redis性能测试

文章目录1.查看性能1）查看实例60秒内最大响应延迟2）查看一段时间内redis的最小、最大、平均访问延迟3）精简测试4）pipeline测试(通过 Redis pipeline 功能，批量提交命令给 Redis Server ，从而提升性能)5）快速测试6）随机key测试2.redis监控1）Ping 判断客户端和服务器连接是否正常2）redis-cli info 获得 Redis 系统的状态报

谢白羽

3387人浏览 · 2021-08-03 19:09:06

谢白羽 · 2021-08-03 19:09:06 发布

文章目录

1.查看性能

1）查看实例60秒内最大响应延迟

redis-cli -h 127.0.0.1 -p 6379 --intrinsic-latency 60
//
963552652 total runs (avg latency: 0.0623 microseconds / 62.27 nanoseconds per run).
Worst run took 31042x longer than the average latency.
//最大延迟为1.933ms，当该redis实例的运行时延为2ms，不算性能变慢
Max latency so far: 1 microseconds.
Max latency so far: 3 microseconds.
Max latency so far: 5 microseconds.
Max latency so far: 17 microseconds.
Max latency so far: 38 microseconds.
Max latency so far: 50 microseconds.
Max latency so far: 54 microseconds.
Max latency so far: 439 microseconds.
Max latency so far: 518 microseconds.
Max latency so far: 943 microseconds.
Max latency so far: 1172 microseconds.
Max latency so far: 1933 microseconds.

2）查看一段时间内redis的最小、最大、平均访问延迟

redis-cli -h 127.0.0.1 -p 6379 --latency-history -i 1
//平均访问延迟为0.06到0.12
min: 0, max: 1, avg: 0.04 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.06 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.07 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.04 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.06 (97 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.07 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.10 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.06 (98 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.09 (97 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.11 (97 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.12 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.07 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.06 (97 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.06 (98 samples) -- 1.01 seconds range
min: 0, max: 1, avg: 0.07 (97 samples) -- 1.00 seconds range
min: 0, max: 1, avg: 0.06 (98 samples) -- 1.00 seconds range

//Redis-cli --latency -h 127.0.0.1 -p 6379
min: 0, max: 1, avg: 0.07 (4590 samples)  #访问延迟时间为70us

3）精简测试:看每秒set/get/incr命令在redis的操作效率

redis-benchmark -t set,get,incr -n 1000000 -q

通过 -t 参数，设置仅仅测试 SET/GET/INCR 命令
通过 -n 参数，设置每个测试执行 1000000 次操作。
通过 -q 参数，设置精简输出结果

//
SET: 53908.36 requests per second	//每秒完成5W3数量的set请求的qps
GET: 54755.52 requests per second	//
INCR: 54513.74 requests per second	//

4）pipeline测试(通过 Redis pipeline 功能，批量提交命令给 Redis Server ，从而提升性能)

redis-benchmark -t set,get,incr -n 1000000 -q -P 10

通过 -P 参数，设置每个 pipeline 执行 10 次 Redis 命令。

//执行效果
SET: 452939.31 requests per second
GET: 537827.94 requests per second
INCR: 502765.22 requests per second

5）快速测试：用redis-benchmark测试redis性能

redis-benchmark参数

以下参数被支持：

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 2)
 -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).
 -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.

如果仔细观察，有效载荷大小是 3 个字节。这是默认设置，您可以使用-d参数进行自定义。以下测试针对 100k 请求，负载为 1mb。
（1Mb）

root@gf-lab:~# redis-benchmark -h 127.0.0.1 -p 6379 -n 100000 -d 1000000
====== PING_INLINE ======
  100000 requests completed in 2.80 seconds
  50 parallel clients
  1000000 bytes payload
  keep alive: 1

73.19% <= 1 milliseconds
99.95% <= 2 milliseconds
100.00% <= 2 milliseconds
35676.06 requests per second

redis-benchmark

//输出效果
====== PING_INLINE ======
  100000 requests completed in 1.93 seconds				//十万请求用1.93秒完成
  50 parallel clients									//50个客户端
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no										//单线程

