12

编辑3：

我创建了一个更短的内存泄漏示例。我希望它可以更容易地推理正在发生的事情。随着迭代的进行，您会看到 gc() VCell 内存使用量稳步增加，而 tables() 报告的内存使用量保持不变。不知何故， unlist(.SD) 调用似乎是负责任的。这里是：

DT = data.table(k = 1:100, g = 1:20, val = rnorm(2e6))
for (i in 1:100){
  tmp = DT[ , unlist(.SD), by = 'k']
  print(gc())
  tables()
}

原帖：

使用 data.table 包时，我看到一些我不理解的内存行为。我将 R-2.13.0 与 data.table 1.8.8 一起使用。我在 64 位 suse linux 上运行。

我的最终目标是使用尽可能少的内存将 data.table 从“长”格式重塑为“宽”格式。我遵循了另一个 [SO 帖子] 中的建议（在多个列上嵌套了 if else 语句）。基本上，我尝试重塑在 j 表达式中返回命名列表的 data.table。

我看到内存使用量稳步增加，这似乎是内存泄漏。data.tables 或其他对象使用的总内存不考虑 gc() 中显示的内容。特别是，Vcells 从大约 17 MB 开始，到接近 30 MB 结束，而 tables() 报告的总内存使用量为 19 MB（最后）。没有其他对象（我可以看到）使用任何有意义的内存量。重复运行下面的代码显示了 print(gc()) 语句的内存使用量增加。

我是否遗漏了什么或者 dogroups.c 中的某些内存分配有问题？

这是重现我看到的问题的代码。有任何想法吗？我真的希望能够相对有效地重塑 data.table，内存使用比速度更重要。

library(data.table)

if(!exists('DT')){
  cat('creating DT\n')
  # make a "long" matrix with 300 columns and keys v,d
  v = 1:250
  d = 1:50
  grid = expand.grid(v,d)
  DT = data.table(v = grid[,1], d = grid[,2])    
  # now add many columns
  DT[,sprintf('col%s',1:100) := 1:nrow(DT)]; 
  # set d as key, we don't care much about v for this example
  setkey(DT,'d')
}

# The following code attempts to cast a "long" data.table to "wide" format
# it is the equivalent the reshape2 call:
#
#   dcast(melt(DT, c('d','v')), d ~ v + variable, value_var='value')
#
# When I run the code I see ever-increasing memory use.  sourcing the file
# repeatedly shows that as well. The total memory used by the input
# and result data.table or any other objects do not account for the total use.


# casting patterned after
# https://stackoverflow.com/questions/15510566/nested-if-else-statements-over-a-number-of-columns/15511689?noredirect=1#comment21968080_15511689

paste.dash <- function(...){ paste(..., sep='-')}    

# assumes keys is  a vector of characters
dt.melt <- function(dt, keys) {
  dt[, list(variable = names(.SD), value = unlist(.SD)), by = keys]
}

# assumes keys is  a vector of characters.
# all.names is all the column names we expect in the wide data.table
# we accommodate for the possibility of missing wide table values 
# for some groups by appending NAs for any column names not present.
# in the particular example above there are no missing values,
# but the data I intend to run this on does.
dt.recast<- function(dat, keys, all.names,verbose=FALSE){

  if (verbose){
    cat(sprintf('dt.recast(): keys = %s\n', paste(keys, collapse=',')))
    print(gc())
  }
  # id, variable, value
  m = dt.melt(dat, keys)

  # m.names will be the wide table column names.
  m.names = do.call(paste.dash, m[, c(keys,'variable'),  with=FALSE])

  #append anything that's missing in this group to end of list with NA values
  missing.names = setdiff(all.names, m.names)
  missing.vals = rep(NA_real_, length(missing.names))
  ret.val = c(m$value, missing.vals)
  # set names and make a list as required by data.table to generate a wide row
  ret.val = as.list(setattr(ret.val,'names', c(m.names,missing.names)))

  if (verbose){
    print(gc())
  }

  return(ret.val)
}

# turn to wide format row key 'd': columns are cartesian product of v and
# current non-key columns

all.wide.names = do.call(paste.dash, expand.grid(unique(DT$v), tail(names(DT),-2)))

print (gc())

