Redis实用监控工具一览

Redis已经成为web应用开发不可或缺的一个组成部分，在项目中的应用越来越广泛，这篇文章就来讲讲那些关于Redis监控的那点事。

vredis-benchmark

1.1 简介

第一个就介绍一下，Redis自带的性能检测工具redis-benchmark, 该工具可以模拟 N 个客户端同时发出 Y 个请求。 可以使用 redis-benchmark -h 来查看基准参数。

1.2 命令格式：

redis-benchmark [-h ] [-p ] [-c ] [-n <requests]> [-k ]

1.3 参数介绍：

序号	选项	描述	默认值
1	-h	指定服务器主机名	127.0.0.1
2	-p	指定服务器端口	6379
3	-s	指定服务器 socket
4	-c	指定并发连接数	50
5	-n	指定请求数	10000
6	-d	以字节的形式指定 SET/GET 值的数据大小	2
7	-k	1=keep alive 0=reconnect	1
8	-r	SET/GET/INCR 使用随机 key, SADD 使用随机值
9	-P	通过管道传输 <numreq> 请求	1
10	-q	强制退出 redis。仅显示 query/sec 值
11	--csv	以 CSV 格式输出
12	-l	生成循环，永久执行测试
13	-t	仅运行以逗号分隔的测试命令列表。
14	-I	Idle 模式。仅打开 N 个 idle 连接并等待。

1.4 实例：

1.4.1 同时执行1000个请求来检测性能：

redis-benchmark -n 1000 -q

1.4.2 50个并发请求，10000个请求，检测Redis性能：

redis-benchmark -h localhost -p 6379 -c 50 -n 10000

[root@localhost toutou]# redis-benchmark -h localhost -p 6379 -c 50 -n 10000
====== PING_INLINE ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

96.25% <= 1 milliseconds
98.38% <= 2 milliseconds
99.01% <= 3 milliseconds
100.00% <= 4 milliseconds
88495.58 requests per second

====== PING_BULK ======
  10000 requests completed in 0.10 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

97.74% <= 1 milliseconds
100.00% <= 2 milliseconds
95238.10 requests per second

====== SET ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.44% <= 1 milliseconds
100.00% <= 1 milliseconds
93457.95 requests per second

====== GET ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.33% <= 1 milliseconds
99.13% <= 2 milliseconds
100.00% <= 2 milliseconds
93457.95 requests per second

====== INCR ======
  10000 requests completed in 0.10 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.28% <= 1 milliseconds
100.00% <= 1 milliseconds
95238.10 requests per second

====== LPUSH ======
  10000 requests completed in 0.10 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.70% <= 1 milliseconds
100.00% <= 1 milliseconds
97087.38 requests per second

====== RPUSH ======
  10000 requests completed in 0.10 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

98.66% <= 1 milliseconds
100.00% <= 1 milliseconds
95238.10 requests per second

====== LPOP ======
  10000 requests completed in 0.15 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

93.78% <= 1 milliseconds
96.51% <= 2 milliseconds
97.35% <= 3 milliseconds
98.41% <= 4 milliseconds
99.02% <= 5 milliseconds
99.23% <= 6 milliseconds
99.46% <= 7 milliseconds
99.96% <= 8 milliseconds
99.97% <= 9 milliseconds
100.00% <= 9 milliseconds
67567.57 requests per second

====== RPOP ======
  10000 requests completed in 0.31 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

65.78% <= 1 milliseconds
84.10% <= 2 milliseconds
90.96% <= 3 milliseconds
94.19% <= 4 milliseconds
95.72% <= 5 milliseconds
97.05% <= 6 milliseconds
98.33% <= 7 milliseconds
98.80% <= 8 milliseconds
99.40% <= 9 milliseconds
99.72% <= 10 milliseconds
100.00% <= 14 milliseconds
31746.03 requests per second

====== SADD ======
  10000 requests completed in 0.19 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

93.00% <= 1 milliseconds
96.88% <= 2 milliseconds
98.33% <= 3 milliseconds
98.92% <= 6 milliseconds
98.94% <= 7 milliseconds
98.95% <= 9 milliseconds
99.04% <= 10 milliseconds
99.48% <= 12 milliseconds
99.61% <= 14 milliseconds
99.62% <= 15 milliseconds
99.99% <= 16 milliseconds
100.00% <= 16 milliseconds
52083.33 requests per second

