在Ruby中2.2的垃圾收集器引发意外CoW的(Garbage collector in Ruby

2019-10-22 20:09发布

如何防止挑起写入时复制,当我叉我的过程中,GC? 我最近一直在分析Ruby的垃圾收集器的行为,因为我在我的计划遇到了一些内存问题(我我60core 0.5TB的机器上运行的内存甚至是相当小的任务)。 对我来说,这真的限制了红宝石的多核服务器上运行的程序的有效性。 我想在这里提出我的实验和结果。

当垃圾回收器分叉过程中运行,这个问题就出现了。 我已经调查三种情况来说明问题。

情况1:我们分配大量的对象使用阵列中的存储器(串超过20个字节不再)。 该字符串使用随机数和字符串格式创建的。 当进程叉和我们迫使GC在孩子跑,所有的共享内存变为私有,导致初始存储的重复。

情况2:我们分配大量使用阵列中的存储器中的对象(串)的,但是使用rand.to_s函数创建字符串,因此我们去掉相比前情况下,数据的格式。 我们最终得到的存储器较小量被使用,这可能是由于以下的垃圾。 当进程叉和我们迫使GC在孩子跑,只有记忆的一部分变为私有。 我们最初的记忆的重复,但程度较小。

案例3:我们比以前更少的分配对象,但对象是大,使得分配的内存大小保持不变,如前面的情况。 当进程叉和我们迫使GC在所有的内存共享撑子上运行,即没有存储重复。

在这里,我贴已用于这些实验的Ruby代码。 案件之间进行切换,你只需要在memory_object功能改变“选项”值。 码一个Ubuntu 14.04机器上使用Ruby 2.2.2,2.2.1,2.1.3,2.1.5和1.9.3中进行测试。

样品输出壳体1:

ruby version 2.2.2 
 proces   pid log                   priv_dirty   shared_dirty 
 Parent  3897 post alloc                   38            0 
 Parent  3897 4 fork                        0           37 
 Child   3937 4 initial                     0           37 
 Child   3937 8 empty GC                   35            5 

完全相同的代码已经写在Python和在所有情况下牛工作完全正常。

样品输出壳体1:

python version 2.7.6 (default, Mar 22 2014, 22:59:56) 
[GCC 4.8.2] 
 proces   pid log                   priv_dirty shared_dirty 
 Parent  4308 post alloc                35             0 
 Parent  4308 4 fork                     0            35 
 Child   4309 4 initial                  0            35 
 Child   4309 10 empty GC                1            34 

Ruby代码

$start_time=Time.new

# Monitor use of Resident and Virtual memory.
class Memory

    shared_dirty = '.+?Shared_Dirty:\s+(\d+)'
    priv_dirty = '.+?Private_Dirty:\s+(\d+)'
    MEM_REGEXP = /#{shared_dirty}#{priv_dirty}/m

    # get memory usage
    def self.get_memory_map( pids)
        memory_map = {}
        memory_map[ :pids_found] = {}
        memory_map[ :shared_dirty] = 0
        memory_map[ :priv_dirty] = 0

        pids.each do |pid|
            begin
                lines = nil
                lines = File.read( "/proc/#{pid}/smaps")
            rescue
                lines = nil
            end
            if lines
                lines.scan(MEM_REGEXP) do |shared_dirty, priv_dirty|
                    memory_map[ :pids_found][pid] = true
                    memory_map[ :shared_dirty] += shared_dirty.to_i
                    memory_map[ :priv_dirty] += priv_dirty.to_i
                end
            end
        end
        memory_map[ :pids_found] = memory_map[ :pids_found].keys
        return memory_map
    end

    # get the processes and get the value of the memory usage
    def self.memory_usage( )
        pids   = [ $$]
        result = self.get_memory_map( pids)

        result[ :pids]   = pids
        return result
    end

    # print the values of the private and shared memories
    def self.log( process_name='', log_tag="")
        if process_name == "header"
            puts " %-6s %5s %-12s %10s %10s\n" % ["proces", "pid", "log", "priv_dirty", "shared_dirty"]
        else
            time = Time.new - $start_time
            mem = Memory.memory_usage( )
            puts " %-6s %5d %-12s %10d %10d\n" % [process_name, $$, log_tag, mem[:priv_dirty]/1000, mem[:shared_dirty]/1000]
        end
    end
end

# function to delay the processes a bit
def time_step( n)
    while Time.new - $start_time < n
        sleep( 0.01)
    end
end

