I also noticed that you mentioned avoiding lazy sequences is not idiomatic in Clojure. I disagree with this since using transducers is still idiomatic. I wonder if you could've noticed some speed improvements moving your filters/maps to transducers. Though I doubt this would get you to Rust speeds anyway, it might just be fast enough.
* Prismatic Schema, immensely popular, was "replaced" by spec, which is not yet complete and still in the research phase
* leiningen (one of the best language tooling out there) was "replaced" by Clojure CLI that can't do half of what leiningen can
* transducers (a brilliant concept) are not easy (as in close at hand) because the code is quite different to normal lazy-sequence based code (I wrote a library [1] to address this)
I still prefer Clojure for all my side projects, but it is very clear that the community is tiny and fragmented.
[0] https://github.com/thi-ng/umbrella/tree/develop/packages/tra...
Odd, I can just step through my code with Cursive if I need to.
In scheme, the srfi-158 based generators are slower than my own transducer SRFI (srfi-171) only in schemes where set! incurs a boxing penalty and where the eagerness of transducers means mutable state can be avoided.
Now, I know very little clojure, but I doubt they would leave such a relatively trivial optimization on the table. A step in a transducer is just a procedure call, which is the same for trivial generator-based laziness.
IME (admittedly in a different context, doing UI development) Clojure's seqs and other immutable data can be a huge performance drag due to additional allocations needed. If you're in a hot loop where you're creating and realizing a sequence immediately, it's probably much faster to bang on a transient vector. Same with creating a bunch of immutable hash maps that you then throw away; better to create a simpler data structure (e.g. a POJO or Map) which doesn't handle complicated structural sharing patterns if it's just going to be thrown away.
Transducer's would help in the author's first case to take the map/filter piped through the `->>`, which is going to do two separate passes and realize two seqs, and combine it into one.
Another idiomatic way to improve performance are transients[0]. From the outside your function is still a function, but on the inside it's cheating by updating in place instead of using persistent data structures. See the frequencies function for a simple example[1].
Clojure and Rust are both very expressive languages and even though they both can be considered niche, they have _massive_ reach: Clojure taps into JVM and the JS ecosystems, Rust can also compile to WASM or be integrated with the JVM via JNI.
The big difference between the two, and why I think they complement each other nicely, is that Clojure is optimized for development, and does its best at runtime, but Rust is optimized for runtime, and tries its best at development. (A similar take in the article). In other words: they both achieve their secondary goal well, but resolve trade-offs by adhering to their primary in the vast majority of cases.
[0] https://clojure.org/reference/transients
[1] https://github.com/clojure/clojure/blob/clojure-1.10.1/src/c...
The Rust vs Java question translates to the age old C++ vs Java argument, where the counterpoint is that Java can be faster because JVM has no significant disadvantage in code generation but JIT and GC can be faster than AOT and malloc, and then there are many back and forth arguments and nobody changes their mind.
In another sense, ease of use and HLL properties of languages can in practice give performance advantages. Given the same amount of time, the programmer of a more expressive high level language might have more time to iterate and to do algorithm work that end up being much bigger effects than the relatively small differences of compiler code generation.
(The word performance of course also has meanings other than code execution speed...)
except that people routinely rewrite java code in C++ in 2020 and run around the Java code in circles, even when tuning GCs etc etc, à la https://www.scylladb.com/2020/10/06/c-scylla-in-battle-royal... or Minecraft Bedrock edition (C++) vs original Java Minecraft
How many times have there been rewrites from C++ to Java that ended up being faster ?
Not if you use the wrong constructs, copy stuff around in the heap, use ref counting everywhere in longer running processes.
I'm not nitpicking here, in Rust you can get really fast, but its on you to make that happen.
For example persistent data-structures (used in Clojure) are really fast and for some operations and cases even close to optimal.
Performance is hard, and I very much agree with your question here. What has been measured and what are the results.
A lot of the performance comes from the different paradigms though, so it's not always an apple to apple comparison. But I also think that's an assumption being made when talking about a Clojure Vs Rust implementation. In the latter, you're most likely implying using mutable collections, fixed size structs, primitive types, and a tighter memory allocation surface. And not surprisingly, those are the same changes you'd make to your Clojure code base to speed it up (most likely).
You're technically correct, but the typical Java program making heavy use of threads has inefficiencies (and incorrectness) that would be avoided with Clojure's higher level async APIs. As it's easier to write idiomatic, performant C than the "faster" ASM.
every language need easy access to a query-ble database many problems are a lot simple when solved declaratively as a query-ble database
the relational model, is functional, and is a very good solution to a wide range of problems
i think the Sqlite engine should be integrated in the standard library of every language, and either use sql, or the language can provide a native sql alternative in the original language itself, or we can create a new standard language (because yes, sql can be improved upon)
I think Chris Date D language can be a place to start to investigate SQL alternative , or as a language that can be more easily emulated in other languages
Chris Dates' D ( not to be confused with Walter Bright D) looks more like a regular programing language
And was functional
So I think an language can add a library or an extension that act like D, or simulate D
Check this to get an idea https://reldb.org/c/wp-content/uploads/2017/12/Rel-and-Tutor...
Also, as I recall, the Notion of RelVars made it sound functional
Each Relational Operator, returned a RelVar So you were passing RelVars between operators until you get the result you want, which was a RelVar
Anyway this is from memory, so I maybe be wrong on many things .. but again from memory D or tutorial D sounded a lot less DSL-ish and lot more functional than SQL , which was an improvement in my mind
The context is a rewrite AKA runtime optimization. So the result is already understood. A great use-case for Rust is top-down implementation.
Also the code doesn't show any of the more painful cases. From the article:
> There are some inefficiencies visible here, and they're probably the most important spots for performance improvements. But they're still there as fixing them was too hard and/or time-consuming for me.
Resolving these "inefficiencies" is where Rust really shines. Because it _can_ resolve them and does it internally consistent on top of it. But at the same time, this is where you really _slow down_ in development and need to think about the more complex and intricate concepts such as lifetimes and borrow semantics.
I have no idea what the author's trying to say here.
Pure speculation on my part, but if one has a lot of experience with imperative, mutable languages, one might design a system that ends up being not so great when written in a functional, immutable language. If so, then seeing improvements when directly porting to an imperative, mutable language might be not so surprising.
Tangent: Regarding the power and importance of code structure, I highly recommend watching "Solving Problems the Clojure Way" by Rafal Dittwald at Clojure North 2019 [3].
[1] I didn't see a link, but if it's available, I'd love to take a look.
[2] The `rule-field-diff` function for example seems to be burdened with some odd choices, e.g., taking in two "rules" as arguments, (which seem to be collections of rules keyed by field), then using two hard-coded "operations" (also keyed by field), and yielding a map whose values are sequences by field (I think). Off the top of my head I don't see why this fn needs to work across multiple fields in the first place (i.e., any field-specific "loop" should be in a surrounding context. Ditto for `diff-rules-by-keys`.
Most developers should watch it.
Just looking at the Clojure code, I feel there's better approaches in Clojure to achieve the same or better results.
More clarity would be helpful.
Also, transducers are a big performance win for long sequences of values.
You cannot even begin to guess where your performance problems are until you use something like YourKit (https://www.yourkit.com) which is an excellent tool. With very little effort and a few type hints you can sometimes more than double your Clojure performance.
