redis-benchmark使用方法总结

redis-benchmark使用方法总结：包括各个指令的详细解释。

redis-3.0.4

1.redis-benchmark -help 

hadoop@Master:~$ redis-benchmark -help
Invalid option "-help" or option argument missing

Usage: redis-benchmark [-h <host>] [-p <port>] [-c <clients>] [-n <requests]> [-k <boolean>]

 -h <hostname>      Server hostname (default 127.0.0.1)                    //服务主机名
 -p <port>          Server port (default 6379)                             //服务器端口号
 -s <socket>        Server socket (overrides host and port)                //socket，覆盖host和port
 -a <password>      Password for Redis Auth                                //密码
 -c <clients>       Number of parallel connections (default 50)            //并行请求的客户端数量，默认50
 -n <requests>      Total number of requests (default 100000)              //请求的总量，默认100000
 -d <size>          Data size of SET/GET value in bytes (default 2)        //set或get的数据尺寸，单位是字节，默认是2
 -dbnum <db>        SELECT the specified db number (default 0)             //选择指定的数据号
 -k <boolean>       1=keep alive 0=reconnect (default 1)                   //布尔量，1代表保持活跃，0代表重连
 -r <keyspacelen>   Use random keys for SET/GET/INCR, random values for SADD //set/get/incr使用随机的keys,SADD使用随机值...
  Using this option the benchmark will expand the string __rand_int__
  inside an argument with a 12 digits number in the specified range
  from 0 to keyspacelen-1. The substitution changes every time a command
  is executed. Default tests use this to hit random keys in the
  specified range.
 -P <numreq>        Pipeline <numreq> requests. Default 1 (no pipeline).    //管道请求，默认是1，没管道（并行？）
 -q                 Quiet. Just show query/sec values                       //安静，仅仅展示每秒的查询值
 --csv              Output in CSV format                                    //输出按CSV格式
 -l                 Loop. Run the tests forever
 -t <tests>         Only run the comma separated list of tests. The test    //仅仅运行tests
                    names are the same as the ones produced as output.
 -I                 Idle mode. Just open N idle connections and wait.       //闲置模型，进打开N个闲置链接且等待

Examples:

 Run the benchmark with the default configuration against 127.0.0.1:6379:
   $ redis-benchmark

 Use 20 parallel clients, for a total of 100k requests, against 192.168.1.1:
   $ redis-benchmark -h 192.168.1.1 -p 6379 -n 100000 -c 20

 Fill 127.0.0.1:6379 with about 1 million keys only using the SET test:
   $ redis-benchmark -t set -n 1000000 -r 100000000

 Benchmark 127.0.0.1:6379 for a few commands producing CSV output:
   $ redis-benchmark -t ping,set,get -n 100000 --csv

 Benchmark a specific command line:
   $ redis-benchmark -r 10000 -n 10000 eval 'return redis.call("ping")' 0

 Fill a list with 10000 random elements:
   $ redis-benchmark -r 10000 -n 10000 lpush mylist __rand_int__

 On user specified command lines __rand_int__ is replaced with a random integer
 with a range of values selected by the -r option.

自己看下就能明白意思。

下面简单抛下：

2.直接测试redis-benchmark：

hadoop@Master:~$ redis-benchmark 
====== PING_INLINE ======
  100000 requests completed in 0.81 seconds     //默认是100000，上面有，请求在0.81s内完成
  50 parallel clients                           //50个客户端并行操作
  3 bytes payload                               //每次写入3个字节的数据
  keep alive: 1                                 //保持一个连接，一台服务器来处理这些请求  ？？未验证是一个服务器还是一个实例？？

99.86% <= 1 milliseconds
99.97% <= 3 milliseconds
100.00% <= 3 milliseconds
123609.39 requests per second 

====== PING_BULK ======
  100000 requests completed in 0.80 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.81% <= 1 milliseconds
99.88% <= 2 milliseconds
100.00% <= 2 milliseconds
124378.11 requests per second

====== SET ======
  100000 requests completed in 0.87 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.33% <= 1 milliseconds
99.95% <= 5 milliseconds
100.00% <= 5 milliseconds
115340.26 requests per second

