newline Podcast Sudo Steph Nate: [00:00:00] Steph, just tell us a little bit about your work and kind of your background with, like AWS and like what you're doing now. Steph: [00:00:06] Yes, so I work as a engineer for a manage services provider called Second Watch. We basically partner with other big companies that use AWS or some other clouds sometimes Azure for managing their cloud infrastructure, which basically just means that. We help big companies who may not, their focus may not be technology, it may not be cloud stuff in general, and we're able to just basically optimize the cost of everything, make sure that things are running reliably and smoothly, and we're able to work with AWS directly to kind of keep people ahead of the curve when. New stuff is coming out and just it changes so much, you know, it's important to be able to adapt. So like personally, my role is I develop automation for our internal operations teams. So we have a bunch of, you know, just really smart people who are always working on customer specific AWS issues. And we see some of the same issues. Pop up over and over. Of course, you know, security , auditing, cost optimization. And so my team makes optimizations that we can distribute to all of these clients who have to maintain their own. You know, they have their own AWS account. It's theirs. And we make it so that we're actually able to distribute these automations same way in all of our customers' accounts. So the idea is that, and it's really wouldn't be doable without serverless because the idea is that everyone has to own their own infrastructure, right? Your AWS account is yours does or your resources, you don't, for security reasons, want to put all of your stuff on somebody else's account. But actually managing them all the same way can be a really difficult, even with scripts, because permissions different places have to be granted through the AWS permissions up with access, I identity and access management, right? So serverless gave us the real tool that we needed to be able to at scale, say, Hey, we came up with a little script that will run on an hourly basis to check to see how much usage these servers are getting, and if they're not production servers, you know, spin them down if they're not in use to save money. Little things like that when it comes to operations and AWS Lambda is just so good for it because it's all about, you know, like I said, doing things reliably. Doing things in a ways that can be audited and logged and doing things for like a decent price. So like background wise, I used to work at AWS in AWS support actually, and I kind of supported some of their dev ops products like OpsWorks, which is based on chef for configuration management, elastic Beanstalk and AWS CloudFormation, specifically. After working there for a bit, I really got to see, you know, how it's made and what the underlying system is like. And it was just crazy just to see how much work goes into all this, just so you can have a supposedly, easier to use for an end. But serverless just kinda changed all that for the better. Luckily. Amelia: [00:02:57] So it sounds like AWS has a ton of different services. What are the main ones and how many are there? Steph: [00:03:04] So I don't think I can even count anymore because they just, they do release new ones all the time. So hundreds at this point, but really main ones, and maybe not hundreds, maybe a little over a hundred would be a better estimate. I mean, EC2 which is elastic compute is. The bread and butter. Historically, AWS is just, they're virtualized servers basically. So EC2, the thing that made AWS really special from the beginning and that made cloud start to take over the world was the concept of auto scaling groups, which are basically definitions you attached to EC2 and it basically allows you to say, Hey, if I start getting a lot of traffic on. This one type of server, right? You know, create a second server that looks exactly the same and load balance the traffic through it. So when they say scaling, that's basically what, how you scale, easy to use auto scaling groups and elastic load balancers and kind of distribute the traffic out. The other big thing besides the scalability of with auto scaling groups is. Redundancy. So there's this idea of regions within AWS, and within each region there's availability zones. So regions are the general, like you can think of it as the place where data center is kind of like located within like a small degree. So it's usually like. Virginia is one, right? That's us East one. It's the oldest one. Another one is in California, but they're all over the world now. So the idea is you pick a region to minimize latency, so you pick the region that's closest to you. And then within the region, there's the idea of availability zones, which are basically just discreet, like physical locations of the servers that you administer them the same way, but they're protected. So like if a tornado runs through and hits one of your data centers. If you happen to have them distributed between two different availability zones, then you'll still be able to, you know, serve traffic. The other one will go down, but then the elastic load balancer will just notice that it's not responding and send the traffic to the other availability zone. So those are the main concepts that make it like EC2 those are what you need to use it effectively. Nate: [00:05:12] So with an easy to instance, that would be like a virtual server. I mean, it's not quite a Docker container, I guess we're getting to nuance there, but it's basically like a server that you would have like command line access to. You could log in and you can do more or less whenever you want on an EC2 instance. Steph: [00:05:29] Right, exactly. And so it used to be AWS used what was called Zen virtualization to do it. And that's just like you can run Zen on your own computer, you can get a computer and set up a virtual machine, almost just like they used to do it . So they are constantly putting out like new ways of virtualizing more efficiently. So they do have new technology now, but it's not something that was really, I mean, it was well known, but they really took it to a new kind of scale, which made it really impressive. Nate: [00:05:56] Okay, so EC2 lets you have full access to the box that's running and you might like load bounce requests against that. How does that contrast with what you do with AWS Lambda and serverless? Steph: [00:06:09] So with , you still have to, you know, either secure shell or, you know, furious and windows. Use RDP or something to actually get in there. You care about what ports are open. You have security groups for that. You care about all the stuff you would care about normally with a server you care about. Is it patched and up today you care about, you know, what's the current memory and CPU usage? All those things don't go away on EC2 just because it's cloud, right? When we start bringing serverless into the mix, suddenly. They do go and away. I mean, and there's still a few limitations. Like for instance, a Lambda has a limit on how much memory it can process with, just because they have to have a way to kind of keep costs down and define the units of them and define where to put them. Right? But at its core, what a Lambda is, it actually runs on a Docker container. You can think of it like a pre-configured Docker container with some pre-installed dependencies. So for Python, it would have. The latest version of Python that it says it has, it would have boto. It would have the stuff that it needs to execute that, and it would also have some basic, it's structured like it was, you know, basic Linux. So there's like a attempt. So slash temp you can write files there, right. But really it's like a Docker container. That runs underneath it on a fleet of . As far as availability zone distribution goes, that's already built into land, but you don't have to think about it with . You do have to think about it. Because if you only run one easy to server and it's in one availability zone, it's not really different from just having a physical server somewhere with a traditional provider. Nate: [00:07:38] So. There are these two terms, there's like serverless and Lambda. Can you talk a little bit about like the difference between those two terms and when to use each appropriately? Steph: [00:07:48] Yeah, so they are in a way sorta interchangeable, right? Because serverless technology just means the general idea of. I have an application, I have it defined it an artifact of we'll say zip from our get repo, right? So that application is my main artifact, and then I pass it to a service somewhere. I don't know. It could be at work. The Google app engine, that's a type of serverless technology and AWS Lambda is just the specific AWS serverless technology. But the reason AWS Lambda is, in my opinion so powerful, is because it integrates really well with the other features of AWS. So permissions management works with AWS Lambda API gateway. there's a lot of really tight integrations you can make with Lambda so that it doesn't, it's not like you have to keep half of your stuff one place and half of your stuff somewhere else. I remember when like Heroku was really big . A lot of people, you know, maybe they were maintaining an AWS account and they were also maintaining a bunch of stuff and Heroku, and they're just trying to make it work together. And even though Heroku does use, you know, AWS on the backend, or at least it did back then, it can just make things more complicated. But the whole server, this idea of the artifact is you make your code general, it's like a little microservice in a way. So I can take my serverless application and ideally, you know, it's just Python. I use NF, I write it the right way. Getting it to work on a different server. This back end, like for, exit. I think Azure has one, and Google app engin