====== HSET ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

95.90% <= 1 milliseconds
99.95% <= 2 milliseconds
100.00% <= 2 milliseconds
90909.09 requests per second

====== SPOP ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

97.04% <= 1 milliseconds
99.75% <= 2 milliseconds
99.78% <= 3 milliseconds
100.00% <= 3 milliseconds
90909.09 requests per second

====== LPUSH (needed to benchmark LRANGE) ======
  10000 requests completed in 0.11 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

96.48% <= 1 milliseconds
99.46% <= 2 milliseconds
99.95% <= 3 milliseconds
100.00% <= 3 milliseconds
87719.30 requests per second

====== LRANGE_100 (first 100 elements) ======
  10000 requests completed in 0.33 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

32.63% <= 1 milliseconds
93.24% <= 2 milliseconds
99.83% <= 3 milliseconds
100.00% <= 3 milliseconds
30303.03 requests per second

====== LRANGE_300 (first 300 elements) ======
  10000 requests completed in 0.85 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

2.65% <= 1 milliseconds
23.01% <= 2 milliseconds
53.33% <= 3 milliseconds
77.25% <= 4 milliseconds
91.47% <= 5 milliseconds
98.58% <= 6 milliseconds
99.99% <= 7 milliseconds
100.00% <= 7 milliseconds
11764.71 requests per second

====== LRANGE_500 (first 450 elements) ======
  10000 requests completed in 1.22 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

1.01% <= 1 milliseconds
9.09% <= 2 milliseconds
28.25% <= 3 milliseconds
50.31% <= 4 milliseconds
68.06% <= 5 milliseconds
81.18% <= 6 milliseconds
90.78% <= 7 milliseconds
96.96% <= 8 milliseconds
99.43% <= 9 milliseconds
100.00% <= 9 milliseconds
8196.72 requests per second

====== LRANGE_600 (first 600 elements) ======
  10000 requests completed in 1.57 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

0.61% <= 1 milliseconds
4.90% <= 2 milliseconds
14.77% <= 3 milliseconds
28.67% <= 4 milliseconds
44.56% <= 5 milliseconds
59.45% <= 6 milliseconds
72.38% <= 7 milliseconds
82.29% <= 8 milliseconds
90.01% <= 9 milliseconds
95.42% <= 10 milliseconds
98.34% <= 11 milliseconds
99.78% <= 12 milliseconds
100.00% <= 12 milliseconds
6357.28 requests per second

====== MSET (10 keys) ======
  10000 requests completed in 0.19 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

68.40% <= 1 milliseconds
98.61% <= 2 milliseconds
100.00% <= 3 milliseconds
53763.44 requests per second

[root@localhost toutou]# 

vredis-cli

2.1 简介

查看redis的连接及读写操作

2.2 命令格式

redis-cli -h xx -p yy monitor

2.3 实例：

2.4 redis-cli info：

Redis 监控最直接的方法就是使用系统提供的 info 命令，只需要执行下面一条命令，就能获得 Redis 系统的状态报告。

# Server
redis_version:5.0.2                    # Redis 的版本
redis_git_sha1:00000000
redis_git_dirty:0
redis_build_id:bf5d1747be5380f
redis_mode:standalone
os:Linux 2.6.32-220.7.1.el6.x86_64 x86_64
arch_bits:64
multiplexing_api:epoll
gcc_version:4.4.7                       #gcc版本
process_id:49324                        # 当前 Redis 服务器进程id
run_id:bbd7b17efcf108fdde285d8987e50392f6a38f48
tcp_port:6379
uptime_in_seconds:1739082               # 运行时间(秒)
uptime_in_days:20                       # 运行时间(天)
hz:10
lru_clock:1734729
config_file:/home/s/apps/RedisMulti_video_so/conf/zzz.conf

# Clients
connected_clients:1                     #连接的客户端数量
client_longest_output_list:0
client_biggest_input_buf:0
blocked_clients:0

# Memory
used_memory:821848                       #Redis分配的内存总量
used_memory_human:802.59K
used_memory_rss:85532672                 #Redis分配的内存总量(包括内存碎片)
used_memory_peak:178987632
used_memory_peak_human:170.70M           #Redis所用内存的高峰值
used_memory_lua:33792
mem_fragmentation_ratio:104.07           #内存碎片比率
mem_allocator:tcmalloc-2.0

