Throw away code
There’s an ongoing discussion about what makes Python better prototyping language than Rust (with Python being probably just the archetype of some scripted weakly-typed language). The thing is, I prefer doing my prototypes in Rust over Python. Apparently, I’m not the only one. So I wanted to share few things about what makes Rust viable for these kinds of throw-away coding sprints, at least for me.
Sometimes, our goal isn’t really to write perfect code that is performant, correct, handles all kinds of errors sanely, has great UX and is maintainable. These projects are what we are proud of, sure. We pin them on github profiles. We write blog posts about them. We write whole handbooks of best practices how to do them.
But sometimes we just need to throw something together really fast and don’t care about the quality as much. That kind of bit of cardboard and huge amount of duck tape thing. These include:
- Single-use debugging tools („I need to throw 10k of these weird requests at the server to see if it triggers the bug. It didn’t? Ok, let’s try something else…“)
- Searching for a counter-example to a claim in a scientific paper („I can prove it’s a counter example once I have it, so I won’t need the code any more“)
- Processing bunch of data just once („I wonder how many of these
.txtfiles have broken unicode in them“)
- Figuring if something has any chance to fly at all, before committing to it („Could I distribute the changes as compressed binary diffs, or would that be too large?“)
- Demonstration purposes („We would like to build something in lines of this, but, you know, actually working“)
Of course, there’s a lot more. I’m not even sure if there’s more of the „proper“ coding or of this „throw away“ coding. Except that we don’t really brag about our throw-away code („Look what terrible monster I’ve stitched together during the lunch break“), we don’t write tutorials how to write them much, etc. So this is exactly the kind of blog post we don’t write 😈.
Instead of writing something proper this time, we are going to talk about how to write terrible code, but fast. We have decided to make an explicitly sloppy job of this one and admit it to ourselves not to feel ashamed of it later:
- We want to spend as little time on it as possible. Just do it, throw the code away after it had its use and move on. This one is going to be over by lunch time.
- We don’t care about performance that much (as long as it finishes running before the lunch too).
- We don’t care about handling all corner cases, only the ones we actually encounter in the data.
- We don’t care about documentation or readability.
- We don’t care about tests, provided we are confident enough the answers are accurate enough.
- Actually, we don’t really care at all…
Note: make sure not to let anyone put this into production 😇. If you don’t delete it, make sure there’s a comment on a prominent place warning people not to use it.
Why do people think Python fits here better than Rust
The thing is, Rust makes us care. That’s one of the points of Rust. It’ll complain that our code is not production quality and that we need to do better to save on the pain down the line. Its type system can be a real prick in insisting on little details, like that ints and strings are not really the same thing and that there’s a difference between owned and borrowed thing and… Well, you know, all that stuff. Rust wants you to make good, proper, maintainable code.
On the other hand, Python doesn’t really insist on anything. Therefore, it is easier to not care in Python.
My own experience
I know a bunch of programming languages and reach for the one that I hope would suit me best in the given time. So, for some really simple things I simply put together few lines of shell (and some slightly less simple ones ‒ I’m ashamed to admit that some 1000 lines long shell monster kept running in real production for years ‒ but it did run). If it can be done by 2 or 3 ugly pipelines, it’s fine.
Over the years, I’ve used Perl a lot (that one doesn’t care if it’s int or string… no, correction, in Perl everything is a string, ints just don’t exist. Well, kind of). It’s probably the language designed for throw away coding. I’ve done some Python too (that’s like Perl, but with proper objects in it, and everything is a dictionary there).
But recently I’ve noticed that if I try to do a similar thing, I do it faster in Rust. Not that it runs faster (well, that usually too, but that’s not the point), but that I’m done with the task at hand sooner and with slightly smaller amount of cursing.
This certainly is in part because I’m more proficient in Rust than in Python. It’s also because the Rust mental model is closer to how my brain works than the Python one. Your mileage will vary ‒ if you’re a Python matador who’s been coding in it for decades and are just learning Rust, you’ll certainly do it faster in Python.
But also, there are some tricks you might employ to do these things in Rust
faster (that is, faster than you do now, not necessarily faster than in
Tricks for faster coding
Rust is known for its slow compile times. Python has no compile times. If you have to wait every time for the compilation just to have a bunch of errors thrown into your face, it’s going to slow you down. Especially because Rust likes to throw bunch of errors at you every time you try to compile it. Rust is known for its great error messages, so it wants to brag how good they are by using them a lot.
You can, however, notice that you don’t really need to build and run every time. That you often just want to check everything is on the right path. For Python, you do need to actually run the thing (because Python doesn’t really have much of a compile time so it likes to throw the bunch of errors into your face at run time), but Rust is the language that „if it compiles, it’s correct“. And by complies, I actually mean mostly type-checks.
