There are three kinds of lies: lies, damned lies, and statistics.

For this benchmark we implemented Treap in a few classic (C++, Java, Python) and hyped (JavaScript, Kotlin, Swift, Rust) programming languages and tested their performance on Linux, Mac OS, and Windows (all of them running on different hardware, so the results should not be compared between platforms).

This turned out to be a good benchmark of memory-intensive operations, which should have been pushed memory management implementations to their edge.

First, we tried to play by the rules of the garbage-collected languages, thus there are "ref-counted" versions of implementations for C++ and Rust, but then we still wanted to compare the results with idiomatic (a.k.a. common practices) implementations for C++ ("raw-pointers") and Rust ("idiomatic").

I must say that most of the implementations (except for C++, Haskell, and OCaml) were implemented by mostly adapting the syntax from the very first implementation of the algorithm in Kotlin. Even Rust, which is considered to have the steepest learning curve among the tested languages, didn't require any "black magic" (the solution does not require either unsafe code or lifetime annotations). C++ was implemented separately, so it has a few shortcuts, and thus it might be not a completely fair comparison (I will try to implement "fair" C++ solution and also "C++"-like Rust solution to see if the performance can be on par).

We define the "naive" implementations as those which a developer with enough experience in a given language would implement as a baseline "good enough" solution where correctness is more important than performance.

However, experienced developers in system programming languages (e.g. C, C++, D) tend to work comfortably with raw pointers, and that makes the comparison of the solutions only by speed and memory consumption unfair. High-level abstractions tend to introduce some performance hit in exchange for safety and expressiveness. Thus, we added other metrics: "Expressiveness" (1 - pure magic, 10 - easy to get started and express your intent) and "Maintenance Complexity" (1 - easy to maintain, 5 - ugly yet safe, 6-10 - hard to keep it right, i.e. risky). The ease of maintenance is estimated for a big project using the given language and the given approach.

Thus, here are the metrics:

• Expressiveness (e12s), scores from 1 to 10 - higher value is better (keep in mind that this is a subjective metric based on the author's experience!)
• Maintenance Complexity (M.C.), scores from 1 to 10 - smaller value is better (keep in mind that this is a subjective metric based on the author's experience!)
• Real Time, seconds - smaller value is better
• Slowdown Time (relative speed compared to the best tuned solution) - smaller value is better
• Memory, megabytes - smaller value is better
• Binary Size, megabytes - smaller value is better

To measure time we used time util on Mac OS and Windows (msys2 environment), and cgmemtime on Linux.

Memory measurement was only available on Linux with cgmemtime util, which leverages CGroup capabilities to capture the high-water RSS+CACHE memory usage, and given the limitations of cgroup subsystem (it counts caches and loaded shared objects unless they are already cached or loaded by other processes), we take the lowest memory footprint among all the executions.

Originally, this benchmark had a goal to implement the same "natural" and "naive" API in all the languages with exception to C++, which would represent a "bare metal" performance. Over time, we received optimized solutions in other languages, but it doesn't seem fair to put them on the same scoreboard. Thus, even though, all the solutions implement the same algorithm, they were created with performance in mind and received quite an intensive profiling and tunning, and that is why they will be presented in a separate scoreboard.

All tables are sorted in an alphabetical order.

(*) C# has a noticeable VM start time (~0.4 seconds), but we still measure real execution time of the whole program.

(**) Having no static types leaves the code clean, but makes it less reliable from the maintenance perspective.

(***) With a minor update (PR #52), Rust solution gets a significant speedup while still keeping its safety guarantees (see the result in the "Tuned Implementations Scoreboard" below).

D demonstrated the best performance among garbage-collected solutions. It even managed to outperform Object Pascal solution which used raw pointers and manual memory management, as well as naive C++ shared_ptr-based implementations and naive "ref-counted" solution in Rust.

C++ "ref-counted" (shared_ptr) has significant performance hit on non-Linux platforms.

C++ unique_ptr has some non-zero runtime overhead over raw pointers.

JVM speeds up if you limit its memory.

Kotlin Native is still much slower than the Kotlin running in JVM.

Kotlin JS produces JS code which is ~25% slower than the manual Kotlin to JS translation.

Nim, D, and Rust can go as fast as C/C++ can when you switch to "unsafe" manual memory management.

With CPython vs PyPy you trade speed for memory.

On Reddit in 2020: r/rust.

On Reddit in 2018: r/programming, r/rust, r/ada, r/python, r/swift, r/dlang, r/kotlin, r/javascript, r/nim,

Completely Unscientific Benchmarks project is licensed under either of

• Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.