# Persistence
loading:0
rdb_changes_since_last_save:0            #上次保存数据库之后，执行命令的次数
rdb_bgsave_in_progress:0                 #后台进行中的 save 操作的数量
rdb_last_save_time:1410848505            #最后一次成功保存的时间点，以 UNIX 时间戳格式显示
rdb_last_bgsave_status:ok
rdb_last_bgsave_time_sec:0
rdb_current_bgsave_time_sec:-1
aof_enabled:0                            #redis是否开启了aof
aof_rewrite_in_progress:0
aof_rewrite_scheduled:0
aof_last_rewrite_time_sec:-1
aof_current_rewrite_time_sec:-1
aof_last_bgrewrite_status:ok
aof_last_write_status:ok

# Stats
total_connections_received:5705          #运行以来连接过的客户端的总数量
total_commands_processed:204013          # 运行以来执行过的命令的总数量
instantaneous_ops_per_sec:0
rejected_connections:0
sync_full:0
sync_partial_ok:0
sync_partial_err:0
expired_keys:34401                       #运行以来过期的 key 的数量
evicted_keys:0                           #运行以来删除过的key的数量
keyspace_hits:2129                       #命中key 的次数
keyspace_misses:3148                     #没命中key 的次数
pubsub_channels:0                        #当前使用中的频道数量
pubsub_patterns:0                        #当前使用中的模式数量
latest_fork_usec:4391

# Replication
role:master                              #当前实例的角色master还是slave
connected_slaves:0
master_repl_offset:0
repl_backlog_active:0
repl_backlog_size:1048576
repl_backlog_first_byte_offset:0
repl_backlog_histlen:0

# CPU
used_cpu_sys:1551.61
used_cpu_user:1083.37
used_cpu_sys_children:2.52
used_cpu_user_children:16.79

# Keyspace
db0:keys=3,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

redis-cli info

结果会返回 Server、Clients、Memory、Persistence、Stats、Replication、CPU、Keyspace 8个部分。从info大返回结果中提取相关信息，就可以达到有效监控的目的。

vshowlog

3.1 简介

redis的slowlog是redis用于记录记录慢查询执行时间的日志系统。由于slowlog只保存在内存中，因此slowlog的效率很高，完全不用担心会影响到redis的性能。Slowlog是Redis从2.2.12版本引入的一条命令。

3.2 命令格式

在redis-cli中有关于slowlog的设置：

CONFIG SET slowlog-log-slower-than 6000

CONFIG SET slowlog-max-len 25

3.3 实例：

---

上面介绍的都是关于Redis自带的命令化性能查询工具。下面介绍介绍一些第三方的Redis可视化性能监控工具。

---

vRedisLive

4.1 简介

RedisLive是由Python编写的开源的图形化监控工具。核心服务部分只包括一个web服务和基于Redis自带的Info命令以及monitor命令的监控服务。支持多实例监控，监控信息可以使用redis存储和sqlite持久化存储。

4.2 安装

4.2.1 安装依赖环境

RedisLive是由Python2.X编写的，所以最好使用Python2.7来运行RedisLive，在CentOS 7中预安装了Python2.7，但没有安装Python的包管理器pip。

yum install epel-release
sudo yum install python-pip
pip install --upgrade pip
pip install tornado
pip install redis
pip install python-dateutil

4.2.2 安装RedisLive

git clone https://github.com/nkrode/RedisLive.git

4.2.3 修改配置文件redis-live.conf

cd RedisLive/src

//按照以下方式修改配置文件
{
    "RedisServers":
    [
        #在此处添加需要监控的redis实例
        {
              "server": "127.0.0.1",                #redis监听地址，此处为本机
              "port" : 6379,                        #redis端口号，可以通过lsof -i | grep redis-ser查看 redis-server端口号
              "password" : "some-password"          #redis认证密码，如果没有可以删除该行，注意json格式
        }
    ],

