Maintainable Infrastructure As Code In Pure Python With Pulumi - Episode 261

Summary

After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.

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Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.
  • Your host as usual is Tobias Macey and today I’m interviewing Luke Hoban about building and maintaining infrastructure as code with Pulumi

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the concept of "infrastructure as code"?
  • What is Pulumi and what is the story behind it?
    • Where does the name come from?
    • How does Pulumi compare to other infrastructure as code frameworks, such as Terraform?
  • What are some of the common challenges in managing infrastructure as code?
    • How does use of a full programming language help in addressing those challenges?
    • What are some of the dangers of using a full language to manage infrastructure?
      • How does Pulumi work to avoid those dangers?
  • Why is maintaining a record of the provisioned state of your infrastructure necessary, as opposed to relying on the state contained by the infrastructure provider?
    • What are some of the design principles and constraints that developers should be considering as they architect their infrastructure with Pulumi?
  • Can you describe how Pulumi is implemented?
    • How does Pulumi manage support for multiple languages while maintaining feature parity across them?
    • How do you manage testing and validation of the different providers?
  • The strength of any tool is largely measured in the ecosystem that exists around it, which is one of the reasons that Terraform has been so successful. How are you approaching the problem of bootstrapping the community and prioritizing platform support?
  • Can you talk through the workflow of working with Pulumi to build and maintain a proper infrastructure?
  • What are some of the ways to approach testing of infrastructure code?
    • What does the CI/CD lifecycle for infrastructure look like?
  • What are the limitations of infrastructure as code?
    • How do configuration management tools fit with frameworks such as Pulumi?
  • The core framework of Pulumi is open source, and your business model is focused around a managed platform for tracking state. How are you approaching governance of the project to ensure its continued viability and growth?
  • What are some of the most interesting, innovative, or unexpected design patterns that you have seen your users include in their infrastructure projects?
  • When is Pulumi the wrong choice?
  • What do you have planned for the future of Pulumi?

Keep In Touch

Picks

Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Click here to read the raw transcript...
Tobias Macey
0:00:13
Hello, and welcome to podcast dotnet, the podcast about Python and the people who make it great. When you're ready to launch your next app or want to try a project to hear about on the show, you'll need somewhere to deploy it. So take a look at our friends over at linode who 200 gigabit and private networking node balancers, 40 gigabit public network fast object storage and a brand new managed Kubernetes platform all controlled by a convenient API, you've got everything you need to scale up. And for your tasks that need fast computation such as training machine learning models or running your ci CD pipelines. They've got dedicated CPU and GPU instances. Go to Python podcast.com slash linode. That's Li n o d today to get a $20 credit and launch a new server in under a minute. And don't forget to thank them for their continued support of this show. You monitor your website to make sure You're the first to know when something goes wrong. But what about your data? tidy data is the data ops monitoring platform that you've been missing. With real time alerts for problems in your database ETL pipelines or data warehouse and integrations with slack pager duty and custom web hooks, you can fix the errors before they become a problem. Go to Python podcast.com slash tidy data today and get started for free with no credit card required. Your host, as usual is Tobias Macey. And today I'm interviewing Luke Oban, about building and maintaining infrastructure as code with pulu. me So Luke, can you start by introducing yourself?
Luke Hoban
0:01:34
Hi, my name is Luke open and CTO at plumie previously spent time at Microsoft doing sort of application developer tools and at Amazon doing cloud infrastructure.
Tobias Macey
0:01:44
And do you remember how you first got introduced to Python?
Luke Hoban
0:01:46
Yes, I've worked in sort of the programming languages space for a lot of my career and even you know, back in sort of in college and things kind of got introduced to Python mostly from sort of an academic perspective, just seeing it as sort of another programming language another interesting space in a special And just using it from that kind of perspective, I think my my next sort of deeper engagement with it was actually working at Microsoft on sort of dotnet. And worked with a lot of the teams building ironpython. Actually, at the time, it's a really sort of seeing Python through the lens of kind of the Microsoft developer ecosystem, which was sort of an interesting lens to see Python through. And then I think more recently, just personally, I got a lot more exposure to Python, just as a lens into kind of machine learning and that kind of thing. Just learning that from a personal perspective. And then I think with with paluma, was where we've really gone. I've personally really gone the deepest in my career so far with Python, which is really kind of building and designing API's and programming models for Python developers to program the cloud and work with that cloud infrastructure.
Tobias Macey
0:02:44
And so in terms of the concept of infrastructure as code, can you give your definition of that and some of the ways that it manifests
Luke Hoban
0:02:52
Yeah, so I think of infrastructures code is really about kind of applying software engineering principles that we kind of use in software development. When we're building our, you know, Python server applications or our JavaScript web browser applications, we use a set of sort of software engineering principles, we use code to write these things down. We use source control, we use IDs to get sort of completion and error checking, we use a whole bunch of different things that are sort of part of being able to scale up the complexity of the software you build, to sort of tackle complex problems in our application development. I think infrastructures code is really being that same trend, bringing that into the cloud infrastructure space, where there's all these amazing building blocks that are available from these cloud providers. But to really take advantage of them, you've got to bring them all together, build complex things on top of them. And to do that and scale up that complexity of what we do with our cloud infrastructure we really need to bring to bear a lot of those same kinds of tools that we use kind of for application development. And so so for me infrastructures code is really about doing that, bringing all those tools to bear and I think, you know, in the industry today, infrastructures code is sort of largely I'd say infrastructure is texts. sort of taking that very first step of being code and making it something you can write down in a in a file, you can put it into source control, you can version it in source control, that starts to give you some of these benefits of being able to repeatably deploy a thing and be able to version it and be able to have multiple people contribute to it. But we think there's so much more that you can do when you bring this idea of software engineering into your cloud infrastructure. And so that's kind of what we're focused on.
Tobias Macey
0:04:24
So can you give a bit more of the background of what plumie is, and some of the story of how it got started, and how you've got to where you are today?
