Chapter 1: What is the main focus of this episode on Rust adoption?
Hello and welcome to Season 6 of Rust in Production, a podcast about companies who use Rust to shape the future of infrastructure. My name is Matthias Endler from Corode, and today I chat with Kian Butler from CloudSmith about oxidizing Python backends with Rust. Kian, thanks so much for taking the time for the interview today. Can you say a few words about yourself?
Yeah, I'm a performance engineer and SRE at CloudSmith. I've been doing Rust in some form or another for the last 10 years, mostly as side projects. But I have been doing it professionally for nearly three years now. Working at CloudSmith, trying to build on the Edge team, where we work on our CDN and all that fun networking stuff. CloudSmith, we're a package management company.
So we do package management as a SaaS. We support like 36 different formats of packages for Node, Cargo, Python, all the big ones. We do public repositories, private repositories, and open source repositories. We're going pretty fast. We've got some big customers that I don't know who I can mention, so I won't mention anyone just in case.
Because of that, we process about 110 million API requests daily. That equates to petabytes of packages downloaded every day. A lot of that is done in Python right now. We have a very old Django monolith that we've had since day one, which is 10 years ago. It's grown. And as we attempt to scale it, we needed to find new ways to scale it. So we started looking at Rust as a way of
making it faster and more efficient.
Great. That means the monolith is exactly as old as your Rust experience was long. So it's 10 years for the monolith and 10 years of Rust for you.
Yeah, yeah. I hadn't even thought about it, but yeah, it's... Nice little commonality there.
And I could imagine you want to use CloudSmith in a situation where you have an organization that manages a bunch of packages, maybe a bunch of packages in different ecosystems, and you want to have a hosted version of that that is secure and safe, like we're talking about supply chain security. Or are there any other reasons for using CloudSmith?
Oh, 100%. Supply chain security is one of those things we're very big on, very focused on. It's not just though security. So if you run different, you could run multiple different formats of packages or just one format. You'd use us to be a proxy to your upstreams. So you could say, pull all your packages through CloudSmith.
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Chapter 2: How did Cloudsmith integrate Rust into their existing Python monolith?
The flow for uploading a package under the HUD is we take a binary and we store it somewhere. But the handshake you do with that and the metadata you store in that differs in each package, which means that you could go into our code base and go into the slash packages folder, and then you'll just see 36 different code bases in there. that are similar.
They have shared bits of code for logging and for metadata processing and tracking of events used internally. And all that kind of business logic that's shared. But each format is different and their code paths are different. So... We'll never, like, we could, like, sit down and very quickly scaffold out a brand new service in Go or Rust that hits those same things.
But you then have the weird edge case of, like, how does that interact with our processing of... our processing of SBOMs generation. And then we need to store that in a way that can be queried by our API to be displayed in our UI. And we also need to track all those data, all those bytes. You care about how many bytes are being downloaded.
We need to ensure that all that data is being tracked correctly. We're in that scale-up phase of startup life, so we're hiring, we're bringing on new engineers, but we're still a small enough team. So if you brought in... You bring in me. We bring in me. Lee, our CTO, made the joke of one day he's going to wake up and everything's going to be rust after hiring me.
And we laugh and it's funny, but we know it's not really going to happen. We're going to have... some core bits that are Rust, but they're still going to be that core Python code that's not changing. Because everyone in our shop knows Python. We have a couple people who know Go. We have me who knows Rust.
We have people willing to learn and who have tried Rust and Go at different times, but they're not like, ready to jump in on a project and start developing today or tomorrow.
Right, but also, even if you were, let's say, an expert in Go, it would be harder to integrate Go into the project because Go has its own runtime. It has... a garbage collector, and you could do so by using the network boundary, but not necessarily integrating it into the existing project as you could do with, for example, PIO3 or so.
100%. And we have actually experimented with Go, and that's where it ended up. So we've moved logic for doing specific things out into a Go microservice previously, and Nothing core to that business. It was specifically supporting for one format and for scaling that format. And yeah, we couldn't... It's nice. It works. It's there and it's solid.
