DDWiki/实践/后端/项目/11.InfluxDB的基本使用.md

# 基本概念

InfluxDB基于行协议(line protocol)，一个行代表这个point的数据。

```
weather,location=us-midwest temperature=82 1465839830100400200

以上代表着：
measurement,tag_set field_set timestamp

weather就是measurement
location=us-midwest就是tag_set, 是一组键值对
temperature就是field_set,是一组键值对
1465839830100400200就是timestamp,即时间戳(016-06-13T17:43:50.1004002Z)

注意：
--measurement和field_set以及field_set和timestamp之间都有一个空格
--timestamp是Unix型纳秒级，如果不填，会默认使用服务器的纳秒级UTC时间戳.当使用服务器集群的时候，这些服务器集群的时间必须同步，否则会造成数据的不准确

举例：
--weather,location=us-midwest,season=summer temperature=82 1465839830100400200
--weather,location=us-midwest temperature=82,humidity=71 1465839830100400200
```

**数据类型**

```
在tag_set中，tag的值是string类型，InfluxDB不能基于tag的string类型值进行运算，即不能把tag的值作为InfluxQL函数的参数

时间戳，timestamp是UNIX类型，最小时间戳-9223372036854775806,即1677-09-21T00:12:43.145224194Z。最大时间戳9223372036854775806，即2262-04-11T23:47:16.854775806Z。默认情况下时间戳的精度是纳秒，可以通过API更换时间戳的精度。

Field值类型可以是float,integer, string, boolean。
--weather,location=us-midwest temperature=82 1465839830100400200这里的82会被看作是float类型
--weather,location=us-midwest temperature=82i 1465839830100400200这里的82会被看作是integer类型
--weather,location=us-midwest temperature="too warm" 1465839830100400200这里的too warm会被看作是string类型
--weather,location=us-midwest too_hot=true 1465839830100400200,这里的true就是boolean类型，表示true的可以是t,T, true, True, TRUE,表示false的可以是f,F, false, False, FALSE

在同一个分片shard中存储不同类型的field值会报错：
--INSERT weather,location=us-midwest temperature=82 1465839830100400200
--INSERT weather,location=us-midwest temperature=82i 1465839830100400300
ERR:{"error":"field type conflict:input field\"temperature\" on measuremetn \"weather\" is type int64}

但是在不同的分片Shard中存储不同类型的field值不会报错：
--INSERT weather,location=us-midwest temperature=82 1465839830100400200
--INSERT weather,location=us-midwest temperature=82i 1465839830100400300
```

**引号**

```
不要在时间戳上加双引号：
--INSERT weather,location=us-midwest temperature=82 "1465839830100400200"
ERR: {"error":"unable to parse 'weather,location=us-midwest temperature=82 \"1465839830100400200\"': bad timestamp"}

不要在字段field值上加单引号：
--INSERT weather,location=us-midwest temperature='too warm'
ERR: {"error":"unable to parse 'weather,location=us-midwest temperature='too warm'': invalid boolean"}

不要在tag的key,value,field的key上加单引号或双引号，这样虽然不会报错，但InfluxDB会把引号看作是measruements的一部分：
--INSERT "weather",location=us-midwest temperature=87 1465839830100400200
--SHOW MEASURMENTS
--会列出"weather"
--这样查询起来会麻烦：SELECT * FROM "\"weather\""

不要在filed值上加双引号，InfluxDB会看作是字符串类型：
--INSERT weather,location=us-midwest temperatrue="82"
```

**特殊字符Special Characters**

```
，通过\转义：
weather,location=us\,midwest temperature=82 1465839830100400200

=通过\转义：
weather,location=us-midwest temp\=rature=82 1465839830100400200

空格通过\转义：
weather,location\ place=us-midwest temperature=82 1465839830100400200

measurement中的,通过\转义：
wea\,ther,lication=us-midwest temperature=82 1465839830100400200

measurement中的空格通过\转义：
wea\ ther,location=us-midwest temperature=82 1465839830100400200

字段filed值中的双引号用\转义：
weather,location=us-midwest temperature="too\"hot\"" 1465839830100400200

/或\的表现：
--weather,location=us-midwest temperature_str="too hot/cold" 1465839830100400201
--weather,location=us-midwest temperature_str="too hot\cold" 1465839830100400202
--weather,location=us-midwest temperature_str="too hot\\cold" 1465839830100400203
--weather,location=us-midwest temperature_str="too hot\\\cold" 1465839830100400204
--weather,location=us-midwest temperature_str="too hot\\\\\cold" 1465839830100400205
--weather,location=us-midwest temperature_str="too hot\\\\\cold" 1465839830100400206

> SELECT * FROM "wather"
name:weather
time                location        temperature_str
1465839830100400201 us-midwest      too hot/cold
1465839830100400202 us-midwest      too hot\cold
1465839830100400203 us-midwest      too hot\cold  两个会去掉一个
1465839830100400204 us-midwest      too hot\\cold 三个去掉一个
1465839830100400205 us-midwest      too hot\\cold 四个去掉两个
1465839830100400206 us-midwest      too hot\\\cold 5个去掉两个
```

