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/235824/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()
pickle.dump(obj, file[, protocol])
这是将对象持久化的方法，参数的含义分别为：
对象被持久化后怎么还原呢？pickle 模块也提供了相应的方法，如下：
pickle.load(file)
只有一个参数 file ，对应于上面 dump 方法中的 file 参数。这个 file 必须是一个拥有一个能接收一个整数为参数的 read() 方法以及一个不接收任何参数的 readline() 方法，并且这两个方法的返回值都应该是字符串。这可以是一个打开为读的文件对象、StringIO 对象或其他任何满足条件的对象。
下面是一个基本的用例：
# -*- 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
不过实际应用中，我们可能还会有一些改进，比如用 cPickle 来代替 pickle ，前者是后者的一个 C 语言实现版本，拥有更快的速度，另外，有时在 dump 时也会将第三个参数设为 True 以提高压缩比。再来看下面的例子：
# -*- 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()
在我的电脑上执行结果为：
dumping… dump1: 1.297s dump2: 4.750s 20992503, 684198, 30.47% loading… load1: 2.797s load2: 10.125s
可以看到，dump 时如果指定了 protocol 为 True，压缩过后的文件的大小只有原来的文件的 30% ，同时无论在 dump 时还是 load 时所耗费的时间都比原来少。因此，一般来说，可以建议把这个值设为 True 。
另外，pickle 模块还提供 dumps 和 loads 两个方法，用法与上面的 dump 和 load 方法类似，只是不需要输入 file 参数，输入及输出都是字符串对象，有些场景中使用这两个方法可能更为方便。
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