Yhyjyk

I am following the Rust track on Exercism.io. I have a fair amount of C/C++ experience. I like the 'functional' elements of Rust but I'm concerned about the relative performance.

I solved the 'run length encoding' problem:

pub fn encode(source: &str) -> String 
 let mut retval = String::new();
 let firstchar = source.chars().next();
 let mut currentchar = match firstchar 
 Some(x) => x,
 None => return retval,
 ;
 let mut currentcharcount: u32 = 0;
 for c in source.chars() 
 if c == currentchar 
 currentcharcount += 1;
 else 
 if currentcharcount > 1 
 retval.push_str(&currentcharcount.to_string());
 
 retval.push(currentchar);
 currentchar = c;
 currentcharcount = 1;
 
 
 if currentcharcount > 1 
 retval.push_str(&currentcharcount.to_string());
 
 retval.push(currentchar);
 retval

I noticed that one of the top-rated answers looked more like this:

extern crate itertools;

use itertools::Itertools;

pub fn encode(data: &str) -> String 
 data.chars()
 .group_by(

I love the top rated solution; it is simple, functional, and elegant. This is what they promised me Rust would be all about. Mine on the other hand is gross and full of mutable variables. You can tell I'm used to C++.

My problem is that the functional style has a SIGNIFICANT performance impact. I tested both versions with the same 4MB of random data encoded 1000 times. My imperative solution took under 10 seconds; the functional solution was ~2mins30seconds.

• Why is the functional style so much slower than the imperative style?


• Is there some problem with the functional implementation which is causing such a huge slowdown?


• If I want to write high performance code, should I ever use this functional style?

Let's review the functional implementation!

Memory Allocations

One of the big issues of the functional style proposed here is the map method which allocates a lot. Every single character is first mapped to a String, before being collected.

It also uses the format machinery, which is known to be relatively slow.

Sometimes, people try way too hard to get a "pure" functional solution, instead:

let mut result = String::new();
for (c, group) in &source.chars().group_by(|&c| c) 
 let count = group.count();
 if count > 1 
 result.push_str(&count.to_string());
 

 result.push(c);

is about as verbose, yet only allocates when count > 1 just like your solution does and does not use the format machinery either.

I would expect a significant performance win compared to the full functional solution, while at the same time still leveraging group_by for extra readability compared to the full imperative solution. Sometimes, you ought to mix and match!

TL;DR

A functional implementation can be faster than your original procedural implementation, in certain cases.

Why is the functional style so much slower than the imperative style? Is there some problem with the functional implementation which is causing such a huge slowdown?

As Matthieu M. already pointed out, the important thing to note is that the algorithm matters. How that algorithm is expressed (procedural, imperative, object-oriented, functional, declarative) generally doesn't matter.

I see two main issues with the functional code:

• Allocating numerous strings over and over is inefficient. In the original functional implementation, this is done via to_string and format!.




• There's the overhead of using group_by, which exists to give a nested iterator, which you don't need just to get the counts.

Using more of itertools (batching, take_while_ref, format_with) brings the two implementations much closer:

pub fn encode_slim(data: &str) -> String match count 
 1 => f(&c),
 n => f(&format_args!("", n, c)),
 )
 .to_string()

A benchmark of 1KiB of random alphanumeric data:

encode (procedural) time: [4.9922 us 5.0386 us 5.0940 us]
Found 15 outliers among 100 measurements (15.00%)
 2 (2.00%) high mild
 13 (13.00%) high severe

encode (fast) time: [6.6025 us 6.6636 us 6.7371 us]
Found 10 outliers among 100 measurements (10.00%)
 4 (4.00%) high mild
 6 (6.00%) high severe

And 4MiB of data, compiled with RUSTFLAGS='-C target-cpu=native':

encode (procedural) time: [21.082 ms 21.620 ms 22.211 ms]

encode (fast) time: [26.457 ms 27.104 ms 27.882 ms]
Found 7 outliers among 100 measurements (7.00%)
 4 (4.00%) high mild
 3 (3.00%) high severe

If you are interested in creating your own iterator, you can mix-and-match the procedural code with more functional code:

struct RunLength<I> 
 iter: I,
 saved: Option<char>,


impl<I> RunLength<I>
where
 I: Iterator<Item = char>,

 fn new(mut iter: I) -> Self 
 let saved = iter.next(); // See footnote 1
 Self iter, saved 
 


impl<I> Iterator for RunLength<I>
where
 I: Iterator<Item = char>,

 type Item = (char, usize);

 fn next(&mut self) -> Option<Self::Item> 
 let c = self.saved.take().or_else(


pub fn encode_tiny(data: &str) -> String 
 match count 
 1 => s.push(c),
 n => write!(&mut s, "", n, c).unwrap(),
 
 s
 )

1 — thanks to Stargateur for pointing out that eagerly getting the first value helps branch prediction.