Luke Hoban
0:04:31
So Blimey, we sort of think of as a modern infrastructures code tool. So really taking the ideas of infrastructures code and taking them to where kind of modern cloud is today, except from a few different perspectives. So one plumie, let's use existing programming languages, not sort of very constrained DSL is like JSON or yamo, or HCl, but real programming languages like Python, to describe your cloud infrastructure and as well as doing that and all this other software engineering benefits you can get to get from that, who really focuses on sort of some of the modern Cloud scenarios. And so things like the ability to work with serverless, the ability to work with containers to go to work with Kubernetes, these kind of technologies that organizations are moving increasingly quickly towards, as they sort of move into the cloud and making it really easy to take all the different building block pieces you need in all these domains and bring them together to support this cloud infrastructure that your applications need. There has to be as much of a divide between the way that you do your application development and the way that you do cloud infrastructure development, these things can now become more integrated, both because you can use Python for both, but also because the same team the same basic engineering principles can be used across the two in terms of the the kind of backstory behind it, I'd say, you know, from my personal perspective, you know, I, as I mentioned, worked on kind of application developer tools, work for Microsoft for for a long time, worked on dotnet, worked on Visual Studio worked on creating the TypeScript project at Microsoft and, and on some other tooling projects there and in so I I've spent a long time kind of building tools that enable application developers to scale up the complexity of what they can build and seeing what that enables, enables developers to do in the JavaScript ecosystem in the dotnet ecosystem, with Windows, etc. And so then, you know, when I moved over to kind of work in the cloud space, I sort of saw that there were all these great building blocks and the cloud providers, but the developers were still really struggling, they had to be very advanced and have a lot of very specialized skill set to figure out how to build the kind of things that people want to build on top of these cloud platforms. And so sort of, like the story behind bloomie was really seeing that there was this huge gap, that there was this inevitable shift towards the cloud for a whole bunch of great productivity and, you know, economic reasons. And yet, you know, developers weren't being empowered to fully take advantage of all that value that was there. And then there was a big opportunity to help with that problem and help with it in a way that we felt like we understood how to how to sort of bring developer tools and software development practices to this space.
Tobias Macey
0:06:59
And one of the elements that I want to draw out from there is this idea of empowering developers to be able to manage the infrastructure where for a while the servers and the infrastructure was the domain of systems administrators. And now it's largely being relegated to the quote unquote DevOps engineer where the intent is to try and bring the operational aspects closer to the software development aspects so that you have people working together and collaborating on that. And I'm wondering what your thoughts are on the background and the context and the specific skill set that's necessary for developers to be able to adopt to be able to effectively design and build out these infrastructures as part of the application lifecycle. And what you see is being the breakdown in responsibilities of developers versus operations engineers for being able to design and manage this infrastructure.
Luke Hoban
0:07:55
Yeah, I think it's, you know, it's great. It's great framing there. And I think there is, you know, there's a couple of big trends. We kind of see, generally when one is, of course, just more and more cloud adoption. And so more and more usage of these sort of cloud API's and things, but at the same time, we see kind of two layers on top of that. One is that, you know, as folks move into some of these modern cloud technologies, it means that there's sort of more and more cloud resources that they're managing, right. And so it's not no longer you know, few years ago, it might have been that you had a handful of VMs, they were managed by some systems administrator, and the development team, you know, either SSH in and did their deploy inside there or ran some very simple kind of ci process onto that. But the infrastructure didn't change all that often, you know, you added a new VM or a new database server or whatever, you know, once a year, you rarely kind of made changes to it. So it's a fairly static kind of environment in which your cloud infrastructure worked. And so you know, the sort of operation of that didn't have to sort of move at the pace of the application development team with the pace of the kind of business itself. And as we move towards a more modern cloud technologies I'd say that iteration cycle infrastructure, the speed at which infrastructure changes, as you know, has shifted fundamentally towards many layers of is actually iterating at the speed of the application development team. So if you look at technologies like serverless, or Kubernetes, for example, a lot of the infrastructure associated with those projects being deployed onto Kubernetes, Rondo serverless technologies actually has to change nearly as fast as the application code itself. So for serverless, kind of every time you want to deploy your application, you're actually changing resources in AWS, or for you know, Kubernetes. You know, you have to sort of write specifications of the sort of the environment variables and you know, context in which your containers are going to run and how they're going to be wired up into load balancers and services and things inside, inside Kubernetes. And so we see that more and more that ownership is sort of shifting closer to the application development teams just from a, you know, at the speed of iteration and the place at which the affinity is with the sort of ownership of the delivery is moving So that the application moment teams have to have a better understanding of some of these cloud primitives if they want to take advantage of the value of some of these sort of modern cloud technologies. And then the other part of that is just as that complexity rises, generally, even within the sort of traditional platform teams, or DevOps teams, or even sort of traditional infrastructure teams, the amount of cloud infrastructure being managed is just going up dramatically, both because of just the shift into Cloud. And also because of the, you know, shift towards these more modern cloud technologies, where there are more more moving pieces and more managed services and that sort of thing. And so that complexity leads to the need for more of these software engineering principles. So even those traditional DevOps are sort of systems administrator teams are finding that they need to bring in more sort of software engineering practices and controls into the way that they do their jobs because the complexity is going up there as well. And so both of those trends that you know, the traditional folks doing this work are finding they need more software engineering tools, and a new class of folks coming into the space from the development of the spectrum are kind of coming with a developer in And self injuring mindset, we think both of those trends are kind of leading to a need for this kind of tool.
Tobias Macey
0:11:06
Another element of that dynamic is the fact that infrastructure engineers and platform engineers are generally trying to optimize for stability and the sort of continuous operation of those systems. And developers are trying to optimize for rapid release cycles and rapid change. And sometimes that can come into conflict if the engineers are put in charge of managing the infrastructure because they might not be optimizing for some of the types of controls that the platform engineers are trying to incorporate and enforce. And so I'm wondering how they can work together effectively in this new landscape of managing the infrastructure as a software artifact and some of the ways that they can enforce some of the things like security policies or infrastructure consistency so that it's easier to manage in the long term. Yes.
Luke Hoban
0:12:00
is a really big part of kind of what we focus on actually with paluma. And I'd say there's two key ways that we think about that problem of that opportunity really, to empower kind of both of those teams to, to succeed to succeed at different goals you just had it at one of them is just the sort of core software engineering idea of being able to build reusable components abstract away, complexity and abstract away best practices and security and compliance things behind reusable components. And so one of the things we see a lot of teams who end up using pulu me doing is actually taking their platform team will build some of these components that have those best practices built in and have the sort of, you know, the security layout for that networking infrastructure or the Iam policies defined that they want to allow folks to use in different situations within applications. And they'll build those reusable components build the sort of packages and components that can be used by application development teams to deploy infrastructure into the database accounts and into the cloud environments managed by that that sort of cloud From team and so that ability to create sort of reusable components that abstract away some of those best practices is really important to the software engineering tool, generally. I mean, that's how that's how we have the, you know, software we have today. You know, like, Apple provides great API's for iOS that don't just give you raw access to the primitives of the device, but they also give you best practices around sort of security and compliance with the things in the platform. And so so here is the same thing. And then we, you know, we want both ourselves provide some of those libraries. But even more important, empower those platform teams to build those best practices into reusable libraries that they can then use to, to empower their application development teams to use those in the second part is you know that using those components is a good thing just to kind of for ease of use for not having to kind of copy paste that compliance criteria over but we really want to also enforce some of that and help to help those platform teams to enforce that anyone who does deploy into this environment must meet all of these compliance criteria, and so bloomie does also actually and New new capability that's coming with plumie. Two, which is launching very soon, is the ability to add policy as code. So not just to describe your infrastructure as code, but also to describe policy about your infrastructure, using code. And so that would let you say, nothing is allowed to successfully deploy with with pulu me inside my organization or inside these projects with the motorisation unless it passes all of these checks, and so checks that, you know, ports haven't been opened on my security groups or that certain images are only certain blessed images are allowed to be used inside containers, any of these sorts of criteria that you have within your organization that platform team wants to enforce. They can then enforce through through kind of this policy offering and so so we do see sort of multi multi layered approaches to this, but but all of it sort of fundamentally empowered by using real code at all these different layers and providing the tools for both that platform team and the kind of ultimate application developer
Tobias Macey
0:14:59
and where does that name come from?
