1 安装配置InfluxDB是一个开源的时序数据库，使用GO语言开发，特别适合用于处理和分析资源监控数据这种时序相关数据。而InfluxDB自带的各种特殊函数如求标准差，随机取样数据，统计数据变化比等，使数据统计和实时分析变得十分方便。在我们的容器资源监控系统中，就采用了InfluxDB存储cadvisor的监控数据。本文对InfluxDB的基本概念和一些特色功能做一个详细介绍，内容主要是翻译整理自官网文档，如有错漏，请指正。
这里说一下使用docker容器运行influxdb的步骤，物理机安装请参照官方文档。拉取镜像文件后运行即可，当前最新版本是1.3.5。启动容器时设置挂载的数据目录和开放端口。InfluxDB的操作语法InfluxQL与SQL基本一致，也提供了一个类似mysql-client的名为influx的CLI。InfluxDB本身是支持分布式部署多副本存储的，本文介绍都是针对的单节点单副本。
# docker pull influxdb
# docker run -idt --name influxdb -p 8086:8086 -v /Users/ssj/influxdb:/var/lib/influxdb influxdb
f216e9be15bff545befecb30d1d275552026216a939cc20c042b17419e3bde31
root@f216e9be15bf:/# influx
Connected to http://localhost:8086 version 1.3.5
InfluxDB shell version: 1.3.5
> create database cadvisor ## 创建数据库cadvisor
> show databases
name: databases
name
----
_internal
cadvisor
> CREATE USER testuser WITH PASSWORD 'testpwd' ## 创建用户和设置密码
> GRANT ALL PRIVILEGES ON cadvisor TO testuser ## 授权数据库给指定用户
> CREATE RETENTION POLICY "cadvisor_retention" ON "cadvisor" DURATION 30d REPLICATION 1 DEFAULT ## 创建默认的数据保留策略，设置保存时间30天，副本为1
influxdb里面有一些重要概念：database，timestamp，field key， field value， field set，tag key，tag value，tag set，measurement， retention policy ，series，point。结合下面的例子数据来说明这几个概念：
name: census
-————————————
time butterflies honeybees location scientist
2015-08-18T00:00:00Z 12 23 1 langstroth
2015-08-18T00:00:00Z 1 30 1 perpetua
2015-08-18T00:06:00Z 11 28 1 langstroth
2015-08-18T00:06:00Z 3 28 1 perpetua
2015-08-18T05:54:00Z 2 11 2 langstroth
2015-08-18T06:00:00Z 1 10 2 langstroth
2015-08-18T06:06:00Z 8 23 2 perpetua
2015-08-18T06:12:00Z 7 22 2 perpetua
timestamp
既然是时间序列数据库，influxdb的数据都有一列名为time的列，里面存储UTC时间戳。
field key，field value，field set
butterflies和honeybees两列数据称为字段(fields)，influxdb的字段由field key和field value组成。其中butterflies和honeybees为field key，它们为string类型，用于存储元数据。
而butterflies这一列的数据12-7为butterflies的field value，同理，honeybees这一列的23-22为honeybees的field value。field value可以为string，float，integer或boolean类型。field value通常都是与时间关联的。
field key和field value对组成的集合称之为field set。如下：
butterflies = 12 honeybees = 23
butterflies = 1 honeybees = 30
butterflies = 11 honeybees = 28
butterflies = 3 honeybees = 28
butterflies = 2 honeybees = 11
butterflies = 1 honeybees = 10
butterflies = 8 honeybees = 23
butterflies = 7 honeybees = 22
在influxdb中，字段必须存在。注意，字段是没有索引的。如果使用字段作为查询条件，会扫描符合查询条件的所有字段值，性能不及tag。类比一下，fields相当于SQL的没有索引的列。
tag key，tag value，tag set
location和scientist这两列称为标签(tags)，标签由tag key和tag value组成。location这个tag key有两个tag value：1和2，scientist有两个tag value：langstroth和perpetua。tag key和tag value对组成了tag set，示例中的tag set如下：
location = 1, scientist = langstroth
location = 2, scientist = langstroth
location = 1, scientist = perpetua
location = 2, scientist = perpetua
tags是可选的，但是强烈建议你用上它，因为tag是有索引的，tags相当于SQL中的有索引的列。tag value只能是string类型 如果你的常用场景是根据butterflies和honeybees来查询，那么你可以将这两个列设置为tag，而其他两列设置为field，tag和field依据具体查询需求来定。
measurement
measurement是fields，tags以及time列的容器，measurement的名字用于描述存储在其中的字段数据，类似mysql的表名。如上面例子中的measurement为census。measurement相当于SQL中的表，本文中我在部分地方会用表来指代measurement。
retention policy
retention policy指数据存储策略，示例数据中的retention policy为默认的autogen。它表示数据存储永不过期，副本数量为1。你也可以指定数据的存储时间，如30天。
series