====== GET ======
  100000 requests completed in 0.80 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.90% <= 2 milliseconds
99.95% <= 3 milliseconds
100.00% <= 3 milliseconds
124688.28 requests per second

====== INCR ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.85% <= 1 milliseconds
99.86% <= 2 milliseconds
99.95% <= 3 milliseconds
100.00% <= 3 milliseconds
126582.27 requests per second

====== LPUSH ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.90% <= 2 milliseconds
99.91% <= 3 milliseconds
99.95% <= 4 milliseconds
100.00% <= 4 milliseconds
126262.62 requests per second

====== LPOP ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.90% <= 1 milliseconds
99.95% <= 2 milliseconds
100.00% <= 2 milliseconds
126742.72 requests per second

====== SADD ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.90% <= 1 milliseconds
99.95% <= 2 milliseconds
100.00% <= 2 milliseconds
127388.53 requests per second

====== SPOP ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.90% <= 2 milliseconds
100.00% <= 2 milliseconds
126903.55 requests per second

====== LPUSH (needed to benchmark LRANGE) ======
  100000 requests completed in 0.79 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.87% <= 1 milliseconds
99.91% <= 2 milliseconds
99.92% <= 3 milliseconds
100.00% <= 3 milliseconds
126262.62 requests per second

====== LRANGE_100 (first 100 elements) ======
  100000 requests completed in 2.01 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.70% <= 1 milliseconds
99.80% <= 2 milliseconds
99.95% <= 3 milliseconds
100.00% <= 3 milliseconds
49800.80 requests per second

====== LRANGE_300 (first 300 elements) ======
  100000 requests completed in 5.39 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

1.82% <= 1 milliseconds
98.70% <= 2 milliseconds
99.43% <= 3 milliseconds
99.75% <= 4 milliseconds
99.96% <= 5 milliseconds
100.00% <= 5 milliseconds
18556.32 requests per second

====== LRANGE_500 (first 450 elements) ======
  100000 requests completed in 7.63 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

0.23% <= 1 milliseconds
59.65% <= 2 milliseconds
98.03% <= 3 milliseconds
99.05% <= 4 milliseconds
99.73% <= 5 milliseconds
99.94% <= 6 milliseconds
99.98% <= 7 milliseconds
100.00% <= 8 milliseconds
100.00% <= 8 milliseconds
13107.88 requests per second

====== LRANGE_600 (first 600 elements) ======
  100000 requests completed in 9.94 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

0.12% <= 1 milliseconds
19.07% <= 2 milliseconds
85.39% <= 3 milliseconds
98.07% <= 4 milliseconds
99.00% <= 5 milliseconds
99.49% <= 6 milliseconds
99.76% <= 7 milliseconds
99.91% <= 8 milliseconds
99.97% <= 9 milliseconds
99.99% <= 10 milliseconds
100.00% <= 10 milliseconds
10059.35 requests per second

====== MSET (10 keys) ======
  100000 requests completed in 1.02 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.78% <= 1 milliseconds
99.90% <= 2 milliseconds
100.00% <= 2 milliseconds
97847.36 requests per second

3.测试实例：redis-benchmark -n 1000000 -c 100

这里-n从

-n <requests>      Total number of requests (default 100000)

可以看出，代表是请求，总计的请求数量，默认是100000，这里改为了1000000；

-c从

 -c <clients>       Number of parallel connections (default 50)

可以看出代表并行链接的客户端数量，默认是50，这里改为了100.

hadoop@Master:~/cloud/redis-3.0.4$ redis-benchmark -n 1000000 -c 100
====== PING_INLINE ======
  1000000 requests completed in 8.18 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.74% <= 1 milliseconds
99.86% <= 2 milliseconds
99.97% <= 3 milliseconds
100.00% <= 4 milliseconds
100.00% <= 4 milliseconds
122279.28 requests per second

====== PING_BULK ======
  1000000 requests completed in 8.33 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.64% <= 1 milliseconds
99.76% <= 2 milliseconds
99.93% <= 3 milliseconds
99.99% <= 4 milliseconds
100.00% <= 4 milliseconds
120062.42 requests per second