Luke Hoban
0:15:00
Yeah, so it's actually, uh, you know, our founders, Joe and Eric, you know, worked at Microsoft as well for a long time. And they had a colleague there who was one of our early advisors when we started the company. And he unfortunately, passed away actually, but his last name was Bruno, and sort of as a tribute to him, the name is broom in Hawaiian. So he is his broom in Hawaiian. So I sort of attribute to him but also a name, which sort of stood out, you know, SEO for it is pretty good. So, you know, it kind of kind of caught caught our eyes.
Tobias Macey
0:15:34
And in terms of the overall landscape of infrastructure is code. One of the most notable entrance to that is terraform, which has grown a fairly large following. And then there are some of the platform specific tools such as cloud formation, and there's some measure of capability built into a number of configuration management frameworks, and I'm wondering if you could compare and contrast some of the capabilities of polygamy and how it fits into that overall. The ecosystem.
Luke Hoban
0:16:00
Yeah, so I mean, you know, done broad strokes kind of sit in the same sort of space and the infrastructures, code space as many of these tools and we're seeing overall like a ton of growth in that infrastructure is code space. And a lot of folks kind of moving from, you know, managing infrastructure by pointing click or by, you know, using a CNS SDK directly to to using infrastructures code, I'd say the biggest kind of difference for pulu me is, you know, this idea of bringing kind of real programming language real software engineering capabilities, and taking that infrastructures code thing kind of beyond infrastructures, text and towards like a real, you know, software based discipline. And so, so that's really the kind of core you know, technical difference, and it leads to lots and lots and lots of, you know, practical, pragmatic differences that folks experience when they're using it kind of just around, you know, having ID support and having the ability to debug and having to build a unit test and having the ability to package and version reusable components and build abstractions on top of those things. And so, so all of these different second order benefits you get once you embrace kind of software engineering are really the core things differentiate the day to day experience kind of using something like lumion. So think of those as sort of superpowers that plumie gives you to go beyond just the role instructors code, and what are all these things which are going to accelerate and enable your ability to really take advantage of the cloud infrastructure you sit on top of one thing that is kind of notable, though, is while plumie does give you these rich, you know, kind of imperative programming languages, it does still have the same kind of desired state model that many of these other infrastructures code tools have. So whether it's cloud formation, or R, or Kubernetes, YAML, or terraform, they all have this model that they're sort of a desired state that you specify with your your code. And the deployment, orchestrator, whichever one of those it is, is going to help make sure that your infrastructure ends up in that state. So it's going to try and drive from the state attend to the state, you want it to be in figure out the minimal amount of changes that has to make inside the cloud environment to accomplish that. So boom, has that same model. And so it's still is sort of declarative in a meaningful sense in the same way that these other infrastructures code tools that folks may be familiar with are the key difference, just being That you get to use a richer model for how you describe that desired state. And so you can harness some of those operating capabilities and harness it and tame complexity in front of that declarative model. But you still get all the benefits of sort of desired state models
Tobias Macey
0:18:15
in terms of being able to manage this infrastructure and control the complexity of being able to deploy it and enforce the different policies and figure out the different interrelations between all the services that you're trying to leverage. What are some of the common patterns that you've seen as far as being able to address that complexity and some of the challenges that come up because of the nature of infrastructure and some of the fundamental principles that exist in that space?
Luke Hoban
0:18:46
Yeah, so I think there's a couple of interesting things here. So one, I'd say is just the the overall complexity of the cloud platforms is pretty impressive. You know, there's just an enormous amount of kind of value in these cloud platforms and I often think of it like comparing it to others. Things which folks think of as complex like you think of like, the browser, for example, is a big API that has a lot of different capabilities and empowers a lot of different very rich experiences that application developers expose. And yet these cloud platforms have probably an order of magnitude more API surface area than the browser does, you know, just an enormously larger amount of capabilities and properties and API's, that that they expose. And they're growing just still at an incredibly fast pace. So you know, every year at reinvent, AWS announces another couple hundred new capabilities and and plus throughout the year, right, and so, so all these platforms are incredibly rich, incredibly complex API's that are growing incredibly quickly. And so one of the fundamental challenges is just Hey, how do you how do you even like, understand that whole surface area and provide access to it and provide, you know, simpler abstractions over it? And so, you know, so for me, does that a through just making sure that everything is available in a really canonical way by auto generating wrappers around the cloud platforms themselves? Based on the cloud providers, sort of schemas and models for their platforms, but then also making sure that you know, where there is specific complexity that we can add these libraries on top that sort of present that complexity in a fundamentally simpler way. So, for example, we have, you know, libraries, like, we have an AWS x library, which is a part of our crosswalk for AWS, that makes it just easier to work with things like, you know, ECS, and containers, a lot of folks work trying to kind of use containers on AWS, and it's quite hard when you just work with the raw primitives database provides you, you know, requires four or five pages of cloudformation, just to sort of bootstrap a basic environment for running a container. And so paluma can make that kind of a one liner, right? By abstracting away a lot of the defaults and complexity and those underlying pieces. And then progressively you can, you can fall back into all of the details and complexity of that as you want. But for the easy things, we can make that easy, and then you can sort of opt in to when you want to pick up more and more of the platform's complexity to take advantage of it, but the other sort of side of That is actually the thing that makes this, you know, a fundamentally more challenging problem, I'd say for infrastructure is just, you know, folks do typically want to tweak a lot of these little settings. And so, you know, compared with, if you look at, if you look at IO API's, for example, you know, Python has some really nice, you know, I O API's just for working with, you know, the file system. And it's, it's cross platform, you know, it's pretty rare that folks writing Python code need to reach for a Windows or Linux or Mac specific API, in terms of working with the file system, even though those file systems are fundamentally fairly different
0:21:33
underneath the hood.
