redis-benchmark是redis官方提供的压测工具，安装好redis后，默认安装。使用简便。
语法：
Usage: redis-benchmark [-h <host>] [-p <port>] [-c <clients>] [-n <requests]> [-k <boolean>]
模拟20个客户端，100000次请求
redis-benchmark -h 192.168.1.1 -p 6379 -n 100000 -c 20
模拟1000000次请求，生成100000000个set结构
redis-benchmark -t set -n 1000000 -r 100000000
模拟ping，set，get 各100000次，结果输出到csv文件
redis-benchmark -t ping,set,get -n 100000 --csv
模拟100000次键foo的存储性能
redis-benchmark -n 100000 -q script load "redis.call('set','foo','bar')"
模拟一下十万次请求： redis-benchmark -n 100000 -q 模拟一下百万级访问近百万key： [root@vm-ArthurGuo-1 ~]# redis-benchmark -n 1000000 -r1000000 -q 模拟一个万级用户的并发： redis-benchmark -c 10000 -n 1000000 -r 1000000 -q
redis-benchmark --help
Usage: redis-benchmark [-h <host>] [-p <port>] [-c <clients>] [-n <requests]> [-k <boolean>] -h <hostname> Server hostname (default 127.0.0.1) --主机ip地址 -p <port> Server port (default 6379) --端口 -s <socket> Server socket (overrides host and port) --socket（如果测试在服务器上测可以用socket方式） -c <clients> Number of parallel connections (default 50) --客户端连接数 -n <requests> Total number of requests (default 10000) --总请求数 -d <size> Data size of SET/GET value in bytes (default 2) --set、get的value大小 -dbnum <db> SELECT the specified db number (default 0) --选择哪个数据库测试（一般0-15） -k <boolean> 1=keep alive 0=reconnect (default 1) --是否采用keep alive模式 -r <keyspacelen> Use random keys for SET/GET/INCR, random values for SADD --随机产生键值时的随机数范围 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). --pipeline的个数（如果使用pipeline会把多个命令封装在一起提高效率） -q Quiet. Just show query/sec values --仅仅查看每秒的查询数 --csv Output in CSV format --用csv方式输出 -l Loop. Run the tests forever --循环次数 -t <tests> Only run the comma separated list of tests. The test --指定命令 names are the same as the ones produced as output. -I Idle mode. Just open N idle connections and wait. --仅打开n个空闲链接 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 --测试set、get、mset、sadd等场景下的性能 Fill 127.0.0.1:6379 with about 1 million keys only using the SET test: $ redis-benchmark -t set -n 1000000 -r 100000000 --测试set随机数的性能 Benchmark 127.0.0.1:6379 for a few commands producing CSV output: $ redis-benchmark -t ping,set,get -n 100000 --csv --使用csv的输出方式测试 Benchmark a specific command line: $ redis-benchmark -r 10000 -n 10000 eval 'return redis.call("ping")' 0 --测试基本命令的速度 Fill a list with 10000 random elements: $ redis-benchmark -r 10000 -n 10000 lpush mylist __rand_int__ --测试list入队的速度 On user specified command lines __rand_int__ is replaced with a random integer with a range of values selected by the -r option. 下面我就测下我的笔记本电脑的redis性能： [root@db1 ~]# redis-benchmark -h 127.0.0.1 -p 6379 -n 100000 -c 20 ====== PING_INLINE ====== 100000 requests completed in 1.09 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.86% <= 1 milliseconds 100.00% <= 2 milliseconds 100.00% <= 2 milliseconds 91659.03 requests per second ====== PING_BULK ====== 100000 requests completed in 1.07 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.94% <= 1 milliseconds 100.00% <= 1 milliseconds 93545.37 requests per second ====== SET ====== 100000 requests completed in 1.03 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.78% <= 1 milliseconds 100.00% <= 1 milliseconds 97087.38 requests per second ====== GET ====== 100000 requests completed in 1.10 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.81% <= 1 milliseconds 100.00% <= 1 milliseconds 90909.09 requests per second ====== INCR ====== 100000 requests completed in 1.09 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.86% <= 1 milliseconds 100.00% <= 1 milliseconds 91911.76 requests per second ====== LPUSH ====== 100000 requests completed in 1.07 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.85% <= 1 milliseconds 100.00% <= 1 milliseconds 93808.63 requests per second ====== LPOP ====== 100000 requests completed in 1.01 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.89% <= 1 milliseconds 100.00% <= 1 milliseconds 98522.17 requests per second ====== SADD ====== 100000 requests completed in 1.04 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.76% <= 1 milliseconds 100.00% <= 1 milliseconds 96153.85 requests per second ====== SPOP ====== 100000 requests completed in 1.11 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.92% <= 1 milliseconds 100.00% <= 1 milliseconds 90171.33 requests per second ====== LPUSH (needed to benchmark LRANGE) ====== 100000 requests completed in 1.09 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.82% <= 1 milliseconds 100.00% <= 1 milliseconds 92081.03 requests per second ====== LRANGE_100 (first 100 elements) ====== 100000 requests completed in 2.53 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.91% <= 1 milliseconds 100.00% <= 2 milliseconds 100.00% <= 2 milliseconds 39603.96 requests per second ====== LRANGE_300 (first 300 elements) ====== 100000 requests completed in 5.17 seconds 20 parallel clients 3 bytes payload keep alive: 1 91.01% <= 1 milliseconds 99.94% <= 2 milliseconds 100.00% <= 2 milliseconds 19346.10 requests per second ====== LRANGE_500 (first 450 elements) ====== 100000 requests completed in 7.41 seconds 20 parallel clients 3 bytes payload keep alive: 1 61.54% <= 1 milliseconds 98.36% <= 2 milliseconds 99.96% <= 3 milliseconds 100.00% <= 4 milliseconds 100.00% <= 4 milliseconds 13498.92 requests per second ====== LRANGE_600 (first 600 elements) ====== 100000 requests completed in 9.49 seconds 20 parallel clients 3 bytes payload keep alive: 1 41.24% <= 1 milliseconds 91.89% <= 2 milliseconds 99.78% <= 3 milliseconds 100.00% <= 4 milliseconds 100.00% <= 4 milliseconds 10541.85 requests per second ====== MSET (10 keys) ====== 100000 requests completed in 1.68 seconds 20 parallel clients 3 bytes payload keep alive: 1 99.28% <= 1 milliseconds 100.00% <= 1 milliseconds 59382.42 requests per second 从以上可以看出，20个客户端，每种场景均有100000次请求：ping、set、get、lpush、lpop、spop等都达到90000多rps，但lrange前100、300、500等就比较慢了，才10000多rps。 再测下set的速度： [root@db1 ~]# redis-benchmark -t set -n 1000000 -r 100000000 ====== SET ====== 1000000 requests completed in 10.56 seconds 50 parallel clients 3 bytes payload keep alive: 1 98.65% <= 1 milliseconds 99.90% <= 2 milliseconds 99.99% <= 3 milliseconds 100.00% <= 3 milliseconds 94741.83 requests per second 每秒94741次，非常快 再来测试下list的入队速度： [root@db1 ~]# redis-benchmark -r 100000 -n 100000 lpush mylist __rand_int__ ====== lpush mylist __rand_int__ ====== 100000 requests completed in 0.97 seconds 50 parallel clients 3 bytes payload keep alive: 1 98.83% <= 1 milliseconds 100.00% <= 1 milliseconds 102774.92 requests per second 超过了10w次。