====== SET ======
  1000000 requests completed in 8.20 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.66% <= 1 milliseconds
99.79% <= 2 milliseconds
99.93% <= 3 milliseconds
100.00% <= 4 milliseconds
100.00% <= 4 milliseconds
122010.73 requests per second

====== GET ======
  1000000 requests completed in 8.18 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.74% <= 1 milliseconds
99.83% <= 2 milliseconds
99.96% <= 3 milliseconds
100.00% <= 3 milliseconds
122234.45 requests per second

====== INCR ======
  1000000 requests completed in 8.13 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.71% <= 1 milliseconds
99.84% <= 2 milliseconds
99.94% <= 3 milliseconds
100.00% <= 3 milliseconds
123061.77 requests per second

====== LPUSH ======
  1000000 requests completed in 8.09 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.67% <= 1 milliseconds
99.83% <= 2 milliseconds
99.96% <= 3 milliseconds
100.00% <= 4 milliseconds
100.00% <= 4 milliseconds
123609.39 requests per second

====== LPOP ======
  1000000 requests completed in 8.14 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.54% <= 1 milliseconds
99.79% <= 2 milliseconds
99.93% <= 3 milliseconds
99.97% <= 4 milliseconds
99.98% <= 5 milliseconds
99.99% <= 6 milliseconds
99.99% <= 7 milliseconds
100.00% <= 7 milliseconds
122865.22 requests per second

====== SADD ======
  1000000 requests completed in 8.04 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.72% <= 1 milliseconds
99.86% <= 2 milliseconds
99.97% <= 3 milliseconds
100.00% <= 3 milliseconds
124393.59 requests per second

====== SPOP ======
  1000000 requests completed in 8.09 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.69% <= 1 milliseconds
99.85% <= 2 milliseconds
99.99% <= 3 milliseconds
100.00% <= 4 milliseconds
100.00% <= 4 milliseconds
123624.68 requests per second

====== LPUSH (needed to benchmark LRANGE) ======
  1000000 requests completed in 8.13 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

99.47% <= 1 milliseconds
99.81% <= 2 milliseconds
99.94% <= 3 milliseconds
99.98% <= 4 milliseconds
99.99% <= 5 milliseconds
100.00% <= 5 milliseconds
123031.49 requests per second

====== LRANGE_100 (first 100 elements) ======
  1000000 requests completed in 20.18 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

42.68% <= 1 milliseconds
99.34% <= 2 milliseconds
99.80% <= 3 milliseconds
99.95% <= 4 milliseconds
100.00% <= 4 milliseconds
49546.65 requests per second

====== LRANGE_300 (first 300 elements) ======
  1000000 requests completed in 54.01 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

0.01% <= 1 milliseconds
2.15% <= 2 milliseconds
75.25% <= 3 milliseconds
98.11% <= 4 milliseconds
99.04% <= 5 milliseconds
99.50% <= 6 milliseconds
99.76% <= 7 milliseconds
99.89% <= 8 milliseconds
99.95% <= 9 milliseconds
99.98% <= 10 milliseconds
100.00% <= 11 milliseconds
100.00% <= 12 milliseconds
100.00% <= 13 milliseconds
100.00% <= 13 milliseconds
18513.72 requests per second

====== LRANGE_500 (first 450 elements) ======
  1000000 requests completed in 75.73 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

0.00% <= 1 milliseconds
0.03% <= 2 milliseconds
11.64% <= 3 milliseconds
59.78% <= 4 milliseconds
98.00% <= 5 milliseconds
99.22% <= 6 milliseconds
99.60% <= 7 milliseconds
99.78% <= 8 milliseconds
99.87% <= 9 milliseconds
99.93% <= 10 milliseconds
99.97% <= 11 milliseconds
99.98% <= 12 milliseconds
99.99% <= 13 milliseconds
100.00% <= 14 milliseconds
100.00% <= 15 milliseconds
100.00% <= 15 milliseconds
13205.16 requests per second

====== LRANGE_600 (first 600 elements) ======
  1000000 requests completed in 98.43 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

