测算Redis处理实际生产请求的QPS/TPS

测算Redis处理实际生产请求的QPS/TPS

Benchmark工具

redis发布版本中自带了redis-benchmark性能测试工具;

示例:
使用50个并发连接,发出100000个请求,每个请求的数据为2kb,
测试host为127.0.0.1 端口为6379的redis服务器性能:

./redis-benchmark -h 127.0.0.1 -p 6379 -c 50 -n 100000 -d 2

...

====== SADD ======
  100000 requests completed in 2.27 seconds
  500 parallel clients
  3 bytes payload
  keep alive: 1

4.66% <= 1 milliseconds
14.15% <= 2 milliseconds
23.87% <= 3 milliseconds
33.59% <= 4 milliseconds
43.13% <= 5 milliseconds
52.69% <= 6 milliseconds
62.08% <= 7 milliseconds
71.43% <= 8 milliseconds
80.66% <= 9 milliseconds
89.10% <= 10 milliseconds
95.23% <= 11 milliseconds
98.76% <= 12 milliseconds
99.59% <= 13 milliseconds
99.78% <= 14 milliseconds
99.87% <= 15 milliseconds
99.95% <= 16 milliseconds
99.99% <= 17 milliseconds
100.00% <= 17 milliseconds
44150.11 requests per second

我们关注结果最后一行:每秒44150.11个请求,既QPS4.4万;
但这里的数据都只是测试数据,测出来的QPS不能代表实际生产的处理能力;

测算redis处理实际生产请求的QPS/TPS

在实际生产中,我们需要关心这个指标,在我们的应用场景中,
redis能够处理的最大的(QPS/TPS)是多少?

测量redis QPS的方式有两种:

  1. 估计生产的报文大小,使用benchmark工具指定-d数据块大小来模拟;

  2. 使用redis-cli中info统计信息计算差值;
    redis-cli的info命令中有一项total_commands_processed表示:从启动到现在处理的所有命令总数,可以通过统计两次info指令间的差值来计算QPS:

//返回redis-cli info中total_commands_processed的结果 
long getCmdProcessNum(redisContext *c)
{
    string strVal;
    getInfo(c,strVal);

    map mpVal;
    parserInfo(strVal,mpVal);

    map::iterator  iter = mpVal.find("total_commands_processed");

    if(iter != mpVal.end())
    {
        return atol(iter->second.c_str());
    }

    cout << "[err] not found total_commands_processed" << endl;
    return 0;
}

程序实现很简单,就不全贴在这里了,完整代码详见github:
https://github.com/me115/cppset/tree/master/redisTPS

在实际生产中,运行这个程序来统计实际的QPS。
运行示例:

/opt/app/redisTPS#./redisTPS 
Time:  1  Process:40962  TPS:40839.48
Time:  1  Process:43741  TPS:43610.17
Time:  1  Process:38935  TPS:38779.88
Time:  1  Process:31724  TPS:31597.61
Time:  1  Process:32169  TPS:32008.96
Time:  1  Process:31634  TPS:31476.62
Time:  1  Process:46007  TPS:45823.71
Time:  1  Process:50460  TPS:50258.96
Time:  1  Process:47309  TPS:47167.50
Time:  1  Process:50511  TPS:50359.92
...

Posted by: 大CC | 14MAR,2015
博客:blog.me115.com [订阅]
微博:新浪微博

转载于:https://www.cnblogs.com/me115/p/4337733.html

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