0.00% <= 0.1 milliseconds
2.93% <= 0.2 milliseconds
17.21% <= 0.3 milliseconds
33.84% <= 0.4 milliseconds
50.86% <= 0.5 milliseconds
68.00% <= 0.6 milliseconds
84.21% <= 0.7 milliseconds
94.67% <= 0.8 milliseconds
98.00% <= 0.9 milliseconds
99.02% <= 1.0 milliseconds
99.45% <= 1.1 milliseconds
99.66% <= 1.2 milliseconds
99.83% <= 1.3 milliseconds
99.90% <= 1.4 milliseconds
99.92% <= 1.5 milliseconds
99.94% <= 1.6 milliseconds
99.96% <= 1.7 milliseconds
99.97% <= 1.8 milliseconds
99.98% <= 1.9 milliseconds
99.98% <= 2 milliseconds
100.00% <= 2 milliseconds
51786.64 requests per second							//5.2w的qps/s

====== PING_BULK ======
  100000 requests completed in 1.93 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.10% <= 1 milliseconds
99.92% <= 2 milliseconds
99.98% <= 3 milliseconds
100.00% <= 3 milliseconds
51894.13 requests per second

====== SET ======
  100000 requests completed in 1.94 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.72% <= 1 milliseconds
99.94% <= 2 milliseconds
99.98% <= 3 milliseconds
100.00% <= 3 milliseconds
51440.33 requests per second

====== GET ======
  100000 requests completed in 1.96 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.86% <= 1 milliseconds
99.97% <= 2 milliseconds
99.99% <= 3 milliseconds
100.00% <= 3 milliseconds
51150.89 requests per second

====== INCR ======
  100000 requests completed in 1.96 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.73% <= 1 milliseconds
99.96% <= 2 milliseconds
100.00% <= 3 milliseconds
100.00% <= 3 milliseconds
51150.89 requests per second

====== LPUSH ======
  100000 requests completed in 1.96 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.13% <= 1 milliseconds
99.97% <= 2 milliseconds
100.00% <= 3 milliseconds
51072.52 requests per second

====== RPUSH ======
  100000 requests completed in 1.89 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.38% <= 1 milliseconds
99.96% <= 2 milliseconds
99.99% <= 3 milliseconds
100.00% <= 3 milliseconds
53022.27 requests per second

====== LPOP ======
  100000 requests completed in 2.08 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

94.68% <= 1 milliseconds
99.92% <= 2 milliseconds
100.00% <= 3 milliseconds
48169.56 requests per second

====== RPOP ======
  100000 requests completed in 1.90 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.25% <= 1 milliseconds
99.96% <= 2 milliseconds
99.98% <= 3 milliseconds
100.00% <= 3 milliseconds
52742.62 requests per second

====== SADD ======
  100000 requests completed in 1.91 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.38% <= 1 milliseconds
99.94% <= 2 milliseconds
100.00% <= 2 milliseconds
52465.90 requests per second

====== HSET ======
  100000 requests completed in 1.90 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.22% <= 1 milliseconds
99.98% <= 2 milliseconds
100.00% <= 2 milliseconds
52659.29 requests per second

====== SPOP ======
  100000 requests completed in 1.90 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.55% <= 1 milliseconds
99.94% <= 2 milliseconds
99.97% <= 3 milliseconds
100.00% <= 3 milliseconds
52742.62 requests per second

====== ZADD ======
  100000 requests completed in 1.92 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.86% <= 1 milliseconds
99.94% <= 2 milliseconds
99.97% <= 3 milliseconds
100.00% <= 3 milliseconds
52137.64 requests per second

====== ZPOPMIN ======
  100000 requests completed in 1.91 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

99.28% <= 1 milliseconds
99.94% <= 2 milliseconds
100.00% <= 3 milliseconds
100.00% <= 3 milliseconds
52246.60 requests per second

====== LPUSH (needed to benchmark LRANGE) ======
  100000 requests completed in 1.91 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

98.91% <= 1 milliseconds
99.96% <= 2 milliseconds
100.00% <= 2 milliseconds
52465.90 requests per second

====== LRANGE_100 (first 100 elements) ======
  100000 requests completed in 3.08 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

