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For people discovering k for the first time, I wrote https://xpqz.github.io/kbook https://xpqz.github.io/kbook as a gentle intro.
by skruger 3y ago
For people discovering k for the first time, I wrote https://xpqz.github.io/kbook https://xpqz.github.io/kbook as a gentle intro.
- graemep 3y agoI am interested, but one thing I have still not quite understood is where to use this family of languages. As far as I can tell they are best for data analysis but not for heavy numerical computation because most or all of them lack GPU support. is that right or have I got it wrong? Are there any other use cases?
- scrawl 3y agoq/kdb+ is used in finance (banking + funds) for heavy numerical computation every day. high-volume realtime data straight from markets, and petabyte/trillion-row historical DBs. it runs on CPU but computation easily parallelizes over cores/clusters. regarding use cases, see https://kx.com/resources/use-cases/ https://kx.com/resources/use-cases/
- graemep 3y agoThanks, that gives me better feel for it. Mostly analytics, good with large datasets, but probably not great for things where you get a big gain from GPU?
- scrawl 3y agowhat tasks are you thinking? i'm not a gpu expert q is good with bulk operations on compact arrays; these are cache-friendly and the interpreter can utilize cache-level parallelism. and with q it's convenient to go from idea -> MVP in short time. it's a high-level language with functional features so expressing algos and complex logic is natural. but it's interpreted and optimized for array ops. so really latency-critical (e.g. high-freq trading) or highly scalar logic will be done with C++. the trade-off is convenience of development.
- cachvico 3y agoHedge funds use ML