redis-benchmark压力测试

快来打我* 2024-02-18 14:49 46阅读 0赞

## [redis-benchmark压力测试][redis-benchmark] ##

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

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

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

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

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。

[redis-benchmark]: http://www.cnblogs.com/jandison/p/7337442.html

redis-benchmark压力测试

发表评论取消回复

还没有评论，来说两句吧...

相关阅读

相关系统压力测试--1.JMeter压力测试

相关 jmeter压力测试(jmeter压力测试并发数)

相关压力测试

相关 EMQTT测试--压力测试

相关服务器压力测试

相关 apache压力测试

相关压力测试——测试MySQL

相关 Monkey压力测试（2）—对指定app进行压力测试和停止压力测试

相关压力测试

相关压力测试分析