Question

我需要实施一个匿名投票系统（用户注册是不行的）。我已经决定，最好的选择是将单个项目的投票限制为每个IP 10个（考虑到学校等）。

解决这个问题的最佳方法是什么？我正在使用PHP + MySQL。在高峰时段，每秒最高可达20票。我正在使用负载均衡的前端和专用的MySQL服务器。

我担心每次投票都会在数据库中插入一行，然后查询该数据以查看它们是否达到了限制，这可能对服务器来说太过分了？

观看MongoDB或其他什么东西会更好吗？

还有其他想法吗？

Answer 1

我建议在Cookie中保存“已投票”状态。这将允许整个学校和办公室投票。每IP执行10次将允许一个地址的用户投票10次。

显然有很多方法可以解决这个问题，例如清除cookies等，但我认为这是一个很好的选择。

Answer 2

我认为关键/价值数据库在这里会更好。
此外，您不需要为每个投票排，每个IP只需要1行，并使用查询LIKE

INSERT INTO .. ON DUPLICATE KEY UPDATE

Answer 3

我需要实施一个匿名投票系统（用户注册是一个没有去）

IP不是解决这个问题的方法，因为很多公司/学校都有数千人映射到几个IP地址。如果您不希望用户因匿名投票而登录，我建议您使用CAPTCHA（recaptcha）来保护群发投票，因为熟练的程序员可以绕过所有其他技术。甚至可以spoof IP address。我相信在许多Linux发行版中你都可以轻松欺骗IP。

alfred@alfred-laptop:~/bash$ apt-cache search ^fake$
fake - IP address takeover tool

http://en.wikipedia.org/wiki/IP_address_spoofing#Defense_against_spoofing：

还建议设计网络协议和服务他们不依赖IP源地址进行身份验证。

但是熟练的程序员不能绕过经过良好测试的CAPTCHA，例如recaptcha。投票有点难，但在我看来，这是对抗虚假投票的唯一方法。验证码也不能使投票系统不受错误投票的影响。制作此类系统的唯一方法是使用身份验证。保留允许投票的用户（身份）列表。

解决这个问题的最佳方法是什么？我正在使用PHP + MySQL。中高峰时段每秒最高可达20票。

这甚至不会出汗Redis，因为它非常快。

Redis是一个开源的高级键值存储。经常这样称为数据结构服务器，因为键可以包含字符串，哈希，列表，集合和排序集。

首先是我的系统信息。我喜欢它，但它已经很老了。

-Computer-
Processor       : 2x Intel(R) Core(TM)2 Duo CPU     T7100  @ 1.80GHz
Memory      : 2051MB (1403MB used)
Operating System        : Ubuntu 10.10
User Name       : alfred (alfred)
Date/Time       : Sat 16 Jul 2011 07:53:20 PM CEST
-Display-
Resolution      : 1280x800 pixels
OpenGL Renderer     : Unknown
X11 Vendor      : The X.Org Foundation
-Multimedia-
Audio Adapter       : HDA-Intel - HDA Intel
-Input Devices-
 Power Button
 Lid Switch
 Sleep Button
 Power Button
 AT Translated Set 2 keyboard
 Dell Dell USB Keyboard
 Logitech Trackball
 PS/2 Logitech Wheel Mouse
 Video Bus
-Printers (CUPS)-
Canon-MP150     : <i>Default</i>
HP-Photosmart-b110
-SCSI Disks-
HL-DT-ST DVDRAM GSA-T20N
ATA WDC WD1600BEVS-2

接下来我将对我的redis-server进行基准测试：

alfred@alfred-laptop:~/database/redis-2.2.0-rc4/src$ ./redis-server --version
Redis server version 2.1.12 (00000000:0)

alfred@alfred-laptop:~/database/redis-2.2.0-rc4/src$ ./redis-benchmark 
====== PING (inline) ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

94.11% <= 1 milliseconds
97.77% <= 2 milliseconds
98.97% <= 3 milliseconds
99.02% <= 4 milliseconds
99.51% <= 6 milliseconds
99.88% <= 7 milliseconds
100.00% <= 7 milliseconds
44052.86 requests per second

====== PING ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

87.97% <= 1 milliseconds
97.44% <= 2 milliseconds
98.83% <= 3 milliseconds
99.41% <= 4 milliseconds
99.51% <= 5 milliseconds
99.70% <= 6 milliseconds
100.00% <= 6 milliseconds
43478.26 requests per second

====== MSET (10 keys) ======
  10000 requests completed in 0.37 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

11.02% <= 1 milliseconds
82.00% <= 2 milliseconds
93.94% <= 3 milliseconds
97.18% <= 4 milliseconds
98.17% <= 5 milliseconds
98.89% <= 6 milliseconds
99.44% <= 7 milliseconds
99.51% <= 9 milliseconds
99.52% <= 10 milliseconds
100.00% <= 10 milliseconds
26881.72 requests per second

====== SET ======
  10000 requests completed in 0.24 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

86.50% <= 1 milliseconds
96.08% <= 2 milliseconds
97.45% <= 3 milliseconds
97.87% <= 4 milliseconds
99.02% <= 5 milliseconds
99.51% <= 6 milliseconds
99.52% <= 7 milliseconds
100.00% <= 7 milliseconds
40983.61 requests per second

====== GET ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

86.06% <= 1 milliseconds
97.51% <= 2 milliseconds
98.89% <= 3 milliseconds
99.65% <= 4 milliseconds
100.00% <= 4 milliseconds
42553.19 requests per second

====== INCR ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

90.72% <= 1 milliseconds
96.92% <= 2 milliseconds
98.12% <= 3 milliseconds
98.33% <= 4 milliseconds
99.27% <= 5 milliseconds
99.51% <= 7 milliseconds
100.00% <= 7 milliseconds
43103.45 requests per second

