Learn multithreading and concurrency concepts with the help of some related interview problems.

Ace the Python Coding Interview

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 module lays the foundations of advanced concurrency and multithreading and explains concepts, such as monitors and deferred callbacks in depth. In the end, it builds simple and complete solutions to popular concurrency problems that can be asked in interviews, like the Reader-writer Problem and the Dining Philosopher Problem.

Concurrency

<div class="card">
   <h3>Multiple Processors</h3>   
   <div class="ans">
<p>We'll rewrite our service from the previous section to use a <code class="clazz">ProcessPoolExecutor</code> to process requests instead of threads. The changes appear in the widget below.</p>
</div>
</div>


<div class="card">
   # Multiple Processors   
   <div class="ans">
<p>We'll rewrite our service from the previous section to use a <code class="clazz">ProcessPoolExecutor</code> to process requests instead of threads. The changes appear in the widget below.</p>
</div>
</div>


<div>
   # Multiple Processors   
   <div>
<p>We'll rewrite our service from the previous section to use a <code>ProcessPoolExecutor</code> to process requests instead of threads. The changes appear in the widget below.</p>
</div>
</div>


This lesson discusses the implementation of the web-service using multiple processors.

Prelude

Basics of Concurrency

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Practice Problems

Conclusion

Multiple Processors

Multiple Processors