Column-wise processing on spark?

Are there efficient ways to process data column-wise (vs row-wise) in spark?

I'd like to do some whole-database analysis of each column. I'd like to iterate through each column in a database and compare it to another column with a significance test.

colA = "select id, colA from table1"

foreach table, t:
   foreach id,colB in t: # "select id, colB from table2"
     # align colA,colB by ID
     ab = join(colA,colB)
     yield comparefunc(ab)

I have ~1M rows but ~10k columns. Issuing ~10k selects is very slow, but shouldn't I be able to do a select * and broadcast each column to a different node for processing.