对主从 Redis 进行 QPS 压测

本文主要是介绍对主从 Redis 进行 QPS 压测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1、对redis读写分离架构进行压测，单实例写QPS+单实例读QPS

redis-3.2.8/src
./redis-benchmark -h 192.168.31.187
-c <clients> Number of parallel connections (default 50) // 并发
-n <requests> Total number of requests (default 100000) // 总数
-d <size> Data size of SET/GET value in bytes (default 2) // 大小
根据你自己的高峰期的访问量，在高峰期，瞬时最大用户量会达到10万+，-c 100000，-n 10000000，-d 50
各种基准测试，直接出来
1核1G，虚拟机
====== PING_INLINE ======
100000 requests completed in 1.28 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.78% <= 1 milliseconds
99.93% <= 2 milliseconds
99.97% <= 3 milliseconds
100.00% <= 3 milliseconds
78308.54 requests per second
====== PING_BULK ======
100000 requests completed in 1.30 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.87% <= 1 milliseconds
100.00% <= 1 milliseconds
76804.91 requests per second
====== SET ======
100000 requests completed in 2.50 seconds
50 parallel clients
3 bytes payload
keep alive: 1
5.95% <= 1 milliseconds
99.63% <= 2 milliseconds
99.93% <= 3 milliseconds
99.99% <= 4 milliseconds
100.00% <= 4 milliseconds
40032.03 requests per second
====== GET ======
100000 requests completed in 1.30 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.73% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 2 milliseconds
76628.35 requests per second
====== INCR ======
100000 requests completed in 1.90 seconds
50 parallel clients
3 bytes payload
keep alive: 1
80.92% <= 1 milliseconds
99.81% <= 2 milliseconds
99.95% <= 3 milliseconds
99.96% <= 4 milliseconds
99.97% <= 5 milliseconds
100.00% <= 6 milliseconds
52548.61 requests per second
====== LPUSH ======
100000 requests completed in 2.58 seconds
50 parallel clients
3 bytes payload
keep alive: 1
3.76% <= 1 milliseconds
99.61% <= 2 milliseconds
99.93% <= 3 milliseconds
100.00% <= 3 milliseconds
38684.72 requests per second
====== RPUSH ======
100000 requests completed in 2.47 seconds
50 parallel clients
3 bytes payload
keep alive: 1
6.87% <= 1 milliseconds
99.69% <= 2 milliseconds
99.87% <= 3 milliseconds
99.99% <= 4 milliseconds
100.00% <= 4 milliseconds
40469.45 requests per second
====== LPOP ======
100000 requests completed in 2.26 seconds
50 parallel clients
3 bytes payload
keep alive: 1
28.39% <= 1 milliseconds
99.83% <= 2 milliseconds
100.00% <= 2 milliseconds
44306.60 requests per second
====== RPOP ======
100000 requests completed in 2.18 seconds
50 parallel clients
3 bytes payload
keep alive: 1
36.08% <= 1 milliseconds
99.75% <= 2 milliseconds
100.00% <= 2 milliseconds
45871.56 requests per second
====== SADD ======
100000 requests completed in 1.23 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.94% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 2 milliseconds
81168.83 requests per second
====== SPOP ======
100000 requests completed in 1.28 seconds
50 parallel clients
3 bytes payload
keep alive: 1
99.80% <= 1 milliseconds
99.96% <= 2 milliseconds
99.96% <= 3 milliseconds
99.97% <= 5 milliseconds
100.00% <= 5 milliseconds
78369.91 requests per second
====== LPUSH (needed to benchmark LRANGE) ======
100000 requests completed in 2.47 seconds
50 parallel clients
3 bytes payload
keep alive: 1
15.29% <= 1 milliseconds
99.64% <= 2 milliseconds
99.94% <= 3 milliseconds
100.00% <= 3 milliseconds
40420.37 requests per second
====== LRANGE_100 (first 100 elements) ======
100000 requests completed in 3.69 seconds
50 parallel clients
3 bytes payload
keep alive: 1
30.86% <= 1 milliseconds
96.99% <= 2 milliseconds
99.94% <= 3 milliseconds
99.99% <= 4 milliseconds
100.00% <= 4 milliseconds
27085.59 requests per second
====== LRANGE_300 (first 300 elements) ======
100000 requests completed in 10.22 seconds
50 parallel clients
3 bytes payload
keep alive: 1
0.03% <= 1 milliseconds
5.90% <= 2 milliseconds
90.68% <= 3 milliseconds
95.46% <= 4 milliseconds
97.67% <= 5 milliseconds
99.12% <= 6 milliseconds
99.98% <= 7 milliseconds
100.00% <= 7 milliseconds
9784.74 requests per second
====== LRANGE_500 (first 450 elements) ======
100000 requests completed in 14.71 seconds
50 parallel clients
3 bytes payload
keep alive: 1
0.00% <= 1 milliseconds
0.07% <= 2 milliseconds
1.59% <= 3 milliseconds
89.26% <= 4 milliseconds
97.90% <= 5 milliseconds
99.24% <= 6 milliseconds
99.73% <= 7 milliseconds
99.89% <= 8 milliseconds
99.96% <= 9 milliseconds
99.99% <= 10 milliseconds
100.00% <= 10 milliseconds
6799.48 requests per second
====== LRANGE_600 (first 600 elements) ======
100000 requests completed in 18.56 seconds
50 parallel clients
3 bytes payload
keep alive: 1
0.00% <= 2 milliseconds
0.23% <= 3 milliseconds
1.75% <= 4 milliseconds
91.17% <= 5 milliseconds
98.16% <= 6 milliseconds
99.04% <= 7 milliseconds
99.83% <= 8 milliseconds
99.95% <= 9 milliseconds
99.98% <= 10 milliseconds
100.00% <= 10 milliseconds
5387.35 requests per second
====== MSET (10 keys) ======
100000 requests completed in 4.02 seconds
50 parallel clients
3 bytes payload
keep alive: 1
0.01% <= 1 milliseconds
53.22% <= 2 milliseconds
99.12% <= 3 milliseconds
99.55% <= 4 milliseconds
99.70% <= 5 milliseconds
99.90% <= 6 milliseconds
99.95% <= 7 milliseconds
100.00% <= 8 milliseconds
24869.44 requests per second

我们这个读写分离这一块的第一讲
大部分情况下来说，看你的服务器的机器性能和配置，机器越牛逼，配置越高
单机上十几万，单机上二十万
很多公司里，给一些低配置的服务器，操作复杂度
大公司里，都是公司会提供统一的云平台，比如京东、腾讯、BAT、其他的一些、小米、美团
虚拟机，低配
搭建一些集群，专门为某个项目，搭建的专用集群，4核4G内存，比较复杂的操作，数据比较大
几万，单机做到，差不多了
redis提供的高并发，至少到上万，没问题
几万~十几万/二十万不等
QPS，自己不同公司，不同服务器，自己去测试，跟生产环境还有区别
生产环境，大量的网络请求的调用，网络本身就有开销，你的redis的吞吐量就不一定那么高了
QPS的两个杀手：一个是复杂操作，lrange，挺多的; value很大，2 byte，我之前用redis做大规模的缓存
做商品详情页的cache，可能是需要把大串数据，拼接在一起，作为一个json串，大小可能都几k，几个byte
2、水平扩容redis读节点，提升度吞吐量
就按照上一节课讲解的，再在其他服务器上搭建redis从节点，单个从节点读请QPS在5万左右，两个redis从节点，所有的读请求打到两台机器上去，承载整个集群读QPS在10万+