0:21:35
And yet, in cloud infrastructure, we haven't gotten to that point yet where we kind of have any meaningful standardization of cross platform or lowest common denominator API's surface area that's broad to us, folks are still heavily specializing for individual individual services and individual cloud platforms. And so offering these kind of higher level abstractions is still meaningfully harder than it is I'd say for typical you know, Python machine learning frameworks. Or filesystem API's or what have you where it is, it's become pretty easy to to offer nice cross platform higher level generalized API's. And so that's a that's a key problem that we're working on is on kind of what are the tools that you need and infrastructure world to empower you to have these these generalized things, but still let folks take advantage of smaller level details when they need to why they want to really get a specific benefit out of AWS or out of a particular service. And so we have a few tools we've built to kind of help with that.
Tobias Macey
0:22:28
And one of the common stances of people who are using some of these, as you put it infrastructure as text tools is that they are more declarative, and that by using a non Turing complete language, you can avoid some of the complexities and problems that can arise as a result of the sort of big ball of mud pattern that can arise or some of the dynamicism that can end up being a foot gun in Python, and I'm wondering what you have found to be some of the potential dangers of using the Full programming languages to manage infrastructure and how pulu me is working to address those dangers. And then on the converse, what the outsize benefits are of using those full programming languages that would motivate somebody to want to use pulu me over some of the available tools.
Luke Hoban
0:23:15
Yeah, so one of my deep beliefs is sort of that we can actually get the best of both worlds here that we can get sort of all the expressivity of kind of full programming languages. But we can still get the reliability of desired state infrastructures code. And so I think the way that we do that is by kind of separating the two the the way that we author our infrastructure can be using expressive programming languages and expressive frameworks and components and packages and that sort of thing. But it can ultimately turn into something where that program, what it does is it builds up a model for the desired state. So when I run my plumie program, it builds up here's the desired state I want to be in and then compares that with the state that we are in and then figures out what changes that need to make. Because of that, we can still provide things like we can provide a preview when you when you go to run your painting. deployment, the first thing that Lumi does is it shows you a preview of what's going to change in your cloud environment. So you can review that you can say, Oh, I didn't expect that thing to change, let me go make sure that my code is correct. And so so we can still get that reliable thing where we can get a preview, we can diff between different states of our infrastructure. As I mentioned earlier, we can run sort of compliance validation on that before we deploy it. So so we can do a lot of things to verify and validate and have confidence in our deployments. Really still from that that declarative model. And so we don't have to give up the idea that we kind of have a fundamentally declarative approach. It's just that we gain stronger tools for authoring what that declarative approach is. And for me, the you know, the thing that I found is, you know, when folks talk about, you know, the benefits of it being just as a yamo document or a JSON document or something like that, that is really declarative, and you can just look at it and see exactly what it's going to be that I totally get the value of that for when you have 10 or 20 resources total. And you can kind of look at it, you can really understand it just by looking At that JSON file and kind of understanding, here's the full set of resources I have. But these days, you know, most the most the folks I see working in the cloud space have hundreds or thousands or 10s of thousands of resources that they're managing, both because of just the complexity of their applications going up, but also just the granularity of some of the building blocks they're working with going up, I mentioned that just using ECS. To deploy your container, you're gonna have 50 or so resources deployed in AWS just to do that. And so at that point, you know, having hundreds of thousands of lines of, of Jason to look at is no longer really the reliable and best practice way to to author anything, you're not going to really be able to get a clearer understanding of the havior of that piece of infrastructure by sitting there and reading 100,000 lines of JSON, until you're going to have to find ways just like we do with software engineering to take pieces of that, give them clear names, give them clear interfaces, describe the contracts they have with the rest of the world and then trust that the implementation of those has been tested and validated to meet that, that description and now go and use that. abstraction instead of the raw building blocks and copying, pasting everywhere. And so I sort of fundamentally think that for anyone working at a meaningful level of scale, and they think that everyone will be working at a meaningful level of scale with where the cloud is going, that the idea that it's going to be sustainable to just have giant JSON documents as the way we do, this just doesn't, doesn't resonate with me in terms of how I've seen kind of software engineering evolve over the last few
Tobias Macey
0:26:24
decades. And one of the common aspects of what you have built and what terraform is doing is this maintenance of the state of the infrastructure as it's been provisioned by those given tools. And I'm wondering why that is seen as a desirable or unnecessary aspect of the design of these tools for being able to maintain that statefulness as opposed to relying on the state as its represented through the API's of the different cloud providers so that it's still able to be a little bit more dynamic or able to more easily incorporate existing infrastructure without having to control it all from day one.
Luke Hoban
0:27:04
Yeah. So in the need to kind of maintain state is actually mostly rooted in just the cloud platforms themselves. Oftentimes, they don't actually provide access to all of the information that you need to create a resource. They don't necessarily give back all that information when they when you read the resource out of the cloud provider. And so because we have to be able to define the desired states to be able to provide this sort of reliable infrastructures code model, we need to know, you know, here's what I specified previously. And here's what I'm specifying now. And here's how those things are different. And unfortunately, we can't go and ask the cloud provider, what did I specify as the the inputs this thing the last time I provisioned it, because even though 80% of that is available, there's that is for from for many API's, and across all the different cloud providers, there is enough of that information that they do not provide back that unfortunately, we do have to maintain a history of kind of what did we ask for the last time and how is what we're asking for now we're going to differ from that and what might change Because of that, and so I think that's something where, you know, ideally, the cloud providers would evolve to where their API's did let you see what was the configuration that got used for every, for every resource being managed last time. And this is something that, you know, for things like cloud formation, they take care of this kind of internally inside AWS. And so you don't see it as much. It's why you kind of see it more in tools like terraform, or plumie, because it's managed outside of the cloud provider. But it the same concept has to apply for for those tools as well, where they have to track kind of what are the diffs in the inputs that were specified between different iterations of the deployment. And so it is it is something that plumie does need to do is manage that state and understand kind of what you deployed the last time and what state things are in. I think one of the things that bloomie does differently is by default, plumie stores that state in the plumie back end service itself. So you know, any user of Columbia when they get started don't really have to think about the state file at all. They don't have to think about managing it on their local machine or storing it themselves. It's just transparently handled by paluma. And then if they need to for kind of compliance reasons or whatever, take over ownership of that file and manage it themselves, they can do that. But we try to make that not something that developers really have to worry about initially when they get started, and the maintenance of that state does add a bit of complexity and sort of upfront planning in terms of how developers think about how to structure their infrastructure code, because of the possibility of having such a large amount of state that is tightly coupled. And so needing to determine what are the dividing lines between different aspects of the infrastructure, I'm wondering if you can talk through some of the design considerations that they should be thinking of and some of the useful practices that you've seen for ensuring that that statefulness doesn't end up being a liability instead of a benefit?
