在j中返回命名列表时，data.table缓慢的内存泄漏（试图重塑data.table） [英] Slow memory leak in data.table when returning named lists in j (trying to reshape a data.table)
问题描述
编辑3：
我创建了一个更短的内存泄漏示例。我希望它使得更容易理解发生了什么。随着迭代的继续，你看到稳定增加gc（）VCell内存使用，而由表（）报告的内存使用保持不变。不知何故，unlist（.SD）调用似乎是负责任的。这是：
I created a much shorter example of the memory leak. I hope it makes it much easier to reason about what's going on. As the iterations proceed, you see steadily increasing gc() VCell memory use, while memory use reported by tables() stays the same. Somehow, the unlist(.SD) call seems to be responsible. Here it is:
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()
}
原始帖子：
我看到一些内存行为我不明白当使用data.table包。
我使用R-2.13.0与data.table 1.8.8。我运行在64位suse linux上。
I am seeing some memory behavior I don't understand when using the data.table package. I am using R-2.13.0 with data.table 1.8.8. I run on 64-bit suse linux.
我的最终目标是使用尽可能少的内存将data.table从long格式重塑为wide格式。我按照另一个[SO post]（嵌套if else语句多列）。基本上我尝试重塑一个data.table在j表达式中返回一个命名列表。
My ultimate objective is to reshape a data.table from "long" to "wide" format using as little memory as possible. I followed a suggestion in another [SO post] ( Nested if else statements over a number of columns). Basically I try to reshape a data.table returning a named list in the j expression.
我看到一个稳定增加的内存使用，这似乎内存泄漏。 data.tables或其他对象使用的总内存不考虑gc（）中显示的内容。特别地，Vcell从大约17MB开始并且以大约30MB结束，而tables（）报告的总内存使用是19MB（结束）。没有其他对象（我可以看到）使用任何有意义的内存量。运行下面的代码重复显示了使用print（gc（））语句增加的内存使用。
I see a steadily increasing memory use, which seems like a memory leak. The total memory used by data.tables or other objects does not account for what is shown in gc(). in particular, Vcells starts at around 17 MB and ends at nearly 30 MB, while the total memory use reported by tables() is 19 MB (at the end). There are no other objects (that I can see) using any meaningful amount of memory. Running the code below repeatedly shows the increasing memory use with print(gc()) statements.
我缺少一些东西或者有一些内存分配的问题dogroups.c？
Am I missing something or is there an issue with some of the memory allocation in dogroups.c ?
这里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，内存使用是比速度更大的考虑。
Here is code to reproduce the problem I see. Any ideas? I really would like to be able to reshape a data.table relatively efficiently, with memory use being a bigger consideration than speed.
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
# http://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：这是连续两次运行的一些输出。
Here is some output from two consecutive runs.
> 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
>
推荐答案
UPDATE 在v1.8.11。从新闻：
UPDATE - Now fixed in v1.8.11. From NEWS :
分组固定的长期未完成（通常很小）的内存泄漏。当
最后一组小于最大组时，
那些尺寸的差异未被释放。也在非平凡聚合
中，其中每个组返回不同数量的行。大多数用户运行
分组查询一次，并且永远不会注意到，但任何循环
调用分组（例如并行运行）可能已经遭受，
＃2648。测试已添加。
Long outstanding (usually small) memory leak in grouping fixed. When the last group is smaller than the largest group, the difference in those sizes was not being released. Also in non-trivial aggregations where each group returns a different number of rows. Most users run a grouping query once and will never have noticed, but anyone looping calls to grouping (such as when running in parallel) may have suffered, #2648. Tests added.
非常感谢vc273，YT和其他人。
Many thanks to vc273, Y T and others.
这个问题顶部的特定（很好）示例被认为是一个非平凡聚合，其中每个组的结果可以是不同数量的行，而不仅仅是一个聚合在一行。添加 verbose = TRUE 会显示：
The particular (great) example at the top of this question is considered a "non-trivial" aggregation where the result of each group can be a different number of rows, not just a single aggregated in one row. Adding verbose=TRUE reveals :
4488000）。
Wrote less rows (4000000) than allocated (4488000).
这就是泄漏的地方。只有重要的是，如果你需要重复分组多次，有时需要。结果是正确的。
and that's where the leak was in this case. Only matters if you need to repeat grouping many times, as is needed sometimes. The result was correct.
为后代留下的上一个答案...
Previous answer retained for posterity ...
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 set（），它们是 data.table 提供的通过引用添加列的方式。 = 与< - 相同;即在 for 循环的每次迭代中，整个 DT 将被复制以为单个额外柱。您描述的内存泄漏将与 for 循环一致。
That isn't using := or set() which are the data.table provided ways of adding columns by reference. = is the same as <-; i.e., on each and every iteration of this for loop the entire DT will be copied to make room for the single extra column. The memory leak you describe would be consistent with this for loop.
一些选项是：
- 使用
cbind - 使用
：=例如，一次性添加列DT[,sprintf(’col%s’,1:100):=1:nrow(DT)] - Keep the
forloop but use:=orset()on each iteration
- Add the many columns in one go using
cbind - Add the columns in one go using
:=e.g.DT[,sprintf('col%s',1:100):=1:nrow(DT)] - Keep the
forloop but use:=orset()on each iteration
I haven’t actually run your code to check so there may be other problems later as well.
I haven't actually run your code to check so there may be other problems later as well.
UPDATE : I have now run your code and I think I might be able to guess what you mean about memory use. But guessing can use up a lot of time, especially in areas like this. Can you please expand significantly upon this :
UPDATE : I have now run your code and I think I might be able to guess what you mean about memory use. But guessing can use up a lot of time, especially in areas like this. Can you please expand significantly upon this :
I see a steadily increasing memory use, which seems like a memory leak.
I see a steadily increasing memory use, which seems like a memory leak.
What precisely do you see; i.e., what are the numbers? What does it start at and what does it end at? How many times did you run it? Please also provide the output of sessionInfo(); although you give the version of R (2.13.0) which is helpful, it helps to know if you are 32bit or 64bit Linux, Mac or Windows as well.
What precisely do you see; i.e., what are the numbers? What does it start at and what does it end at? How many times did you run it? Please also provide the output of sessionInfo(); although you give the version of R (2.13.0) which is helpful, it helps to know if you are 32bit or 64bit Linux, Mac or Windows as well.
这篇关于在j中返回命名列表时，data.table缓慢的内存泄漏（试图重塑data.table）的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
