join performance regression

[jangorecki]

introduced between a45cc50...31fbad7

reproduced using

cd db-benchmark
source ./pydatatable/py-pydatatable/bin/activate
pip install --upgrade git+https://github.com/h2oai/datatable.git@a45cc503494571bfbf0feb00ece03ad0bab16dfc
./_launcher/solution.R --solution=pydatatable --task=join --nrow=1e7 --quiet=true --out=dt-time.csv
sleep 5
./_launcher/solution.R --solution=pydatatable --task=join --nrow=1e8 --quiet=true --out=dt-time.csv
sleep 5
pip install --upgrade git+https://github.com/h2oai/datatable.git@31fbad7b471113e30f34de8cc2b321c76f926c32
./_launcher/solution.R --solution=pydatatable --task=join --nrow=1e7 --quiet=true --out=dt-time.csv
sleep 5
./_launcher/solution.R --solution=pydatatable --task=join --nrow=1e8 --quiet=true --out=dt-time.csv
deactivate
R

library(data.table)
fctr = function(x) factor(x, levels=unique(x)) ## this retains order for strings
d = fread("dt-time.csv")
dd = d[run==1L, .(time_sec, in_rows, question=fctr(question), git=fctr(substr(git,1,7)))]
dcast(dd, in_rows+question~git, value.var="time_sec")

      in_rows               question a45cc50 31fbad7
 1:  10000000     small inner on int   7.419  10.852
 2:  10000000    medium inner on int  12.999  13.902
 3:  10000000    medium outer on int   8.128   6.900
 4:  10000000 medium inner on factor  14.145  10.085
 5:  10000000       big inner on int  16.316  27.621
 6: 100000000     small inner on int  39.371 154.445
 7: 100000000    medium inner on int  33.088 208.082
 8: 100000000    medium outer on int  24.478  94.655
 9: 100000000 medium inner on factor  33.831 163.471
10: 100000000       big inner on int 114.547 153.472