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;
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:
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;
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<string,string> mpVal; parserInfo(strVal,mpVal); map<string,string>::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
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