将python对象写入磁盘而不加载到内存？

我正在运行大量的计算，我希望每次将计算结果保存到磁盘上，因为整个数据太大，无法保存在内存中。我尝试使用

shelve

保存它，但出现错误：

HASH: Out of overflow pages.  Increase page size

我的代码如下。在python中实现这一点的正确方法是什么<代码>pickle将对象加载到内存中

shelve

支持磁盘写入，但强制使用字典结构，其中键的数量有限。我保存的最终数据只是一个列表，不需要是字典形式。只需要能够一次读一个项目

import shelve
def my_data():
  # this is a generator that yields data points
  for n in xrange(very_large_number):
    yield data_point

def save_result():
  db = shelve.open("result")
  n = 0
  for data in my_data():
    # result is a Python object (a tuple)
    result = compute(data)
    # now save result to disk
    db[str(n)] = result
  db.close()

---

以下程序演示了您可能希望如何执行问题中描述的流程。它模拟应用程序可能需要复制的数据的创建、写入、读取和处理。默认情况下，代码生成大约32GB的数据并将其写入磁盘。经过一些实验后，启用

gzip

压缩提供了很好的速度，并将文件大小减少到大约195MB。您应该针对自己的问题调整示例，通过反复试验，可能会发现不同的压缩技术比其他压缩技术更合适

#! /usr/bin/env python3
import os
import pickle


# Uncomment one of these imports to enable file compression:
# from bz2 import open
# from gzip import open
# from lzma import open


DATA_FILE = 'results.dat'
KB = 1 << 10
MB = 1 << 20
GB = 1 << 30
TB = 1 << 40


def main():
    """Demonstrate saving data to and loading data from a file."""
    save_data(develop_data())
    analyze_data(load_data())


def develop_data():
    """Create some sample data that can be saved for later processing."""
    return (os.urandom(1 * KB) * (1 * MB // KB) for _ in range(32 * GB // MB))


def save_data(data):
    """Take in all data and save it for retrieval later on."""
    with open(DATA_FILE, 'wb') as file:
        for obj in data:
            pickle.dump(obj, file, pickle.HIGHEST_PROTOCOL)


def load_data():
    """Load each item that was previously written to disk."""
    with open(DATA_FILE, 'rb') as file:
        try:
            while True:
                yield pickle.load(file)
        except EOFError:
            pass


def analyze_data(data):
    """Pretend to do something useful with each object that was loaded."""
    for obj in data:
        print(hash(obj))


if __name__ == '__main__':
    main()

#/usr/bin/env蟒蛇3
导入操作系统
进口泡菜
#取消对其中一个导入的注释以启用文件压缩：
#从bz2导入打开
#从gzip导入打开
#从lzma导入打开
数据文件='results.dat'
KB=1如果您使用
klepot
，它将使您能够在文件或数据库中透明地存储对象，这将非常容易。首先，我展示了如何直接使用归档后端（即直接写入磁盘）

然后重新启动，创建到磁盘上“数据库”的新连接

>>> import klepto
>>> db = klepto.archives.dir_archive('db', serialized=True, cached=False)
>>> db['n'] = 69     
>>> db['add'] = lambda x,y: x+y
>>> db['x'] = 42
>>> db['y'] = 11
>>> db['sub'] = lambda x,y: y-x
>>> 

Python 2.7.11 (default, Dec  5 2015, 23:50:48) 
[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import klepto
>>> db = klepto.archives.dir_archive('db', serialized=True, cached=False)
>>> db     
dir_archive('db', {'y': 11, 'x': 42, 'add': <function <lambda> at 0x10e500d70>, 'sub': <function <lambda> at 0x10e500de8>, 'n': 69}, cached=False)
>>> 

Python 2.7.11 (default, Dec  5 2015, 23:50:48) 
[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import klepto
>>> db = klepto.archives.dir_archive('db', serialized=True, cached=True)
>>> db
dir_archive('db', {}, cached=True)
>>> db.load('x', 'y')  # read multiple
>>> db.load('add')     # read one at a time
>>> db
dir_archive('db', {'y': 11, 'x': 42, 'add': <function <lambda> at 0x1079e7d70>}, cached=True)
>>> db['result'] = db['add'](db['x'],db['y'])
>>> db['result']
53
>>>

>>> db.dump('result')
>>>