    "DataStoreType" : "redis",        #监控数据存储方案的配置，可选择redis或sqllite
    #用来存储监控数据的 Redis 实例
    "RedisStatsServer":
    {
        "server" : "127.0.0.1",
        "port" : 6379,
        "password" : "some-password"
    },
    #监控数据持久化数据存储配置
    "SqliteStatsStore" :
    {
        "path":  "db/redislive.sqlite"    #redis数据文件
    }
}

redis-live.conf的配置可以参考redis-live.conf.example

4.3 启动

启动监控服务，每60秒监控一次

./redis-monitor.py --duration=60

再次开启一个终端，进入/root/RedisLive/src目录，启动web服务

./redis-live.py

4.4 效果图

vredis-faina

5.1 简介

5.1.1 背景

redis-faina是由Instagram开发并开源的一个 Redis 查询分析小工具。Instagram团队曾经使用 PGFouine 来作为其PostgreSQL的查询分析工具，他们觉得Redis也需要一个类似的工具来进行query分析工作，于是开发了 redis-faina。

5.1.1 概念

redis-faina 是通过Redis的 MONITOR命令来实现的，通过对在Redis上执行的query进行监控，统计出一段时间的query特性。

5.2 安装

git clone https://github.com/facebookarchive/redis-faina.git

5.3 命令介绍

[root@localhost toutou]# cd redis-faina/
[root@localhost redis-faina]# ls
heroku-redistogo-faina.sh  LICENSE  README.md  redis-faina.py
[root@localhost redis-faina]# ./redis-faina.py -h
usage: redis-faina.py [-h] [--prefix-delimiter PREFIX_DELIMITER]
                      [--redis-version REDIS_VERSION]
                      [input]

positional arguments:
  input                 File to parse; will read from stdin otherwise

optional arguments:
  -h, --help            show this help message and exit
  --prefix-delimiter PREFIX_DELIMITER
                        String to split on for delimiting prefix and rest of
                        key
  --redis-version REDIS_VERSION
                        Version of the redis server being monitored
[root@localhost redis-faina]# 

其中 --prefix-delimiter 主要用于统计前缀的key的数据。

可以通过 redis MONITOR 命令以及管道进行分析，例如：

redis-cli -p 6379 MONITOR | head -n | ./redis-faina.py [options]

或者

redis-cli -p 6379 MONITOR > outfile.txt

./redis-faina.py ./outfile.txt

Overall Stats
========================================
Lines Processed     117773
Commands/Sec        11483.44

Top Prefixes
========================================
friendlist          69945
followedbycounter   25419
followingcounter    10139
recentcomments      3276
queued              7

Top Keys
========================================
friendlist:zzz:1:2     534
followingcount:zzz     227
friendlist:zxz:1:2     167
friendlist:xzz:1:2     165
friendlist:yzz:1:2     160
friendlist:gzz:1:2     160
friendlist:zdz:1:2     160
friendlist:zpz:1:2     156

Top Commands
========================================
SISMEMBER   59545
HGET        27681
HINCRBY     9413
SMEMBERS    9254
MULTI       3520
EXEC        3520
LPUSH       1620
EXPIRE      1598

Command Time (microsecs)
========================================
Median      78.25
75%         105.0
90%         187.25
99%         411.0

Heaviest Commands (microsecs)
========================================
SISMEMBER   5331651.0
HGET        2618868.0
HINCRBY     961192.5
SMEMBERS    856817.5
MULTI       311339.5
SADD        54900.75
SREM        40771.25
EXEC        28678.5

Slowest Calls
========================================
3490.75     "SMEMBERS" "friendlist:zzz:1:2"
2362.0      "SMEMBERS" "friendlist:xzz:1:3"
2061.0      "SMEMBERS" "friendlist:zpz:1:2"
1961.0      "SMEMBERS" "friendlist:yzz:1:2"
1947.5      "SMEMBERS" "friendlist:zpz:1:2"
1459.0      "SISMEMBER" "friendlist:hzz:1:2" "zzz"
1416.25     "SMEMBERS" "friendlist:zhz:1:2"
1389.75     "SISMEMBER" "friendlist:zzx:1:2" "zzz"

v博客总结

关于Redis的监控工具还有很多，这里就不一一列举了，下面给出其它几款优秀的Redis监控工具链接，感兴趣的可以看看。

其他监控工具：

• https://github.com/junegunn/redis-stat
• https://github.com/steelThread/redmon
• https://github.com/oliver006/redis_exporter

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