And especially having performant and actively maintained default choices built in. With C, as described in the post you responded to, you'll typically end up building a personal collection of dusty old libraries that work well enough for most of the time.
Either way, I would like to reiterate that the comparison is flawed at a more fundamental level because hash tables and B-trees are different data structures with different performance characteristics. O(1) average lookup vs O(log n) with cache-friendly ordered traversal. These are not interchangeable.
If BTreeMap outperformed his hash table, that is either because the hash table implementation was poor, or because the access patterns favored B-tree cache locality. Neither tells you anything about Rust vs C. It is a data structure benchmark.
More importantly, choosing between a hash table and a tree is an architectural decision with real trade-offs. It is not something that should be left to "whatever the standard library defaults to". If you are picking data structures without understanding why, that is on you, not on C's lack of a blessed standard library (BTW one size cannot fit all).
The specific thing it tells you about Rust vs C is that Rust makes using an optimized BTreeMap the default, much-easier thing to do when actually writing code. This is a developer experience feature rather than a raw language performance feature, since you could in principle write an equally-performant BTreeMap in C. But in practice Bryan Cantrill wasn't doing that.
> More importantly, choosing between a hash table and a tree is an architectural decision with real trade-offs. It is not something that should be left to "whatever the standard library defaults to". If you are picking data structures without understanding why, that is on you, not on C's lack of a blessed standard library (BTW one size cannot fit all).
The Rust standard library provides both a hash table and a b-tree map, and it's pretty easy to pull in a library that provides a more specialized map data structure if you need one for something (because in general it's easier to pull in any library for anything in a Rust project set up the default way). Again, a better developer experience that leads to developers making better decisions writing their software, rather than a fundamentally more performant language.
What churn? Rust hasn't broken compatibility since 1.0, over a decade ago. These days it feels like rust changes slower than C and C++.
> Either way, I would like to reiterate that the comparison is flawed at a more fundamental level because hash tables and B-trees are different data structures with different performance characteristics. O(1) average lookup vs O(log n) with cache-friendly ordered traversal. These are not interchangeable.
They're mostly interchangeable when used as a map! In rust code, in most cases you can just replace HashMap with BTreeMap. In practice, O(log n) and O(1) are very similar bounds owing to how slowly log(n) grows with respect to n. Cache locality often matters much more than a O(log n) factor in your algorithm.
If you read the actual article, you'll see that Cantrill benchmarked his library using rust's b-tree and hash table implementation. Both maps outperformed his C based hash table implementation.
> Neither tells you anything about Rust vs C.
It tells you rust's standard library has a faster hash map implementation than Bryan Cantrill. If you need a hash table, you're almost certainly better off using rust than rolling your own in C.
Quite frankly, writing the same in Rust seems far, far more "arduous", and you'd only realistically be writing something using BTreeMap because someone else did the work for you.
However, being right there in std makes use much easier than searching around for an equivalent library to pull into your C codebase. That's the benefit.
You should have practiced better restraint then, as this was not a productive addition to the discussion.
My experience disagrees with your opinion and I see no value in engaging further.
Can you mention 3 cases of breakage the language has had in the last, let's say, 5 years? I've had colleagues in different companies responsible for updating company-wide language toolchains tell me that in their experience updating Rust was the easiest of their bunch.
> edition migrations
One can write Rust 2015 code today and have access to pretty much every feature from the latest version. Upgrading editions (at your leisure) can be done most of the time just by using rustfix, but even if done by hand, the idea that they are onerous is overstating their effect.
Last time I checked there were <100 checks in the entire compiler for edition gates, with many checks corresponding to the same feature. Adding support for new features that doesn't affect prior editions and by extension existing code (like adding async await keywords, or support for k# and r# tokens) is precisely the point of editions.
> dependency hell in Cargo.lock
Could you elaborate on what you mean?
That's a thin, thin line of argumentation. The distinction between the ecosystem and language may as well not exist.
A lot of improvements of modern languages come down to convenience, and the more convenient something is, the more likely it is to be used. So it is meaningful to say that the average Rust program will perform better than the average C program given that there exist standard, well-performing, generic data structure libraries in Rust.
> It is a library benchmark, not a language one.
If you have infinite time to tune performance, perhaps. It is also meaningful to say that while importing a library may take a minute, writing equivalently performant code in C may take an hour.
I acknowledge that C needs a tool as good as cargo, but if we are comparing language, we should restrict to language.
Are you surprised? Rust is never inherently faster than C. When it appears faster, it boils down to library quality and algorithm choice, not the language.
Also worth noting that hash tables and B-trees have fundamentally different performance characteristics. If BTreeMap won, it is either the hash table implementation, or access patterns that favor B-tree cache locality. Neither says anything about Rust vs C. It is a library benchmark, not a language one.