4MiB of data, compiled with RUSTFLAGS='-C target-cpu=native':

encode (procedural) time: [19.888 ms 20.301 ms 20.794 ms]
Found 4 outliers among 100 measurements (4.00%)
 3 (3.00%) high mild
 1 (1.00%) high severe

encode (tiny) time: [19.150 ms 19.262 ms 19.399 ms]
Found 11 outliers among 100 measurements (11.00%)
 5 (5.00%) high mild
 6 (6.00%) high severe

I believe this more clearly shows the main fundamental difference between the two implementations: an iterator-based solution is resumable. Every time we call next, we need to see if there was a previous character that we've read (self.saved). This adds a branch to the code that isn't there in the procedural code.

On the flip side, the iterator-based solution is more flexible — we can now compose all sorts of transformations on the data, or write directly to a file instead of a String, etc. The custom iterator can be extended to operate on a generic type instead of char as well, making it very flexible.

See also:

• How can I add new methods to Iterator?

If I want to write high performance code, should I ever use this functional style?

I would, until benchmarking shows that it's the bottleneck. Then evaluate why it's the bottleneck.

Supporting code

Always got to show your work, right?

benchmark.rs

use criterion::criterion_group, criterion_main, Criterion; // 0.2.11
use rle::*;

fn criterion_benchmark(c: &mut Criterion) 
 let data = rand_data(4 * 1024 * 1024);

 c.bench_function("encode (procedural)", 
 let data = data.clone();
 move );

 c.bench_function("encode (functional)", encode_iter(&data))
 );

 c.bench_function("encode (fast)", b);

 c.bench_function("encode (tiny)", b);


criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);

lib.rs

use itertools::Itertools; // 0.8.0
use rand; // 0.6.5

pub fn rand_data(len: usize) -> String 
 use rand::distributions::Alphanumeric, Distribution;
 let mut rng = rand::thread_rng();
 Alphanumeric.sample_iter(&mut rng).take(len).collect()


pub fn encode_proc(source: &str) -> String 
 let mut retval = String::new();
 let firstchar = source.chars().next();
 let mut currentchar = match firstchar 
 Some(x) => x,
 None => return retval,
 ;
 let mut currentcharcount: u32 = 0;
 for c in source.chars() 
 if c == currentchar 
 currentcharcount += 1;
 else 
 if currentcharcount > 1 
 retval.push_str(&currentcharcount.to_string());
 
 retval.push(currentchar);
 currentchar = c;
 currentcharcount = 1;
 
 
 if currentcharcount > 1 
 retval.push_str(&currentcharcount.to_string());
 
 retval.push(currentchar);
 retval


pub fn encode_iter(data: &str) -> String 
 data.chars()
 .group_by(

pub fn encode_slim(data: &str) -> String match count 
 1 => f(&c),
 n => f(&format_args!("", n, c)),
 )
 .to_string()


struct RunLength<I> 
 iter: I,
 saved: Option<char>,


impl<I> RunLength<I>
where
 I: Iterator<Item = char>,

 fn new(mut iter: I) -> Self 
 let saved = iter.next();
 Self iter, saved 
 


impl<I> Iterator for RunLength<I>
where
 I: Iterator<Item = char>,

 type Item = (char, usize);

 fn next(&mut self) -> Option<Self::Item> 
 let c = self.saved.take().or_else(


pub fn encode_tiny(data: &str) -> String 
 match count 
 1 => s.push(c),
 n => write!(&mut s, "", n, c).unwrap(),
 
 s
 )


#[cfg(test)]
mod test 
 use super::*;

 #[test]
 fn all_the_same() 
 let data = rand_data(1024);

 let a = encode_proc(&data);
 let b = encode_iter(&data);
 let c = encode_slim(&data);
 let d = encode_tiny(&data);

 assert_eq!(a, b);
 assert_eq!(a, c);
 assert_eq!(a, d);