参考文献
influxdb文档 - 聚合函数说明
使用help查看常用命令
> help
Usage:
connect connects to another node specified by host:port
auth prompts for username and password
pretty toggles pretty print for the json format
chunked turns on chunked responses from server
chunk size sets the size of the chunked responses. Set to 0 to reset to the default chunked size
use sets current database
format specifies the format of the server responses: json, csv, or column
precision specifies the format of the timestamp: rfc3339, h, m, s, ms, u or ns
consistency sets write consistency level: any, one, quorum, or all
history displays command history
settings outputs the current settings for the shell
clear clears settings such as database or retention policy. run 'clear' for help
exit/quit/ctrl+d quits the influx shell
show databases show database names
show series show series information
show measurements show measurement information
show tag keys show tag key information
show field keys show field key information
A full list of influxql commands can be found at:
https://docs.influxdata.com/influxdb/latest/query_language/spec/
>
查看数据库
> show databases
name: databases
name
----
_internal
nmon_reports
nmon2influxdb_log
>
删除数据库
> drop database nmon_reports
> drop database nmon2influxdb_log
使用\切换数据库
> use nmon_reports
Using database nmon_reports
查看所有表数据
> show measurements
name: measurements
name
----
CPU_ALL
...
>
删除单表数据
> drop measurement CPU_ALL
查看所有表中的索引tag
> show tag keys;
name: cpu
tagKey
------
cpu
host
name: disk
tagKey
------
device
fstype
host
mode
path
name: diskio
tagKey
------
host
name
查看表中有哪些字段是tag
> show tag keys from cpu;
name: cpu
tagKey
------
cpu
host
>
查看表中有哪些field字段
> show field keys from cpu;
name: cpu
fieldKey fieldType
-------- ---------
usage_guest float
usage_guest_nice float
usage_idle float
usage_iowait float
usage_irq float
usage_nice float
usage_softirq float
usage_steal float
usage_system float
usage_user float
>
查询表中的点series
> show series from cpu;
key
---
cpu,cpu=cpu-total,host=QC_PREDEPL_API_02
cpu,cpu=cpu-total,host=QC_PREDEPL_CMS-NGX-02
cpu,cpu=cpu-total,host=QC_PREDEPL_Mongo_Mysql_02
cpu,cpu=cpu-total,host=QC_PREDEPL_Redis_Memcache_02
cpu,cpu=cpu-total,host=locust01
cpu,cpu=cpu-total,host=locust02
查询单表10条数据
> select * from CPU_ALL limit 10
name: CPU_ALL
time host name value
---- ---- ---- -----
1551694907000000000 qc_predepl_cms-ngx-02 CPUs 4
1551694907000000000 qc_predepl_cms-ngx-02 Idle% 96.9
1551694907000000000 qc_predepl_cms-ngx-02 Steal% 0
1551694907000000000 qc_predepl_cms-ngx-02 Sys% 0.9
1551694907000000000 qc_predepl_cms-ngx-02 User% 0.7
1551694907000000000 qc_predepl_cms-ngx-02 Wait% 1.5
1551694910000000000 qc_predepl_cms-ngx-02 CPUs 4
1551694910000000000 qc_predepl_cms-ngx-02 Idle% 99.7
1551694910000000000 qc_predepl_cms-ngx-02 Steal% 0
1551694910000000000 qc_predepl_cms-ngx-02 Sys% 0.1
>
查询表中最大的值
> select max(*) from CPU_ALL
name: CPU_ALL
time max_value
---- ---------
1551695447000000000 100
>
设置时间显示格式
在influxDB的CLI界面执行precision rfc3339即可，但是显示是UTC的时区，与中国时区差了8个小时，需要在查询语句的最后加上tz('Asia/Shanghai')，这样查询的时间才是纠正为中国时区显示。
> precision rfc3339
> select * from CPU_ALL where time >= '2018-11-23 14:30:39' and time <= '2019-11-23 14:32:32' limit 10
name: CPU_ALL
time host name value
---- ---- ---- -----
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 CPUs 4
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 Idle% 96.9
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 Steal% 0
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 Sys% 0.9
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 User% 0.7
2019-03-04T10:21:47Z qc_predepl_cms-ngx-02 Wait% 1.5
2019-03-04T10:21:50Z qc_predepl_cms-ngx-02 CPUs 4
2019-03-04T10:21:50Z qc_predepl_cms-ngx-02 Idle% 99.7
2019-03-04T10:21:50Z qc_predepl_cms-ngx-02 Steal% 0
2019-03-04T10:21:50Z qc_predepl_cms-ngx-02 Sys% 0.1
>
> select * from CPU_ALL where time >= '2018-11-23 14:30:39' and time <= '2019-11-23 14:32:32' limit 10 tz('Asia/Shanghai')
name: CPU_ALL
time host name value
---- ---- ---- -----
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 CPUs 4
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 Idle% 96.9
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 Steal% 0
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 Sys% 0.9