**关键字Keywords**

```
time可以是database, measurement, retension plocy, subscription, user的名称，time不能作为tag或field的key
```

**聚合aggregation**

InfluxQL函数，对一组数据进行计算。

```

==COUNT()
> SELECT COUNT("water_level") FROM "h2o_feet"
返回h2o_feet"这个measurement中water_level这个字段field值不为空的数量

> SELECT COUNT(*) FROM "h2o_feet"
返回h2o_feet"这个measurement中所有字段字段field值不为空的数量

> SELECT COUNT(/water/) FROM "h2o_feet"
返回h2o_feet"这个measurement中字段包含water并且值不为空的数量

> SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(12m),* fill(200) LIMIT 7 SLIMIT 1
时间范围，12分钟的时间间隔进行分组，没有值的用200填充，数据点个数最多为7，序列个数最多为1
```

# InfluxQL-基本

**连接和退出数据库**

```
$ .\influx -precision rfc3339
Connected to http://localhost:8086 version1.7.7
InfluxDB shell version:1.7.1

rfc3339的时间戳格式是：YYYY-MM-DDTHH:MM:SS.nnnnnnnnnZ

$ exit

```

**创建数据库**

- 运行`influxd.exe`文件
- 启动influx: `./influx -precision rfc3339`
- 创建数据库
```
$ CREATE DATABASE NOAA_water_database
```

**下载测试数据并写入本地数据库**

```
下载数据：
$ curl https://s3.amazonaws.com/noaa.water-database/NOAA_data.txt -o NOAA_data.txt
这样在目录中多了一个NOAA_data.txt文件

导入本地数据库：
$ ./influx -import -path=NOAA_data.txt -precision=s -database=NOAA_water_database
这时会报错：unknown arguments: .txt -precision=s
在`influx.exe`文件所在目录，把`NOAA_data.txt`改成`NOAA_data`
$ ./influx -import -path=NOAA_data -precision=s -database=NOAA_water_database

连接数据库：
$ ./influx -precsion rfc3339 -database NOAA_water_database

查询所有的表，即measument:
$ SHOW measurements
```

# InfluxQL-Data exploration
> 查询

**统计某个非空值字段的数量**

```
SELECT COUNT("water_level") FROM h2o_feet
```

**选择前几个**

```
SELECT * FROM h2o_feet LIMIT 5
```

**查询所有fields和tags**
```
 SELECT * FROM "h2o_feet"
```

**选择特定的tag和field**

```
$ ./influx -precsion rfc3339
$ USE NOAA_water_database
$ SELECT "level description","location","water_level" FROM "h2o_feet"
```

**选择tag和field，用类型区分**

```
SELECT "level description"::field,"location"::tag,"water_level"::field FROM "h2o_feet"
```

**选择所有的field**

```
SELECT *::field FROM "h2o_feet"
```

**field简单计算**
```
SELECT ("water_level" * 2) + 4 from "h2o_feet"
```

**从多个measurements中查询数据**
```
select * from "h2o_feet","h2o_PH"
```

**从多个measurements中查询数据，用上数据库名**

```
select * from "NOAA_water_database"."autogen"."h2o_feet"
```

**查询某个数据库中某个measuremnt的所有数据**

```
select * from "NOAA_water_database".."h2o_feet"
```

**查询与tag相关的数据必须至少带一个field**
```
select "water_level","location" from "h2o_feet"
```

> 过滤

**Where语句语法**

```
field支持的操作符：
field_key <operator> ['string' | boolean | float | integer]
= <> != > >= < <=

tag支持的操作符：
tag_key <operator> ['tag_value']
= <> !=
```

**根据字段值筛选**
```
select * from "h2o_feet" where "water_level">8
```

**根据某个字段的字符串值筛选**

```
select * from "h2o_feet" where "level description" = 'below 3 feet'
```

**根据某个计算筛选**
```
select * from "h2o_feet" where "water_level" + 2 > 11.9
```

**根据某个tag值筛选**
```
select "water_level" from "h2o_feet" wehre "location" = 'santa_monica'
```

**根据tag和field筛选**
```
select "water_level" from "h2o_feet" where "location" <> 'santa_monica' adn (water_level < -0.59 OR water_level > 9.95)
```