But it is a separate microservice and it goes against that belief we have that everything should be in the monolith. This is one of those core tenants we have that we should scale our monolith. We should... focus on making sure code is in the monolith.
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Chapter 3: What performance issues led to the decision to use Rust?
I sat down and I just started looking at those traces, started looking at Datadog, started looking at where the bottlenecks in our service were. We had load tests running. We were getting information back about what was slow, what were our slowest endpoints, all that kind of stuff.
So the things that came out when you look at that data was we would sit waiting on IO, and it would be serialization. These were two of our biggest things. The IO was two different types of IO. Our database, we query the database a lot, probably too much, but we do it. Eats up a lot of resources. The other side is the network.
So we call out to upstreams like PyPy and Cargo to pull in information. And then we have the inbound requests. So that's requests from our customers to us. And how many requests per second can we pull in from the network and process concurrently? The other bits being serialization, that's serializing large JSON payloads, large XML payloads, and that kind of stuff.
So we sat down and said, how can we go about fixing this? And it wasn't a one shot of like, we need to fix it all at once, or we need to roll everything out, switch everything up at once, or let's build it ourselves. We try not to be a shop that suffers from not built here kind of thing.
We like to use open source software where possible or use SaaS where possible because there's only so many people we have. So I started Googling because I knew a solution to the JSON serialization already. Back in two jobs ago, back when I worked in video games, we had a very large logging pipeline where we would serialize everything to JSON across the whole fleet.
And so we were also a Python shop, and I was working on the metrics team. And we rolled out a logging change that switched how we serialize JSON in all of our microservices with a Rust library called orjson. Oh, yeah. It's a great library. Well, it's a Rust library and a Python library.
It's written in Rust, and it's got nice Python bindings that look similar enough to the normal ones, the normal JSON Python bindings. So I knew from then that the speedup Varies somewhere between 7 and 10x, depending on what you're doing and what it looks like. And I know that when we did the change in that company, I saw...
about a 1% to 2% change of CPU usage across our data center over a couple weeks. It takes time for changes to go out, but we definitely saw improvements. And at that scale, it was really important to kind of like, you get a lot of, those small gains, they really add up over time. So I reached for that library because I had such success with it before.
And when we went to reach for it, it turns out Django already has a wrapper for it. It was even easier than that. So we installed the Django or JSON serialization library. And it swapped out our JSON serialization, which is just the normal Python JSON serialization with a Rust-based one. We then had to go through all the code base and find every place we imported JSON and replace it with orJSON.
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Chapter 4: What challenges did Cloudsmith face when transitioning to Rust?
it's not that we have one caching mechanism, it's that we have different caching mechanisms. So we were using the Python caching library for in-memory cache. And then we were using our memcache with our database to cache responses from the database. So these are actually two different caches. The memcache one is just... could we stop ourselves from going to database?
And we would totally check that on every request. So if we had done a very expensive DB query, it should be in that memcache. So on the retry, it would come from the memcache. What wasn't being cached were those pure functions we were running inside the monolith that were in the Python cache.
Got it. So the new bottleneck right now is between the network layer, which was your WSGI and the Django monolith. There's where you lose a lot of the performance now.
Yeah.
Yeah, and my goal was something we're still working on, was I wanted to be able to do request cancellation. So I wanted to be able to say, that's timed out upstream, I want to cancel it. Something I had previously done in a Tokyo service, so kind of was like, totally, let's do this. So I sat down to try and figure out how I could map a Tokyo request managed service to our Whiskey app. And...
It was, and I was reading PyOtree docs and I was playing around with a library called Py, no, not Py import, Rust import, which lets you like very quickly write PyOtree bindings for your Rust libraries. You can get like a very rough and ready code in like 20 lines with some, not decorators, that's Python, with some macros.
And you can have this very rough importing of Rust code directly into your Python code without a lot of overhead. Great for prototyping. I had found some places where I thought I would probably change this if I wanted to bring it to prod and just use Pyotree for creating the interface exactly as I wanted to. But it was definitely great for prototyping.