====== LPUSH ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

87.92% <= 1 milliseconds
96.35% <= 2 milliseconds
98.26% <= 3 milliseconds
99.51% <= 7 milliseconds
100.00% <= 7 milliseconds
42735.04 requests per second

====== LPOP ======
  10000 requests completed in 0.24 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

87.75% <= 1 milliseconds
96.67% <= 2 milliseconds
97.77% <= 3 milliseconds
98.64% <= 4 milliseconds
98.65% <= 5 milliseconds
99.80% <= 6 milliseconds
100.00% <= 6 milliseconds
41841.00 requests per second

====== SADD ======
  10000 requests completed in 0.23 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

89.55% <= 1 milliseconds
96.56% <= 2 milliseconds
97.80% <= 3 milliseconds
98.76% <= 4 milliseconds
99.50% <= 5 milliseconds
99.63% <= 6 milliseconds
100.00% <= 6 milliseconds
42553.19 requests per second

====== SPOP ======
  10000 requests completed in 0.25 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

88.12% <= 1 milliseconds
96.21% <= 2 milliseconds
97.45% <= 3 milliseconds
97.99% <= 4 milliseconds
98.53% <= 5 milliseconds
99.51% <= 6 milliseconds
100.00% <= 6 milliseconds
40322.58 requests per second

====== LPUSH (again, in order to bench LRANGE) ======
  10000 requests completed in 0.24 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

89.41% <= 1 milliseconds
96.05% <= 2 milliseconds
97.76% <= 3 milliseconds
98.76% <= 4 milliseconds
99.01% <= 5 milliseconds
99.51% <= 7 milliseconds
99.96% <= 8 milliseconds
100.00% <= 8 milliseconds
42016.81 requests per second

====== LRANGE (first 100 elements) ======
  10000 requests completed in 0.40 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

11.56% <= 1 milliseconds
76.23% <= 2 milliseconds
91.93% <= 3 milliseconds
94.47% <= 4 milliseconds
97.80% <= 5 milliseconds
99.23% <= 6 milliseconds
99.87% <= 9 milliseconds
100.00% <= 9 milliseconds
24937.66 requests per second

====== LRANGE (first 300 elements) ======
  10000 requests completed in 0.86 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

2.28% <= 1 milliseconds
10.90% <= 2 milliseconds
35.68% <= 3 milliseconds
63.74% <= 4 milliseconds
86.00% <= 5 milliseconds
92.65% <= 6 milliseconds
94.96% <= 7 milliseconds
97.50% <= 8 milliseconds
98.04% <= 9 milliseconds
98.75% <= 10 milliseconds
99.56% <= 11 milliseconds
99.96% <= 12 milliseconds
100.00% <= 12 milliseconds
11682.24 requests per second

====== LRANGE (first 450 elements) ======
  10000 requests completed in 1.15 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

1.13% <= 1 milliseconds
6.20% <= 2 milliseconds
10.38% <= 3 milliseconds
27.37% <= 4 milliseconds
53.45% <= 5 milliseconds
74.60% <= 6 milliseconds
89.41% <= 7 milliseconds
95.40% <= 8 milliseconds
98.04% <= 9 milliseconds
98.98% <= 10 milliseconds
99.46% <= 11 milliseconds
99.58% <= 12 milliseconds
99.73% <= 13 milliseconds
99.87% <= 14 milliseconds
100.00% <= 14 milliseconds
8695.65 requests per second

====== LRANGE (first 600 elements) ======
  10000 requests completed in 1.45 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

0.52% <= 1 milliseconds
6.23% <= 2 milliseconds
10.67% <= 3 milliseconds
16.37% <= 4 milliseconds
27.51% <= 5 milliseconds
46.06% <= 6 milliseconds
60.82% <= 7 milliseconds
79.70% <= 8 milliseconds
90.96% <= 9 milliseconds
96.01% <= 10 milliseconds
97.99% <= 11 milliseconds
99.43% <= 12 milliseconds
99.90% <= 13 milliseconds
100.00% <= 13 milliseconds
6896.55 requests per second

incr操作就是您所需要的，您可以看到我的系统可以处理43103.45 requests per second。

观看MongoDB或其他什么东西会更好吗？

我建议redis如上所述。

Answer 4

只需确保索引IP字段，并考虑将其表示为字符串以外的其他内容（例如整数）。有关详情，请参阅此处：http://daipratt.co.uk/mysql-store-ip-address/

此外，adlawson的cookie创意很好。你可以使用两者，也许让IP地址只是触发你的警报，在那里你可以去一些管理员屏幕，并决定那些IP看起来像是某人试图欺骗系统而不是学校。

关于ipv6的更新：我对ipv6没有太多了解w /关于当前的虚拟主机，所以不确定是否有用户专门在ipv6上。如果是这样，您可以考虑这些帖子中提供的一些想法如何存储它们：

Answer 5

正如您所描述的负载平衡应该没问题。专用的MySQL服务器不应该对该查询率有任何问题。我不认为MongoDB可以帮助解决这样的问题。像memcached这样的东西性能要高得多，但你仍然需要在某些时候将数据发送到更持久的MySQL数据库。

我同意adlawston使用cookies代替。您仍然可以使用单个IP

进行投票的上限

Answer 6

10是完全随意的值，并不会考虑单个面向公众的IPv4地址背后可能有数百甚至数千人的办公室。更不用说你可能允许个人投票十次了。

显然，这不是一个强大的或适合用途的解决方案。

找到另一种独特识别人的方式。

PHP + MySQL投票系统受IP限制

6 个答案: