datafusion-ballista VS regex-benchmark

Not super on topic because this is all immature and not integrated with one another yet, but there is a scaled-out rust data-frames-on-arrow implementation called ballista that could maybe? form the backend of a polars scale out approach: https://github.com/apache/arrow-ballista

> Is Rust's compile-time GC about something other than performance somehow?

AFAIK, memory safety and language features as RAII is also available in C++, for instance. About the reasons for slow compilation, take a look at https://www.reddit.com/r/rust/comments/xna9mb/why_are_rust_p...

Not having a GC is also about not having a runtime as you mention (e.g. nice for creating Python extensions and embedded systems programming) and also more runtime deterministic performance: on that, if I'm not mistaken that was the reason for Discourse switching to Rust and also, e.g.: "the choice of Rust as the main execution language avoids the overhead of GC pauses and results in deterministic processing times" https://github.com/apache/arrow-ballista/blob/main/README.md

Ballista (Arrow-Rust), which is largely inspired by Apache Spark, there are some interesting differences.

https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb

Source: https://github.com/apache/arrow-ballista

Can you explain how this might differ from something like https://github.com/apache/arrow-ballista

I've seen several variants of "next-gen" spark, but nowhere have I really seen the different tradeoffs/advantages/disadvantages between them.

Rust has amazing integration with Python through PyO3 [1] so see it like a safe alternative for high performance calculations. The ecosystem itself is starting to come together exciting projects like Polars [2] (Pandas alternative), nalgebra [3], Datafusion [4] and Ballista [5]

[1] https://github.com/PyO3/pyo3

[2] https://github.com/pola-rs/polars/

[3] https://docs.rs/nalgebra/latest/nalgebra/

[4] https://github.com/apache/arrow-datafusion

[5] https://github.com/apache/arrow-ballista

Before we start comparing the aforementioned solutions, it is worth to show how bad things are with the standard regex library in Go. I found the project where the author compares the performance of standard regex engines of various languages. The point of this benchmark is to repeatedly run 3 regular expressions over a predefined text. Go came in 3rd place in this benchmark! From the end....

* Let you clone a map without rehashing every key to a new seed. I generally measure at least 15x speedup from this alone, unlocking very useful design patterns like "clone a map and apply a few temporary updates for a one-off operation like validation or simulation" with no extra code complexity. Go gives you no better option than slowly rehashing the entire map.

And that's just hash maps. How about Go's regex engine being one of the slowest in the world while Rust's regex crate being one of the fastest:

https://github.com/mariomka/regex-benchmark#optimized

Languages Regex Benchmark

Java is still pretty bad compared to C# (not to mention Rust or Nim)

https://github.com/mariomka/regex-benchmark

And the always interesting techempower Project, which leaves the implementation to participants of each round. https://www.techempower.com/benchmarks/#section=data-r21&tes...

Choose whatever category you wish there, js is faster in then go in almost all categories there.

Even though I said it before, I'm going to repeat myself as I expect you to ignore my previous message: the language doesn't make any implementation fast or slow. You can have a well performing search engine in go, and JS. The performance difference will most likely not be caused by the language with these two choices. And the same will apply with C/Rust. The language won't make the engine performant creating a maximally performant search engine is hard

Also, regex engines are not created equally, at all. One of the best writeups I've ever read is from the ripgrep blog. Burntsushi knows regex. There's also this benchmark site which illustrates how general language performance is an entirely different metric than regex performance. Don't assume those benchmarks will cover your particular use case, though--different regex engines might handle your particular situation differently.

Interesting. Looking at this repo, they have

Rust -> Ruby -> Java -> Golang

https://github.com/mariomka/regex-benchmark

Though it appears the numbers are two years old or so, and only for 3 specific regexes.

id also be careful in claiming C++ std regex is faster than python, unless you actually have proof. there's a ton of information that in many cases its actually slower. https://github.com/mariomka/regex-benchmark. have you actually benchmarked your code? or was it just a naive assumption that because its C++ its just fast?

It is a matter of personal preference.

I find that regular expressions and text-wrangling tasks are faster and easier in Perl than in other programming languages due to its accessible syntax and regular expression engine speed.

This article shows the regular expression syntax in several popular programming languages: https://cs.lmu.edu/~ray/notes/regex/

This GitHub repo gives some regex performance test benchmarks: https://github.com/mariomka/regex-benchmark Perl is pretty fast among the scripting languages that were benchmarked.

If you are familiar with C / C++, then learning Perl is relatively fast and easy: https://perldoc.perl.org/perlintro