**根据timestamp筛选**
```
select * from h2o_feet wehre time > now() -7d
```

> 分组

**根据tag分组**

```
 select MEAN(water_level) from h2o_feet group by location
 根据location分组后，取每个分组中water_level字段的平均值
```

**根据多个tag分组**
```
select MEAN(index) from h2o_feet group by lcoation,randtag
```

**根据所有tag分组**
```
select MEAN(index) from h2o_feet group by *
```

**根据时间间隔分组**
```
SELECT COUNT("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
```

**根据时间间隔和tag分组**
```
SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),"location"
```

**根据时间间隔分组并移前**
```
SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(18m,6m)
```

**groupby和fill的结合**

```
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m) fill(100)
```

> INTO

**在原来数据库基础上复制出一个新的数据库**

重命名一个数据库是不可能的，只能在原来数据库基础上创建一个新的数据库，用INTO语法。
```
SELECT * INTO "copy_NOAA_water_database"."autogen".:MEASUREMENT FROM "NOAA_water_database"."autogen"./.*/ GROUP BY *

:MEASUREMENT表示原先数据库的measuments都复制到新的数据库。

autogen是数据保留策略，原先数据库和新的数据库都必须有，否则INTO语句无法执行。

GROUP BY *很关键，意思是把NOAA_water_database数据库中所有measuments下的所有tag也复制到copy_NOAA_water_database数据库。如果不这样写，原先数据库中measuments下的tag会变成copy_NOAA_water_database下的字段。

具体步骤：
--创建新的数据库：create database copy_NOAA_water_database
--进入源数据库：use NOAA_water_database
--使用INTO语句复制数据： SELECT * INTO "copy_NOAA_water_database"."autogen".:MEASUREMENT FROM "NOAA_water_database"."autogen"./.*/ GROUP BY *
--进入新数据库：use copy_NOAA_water_database
--查询新数据库的所有measurments:show measurements
--查询新数据库是否有数据：select * from h2o_feet LIMIT 5

```

**如果数据量很大，建议按measuement和时间范围，循序渐进地复制**

```
SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>
WHERE time > now() - 100w and time < now() - 90w GROUP BY *

SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>}
WHERE time > now() - 90w  and time < now() - 80w GROUP BY *

SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>
WHERE time > now() - 80w  and time < now() - 70w GROUP BY *
```

**把查询结果复制到measument中去**

```
SELECT "water_level" INTO "h2o_feet_copy_1" FROM "h2o_feet" WHERE "location" = 'coyote_creek'
```

> 排序

```
根据时间降序：
SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' ORDER BY time DESC

分组排序：
SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY time(12m) ORDER BY time DESC
```

>LIMIT和SLIMIT

```
限制point返回数量：
SELECT "water_level","location" FROM "h2o_feet" LIMIT 3

限制series返回数量：
SELECT "water_level" FROM "h2o_feet" GROUP BY * SLIMIT 1
```

> OFFSET SOFFSET

```
显示point的第4，5，6条数据
SELECT "water_level","location" FROM "h2o_feet" LIMIT 3 OFFSET 3

显示point的第1，2，3条数据
SELECT "water_level","location" FROM "h2o_feet" LIMIT 3 

SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) ORDER BY time DESC LIMIT 2 OFFSET 2 SLIMIT 1

显示serie的第2条数据
SELECT "water_level" FROM "h2o_feet" GROUP BY * SLIMIT 1 SOFFSET 1
```
> Time Zone

```
选择时区基准
SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:18:00Z' tz('America/Chicago')

SELECT语句即使没有选择时间范围，也有默认时间范围：
1677-09-21 00:12:43.145224194 and 2262-04-11T23:47:16.854775806Z

GROUP BY time()的时间范围是从过去到现在：
1677-09-21 00:12:43.145224194到现在

使用RFC3339的时间类型字符串：
SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00.000000000Z' AND time <= '2015-08-18T00:12:00Z'

使用RFC3339-like的时间类型字符串：
SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18' AND time <= '2015-08-18 00:12:00'

使用epoch时间戳：
 SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= 1439856000000000000 AND time <= 1439856720000000000

 使用second-precision epoch时间戳：
 SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= 1439856000s AND time <= 1439856720s

 在RFC3339-like的时间类型字符串上运行计算：
 SELECT "water_level" FROM "h2o_feet" WHERE time > '2015-09-18T21:24:00Z' + 6m

在epoch时间戳上运行计算：
SELECT "water_level" FROM "h2o_feet" WHERE time > 24043524m - 6m
```

