julia VS duckdf

34. Julia - $74,963

I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.

3. dispatch on all the arguments

the first solution is clean, but people really like dispatch.

the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.

the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.

the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.

nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html

lisps of course lack UFCS.

see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779

so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!

https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md

Visit official site: https://julialang.org/

No. It runs natively on ARM.

julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release

https://github.com/JuliaLang/julia/issues/51086#issuecomment...

So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.

But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.

Still, the musl author has some reservations against making `jemalloc` the default:

https://www.openwall.com/lists/musl/2018/04/23/2

> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.

With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".

I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.

https://github.com/JuliaLang/julia/issues/34753

I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.

However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.

The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.

Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.

Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...

I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...

but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...

Quite a while ago, when duckdb was just a duckling, I wrote an R package that supported direct manipulation of R dataframes using SQL.[1] duckdb was the engine for this.

The approach was never as fast as data.table but did approach the speed of dplyr for more complex queries.

Life had other things in store for me and I haven’t touched this library for a while now.

At the time there was no Julia connector for duckdb, but now that there is, I’d like to try this approach in that language.

[1] https://github.com/phillc73/duckdf

Yeah, I agree sqldf is quite slow. Fair point.

As you've seen, duckdb registers an "R data frame as a virtual table." I'm not sure what they mean by "yet" either.

Of course it is possible to write an R dataframe to an on-disk duckdb table, if that's what you want to do.

There are some simple benchmarks on the bottom of the duckdf README[1]. Essentially I found for basic SQL SELECT queries, dplyr is quicker, but for much more complex queries, the duckdf/duckdb combination performs better.

If you really want speed of course, just use data.table.

[1] https://github.com/phillc73/duckdf

That's a really good point that I'd not really thought about. I'd never really considered the difference between calling just functions versus macros.

Thinking about Query.jl and DataFramesMeta.jl, and I am for sure not an expert in either, I can't specifically speak to your `head` example, but other base functions can be combined with macros. For example, see the LINQ examples from DataFramesMeta.jl[1] where `mean` is being used. Or again the LINQ style examples in Query.jl[2], where `descending` is used in the first example, or `length` later in the Grouping examples.

Is that the kind of thing you meant?

For whatever reason, with the way my brain is wired, the LINQ style of query just works for me. I have never directly used LINQ, but do have some SQL experience. In fact, I wrote some dinky little wrapper functions[3] around duckdb[4] so I could directly query R dataframes and datatables with SQL using that backend, rather than sqldf[5].

[1] https://juliadata.github.io/DataFramesMeta.jl/stable/#@linq-...

[2] https://www.queryverse.org/Query.jl/stable/linqquerycommands...

[3] https://github.com/phillc73/duckdf

[4] https://duckdb.org/

[5] https://cran.r-project.org/web/packages/sqldf/index.html