89.35% <= 1 milliseconds
99.89% <= 2 milliseconds
99.99% <= 3 milliseconds
100.00% <= 3 milliseconds
32467.53 requests per second

====== LRANGE_300 (first 300 elements) ======
  100000 requests completed in 6.28 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

0.00% <= 1 milliseconds
93.42% <= 2 milliseconds
99.83% <= 3 milliseconds
100.00% <= 4 milliseconds
100.00% <= 4 milliseconds
15915.96 requests per second

====== LRANGE_500 (first 450 elements) ======
  100000 requests completed in 9.32 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

0.00% <= 1 milliseconds
35.27% <= 2 milliseconds
82.62% <= 3 milliseconds
99.39% <= 4 milliseconds
99.97% <= 5 milliseconds
100.00% <= 5 milliseconds
10730.76 requests per second

====== LRANGE_600 (first 600 elements) ======
  100000 requests completed in 10.62 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

0.00% <= 1 milliseconds
0.01% <= 2 milliseconds
91.79% <= 3 milliseconds
98.37% <= 4 milliseconds
99.76% <= 5 milliseconds
99.98% <= 6 milliseconds
99.99% <= 7 milliseconds
100.00% <= 7 milliseconds
9411.76 requests per second

====== MSET (10 keys) ======
  100000 requests completed in 1.87 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1
  host configuration "save": 900 1 300 10 60 10000
  host configuration "appendonly": no
  multi-thread: no

96.90% <= 1 milliseconds
99.92% <= 2 milliseconds
99.95% <= 3 milliseconds
100.00% <= 3 milliseconds
53619.30 requests per second

6）随机key测试redis性能

未使用随机key
root@iZbp1do67v9l7zwkcs2b22Z:~/redis-6.0.8/src# redis-benchmark -t set -n 1000 -q
SET: 47619.05 requests per second

使用随机key
root@iZbp1do67v9l7zwkcs2b22Z:~/redis-6.0.8/src# redis-benchmark -t set -n 1000 -q -r 10
SET: 45454.55 requests per second

2.redis监控

1）Ping 判断客户端和服务器连接是否正常

返回Pong就表示连接正常

2）redis-cli info 获得 Redis 系统的状态报告：包括服务器、客户端、内存、持久化、主从复制、CPU集群情况

root@iZbp1do67v9l7zwkcs2b22Z:~/projects/Project1# redis-cli info

#服务器
# Server	
redis_version:6.0.8						# redis版本
redis_git_sha1:00000000					# git上版本
redis_git_dirty:0						# git的版本是否修改
redis_build_id:f9f1fed6697271fc			# 编译时id
redis_mode:standalone					# redis运行模式
os:Linux 4.15.0-135-generic x86_64		# 服务器宿主操作系统
arch_bits:64							# 架构是64位的
multiplexing_api:epoll					# redis基于epoll模型
atomicvar_api:atomic-builtin			
gcc_version:7.5.0						# 编译redis的gcc版本
process_id:518						    # 当前redis服务器进程id
run_id:974027d18c83c7ded724cf6c48068a0f634ca3cb # redis的随机标识符（用于 Sentinel 和集群）
tcp_port:6379							# redis的tcp端口
uptime_in_seconds:189856				# 运行时间(单位：秒)
uptime_in_days:2						# 运行时间(单位：天)
hz:10									
configured_hz:10
lru_clock:587530						# 以分钟为单位进行自增的时钟，用于LRU管理
executable:/usr/local/bin/redis-server	# 
config_file:/etc/redis/6379.conf		# 配置文件
io_threads_active:0

#客户端
# Clients
connected_clients:1						#连接的客户端数量，Redis默认允许客户端连接的最大数量是10000。若是看到连接数超过5000以上，那可能会影响Redis的性能，已连接客户端的数量（不包括通过从属服务器连接的客户端）
client_recent_max_input_buffer:2
client_recent_max_output_buffer:0
blocked_clients:0						# ##正在等待阻塞命令（BLPOP、BRPOP、BRPOPLPUSH）的客户端的数量
tracking_clients:0
clients_in_timeout_table:0

