R 内存未在 Windows 中释放

标签 r memory memory-management

我在 Windows 7 中使用 RStudio，但在向操作系统释放内存时遇到问题。下面是我的代码。在 for 循环中:

  • 我通过 Census.gov 网站的 API 调用读取数据，并使用包 acs 通过临时对象 将它们保存在 .csv 文件中>表格。
  • 我删除了 table(通常大小:几 MB)，并使用包 pryr 来检查内存使用情况。

根据函数mem_used()，在移除table后，R总是返回到一个常数的内存使用量；相反，根据 Windows 任务管理器，rsession.exe(不是 Rstudio)的内存分配在每次迭代时都会增加，最终导致 rsession 崩溃。 gc() 的使用没有帮助。我已经阅读了很多类似的问题，但似乎释放内存的唯一解决方案是重新启动 R session ，这似乎很愚蠢。 有什么建议吗？

   library(acs)
   library(pryr) 
   # for loop to extract tables from API and save them on API
   for (i in 128:length(tablecodes)) {
           tryCatch({table <- acs.fetch(table.number = tablecodes[i],endyear = 2014, span=5, 
                 geography = geo.make(state = "NY", county = "*", tract = "*"), 
                 key = "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851",col.names="pretty")},
             error = function(e){print("Table skipped") })

    # if the table is actually fetched then we save it 
    if (exists("table", mode="S4")) {         
         print(paste("Table",i,"fetched")
         if (!is.na(table)){
                   write.csv(estimate(table),paste("./CENSUS_tables/NY/",tablecodes[i],".csv",sep = ""))       
         }
    print(mem_used())  
    print(mem_change(rm(table)))
    gc()
    }
   }

最佳答案

我能够确认 Windows 7 上存在内存问题。(在 MacOSX 上通过 VMware Fusion 运行)。它似乎也存在于 MacOSX 上，尽管内存使用似乎相当缓慢 [未经证实，但表示内存泄漏]。 MacOSX 有点棘手，因为如果操作系统看到高使用率，它会压缩内存。

建议的解决方法:

鉴于上述情况，我的建议是当您从美国人口普查局下载时，将表格下载集分成更小的组。为什么？好吧，看看您正在下载数据以存储在 .CSV 文件中的代码。因此，短期内的解决方法是分解您正在下载的表格列表。您的程序应该能够在一组运行中成功完成。

一种选择是创建一个包装器 RScript 并让它在 N 次运行中运行，每次运行都调用一个单独的 R session 。即Rscript依次调用N个RSession，每个 session 下载N个文件

注意。根据您的代码和观察到的内存使用情况，我的感觉是您正在下载很多表，因此在 R session 中拆分可能是最好的选择。

nb. 以下内容应在 Windows 7 上的 cgiwin 下工作。

调用脚本

示例:下载主表 01 到 27 - 如果它们不存在，请跳过...

!#/bin/bash

#Ref: https://censusreporter.org/topics/table-codes/
# Params: Primary Table Year Span

for CensusTableCode in $(seq -w 1 27)
do
  R --no-save -q --slave < ./PullCensus.R --args B"$CensusTableCode"001 2014 5
done

PullCensus.R

if (!require(acs)) install.packages("acs")
if (!require(pryr)) install.packages("pryr")

# You can obtain a US Census key from the developer site
# "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
api.key.install(key = "** Secret**")

setwd("~/dev/stackoverflow/37264919")
    
# Extract Table Structure
#
# B = Detailed Column Breakdown
# 19 = Income (Households and Families)
# 001 =
# A - I = Race
#

args <- commandArgs(trailingOnly = TRUE) # trailingOnly=TRUE means that only your arguments are returned

if ( length(args) != 0 ) {
    tableCodes <- args[1]
    defEndYear = args[2]
    defSpan = args[3]
  } else {
  tableCodes <- c("B02001")
  defEndYear = 2014
  defSpan = 5
}

# for loop to extract tables from API and save them on API
for (i in 1:length(tableCodes))
{
  tryCatch(
    table <- acs.fetch(table.number = tableCodes[i],
                       endyear = defEndYear,
                       span = defSpan,
                       geography = geo.make(state = "NY",
                                            county = "*",
                                            tract = "*"),
                       col.names = "pretty"),
    error = function(e) { print("Table skipped")} )

  # if the table is actually fetched then we save it
  if (exists("table", mode = "S4"))
  {
    print(paste("Table", i, "fetched"))
    if (!is.na(table))
    {
      write.csv(estimate(table), paste(defEndYear,"_",tableCodes[i], ".csv", sep = ""))
    }
    print(mem_used())
    print(mem_change(rm(table)))
    gc(reset = TRUE)
    print(mem_used())
  }
}

我希望以上内容可以作为示例有所帮助。这是一种方法。 ;-)

T.