DT.wide = DT[ , dt.recast(.SD, 'v', all.wide.names, verbose = TRUE),
  by = 'd',
  verbose=TRUE ]

print (gc())

编辑：

#Here is the output of sessionInfo
> sessionInfo()
R version 2.13.0 (2011-04-13)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=C              LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C   \
               LC_ADDRESS=C
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] data.table_1.8.8
>

Edit2：这是两次连续运行的一些输出。

> source('memory-leak.R')
data.table 1.8.8  For help type: help("data.table")
creating DT
         used (Mb) gc trigger (Mb) max used (Mb)
Ncells 231906 12.4     407500 21.8   350000 18.7
Vcells 272022  2.1     786432  6.0   773683  6.0
Finding groups (bysameorder=TRUE) ... done in 0.001secs. bysameorder=TRUE and o__ is length 0
Optimization is on but j left unchanged as 'dt.recast(.SD, "v", all.wide.names, verbose = TRUE)'
Starting dogroups ... dt.recast(): keys = v
         used (Mb) gc trigger (Mb) max used (Mb)
Ncells 233168 12.5     467875   25   350000 18.7
Vcells 292303  2.3     786432    6   773683  6.0
         used (Mb) gc trigger (Mb) max used (Mb)
Ncells 258224 13.8     531268 28.4   350000 18.7
Vcells 474776  3.7     905753  7.0   773683  6.0
The result of j is a named list. It's very inefficient to create the same names over and over again for each group. When j=list(...), any names are detected, removed and put back after grouping has completed, for efficiency. Using j=transform(), for example, prevents that speedup (consider changing to :=). This message may be upgraded to warning in future.
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283206 15.2     531268 28.4   350000 18.7
Vcells 1699595 13.0    2029708 15.5  1699607 13.0
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308232 16.5     597831   32   350000 18.7
Vcells 1882303 14.4    2221551   17  2029708 15.5
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1732347 13.3    2412628 18.5  2029708 15.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831   32   350000 18.7
Vcells 1915666 14.7    2613259   20  2284358 17.5
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1764847 13.5    2823921 21.6  2284358 17.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 1948166 14.9    3045117 23.3  2316858 17.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1797347 13.8    3045117 23.3  2316858 17.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 1980666 15.2    3277372 25.1  2349358 18.0
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1829847 14.0    3277372 25.1  2349358 18.0
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2013166 15.4    3277372 25.1  2381858 18.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1862347 14.3    3277372 25.1  2381858 18.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2045666 15.7    3277372 25.1  2414358 18.5
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1894847 14.5    3277372 25.1  2414358 18.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2078166 15.9    3277372 25.1  2446858 18.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1927347 14.8    3277372 25.1  2446858 18.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2110666 16.2    3277372 25.1  2479358 19.0
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1959847 15.0    3277372 25.1  2479358 19.0
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2143166 16.4    3521240 26.9  2511858 19.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 1992347 15.3    3521240 26.9  2511858 19.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2175666 16.6    3521240 26.9  2544358 19.5
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2024847 15.5    3521240 26.9  2544358 19.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2208166 16.9    3521240 26.9  2576858 19.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2057347 15.7    3521240 26.9  2576858 19.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2240666 17.1    3521240 26.9  2609358 20.0
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2089847 16.0    3521240 26.9  2609358 20.0
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2273166 17.4    3521240 26.9  2641858 20.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2122347 16.2    3521240 26.9  2641858 20.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2305666 17.6    3521240 26.9  2674358 20.5
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2154847 16.5    3521240 26.9  2674358 20.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2338166 17.9    3777302 28.9  2706858 20.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2187347 16.7    3777302 28.9  2706858 20.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2370666 18.1    3777302 28.9  2739358 20.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2219847 17.0    3777302 28.9  2739358 20.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2403166 18.4    3777302 28.9  2771858 21.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2252347 17.2    3777302 28.9  2771858 21.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2435666 18.6    3777302 28.9  2804358 21.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2284847 17.5    3777302 28.9  2804358 21.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2468166 18.9    3777302 28.9  2836858 21.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2317347 17.7    3777302 28.9  2836858 21.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2500666 19.1    4046167 30.9  2869358 21.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2349847 18.0    4046167 30.9  2869358 21.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2533166 19.4    4046167 30.9  2901858 22.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2382347 18.2    4046167 30.9  2901858 22.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2565666 19.6    4046167 30.9  2934358 22.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2414847 18.5    4046167 30.9  2934358 22.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2598166 19.9    4046167 30.9  2966858 22.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2447347 18.7    4046167 30.9  2966858 22.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2630666 20.1    4046167 30.9  2999358 22.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2479847 19.0    4046167 30.9  2999358 22.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2663166 20.4    4046167 30.9  3031858 23.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2512347 19.2    4046167 30.9  3031858 23.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2695666 20.6    4328475 33.1  3064358 23.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2544847 19.5    4328475 33.1  3064358 23.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2728166 20.9    4328475 33.1  3096858 23.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2577347 19.7    4328475 33.1  3096858 23.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2760666 21.1    4328475 33.1  3129358 23.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2609847 20.0    4328475 33.1  3129358 23.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2793166 21.4    4328475 33.1  3161858 24.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2642347 20.2    4328475 33.1  3161858 24.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2825666 21.6    4328475 33.1  3194358 24.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2674847 20.5    4328475 33.1  3194358 24.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2858166 21.9    4328475 33.1  3226858 24.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2707347 20.7    4328475 33.1  3226858 24.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2890666 22.1    4624898 35.3  3259358 24.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2739847 21.0    4624898 35.3  3259358 24.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2923166 22.4    4624898 35.3  3291858 25.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2772347 21.2    4624898 35.3  3291858 25.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2955666 22.6    4624898 35.3  3324358 25.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2804847 21.4    4624898 35.3  3324358 25.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 2988166 22.8    4624898 35.3  3356858 25.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2837347 21.7    4624898 35.3  3356858 25.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 3020666 23.1    4624898 35.3  3389358 25.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 2869847 21.9    4624898 35.3  3389358 25.9