# create an object of specified size. The option argument can be changed from 0 to 2 to visualize the behavior of the GC in various cases
#
# case 0 (default) : we make a huge array of small objects by formatting a string
# case 1 : we make a huge array of small objects without formatting a string (we use the to_s function)
# case 2 : we make a smaller array of big objects
def memory_object( size, option=1)
    result = []
    count = size/20

    if option > 3 or option < 1
        count.times do
            result << "%20.18f" % rand
        end
    elsif option == 1
        count.times do
            result << rand.to_s
        end
    elsif option == 2
        count = count/10
        count.times do
            result << ("%20.18f" % rand)*30
        end
    end

    return result
end

##### main #####

puts "ruby version #{RUBY_VERSION}"

GC.disable

# print the column headers and first line
Memory.log( "header")

# Allocation of memory
big_memory = memory_object( 1000 * 1000 * 10)

Memory.log( "Parent", "post alloc")

lab_time = Time.new - $start_time
if lab_time < 3.9
    lab_time = 0
end

# start the forking
pid = fork do
    time = 4
    time_step( time + lab_time)
    Memory.log( "Child", "#{time} initial")

    # force GC when nothing happened
    GC.enable; GC.start; GC.disable

    time = 8
    time_step( time + lab_time)
    Memory.log( "Child", "#{time} empty GC")

    sleep( 1)
    STDOUT.flush
    exit!
end

time = 4
time_step( time + lab_time)
Memory.log( "Parent", "#{time} fork")

# wait for the child to finish
Process.wait( pid)

Python代码

import re
import time
import os
import random
import sys
import gc

start_time=time.time()

# Monitor use of Resident and Virtual memory.
class Memory:   

    def __init__(self):
        self.shared_dirty = '.+?Shared_Dirty:\s+(\d+)'
        self.priv_dirty = '.+?Private_Dirty:\s+(\d+)'
        self.MEM_REGEXP = re.compile("{shared_dirty}{priv_dirty}".format(shared_dirty=self.shared_dirty, priv_dirty=self.priv_dirty), re.DOTALL)

    # get memory usage
    def get_memory_map(self, pids):
        memory_map = {}
        memory_map[ "pids_found" ] = {}
        memory_map[ "shared_dirty" ] = 0
        memory_map[ "priv_dirty" ] = 0

        for pid in pids:
            try:
                lines = None

                with open( "/proc/{pid}/smaps".format(pid=pid), "r" ) as infile:
                    lines = infile.read()
            except:
                lines = None

            if lines:
                for shared_dirty, priv_dirty in re.findall( self.MEM_REGEXP, lines ):
                    memory_map[ "pids_found" ][pid] = True
                    memory_map[ "shared_dirty" ] += int( shared_dirty )
                    memory_map[ "priv_dirty" ] += int( priv_dirty )     

        memory_map[ "pids_found" ] = memory_map[ "pids_found" ].keys()
        return memory_map

    # get the processes and get the value of the memory usage   
    def memory_usage( self):
        pids   = [ os.getpid() ]
        result = self.get_memory_map( pids)

        result[ "pids" ]   = pids

        return result

    # print the values of the private and shared memories
    def log( self, process_name='', log_tag=""):
        if process_name == "header":
            print " %-6s %5s %-12s %10s %10s" % ("proces", "pid", "log", "priv_dirty", "shared_dirty")
        else:
            global start_time
            Time = time.time() - start_time
            mem = self.memory_usage( )
            print " %-6s %5d %-12s %10d %10d" % (process_name, os.getpid(), log_tag, mem["priv_dirty"]/1000, mem["shared_dirty"]/1000)

# function to delay the processes a bit
def time_step( n):
    global start_time
    while (time.time() - start_time) < n:
        time.sleep( 0.01)

# create an object of specified size. The option argument can be changed from 0 to 2 to visualize the behavior of the GC in various cases
#
# case 0 (default) : we make a huge array of small objects by formatting a string
# case 1 : we make a huge array of small objects without formatting a string (we use the to_s function)
# case 2 : we make a smaller array of big objects                                       
def memory_object( size, option=2):
    count = size/20

    if option > 3 or option < 1:
        result = [ "%20.18f"% random.random() for i in xrange(count) ]

    elif option == 1:
        result = [ str( random.random() ) for i in xrange(count) ]

    elif option == 2:
        count = count/10
        result = [ ("%20.18f"% random.random())*30 for i in xrange(count) ]

    return result

##### main #####

print "python version {version}".format(version=sys.version)

memory = Memory()

gc.disable()

# print the column headers and first line
memory.log( "header")   # Print the headers of the columns

# Allocation of memory
big_memory = memory_object( 1000 * 1000 * 10)   # Allocate memory

memory.log( "Parent", "post alloc")

lab_time = time.time() - start_time
if lab_time < 3.9:
    lab_time = 0

# start the forking
pid = os.fork()     # fork the process
if pid == 0:
    Time = 4
    time_step( Time + lab_time)
    memory.log( "Child", "{time} initial".format(time=Time))

