加速write.table的性能

Question

我有一个data.frame，我想把它写出来。我的data.frame的尺寸是256x65536列。write.csv有哪些速度更快的替代方案

Answer 1

如果所有列都属于同一类，则在写出之前转换为矩阵，可提供近6倍的速度。此外，您还可以从MASS包中研究如何使用write.matrix()，尽管在本例中并没有证明它更快。也许我没有正确设置一些东西：

#Fake data
m <- matrix(runif(256*65536), nrow = 256)
#AS a data.frame
system.time(write.csv(as.data.frame(m), "dataframe.csv"))
#----------
#   user  system elapsed 
# 319.53   13.65  333.76 

#As a matrix
system.time(write.csv(m, "matrix.csv"))
#----------
#   user  system elapsed 
#  52.43    0.88   53.59 

#Using write.matrix()
require(MASS)
system.time(write.matrix(m, "writematrix.csv"))
#----------
#   user  system elapsed 
# 113.58   59.12  172.75 

编辑

为了解决下面提出的上述结果对data.frame不公平的问题，这里有一些更多的结果和时间，以表明总体消息仍然是“如果可能，将您的数据对象转换为矩阵。如果不可能，请处理它。或者，如果时间非常重要，请重新考虑为什么需要以CSV格式写出200MB+文件”：

#This is a data.frame
m2 <- as.data.frame(matrix(runif(256*65536), nrow = 256))
#This is still 6x slower
system.time(write.csv(m2, "dataframe.csv"))
#   user  system elapsed 
# 317.85   13.95  332.44
#This even includes the overhead in converting to as.matrix in the timing 
system.time(write.csv(as.matrix(m2), "asmatrix.csv"))
#   user  system elapsed 
#  53.67    0.92   54.67 

所以，没有什么真正的改变。要确认这是合理的，请考虑as.data.frame()的相对时间成本

m3 <- as.matrix(m2)
system.time(as.data.frame(m3))
#   user  system elapsed 
#   0.77    0.00    0.77 

所以，并不像下面的评论所相信的那样，这真的是一个大问题或歪曲信息。如果您仍然不相信在大型data.frames上使用write.csv()是一个性能方面的坏主意，请参考Note下面的手册

write.table can be slow for data frames with large numbers (hundreds or more) of
columns: this is inevitable as each column could be of a different class and so must be
handled separately. If they are all of the same class, consider using a matrix instead.

最后，如果您仍然因为更快地保存东西而失眠，请考虑转移到本机RData对象

system.time(save(m2, file = "thisisfast.RData"))
#   user  system elapsed 
#  21.67    0.12   21.81

Answer 2

data.table::fwrite()由Otto Seiskari贡献，并在1.9.8+版本中可用。Matt在top上做了额外的增强(包括并行化)，并写了关于它的an article。请在tracker上报告任何问题。

首先，这里是与上面的@chase使用的相同维度的比较(例如，非常大量的列：65,000列(!) x 256行)，以及fwrite和write_feather，这样我们就有了一些跨机器的一致性。注意compress=FALSE在base R中产生的巨大差异。

# -----------------------------------------------------------------------------
# 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  |

注意，fwrite()是并行运行的。这里显示的时序是在13英寸的Macbook Pro上，具有2个内核和1个线程/内核(+2个虚拟线程通过超线程)，512 L4固态硬盘，256KB/内核L2缓存和4MB L4缓存。根据您的系统规格，可以选择YMMV。

我还在相对更可能(也更大)的data上重新运行了基准测试

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  |
# -------------------------------------------------------------------------------

因此，在此测试中，fwrite比feather快约2倍。这是在上面提到的同一台机器上运行的，fwrite在2个内核上并行运行。

feather似乎也是非常快的二进制格式，但还没有压缩。

这里尝试展示fwrite在规模方面的比较：

注意:基准测试已经通过使用compress = FALSE运行base R的save()进行了更新(因为羽化也是未压缩的)。

​

​

因此，在所有这些数据上，fwrite是最快的(在2个内核上运行)，而且它还创建了一个.csv，可以轻松地查看、检查和传递到grep、sed等。

用于再现的代码：

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()

Answer 3

另一种选择是使用feather文件格式。

df <- as.data.frame(matrix(runif(256*65536), nrow = 256))

system.time(feather::write_feather(df, "df.feather"))
#>   user  system elapsed 
#>  0.237   0.355   0.617 

Feather是一种二进制文件格式，其读写效率非常高。它被设计用于多种语言:目前有R和python客户端，julia客户端正在开发中。

为了便于比较，下面是saveRDS所需的时间：

system.time(saveRDS(df, "df.rds"))
#>   user  system elapsed 
#> 17.363   0.307  17.856

现在，这是一个有点不公平的比较，因为saveRDS的默认设置是压缩数据，而这里的数据是不可压缩的，因为它是完全随机的。关闭压缩会显著提高saveRDS的速度：

system.time(saveRDS(df, "df.rds", compress = FALSE))
#>   user  system elapsed 
#>  0.181   0.247   0.473     

事实上，它现在比羽毛稍微快一点。那么为什么要使用羽毛呢？嗯，它通常比readRDS()快，而且与读取数据的次数相比，通常写入数据的次数相对较少。

system.time(readRDS("df.rds"))
#>   user  system elapsed 
#>  0.198   0.090   0.287 

system.time(feather::read_feather("df.feather"))
#>   user  system elapsed 
#>  0.125   0.060   0.185