公司用到redis 的地方非常多 我测试下自己的QPS
使用 redis发布版本中自带了redis-benchmark性能测试工具;
使用50个并发连接，发出100000个请求，每个请求的数据为2kb，
测试host为127.0.0.1 端口为6379的redis服务器性能：
命令:
redis-benchmark -h 127.0.0.1 -p 6379 -c 50 -n 100000 -d 2
输出 :
====== PING_INLINE ======
100000 requests completed in 1.47 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.62% <= 1 milliseconds
99.95% <= 41 milliseconds
100.00% <= 41 milliseconds
67888.66 requests per second
====== PING_BULK ======
100000 requests completed in 1.49 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.57% <= 1 milliseconds
100.00% <= 1 milliseconds
66934.41 requests per second
====== SET ======
100000 requests completed in 1.70 seconds
50 parallel clients
2 bytes payload
keep alive: 1
98.37% <= 1 milliseconds
99.83% <= 2 milliseconds
99.85% <= 4 milliseconds
99.90% <= 15 milliseconds
99.95% <= 215 milliseconds
99.97% <= 216 milliseconds
100.00% <= 216 milliseconds
58858.15 requests per second
====== GET ======
100000 requests completed in 1.45 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.55% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 2 milliseconds
69013.11 requests per second
====== INCR ======
100000 requests completed in 1.31 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.52% <= 1 milliseconds
100.00% <= 2 milliseconds
76219.51 requests per second
====== LPUSH ======
100000 requests completed in 1.42 seconds
50 parallel clients
2 bytes payload
keep alive: 1
98.82% <= 1 milliseconds
99.95% <= 2 milliseconds
100.00% <= 2 milliseconds
70422.54 requests per second
====== LPOP ======
100000 requests completed in 1.39 seconds
50 parallel clients
2 bytes payload
keep alive: 1
98.55% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 2 milliseconds
72150.07 requests per second
====== SADD ======
100000 requests completed in 1.43 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.47% <= 1 milliseconds
100.00% <= 1 milliseconds
70028.02 requests per second
====== SPOP ======
100000 requests completed in 1.47 seconds
50 parallel clients
2 bytes payload
keep alive: 1
99.45% <= 1 milliseconds
100.00% <= 2 milliseconds
67934.78 requests per second
====== LPUSH (needed to benchmark LRANGE) ======
100000 requests completed in 1.43 seconds
50 parallel clients
2 bytes payload
keep alive: 1
98.53% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 2 milliseconds
69832.40 requests per second
====== LRANGE_100 (first 100 elements) ======
100000 requests completed in 4.10 seconds
50 parallel clients
2 bytes payload
keep alive: 1
32.39% <= 1 milliseconds
99.56% <= 2 milliseconds
100.00% <= 2 milliseconds
24378.35 requests per second
====== LRANGE_300 (first 300 elements) ======
100000 requests completed in 9.28 seconds
50 parallel clients
2 bytes payload
keep alive: 1
0.00% <= 1 milliseconds
0.01% <= 2 milliseconds
99.33% <= 3 milliseconds
99.98% <= 4 milliseconds
100.00% <= 4 milliseconds
10770.06 requests per second
====== LRANGE_500 (first 450 elements) ======
100000 requests completed in 12.75 seconds
50 parallel clients
2 bytes payload
keep alive: 1
0.00% <= 2 milliseconds
7.22% <= 3 milliseconds
98.10% <= 4 milliseconds
99.14% <= 5 milliseconds
99.54% <= 6 milliseconds
99.75% <= 7 milliseconds
99.89% <= 8 milliseconds
99.98% <= 9 milliseconds
99.98% <= 10 milliseconds
100.00% <= 11 milliseconds
100.00% <= 11 milliseconds
7841.91 requests per second
====== LRANGE_600 (first 600 elements) ======
100000 requests completed in 16.08 seconds
50 parallel clients
2 bytes payload
keep alive: 1
0.00% <= 2 milliseconds
0.05% <= 3 milliseconds
57.90% <= 4 milliseconds
99.17% <= 5 milliseconds
99.93% <= 6 milliseconds
100.00% <= 7 milliseconds
100.00% <= 7 milliseconds
6217.36 requests per second
====== MSET (10 keys) ======
100000 requests completed in 2.92 seconds
50 parallel clients
2 bytes payload
keep alive: 1
0.39% <= 1 milliseconds
97.30% <= 2 milliseconds
99.15% <= 3 milliseconds
99.50% <= 4 milliseconds
99.85% <= 5 milliseconds
99.90% <= 6 milliseconds
99.95% <= 30 milliseconds
100.00% <= 31 milliseconds
100.00% <= 31 milliseconds
34258.31 requests per second
仅看 每个操作的 requests per second 参数
测算redis处理实际生产请求的QPS/TPS在实际生产中，我们需要关心这个指标，在我们的应用场景中，
redis能够处理的最大的(QPS/TPS)是多少？
测量redis QPS的方式有两种：
估计生产的报文大小，使用benchmark工具指定-d数据块大小来模拟；
使用redis-cli中info统计信息计算差值；
redis-cli的info命令中有一项total_commands_processed表示：从启动到现在处理的所有命令总数，可以通过统计两次info指令间的差值来计算QPS：
生产环境统计
redis-cli -h 127.0.0.1 -p 6379 info |grep -i total_commands_processed | awk -F ':' '{print "'$(date +%H:%M:%S)' " $2}'
使用watch -n1 每秒查看一次 这样状态就回每秒更新
watch -n1 redis-cli -h 127.0.0.1 -p 6379 info stats
Every 1.0s: redis-cli -h 127.0.0.1 -p 6379 info stats Sat Jul 1 15:48:18 2017
# Stats
total_connections_received:6053962
total_commands_processed:596138332
instantaneous_ops_per_sec:292
total_net_input_bytes:47770646646
total_net_output_bytes:35112660886004
instantaneous_input_kbps:18.51
instantaneous_output_kbps:27963.46
rejected_connections:0
sync_full:0
sync_partial_ok:0
sync_partial_err:0
expired_keys:0
evicted_keys:0
keyspace_hits:861725
keyspace_misses:1158168
pubsub_channels:0
pubsub_patterns:0
latest_fork_usec:0
migrate_cached_sockets:0