性能测试的使用场景有很多,例如以下几个:
等等,诸如此类的情况,我们都需要进行性能测试。
既然性能测试使用场景那么多,那要怎么进行性能测试呢?
目前比较主流的性能测试分为两种:
因为自己编写代码进行性能测试的方式不够灵活,且很难短时间内模拟大量的并发数,所有作者并不建议使用这种方式。幸运的是 Redis 本身给我们提供了性能测试工具 redis-benchmark(Redis 基准测试),因此我们本文重点来介绍 redis-benchmark 的使用。
redis-benchmark 位于 Redis 的 src 目录下,我们可以使用 ./redis-benchmark -h
来查看基准测试的使用,执行结果如下:
Usage: redis-benchmark [-h <host>] [-p <port>] [-c <clients>] [-n <requests>] [-k <boolean>]
-h <hostname> Server hostname (default 127.0.0.1)
-p <port> Server port (default 6379)
-s <socket> Server socket (overrides host and port)
-a <password> Password for Redis Auth
-c <clients> Number of parallel connections (default 50)
-n <requests> Total number of requests (default 100000)
-d <size> Data size of SET/GET value in bytes (default 3)
--dbnum <db> SELECT the specified db number (default 0)
-k <boolean> 1=keep alive 0=reconnect (default 1)
-r <keyspacelen> Use random keys for SET/GET/INCR, random values for SADD
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).
-e If server replies with errors, show them on stdout.
(no more than 1 error per second is displayed)
-q Quiet. Just show query/sec values
--csv Output in CSV format
-l Loop. Run the tests forever
-t <tests> Only run the comma separated list of tests. The test
names are the same as the ones produced as output.
-I Idle mode. Just open N idle connections and wait.
可以看出 redis-benchmark 支持以下选项:
-h <hostname>
:服务器的主机名(默认值为 127.0.0.1)。-p <port>
:服务器的端口号(默认值为 6379)。-s <socket>
:服务器的套接字(会覆盖主机名和端口号)。-a <password>
:登录 Redis 时进行身份验证的密码。-c <clients>
:并发的连接数量(默认值为 50)。-n <requests>
:发出的请求总数(默认值为 100000)。-d <size>
:SET/GET 命令所操作的值的数据大小,以字节为单位(默认值为 2)。–dbnum <db>
:选择用于性能测试的数据库的编号(默认值为 0)。-k <boolean>
:1 = 保持连接;0 = 重新连接(默认值为 1)。-r <keyspacelen>
:SET/GET/INCR 命令使用随机键,SADD 命令使用随机值。通过这个选项,基准测试会将参数中的 __rand_int__
字符串替换为一个 12 位的整数,这个整数的取值范围从 0 到 keyspacelen-1。每次执行一条命令的时候,用于替换的整数值都会改变。通过这个参数,默认的测试方案会在指定范围之内尝试命中随机键。-P <numreq>
:使用管道机制处理 <numreq>
条 Redis 请求。默认值为 1(不使用管道机制)。-q
:静默测试,只显示 QPS 的值。–csv
:将测试结果输出为 CSV 格式的文件。-l
:循环测试。基准测试会永远运行下去。-t <tests>
:基准测试只会运行列表中用逗号分隔的命令。测试命令的名称和结果输出产生的名称相同。-I
:空闲模式,只会打开 N 个空闲的连接,然后等待。可以看出 redis-benchmark 带的功能还是比较全的。
在安装 Redis 服务端的机器上,我们可以不带任何参数直接执行 ./redis-benchmark
执行结果如下:
[@iZ2ze0nc5n41zomzyqtksmZ:src]$ ./redis-benchmark
====== PING_INLINE ======
100000 requests completed in 1.26 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.81% <= 1 milliseconds
100.00% <= 2 milliseconds
79302.14 requests per second
====== PING_BULK ======
100000 requests completed in 1.29 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.83% <= 1 milliseconds
100.00% <= 1 milliseconds
77459.34 requests per second
====== SET ======
100000 requests completed in 1.26 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.80% <= 1 milliseconds
99.99% <= 2 milliseconds
100.00% <= 2 milliseconds
79239.30 requests per second
====== GET ======
100000 requests completed in 1.19 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.72% <= 1 milliseconds
99.95% <= 15 milliseconds
100.00% <= 16 milliseconds
100.00% <= 16 milliseconds
84104.29 requests per second
====== INCR ======
100000 requests completed in 1.17 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.86% <= 1 milliseconds
100.00% <= 1 milliseconds
85397.09 requests per second
====== LPUSH ======
100000 requests completed in 1.22 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.79% <= 1 milliseconds
100.00% <= 1 milliseconds
82169.27 requests per second
====== RPUSH ======
100000 requests completed in 1.22 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.71% <= 1 milliseconds