... <snip> ...

dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 3162347 24.2    5262949 40.2  3681858 28.1
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 3345666 25.6    5262949 40.2  3714358 28.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 3194847 24.4    5262949 40.2  3714358 28.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 3378166 25.8    5262949 40.2  3746858 28.6
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 3227347 24.7    5262949 40.2  3746858 28.6
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 3410666 26.1    5262949 40.2  3779358 28.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  283211 15.2     597831 32.0   350000 18.7
Vcells 3259847 24.9    5262949 40.2  3779358 28.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  308247 16.5     597831 32.0   350000 18.7
Vcells 3443166 26.3    5262949 40.2  3811858 29.1
done dogroups in 10.972 secs
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  258292 13.8     597831 32.0   350000 18.7
Vcells 3247919 24.8    5262949 40.2  3811858 29.1
> tables()
     NAME      NROW MB COLS                                                                             KEY
[1,] DT      12,500  5 v,d,col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,c d  
[2,] DT.wide     50 14 d,1-col1,1-col2,1-col3,1-col4,1-col5,1-col6,1-col7,1-col8,1-col9,1-col10,1-col11 d  
Total: 19MB
> source('/memory-leak.R')
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  260024 13.9     597831 32.0   350000 18.7
Vcells 3279245 25.1    5262949 40.2  3859228 29.5
Finding groups (bysameorder=TRUE) ... done in 0.001secs. bysameorder=TRUE and o__ is length 0
Optimization is on but j left unchanged as 'dt.recast(.SD, "v", all.wide.names, verbose = TRUE)'
Starting dogroups ... dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  260400 14.0     597831 32.0   350000 18.7
Vcells 3297670 25.2    5262949 40.2  3859228 29.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  285438 15.3     597831 32.0   350000 18.7
Vcells 3480986 26.6    5262949 40.2  3859228 29.5
The result of j is a named list. It's very inefficient to create the same names over and over again for each group. When j=list(...), any names are detected, removed and put back after grouping has completed, for efficiency. Using j=transform(), for example, prevents that speedup (consider changing to :=). This message may be upgraded to warning in future.
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   350000 18.7
Vcells 4705194 35.9    5606096 42.8  4781165 36.5
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 4888513 37.3    5966400 45.6  5257204 40.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   374617 20.1
Vcells 4737694 36.2    6344720 48.5  5257204 40.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 4921013 37.6    6741956 51.5  5289704 40.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   374617 20.1
Vcells 4770194 36.4    7159053 54.7  5289704 40.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 4953513 37.8    7597005   58  5322204 40.7
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4802694 36.7    7597005   58  5322204 40.7
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 4986013 38.1    7597005   58  5354704 40.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4835194 36.9    7597005   58  5354704 40.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 5018513 38.3    7597005   58  5387204 41.2
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4867694 37.2    7597005   58  5387204 41.2
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 5051013 38.6    7597005   58  5419704 41.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4900194 37.4    7597005   58  5419704 41.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 5083513 38.8    7597005   58  5452204 41.6
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4932694 37.7    7597005   58  5452204 41.6
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 5116013 39.1    7597005   58  5484704 41.9
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4965194 37.9    7597005   58  5484704 41.9
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831   32   374617 20.1
Vcells 5148513 39.3    7597005   58  5517204 42.1
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831   32   374617 20.1
Vcells 4997694 38.2    7597005   58  5517204 42.1
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 5181013 39.6    8056855 61.5  5549704 42.4
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   374617 20.1
Vcells 5030194 38.4    8056855 61.5  5549704 42.4
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 5213513 39.8    8056855 61.5  5582204 42.6
dt.recast(): keys = v
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   374617 20.1
Vcells 5062694 38.7    8056855 61.5  5582204 42.6
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 5246013 40.1    8056855 61.5  5614704 42.9
dt.recast(): keys = v

