InfluxDB基本使用

实践/后端/项目/11.InfluxDB的基本使用.md

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+# 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 ""
+```
+