几天前，我发布了一个类似的问题，但是没有任何代码，现在我创建了一个测试代码，以期寻求帮助。

代码在底部。

我得到了一些数据集，其中有一堆大文件（〜100个），我想从这些文件中高效地提取特定行（在内存和速度上）。

我的代码获取了一个相关文件的列表，代码使用[第1行]打开了每个文件，然后使用[第2行]将文件映射到了内存，对于每个文件，我都收到一个索引列表并遍历检索到的索引相关信息（此示例为10个字节），如下所示：[3-4行]，最后我用[5-6行]关闭了句柄。

binaryFile = open(path, "r+b")
binaryFile_mm = mmap.mmap(binaryFile.fileno(), 0)
for INDEX in INDEXES:
    information = binaryFile_mm[(INDEX):(INDEX)+10].decode("utf-8")
binaryFile_mm.close()
binaryFile.close()

该代码并行运行，每个文件具有数千个索引，并连续数小时连续数小时执行。

现在解决问题 -当我将索引限制为较小时，代码运行良好（意思是-
当我要求代码从文件开头获取信息时）。但！当我增加索引范围时，一切都会减慢到（几乎）停止并且buff /
cache内存已满（我不确定内存问题是否与减速有关）。

所以我的问题是，为什么要从文件的开头或结尾检索信息，这又有什么用呢？如何克服这个问题，以便从文件的结尾即时访问信息，而又不减慢和增加缓冲/缓存的内存采用。

PS-
一些数字和大小：所以我得到了约100个文件，每个文件的大小约为1GB，当我将索引限制为文件的0％-10％时，它运行良好，但是当我允许索引位于文件停止工作。

代码 -使用python 3.5在Linux和Windows上进行测试， 需要10 GB的存储空间
（创建3个文件，每个文件的随机字符串在3GB之内）

import os, errno, sys
import random, time
import mmap



def create_binary_test_file():
    print("Creating files with 3,000,000,000 characters, takes a few seconds...")
    test_binary_file1 = open("test_binary_file1.testbin", "wb")
    test_binary_file2 = open("test_binary_file2.testbin", "wb")
    test_binary_file3 = open("test_binary_file3.testbin", "wb")
    for i in range(1000):
        if i % 100 == 0 :
            print("progress -  ", i/10, " % ")
        # efficiently create random strings and write to files
        tbl = bytes.maketrans(bytearray(range(256)),
                          bytearray([ord(b'a') + b % 26 for b in range(256)]))
        random_string = (os.urandom(3000000).translate(tbl))
        test_binary_file1.write(str(random_string).encode('utf-8'))
        test_binary_file2.write(str(random_string).encode('utf-8'))
        test_binary_file3.write(str(random_string).encode('utf-8'))
    test_binary_file1.close()
    test_binary_file2.close()
    test_binary_file3.close()
    print("Created binary file for testing.The file contains 3,000,000,000 characters")




# Opening binary test file
try:
    binary_file = open("test_binary_file1.testbin", "r+b")
except OSError as e: # this would be "except OSError, e:" before Python 2.6
    if e.errno == errno.ENOENT: # errno.ENOENT = no such file or directory
        create_binary_test_file()
        binary_file = open("test_binary_file1.testbin", "r+b")




## example of use - perform 100 times, in each itteration: open one of the binary files and retrieve 5,000 sample strings
## (if code runs fast and without a slowdown - increase the k or other numbers and it should reproduce the problem)

## Example 1 - getting information from start of file
print("Getting information from start of file")
etime = []
for i in range(100):
    start = time.time()
    binary_file_mm = mmap.mmap(binary_file.fileno(), 0)
    sample_index_list = random.sample(range(1,100000-1000), k=50000)
    sampled_data = [[binary_file_mm[v:v+1000].decode("utf-8")] for v in sample_index_list]
    binary_file_mm.close()
    binary_file.close()
    file_number = random.randint(1, 3)
    binary_file = open("test_binary_file" + str(file_number) + ".testbin", "r+b")
    etime.append((time.time() - start))
    if i % 10 == 9 :
        print("Iter ", i, " \tAverage time - ", '%.5f' % (sum(etime[-9:]) / len(etime[-9:])))
binary_file.close()


## Example 2 - getting information from all of the file
print("Getting information from all of the file")
binary_file = open("test_binary_file1.testbin", "r+b")
etime = []
for i in range(100):
    start = time.time()
    binary_file_mm = mmap.mmap(binary_file.fileno(), 0)
    sample_index_list = random.sample(range(1,3000000000-1000), k=50000)
    sampled_data = [[binary_file_mm[v:v+1000].decode("utf-8")] for v in sample_index_list]
    binary_file_mm.close()
    binary_file.close()
    file_number = random.randint(1, 3)
    binary_file = open("test_binary_file" + str(file_number) + ".testbin", "r+b")
    etime.append((time.time() - start))
    if i % 10 == 9 :
        print("Iter ", i, " \tAverage time - ", '%.5f' % (sum(etime[-9:]) / len(etime[-9:])))
binary_file.close()

我的结果是：（ 从文件中获取信息的平均时间比从一开始就获取信息的速度慢了将近4倍，在大约100个文件和并行计算的情况下，这种差异变得更大）

Getting information from start of file
Iter  9         Average time -  0.14790
Iter  19        Average time -  0.14590
Iter  29        Average time -  0.14456
Iter  39        Average time -  0.14279
Iter  49        Average time -  0.14256
Iter  59        Average time -  0.14312
Iter  69        Average time -  0.14145
Iter  79        Average time -  0.13867
Iter  89        Average time -  0.14079
Iter  99        Average time -  0.13979
Getting information from all of the file
Iter  9         Average time -  0.46114
Iter  19        Average time -  0.47547
Iter  29        Average time -  0.47936
Iter  39        Average time -  0.47469
Iter  49        Average time -  0.47158
Iter  59        Average time -  0.47114
Iter  69        Average time -  0.47247
Iter  79        Average time -  0.47881
Iter  89        Average time -  0.47792
Iter  99        Average time -  0.47681