100.00% <= 1 milliseconds
81900.09 requests per second
====== LPOP ======
100000 requests completed in 1.29 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.78% <= 1 milliseconds
99.95% <= 13 milliseconds
99.97% <= 14 milliseconds
100.00% <= 14 milliseconds
77399.38 requests per second
====== RPOP ======
100000 requests completed in 1.25 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.82% <= 1 milliseconds
100.00% <= 1 milliseconds
80192.46 requests per second
====== SADD ======
100000 requests completed in 1.25 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.74% <= 1 milliseconds
100.00% <= 1 milliseconds
80192.46 requests per second
====== HSET ======
100000 requests completed in 1.21 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.86% <= 1 milliseconds
100.00% <= 1 milliseconds
82440.23 requests per second
====== SPOP ======
100000 requests completed in 1.22 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.92% <= 1 milliseconds
100.00% <= 1 milliseconds
81699.35 requests per second
====== LPUSH (needed to benchmark LRANGE) ======
100000 requests completed in 1.26 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.69% <= 1 milliseconds
99.95% <= 13 milliseconds
99.99% <= 14 milliseconds
100.00% <= 14 milliseconds
79176.56 requests per second
====== LRANGE_100 (first 100 elements) ======
100000 requests completed in 1.25 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.57% <= 1 milliseconds
99.98% <= 2 milliseconds
100.00% <= 2 milliseconds
80128.20 requests per second
====== LRANGE_300 (first 300 elements) ======
100000 requests completed in 1.25 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.91% <= 1 milliseconds
100.00% <= 1 milliseconds
80064.05 requests per second
====== LRANGE_500 (first 450 elements) ======
100000 requests completed in 1.30 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.78% <= 1 milliseconds
100.00% <= 1 milliseconds
76863.95 requests per second
====== LRANGE_600 (first 600 elements) ======
100000 requests completed in 1.20 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.85% <= 1 milliseconds
100.00% <= 1 milliseconds
83263.95 requests per second
====== MSET (10 keys) ======
100000 requests completed in 1.27 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.65% <= 1 milliseconds
100.00% <= 1 milliseconds
78740.16 requests per second
可以看出以上都是对常用的方法 Set、Get、Incr 等进行测试,基本能达到每秒 8W 的处理级别。
我们可以使用 ./redis-benchmark -t set,get,incr -n 1000000 -q
命令,来对 Redis 服务器进行精简测试,测试结果如下:
[@iZ2ze0nc5n41zomzyqtksmZ:src]$ ./redis-benchmark -t set,get,incr -n 1000000 -q
SET: 81726.05 requests per second
GET: 81466.40 requests per second
INCR: 82481.03 requests per second
可以看出以上测试展示的结果非常的精简,这是因为我们设置了 -q
参数,此选项的意思是设置输出结果为精简模式,其中 -t
表示指定测试指令,-n
设置每个指令测试 100w 次。
本课程的前面章节介绍了 Pipeline(管道)的知识,它是用于客户端把命令批量发给服务器端执行的,以此来提高程序的整体执行效率,那接下来我们测试一下 Pipeline 的吞吐量能到达多少,执行命令如下:
[@iZ2ze0nc5n41zomzyqtksmZ:src]$ ./redis-benchmark -t set,get,incr -n 1000000 -q -P 10
SET: 628535.50 requests per second
GET: 654450.25 requests per second
INCR: 647249.19 requests per second
我们发现 Pipeline 的测试很快就执行完了,同样是每个指令执行 100w 次,可以看出 Pipeline 的性能几乎是普通命令的 8 倍, -P 10
表示每次执行 10 个 Redis 命令。
为什么每次执行 10 个 Redis 命令,Pipeline 的效率为什么达不到普通命令的 10 倍呢?
这是因为基准测试会受到很大外部因素的影响,例如以下几个:
本文介绍了 Redis 自带的性能测试工具 redis-benchmark 也是 Redis 主流的性能测试工具,我们可以轻松模拟指定并发量和指定命令的测试条件,也可以模拟管道测试。测试的结果对于我们做技术选型、版本选择以及数据类型的选择上都有一定的指导意义,但需要注意 Redis 的吞吐量还受到其他因素的影响,例如带宽、CPU 等因素。
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