The ultimate guide to Ruby concurrency interviews. Developed by FAANG engineers, this course covers Monitors, Deferred Callbacks and problem-solving strategies for Reader-Writer and Dining Philosopher Problems.

Concurrency in Ruby 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 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 basics of Ruby would be helpful.

Ruby Concurrency for Senior Engineering Interviews

<div class="card">
   # Throughput vs Latency   
   <div class="ans">
## Throughput
<p>Throughput is defined as <span class="hi-yl">the <i>rate of doing work</i> </span>or how much work gets done per unit of time. If you are an Instagram user, you could define throughput as the number of images downloaded by your phone or browser per unit of time.</i></p>

## Latency
<p>Latency is defined as <span class="hi-yl">the <i>time required to complete a task or produce a result</span></i>. Latency is also referred to as <i>response time</i>. The time it takes for a web browser to download Instagram images from the internet is the latency for downloading the images.
</p>

## Throughput vs Latency

<p>The two terms are more frequently used when describing networking links and have more precise meanings in that domain. In the context of concurrency, throughput can be thought of as time taken to execute a program or computation.
For instance, imagine a program that is given hundreds of files containing integers and asked to sum up all the numbers. Since addition is commutative, each file can be worked on in parallel. In a single-threaded environment, each file will be sequentially processed, but in a concurrent system, several threads can work in parallel on distinct files. Of course, there will be some overhead to manage the state, including already processed files. However, such a program will complete the task much faster than a single thread. The performance difference will become more and more apparent as the number of input files increases. <span class="hi-yl">The throughput in this example can be defined as the number of files processed by the program in a minute. Latency can be defined as the total time taken to process all the files completely.</span> As you observe, in a multithreaded implementation, throughput will go up, and latency will go down. More work gets done in less amount of time. In general, the two have an inverse relationship.</p>

</div>
</div>


<div>
   # Throughput vs Latency   
   <div>
## Throughput
<p>Throughput is defined as <span>the <i>rate of doing work</i> </span>or how much work gets done per unit of time. If you are an Instagram user, you could define throughput as the number of images downloaded by your phone or browser per unit of time.</i></p>

## Latency
<p>Latency is defined as <span>the <i>time required to complete a task or produce a result</span></i>. Latency is also referred to as <i>response time</i>. The time it takes for a web browser to download Instagram images from the internet is the latency for downloading the images.
</p>

## Throughput vs Latency

<p>The two terms are more frequently used when describing networking links and have more precise meanings in that domain. In the context of concurrency, throughput can be thought of as time taken to execute a program or computation.
For instance, imagine a program that is given hundreds of files containing integers and asked to sum up all the numbers. Since addition is commutative, each file can be worked on in parallel. In a single-threaded environment, each file will be sequentially processed, but in a concurrent system, several threads can work in parallel on distinct files. Of course, there will be some overhead to manage the state, including already processed files. However, such a program will complete the task much faster than a single thread. The performance difference will become more and more apparent as the number of input files increases. <span>The throughput in this example can be defined as the number of files processed by the program in a minute. Latency can be defined as the total time taken to process all the files completely.</span> As you observe, in a multithreaded implementation, throughput will go up, and latency will go down. More work gets done in less amount of time. In general, the two have an inverse relationship.</p>

</div>
</div>


This lesson discusses throughput and latency in the context of concurrent systems.

The Basics

API Overview

Brief Intro to Asynchronous Programming

Interview Practice Problems

Bonus Questions

Epilogue

Throughput vs Latency