series是共享同一个retention policy，measurement以及tag set的数据集合。示例中数据有4个series，如下:
| Arbitrary series number | Retention policy | Measurement | Tag set |
|---|---|---|---|
| series 1 | autogen | census | location = 1,scientist = langstroth |
| series 2 | autogen | census | location = 2,scientist = langstroth |
| series 3 | autogen | census | location = 1,scientist = perpetua |
| series 4 | autogen | census | location = 2,scientist = perpetua |
point
point则是同一个series中具有相同时间的field set，points相当于SQL中的数据行。如下面就是一个point：
name: census
-----------------
time butterflies honeybees location scientist
2015-08-18T00:00:00Z 1 30 1 perpetua
database
上面提到的结构都存储在数据库中，示例的数据库为my_database。一个数据库可以有多个measurement，retention policy， continuous queries以及user。influxdb是一个无模式的数据库，可以很容易的添加新的measurement，tags，fields等。而它的操作却和传统的数据库一样，可以使用类SQL语言查询和修改数据。
influxdb不是一个完整的CRUD数据库，它更像是一个CR-ud数据库。它优先考虑的是创建和读取数据而不是更新和删除数据的性能，而且它阻止了某些更新和删除行为使得创建和读取数据更加高效。
3 特色函数influxdb函数分为聚合函数，选择函数，转换函数，预测函数等。除了与普通数据库一样提供了基本操作函数外，还提供了一些特色函数以方便数据统计计算，下面会一一介绍其中一些常用的特色函数。
FILL()INTEGRAL()SPREAD()STDDEV()MEAN()MEDIAN()SAMPLE()PERCENTILE()FIRST()LAST()TOP()BOTTOM()DERIVATIVE()DIFFERENCE()HOLT_WINTERS()先从官网导入测试数据（注：这里测试用的版本是1.3.1，最新版本是1.3.5）:
$ curl https://s3.amazonaws.com/noaa.water-database/NOAA_data.txt -o NOAA_data.txt
$ influx -import -path=NOAA_data.txt -precision=s -database=NOAA_water_database
$ influx -precision rfc3339 -database NOAA_water_database
Connected to http://localhost:8086 version 1.3.1
InfluxDB shell 1.3.1
> show measurements
name: measurements
name
----
average_temperature
distincts
h2o_feet
h2o_pH
h2o_quality
h2o_temperature
> show series from h2o_feet;
key
---
h2o_feet,location=coyote_creek
h2o_feet,location=santa_monica
h2o_feetlocationlevel descriptionwater_level> SELECT * FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time level description location water_level
---- ----------------- -------- -----------
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:06:00Z between 6 and 9 feet coyote_creek 8.005
2015-08-18T00:06:00Z below 3 feet santa_monica 2.116
2015-08-18T00:12:00Z between 6 and 9 feet coyote_creek 7.887
2015-08-18T00:12:00Z below 3 feet santa_monica 2.028
2015-08-18T00:18:00Z between 6 and 9 feet coyote_creek 7.762
2015-08-18T00:18:00Z below 3 feet santa_monica 2.126
2015-08-18T00:24:00Z between 6 and 9 feet coyote_creek 7.635
2015-08-18T00:24:00Z below 3 feet santa_monica 2.041
2015-08-18T00:30:00Z between 6 and 9 feet coyote_creek 7.5
2015-08-18T00:30:00Z below 3 feet santa_monica 2.051
GROUP BY，FILL()
GROUP BY time(12m),*GROUP BY time(12m)2015-08-17T:23:48:00Z2015-08-17T23:48:00Z <= t < 2015-08-18T00:00:00Zlocation=coyote_creekwater_level2015-08-18T00:00:00Z <= t < 2015-08-18T00:12:00Zlocation=coyote_creekwater_level（8.12+8.005）/ 2 = 8.0625GROUP BY time(10m)2015-08-17T23:40:00ZSOFFSET 1GROUP BY time(10s)## GROUP BY time(12m)
> SELECT mean("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),* fill(200) LIMIT 7 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-17T23:48:00Z 200
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:24:00Z 7.5675
## GROUP BY time(10m)，SOFFSET设置为1
> SELECT mean("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(10m),* fill(200) LIMIT 7 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