后续步骤:

我将查看软件包源代码，看看我是否能看出实际有什么问题。或者，您自己也可以缩小范围并针对包提交错误。

背景/工作示例:

我的感觉是，这可能有助于提供一个工作代码示例来构建上述解决方法。为什么？此处的目的是提供一个示例，供人们用来测试和考虑正在发生的事情。为什么？嗯，这样更容易理解您的问题和意图。

基本上，(据我了解)您是从美国人口普查网站批量下载美国人口普查数据。表格代码用于指定您希望下载的数据。好的，所以我刚刚创建了一组表代码并测试了内存使用情况，看看是否按照您的说明开始消耗内存。

library(acs)
library(pryr)
library(tigris)
library(stringr)  # to pad fips codes
library(maptools)

# You can obtain a US Census key from the developer site
# "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
api.key.install(key = "<INSERT KEY HERE>")

# Table Codes
#
# While Census Reporter hopes to save you from the details, you may be
# interested to understand some of the rationale behind American Community
# Survey table identifiers.
#
# Detailed Tables
#
# The bulk of the American Community Survey is the over 1400 detailed data
# tables. These tables have reference codes, and knowing how the codes are
# structured can be helpful in knowing which table to use.
#
# Codes start with either the letter B or C, followed by two digits for the
# table subject, then 3 digits that uniquely identify the table. (For a small
# number of technical tables the unique identifier is 4 digits.) In some cases
# additional letters for racial iterations and Puerto Rico-specific tables.
#
# Full and Collapsed Tables
#
# Tables beginning with B have the most detailed column breakdown, while a
# C table for the same numbers will have fewer columns. For example, the
# B02003 table ("Detailed Race") has 71 columns, while the "collapsed
# version," C02003 has only 19 columns. While your instinct may be to want
# as much data as possible, sometimes choosing the C table can simplify
# your analysis.
#
# Table subjects
#
# The first two digits after B/C indicate the broad subject of a table.
# Note that many tables have more than one subject, but this reflects the
# main subject.
#
# 01 Age and Sex
# 02 Race
# 03 Hispanic Origin
# 04 Ancestry
# 05 Foreign Born; Citizenship; Year or Entry; Nativity
# 06 Place of Birth07Residence 1 Year Ago; Migration
# 08 Journey to Work; Workers' Characteristics; Commuting
# 09 Children; Household Relationship
# 10 Grandparents; Grandchildren
# 11 Household Type; Family Type; Subfamilies
# 12 Marital Status and History13Fertility
# 14 School Enrollment
# 15 Educational Attainment
# 16 Language Spoken at Home and Ability to Speak English
# 17 Poverty
# 18 Disability
# 19 Income (Households and Families)
# 20 Earnings (Individuals)
# 21 Veteran Status
# 22 Transfer Programs (Public Assistance)
# 23 Employment Status; Work Experience; Labor Force
# 24 Industry; Occupation; Class of Worker
# 25 Housing Characteristics
# 26 Group Quarters
# 27 Health Insurance
#
# Three groups of tables reflect technical details about how the Census is
# administered. In general, you probably don't need to look at these too
# closely, but if you need to check for possible weaknesses in your data
# analysis, they may come into play.
#
# 00 Unweighted Count
# 98 Quality Measures
# 99 Imputations
#
# Race and Latino Origin
#
# Many tables are provided in multiple racial tabulations. If a table code
# ends in a letter from A-I, that code indicates that the table universe is
# restricted to a subset based on responses to the race or
# Hispanic/Latino-origin questions.
#
# Here is a guide to those codes:
#
#   A White alone
#   B Black or African American Alone
#   C American Indian and Alaska Native Alone
#   D Asian Alone
#   E Native Hawaiian and Other Pacific Islander Alone
#   F Some Other Race Alone
#   G Two or More Races
#   H White Alone, Not Hispanic or Latino
#   I Hispanic or Latino


setwd("~/dev/stackoverflow/37264919")

# Extract Table Structure
#
# B = Detailed Column Breakdown
# 19 = Income (Households and Families)
# 001 =
# A - I = Race
#
tablecodes <- c("B19001", "B19001A", "B19001B", "B19001C", "B19001D",
                "B19001E", "B19001F", "B19001G", "B19001H", "B19001I" )

# for loop to extract tables from API and save them on API
for (i in 1:length(tablecodes))
{
  print(tablecodes[i])
  tryCatch(
    table <- acs.fetch(table.number = tablecodes[i],
                       endyear = 2014,
                       span = 5,
                       geography = geo.make(state = "NY",
                                            county = "*",
                                            tract = "*"),
                       col.names = "pretty"),
    error = function(e) { print("Table skipped")} )

