influxDB-查询操作
转自：https://www.cnblogs.com/Bluebells/p/14120368.html
1 #----综合使用 2 书写顺序 3 select distinct * from ‘表名‘ where ‘限制条件‘ group by ‘分组依据‘ having ‘过滤条件‘ order by limit ‘展示条数‘ 4 执行顺序 5 from -- 查询 6 where -- 限制条件 7 group by -- 分组 8 having -- 过滤条件 9 order by -- 排序 10 limit -- 展示条数 11 distinct -- 去重 12 select -- 查询的结果
1.查询数据表weather 的所有记录:
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> select * from weather
name: weather
time altitude area humidity temperature
---- -------- ---- -------- -----------1607604432455278300 1001 南 -5 101607656595672442800 1000 东 -4 91607656662027484500 1001 南 -5 111607656706278952000 999 南 -5 111607656751612223600 1002 西 -2 111607656799728402900 1003 东 -2 11 |
2.按条件查询
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#查询temperature=11的数据> select * from weather where temperature=11
name: weather
time altitude area humidity temperature
---- -------- ---- -------- -----------1607656662027484500 1001 南 -5 111607656706278952000 999 南 -5 111607656751612223600 1002 西 -2 111607656799728402900 1003 东 -2 11 |
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#查询altitude,temperature两列的数据> select altitude,temperature from weather
name: weather
time altitude temperature
---- -------- -----------1607604432455278300 1001 101607656595672442800 1000 91607656662027484500 1001 111607656706278952000 999 111607656751612223600 1002 111607656799728402900 1003 11 |
3.排序
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#按最新时间排序> select * from weather order by time desc
name: weather
time altitude area humidity temperature
---- -------- ---- -------- -----------1607656799728402900 1003 东 -2 111607656751612223600 1002 西 -2 111607656706278952000 999 南 -5 111607656662027484500 1001 南 -5 111607656595672442800 1000 东 -4 91607604432455278300 1001 南 -5 10#按最早时间排序> select * from weather order by time asc
name: weather
time altitude area humidity temperature
---- -------- ---- -------- -----------1607604432455278300 1001 南 -5 101607656595672442800 1000 东 -4 91607656662027484500 1001 南 -5 111607656706278952000 999 南 -5 111607656751612223600 1002 西 -2 111607656799728402900 1003 东 -2 11 |
4.去重 (distinct)
> select distinct humidity from weather name: weather time distinct ---- -------- 0 -5 0 -4 0 -2
5.group by
select 查询字段1,查询字段2,... from 表名
where 过滤条件
group by分组依据 # 分组后取出的是每个组的第一条数据
> select * from weather group by area name: weather tags: area=东 time altitude humidity temperature ---- -------- -------- ----------- 1607656595672442800 1000 -4 9 1607656799728402900 1003 -2 11 name: weather tags: area=南 time altitude humidity temperature ---- -------- -------- ----------- 1607604432455278300 1001 -5 10 1607656662027484500 1001 -5 11 1607656706278952000 999 -5 11 name: weather tags: area=西 time altitude humidity temperature ---- -------- -------- ----------- 1607656751612223600 1002 -2 11
6.聚合
①count()函数
返回一个（field）字段中的非空值的数量。
SELECT COUNT(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]
> select count(humidity) from weather name: weather time count ---- ----- 0 6
②MEAN() 函数
返回一个字段（field）中的值的算术平均值（平均值）。字段类型必须是长整型或float64。
语法格式：SELECT MEAN(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]
> SELECT MEAN(humidity) from weather name: weather time mean ---- ---- 0 -3.8333333333333335
③MEDIAN()函数
从单个字段（field）中的排序值返回中间值（中位数）。中值是在一组数值中居于中间的数值。字段值的类型必须是长整型或float64格式。
语法：SELECT MEDIAN(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]
> SELECT MEAN(humidity) from weather name: weather time mean ---- ---- 0 -3.8333333333333335
④SPREAD()函数
返回字段的最小值和最大值之间的差值。数据的类型必须是长整型或float64。
语法：SELECT SPREAD(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]
> select spread(humidity) from weather name: weather time spread ---- ------ 0 3
⑤SUM()函数
返回一个字段中的所有值的和。字段的类型必须是长整型或float64。
语法：SELECT SUM(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>
> select sum(humidity) from weather name: weather time sum ---- --- 0 -23
⑥INTEGRAL()函数
返回曲线
语法：SELECT INTEGRAL( [ * | <field_key> | /<regular_expression>/ ] [ , <unit> ] ) [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] [SLIMIT_clause] [SOFFSET_clause]
> select INTEGRAL(temperature) from weather name: weather time integral ---- -------- 0 497728.82358215
⑦distinc()函数
> select distinct(temperature) from weather name: weather time distinct ---- -------- 0 10 0 9 0 11
7.limit限制条数
#显示一条信息 > select * from weather limit 1 name: weather time altitude area humidity temperature ---- -------- ---- -------- ----------- 1607604432455278300 1001 南 -5 10 #limit 10 offset 15，就是从第15行开始之后的10条数据 > select * from weather limit 2 offset 2 name: weather time altitude area humidity temperature ---- -------- ---- -------- ----------- 1607656662027484500 1001 南 -5 11 1607656706278952000 999 南 -5 11
8.or
influxDB中没有in的操作，但是有or。对于习惯了mysql的in来说，用or就需要在代码中循环了。
> select * from weather where altitude=1001 or temperature=11 name: weather time altitude area humidity temperature ---- -------- ---- -------- ----------- 1607656662027484500 1001 南 -5 11 1607656706278952000 999 南 -5 11 1607656751612223600 1002 西 -2 11 1607656799728402900 1003 东 -2 11
9.模糊查询
> select * from test name: test time app count host monitor_name num ---- --- ----- ---- ------------ --- 1585897703920290000 1 127.0.0.1 test 1585897983909417000 ios 2 127.0.0.1 test1 3 1585898383503216000 ios 2 127.0.0.1 test1 3 1585901694441000000 ios 2 127.0.0.1 app1 3 1585901704179677000 ios 2 127.0.0.1 ios1 3 ## =~/给定字段/ 包含指定字段的 > select * from test where monitor_name =~/app/ name: test time app count host monitor_name num ---- --- ----- ---- ------------ --- 1585901694441000000 ios 2 127.0.0.1 app1 3 ##=~/^给定字段/ 以指定字段开始的 > select * from test where monitor_name =~/^app/ name: test time app count host monitor_name num ---- --- ----- ---- ------------ --- 1585901694441000000 ios 2 127.0.0.1 app1 3 ##=~/给定字段$/ 以指定字段结尾的 > select * from test where monitor_name =~/p1$/ name: test time app count host monitor_name num ---- --- ----- ---- ------------ --- 1585901694441000000 ios 2 127.0.0.1 app1 3
10.展示tag
> show tag keys from weather name: weather tagKey ------ altitude area
#查询单个tag的value值 #查询所以tag为altitude的value的值 > show tag values from weather with key="altitude" name: weather key value --- ----- altitude 1000 altitude 1001 altitude 1002 altitude 1003 altitude 999