生成全局唯一Id

生成全局唯一Id

参考了: /article/4895649.html

GUID

import uuid
uuid.uuid1()

优点： 确保唯一， 速度快

缺点： 太长， 不友好, 不好索引

数据库唯一索引

时间戳加上随机数，然后通过数据库做唯一性校验

import time
import random
import string

m = time.strftime('%y%m%d%H%M%S') + ''.join([random.choice(string.lowercase + string.digits) for _ in range(5)])
#检查m在数据库中是否存在，存在则重复上述过程，不存在则存入数据库并返回

优点：适合简单应用，id较短，有一定亲和力

缺点：每秒id总数有限制，并发越大性能越低, 加大数据库访问压力，需要锁表

优化：将时间戳转成62进制数

digit62 = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
#整数转化为62进制字符串
#入口：
#   x : 整数
#返回： 字符串
def int_to_str62( x ):
try:
x=int(x)
except:
x=0
if x<0:
x=-x
if x==0:
return "0"
s=""
while x>=62:
x1= x % 62
s = digit62[x1]+s
x = x // 62
if x>0:
s = digit62[x]+s
return s

#62进制字符串转化为整数
#入口：
#   s : 62进制字符串
#返回： 整数
def str62_to_int( s ):
x = 0
s = str(s).strip()
if s=="":
return x
for y in s:
k = digit62.find(y)
if k>=0:
x=x*62+k
return x

import time
import random
import string

t = time.strftime('%y%m%d%H%M%S')
cut = [ t[i:i+2] for i in range(0, len(t), 2) ]
62t = ''.join([ int_to_str62(int(x)) for x in cut])

m = 62t + ''.join([random.choice(string.lowercase + string.digits) for _ in range(6)])

再ps. 有人说random.choice慢而且随机不均匀，我就写了两个小程序测试一下

import random
import time
import string
import timeit
import hashlib
import uuid
import threading

def randomchoice():
return ''.join([ random.choice(string.lowercase + string.digits) for _ in range(6)])

def _time(f, n=1000000):
print 'start timeit function ', f
t = timeit.timeit(f, number=n)
print 'repeat %s times and used %ss' % (n, t)
print 'end timeit function ', f
print

_time(randomchoice)

result

start timeit function  <function randomchoice at 0x2a7d6e0>
repeat 1000000 times and used 3.97338795662s
end timeit function  <function randomchoice at 0x2a7d6e0>

随机分布

from random import choice
import string
import collections
from matplotlib.pyplot import plot, show, barh, yticks, xlabel, title, figure
import numpy as np

tables = string.ascii_letters + string.digits

counter = collections.Counter()

for _ in range(1000000):
counter[choice(tables)] += 1

alphats = counter.keys()
y_pos = np.arange(len(alphats))
freq = counter.values()

figure(figsize=(100,100))
barh(y_pos, freq, align='edge', alpha=1, height=0.05)
yticks(y_pos, alphats)
xlabel('frequence')
title('random choice')

show()

结果图:



可见分布还是比较平均的

like mongo objectid

时间 ＋ md5(hostname) + pid + 递增id

import struct
import socket
import os
import time
from hashlib import md5
import threading
import random
import binascii

_inc = random.randint(0, 0xFFFFFF)
_inc_lock = threading.Lock()

oid = ""

oid += struct.pack(">i", int(time.time()))

m = md5()
m.update(socket.gethostname())
oid += m.digest()[0:3]

oid += struct.pack(">H", os.getpid() % 0xFFFF)

_inc_lock.acquire()
oid += struct.pack(">i", _inc)[1:4]
_inc = (_inc + 1) % 0xFFFFFF
_inc_lock.release()

print len(oid)
print binascii.hexlify(oid)