Effective Performance Testing to your Server with Autocannon

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Performance testing expertise that is developed for a long time. In order to measure your server performance you need a tool that can efficiently simulate a lot of abilities and give you good measurements according your analysing criteria.

Autocannon NPM library gave me exactly that - that library is super easy to install and has a very simple API to work with. Within a really short amount of time you can start do performance testing to your application and get good measurements in development environment and in your performance labs, and generate complicated testing scenarios.

In this talk I will introduce Autocannon, explain how to efficiently analyse your server performance with it, and show how it helped me to understand complicated performance issues in my Node.js servers. At the end of this lecture, developers will be able to have the ability to integrate a fast and easy tool in order to measure your server performance.

Tamar Twena-Stern
Tamar Twena-Stern
36 min
19 Nov, 2021

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Video Summary and Transcription

Tamar is an experienced code writer and architect with expertise in Node.js. Performance testing can be confusing, but understanding terms like throughput and the 99th percentile is crucial. The 99th percentile is important for making commitments and ensuring customer satisfaction. AutoCanon is a powerful tool for simulating requests and analyzing server performance. It can be installed globally or used as a library in Node.js. Autocannon is preferred over Gatling for performance testing and can be integrated with end-to-end tests in Cypress.

Available in Español

1. Introduction to Tamar and her expertise

Short description:

Hi, everyone. I'm Tamar, a passionate code writer with extensive experience in managing development groups and working as an architect. Currently leading back-end development at XM Cyber, a startup that simulates hacker activity. I'm an expert in Node.js and have a deep understanding of its inner workings. Follow me on Twitter for more insights and check out my previous lectures on YouTube. I'm also a professional violin player and a community leader in the JavaScript Israel community. Join our meetups if you're in Israel.

Hi, everyone. I'm very happy that you have come to my session about performance testing with Autocanon. But first, before we will really go and do some technical stuff, I would like to introduce myself. So, I am Tamar. I'm writing code for a lot of years. And it's my passion to write code. In addition to that, I was managing large development groups and I was working as an architect in several places. Currently I lead the back-end development in a startup called XM Cyber. It's a really cool startup. What we're doing is we're imitating the activity of a hacker in a computer network. In addition to that, well, I'm an expert in Node.js. And my interest in Node.js had started when I founded my own startup and I wrote my entire back-end with Node.js. At that point I really fell in love with that technology. And I started to investigate it and start to understand the deeper parts of it. And from that point I was really focusing on that technology. And it's definitely my favorite. You can follow me on Twitter and you can find previous lectures of mine in YouTube. In addition to that I have three kids. I'm a professional violin player as well. And I'm a community leader in JavaScript Israel community. We organize really cool lines of meetups. So if you happen to be around and if you happen to be around and in Israel and you encounter a meetup of JavaScript Israel then it's really nice to be there. It's recommended.

2. The Mystery of Performance Testing

Short description:

Let's talk about the mystery of performance testing. It can be confusing due to unfamiliar terminology and measurements. Key concepts include throughput, concurrent users, the 99th percentile, and average response time. Understanding these terms is crucial for simulating servers and improving performance. The main goal of performance testing is to determine the server's load capacity. Working with one docker container helps gauge throughput, and duplicating containers increases the number of concurrent requests. The 99th percentile of response time and average throughput are essential metrics to consider.

Okay, so now let's go to the technical part of the lecture. And I would like to talk a little bit about the mystery of performance testing. Why do I call it mystery? Because let's say that at the first time that I did performance testing I felt like I was climbing on a mountain. Um, well, it was very, very hard and confusing. So why it was very hard and confusing? Because I had so many questions because everybody was talking a lot about a lot of terminology that I did not understand.

So, to which terminology I mean. Well, when you're doing performance testing you're talking about a lot of terms and a lot of measurements that you're not familiar with. And at least for me, at the beginning, left me a little bit confused. So, first of all the throughput. The throughput of the server. So how do you measure the throughput of the server? What does that mean? I mean, I can simulate a lot of scenarios in a lot of ways. So, what is the best way to actually, what is the best way to measure the throughput of the server? In addition to that, concurrent users. So, well, concurrent users, how would that affect my scale? What is it, I mean. What is a concurrent users? What is that mean? What is that measurement? How to simulate that? What is the difference between that and between HTTP pipeline? Another thing that, you know, is very common when you're talking about performance testing and talking about benchmarking, is the 99th percentile. What is the 99th percentile? Why it's very important. Because sometimes when I measure and when people measure, we're looking at the 99th percentile much more than what we're looking at the average. So why the 99th percentile is so important? And the last thing is the average response time, or the response time. So the response time, how you measure it, whether you have to look at the average or the 99th percentile, there is also the standard deviation of the benchmark that needs to be taken into account. So all of those when you first encounter them left me very confused. And I had to understand exactly what I'm doing in order to understand how to simulate my server in order for it to test mean something and really improve my performance.

So let's explain a little bit about all those terms and just a little bit in a high level to like get you in order to all of this. So first of all, of course, the main goal for performance testing is to understand how much load our server can handle. Well, usually you're working with one docker container to my opinion in performance testing and then you're like simulating HTTP request to that like one docker in order to understand what throughput this one docker can handle. And if this one container can handle 100 concurrent requests, when you duplicate it and you create another instance of it, you create another replica, then you're able to handle 200 requests, et cetera. If you create three replicas, then 300 requests. But it's really important to understand how much load one docker container actually can handle. So, important question that was needed to ask. So, what is the 99th percentile of our response time? And what is the throughput? How many concurrent requests can we handle on average? I mean, those are, like, very important questions. And why those questions are important. First of all, the 99th percentile of the response time.

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