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 User% 0.7
2019-03-04T18:21:47+08:00 qc_predepl_cms-ngx-02 Wait% 1.5
2019-03-04T18:21:50+08:00 qc_predepl_cms-ngx-02 CPUs 4
2019-03-04T18:21:50+08:00 qc_predepl_cms-ngx-02 Idle% 99.7
2019-03-04T18:21:50+08:00 qc_predepl_cms-ngx-02 Steal% 0
2019-03-04T18:21:50+08:00 qc_predepl_cms-ngx-02 Sys% 0.1
>
条件查询
- 查询某时间下的数据
> select * from CPU_ALL where "time" = 1551694910000000000
name: CPU_ALL
time host name value
---- ---- ---- -----
1551694910000000000 qc_predepl_cms-ngx-02 CPUs 4
1551694910000000000 qc_predepl_cms-ngx-02 Idle% 99.7
1551694910000000000 qc_predepl_cms-ngx-02 Steal% 0
1551694910000000000 qc_predepl_cms-ngx-02 Sys% 0.1
1551694910000000000 qc_predepl_cms-ngx-02 User% 0.2
1551694910000000000 qc_predepl_cms-ngx-02 Wait% 0
>
- 查询某个时间返回的数据，设置时区为上海时区
> select * from CPU_ALL where time >= '2018-11-23 14:30:39' and time <= '2019-11-23 14:32:32' tz('Asia/Shanghai')
- 查询特定字段数据
select * from table_name where "字段1" =~ /匹配值/
> select * from CPU_All3 limit 10
name: CPU_All3
time Cpus Idle% Steal% Sys% User% Wait% host
---- ---- ----- ------ ---- ----- ----- ----
1551689409000000000 4 94.5 0 0.9 0.7 3.9 qc_predepl_cms-ngx-02
1551689412000000000 4 99.8 0 0.2 0.1 0 qc_predepl_cms-ngx-02
1551689415000000000 4 99.5 0 0 0.1 0.4 qc_predepl_cms-ngx-02
1551689418000000000 4 99.4 0 0.1 0.1 0.4 qc_predepl_cms-ngx-02
1551689421000000000 4 99.7 0 0.2 0.2 0 qc_predepl_cms-ngx-02
1551689424000000000 4 99.7 0 0.1 0.1 0.2 qc_predepl_cms-ngx-02
1551689427000000000 4 99.5 0 0.2 0.2 0.2 qc_predepl_cms-ngx-02
1551689430000000000 4 99.7 0 0.2 0.2 0 qc_predepl_cms-ngx-02
1551689433000000000 4 99.7 0 0.1 0.2 0.1 qc_predepl_cms-ngx-02
1551689436000000000 4 99.8 0 0.1 0.1 0 qc_predepl_cms-ngx-02
>
>
> SELECT * FROM "CPU_All3" WHERE time < now() - 5m and "Idle%" =~ /94/
name: CPU_All3
time Cpus Idle% Steal% Sys% User% Wait% host
---- ---- ----- ------ ---- ----- ----- ----
1551689409000000000 4 94.5 0 0.9 0.7 3.9 qc_predepl_cms-ngx-02
1551694925000000000 4 94.8 0 3.5 1.3 0.4 qc_predepl_cms-ngx-02
1551694937000000000 4 94.2 0 4.3 1.3 0.3 qc_predepl_cms-ngx-02
>
> SELECT * FROM "CPU_All3" WHERE time < now() - 5m and "Idle%" =~ /94.5/
name: CPU_All3
time Cpus Idle% Steal% Sys% User% Wait% host
---- ---- ----- ------ ---- ----- ----- ----
1551689409000000000 4 94.5 0 0.9 0.7 3.9 qc_predepl_cms-ngx-02
>
>
> SELECT * FROM "CPU_All3" WHERE time < now() - 5m and "Idle%" =~ /94.5/ and host =~ /qc_predepl_cms/
name: CPU_All3
time Cpus Idle% Steal% Sys% User% Wait% host
---- ---- ----- ------ ---- ----- ----- ----
1551689409000000000 4 94.5 0 0.9 0.7 3.9 qc_predepl_cms-ngx-02
>
倒序查询
> select * from CPU_ALL order by time desc limit 10 tz('Asia/Shanghai')
name: CPU_ALL
time host name value
---- ---- ---- -----
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 Wait% 0.3
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 User% 0.1
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 Sys% 0.1
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 Steal% 0
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 Idle% 99.5
2019-03-04T18:31:44+08:00 qc_predepl_cms-ngx-02 CPUs 4
2019-03-04T18:31:41+08:00 qc_predepl_cms-ngx-02 Wait% 0
2019-03-04T18:31:41+08:00 qc_predepl_cms-ngx-02 User% 0.1
2019-03-04T18:31:41+08:00 qc_predepl_cms-ngx-02 Sys% 0.2
2019-03-04T18:31:41+08:00 qc_predepl_cms-ngx-02 Steal% 0
Distinct去重查询
> SELECT COUNT(DISTINCT("level description")) FROM "h2o_feet"
name: h2o_feet
time count
---- -----
1970-01-01T00:00:00Z 4
Max()最大值、Min() 最小值
> select min(*) from CPU_ALL tz('Asia/Shanghai')
name: CPU_ALL
time min_value
---- ---------
2019-03-04T18:21:47+08:00 0
>
> select max(*) from CPU_ALL tz('Asia/Shanghai')
name: CPU_ALL
time max_value
---- ---------
2019-03-04T18:30:47+08:00 100
>
Mean()查询平均值
> select mean(*) from CPU_ALL tz('Asia/Shanghai')
name: CPU_ALL
time mean_value
---- ----------
1970-01-01T08:00:00+08:00 17.336166666666678
>
MEDIAN() 中位数
返回查询结果中间的数值。
> select median(*) from CPU_ALL tz('Asia/Shanghai')
name: CPU_ALL
time median_value
---- ------------
1970-01-01T08:00:00+08:00 0.4
>
SPREAD()最小值与最大值之间的数值差距
> select spread(*) from CPU_ALL tz('Asia/Shanghai')
name: CPU_ALL
time spread_value
---- ------------
1970-01-01T08:00:00+08:00 100
>