0:29:46
Yeah, so I mean, generally in terms of sort of, you know, how folks we work with with Blimey, sort of think about breaking up the infrastructure and things I take, for me, I often sort of analogize it to sort of the model was first Microsoft versus kind of debate kind of from a server application. development perspective. And it follows a lot of the same kind of lines of reasoning. So monoliths, you know, in all cases tend to be sort of easier to reason about up to a certain scale. And where we just have kind of one unit that's versioning cohesively, and can sort of have boundaries within it that are we still have sort of software engineering practices in terms of we have good good boundaries between API's inside that monolith, but they don't have to be versioned boundaries that are backwards and forwards compatible, and that kind of thing to cause roll. versioning is single unit, we're all you know, the the team is all working in a single code base, that sort of thing. And so, in some sense, with limits, it's easiest. Still to kind of keep things in one, one unit of deployment, as long as the team working on it is sort of working together and as long as it makes sense to be deploying that in a single version unit. But as teams grow and evolve, and as the software being deployed, grows and evolves, it often becomes valuable to split these things up into multiple units that have maybe different teams working on them that are delivering and versioning at different cadences. And that are ultimately going to have boundaries between them that are well defined interfaces that have that are, they're much more sort of fixed. In a sense, those those interfaces are things which have to have high levels of compatibility, because they may intermix in various directions with other components. And they may not even 100% know, all of their consumers have to have a very clean versioning guarantees around them. And so with bloomie, we can do all those same things you can break up our infrastructure into into multiple pieces that are owned maybe by different teams, or that kind of a written version at fundamentally different cadences. And we can define those those contracts between those layers. So I can expose a set of infrastructure definitions from a lower level stack, for instance, and then consume that from a higher level stack effectively define that API in between those two components. And so we see things like maybe that sort of core networking and security infrastructure is is one stack that is owned by a platform team somewhere and then versions fairly slowly, and then there's potentially application related stacks that include specific things About how that application gets deployed into the compute infrastructure or you know what the IM roles and policies related to that specific application are or maybe even some of the data stores that it might want to use, like, maybe it uses a queue or a dynamodb database or something like that, that are application specific. And those things are going to iterate more with the speed of the application delivery itself. And so give me one by that different team have that different cadence of delivery, but they may need to reference that lower level stack and understand, hey, what was the what was the network that I want to run in? What was the core security groups that I want to apply to run my compute within this environment? And so is that something that we support and and in general, I think people's intuitions from kind of the monolith first microservices lines of thinking and how that applies into the way they build their services and apply very, very cleanly into their infrastructure as well.
Tobias Macey
0:32:49
Digging further into the specifics of paluma itself. Can you describe a bit about how it's implemented and how the overall system is architected? And some of the evolution that it's gone through since you've read began working on it.
Luke Hoban
0:33:00
Yeah, definitely. So you know, the core of the the the tool is actually just a CLA tool vcli. And that poignancy ally, they sort of two deep things that it kind of wants to scale really well with. So one is with cloud platforms. So we support, you know, AWS, Azure, GCP Kubernetes, but also 50 or so other sass and cloud platforms, like if you want to work with CloudFlare, or you want to manage users and databases in my sequel, or you want to work with Okta, there's lots and lots of different cloud infrastructure you may want to interact with, and plumie has providers for all those. And so there's a lot of different providers, and we want to make sure that we have kind of a nice plugin model around how it's easy to build new providers and have those get consumed inside gloomy. And the other direction is languages, of course. So plumie supports today Python, JavaScript, and with two Oh, which, which is launching again, very soon where we're ga in our support for dotnet and go as well. And so we know we have a variety of different languages today. We're expecting that to keep growing over time. And so we want to make sure that plumie kind of scales well. Both on the cloud platforms and on the languages side. And so both of those, we have kind of a plug in model where that flew me see Li can can know how to launch a language plugin, you know, if users are putting the application says it's a no JS application, we'll go and launch an OJS host run their code, and then that host will communicate back to the engine, what resources are being defined by that code in a language agnostic way. And then where the engine will figure out what the previous state of that was, what the how that differs from the current state. And if it wasn't there in the previous state, we'll go and ask the cloud provider to create it. If it was there, we'll ask the cloud provider updated. And so that the plumie COI effectively just does this sort of orchestration job in between languages, which actually run the user code and cloud provider, resource providers which know how to provision you know, create, read, update, and delete resources inside the cloud providers. And so the architecture plumie is really an engine which sits in the middle and then a bunch of different language hosts for each of these different language ecosystems and then a whole bunch of clouds providers for ecosystems.
Tobias Macey
0:35:02
And I know that another element of the paluma ecosystem that is more focused on the business side of it is the managed platform that you provide for hosting the statefulness of those different deployments and also handling some of the collaboration capabilities. So I'm wondering if you can talk a bit more about how that fits into the overall lifecycle of working with plumie. And some of the ways that you're approaching the business model to ensure that this is a sustainable project?
Luke Hoban
0:35:32
Yes, I mean, one one key thing actually to note is that the plumie sat so apt up blooming calm as our as our console that basically provides users access to all the different deployments, they've done, the history of kind of what resources have been under management links into the cloud providers, a whole bunch of capabilities to as well as to driving your deployments from the CLR to actually understand the state of the infrastructure that you're managing. And, and that's available. There's a free tier of that. So every everyone who uses plumie actually access to those capabilities. But But like you said, it's also part of, you know, kind of where we offer paid offerings as well for teams enterprises. And so, you know, we see a lot of teams who go and use pulu me to deploy their infrastructure. At the point where they're sort of taking that into production, they they kind of find they want to be a part of a team of folks collaborate on some infrastructure, and they want to bring some more controls into that environment. So they want role based access control for the different users within their team and what role but what access those users should have. They want the ability to sort of automate things around the service. So web hooks and rest API's and that sort of thing. And ultimately, for enterprises, they want the ability to enforce policy organizationally across everything they're doing. And so those paid offerings in the in the service side really focus on helping teams and enterprises to apply plumie. at scale, we really tried to make sure that every all sort of core core capabilities of plumie are available as part of the open source project and as part of that free tier, including the back end service, but really, the things in those paid offerings are about enabling organizations. at scale to really apply plumie successfully as they apply it across different parts.
