Comment by eggy | Hacker News Reader

Comment by eggy | Hacker News Reader

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TS enhanced version of riak_core, got it.

Most TS, or tick DBs, are columnar-based, memory-mapped, fast and light systems.

Are there any benchmarks similar to STAC-M3, which is a year's worth of NYSE data run on different hardware to gauge kdb+'s effectiveness on different hardware configurations [1]? It's a great way to gauge performance and TCO.

Does it do both memory (streaming data) and disk-based (historical) storage for big data set analytics in realtime?

I'd be interested to see numbers there.

A lot of people think kdb+ is only for finance. There is a conference coming up in May that will have talks on q (the language for the kdb+ database) about natural language processing and machine learning in q to name a few. Another is about using it at a power plant to most efficiently route power based upon realtime data [2].

I only got into kdb+ and q with the free, non-commercial 32-bit version. I usually use J and sometimes APL, which had MapReduce since at least the 80s for APL.Check out this post from 2009 [3]. I guess the 'new shiny' bit threw me in your chosen title.

[1] https://stacresearch.com/news/2014/02/13/stac-reports-intel-...

[2] https://kxcon2016.com/agenda/

[3] http://blog.data-miners.com/2009/04/mapreduce-hadoop-everyth...

You might find these benchmarks interesting: http://kparc.com/q4/readme.txt

I was inquiring about benchmarks for RiakTS, but your link was perfect. I am a J/APL dabbler, and quite recently learning kdb+/q (I prefer k).

As much as I step away from these languages, I always find my way back to them in strange ways. I was studying music, and there was a great J article in Vector magazine written in August 2006 [1] that walks through scales, and other musical concepts in J.

A Forth-based music software called Sporth [2] has a kona ugen in it, so you can generate scales or other musical items in kona, and then use them in the stack-based Sporth audio language.

My interests in kdb+/q, k, J and APL are in applying them to mathematical investigations of music, visuals, doing data analysis, and then just code golfing, or toying around. They're so much fun!

I need more time on large streaming datasets (Time Series data), than large disk-based datasets to really test latencies. I am building a box much better suited for it than my current machine. The goal is to stay in RAM as much as possible.

[1] http://archive.vector.org.uk/art10010610

[2] https://github.com/PaulBatchelor/Sporth

You should definitely check out JohnEarnest/RodgerTheGreat's iKe, built on his open source k interpreter in JS. Fun examples: http://johnearnest.github.io/ok/ike/ike.html?gist=bbab46d613... and http://johnearnest.github.io/ok/ike/ike.html?gist=b741444d04...

https://github.com/JohnEarnest/ok/tree/gh-pages/ike

https://github.com/JohnEarnest/ok

And related APL/J/K subreddit: https://www.reddit.com/r/apljk/

I had stumbled upon John's work before. I am currently dabbling with a stack-based audio language called Sporth [1], and messing with the idea of somehow mashing it up with John's ike project.

See, vector/array languages aren't just for FinTech or Time Series!

[1] https://github.com/PaulBatchelor/Sporth

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