How to speed up rbind?

Question

I'm supposed to download a table from MS-SQL server.

The number of row is larger than 6million. The server cannot return entire data at once.

So, I wrote a code that downloads 10,000 rows at a time. and, it binds rows in the loop.

Assume that getData() function returns a data frame contains 10000 rows at a time. (Pseudo Code)

for(i in 1:600) {     tempValue <- getData()     wannagetValue <- rbind(wannagetValue,tempValue)     print(i) } 

The problem is that it gets slower as time goes by.

I think using rbind like that way is not a good idea.

Any advice will be very helpful. Thank you in advance.

Answer 1

Here are a few options that I'm sure could be better:

library(data.table) library(microbenchmark)  #function to generate your data getData <- function(){   data.frame(x=rnorm(10000),y=rnorm(10000),z=rnorm(10000)) }  #using data table's rbindlist each iteration fDT1 <- function(n){   dat <- getData()   for(i in 1:n){     dat <- rbindlist(list(dat,getData()))   }   return(data.frame(dat)) }  #using data table's rbindlist all at once fDT2 <- function(n){   return(data.frame(rbindlist(lapply(1:n,function(x) getData())))) }  #pre-allocating a data frame fPre <- function(n){   dat <- data.frame(x=rep(0,n*10000),y=rep(0,n*10000),z=rep(0,n*10000))   j <- 1   for(i in 1:n){     dat[j:(j+10000-1),] <- getData()     j <- j + 10000   }   return(dat) }  #standard do.call rbind f2 <- function(n){   return(do.call(rbind,lapply(1:n,function(x) getData()))) }  #current approach f <- function(n){   dat <- getData()   for(i in 1:n){     dat <- rbind(dat,getData())   }   return(dat) } 

As you can see using data.table's rbindlist() is a big improvement over base R's rbind() and there is a big benefit in appending rows all at once instead of in interations, however that may not be possible if there are memory concerns. You may also note that the speed improvements are nowhere near linear as the size of data increases.

 > microbenchmark(fDT2(5),fDT1(5),fPre(5),f2(5),f(5), +                fDT2(25),fDT1(25),fPre(25),f2(25),f(25), +                fDT2(75),fDT1(75),fPre(75),f2(75),f(75), +                times=10) Unit: milliseconds      expr        min         lq     median         uq         max neval   fDT2(5)   18.31207   18.63969   24.09943   25.45590    72.01725    10   fDT1(5)   27.65459   29.25147   36.34158   77.79446    88.82556    10   fPre(5)   34.96257   39.39723   41.24445   43.30319    68.75897    10     f2(5)   30.85883   33.00292   36.29100   43.53619    93.15869    10      f(5)   87.40869   97.97500  134.50600  138.65354   147.67676    10  fDT2(25)   89.42274   99.39819  103.90944  146.44160   156.01653    10  fDT1(25)  224.65745  229.78129  261.52388  280.85499   300.93488    10  fPre(25)  371.12569  412.79876  431.80571  485.37727  1046.96923    10    f2(25)  221.03669  252.08998  265.17357  271.82414   281.47096    10     f(25) 1446.32145 1481.01998 1491.59203 1634.99936  1849.00590    10  fDT2(75)  326.66743  334.15669  367.83848  467.85480   520.27142    10  fDT1(75) 1749.83842 1882.27091 2066.95241 2278.55589  2419.07205    10  fPre(75) 3701.16220 3968.64643 4162.70585 4234.39716  4356.09462    10    f2(75) 1174.47546 1183.98860 1314.64585 1421.09483  1537.42903    10     f(75) 9139.36935 9349.24412 9510.90888 9977.24621 10861.51206    10