I don't think the authors claim that "The only thing comparable to it from an engineering standpoint are the other APL based databases such as KDB and Jd" is co

I don't think the authors claim that "The only thing comparable to it from an engineering standpoint are the other APL based databases such as KDB and Jd" is correct, at least not based on this blog post. Numpy+pandas also seems pretty similar, as is breeze, and Julia has similar features also. Let me emphasize that while I'm about to implement most of his blog post in numpy/pandas, I don't mean to come off as critical or dismiss building a new one. Kerf looks pretty cool. I just don't think this post gives much reason to believe there is nothing comparable to it. Idiomatic numpy: def wavg(x,y): return sum(x*y) / sum(x) Less idiomatic, but analogous to kerf or J: def wavg(x,y): return add.reduce(x*y) / add.reduce(x) arange(1,6) is equivalent to his range(5), and full((5,), 1) is equivalent to repeat(5,1). The SQL-ish engine looks fairly similar to pandas: df = pandas.DataFrame({'a' : arange(5), 'b' : ['a', 'b', 'c', 'd', 'e']}) #Result is: a b 0 0 a 1 1 b 2 2 c 3 3 d 4 4 e df[df.a > 2] #Result is a b 3 3 d 4 4 e Pandas similarly has joins, averages and other aggregates, etc. E.g.: pandas.merge(df1, df2, how='inner', left_on='a', right_on='b') is equivalent to: SELECT * FROM df1 INNER JOIN df2 ON df1.a = df2.b; Syntactically it's a bit different (embedded SQL is pretty cool) but from this blog post I don't see any major semantic differences. However, if there are important differences under the hood, I'd love to hear about them. Again, I'm really not trying to criticize Locklin for rolling his own, I'm just disputing a minor point.