2015-08-17T23:40:00Z 200
2015-08-17T23:50:00Z 200
2015-08-18T00:00:00Z 2.09
2015-08-18T00:10:00Z 2.077
2015-08-18T00:20:00Z 2.041
2015-08-18T00:30:00Z 2.051
INTEGRAL(field_key, unit)
计算数值字段值覆盖的曲面的面积值并得到面积之和。测试数据如下：
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
2015-08-18T00:18:00Z 2.126
2015-08-18T00:24:00Z 2.041
2015-08-18T00:30:00Z 2.051
3732.66=2.028*1800+分割出来的梯形和三角形面积3732.66/60 = 62.2113732.66/120 = 31.1055# unit为默认的1秒
> SELECT INTEGRAL("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral
---- --------
1970-01-01T00:00:00Z 3732.66
# unit为1分
> SELECT INTEGRAL("water_level", 1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time integral
---- --------
1970-01-01T00:00:00Z 62.211
SPREAD(field_key)
计算数值字段的最大值和最小值的差值。
> SELECT SPREAD("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),* fill(18) LIMIT 3 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time spread
---- ------
2015-08-17T23:48:00Z 18
2015-08-18T00:00:00Z 0.052000000000000046
2015-08-18T00:12:00Z 0.09799999999999986
STDDEV(field_key)
计算字段的标准差。influxdb用的是贝塞尔修正的标准差计算公式 ，如下：
- mean=(v1+v2+...+vn)/n;
- stddev = math.sqrt(
((v1-mean)2 + (v2-mean)2 + ...+(vn-mean)2)/(n-1)
)
> SELECT STDDEV("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),* fill(18) SLIMIT 1;
name: h2o_feet
tags: location=coyote_creek
time stddev
---- ------
2015-08-17T23:48:00Z 18
2015-08-18T00:00:00Z 0.08131727983645186
2015-08-18T00:12:00Z 0.08838834764831845
2015-08-18T00:24:00Z 0.09545941546018377
PERCENTILE(field_key, N)
选取某个字段中大于N%的这个字段值。
MAX(field_key)MEDIAN(field_key)> SELECT PERCENTILE("water_level",20) FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
time percentile
---- ----------
2015-08-17T23:48:00Z
2015-08-18T00:00:00Z 2.064
2015-08-18T00:12:00Z 2.028
2015-08-18T00:24:00Z 2.041
> SELECT PERCENTILE("water_level",40) FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
time percentile
---- ----------
2015-08-17T23:48:00Z
2015-08-18T00:00:00Z 2.116
2015-08-18T00:12:00Z 2.126
2015-08-18T00:24:00Z 2.051
SAMPLE(field_key, N)
GROUP BY time()> SELECT SAMPLE("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z';
name: h2o_feet
time sample
---- ------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:12:00Z 2.028
> SELECT SAMPLE("water_level",2) FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m);
name: h2o_feet
time sample
---- ------
2015-08-18T00:06:00Z 2.116
2015-08-18T00:06:00Z 8.005
2015-08-18T00:12:00Z 7.887
2015-08-18T00:18:00Z 7.762
2015-08-18T00:24:00Z 7.635
2015-08-18T00:30:00Z 2.051
CUMULATIVE_SUM(field_key)
计算字段值的递增和。
> SELECT CUMULATIVE_SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-17T23:48:00Z' AND time <= '2015-08-18T00:30:00Z';
name: h2o_feet
time cumulative_sum
---- --------------
2015-08-18T00:00:00Z 8.12
2015-08-18T00:00:00Z 10.184
2015-08-18T00:06:00Z 18.189
2015-08-18T00:06:00Z 20.305
2015-08-18T00:12:00Z 28.192
2015-08-18T00:12:00Z 30.22
2015-08-18T00:18:00Z 37.982
2015-08-18T00:18:00Z 40.108
2015-08-18T00:24:00Z 47.742999999999995
2015-08-18T00:24:00Z 49.78399999999999
2015-08-18T00:30:00Z 57.28399999999999