Tobias Macey
0:37:06
And given the fact that you have this many to many engine in terms of mapping the different languages to the different providers. So I'm wondering how you approach maintenance and feature parity across those different languages and ensuring that the providers are properly tested and validated to ensure that there aren't any surprises for people who are trying to use it?
Luke Hoban
0:37:30
Yes, I mean, this is I think one of the one of the interesting kind of core challenges of kind of what we do with them is that and I think we really do lean on the ability to sort of auto generate and drive off of sort of schematize models, both from the cloud providers and, and even within our language SDKs. So that we can sort of build, you know, instead of m times in, we can build like m plus n right things and validate those and make sure that they they work really reliably and and so we've put a lot of investment into sort of the tool chain that we use internally around Doing that around making sure it's easy for us to light things up for all languages at once. So when when a capability comes in, and in a cloud provider, for example, that automatically gets lit up across every language that Clooney supports, and making sure that you know, as we add new capabilities into the core platform into the core plumie programming model, that we have a sort of consistent approach to how we do that across all the different languages that respects the ways in which languages are different, and the programming models and different language different, but still is able to express all the same to the core concepts that we want to include the programming model, so that it's easy for us to add in new capabilities into all those different programming models in a natural way. Yeah. And so that's, that's sort of a key API design challenge. And luckily, you know, we've got a handful of folks on the team who have spent, you know, a good amount of time in their careers working on API design and in a lot of different environments. And so doing that here with pulu me is sort of a fun challenge just because of the sort of two dimensional matrix here.
Tobias Macey
0:38:57
And you mentioned, the fact that you're working to To maintain some of the idiomatic principles of the different languages, rather than just sort of spitting out a bunch of auto generated code for these different providers, I'm wondering how you are managing the sort of mapping of what those idioms are and the different languages to just the base schema that's actually driving a lot of this logic.
Luke Hoban
0:39:22
Yeah. So I think, you know, some of it was just for each one of these languages, we, before we bring up the language in the first place, we go pretty deep on experimenting with a lot of different API designs and how we could embed that sort of corporate Looney model into that language in a way which would feel really natural for the users of that programming language, but would also would also sort of faithfully and in a way, which is going to work well with illumi let people express what they want to and so so let's say that, you know, for each new language we bring up that's a big part of what we have to do is really spend the time kind of thinking about what it looks like to do pulu me in that language in a really natural way. And so yeah, so I'd say you know, that That is an important thing. Once we once we kind of come up with a core model, then we just need to make sure as we evolve that core kind of plumie programming model that we do so in a way, which respects the constraints that apply in each of these different languages. And in that part, that part was generally found to be a bit easier. But there is there's some work to do to kind of find an API design that's going to fit well in the first place. It needs to be language ecosystems.
Tobias Macey
0:40:23
And one of the main tests of the viability of any tool is how much the community rallies around it and how much community contribution there is to build out an ecosystem for that particular tool or platform. And one of the big successes of terraform is that it has grown that community and so there are a large variety of different providers for different platforms. So I'm curious how you're approaching the bootstrapping process of building out the ecosystem for plumie and some of the challenges that you're facing in terms of being able to build out some of that broad support that will helped to create a useful feedback cycle to bring more people in to continue growing that ecosystem.
Luke Hoban
0:41:07
Yeah. So I mean, I think one of the key things is, you know, pulling is also open source. And so all pretty much everything we've talked about here today is kind of open source components. So, you know, a lot of folks jump into that ecosystems in the various different open source projects, big contributions, everything from small things, to to significant feature capabilities. And of course, by setting up that sort of kind of core open source of the projects that that we contribute to also seeing a lot of other projects pop up, around pulu me to sort of apply plumie in interesting ways. So you know, some test framework, some some higher level libraries, various different open source projects built by folks in the in the broader ecosystem, to enable to take pulu me kind of even further until we do we do very much believe in kind of trying doing things in core to empower and enable folks in that ecosystem to kind of expand what they do. They've seen huge growth there. I think we've seen massive growth. Interesting usage and in the sort of contributions ecosystem over the last year or so. And so we're seeing great growth there. Of course, you know, some of these other ecosystems have been around, you know, terraform, as an ecosystem has been around for a while. And so there's a ton of great value there and a really nice ecosystem. And so one of the things we do there is, you know, we say, hey, because there is such a great ecosystem around terraform providers in particular, you know, we let folks kind of use those terraform providers as well if they want to. So the terraform providers, you can wrap those in kind of a paluma API, and now get that from your your Python or your JavaScript code. And so we don't want to just compete head on with the kind of terraform resource provider ecosystem is a great open source ecosystem there. We want to actually let folks take advantage of all the value being built in ecosystems like that from me as well.
Tobias Macey
0:42:45
And in terms of the actual workflow of working with plumie to build and maintain infrastructure, what's the overall process and lifecycle of the code and how it fits into the development cycle?
Luke Hoban
0:42:59
Yes, I think we See, you know, folks, when they get started with Bhumi, they, you know, download the plumie COI and they run plumie up to deploy some infrastructure they've authored locally on their machine. And so I think then they'll kind of iterate for a little while making changes that infrastructure, deploying it into their environment, as they're sort of doing development of a new piece of infrastructure. And then at some point, they'll decide that that's ready to kind of be something which deploys into a staging environment or production. And they want to have a reliable way to deploy that into typically move that into a CI CD pipeline. And so they'll move from running that plumie up, you know, locally on the machine during that plumie up inside a continuous delivery environment. And so plumie plugs well into sort of any kind of ci CD setup you have, we have kind of documented guides for doing that with with 10 or so of the most popular tools out there. But basically Now once you've moved there, now you're going to do something like either get push or some other kind of workflow, that where you push new code into some particular branch that's going to trigger a deployment into a corresponding environment, whether that's staging environments where your production environment or what have you. And so once folks moved in that kind of model, now, they're sort of doing proper kind of get ops or ci CD around their deployment. But they still may want to actually, as they're building up a new capability on that feature, they may want to spin up a dev environment, for example. And one of the things that the plumie makes really, really nice is in a sense, it becomes easy to spin up a whole nother copy of your infrastructure, or some subset of infrastructure to iterate quickly on potential changes you might want to make to that and so might spin up a new stack, that's for your, your development environment locally on your machine and make changes locally, verify those, see if it's doing what you want, and then open up a pull request or whatever to to contribute that as those changes back into the shared staging environment, for example, I went to so we see a lot of different kind of workflows around that core idea. But in general, there's that combination of using plumie as a deployment tool locally on your machine while you're while you're iterating. and developing capabilities and using it in ci CD for delivery into fixed consistent infrastructure that exists over a period of time.