  # if the table is actually fetched then we save it
  if (exists("table", mode="S4"))
  {
    print(paste("Table", i, "fetched"))
    if (!is.na(table))
    {
      write.csv(estimate(table), paste("T",tablecodes[i], ".csv", sep = ""))
    }
    print(mem_used())
    print(mem_change(rm(table)))
    gc()
    print(mem_used())
  }
}

运行时输出

> library(acs)
> library(pryr)
> library(tigris)
> library(stringr)  # to pad fips codes
> library(maptools)
> # You can obtain a US Census key from the developer site
> # "e24539dfe0e8a5c5bf99d78a2bb8138abaa3b851"
> api.key.install(key = "...secret...")
> 
...
> setwd("~/dev/stackoverflow/37264919")
> 
> # Extract Table Structure
> #
> # B = Detailed Column Breakdown
> # 19 = Income (Households and Families)
> # 001 =
> # A - I = Race
> #
> tablecodes <- c("B19001", "B19001A", "B19001B", "B19001C", "B19001D",
+                 "B19001E", "B19001F", "B19001G", "B19001H", "B19001I" )
> 
> # for loop to extract tables from API and save them on API
> for (i in 1:length(tablecodes))
+ {
+   print(tablecodes[i])
+   tryCatch(
+     table <- acs.fetch(table.number = tablecodes[i],
+                        endyear = 2014,
+                        span = 5,
+                        geography = geo.make(state = "NY",
+                                             county = "*",
+                                             tract = "*"),
+                        col.names = "pretty"),
+     error = function(e) { print("Table skipped")} )
+ 
+   # if the table is actually fetched then we save it
+   if (exists("table", mode="S4"))
+   {
+     print(paste("Table", i, "fetched"))
+     if (!is.na(table))
+     {
+       write.csv(estimate(table), paste("T",tablecodes[i], ".csv", sep = ""))
+     }
+     print(mem_used())
+     print(mem_change(rm(table)))
+     gc()
+     print(mem_used())
+   }
+ }
[1] "B19001"
[1] "Table 1 fetched"
95.4 MB
-1.88 MB
93.6 MB
[1] "B19001A"
[1] "Table 2 fetched"
95.4 MB
-1.88 MB
93.6 MB
[1] "B19001B"
[1] "Table 3 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001C"
[1] "Table 4 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001D"
[1] "Table 5 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001E"
[1] "Table 6 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001F"
[1] "Table 7 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001G"
[1] "Table 8 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001H"
[1] "Table 9 fetched"
95.5 MB
-1.88 MB
93.6 MB
[1] "B19001I"
[1] "Table 10 fetched"
95.5 MB
-1.88 MB
93.6 MB

输出文件

>ll
total 8520
drwxr-xr-x@ 13 hidden  staff   442B Oct 17 20:41 .
drwxr-xr-x@ 40 hidden  staff   1.3K Oct 17 23:17 ..
-rw-r--r--@  1 hidden  staff   4.4K Oct 17 23:43 37264919.R
-rw-r--r--@  1 hidden  staff   492K Oct 17 23:50 TB19001.csv
-rw-r--r--@  1 hidden  staff   472K Oct 17 23:51 TB19001A.csv
-rw-r--r--@  1 hidden  staff   414K Oct 17 23:51 TB19001B.csv
-rw-r--r--@  1 hidden  staff   387K Oct 17 23:51 TB19001C.csv
-rw-r--r--@  1 hidden  staff   403K Oct 17 23:51 TB19001D.csv
-rw-r--r--@  1 hidden  staff   386K Oct 17 23:51 TB19001E.csv
-rw-r--r--@  1 hidden  staff   402K Oct 17 23:51 TB19001F.csv
-rw-r--r--@  1 hidden  staff   393K Oct 17 23:52 TB19001G.csv
-rw-r--r--@  1 hidden  staff   465K Oct 17 23:44 TB19001H.csv
-rw-r--r--@  1 hidden  staff   417K Oct 17 23:44 TB19001I.csv

关于R 内存未在 Windows 中释放，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/37264919/

上一篇：c++ - 如何在 Linux(和 OSX)上查询分配的内存量？

下一篇：Python 内存错误 : cannot allocate array memory

相关文章：

r - 在 R 中删除基于名称的列表列表

r - 从非序列表计算经过的天数

c - Next Fit 内存分配问题

php - 如何修复 PHPExcel 耗尽的内存？

r - 对应列为1时如何赋值？

r - 使用 tidyverse 和 mutate_all 对每一列应用相同的函数

java - 释放本地对象

java - JVM 能否优化引用 null(null 类型的实例)所需的内存？

java - 在 Java 中分配大量数组时避免内存碎片

python - 为什么此 python 代码中的内存使用量增加？

©2024 IT工具网  联系我们