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">
   # Event Loop   
   <div class="ans">
<p><span class="hi-yl">The event loop is a programming construct that waits for events to happen and then dispatches them to an event handler.</span> An event can be a user clicking on a UI button or a process initiating a file download. At the core of asynchronous programming, sits the event loop. The concept isn't novel to Python. In fact, many programming languages enable asynchronous programming with event loops. In Python, event loops run asynchronous tasks and callbacks, perform network IO operations, run subprocesses and delegate costly function calls to pool of threads.</p>

<p>One of the most common use cases you'll find in the wild is of webservers implemented using asynchronous design. A webserver waits for an HTTP request to arrive and returns the matching resource. Folks familiar with JavaScript would recall NodeJS works on the same principle. It is a webserver that runs an event loop to receive web requests in a single thread. Contrast that to webservers which create a new thread or worse fork a new process, to handle each web request. In some benchmarks, the asynchronous event loop based webservers outperformed multithreaded ones, which may seem counterintuitive.</p>

## Rationale for Event Loops
<p>In order to truly appreciate event loops, we'll present Ryan Dahl's motivation for creating NodeJS which also runs an event loop. Ryan classifies disk and network I/O operations as blocking operations and <a href="https://www.youtube.com/watch?v=ztspvPYybIY" target="_blank">presents</a> the following table to put the latency for various operations in perspective.</p>

<table>
<tr><th>Device</th><th>CPU Cycles</th><th>Humanified</th></tr>
<tr><td>L1 cache</td><td>3</td><td>1 seconds</td></tr>

<tr><td>L2 cache</td><td>14</td><td>4.6 seconds</td></tr>
<tr><td>RAM</td><td>250</td><td>83 seconds</td></tr>
<tr><td>Disk</td><td>41000000</td><td>158 days</td></tr>
<tr><td>Network</td><td>240000000</td><td>2.5 years</td></tr>
</table>

<p>The third column <i>humanizes</i> the CPU cycles by assuming if 3 CPU cycles equaled one second then a network I/O would feel equivalent of 2.5 years. This may dawn upon you how slow can disk and network I/O be in comparison to access times for other devices. The long waiting times suggest that if these blocking calls are synchronous then we end up wasting a lot of CPU cycles. The CPU can be better utilized especially in environments with a lot of I/O.</p>

<p>Synchronous blocking I/O calls can be made non-blocking by either one of the following two methods:</p>

<ul>
<li><p>Use threads to make blocking calls.</p></li>
<li><p>Convert blocking calls to nonblocking asynchronous calls.</p></li>
</ul>    

<p>Threads don't come cheap. Creating, maintaining and tearing down threads takes CPU cycles in addition to memory. In fact, this difference becomes more visible in webservers which use threads to handle HTTP web requests vs which use an event loop. Apache is an example of the former and NGINX of the latter. NGINX outshines Apache in memory usage under high load.</p>


<p>Taking the discussion back to Python, you'll rarely need to access the event loop directly unless you are working on low level libraries or functionality. The below code snippet retrieves the event loop object and prints it.</p>
</div>
</div>


<div>
   # Event Loop   
   <div>
<p><span>The event loop is a programming construct that waits for events to happen and then dispatches them to an event handler.</span> An event can be a user clicking on a UI button or a process initiating a file download. At the core of asynchronous programming, sits the event loop. The concept isn't novel to Python. In fact, many programming languages enable asynchronous programming with event loops. In Python, event loops run asynchronous tasks and callbacks, perform network IO operations, run subprocesses and delegate costly function calls to pool of threads.</p>

<p>One of the most common use cases you'll find in the wild is of webservers implemented using asynchronous design. A webserver waits for an HTTP request to arrive and returns the matching resource. Folks familiar with JavaScript would recall NodeJS works on the same principle. It is a webserver that runs an event loop to receive web requests in a single thread. Contrast that to webservers which create a new thread or worse fork a new process, to handle each web request. In some benchmarks, the asynchronous event loop based webservers outperformed multithreaded ones, which may seem counterintuitive.</p>

## Rationale for Event Loops
<p>In order to truly appreciate event loops, we'll present Ryan Dahl's motivation for creating NodeJS which also runs an event loop. Ryan classifies disk and network I/O operations as blocking operations and <a href="https://www.youtube.com/watch?v=ztspvPYybIY" target="_blank">presents</a> the following table to put the latency for various operations in perspective.</p>

<table>
<tr><th>Device</th><th>CPU Cycles</th><th>Humanified</th></tr>
<tr><td>L1 cache</td><td>3</td><td>1 seconds</td></tr>

<tr><td>L2 cache</td><td>14</td><td>4.6 seconds</td></tr>
<tr><td>RAM</td><td>250</td><td>83 seconds</td></tr>
<tr><td>Disk</td><td>41000000</td><td>158 days</td></tr>
<tr><td>Network</td><td>240000000</td><td>2.5 years</td></tr>
</table>

<p>The third column <i>humanizes</i> the CPU cycles by assuming if 3 CPU cycles equaled one second then a network I/O would feel equivalent of 2.5 years. This may dawn upon you how slow can disk and network I/O be in comparison to access times for other devices. The long waiting times suggest that if these blocking calls are synchronous then we end up wasting a lot of CPU cycles. The CPU can be better utilized especially in environments with a lot of I/O.</p>

<p>Synchronous blocking I/O calls can be made non-blocking by either one of the following two methods:</p>

<ul>
<li><p>Use threads to make blocking calls.</p></li>
<li><p>Convert blocking calls to nonblocking asynchronous calls.</p></li>
</ul>    

<p>Threads don't come cheap. Creating, maintaining and tearing down threads takes CPU cycles in addition to memory. In fact, this difference becomes more visible in webservers which use threads to handle HTTP web requests vs which use an event loop. Apache is an example of the former and NGINX of the latter. NGINX outshines Apache in memory usage under high load.</p>


<p>Taking the discussion back to Python, you'll rarely need to access the event loop directly unless you are working on low level libraries or functionality. The below code snippet retrieves the event loop object and prints it.</p>
</div>
</div>


This lesson introduces the concept of the event loop and the central role it plays in asynchronous programming models.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Event Loop

Event Loop