The goal of a developer retreat is to stop what you are doing for awhile and explore something new. This usually requires a shift in mindset, and the biggest shift is to suspend the focus around productivity and urgency. It’s important to give up the idea that “we must accomplish something in an amount of time.” Only with the sigh of relief that comes from liberating yourself from goals is your brain allowed to float to the most interesting places.
My friend Jeremy Cerise, who comes to most of these retreats, said that he had become re-interested in the Rust language and wanted to explore it. We had touched on Rust a couple of years before, but that retreat was invested with urgency and goals and Rust ended up feeling too complicated and cumbersome. I was skeptical, but I’ve found that if Jeremy is interested in something, there’s probably something there, so Rust was worth a second look.
We had both gotten Covid at the Winter Tech Forum in March, so it seemed like we could probably spend time together reasonably safely.
My initial plan was to break Rust up into small topics and create exercises around those topics, as if it were a framework for a book. I don’t want to write a Rust book, but I thought it might be helpful for someone who does.
That idea went out the window as soon as I started reading the Rust documentation/book, which you can find here. Although it is definitely designed for experienced programmers, I found the thinking and writing in this book incredibly clear and compelling, and whether I ever do anything serious with Rust I want to read the book just for its insights about programming.
Rust itself is a bit of a conundrum to describe. Chapter 4 of the book begins by stating “Rust’s central feature is ownership.” Basically, this means that Rust is designed around not having a garbage collector. To do that, it provides as much language and compiler support as possible to guarantee that you don’t mis-manage your memory, because you’re doing it all by hand.
This is what can seem weird at first about Rust. Garbage collectors do a lot of valuable work for the programmer. They liberate programmers to focus on the problem at hand rather than being forced to pay attention to low-level details. There is runtime overhead, but that overhead almost universally pays off in the form of greatly-reduced programmer effort. Giving up a garbage collector feels like a significant step backward.
This focus on low-level efficiency pervades Rust. Any Rust code you look at leaves a metallic taste in your mouth of being right next to the hardware. That awareness of hardware is ever-present, and for that reason I’ve started calling Rust a “very-high-level assembly language.”
The “very-high-level” part is compelling. I don’t want to write in assembly language or anything like it (I spent the first several years of my career writing assembly for embedded systems). But if I must, I’d rather have the highest-level tools helping me. I don’t think I’ve seen anything that comes close to Rust for solving the problem of writing low-level code.
Both Jeremy and I have strong Python backgrounds, and we both became fascinated with a problem that has plagued the Python community from its beginning: low-level extensions. Python was designed to be able to call C components, but this was always messy and complicated. Over the years there have been many attempts to solve this problem, some better than others but none I have ever looked at and felt no intimidation.
So we wondered whether Rust might be a good solution for speeding up slow parts of a Python program. We started with this article, but couldn’t get it to work because of what turned out to be a simple renaming problem that doesn’t seem to be mentioned anywhere. Upon giving up, we discovered PyO3, which beautifully solves most or all of the setup for interfacing Python and Rust. If you want to write Rust extensions for Python, PyO3 appears to be the nicest solution.
We then turned our attention to Go, which also has nice potential as an extension language for Python. When we looked for something similar to PyO3 for Go, there appeared to be several abandoned projects. This one looked the most promising:
However there were statements like This is a newly-improved version that works with current (e.g., 1.12) versions of Go. (the current version of Go is 1.15—1.12 was about two years ago). Also: Limitations: Windows completely untested, likely needs something special. (Windows dwarfs every other platform out there. You’ve got to support it).
Has the Go community decided their language wasn’t worth adapting to Python? Maybe we didn’t look hard enough? Then we remembered gRPC, Google’s super-performant remote procedure call (RPC) system, which they use virtually everywhere and which apparently supports billions of calls per second across Google.
I’ve always been fascinated with getting different languages to interact with each other. It seems very powerful to be able to use the best features of each language. I first encountered the idea of RPCs when I was on the C++ standards committee as the CORBA initiative began. A core part of CORBA was the goal of making language-agnostic function calls across networks.
In most RPC protocols you describe data and functions using an interface description language (IDL). An IDL is like a restricted programming language that can only describe data structures and function interfaces. You run the IDL through a generator that produces code for your specific programming language. You then add application-specific code and deploy the result. Using a common format for interfacing greatly simplifies the process of making RPCs.
The Object Management Group that created CORBA was (at least at the time) bogged down with bureacracy and was controlled predominantly by the larger organizations that could pay the costs of being on that committee. The focus was on the specification and you had to implement it by hand, which wasn’t easy. My first experience of creating a CORBA system didn’t happen until it was available (and easy) on Python. The specification-first, design-by-special-interests approach floundered for a long time and never gained traction, despite huge investments in creating and promoting it.
Other RPC systems learned from CORBA’s mistakes; in particular the need for implementations and supporting tools. We saw solutions like XML/RPC which worked nicely but had limitations and enough performance overhead that you needed to balance the cost of making calls over the wire to the benefits of the remote call.
There’s even a system being developed called ALPS that uses a meta-IDL to take a spec and generate different IDLs for systems like GraphQL, gRPC and REST:
Rust or Go?
Ideally, calling overhead should not be a factor in deciding whether to use remote procedure calls. And consider Google’s massive system: tiny amounts of memory or delays could produce immense impacts. They were compelled to make gRPC as efficient as humanly possible. If overhead is a non-issue, calling from Python to another language becomes a much easier decision. But is this the case?
This article shows that Rust is at least 30% faster than Go in all their testing algorithms, and sometimes much faster than that:
The gRPC approach is a little more complicated, as well, because you must start a server in another process to provide the gRPC service in Go. However, if Go has features you want, even with the speed differences with Rust, it could still be so much faster than what you were trying to do in Python that it might never be an issue. If you already understand Go and are comfortable with it and don’t have time to delve into Rust right now, then Go and gRPC will almost certainly solve your problem. But if you know from the start that squeezing out every drop of performance is essential, then you want Rust for your Python extension.