2025-03-11 08:00:00
Rust makes a distinction between values and references that you generally learn to work with while using the language. This week I learned an interesting new corner around how that distinction applies to trait objects, despite using the language for quite a long time. Maybe it will surprise you too!
When you have some x: usize you can say x is the usize itself, while some
y: &usize = &x is a reference to that value. y's concrete value is a
pointer.
Similarly, the type [u8; 40] means 40 bytes itself. If you put it in a struct
or pass it to a function, you're moving around 40 bytes.
Finally, the type [u8] means a series of bytes of (compile-time) unknown size.
You don't interact with these often because you can't usually put these in a
struct or pass them to a function because of their unknown size. Instead you
typically work with references to these as &[u8], which concretely are a
pointer and a length.
(cheats.rs has nice pictures of this.)
But you still do sometimes see [u8] as a type without a reference, in types
like Box<[u8]>. And further, you can wrap a dynamically sized type in a struct
as the last member, making that struct dynamically sized as well:
struct Test<X: ?Sized> {
inner: X,
}
// the type Test<[u8]> is now also dynamically sized
The type File represents an open file. Concretely, it's a struct with some
private fields.
The trait Read represents things that can be read from, with a .read()
method. The trait implies a trait object type dyn Read, which is the type of
things that implement the trait. File implements Read, so I will use File
and Read for the following examples.
Concretely, the layout of a trait object dyn Read is the same as the
underlying value it's wrapping, e.g. a File
(spec ref).
(This is possibly only useful to know as compiler trivia; even cheats.rs doesn't
document this!) Much like [u8], because their concrete size is not known at
compile time, you don't typically interact with these directly but instead via
references.
The type &dyn Read is a reference to a trait object. Concretely it's a pointer
to the object and a pointer to a static vtable of the methods for that type that
implement the trait.
(More pictures from cheats.rs.) Also like
[u8], you might more often use Box<dyn Read>, which holds ownership over the
underlying Read-able type.
(It was useful for my understanding to contrast these with C++ objects and vtables. In C++, the vtable pointer is always embedded in the struct itself. In Rust, the struct never contains a vtable pointer. Instead the reference to the trait object is two pointers, to the value and the vtable.)
Though it's relatively rare in Rust, there are a few places where one type will
silently convert to another. One you may have used without thinking is using a
&mut T in a place that needs a &T.
When using trait objects there is another coercion:
let f: File = ...;
let r: &dyn Read = &f;
Here, &f is &File, but the compiler converts it to &dyn Read.
Did you know that there is another trait-related coercion involving generics? Consider:
let f: File = ...;
let b: BufReader<File> = BufReader::new(f);
let r: &BufReader<dyn Read> = &b; // !!! legal
Here, the File inside the BufReader<> was able to coerce to a trait object.
Concretely, r here is like to a reference to a trait object, in that it is a
pair of a pointer to the BufReader along with a pointer to a Read vtable.
(Poking at the compiler, it uses the same Read vtable as you would get from a
plain File.)
The
underlying spec reference: [coerce.unsized.composite]
for why this is allowed is pretty involved! But at a hand-wavy level it's
allowed because BufReader<dyn Read> is a dynamically sized type where the last
field is the place where the dyn Read is used. Note that, for example, you
cannot have a similar coercion to two traits &SomeType<dyn A, dyn B> because
the reference can only carry a single vtable.
(How is this useful? It came up in some code I was reviewing from someone new to Rust; I'm not sure.)
(Bonus question: The above doesn't compile if you substitute Box or Rc for
BufReader. Why not? Something about the CoercedUnsized impls on those? I don't
know the answer.)
This does compile though:
let f: File = ...;
let b: Box<File> = Box::new(f);
let r: Box<dyn Read> = b; // consumes b
This is due to some magic traits implemented by Box (and also Rc, etc.).
I believe this behavior is the ultimate reason for these corners of support in
the compiiler. You want to sometimes be able to implicitly convert between a
struct and a trait object, and you also sometimes want to be able to wrap things
(in e.g. Box or Rc) of unknown size, and then you want those two features to
combine.
PS: Playground link, if you'd like to poke at it yourself.
2025-03-04 08:00:00
Two related threads, both views on limits to the useful size of data.
I have recently been working with WebAssembly and Win32, which both use 32-bit pointers and are limited to 4gb of memory. Meanwhile, modern computers usually use 64-bit pointers. 64-bit bit pointers let you address more than 4gb of memory. (They also have other important uses, including pointer tricks around memory mapping like ASLR.)
But these larger pointers also cost memory. Fancy VMs like v8 employ "pointer compression" to try to reduce the memory cost. As always, you end up trading off CPU and memory. Is Memory64 actually worth using? talks about this tradeoff in the context of WebAssembly's relatively recent extension to support 64-bit pointers. The costs of the i386 to x86-64 upgrade dive in this in the context of x86, where it's difficult to disentangle the separate instruction set changes that accompanied x86-64 from the increased pointer size.
I provide these links as background for an interesting observation I heard about why a 4gb limit turns out to be "big enough" much of the time: to the extent a program needs to work with data sets larger than 4gb, the data is large enough that you end up working with it differently anyway.
In other words, the simple small data that programs typically work with, like command-line flags or configuration files, comfortably fits within a 4gb limit. And in contrast, the kind of bigger data that crosses the 4gb limit fundamentally will have different access patterns — typically smaller views onto the larger space — because it is too large to traverse quickly.
Imagine a program that generates 4gb of data or loads it from disk or network. Even if the program grinds through hundreds of megabytes per second, it still takes over 30 seconds to work through 4gb. This is enough time to probably require a different user interface such as a progress bar. Such a program likely will work with the data in a streaming fashion, paging in/out smaller blocks of the data via some file API that manages blocks of the >4gb data.