What does this all mean? You can check out:
rust-analyzerlanguage server. You’ll be getting red squiggles in the editor instead of having to compile. It’s not perfect (sometimes the list of errors is different, sometimes it just gives up on that particular project), but it’s getting better and it points out most of the errors without any compilation at all.
cargo checkperforms just the first stages of compilation and will stop before codegen. It means it doesn’t produce anything that could be run, but it is so much faster and provides the bunch of errors we so much want to have.
- You can let
cargo watchkeep recompiling the code asynchronously in another terminal. I just glance at it to check if there are any errors around, but I don’t wait for it ‒ at worst, the list of errors is one iteration outdated. It can be used for other things, like keeping the documentation of the current crate up to date, or having a head start at compiling the executable, or even having all the tests being re-run on each save (I’m getting off topic here; we are being sloppy here on purpose, so what tests are we talking about?)
These don’t make the compile times shorter, but it eliminates the waiting for them from the hot coding path. It still takes some time to compile (especially if you have a lot of dependencies and do a clean release build), but that doesn’t mean it has to slow you down.
Embrace the type system (and borrow checker and all of these things)
After some time working with Rust, one learns to lean onto them instead of fighting them.
This is where most of my own speed up comes from and what I miss about Python. When I want to know if my code is working, I actually have to run the Python thing and feed it with data. Which means I either need to set up a smaller input or wait for the whole thing to get crunched, only to have it explode on some typo or switched order of parameters after 5 minutes of running. After 10 iterations of running the Python code (each crashing later and later in the code), it finally finishes. By that time, I’m no longer confident it does what it should, after all these retries, so I go back and have to figure a way to double-check it.
In Rust not only I don’t have to run the code until it is almost finished and even when I feed it the whole input (which I usually do), it’s usually faster and it runs to completion the first time. I also can move through the code much faster. With Python, I stop to check the documentation, think about what type goes where, etc, exactly because it’s so painful to find out only at runtime. I need to be careful while writing the code. With Rust, I just type the code, get the red squiggly, fix it and move on. I outsource that effort of checking if these things click together in any meaningful way to the compiler.
This is kind of in the theme of „hurry slowly“ approach. By making sure everything has the right types and aligns well, it makes each iteration slower. But it also makes it possible to have much fewer iterations before the whole thing works well enough.
Also, don’t fall for the impression that throwing
unsafe in there to bypass
some of the checking will save you time. It won’t. It’s a trap. If you don’t
know for sure that you need it, then you probably don’t and doing
is a lot of work. Doing it wrong is easy, maybe easier than doing it safely, but
you’ll pay for it later on, when trying to figure out why the thing does
something arcanely weird. If you put any non-trivial
unsafe in the code,
you’re risking spending days and nights in front of a debugger. The checks are
there for a reason.
Take the easy way out
I don’t say to clone everything. Even in prototyping code, I often take
as parameter if it’s just „looking at it“. But I do so in the obvious, trivial
cases. The ones I don’t really need to think about any more.
But if you ever find yourself thinking about writing any kind of
-> impl Iterator<Item = &impl Display> + '_, just stop and throw a
Vec<String> in there. Where you would care about limiting the number of
allocations or conversion in true production code, or would design the right
schemes to borrow things, shamelessly let the computer do a bit more work to
conserve the time and mental energy. Don’t feel bad about not putting lifetimes
RefCells if it makes you move faster. That may mean
the design is sub-optimal, but if it works…
After some time of Rust coding, you get the itch of „this really should be possible without this one particular heap allocation“. Nurture that intuition, it’s useful one. But when on a speed run, make sure to set it aside.
But also do listen to Rust telling you the design sucks too much. If the code
gets too infested with
RefCells or other smell indicators to the point where
it’s impossible to see through it, it’s a signal it’s not well thought through
and that you might not know what you’re trying to do. Sure, you could force your
way through all that, but it would probably produce the wrong results anyway.
The goal here is not some abstract elegance, the job needs to be done fast, but
it’s often faster to make sure one understands the dealings first than to write
it without the understanding. Rust seems particularly good at hinting at the
places where the design is just lacking.
But of course, the balance here is different between production and throw away
code. Let yourself wade through some amount of
if you see why the design is suboptimal but it’s still less work to write. The
trade off is different for this and for a code that’s supposed to be around in 6
months or 6 years.
Other shortcuts one can take:
- Relax with the error handling. I personally avoid
unwraps for user errors even in this kind of code, but don’t hesitate to just put
anyhow::Erroranywhere and let it bubble out of
main(I want to see at glance what I think is a bug in the code and what is just me giving it non-existing file name). Sometimes, I attach a context to it (like the file name it is processing) because I expect to mess up something in around there and knowing the file name will save me some time in the whole session, but not too much. I expect to know the code when using it, so I won’t need too many details ‒ after all, it’s going to be short and used just 5 minutes from now. The error messages don’t have to be perfect and I’m just not going to design my own error types.
