Python中的数据对象持久化存储模块pickle的使用示例

import cPickle as pickle

# dumps and loads
# 将内存对象dump为字符串，或者将字符串load为内存对象
def test_dumps_and_loads():
  t = {'name': ['v1', 'v2']}
  print t

  o = pickle.dumps(t)
  print o
  print 'len o: ', len(o)

  p = pickle.loads(o)
  print p

 

# 关于HIGHEST_PROTOCOL参数，pickle 支持3种protocol，0、1、2：
# http://stackoverflow.com/questions/23582489/python-pickle-protocol-choice
# 0：ASCII protocol，兼容旧版本的Python
# 1：binary format，兼容旧版本的Python
# 2：binary format，Python2.3 之后才有，更好的支持new-sytle class
def test_dumps_and_loads_HIGHEST_PROTOCOL():
  print 'HIGHEST_PROTOCOL: ', pickle.HIGHEST_PROTOCOL

  t = {'name': ['v1', 'v2']}
  print t

  o = pickle.dumps(t, pickle.HIGHEST_PROTOCOL)
  print 'len o: ', len(o)

  p = pickle.loads(o)
  print p


# new-style class
def test_new_sytle_class():
  class TT(object):
    def __init__(self, arg, **kwargs):
      super(TT, self).__init__()
      self.arg = arg
      self.kwargs = kwargs

    def test(self):
      print self.arg
      print self.kwargs

  # ASCII protocol
  t = TT('test', a=1, b=2)
  o1 = pickle.dumps(t)
  print o1
  print 'o1 len: ', len(o1)
  p = pickle.loads(o1)
  p.test()

  # HIGHEST_PROTOCOL对new-style class支持更好，性能更高
  o2 = pickle.dumps(t, pickle.HIGHEST_PROTOCOL)
  print 'o2 len: ', len(o2)
  p = pickle.loads(o2)
  p.test()


# dump and load
# 将内存对象序列化后直接dump到文件或支持文件接口的对象中
# 对于dump，需要支持write接口，接受一个字符串作为输入参数，比如：StringIO
# 对于load，需要支持read接口，接受int输入参数，同时支持readline接口，无输入参数，比如StringIO

# 使用文件，ASCII编码
def test_dump_and_load_with_file():
  t = {'name': ['v1', 'v2']}

  # ASCII format
  with open('test.txt', 'w') as fp:
    pickle.dump(t, fp)

  with open('test.txt', 'r') as fp:
    p = pickle.load(fp)
    print p


# 使用文件，二进制编码
def test_dump_and_load_with_file_HIGHEST_PROTOCOL():
  t = {'name': ['v1', 'v2']}
  with open('test.bin', 'wb') as fp:
    pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL)

  with open('test.bin', 'rb') as fp:
    p = pickle.load(fp)
    print p


# 使用StringIO，二进制编码
def test_dump_and_load_with_StringIO():
  import StringIO

  t = {'name': ['v1', 'v2']}

  fp = StringIO.StringIO()
  pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL)

  fp.seek(0)
  p = pickle.load(fp)
  print p

  fp.close()


# 使用自定义类
# 这里演示用户自定义类，只要实现了write、read、readline接口，
# 就可以用作dump、load的file参数
def test_dump_and_load_with_user_def_class():
  import StringIO

  class FF(object):
    def __init__(self):
      self.buf = StringIO.StringIO()

    def write(self, s):
      self.buf.write(s)
      print 'len: ', len(s)

    def read(self, n):
      return self.buf.read(n)

    def readline(self):
      return self.buf.readline()

    def seek(self, pos, mod=0):
      return self.buf.seek(pos, mod)

    def close(self):
      self.buf.close()

  fp = FF()
  t = {'name': ['v1', 'v2']}
  pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL)

  fp.seek(0)
  p = pickle.load(fp)
  print p

  fp.close()


