[Chirono]

I used to use K professionally inside a hedge fund a few years back. Aside from the terrible user experience (if your code isn’t correct you will often just get ‘error’ or ‘not implemented’ with no further detail), if the performance really was as stellar as claimed, then there wouldn’t need to be a no benchmark clause in the license. It can be fast, if your data is in the right formats, but not crazy fast. And easy to beat if you can run your code on the GPU.

[lmeyerov]

The last point is spot on... Pandas on GPUs (cudf) gets you both the perf + usability, without having to deal with issues common to stack/array languages (k) and lazy languages (dask, polars). My flow is pandas -> cudf -> dask cudf , spark, etc.

More recently, we have been working on GFQL with users at places like banks (graph dataframe query language), where we translate down to tools like pandas & cudf. A big "aha" is that columnar operations are great -- not far from what array languages focus on -- and having a static/dynamic query planner so optimizations around that helps once you hit memory limits. Eg, dask has dynamic DFS reuse of partitions as part of its work stealing. More SQL-y tools like Spark may make plans like that ahead of time. In contrast, that lands more on the user if they stick with pandas or k, eg, manual tiling.

[anonu]

I've been using kdb/q since 2010. Started at a big bank and have used it ever since.

Kdb/q is like minimalist footwear. But you can run longer and faster with it on. There's a tipping point where you just "get it". It's a fantastic language and platform.

The problem is very few people will pay 100k/month for shakti. I'm not saying people won't pay and it won't be a good business. But if you want widespread adoption you need to create and an ecosystem. Open sourcing it is a start. Creating libraries and packages comes after. The mongodb model is the right approach IMO

[ccorcos]

Can you elaborate on what mongo did right? My understanding is that AWS is stealing their business by creating a compatible api

[wink]

MongoDB's biggest feat is marketing. Recovering from people comparing your database with a black hole is... something.

[tiffanyh]

Would you recommend K?

Is something else better (if so what)?

[chongli]

I think the main reason to use any of these array languages (for work) is job security. Since it's so hard to find expert programmers if you can get your employer to sign off on an array language for all the mission-critical stuff then you can lock in your job for life! How can they possibly replace you if they can't find anyone else who understands the code?

Otherwise, I don't see anything you can do in an array language that you couldn't do in any other language, albeit less verbosely. But I believe in this case a certain amount of verbosity is a feature if you want people to be able to read and understand the code. Array languages and their symbol salad programs are like the modern day equivalent of medieval alchemists writing all their lab notes in a bespoke substitution cipher. Not unbreakable (like modern cryptography) but a significant enough barrier to dissuade all but the most determined investigators.

As an aside, I think the main reason these languages took off among quants is that investing as an industry tends toward the exultation of extremely talented geniuses. Perhaps unintelligible "secret sauce" code has an added benefit of making industrial espionage more challenging (and of course if a rival firm steals all your code they can arbitrage all of your trades into oblivion).

[rscho]

I'm sorry but you really sound like you judge APLs from an outsider pov. For sure, it's not job security that's keeping APLs afloat, because APLs are very easy to learn. A pro programmer would never use K or any APL. But pro mathematicians or scientists needing some array programming for their job will.

[nathan_compton]

I have been a programmer, scientist, etc, at various times in my life and I have programmed in J. I don't think there is any compelling reason to use J over Matlab, R, or Python and very many reasons not to. Vector languages are mind expanding, for sure, but they have done a very poor job keeping up with the user experience and network effects of newer languages.

A few years ago I wrote a pipeline in J and then re-implemented it R. The J code was exactingly crafted and the R code was naive, but the R code was still faster, easier to read and maintain and, frankly, easier to write. J gives a certain perverse frisson, but beyond that I don't really see the use case.

[rscho]

I disagree. For the stuff I'm doing, I've been rewriting the same 1-screen program in different ways many times. J made that easy, R would make that much more tedious since I'd have >250 lines instead of 30. Of course if I'm doing something for which R has libs, I'll do it in R. Additionally, I'll very probably end up rewriting my final program in another language for speed and libs, because by then I'll know precisely what I want to write. IMO, the strength of array languages lies in easy experimentation.

[nathan_compton]

I think there is an element of truth to this, but the context where this ends up being an advantage is personal and idiosyncratic.

[rscho]

Are you a quant, or doing very specialized numerical things that aren't in libs on 2D datasets? Then 10X yes. If not, no. Everything else will be better

[nathan_compton]

I'm not a quant but having used "data science" languages, ocaml, J, R, etc, I strongly doubt that an array language offers any substantial advantages at this date. I could be wrong, of course, but it seems unlikely.

[rscho]

J only is a data science language in the sense that its core is suited to dataset manipulation, but it's severely lacking in modelling librairies. If you're doing exotic stuff and you know exactly what you're doing, I can see the rationale but otherwise it's R/Python any day. It makes plenty of sense for custom mathematical models, such as simulations though.

[koolala]

J and OPFS

[koolala]

no benchmark clause sounds like webgpu