#内存
# Memory
used_memory:1370688						# redis使用的内存总量，由 Redis 分配器分配的内存总量，以字节（byte）为单位
used_memory_human:1.31M					# 由Redis分配的内存的总量，单位G或M给人看的
used_memory_rss:5423104					# redis分配的内存总量(包括内存碎片)，从操作系统上显示已经分配的内存总量
used_memory_rss_human:5.17M				# redis进程从OS角度分配的物理内存，如key被删除后，malloc不一定把内存归还给OS,但可以Redis进程复用，代表redis使用的总内存，给人看的
used_memory_peak:17340992				# Redis使用内存的峰值，字节数    给机器看的
used_memory_peak_human:16.54M			# redis所用内存的高峰值
used_memory_peak_perc:7.90%
used_memory_overhead:803744
used_memory_startup:803056
used_memory_dataset:566944
used_memory_dataset_perc:99.88%
allocator_allocated:1460800
allocator_active:1806336
allocator_resident:4194304
total_system_memory:2004525056
total_system_memory_human:1.87G
used_memory_lua:37888				#Lua脚本引擎所使用的内存大小。
used_memory_lua_human:37.00K
used_memory_scripts:0
used_memory_scripts_human:0B
number_of_cached_scripts:0
maxmemory:0							# redis可以使用的最大的内存，字节数，给机器看的
maxmemory_human:0B					# redis可以使用的最大的内存，给人看的
maxmemory_policy:noeviction			# 设置删除key的规则（有6种
		# allkeys-lru：不管 key 是否设置了过期，淘汰最近最少访问的 
		# keyvolatile-lru：只淘汰最近最少访问、并设置了过期时间的 
		# keyallkeys-random：不管 key 是否设置了过期，随机淘汰 
		# keyvolatile-random：只随机淘汰设置了过期时间的 
		# keyallkeys-ttl：不管 key 是否设置了过期，淘汰即将过期的 
		# keynoeviction：不淘汰任何 key，实例内存达到 
		# maxmeory 后，再写入新数据直接返回错误
		# allkeys-lfu：不管 key 是否设置了过期，淘汰访问频率最低的 key（4.0+版本支持）
		# volatile-lfu：只淘汰访问频率最低、并设置了过期时间 key（4.0+版本支持）
allocator_frag_ratio:1.24
allocator_frag_bytes:345536
allocator_rss_ratio:2.32
allocator_rss_bytes:2387968
rss_overhead_ratio:1.29
rss_overhead_bytes:1228800
mem_fragmentation_ratio:4.15		#内存碎片比率
mem_fragmentation_bytes:4115872
mem_not_counted_for_evict:0
mem_replication_backlog:0
mem_clients_slaves:0
mem_clients_normal:0
mem_aof_buffer:0
mem_allocator:jemalloc-5.1.0 		# 在编译时指定的Redis使用的内存分配器，可以是libc、jemalloc、tcmalloc,Redis内存管理器
active_defrag_running:0
lazyfree_pending_objects:0

# Persistence
loading:0							# 是否加载rdb文件,标志位，是否在载入数据文件，0代表没有，1代表正在载入
rdb_changes_since_last_save:0		# 上次保存数据库后，执行命令的次数;从最近一次dump快照后，未被dump的变更次数(和save里变更计数器类似)
rdb_bgsave_in_progress:0			# 标志位，记录当前是否在后台创建RDB快照保存任务
rdb_last_save_time:1627962982		# 最后一次成功rdb保存任务的时间点，以unix时间戳格式显示
rdb_last_bgsave_status:ok			# 标志位，记录最近一次bgsave操作是否创建成功
rdb_last_bgsave_time_sec:0			
rdb_current_bgsave_time_sec:-1		# 如果当前正在执行rdb保存任务，则为当前rdb任务已经消耗的时间，否则为-1
rdb_last_cow_size:421888			# 最后一次执行rdb保存任务消耗的内存
aof_enabled:0						# 0表示没开aof持久化，1表示开启aof持久化
aof_rewrite_in_progress:0			# 最近一次bgsave操作耗时秒数
aof_rewrite_scheduled:0				# 是否等待调度一次aof重写任务，如果触发了一次aof重写，但是后台正在执行rdb保存任务时会将该状态置为1
aof_last_rewrite_time_sec:-1		# 上次bgsave执行耗时秒数(-1 还没有执行)
aof_current_rewrite_time_sec:-1		# 
aof_last_bgrewrite_status:ok		# 最后一次执行aof缓冲区写入的状态
aof_last_write_status:ok
aof_last_cow_size:0
module_fork_in_progress:0
module_fork_last_cow_size:0

