <span>The answer lies in how memory management works in Python - reference counter.</span></p>
</div>
</div>


This lesson discusses the global interpreter lock also known as GIL and its effects.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Global Interpreter Lock

Global Interpreter Lock