2015-08-18T00:30:00Z 59.334999999999994
DERIVATIVE(field_key, unit) 和 NON_NEGATIVE_DERIVATIVE(field_key, unit)
计算字段值的变化比。unit默认为1s，即计算的是1秒内的变化比。
(2.116-2.064)/(6*60) = 0.00014..> SELECT DERIVATIVE("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time derivative
---- ----------
2015-08-18T00:06:00Z 0.00014444444444444457
2015-08-18T00:12:00Z -0.00024444444444444465
2015-08-18T00:18:00Z 0.0002722222222222218
2015-08-18T00:24:00Z -0.000236111111111111
2015-08-18T00:30:00Z 0.00002777777777777842
> SELECT DERIVATIVE("water_level", 6m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
time derivative
---- ----------
2015-08-18T00:06:00Z 0.052000000000000046
2015-08-18T00:12:00Z -0.08800000000000008
2015-08-18T00:18:00Z 0.09799999999999986
2015-08-18T00:24:00Z -0.08499999999999996
2015-08-18T00:30:00Z 0.010000000000000231
而DERIVATIVE结合GROUP BY time，以及mean可以构造更加复杂的查询，如下所示:
> SELECT DERIVATIVE(mean("water_level"), 6m) FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' group by time(12m), *
name: h2o_feet
tags: location=coyote_creek
time derivative
---- ----------
2015-08-18T00:12:00Z -0.11900000000000022
2015-08-18T00:24:00Z -0.12849999999999984
name: h2o_feet
tags: location=santa_monica
time derivative
---- ----------
2015-08-18T00:12:00Z -0.00649999999999995
2015-08-18T00:24:00Z -0.015499999999999847
(7.8245-8.0625)/2 = -0.1190> SELECT mean("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' group by time(12m), *
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:24:00Z 7.5675
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
2015-08-18T00:24:00Z 2.0460000000000003
NON_NEGATIVE_DERIVATIVEDERIVATIVE> SELECT DERIVATIVE(mean("water_level"), 6m) FROM "h2o_feet" WHERE location='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' group by time(6m), *
name: h2o_feet
tags: location=santa_monica
time derivative
---- ----------
2015-08-18T00:06:00Z 0.052000000000000046
2015-08-18T00:12:00Z -0.08800000000000008
2015-08-18T00:18:00Z 0.09799999999999986
2015-08-18T00:24:00Z -0.08499999999999996
2015-08-18T00:30:00Z 0.010000000000000231
> SELECT NON_NEGATIVE_DERIVATIVE(mean("water_level"), 6m) FROM "h2o_feet" WHERE location='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' group by time(6m), *
name: h2o_feet
tags: location=santa_monica
time non_negative_derivative
---- -----------------------
2015-08-18T00:06:00Z 0.052000000000000046
2015-08-18T00:18:00Z 0.09799999999999986
2015-08-18T00:30:00Z 0.010000000000000231
4.1 基本语法
连续查询(CONTINUOUS QUERY，简写为CQ)是指定时自动在实时数据上进行的InfluxQL查询，查询结果可以存储到指定的measurement中。基本语法格式如下：
CREATE CONTINUOUS QUERY <cq_name> ON <database_name>
BEGIN
<cq_query>
END
cq_query格式：
SELECT <function[s]> INTO <destination_measurement> FROM <measurement> [WHERE <stuff>] GROUP BY time(<interval>)[,<tag_key[s]>]
GROUP BY time()GROUP BY time()GROUP BY time(1h)now()-GROUP BY time(1h)now()transportationbus_databus_data.txt# DDL
CREATE DATABASE transportation
# DML
# CONTEXT-DATABASE: transportation
bus_data,complaints=9 passengers=5 1472367600
bus_data,complaints=9 passengers=8 1472368500
bus_data,complaints=9 passengers=8 1472369400
bus_data,complaints=9 passengers=7 1472370300
bus_data,complaints=9 passengers=8 1472371200
