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chrisaycock • yesterday at 10:36 PM • 2 replies • view on HN

I first encountered q/kdb+ at a quant job in 2007. I learned so much from the array semantics about how to concisely represent time-series logic that I can't imagine ever using a scalar language for research.

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...

Replies

leprechaun1066 • today at 12:07 AM

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.

  aj[`sym`time;t;q]
becomes

  t,'(`sym`time _q)(`sym`time#q)bin`sym`time#t
The rest of the aj function internals are there to handle edge cases, handling missing columns and options for filling nulls.

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)#x
➕ show 1 reply
zX41ZdbW • yesterday at 11:03 PM

Similarly, this is how it was introduced in ClickHouse in 2019: https://github.com/ClickHouse/ClickHouse/pull/4774