    # force GC when nothing happened
    gc.enable(); gc.collect(); gc.disable();

    Time = 10
    time_step( Time + lab_time)
    memory.log( "Child", "{time} empty GC".format(time=Time))

    time.sleep( 1)

    sys.exit(0)

Time = 4
time_step( Time + lab_time)
memory.log( "Parent", "{time} fork".format(time=Time))

# Wait for child process to finish
os.waitpid( pid, 0)

编辑

事实上,调用GC多次分叉过程之前解决这个问题,我很惊讶。 我也运行使用Ruby 2.0.0的代码,这个问题甚至没有出现,所以它必须与就像你提到的这代GC。 但是,如果我叫memory_object功能不分配输出到任何变量(我只创建垃圾),那么内存被复制。 被复制的内存量取决于我创作的垃圾量 - 越垃圾,更多的内存变为私有。

任何想法如何避免这种情况?

这里有一些结果

运行在2.0.0的GC

ruby version 2.0.0
 proces   pid log          priv_dirty shared_dirty
 Parent  3664 post alloc           67          0
 Parent  3664 4 fork                1         69
 Child   3700 4 initial             1         69
 Child   3700 8 empty GC            6         65

在调用子memory_object(1000 * 1000)

ruby version 2.0.0
 proces   pid log          priv_dirty shared_dirty
 Parent  3703 post alloc           67          0
 Parent  3703 4 fork                1         70
 Child   3739 4 initial             1         70
 Child   3739 8 empty GC           15         56

调用memory_object(1000 * 1000 * 10)

ruby version 2.0.0
 proces   pid log          priv_dirty shared_dirty
 Parent  3743 post alloc           67          0
 Parent  3743 4 fork                1         69
 Child   3779 4 initial             1         69
 Child   3779 8 empty GC           89          5

Answer 1:

UPD2

突然想通了,为什么如果格式化字符串所有内存都被私有化 - 格式化过程中,你产生的垃圾,有GC禁用,然后启用GC,并且你已经在你生成的数据得到了释放对象的孔。 然后你叉,以及新的垃圾开始占据这些孔,越垃圾 - 更多的私人页面。

所以我加了一个清理函数运行GC每2000次(只是让懒惰的GC没有帮助):

count.times do |i|
  cleanup(i)
  result << "%20.18f" % rand
end

#......snip........#

def cleanup(i)
      if ((i%2000).zero?)
        GC.enable; GC.start; GC.disable
      end
end   

##### main #####

这导致(与生成memory_object( 1000 * 1000 * 10)后叉):

RUBY_GC_HEAP_INIT_SLOTS=600000 ruby gc-test.rb 0
ruby version 2.2.0
 proces   pid log          priv_dirty shared_dirty
 Parent  2501 post alloc           35          0
 Parent  2501 4 fork                0         35
 Child   2503 4 initial             0         35
 Child   2503 8 empty GC           28         22

是的,它会影响性能,但只有前分叉,即增加加载时间,你的情况。


UPD1

刚刚发现的标准由红宝石2.2套老物件位,这是3 GC的,因此,如果您添加分叉之前如下:

GC.enable; 3.times {GC.start}; GC.disable
# start the forking

你会得到(选项为1在命令行):

$ RUBY_GC_HEAP_INIT_SLOTS=600000 ruby gc-test.rb 1
ruby version 2.2.0
 proces   pid log          priv_dirty shared_dirty
 Parent  2368 post alloc           31          0
 Parent  2368 4 fork                1         34
 Child   2370 4 initial             1         34
 Child   2370 8 empty GC            2         32

但是,这需要有关未来的GC的,这些对象的行为进行进一步测试,至少要经过100 GC的:old_objects保持不变,所以我想应该没问题

登录与GC.stat是这里


顺便说这里还有选项RGENGC_OLD_NEWOBJ_CHECK从一开始就创建旧的对象,但我怀疑这是一个好主意,但可以为特定的情况下是有用的。

第一个答案

上面我在评论命题是错误的,实际上是位图表是救世主。

(option = 1)

ruby version 2.0.0
 proces   pid log          priv_dirty shared_dirty
 Parent 14807 post alloc           27          0
 Parent 14807 4 fork                0         27
 Child  14809 4 initial             0         27
 Child  14809 8 empty GC            6         25 # << almost everything stays shared <<

此外,通过手里拿着和测试Ruby企业版是只比最坏的情况下更好的一半。

ruby version 1.8.7
 proces   pid log          priv_dirty shared_dirty
 Parent 15064 post alloc           86          0
 Parent 15064 4 fork                2         84
 Child  15065 4 initial             2         84
 Child  15065 8 empty GC           40         46

(我做了脚本运行严格GC 1,通过增加RUBY_GC_HEAP_INIT_SLOTS到600K)



文章来源: Garbage collector in Ruby 2.2 provokes unexpected CoW