- Don’t overdo it with performance tuning (both CPU and memory). Will it make
your life easier to load the whole file into a
std::fs::read_to_stringinstead of reading line by line? Sure thing, go for it. If it doesn’t fit into RAM, still prefer
.lines()over the faster option of manually looping over
.read_linewith reusing the allocation.
- Allow yourself not to care about warnings. Does clippy complain about
unwrap_orbeing slower than
unwrap_or_with? Let it complain. I still keep the warnings on. In part because I’m lazy to turn them off, but if the thing doesn’t work in the end, warnings are the first thing to check if there’s something fishy. Just mentally tick them off as harmless and move on, don’t spend time refactoring and rewriting the code to silence them.
- Log or print stuff all over the place. It doesn’t have to be perfect, tunable,
or whatever. But having a way to check that it did or did not run well is
nice. And debugging by these
dbg!macros, if they are already in, is usually faster than running the whole thing again in a debugger. You can also use them for eyeball-level benchmarking (does that part feel slow, or does it print its line fast enough) and you don’t need any better benchmarks than these.
- Don’t bother with
async/await. You’re not handling millions of concurrent connections in that prototype. If you can’t just handle one connection after another in a sequential manner, launch threads (and forget about them/log if they error out).
- Use indexes into a big
Vecinstead of references in complex data structures.
- Use simpler algorithms. Do you need to compute a median? Sort the array and pick the thing in the middle. Is that optimal? When the measured metric is developer time, sure it is.
- Don’t hesitate to generate code ‒ use existing derive crates, write simple
macros (simple ones, though, don’t bother caring about them working in all
possible situations, your copy-pasting spree is what matters), or write a
sedcommand that spits out something close enough to Rust code that can be manually edited to compile by changing 5 characters here and there.
Know when to step back
If on a short time budget, it’s important to remember to not keep banging the
head against one single problem for too long. If you get stuck on the same
lifetime issue for more than 5 minutes, decide you should abandon that
particular road. You don’t need to win this particular fight. The API doesn’t
need to be perfect, or whatever. Just
Rc it or something like that. Or just
decide that part of code is really not necessary at all 😇.
Know the stack
Both the standard library and the crates out there contain a lot of goodies that
can make you move faster. Give them a read, explore. This can be done during a
low-productivity time. Instead of browsing the facebook during the train ride or
after lunch, one can browse the methods on
Result. Did you know
that you can
to get a static reference? Similarly, there are some useful crates that are
worth checking out and can make the life easier (naturally, the list is
anyhow(or something similar) for errors.
itertoolsfor extra methods on iterators.
rayonwhich often lets one to speed things up almost for free. Depending on how fast you need to be, but if you can make it run for 1 minute instead of 5, it can speed up your iteration process and it is worth it if the only change you need to do is put
into_par_iterat the right place in the code.
bumpalois a bump allocator. While the main motivation is performance, it also may make your memory handling and lifetimes in complex data structures much easier.
once_cellif you need global variables that need some initialization.
crossbeam_utils::threadmake it easier to write things like producer-consumer patterns. Do check that you need them, though. Oftentimes, just producing everything ahead of the time into a
Vec<_>is good enough.
StructOptif you need command line.
env_loggerif you need to produce logs
serdeif you need to serialize or parse some common structured data formats.
The idea is to have the feeling of „I think there was something around there that did just the thing I need right now“ and know where to look for it, not necessarily remembering each detail about them.
If you need something else, make sure to check if there’s a crate for it. These 5 minutes of searching can save hours of fixing the hand-rolled CSV parser. The ecosystem contains a lot and during these prototypes, it’s often acceptable to use an outdated unmaintained half-unfinished dependency (while one would be very reluctant to put that into production code, of course). Pick the one with good documentation and convenient API, not the one with most impressive benchmarks. Pull dependencies in instead of rewriting the wheel once again.
Depending on how proficient one is in either language, Rust can be a viable option when writing one-off bits of code. It does however require a mental shift. The way one writes these throw away codes is different than the way one does it in Python or Perl. The goal here is to take shortcuts strategically, to know which things just don’t matter. Make sure to streamline the general work experience. But in the end, the optimal result would be to write it correctly on the first try (even though this first try is going to take longer) than to iterate many times, like one would do with the scripting languages.
Also, it needs a mental shift from the usual production quality Rust coding ‒ the one that’s described in all the tutorials and books. Keep in mind that the optimization metric is developer time and make strategic decisions based on that. Make sure to keep it in mind that the maintainability doesn’t matter, that running 4 times slower doesn’t matter and that you’re allowed to take shortcuts as long as they won’t have time to bite you during the speed run.
Of course, some of these techniques are not exactly Rust specific, but they need to be reminded in context of Rust, which doesn’t exactly recommend them.
And, by the way, don’t forget to ask if you actually need to code anything at
all, or if there’s a whole already existing tool doing just what you need, or if
greps and one
sed will get the job done.