r - Speeding up the performance of write.table

Question

I have a data.frame and I want to write it out. The dimensions of my data.frame are 256 rows by 65536 columns. What are faster alternatives to write.csv?

Answer 1

data.table::fwrite() was contributed by Otto Seiskari and is available in versions 1.9.8+. Matt has made additional enhancements on top (including parallelisation) and wrote an article about it. Please report any issues on the tracker.

First, here's a comparison on the same dimensions used by @chase above (i.e., a very large number of columns: 65,000 columns (!) x 256 rows), together with fwrite and write_feather, so that we have some consistency across machines. Note the huge difference compress=FALSE makes in base R.

# -----------------------------------------------------------------------------
# function  | object type |  output type | compress= | Runtime | File size |
# -----------------------------------------------------------------------------
# save      |      matrix |    binary    |   FALSE   |    0.3s |    134MB  |
# save      |  data.frame |    binary    |   FALSE   |    0.4s |    135MB  |
# feather   |  data.frame |    binary    |   FALSE   |    0.4s |    139MB  |
# fwrite    |  data.table |    csv       |   FALSE   |    1.0s |    302MB  |
# save      |      matrix |    binary    |   TRUE    |   17.9s |     89MB  |
# save      |  data.frame |    binary    |   TRUE    |   18.1s |     89MB  |
# write.csv |      matrix |    csv       |   FALSE   |   21.7s |    302MB  |
# write.csv |  data.frame |    csv       |   FALSE   |  121.3s |    302MB  |

Note that fwrite() runs in parallel. The timing shown here is on a 13' Macbook Pro with 2 cores and 1 thread/core (+2 virtual threads via hyperthreading), 512GB SSD, 256KB/core L2 cache and 4MB L4 cache. Depending on your system spec, YMMV.

I also reran the benchmarks on relatively more likely (and bigger) data:

library(data.table)
NN <- 5e6 # at this number of rows, the .csv output is ~800Mb on my machine
set.seed(51423)
DT <- data.table(
  str1 = sample(sprintf("%010d",1:NN)), #ID field 1
  str2 = sample(sprintf("%09d",1:NN)),  #ID field 2
  # varying length string field--think names/addresses, etc.
  str3 = replicate(NN,paste0(sample(LETTERS,sample(10:30,1),T), collapse="")),
  # factor-like string field with 50 "levels"
  str4 = sprintf("%05d",sample(sample(1e5,50),NN,T)),
  # factor-like string field with 17 levels, varying length
  str5 = sample(replicate(17,paste0(sample(LETTERS, sample(15:25,1),T),
      collapse="")),NN,T),
  # lognormally distributed numeric
  num1 = round(exp(rnorm(NN,mean=6.5,sd=1.5)),2),
  # 3 binary strings
  str6 = sample(c("Y","N"),NN,T),
  str7 = sample(c("M","F"),NN,T),
  str8 = sample(c("B","W"),NN,T),
  # right-skewed (integer type)
  int1 = as.integer(ceiling(rexp(NN))),
  num2 = round(exp(rnorm(NN,mean=6,sd=1.5)),2),
  # lognormal numeric that can be positive or negative
  num3 = (-1)^sample(2,NN,T)*round(exp(rnorm(NN,mean=6,sd=1.5)),2))

# -------------------------------------------------------------------------------
# function  |   object   | out |        other args         | Runtime  | File size |
# -------------------------------------------------------------------------------
# fwrite    | data.table | csv |      quote = FALSE        |   1.7s   |  523.2MB  |
# fwrite    | data.frame | csv |      quote = FALSE        |   1.7s   |  523.2MB  |
# feather   | data.frame | bin |     no compression        |   3.3s   |  635.3MB  |
# save      | data.frame | bin |     compress = FALSE      |  12.0s   |  795.3MB  |
# write.csv | data.frame | csv |    row.names = FALSE      |  28.7s   |  493.7MB  |
# save      | data.frame | bin |     compress = TRUE       |  48.1s   |  190.3MB  |
# -------------------------------------------------------------------------------

So fwrite is ~2x faster than feather in this test. This was run on the same machine as noted above with fwrite running in parallel on 2 cores.

feather seems quite fast binary format as well, but no compression yet.

---

Here's an attempt at showing how fwrite compares with respect to scale:

NB: the benchmark has been updated by running base R's save() with compress = FALSE (since feather also is not compressed).

So fwrite is fastest of all of them on this data (running on 2 cores) plus it creates a .csv which can easily be viewed, inspected and passed to grep, sed etc.

Code for reproduction:

require(data.table)
require(microbenchmark)
require(feather)
ns <- as.integer(10^seq(2, 6, length.out = 25))
DTn <- function(nn)
    data.table(
          str1 = sample(sprintf("%010d",1:nn)),
          str2 = sample(sprintf("%09d",1:nn)),
          str3 = replicate(nn,paste0(sample(LETTERS,sample(10:30,1),T), collapse="")),
          str4 = sprintf("%05d",sample(sample(1e5,50),nn,T)),
          str5 = sample(replicate(17,paste0(sample(LETTERS, sample(15:25,1),T), collapse="")),nn,T),
          num1 = round(exp(rnorm(nn,mean=6.5,sd=1.5)),2),
          str6 = sample(c("Y","N"),nn,T),
          str7 = sample(c("M","F"),nn,T),
          str8 = sample(c("B","W"),nn,T),
          int1 = as.integer(ceiling(rexp(nn))),
          num2 = round(exp(rnorm(nn,mean=6,sd=1.5)),2),
          num3 = (-1)^sample(2,nn,T)*round(exp(rnorm(nn,mean=6,sd=1.5)),2))

count <- data.table(n = ns,
                    c = c(rep(1000, 12),
                          rep(100, 6),
                          rep(10, 7)))

mbs <- lapply(ns, function(nn){
  print(nn)
  set.seed(51423)
  DT <- DTn(nn)
  microbenchmark(times = count[n==nn,c],
               write.csv=write.csv(DT, "writecsv.csv", quote=FALSE, row.names=FALSE),
               save=save(DT, file = "save.RData", compress=FALSE),
               fwrite=fwrite(DT, "fwrite_turbo.csv", quote=FALSE, sep=","),
               feather=write_feather(DT, "feather.feather"))})

png("microbenchmark.png", height=600, width=600)
par(las=2, oma = c(1, 0, 0, 0))
matplot(ns, t(sapply(mbs, function(x) {
  y <- summary(x)[,"median"]
  y/y[3]})),
  main = "Relative Speed of fwrite (turbo) vs. rest",
  xlab = "", ylab = "Time Relative to fwrite (turbo)",
  type = "l", lty = 1, lwd = 2, 
  col = c("red", "blue", "black", "magenta"), xaxt = "n", 
  ylim=c(0,25), xlim=c(0, max(ns)))
axis(1, at = ns, labels = prettyNum(ns, ","))
mtext("# Rows", side = 1, las = 1, line = 5)
legend("right", lty = 1, lwd = 3, 
       legend = c("write.csv", "save", "feather"),
       col = c("red", "blue", "magenta"))
dev.off()