Programming in K(github.com)
github.com
Programming in K
https://github.com/JohnEarnest/ok/blob/gh-pages/docs/Programming.md
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Similarly, this is how it was introduced in ClickHouse in 2019: https://github.com/ClickHouse/ClickHouse/pull/4774
The aj function at its heart is a bin (https://code.kx.com/q/ref/bin/) search between the two tables, on the requested columns, to find the indices of the right table to zip onto the left table.
A lot of the joins can be distilled to the core operators/functions in a similar manner. For example the plus-join is
aj[`sym`time;t;q]
t,'(`sym`time _q)(`sym`time#q)bin`sym`time#t
A lot of the joins can be distilled to the core operators/functions in a similar manner. For example the plus-join is
x+0i^y(cols key y)#xIndeed, my very first attempt used numpy.searchsorted:
https://numpy.org/doc/2.2/reference/generated/numpy.searchso...
I couldn't figure-out how Arthur's bin matched on symbol though, so I switched to a linear scan on the right table to record the last-seen index for each "by" element. While it worked, my hash table was messy because I relied on Python to handle a whole tuple as a key, which had some issues during initial testing.
The asof join I wrote for Empirical properly categorizes the keys before they are matched. That approach worked far better.
https://www.empirical-soft.com/tutorial.html#dataframes
https://numpy.org/doc/2.2/reference/generated/numpy.searchso...
I couldn't figure-out how Arthur's bin matched on symbol though, so I switched to a linear scan on the right table to record the last-seen index for each "by" element. While it worked, my hash table was messy because I relied on Python to handle a whole tuple as a key, which had some issues during initial testing.
The asof join I wrote for Empirical properly categorizes the keys before they are matched. That approach worked far better.
https://www.empirical-soft.com/tutorial.html#dataframes
dang I had no idea you wrote the asof join for pandas. thank you for that
I wish there was a language like K that worked with single precision floats. Would be great to use with graphics.
the github repo linked, under "manual.md", has a link which redirects to malware, such as that ostensibly to "k6".
ty, I made a pull request: https://github.com/JohnEarnest/ok/pull/111
K fucking rules if you're trying to minmax a game or balance your own.
Figuring out things like "what percentage of the time will my starting hand contain the cards I need for my deck to function, and if it doesn't, how many mulligans will it take" will basically ruin competitive MTG for you. I used to buy physical verisons of the decks I made it to top 500 with in Arena, stopped because it wasn't a fun challenge anymore (and lost all interest when they started dragging other IPs in, they'll never get another cent from me)
Figuring out things like "what percentage of the time will my starting hand contain the cards I need for my deck to function, and if it doesn't, how many mulligans will it take" will basically ruin competitive MTG for you. I used to buy physical verisons of the decks I made it to top 500 with in Arena, stopped because it wasn't a fun challenge anymore (and lost all interest when they started dragging other IPs in, they'll never get another cent from me)
K[ryptic!]*
*I appreciate mathematical notation, but this is not it.
*I appreciate mathematical notation, but this is not it.
Fun fact: the aj (asof join) function was my inspiration for pandas.merge_asof. I added the extra parameters (direction, tolerance, allow_exact_matches) because of the limitations I kept hitting in kdb.
https://code.kx.com/q/ref/aj/
https://pandas.pydata.org/docs/reference/api/pandas.merge_as...