0.00% <= 1 milliseconds
0.01% <= 2 milliseconds
0.17% <= 3 milliseconds
15.85% <= 4 milliseconds
53.14% <= 5 milliseconds
89.14% <= 6 milliseconds
99.06% <= 7 milliseconds
99.53% <= 8 milliseconds
99.75% <= 9 milliseconds
99.85% <= 10 milliseconds
99.91% <= 11 milliseconds
99.94% <= 12 milliseconds
99.96% <= 13 milliseconds
99.98% <= 14 milliseconds
99.99% <= 15 milliseconds
99.99% <= 16 milliseconds
100.00% <= 17 milliseconds
100.00% <= 17 milliseconds
10159.92 requests per second

====== MSET (10 keys) ======
  1000000 requests completed in 10.11 seconds
  100 parallel clients
  3 bytes payload
  keep alive: 1

83.03% <= 1 milliseconds
99.25% <= 2 milliseconds
99.88% <= 3 milliseconds
99.96% <= 4 milliseconds
99.98% <= 5 milliseconds
99.99% <= 6 milliseconds
100.00% <= 6 milliseconds
98872.84 requests per second

4.测试实例： redis-benchmark -t set -n 1000000 -r 100000000

Fill 127.0.0.1:6379 with about 1 million keys only using the SET test: 仅仅使用set测试，一百万请求

hadoop@Master:~/cloud/redis-3.0.4$ redis-benchmark -t set -n 1000000 -r 100000000
====== SET ======
  1000000 requests completed in 8.29 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.42% <= 1 milliseconds
99.85% <= 2 milliseconds
99.96% <= 3 milliseconds
99.99% <= 4 milliseconds
100.00% <= 5 milliseconds
100.00% <= 5 milliseconds
120685.49 requests per second

5.测试实例： redis-benchmark -t ping,set,get -n 100000 --csv

测试ping，set,get，请求书为100000，输出格式为CSV

hadoop@Master:~/cloud/redis-3.0.4$  redis-benchmark -t ping,set,get -n 100000 --csv
"PING_INLINE","123304.56"
"PING_BULK","123609.39"
"SET","81967.21"
"GET","125470.52"

6.执行一个特别的指令：redis-benchmark -r 10000 -n 10000 eval 'return redis.call("ping")' 0

随机值在10000以内，请求数为10000，

hadoop@Master:~/cloud/redis-3.0.4$ redis-benchmark -r 10000 -n 10000 eval 'return redis.call("ping")' 0
====== eval return redis.call("ping") 0 ======
  10000 requests completed in 0.12 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.90% <= 1 milliseconds
100.00% <= 1 milliseconds
80000.00 requests per second

7.测试实例：redis-benchmark -r 10000 -n 10000 lpush mylist __rand_int__

用10000个随机元素填充一个list，列表。随机数的值为10000以内

hadoop@Master:~/cloud/redis-3.0.4$ redis-benchmark -r 10000 -n 10000 lpush mylist __rand_int__
====== lpush mylist __rand_int__ ======
  10000 requests completed in 0.18 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

49.79% <= 1 milliseconds
98.90% <= 2 milliseconds
99.22% <= 4 milliseconds
99.48% <= 6 milliseconds
99.51% <= 8 milliseconds
99.54% <= 14 milliseconds
100.00% <= 14 milliseconds
55865.92 requests per second

8.测试实例：redis-benchmark  -q -d 100  

测试存取大小为100字节的数据包的性能

hadoop@Master:~/cloud/redis-3.0.4$ redis-benchmark  -q -d 100  
PING_INLINE: 124533.01 requests per second
PING_BULK: 124378.11 requests per second
SET: 80775.45 requests per second
GET: 123001.23 requests per second
INCR: 126582.27 requests per second
LPUSH: 125786.16 requests per second
LPOP: 125000.00 requests per second
SADD: 126262.62 requests per second
SPOP: 127713.92 requests per second
LPUSH (needed to benchmark LRANGE): 126582.27 requests per second
LRANGE_100 (first 100 elements): 40241.45 requests per second
LRANGE_300 (first 300 elements): 15167.60 requests per second
LRANGE_500 (first 450 elements): 10256.41 requests per second
LRANGE_600 (first 600 elements): 7227.00 requests per second
MSET (10 keys): 92850.51 requests per second