> 相对时间

```
仅仅相对时间：
SELECT "water_level" FROM "h2o_feet" WHERE time > now() - 1h

相对时间和绝对时间结合：
SELECT "level description" FROM "h2o_feet" WHERE time > '2015-09-18T21:18:00Z' AND time < now() + 1000d
```

> 正则表达式

```
选择tag或field中包含1：
SELECT /l/ FROM "h2o_feet" LIMIT 1

选择所有包含temperature的measurment中的degrees的平均值
SELECT MEAN("degrees") FROM /temperature/

location这个tag包含m, water_level这个field大于3：
SELECT MEAN(water_level) FROM "h2o_feet" WHERE "location" =~ /[m]/ AND "water_level" > 3

location这个tag没有值：
SELECT * FROM "h2o_feet" WHERE "location" !~ /./

location这个tag有值：
SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" =~ /./

level description这个字段的值包含between
SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND "level description" =~ /between/

分组时使用正则表达式：
SELECT FIRST("index") FROM "h2o_quality" GROUP BY /l/
```
> 数据类型

```
返回water_level这个字段的类型是float:
SELECT "water_level"::float FROM "h2o_feet" LIMIT 4
```

> 数据类型转换

```
把water_level的float类型的值转换成integer:
SELECT "water_level"::integer FROM "h2o_feet" LIMIT 4

把water_level的float类型的值转换成string(不支持):
SELECT "water_level"::string FROM "h2o_feet" LIMIT 4
```

> 合并行为

```
默认把两个serie自动合并：
SELECT MEAN("water_level") FROM "h2o_feet"

避免自动合并：
SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'coyote_creek'

分别得到两个serie的数据：
SELECT MEAN("water_level") FROM "h2o_feet" GROUP BY "location"
```

> 多条语句

```
SELECT MEAN("water_level") FROM "h2o_feet"; SELECT "water_level" FROM "h2o_feet" LIMIT 2
```

> 子语句

```
SELECT SUM("max") FROM (SELECT MAX("water_level") FROM "h2o_feet" GROUP BY "location")
```


# InfluxQL-Schema exploration

```
展示所有数据库：
SHOW DATABASES

展示数据库的数据保留策略：
SHOW RETENTION POLICIES ON NOAA_water_database

展示某个数据库的所有时间序列：
SHOW SERIES ON NOAA_water_database

展示某个数据库某个表符合条件的时间序列：
SHOW SERIES ON NOAA_water_database FROM "h2o_quality" WHERE "location" = 'coyote_creek' LIMIT 2

展示某个数据库的所有表：
SHOW MEASUREMENTS ON NOAA_water_database

展示某个数据库某个以h2o开头的表，randtag这个tag的值包含整型：
SHOW MEASUREMENTS ON NOAA_water_database WITH MEASUREMENT =~ /h2o.*/ WHERE "randtag"  =~ /\d/

展示某个数据库的所有tag的key:
SHOW TAG KEYS ON "NOAA_water_database"

展示TAG的值：
SHOW TAG VALUES ON "NOAA_water_database" WITH KEY = "randtag"

展示数据库字段的key:
SHOW FIELD KEYS ON "NOAA_water_database"
```

# InfluxQL-Data management
```
创建数据库使用默认配置：
CREATE DATABASE "NOAA_water_database"

创建数据库自定义配置：
CREATE DATABASE "NOAA_water_database" WITH DURATION 3d REPLICATION 1 SHARD DURATION 1H NAME "liquid"

删除数据库：
DROP DATABASE "NOAA_water_database"

删除表中的时间序列：
DROP SERIES FROM "h2o_feet"

根据tag值删除时间序列：
DROP SERIES FROM "h2o_feet" WHERE "location" = 'santa_monica'

删除所有表记录：
DELETE FROM "h2o_feet"

带条件的删除：
DELETE FROM "h2o_quality" WHERE "randtag" = '3'
DELETE WHERE "h2o_quality" WHERE time < '2016-01-01'

删除表：
DROP MEASUREMENT "h2o_feet"

删除shard:
DROOP SHARD 1

数据保留策略：DURATION最小1个小时，最大INF表示无穷；REPLICATION，决定了每个point在集群中有几份，默认是3份，为了确保数据及时响应给请求，这里的值最好小于等于集群中的数据节点。在单结点实例中REPLICATION的设置无效；SHARD DURATION设置Shard Group的时间范围，这里的值没有无线INF一说。默认情况下SHARD DURATION的值受RETENTION POLICY影响。SHARD DURATION的默认值是1小时。
--CREATE RETENTION POLICY "one_day_only" ON "NOAA_water_database" DURATION 1d REPLICATION 1
--把新的策略设置成默认策略：CREATE RETENTION POLICY "one_day_only" ON "NOAA_water_database" DURATION 23h60m REPLICATION 1 DEFAULT

创建并修改策略：
--创建策略：CREATE RETENTION PPLICY "what_is_time" ON "NOAA_water_database" DURATION 2d REPLICATION 1
--修改策略：ALTER RETENTION POLICY "what_is_time" ON "NOAA_water_database" DURAITON 3w SHARD DURATION 2H DEFAULT

删除策略：
DROP RETENTION POLICY "what_is_time" ON "NOAA_water_database"
```