Tobias Macey
0:45:09
And as far as the testing of infrastructure, what are some of the useful practices and patterns for being able to do some of that validation without necessarily having to deploy and destroy full stacks of infrastructure?
Luke Hoban
0:45:24
Yeah. So So we really see kind of three patterns that, you know, we've worked a lot of teams, I think, when when, when we talk about pulu, me to know two different teams. When we talk about bringing software engineering practices into infrastructure, I'd say one of the first things that comes to people's mind is Oh, does this mean I can test my infrastructure. And you know, today, the first approximation nobody doing infrastructure is is really testing it. And this is a big hole. And a lot of organizations feel a lot of concerned about the fact that they're not validating their infrastructure earlier. And so the idea that, hey, we can we bring a lot of these testing capabilities that we're used to using for application delivery and bring those into our infrastructure? This is sort of one of the first things that that comes to people's minds. And so we kind of see three patterns of how we can apply that with lumion. The first is just unit testing and sort of the traditional sense, which is writing tests that kind of validate individual pieces of your infrastructure, but without having to deploy it. So so we can validate that, if I provide these inputs to this component, it will create these resources with with these configurations. And so it's more about the correctness of the logic of my program, then of the ultimate desired state of my program. And so this is something that helps us tame some of that as we're increasing that complexity and, and moving away from a declarative way of authoring and authoring using some of these richer programming constructs we can we can validate the correctness of that logic using unit tests. And so this is the first thing that the teams reach for and is really kind of fundamentally something that paluma enables. That's kind of hard to do if you're not using existing programming languages. And of course, we can plug that into any programming languages, testing ecosystem. So you can go and use the Python integrated Python testing frameworks, Python unit tests to test your infrastructure using this and again, that that model doesn't actually deploy any resources to your cloud, it just tests the logic of your, of your code is correct. The second one is sort of validating invariants, that you want to be true. And so I talked kind of earlier about some of the policy enforcement we have. And some of that policy enforcement can also be used to validate just correctness in a sense of the infrastructure. So when you do end up actually going into putting infrastructure into a cloud, immediately before we actually deploy something, run some validation that we didn't violate an invariant, like, you know, we didn't open up a port that shouldn't be open, or we didn't deploy that image that shouldn't be deployed. And so we can run that kind of testing, which is sort of testing or catch just before we deploy it, make sure that we didn't do anything to violate invariants. And then the last is actually deploying whole copies of my infrastructure and really doing I think we think of it sort of integration testing, and, and this one is actually really valuable. I mean, ultimately, a lot of what you want to test Related infrastructure is fundamentally about the behavior of the cloud provider and whether you've wired things up correctly to get the behavior you want out of the cloud provider. And so some of the a lot of the validation that we might want to do of our infrastructure really does rely on spinning up the infrastructure. And so for that, you know, plumie provides some test frameworks and things which let you take some infrastructure, you have stand up a copy of it, run some validation against that to make sure it's behaving correctly, and then tear it down, and to do that sort of ephemeral environment testing. And one of the things we found there as well is that, you know that that approach generally is valuable. But it's particularly valuable when you do this sort of software engineering idea of building reusable components, because now I can build that reusable component with that well defined interface. And I can write a bunch of tests that sort of tests the parameterization of that interface. And so if I have a abstraction that makes it really easy to work with containers on, on AWS, I can sort of say, Well, here's all the different ways I can parameterize that I'm going to write a set of tests which try out you know, Have a bunch of different points in that spectrum, and stand up copies, validate that it works and tear it down. And so I can run, you know, 100 of these tests in parallel, like among those in a couple of minutes, the total cost of running that is sense, because it's only lasting of the infrastructure is only up for a couple of minutes. And I can validate that that component does what it says it does, even even to the extent of validating the correctness against the cloud provider. And so this kind of testing we find is really valuable, and really kind of ties back into that idea of building reusable components in software.
Tobias Macey
0:49:32
And then as far as the infrastructure is code space itself, what are some of the limitations of it in terms of being able to actually get fully working sets of applications up and running? And where do things like configuration management tools fit into this overall landscape? And what are some of the options for integrating them with pulu? Me for things like saltstack or Ansible?
Luke Hoban
0:49:55
Yeah, so I think you know, kind of addressing the second part of the question first, and then get into the first part and say you Today, you know, plumie can work with sort of the wide variety of different kind of compute primitives out there, everything from kind of VMs, to containers to serverless, what what have you and, and also combine those all really well. And I think, in particular, for folks working with kind of VM based cloud deployments, that's where they're, they're sort of looking at some of these configuration management tools to sort of do the guest provisioning. And to be able to consistently apply kind of that that same infrastructures code model, but to the things they need to run inside their VMs. So when we look at sort of containers, and we look at serverless, some of it gets a bit inverted, because we don't tend to use configuration management tools as much anymore once we're using Docker or serverless. And instead, sort of the cloud provisioning model that paluma tends to manage is actually the thing that contains that specification. So my Kubernetes deployment and my community service effectively are my configuration management. And that's something that I'm going to define kind of using pulu me. And so as we move to some of these more modern kind of cloud compute technologies, we find that a lot of the the sort of work to the instructors code work to manage that stuff. He's actually moving out of some of these configuration management tools and into the cloud infrastructures, code tools like polluting, but for folks who are working with VMs, you can certainly mix and match tools. And so at that VM boundary, you can sort of do whatever you want. So you kind of have the ability to connect user data and those sorts of things to bootstrap VMs and bootstrap into something like a salt stack or what have you. and higher level integrations, like we have this concept of sort of dynamic providers where you can provide custom providers effectively inside the plumie model that can do things like SSH into a VM and then run some some bootstrap scripts or whatever. And so lots of lots of ways to bridge between these two worlds. But we also see that generally a lot more of this, this workload is directionally moving into these cloud infrastructures to come tools and kind of modern infrastructures code tools, like
0:51:55
likely
Tobias Macey
0:51:56
and what are some of the most interesting or innovative or Unexpected design patterns or integrations into the broader Python ecosystem that you have seen your users use with plumie. In their infrastructure projects.
