只是为了实验，而且乐趣...... 我正在尝试创建一个可以“有目的”消耗RAM的应用程序，就像我们立即指定的那样。 例如我想消耗512 MB RAM，然后该应用程序将直接消耗512 MB。
我在网上搜索，他们中的大多数都在使用while循环用变量或数据填充ram。但我认为这是填充RAM的缓慢方式，可能也不准确。
我在python中寻找一个关于内存管理的库。并遇到了这些http://docs.python.org/library/mmap.html。但无法弄清楚如何使用这些库一次性吃掉RAM空间。
我曾经看过一个mem-eater应用程序，但不知道它们是如何编写的......
那么，还有其他更好的建议立即用随机数据填充RAM吗？ 或者我应该只使用while循环来手动填充数据，但使用多线程来加快速度？
答案 0 :(得分：34)
一种简单的方法可能是：
some_str = ' ' * 512000000
在我的测试中似乎工作得很好。
编辑：在Python 3中，您可能希望改为使用bytearray(512000000)。
答案 1 :(得分：6)
您将无法使用
等构造分配所有内存s = ' ' * BIG_NUMBER
最好在
中附加一个列表a = []
while True:
print len(a)
a.append(' ' * 10**6)
这是一个更长的代码，可以更深入地了解内存分配限制：
import os
import psutil
PROCESS = psutil.Process(os.getpid())
MEGA = 10 ** 6
MEGA_STR = ' ' * MEGA
def pmem():
tot, avail, percent, used, free = psutil.virtual_memory()
tot, avail, used, free = tot / MEGA, avail / MEGA, used / MEGA, free / MEGA
proc = PROCESS.get_memory_info()[1] / MEGA
print('process = %s total = %s avail = %s used = %s free = %s percent = %s'
% (proc, tot, avail, used, free, percent))
def alloc_max_array():
i = 0
ar = []
while True:
try:
#ar.append(MEGA_STR) # no copy if reusing the same string!
ar.append(MEGA_STR + str(i))
except MemoryError:
break
i += 1
max_i = i - 1
print 'maximum array allocation:', max_i
pmem()
def alloc_max_str():
i = 0
while True:
try:
a = ' ' * (i * 10 * MEGA)
del a
except MemoryError:
break
i += 1
max_i = i - 1
_ = ' ' * (max_i * 10 * MEGA)
print 'maximum string allocation', max_i
pmem()
pmem()
alloc_max_str()
alloc_max_array()
这是我得到的输出：
process = 4 total = 3179 avail = 2051 used = 1127 free = 2051 percent = 35.5
maximum string allocation 102
process = 1025 total = 3179 avail = 1028 used = 2150 free = 1028 percent = 67.7
maximum array allocation: 2004
process = 2018 total = 3179 avail = 34 used = 3144 free = 34 percent = 98.9
答案 2 :(得分：1)
以下是markolopa的答案版本，对我有用：
import os
import psutil
PROCESS = psutil.Process(os.getpid())
MEGA = 10 ** 6
MEGA_STR = ' ' * MEGA
def pmem():
try:
tot, avail, percent, used, free, active, inactive, buffers = psutil.virtual_memory()
except ValueError:
tot, avail, percent, used, free, active, inactive, buffers, cached, shared = psutil.virtual_memory()
tot, avail, used, free = tot / MEGA, avail / MEGA, used / MEGA, free / MEGA
proc = PROCESS.memory_info()[1] / MEGA
print('process = %s total = %s avail = %s used = %s free = %s percent = %s'
% (proc, tot, avail, used, free, percent))
def alloc_max_array():
i = 0
ar = []
while True:
try:
#ar.append(MEGA_STR) # no copy if reusing the same string!
