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

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   # Introduction   
   <div class="ans">
<p>Python offers the ability to execute tasks as processes using the <code class="abtc">multiprocessing</code> module. Most of the APIs in the module mirror the APIs found in the <code class="abtc">threading</code> module. In fact if you have a program written using the <code class="abtc">threading</code> module APIs, it is trivial to change the program to work with the <code class="abtc">multiprocessing</code> module.</p>

<p>The limitations of the global interpreter lock are to an extent addressed by the <code class="abtc">multiprocessing</code> module. One of the most common critiques of Python has been its inability to schedule threads on multiple processors in a multicore system. So much so, that alternative implementations such as Jython, IronPython, etc have cropped up to address this shortcoming of the standard Python implementation. The <code class="abtc">multiprocessing</code> module offers hope by allowing a program to spin-off tasks as separate processes that can then run on individual processors. This <span class="hi-yl">allows Python to be both concurrent and parallel, whereas with the threading module, Python is only concurrent and not parallel.</span> Languages without a GIL in their design, such as Java, can exhibit both concurrency and parallelism using only threads on a multicore system.</p>

<p>Creating, managing and tearing down processes is more expensive than doing the same for threads. Furthermore, inter-process communication is relatively slower than inter-thread communication. Both these drawbacks may not make Python a practical technology choice for super-critical or time-sensitive use-cases. </p>

<p>Given the above context, let's explore the <code class="abtc">multiprocessing</code> module and the APIs it offers.</p>

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</div>


<div>
   # Introduction   
   <div>
<p>Python offers the ability to execute tasks as processes using the <code>multiprocessing</code> module. Most of the APIs in the module mirror the APIs found in the <code>threading</code> module. In fact if you have a program written using the <code>threading</code> module APIs, it is trivial to change the program to work with the <code>multiprocessing</code> module.</p>

<p>The limitations of the global interpreter lock are to an extent addressed by the <code>multiprocessing</code> module. One of the most common critiques of Python has been its inability to schedule threads on multiple processors in a multicore system. So much so, that alternative implementations such as Jython, IronPython, etc have cropped up to address this shortcoming of the standard Python implementation. The <code>multiprocessing</code> module offers hope by allowing a program to spin-off tasks as separate processes that can then run on individual processors. This <span>allows Python to be both concurrent and parallel, whereas with the threading module, Python is only concurrent and not parallel.</span> Languages without a GIL in their design, such as Java, can exhibit both concurrency and parallelism using only threads on a multicore system.</p>

<p>Creating, managing and tearing down processes is more expensive than doing the same for threads. Furthermore, inter-process communication is relatively slower than inter-thread communication. Both these drawbacks may not make Python a practical technology choice for super-critical or time-sensitive use-cases. </p>

<p>Given the above context, let's explore the <code>multiprocessing</code> module and the APIs it offers.</p>

</div>
</div>


This lesson lays the groundwork for discussing the various APIs and capabilities of the multiprocessing module.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Introduction