# Pickler/Unpickler
# Pickler(file, protocol).dump(obj) 等价于 pickle.dump(obj, file[, protocol])
# Unpickler(file).load() 等价于 pickle.load(file)
# Pickler/Unpickler 封装性更好，可以很方便的替换file
def test_pickler_unpickler():
  t = {'name': ['v1', 'v2']}

  f = file('test.bin', 'wb')
  pick = pickle.Pickler(f, pickle.HIGHEST_PROTOCOL)
  pick.dump(t)
  f.close()

  f = file('test.bin', 'rb')
  unpick = pickle.Unpickler(f)
  p = unpick.load()
  print p
  f.close()

# -*- coding: utf-8 -*-

import pickle
# 也可以这样：
# import cPickle as pickle

obj = {"a": 1, "b": 2, "c": 3}

# 将 obj 持久化保存到文件 tmp.txt 中
pickle.dump(obj, open("tmp.txt", "w"))

# do something else ...

# 从 tmp.txt 中读取并恢复 obj 对象
obj2 = pickle.load(open("tmp.txt", "r"))

print obj2

# -*- coding: utf-8 -*-
 
import pickle
# 也可以这样：
# import cPickle as pickle
 
obj = {"a": 1, "b": 2, "c": 3}
 
# 将 obj 持久化保存到文件 tmp.txt 中
pickle.dump(obj, open("tmp.txt", "w"))
 
# do something else ...
 
# 从 tmp.txt 中读取并恢复 obj 对象
obj2 = pickle.load(open("tmp.txt", "r"))
 
print obj2

# -*- coding: utf-8 -*-

import cPickle as pickle
import random
import os

import time

LENGTH = 1024 * 10240

def main():
 d = {}
 a = []
 for i in range(LENGTH):
 a.append(random.randint(0, 255))

 d["a"] = a

 print "dumping..."

 t1 = time.time()
 pickle.dump(d, open("tmp1.dat", "wb"), True)
 print "dump1: %.3fs" % (time.time() - t1)

 t1 = time.time()
 pickle.dump(d, open("tmp2.dat", "w"))
 print "dump2: %.3fs" % (time.time() - t1)

 s1 = os.stat("tmp1.dat").st_size
 s2 = os.stat("tmp2.dat").st_size

 print "%d, %d, %.2f%%" % (s1, s2, 100.0 * s1 / s2)

 print "loading..."

 t1 = time.time()
 obj1 = pickle.load(open("tmp1.dat", "rb"))
 print "load1: %.3fs" % (time.time() - t1)

 t1 = time.time()
 obj2 = pickle.load(open("tmp2.dat", "r"))
 print "load2: %.3fs" % (time.time() - t1)


if __name__ == "__main__":
 main()

# -*- coding: utf-8 -*-
 
import cPickle as pickle
import random
import os
 
import time
 
LENGTH = 1024 * 10240
 
def main():
 d = {}
 a = []
 for i in range(LENGTH):
 a.append(random.randint(0, 255))
 
 d["a"] = a
 
 print "dumping..."
 
 t1 = time.time()
 pickle.dump(d, open("tmp1.dat", "wb"), True)
 print "dump1: %.3fs" % (time.time() - t1)
 
 t1 = time.time()
 pickle.dump(d, open("tmp2.dat", "w"))
 print "dump2: %.3fs" % (time.time() - t1)
 
 s1 = os.stat("tmp1.dat").st_size
 s2 = os.stat("tmp2.dat").st_size
 
 print "%d, %d, %.2f%%" % (s1, s2, 100.0 * s1 / s2)
 
 print "loading..."
 
 t1 = time.time()
 obj1 = pickle.load(open("tmp1.dat", "rb"))
 print "load1: %.3fs" % (time.time() - t1)
 
 t1 = time.time()
 obj2 = pickle.load(open("tmp2.dat", "r"))
 print "load2: %.3fs" % (time.time() - t1)
 
 
if __name__ == "__main__":
 main()

dumping…
dump1: 1.297s
dump2: 4.750s
20992503, 68894198, 30.47%
loading…
load1: 2.797s
load2: 10.125s