ar.append(MEGA_STR + str(i))
except MemoryError:
break
i += 1
max_i = i - 1
print('maximum array allocation:', max_i)
pmem()
def alloc_max_str():
i = 0
while True:
try:
a = ' ' * (i * 10 * MEGA)
del a
except MemoryError:
break
i += 1
max_i = i - 1
_ = ' ' * (max_i * 10 * MEGA)
print('maximum string allocation', max_i)
pmem()
pmem()
alloc_max_str()
alloc_max_array()
答案 3 :(得分：1)
您可以执行以下操作分配大量内存：
while True:
for i in range(0,100000000):
Gig = 1024*1024*1024*2#A Gig multiplied by 2
a = 787878788888888888888888888888 * (i * Gig)
a = a * i
print str(a)*2
我发现此代码在5分钟内冻结了我的电脑
将其保存在.pyw中以进行后台ram分配
如果它没有冻结你的电脑，请尝试增加a的值
要快速停止，请将此代码保存在.py文件中：
#First we send signals
os.system("TASKKILL /im pythonw.exe")
os.system("TASKKILL /im python.exe")
print "Forcefull termination"
#Now we forcefully terminate
#pythonw.exe if running in idle or background
os.system("TASKKILL /im python.exe /f")
os.system("TASKKILL /im pythonw.exe /f")
os.system("pause")
答案 4 :(得分：1)
x = bytearray(1024*1024*1000)
可以吃掉大约1GB的内存
答案 5 :(得分：1)
这个函数将内存分配到一个字节对象列表中。列表中的每个项目实际上都是唯一的并且长度相同。该函数还记录其分配。我已经对其进行了高达 3.7 TiB 的测试。它使用 humanfriendly 包，但如果您不想要它，您可以删除它。
它确实使用了循环，但至少它可以让您有选择地自定义在每次迭代中分配多少。例如，您可以将 multiplier_per_allocation 的值提高 8 倍。
import logging
import secrets
from typing import Optional
from humanfriendly import format_size
log = logging.getLogger(__name__)
def fill_memory(*, num_unique_bytes_per_allocation: int = 1024, multiplier_per_allocation: int = 1024 ** 2, max_allocations: Optional[int] = None) -> None:
"""Allocate available memory into a list of effectively unique bytes objects.
This function is for diagnostic purposes.
:param num_unique_bytes_per_allocation: Each allocation is created by multiplying a random sequence of bytes of this length.
:param multiplier_per_allocation: Each allocation is created by multiplying the random sequence of bytes by this number.
:param max_allocations: Optional number of max allocations.
"""
# Ref: https://stackoverflow.com/a/66109163/
num_allocation_bytes = num_unique_bytes_per_allocation * multiplier_per_allocation
log.info(
f"Allocating cumulative instances of {num_allocation_bytes:,} bytes ({format_size(num_allocation_bytes)}) each. "
f"Each allocation uses {num_unique_bytes_per_allocation:,} unique bytes ({format_size(num_unique_bytes_per_allocation)}) "
f"with a multiplier of {multiplier_per_allocation:,} ({format_size(multiplier_per_allocation)})."
)
# Allocate memory
allocated = []
num_allocation = 1
while True:
unique_bytes_for_allocation = secrets.token_bytes(num_unique_bytes_per_allocation)
allocated.append(unique_bytes_for_allocation * multiplier_per_allocation)
num_total_bytes_allocated = num_allocation * num_allocation_bytes
log.info(f"Used a total of {num_total_bytes_allocated:,} bytes ({format_size(num_total_bytes_allocated)}) via {num_allocation:,} allocations.")
if max_allocations and (max_allocations == num_allocation):
break
num_allocation += 1
示例输出：
>>> import logging
>>> logging.basicConfig(level=logging.INFO)
>>> fill_memory()
INFO:Allocating cumulative instances of 1,073,741,824 bytes (1 GiB) each. Each allocation uses 1,024 unique bytes (1 KiB) with a multiplier of 1,048,576 (1 MiB).
INFO:Used a total of 1,073,741,824 bytes (1 GiB) via 1 allocations.
INFO:Used a total of 2,147,483,648 bytes (2 GiB) via 2 allocations.
INFO:Used a total of 3,221,225,472 bytes (3 GiB) via 3 allocations.
INFO:Used a total of 4,294,967,296 bytes (4 GiB) via 4 allocations.