SQLite vs R data.table

sqlite_vs_datatable.txt

sqlite vs R's data.table

TLDR;   sqlite (:memory:) 250 sec    data.table 7 sec


data: 100 million rows, 1 million groups
generated by: https://github.com/szilard/benchm-databases/blob/master/0-gendata.txt


sqlite3 :memory:
## SQLite version 3.8.4.1

create table d(x int, y double);
.mode csv
.import d-noh.csv d  
.timer on 

select x, avg(y) as ym 
from d 
group by x
order by ym desc 
limit 5;

## Run Time: real 249.674 user 227.471406 sys 13.416719



R
## R version 3.2.2

library(data.table)
## data.table 1.9.6

d <- fread("d-noh.csv")
setnames(d, c("x","y"))

system.time(
  print(head(d[, list(ym=mean(y)), by=x][order(-ym)],5))
)

##   user  system elapsed
##  6.811   0.176   7.005

Ah so :) Perhaps you can consider adding the obvious indexes to the db benchmarks - I think it represents a more realistic use of databases. If not, the benchmarks will benefit from an explanation about why db indexes are left out, imho. Anyways, interesting work!

Hi Szilard,

just sent you a PR to also check MonetDBLite (R package, https://www.monetdb.org/blog/monetdblite-r), which does these queries in < 10 seconds on my box.

When running on the m3.2xlarge instance and following your test protocol, I get the following timings: Group query: 7.0s Join query: 1.5s.

Awesome, thanks :) I added it to the benchmark: https://github.com/szilard/benchm-databases