12

編集3：

メモリ リークのはるかに短い例を作成しました。何が起こっているのかを推論するのがはるかに簡単になることを願っています. 反復が進むにつれて、gc() VCell のメモリ使用量が着実に増加していることがわかりますが、tables() によって報告されるメモリ使用量は同じままです。どういうわけか、unlist(.SD) 呼び出しが原因のようです。ここにあります：

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()
}

元の投稿:

data.table パッケージを使用すると、理解できないメモリ動作が見られます。R-2.13.0 と data.table 1.8.8 を使用しています。64 ビットの Suse Linux で実行しています。

私の最終的な目的は、できるだけ少ないメモリを使用して、data.table を「長い」形式から「広い」形式に再形成することです。別の [SO 投稿] の提案に従いました (複数の列にネストされた if else ステートメント)。基本的に、j 式で名前付きリストを返す data.table を再形成しようとします。

メモリ リークのように、メモリ使用量が着実に増加しています。data.tables またはその他のオブジェクトによって使用される合計メモリは、gc() に表示されるものを考慮していません。特に、Vcells は約 17 MB で始まり、ほぼ 30 MB で終了しますが、tables() によって報告される合計メモリ使用量は (最後で) 19 MB です。意味のある量のメモリを使用している (私が見ることができる) 他のオブジェクトはありません。以下のコードを繰り返し実行すると、print(gc()) ステートメントでメモリ使用量が増加していることがわかります。

何か足りないのでしょうか、それとも dogroups.c のメモリ割り当てに問題がありますか?

これは、私が見ている問題を再現するためのコードです。何か案は？速度よりもメモリの使用を重視して、data.table を比較的効率的に再形成できるようにしたいと考えています。

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())

編集：

#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: これは、2 つの連続した実行からの出力です。

> 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 に答える 1

7

更新- v1.8.11 で修正されました。NEWSより：

グループ化における長い未解決の (通常は小さい) メモリ リークが修正されました。最後のグループが最大のグループよりも小さい場合、それらのサイズの違いは解放されていませんでした。また、各グループが異なる数の行を返す重要な集計でも。ほとんどのユーザーはグループ化クエリを 1 回実行し、決して気付かないでしょうが、グループ化への呼び出しをループしているユーザー (並列実行時など) は、問題を抱えている可能性があります (#2648)。テストが追加されました。

vc273、YT などに感謝します。

この質問の上部にある特定の (素晴らしい) 例は、各グループの結果が 1 つの行に集約された 1 つのみではなく、異なる数の行になる可能性がある「重要な」集約と見なされます。リビールの追加verbose=TRUE:

割り当てられた (4488000) よりも少ない行 (4000000) を書き込みました。

この場合のリークはここにありました。必要に応じて、グループ化を何度も繰り返す必要がある場合にのみ重要です。結果は正しかった。

以前の回答は後世のために保持されています...

この部分を考慮してください：

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

それは使用していない:=か、参照によって列を追加する提供されset()た方法です。と同じです。つまり、このループの反復ごとに、全体がコピーされて、余分な 1 列分のスペースが確保されます。あなたが説明するメモリリークは、このループと一致します。data.table=<-forDTfor

いくつかのオプションは次のとおりです。

  • を使用して一度に多くの列を追加しますcbind
  • :=たとえば 、列を一度に追加しますDT[,sprintf('col%s',1:100):=1:nrow(DT)]
  • forループを維持しますが、反復ごとに:=orを使用しますset()

コードを実際に実行して確認していないため、後で他の問題が発生する可能性があります。

更新:私は今あなたのコードを実行しました。私はあなたがメモリ使用について何を意味するのか推測できると思います。しかし、特にこのような分野では、推測に多くの時間が費やされる可能性があります。これを大幅に拡張してください：

メモリ使用量が着実に増加していることがわかります。これは、メモリ リークのようです。

あなたは正確に何を見ますか。つまり、数字は何ですか？何に始まり何に終わるの？何回実行しましたか？の出力も提供してくださいsessionInfo()。参考になる R (2.13.0) のバージョンを示しますが、32 ビットまたは 64 ビットの Linux、Mac、または Windows も知っておくと役立ちます。

于 2013-03-27T07:57:19.463 に答える