Tables.jl VS julia

Good questions!

> How exactly does Julia fit into your software architecture?

In a variety of ways:

- We have a bunch of external/internal Julia packages; Julia's package manager is really great at facilitating the development of "tooling ecosystems" comprised of lightweight libraries that compose well together. For example, we use Legolas.jl [1] in conjunction with a well-curated Arrow-in-S3 lake to help teams define lightweight, self-serviceable schemas for Arrow tables in a manner that integrates well with the wider Tables.jl ecosystem [2], interactive analysis workflows, and our own ETL/ELT-ish patterns.

- Julia powers some interesting services within Beacon's Platform. For example, one of our Julia services provides dynamic streaming DSP (multiplexing, filtering, statistics) for biosignal data, atop which we build other applications/pipelines for both product development and internal analysis work.

- We use Julia for exploratory distributed computing on K8s [3], which is awesome because Julia has a lot of potential in the distributed computing landscape (IMO [4]).

> Is your product a cloud offering and/or does it have a client side application?

We work with our clients to do neurobiomarker discovery, clinical trial design, deploy our analysis pipelines into clinical trials, and a few other interesting things :) One of the critical differentiators of Beacon is that we can precisely target and harness key EEG features to a degree that isn't possible without the kind of algorithms/tools we've developed.

> what do you even mean by data architecture for science-first teams

I want to do a blog post on this at some point, but a core value for us - across all of our processes, tooling, and data interactions - is self-serviceability and composability. IMO, the two are inextricably linked. Our goal is to empower each Beaconeer to perform analyses in an afternoon atop terabytes of data that would take them months in a lab atop gigabytes of data.

To achieve this, we treat large-scale data curation/manipulation as an activity that we're all empowered to participate in and contribute to, as opposed to an environment where separate data engineering teams have to administrate siloed systems. Tools like K8s/Julia/Arrow are key enablers here, by surfacing capabilities to domain experts that let them to iterate fast without needing to "throw problems over the wall" to other teams/systems.

It's not a perfect match, and it's a bit abstract, but I remember reading this post about "data meshes" [5] a while back and thinking "Hey, that's similar to what we're chasing after!"

[1] https://github.com/beacon-biosignals/Legolas.jl

[2] https://github.com/JuliaData/Tables.jl

[3] https://github.com/beacon-biosignals/K8sClusterManagers.jl

[4] https://news.ycombinator.com/item?id=24842084

[5] https://martinfowler.com/articles/data-mesh-principles.html

As you progress you'll likely learn to be a bit more relaxed about types - there's a Table Interface that JuliaDB implements along with many other data sources.But this should get you going.

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...