db-benchmark VS Nim

Real-world performance is complicated since data science covers a lot of use cases.

If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.

Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/

I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.

[1] https://h2oai.github.io/db-benchmark/

https://news.ycombinator.com/item?id=33270638 :

> Apache Ballista and Polars do Apache Arrow and SIMD.

> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *

LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database

/? inurl:awesome site:github.com "vector database"

I agree with your conclusion but want to add that switching from Julia may not make sense either.

According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.

For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks

The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.

If interested in benchmarks comparing different dataframe implementations, here is one:

https://h2oai.github.io/db-benchmark/

Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.

22. Nim - $80,000

I think the right answer to your question would be NimLang[0]. In reality, if you're seeking to use this in any enterprise context, you'd most likely want to select the subset of C++ that makes sense for you or just use C#.

[0]https://nim-lang.org/

I don't think it's a rust-inspired language, but since it has strong typing and compiles to javascript, did you give a look at nim [0] ?

For what it takes, I find the language very expressive without the verbosity in rust that reminds me java. And it is also very flexible.

[0] : https://nim-lang.org/

I see a valid cert for https://nim-lang.org/

FYI, on the front page, https://nim-lang.org, in large type you have this:

> Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.

You better off with using a compiled language.

If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).

And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)

Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.

Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).

But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.