# Stats
total_connections_received:5535		# 运行以来连接过的客户端的总数量
total_commands_processed:41388572	# 运行以来执行过的命令的总数量
instantaneous_ops_per_sec:0			# 服务器每秒钟执行的命令数量 
total_net_input_bytes:1975641730	# Redis每秒网络输入的字节数
total_net_output_bytes:3080132453 	# Redis每秒网络输出的字节数
instantaneous_input_kbps:0.00		# 瞬间的Redis输入网络流量(kbps)
instantaneous_output_kbps:0.00		# 瞬间的Redis输出网络流量(kbps)    
rejected_connections:0				# 因连接数达到maxclients上限后，被拒绝的连接个数
sync_full:0							# --累计Master full sync的次数;如果值比较大，说明常常出现全量复制，就得分析原因，或调整repl-backlog-size
sync_partial_ok:0					# 累计Master psync成功的次数
sync_partial_err:0					# 累计Master pysync 出错失败的次数
expired_keys:0						# 运行以来过期的 key 的数量
expired_stale_perc:0.00
expired_time_cap_reached_count:0	# 
expire_cycle_cpu_milliseconds:2802	# 因为过期而被自动删除的数据库键数量    
evicted_keys:0						# 运行以来删除过的key的数量，因内存used_memory达到maxmemory后，每秒被驱逐的key个数    
keyspace_hits:12836252				# 查找键命中的次数
keyspace_misses:4					# 查找键未命中的次数
pubsub_channels:0					# 目前被订阅的频道数量
pubsub_patterns:0					# 目前被订阅的模式数量
latest_fork_usec:270				# --最近一次fork操作的耗时的微秒数(BGREWRITEAOF,BGSAVE,SYNC等都会触发fork),当并发场景fork耗时过长对服务影响较大
migrate_cached_sockets:0			# 迁移缓存的套接字
slave_expires_tracked_keys:0
active_defrag_hits:0
active_defrag_misses:0
active_defrag_key_hits:0
active_defrag_key_misses:0
tracking_total_keys:0
tracking_total_items:0
tracking_total_prefixes:0
unexpected_error_replies:0
total_reads_processed:13512707
total_writes_processed:13497119
io_threaded_reads_processed:0
io_threaded_writes_processed:0

# Replication
role:master							# 当前Redis的主从状态，实例的角色master还是slave
connected_slaves:0					# 下面有几个slave
master_replid:3b2930656714d49b6aef2493905397c11e83e5b4
master_replid2:0000000000000000000000000000000000000000
master_repl_offset:0				# master复制的偏移量、
second_repl_offset:-1				
repl_backlog_active:0				# 标志位，master是否开启了repl_backlog,有效地psync(2.8+)
repl_backlog_size:1048576			# --repl_backlog的长度(repl-backlog-size)，网络环境不稳定的，建议调整大些。(主从之间如何网络延时过大可以调整此参数，避免重复的触发全量同步)
repl_backlog_first_byte_offset:0	# repl_backlog中首字节的复制偏移位    
repl_backlog_histlen:0				# repl_backlog当前使用的字节数

# CPU
used_cpu_sys:163.911361				# Redis进程消耗的sys cpu
used_cpu_user:176.846898			# Redis进程消耗的user cpu
used_cpu_sys_children:0.006340		# 后台进程耗费的系统 CPU
used_cpu_user_children:0.014579		# 后台进程耗费的用户 CPU

# Modules

# Cluster
cluster_enabled:0					# #从服务

# Keyspace
db0:keys=14,expires=0,avg_ttl=0		# 各个数据库的 key 的数量，以及带有生存期的 key 的数量