Luke Hoban
0:52:10
Yes, I've seen a ton of really interesting things. You know, one is, I think one pattern we've seen a ton of is actually folks moving towards manage cloud, so using AWS, or Azure or GCP, but also using Kubernetes. And so that combination of getting the best sort of managed services that are available inside these cloud platforms and getting the operational benefits of those, including have this in a managed Kubernetes clusters, but then being able to use sort of the standards based API that communities provides over compute manages the richness of that API and pulu me sort of really makes it uniquely easy to work with that combination of things and to take advantage of the best of both managed cloud and Kubernetes. And so I've seen a lot of folks doing really interesting things, combining those two, using those to kind of get the best of both worlds for managed services and Kubernetes. Other things I think we've seen that are interesting, one of the one pattern that I you know I'm kind of excited about is a shift from infrastructures code just as a tool for, you know, a human in the loop kind of tool where the user is going to make a discrete change and validate that change and deploy it to a model where we're actually building software systems that drive these deployment processes as well. And so, we work with a lot of companies and teams that are actually provisioning, you know, provisioning tenants, for example, in their back end for their customers or for internal teams within the organization. And they're doing that through automated processes. It's not like a human is going and saying, Oh, I'm going to go and provision this tenant to update these tenant. They actually have automation that's driving a lot of that. And so that requires a much more reliable tool chain around how we do these infrastructure deployments to be able to do that. But we're seeing that as software engineers are taking over and as the complexity is becoming higher here, and they're really looking as well for how do I turn this from a human process into a automated process at an even another level, and so cool gimmies fitting really well into those kind of patterns. And I've been excited to see some of the successes we've seen there.
Tobias Macey
0:54:04
What are the cases when plumie is the wrong choice, and somebody might be better served by either a different infrastructure as code tool, or just avoiding that overall approach and design pattern in general?
Luke Hoban
0:54:16
Yes, I think, you know, pulu me really does try to make a lot of things easier. But I'd say you know, if you only have a small number of resources, or if you know, if you're just managing a VM, and you're primarily going to be doing you know, kind of saltstack or Chef or Puppet or something for your provisioning, pulling, it could well be overkill, right, you might not need to sort of do some of the Taming of complexity, you might not need to bring some of the stuff when you're doing practices, you might honestly be fine pointing and clicking in a console for for a small number of resources. Or if you're using just you know, if you're going to be using Elastic Beanstalk, for example, and you just want to click a button to kind of stand up your beanstalk instance and now you're going to work with with their customized tooling for their platform as a service offering. You may not need pony to stand that And the incremental value around that may not may not be as significant. But we see as folks as folks, sort of the complexity of their their cloud infrastructure goes beyond 10s of resources, they, they're almost always going to be looking for some subset of the things that pulu me kind of brings and helps with. And so so we think kind of as, as they hit a certain level of kind of cloud complexity, they're gonna they're going to be looking for tools like,
Tobias Macey
0:55:26
what do you have planned for the future of the plumie, tool chain and the business itself?
Luke Hoban
0:55:31
Yes, I mean, we're really just getting started still, I mean, the company's been around for a few years and, and we've been growing really quickly. But we see we still see an enormous amount of opportunity around the core programming tool chain, and also around expanding from sort of just the core kind of infrastructures code tooling to provide software engineering focus tools for more of the cloud, you know, cloud engineering jobs, and so so we have a lot more we expect to do over the coming years. But we're really excited right now to just continue to be growing how People are doing software engineering for cloud infrastructure. And yeah, I think there's a lot of specific things there. I think, you know, we're continuing support more cloud platforms, canoes for more languages cueing support, more richness in the kind of high level libraries that we expose to users. So they can more easily work with a lot of these, these newer and more complex modern technologies. And ultimately, just making it more fun and easy to deploy infrastructure, making it something that developers are going to love working with instead of something they're going to fear working with. And so they put a lot of effort into just making the CLR user experience nice making the documentation nice, you know, these sorts of things to make sure that infrastructure can be a kind of lovable thing for developers.
Tobias Macey
0:56:38
Are there any other aspects of paluma itself, or infrastructure as code in general, or the benefits and integrations of using full programming languages with this space that we didn't discuss that you'd like to cover before we close out the show?
Luke Hoban
0:56:52
Yeah, I think it just there's so many fun, fun topics near one thing that we kind of didn't discuss that I think is sort of an interesting thing as we move forward is also kind of the way that the infrastructure deployments and the application deployments end up getting potentially merged together today, most teams really think about their infrastructure deployments and their application deployments as being two totally different sort of trains. When they make updates to their infrastructure. they deploy those through one ci pipeline. And when they make updates, there are applications that deliver those to another ci pipeline. And one of the things we're seeing increasingly is, especially again, around serverless, and containers and Kubernetes, that there's a lot of different ways in which the application code and the infrastructure runs on are getting more and more tightly coupled. There's dependencies on a managed queuing service or a managed database or a managed you know, Dynamo table or managed cache. The application code really wants to tightly coupled its versioning. And so to actually see a lot of interesting things happening around how those two are getting merged and, and the application delivery and the infrastructure delivery are kind of moving into a single unit in some places. I think that's a really interesting trend is something that pulu me is kind of It's been used for quite a lot relative to kind of where that trend is in the industry. But I'm kind of excited to see where that trend goes as well.
Tobias Macey
0:58:06
Well, for anybody who wants to follow along with you or get in touch or get involved with the project, I'll have you add your preferred contact information to the show notes. And so without we'll move into the picks. And this week, I'm going to choose an app that I found for being able to catalogue the different books that I have, because it's something that I've had in mind for a while. And now that I'm spending a lot more time at home, I'm starting to get involved with that. And it's just makes it very easy to be able to scan the ESPN code of your books and add it to your virtual bookshelf so you can keep track of what you've got and categorize them. So I'll add a link to that in the show notes. It's just called bookshelf. And so with that I'll pass it to you Luke, do you have any pics this week?
Luke Hoban
0:58:46
Yo give it kind of random one now. It's something I was looking at over the weekend that I thought was was really neat. Just a little service called go binaries.com and you know we plumie work a bit in the with go that sort of language. We use for, for building our CI and that sort of thing. And so go binary is just a little tool built by TJ Holloway, Chuck that, that lets you just serve go binaries from a simple HTTP endpoint. And so instead of you having to build installers and that sort of thing as a, as somebody's building little little applications or services, you can point folks at go binaries, and they'll do the build for the users platform and deliver it in just a really beautiful, simple little service. I think it's a fun thing, though, to go look at the code if folks are interested in kind of seeing a little bit of go code or they'll service and just kind of fun, nice,
0:59:33
nice little piece of software that they had that he built.
0:59:36
And actually over the weekend, I kind of played with kind of re implementing that just as a pure polygamy thing, just to kind of play with some ideas there. And I think it's just a neat little example to look at.
Tobias Macey
0:59:45
Well, thank you very much for taking the time today to join me and discuss your experience of building polygamy and working in the infrastructure as code space. It's definitely something that is very challenging and complex and something that a lot more people are having To get involved with so it's great to see more projects coming into the space. So I appreciate all your time and effort on that and I hope you enjoy the rest of your day.
Luke Hoban
1:00:08
Likewise, thank you very much for having me.
Tobias Macey
1:00:12
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