# InfluxQL-Continuous Queries

自动或间隔运行并且保存在measurement中。

**自动统计数据**：
```
CREATE CONTINUOUS QUERY "cq_basic" ON "transporation"
BEGIN
    SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h)
END

cq_basic是自动运行的query的名称，每小时从bus_data这个measurment中统计出来的数据保存到trasporation数据库中的average_passengers这个measurement中。

select * from "average_passengers"
```

**自动统计数据，并保存到不同的RETENTION POLICY上**：
```
CREATE CONTINUOUS QUERY "cq_basic_rp" ON "transporation"
BEGIN
    SELECT mean("passengers") INTO "transporation"."three_weeks"."average_passengers" FROM "bus_data" GROUP BY time(1h)

SELECT * FROM "transporation"."three_weeks"."average_passengers"
```

**自动统计数据，保存到不同的数据库**：
```
CREATE CONTINUOUS QUERY "cq_basic_br" ON "transporation"
BEGIN
    SELECT mean(*) INTO "downsampled_trasporation"."autogen".:MEASUREMENT FROM /.*/ GROUP BY time(30m)
END
```

**自动统计数据，延迟保存到另外的表**：
```
CREATE CONTINUOUS QUERY "cq_basic_offset" ON "transporation"
BEGIN
    SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h,15m)
```

自动统计数据，每隔1小时统计一次，然后每30分钟统计一次，即半点的时候统计一次，最终半点的数据会被下一个整点的数据替换掉。
```
CREATE CONTINUOUS QUERY "cq_advanced_every" ON "transportation"
RESAMPLE EVERY 30m
BEGIN
  SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h)
END
```

自动统计数据,每30分钟统计一次数据，统计前1个小时的数据。
```
CREATE CONTINUOUS QUERY "cq_advanced_for" ON "transportation"
RESAMPLE FOR 1h
BEGIN
  SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(30m)
END
```

自动统计for和every结合起来：
```
CREATE CONTINUOUS QUERY "cq_advanced_every_for" ON "transportation"
RESAMPLE EVERY 1h FOR 90m
BEGIN
  SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(30m)
END
```

自动统计，填上空值
```
CREATE CONTINUOUS QUERY "cq_advanced_for_fill" ON "transportation"
RESAMPLE FOR 2h
BEGIN
  SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h) fill(1000)
END
```

展示所有Continuous Query
```
SHOW CONTINUOUS QUERIES
```

删除Continius Query
```
DROP CONTINOUS QUERY "idle_hands" ON ""
```

# InfluxQL-Functions

- COUNT()
- DISTNCT()
- INTEGRAL()
- MEAN()
- MEDIAN()排好序的中位数
- MODE()字段值中出现频率最高的值
- SPREAD()字段值最大最小之差
- STDDEV()字段值标准差
- SUM()
- BOTTOM()
- FIRST()
- LAST()
- MAX()
- MIN()
- PERCENTILE()字段值某个百分位上的值
- SAMPLE()随机样本
- TOP()
- ABS()
- ACOS()
- ASIN()
- ATAN()
- ATAN2()
- CEL()
- COS()
- CUMULATIVE_SUM()
- DERIVATIVE()变化率
- DIFFERENCE()差值
- ELAPSED()时间戳差值
- EXP()指数
- FLOOR()
- LN()自然对数
- LOG()
- LOG2()
- LOG10()
- MOVING_AVERAGE()滚动窗口的平均值
- NON_NEGATIVE_DERIVATIVE()非负变换率
- NON_NEGATIVE_DIFFERENCE()非负差值
- POW()
- ROUND()
- SIN()
- SQRT()
- TAN()



# InfluxQL-Mathematical operations

```
加法：
SELECT "A" + 5 FROM "add"

减法：
SELECT "A" - "B" from ""

乘法：
SELECT "A" * "B" * "C" from ""

除法：
SELECT 10 / "A" FROM ""

取余：
SELECT "B" % 2 FROM ""
```