But saying that, while prototyping, I started looking at prior art. And I had found someone had this idea already, which is, I want to say the best thing about open source is sometimes you go and look and say, has someone already had this idea? And more often than not, someone has.
Yeah, and also you could have gone and completely ignored that and not have done any more research and you would have that liability on your side. Whereas now you looked at prior art, as you said, and you found a thing that someone else worked on before. So that also shows that you took a very level-headed approach to that.
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Chapter 5: How did the team handle the integration of Rust with existing Python code?
So there might be things in your business logic or timeouts with upstream, which mean that they drive up the P90 or P95 signal but overall this is also a thing that you see a lot with replacing code with faster code on the backend side is if you do it right then the outliers become more prominent right
Yeah, no, 100%. We were definitely seeing that where it was these very slow paths that were blocking us were still the slow ones. But the very quick paths, they just became quicker. And there was a lot of differences in how UWSKI and Gradient were configured in those early load tests that I now know were silently masking different things about.
They were handling switching context differently, how thread management worked. So the memory footprint was amazing. little more stable in one while it correlated to workload better in the other. That's got good and bad. It meant that previously we would have like the memory and CPU would stay flat.
But now like as requests went up, you could actually see the CPU was going up and down because we were doing more work. And we were like, that's a good signal for us scaling now. We could use that to do some auto scaling where previously we couldn't do that auto scaling.
Yeah, because you could never go down to zero.
Exactly, yeah. So we sat down and we drew up a testing scenario, like some numbers we wanted to see, some testing we want to do. Which parts of the stack could we try removing now that we just, and could we just replace it with Kranian? So we did a lot of different load tests to the point we actually managed to bottleneck in the load test tooling.
We hadn't scaled the load test tooling up high enough that it could push enough throughput in one of our tests that we needed to step back and change the load test tooling out.
for, we were previously using Locust, which is a fantastic load test tool for where you manage stuff in, you write your load test in Python and you can, and then you spin on top lots of Python workers that are managed and it does the load test from different places. But those workers were becoming our bottleneck. So, well, they're not really a bottleneck.
How much money we were willing to spend on those workers became the bottleneck. Like how many workers could you spin up for a load test was the bottleneck. So we switched out for a tool called Goose, which was a reimagining of that in Rust. Managed to push for the same amount of workers.
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Chapter 6: What are the advantages of using Rust over Python in this context?
As I've noted, I work with some of the best and worst clients. They do retries. They expect really good responses. But I don't own the API contract on them. I have to just follow the API contract. I would love to say that we as an industry should be following the standards, being so strict to them.
And I can totally see that if I look back at me five years ago, I would be there shouting, no, no, follow the standards. We should make everyone who doesn't follow the standards feel the pain. The issue is there, that's a lot of people. That's a lot of pain and it's not something you can fix overnight.
Like we, I know because I work in a package company, a lot of people run a lot of different versions of the same software. So even if like we started making tools stricter, every, everyone on December, on February 28th decided to do one launch where everything switched to strict mode, they
the in every library we don't have to get that rolled out to every version of that software it's not going to be it's going to be a painful rollout you need to have a level of permissiveness in the clients. But saying that, I don't want a default to be permissive. The default should be perfect. It should be the best way a client should run. The client should have timeouts.
It should have sane defaults and should follow the standard. But when you run a legacy system, you're going to have a lot of weird legacy issues and you need to be able to flip those switches off. to main that you can enable these things. Otherwise, you're going to end up with a lot of duct tape around your very strict system to flip those switches off.
Yeah. Be very strict initially and then lower the guard. Yeah, exactly. Now, when you look back on the project, what would you say were your key learnings? I'm talking about things that you would have done differently, but also things where you believe Rust is a good fit and How did that project go? Maybe you can summarize it in a few sentences.
Yeah.
The project could have gone a lot better. Like, it's still underway. We're using it in specific environments now. We haven't rolled out 100% everywhere because of these weird edge cases we found with Docker. And the other issue we found was about connection management to our database. We were...
