一. 压测
你如果要对自己刚刚搭建好的redis做一个基准的压测，测一下你的redis的性能和QPS（query per second）。redis自己提供的redis-benchmark压测工具，是最快捷最方便的，当然啦，这个工具比较简单，用一些简单的操作和场景去压测。
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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) -n <requests> Total number of requests (default 100000) -d <size> Data size of SET/GET value in bytes (default 2)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(1) redis性能一般根据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。
(2) 但在很多公司里，一般是一些低配置的服务器，业务操作又复杂度。如在大公司里，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(3) 一般redisf支持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
(4) QPS的两个杀手：一个是复杂操作，lrange，挺多的; value很大，2 byte
如：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
二. 水平扩容redis读节点，提升度吞吐量
使用Master+多slave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+。