 ... <snip> ...

          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  310409 16.6     597831 32.0   374617 20.1
Vcells 6265194 47.8    9579015 73.1  6784704 51.8
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  335445 18.0     597831 32.0   374617 20.1
Vcells 6448513 49.2    9579015 73.1  6817204 52.1
done dogroups in 11.53 secs
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  260003 13.9     597831 32.0   374617 20.1
Vcells 4978149 38.0    9579015 73.1  6817204 52.1
> tables()
     NAME      NROW MB COLS                                                                             KEY
[1,] DT      12,500  5 v,d,col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,c d  
[2,] DT.wide     50 14 d,1-col1,1-col2,1-col3,1-col4,1-col5,1-col6,1-col7,1-col8,1-col9,1-col10,1-col11 d  
Total: 19MB
>
4

1 回答 1

7

更新- 现在在 v1.8.11 中修复。来自新闻：

固定分组中长期未解决的（通常很小）内存泄漏。当最后一组小于最大组时，这些大小的差异不会被释放。同样在非平凡的聚合中，每个组返回不同数量的行。大多数用户运行一次分组查询并且永远不会注意到，但是任何循环调用分组的人（例如并行运行时）都可能受到影响，#2648。添加了测试。

非常感谢 vc273、YT 和其他人。

这个问题顶部的特定（伟大）示例被认为是“非平凡的”聚合，其中每个组的结果可以是不同数量的行，而不仅仅是一行中的单个聚合。添加verbose=TRUE揭示：

写入的行数 (4000000) 少于分配的行数 (4488000)。

这就是在这种情况下泄漏的地方。仅当您需要多次重复分组时才重要，因为有时需要。结果是正确的。

为后代保留以前的答案...

考虑这部分：

#now add many columns
for (i in 1:100){
    DT[[sprintf('col%s',i)]] = 1:nrow(DT);
}

这不是使用:=或提供的通过引用添加列set()的data.table方法。与;=相同 <-即，在此循环的每次迭代中，将复制for整个内容，以便为单个额外列腾出空间。DT您描述的内存泄漏将与此for循环一致。

一些选项是：

  • 一次添加许多列cbind
  • :=使用例如 一次性添加列DT[,sprintf('col%s',1:100):=1:nrow(DT)]
  • 保持for循环，但在每次迭代中使用:=orset()

我实际上并没有运行你的代码来检查，所以以后可能还会有其他问题。

更新：我现在已经运行了你的代码，我想我可能能够猜到你对内存使用的意思。但是猜测会占用很多时间，尤其是在这样的领域。您能否对此进行大量扩展：

我看到内存使用量稳步增加，这似乎是内存泄漏。

你到底看到了什么；即，数字是多少？它从什么开始，又在什么结束？你跑了多少次？还请提供输出sessionInfo()；尽管您提供的 R (2.13.0) 版本很有帮助，但它有助于了解您是 32 位还是 64 位 Linux、Mac 或 Windows。

于 2013-03-27T07:57:19.463 回答