It's a big problem where you need to do some upgrades, which means we've held off and we haven't got there. And that was the biggest things about the project that was the unknown unknowns. We sat down and I keep saying we, there was maybe me, a principal to review my work and a manager to like sign off on it.
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Chapter 7: What lessons were learned from the Rust integration project?
So we had to read the docs and add those optional args. That was not a big change. It took maybe an hour of my time to just do that. And that was fine. And in saying that, it came with improvements. It came with cache improvements and all that kind of stuff. So taking in those changes was good. It's obviously feature changes, not bug fixes. So I'm happy to take that stuff in. But when I...
We're trying out more Rust and I'm bringing more people in to look at Rust who are coming from a Python world and coming from different worlds. And they look at a lock file and they say, why are none of these things stable? I have to have that conversation with them about why we're still using pre-release software and why it might be years before that pre-release software comes in.
And I don't think it's a problem you need to fix, but maybe it's a problem of education. And how do we talk about the V0 of packages to make people understand that this is, should this be production or should this not be production? It's not, a V1 isn't a signal that this should be production or not. It's just a signal of stability of the API.
Do you think you will use Rust in 10 years?
I hope so. Like there is an answer of I hope so. I think languages change a lot and the language ecosystem change a lot. I didn't think 10 years ago I'd be still writing Python or JavaScript, but I'm still writing Python and JavaScript. But you look at them, and they're a lot different to the Python and JavaScript you wrote 10 years ago. So I think Rust is here to stay.
I said earlier, it's in the Linux kernel now. It's in low-level libraries for Python. It's in UV. It's in TI. It's becoming a core part of our industry. But how will I be writing it or will someone else writing it? I don't know. Maybe we'll have got to a point where we have saturated the amount of rust we need to write and we can use... higher level tooling built on top of that Rust?
Could we have a language that's less verbose than Rust that gives us the same memory safety? Could we take the lessons we learned from the borrow checker and apply that to a language that looks something like a Python for business logic? and call in and out of it. And maybe that's better for us.
Maybe that's actually what I want is a language that takes all the learnings from Rust and takes the stability from Rust, but is a little friendlier for newcomers or a little easier for people fresh for graduates fresh out of college to get started with without feeling like they're writing a systems language. Because that's something you always hear. Rust is a systems language.
It's for systems programming. It's for systems problems, which isn't true. You can write anything. Rust is a language. It's a tool. You can do whatever you want with that tool. I've written business APIs in it. I've written load balancers in it. I've written CLIs in it. It's great for all of those things. And we've learned a lot from it that we could apply to other places.
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Chapter 8: What is the future outlook for Rust in Cloudsmith's technology stack?
It's such a nice way to learn. And I think we have to keep focusing on ways to make it easy to get new people into learning the language, to make it a better language, and to make people not think of it as a fad or... a systems programming language. We have to like focus on that path for beginners. Tools like Clippy have done massive improvements there. Like, it's more than just a linter.
It's a tool for helping you learn how to write good and idiomatic Rust. Like, And when we focus on tooling that's natural to humans, I think we just come up with a better language. And I think we have to keep that in mind when we develop Rust. It's tooling to make you as a human enjoy writing Rust and make sure it's not a pain.
Where can people learn more about CloudSmith?
So cloudsmith.com is our website. If you want to use CloudSmith or think that you need better package management, check it out. If you are interested in joining us, we are always hiring. My team is experimenting with Rust. So if you're a Rust developer and want to write some Rust in production, reach out. reach out to me. I'll get my email dropped in the show notes so people can reach out.
And if they want to just talk about CloudSmith or package management or Rust, you can also reach out.
Amazing. Kian, thanks so much for taking the time for the interview today. Thank you. It's been a very pleasurable chat. Rust in Production is a podcast by Corot. It is hosted by me, Matthias Endler, and produced by Simon BrĆ¼ggen. For show notes, transcripts, and to learn more about how we can help your company make the most of Rust, visit corot.dev. Thanks for listening to Rust in Production.
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