A standard example application that will make use of a ton of memory is a database. But a database is expressly designed around managing the larger size of the data, using indexing data structures so that a given query knows exactly which subsets of the larger data to access. Otherwise, large queries like table scans work with the data in a streaming fashion as in the previous paragraph.
Another canonical "needs a lot of memory" application is an image editor, which operates on a lot of pixels. I worked on one of those! To make the software grind through pixels fast you will take efforts to avoid needing to individually traverse all your pixels. To get the pixels onto the screen quickly, you instead load data piecewise into the GPU and let the GPU handle the rest. Or you write specialized "shader" GPU code. Both are again specialized APIs that work with
How about audio and video? These involve large amounts of data, but also have a time dimension, where most operations only work with a portion of the data near a particular timestamp.
In all, a 32-bit address space for the program's normal data structures coupled with some alternative mechanism for poking at the larger data indirectly has surprisingly ended up working out almost as well as a larger flat address space.
It's interesting to contrast this observation to a joke from the DOS era: "640kb ought to be enough for anybody". 640kb was a memory limit back then and the phrase was thrown around ironically to remark that reasonable programs actually need more. According to Gates (who it was apocryphally, falsely, attributed to) at the time, 640kb was already understood to be a painful limit.
In contrast, we've been easily fitting most programs in 4gb for the last 30 years — from the 1990s through today, where browsers still limit web pages to 4gb.
Why has this arbitrary threshold held up? You could argue it's a lack of imagination: maybe we have sized our programs to the limits we had? But 64-bit has been around for quite a while, long enough that there ought to be better examples of different kinds of programs that truly make use of it.
One answer is to observe is that many of the limits above are related to human limits. Humans won't wait 30s for a program to load its data; humans can't listen to 4 minutes of audio simultaneously, humans don't write >4gb of configuration files... or to put it together, humans don't consume 4gb of data in one gulp.
And that observation links to an interesting related phenomenon, which I sometimes call:
Data visualization is the problem of presenting data in a form where your brain can ingest it. What if you have a lot of data? We use the tools of statistics, to summarize collections of data into smaller representations that capture some of its essence.
Imagine building a stock chart, a simple line chart of a value over time. Even though you have data for every second of the day, when charting the data over a larger timeline, it's not useful to draw all of that data. Instead we will summarize, with either an average price per time window, or something that attempts to reduce each time window's data to a few numbers like a violin plot or OLHC.
Why do we do this? Visually, there's only so much you can usefully see. Even with a higher resolution display, shoving more details into smaller pixels does not convey usefully more information. In fact, it's often a better chart when it has fewer visual marks that still tells the same story. Much like the 4gb limit, the bandwidth of information into your brain sets an upper bound on the amount of useful data that can go into a chart.
From this you can derive an interesting sort of principle about data visualization software: your data visualization does not need be especially fast in supporting a lot of marks, beacuse you won't ever need to display many of them. For example, libraries like d3 are fine to do relatively low performance DOM traversal for rendering.
This is kind of a subtle point, so let me be clear: it's of course useful and important to be able to work with large data sets, and data visualization software often will provide critical API for processing them. The point is that once you are at the level of putting actual objects on the screen, you should have already reduced the data mathematically to a relatively easy small set of data, so you don't really need speedy handling of screen objects.
But wait, you say, this is a dumb argument — it really is more convenient to just have 64-bit pointers everywhere and not worry about any limits. And wouldn't a charting API where you can hand a million points to a scatter plot be more useful than one where you can't?
I think both of those are preferable — in the absence of performance constraints. I prefer those in the same way I'd prefer, in a hypothetical future where performance is completely free, making a database with no need for indexes and running big AI matmuls without specialized GPU code or hardware. But at least in today's software it is the case that even 64-bit pointers are costly enough that implementers will go to lengthy efforts to reduce their cost.
So don't take this post as advocating that these lower limits are somehow just or correct. Rather, I aimed only to observe why it is that the memory limits from the 90s, or the visualization limits from the pen and paper days, have not been as constraining as you might first expect.
2025-01-24 08:00:00
"Blazing"
What you meant: Very fast.
What it comes across as: This term in particular is overused to describe
software that isn't particularly fast in any useful sense, but which has random
micro-optimizations that don't matter.
What to do instead: If it's actually fast, just say fast, and describe what
it's fast in relation to.
"Modern"
What you meant: New, which implies good under the assumption that newer is
implicitly better? Or maybe it means based on newer design principles? I
actually struggled on this one for a while and I think it's most often used to
just mean good in general, nonspecific ways...?
What it comes across as: New for the sake of being new, possibly with new
bugs. Ignorant of the past. An empty filler word.
What to do instead: Describe the actual difference. When new things are good,
it's because of specific benefits such as active maintenance, a new take on an
old problem, a reduced and focused scope by discarding previous compatibility
concerns, etc.
"Isomorphic"
What you meant: The same as, or matching.
What it comes across as: Abusing a math term because it looks cool.
Sesquipedalianism.
What to do instead: Isomorphim is a technical term that describes
a specific
scenario distinct from equality.
Similar terms like "equivalent" or "one-to-one" can often better express the
intended idea.
"Magic"
What you meant: Something with surprisingly helpful behavior.
What it comes across as: Something with surprisingly unpredictable behavior.
What to do instead: Perhaps "intuitive", or something like "handles common
configuration out of the box". For most things I work with, the descriptive
words "simple" and "predictable" have more sparkle to them than "magical".