bus_data,complaints=7 passengers=15 1472372100
bus_data,complaints=7 passengers=15 1472373000
bus_data,complaints=7 passengers=17 1472373900
bus_data,complaints=7 passengers=20 1472374800
导入数据，命令如下：
root@f216e9be15bf:/# influx -import -path=bus_data.txt -precision=s
root@f216e9be15bf:/# influx -precision=rfc3339 -database=transportation
Connected to http://localhost:8086 version 1.3.5
InfluxDB shell version: 1.3.5
> select * from bus_data
name: bus_data
time complaints passengers
---- ---------- ----------
2016-08-28T07:00:00Z 9 5
2016-08-28T07:15:00Z 9 8
2016-08-28T07:30:00Z 9 8
2016-08-28T07:45:00Z 9 7
2016-08-28T08:00:00Z 9 8
2016-08-28T08:15:00Z 7 15
2016-08-28T08:30:00Z 7 15
2016-08-28T08:45:00Z 7 17
2016-08-28T09:00:00Z 7 20
示例1 自动缩小取样存储到新的measurement中
对单个字段自动缩小取样并存储到新的measurement中。
CREATE CONTINUOUS QUERY "cq_basic" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h)
END
bus_dataaverage_passengersAt 8:00 cq_basic 执行查询，查询时间范围 time >= '7:00' AND time < '08:00'.
cq_basic写入一条记录到 average_passengers:
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
At 9:00 cq_basic 执行查询，查询时间范围 time >= '8:00' AND time < '9:00'.
cq_basic写入一条记录到 average_passengers:
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 13.75
# Results
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
示例2 自动缩小取样并存储到新的保留策略（Retention Policy）中
CREATE CONTINUOUS QUERY "cq_basic_rp" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "transportation"."three_weeks"."average_passengers" FROM "bus_data" GROUP BY time(1h)
END
autogenthree_weeksCREATE RETENTION POLICY "three_weeks" ON "transportation" DURATION 3w REPLICATION 1> SELECT * FROM "transportation"."three_weeks"."average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
示例3 使用后向引用(backreferencing)自动缩小取样并存储到新的数据库中
CREATE CONTINUOUS QUERY "cq_basic_br" ON "transportation"
BEGIN
SELECT mean(*) INTO "downsampled_transportation"."autogen".:MEASUREMENT FROM /.*/ GROUP BY time(30m),*
END
:MEASUREMENT/.*/transportationdownsampled_transportation最终结果如下：
> SELECT * FROM "downsampled_transportation."autogen"."bus_data"
name: bus_data
--------------
time mean_complaints mean_passengers
2016-08-28T07:00:00Z 9 6.5
2016-08-28T07:30:00Z 9 7.5
2016-08-28T08:00:00Z 8 11.5
2016-08-28T08:30:00Z 7 16
示例4 自动缩小取样以及配置CQ的时间范围
CREATE CONTINUOUS QUERY "cq_basic_offset" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h,15m)
END
GROUP BY time(1h, 15m)cq_basic_offsetAt 8:15 cq_basic_offset 执行查询的时间范围 time >= '7:15' AND time < '8:15'.
name: average_passengers
------------------------
time mean
2016-08-28T07:15:00Z 7.75
At 9:15 cq_basic_offset 执行查询的时间范围 time >= '8:15' AND time < '9:15'.
name: average_passengers
------------------------
time mean
2016-08-28T08:15:00Z 16.75
最终结果:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:15:00Z 7.75
2016-08-28T08:15:00Z 16.75
4.2 高级语法
InfluxDB连续查询的高级语法如下：
CREATE CONTINUOUS QUERY <cq_name> ON <database_name>
RESAMPLE EVERY <interval> FOR <interval>
BEGIN
<cq_query>
END
RESAMPLE15:00-16:59.999999示例的数据表如下，比之前的多了几条记录为了示例3和示例4的测试:
name: bus_data
--------------
time passengers
2016-08-28T06:30:00Z 2
2016-08-28T06:45:00Z 4
2016-08-28T07:00:00Z 5
2016-08-28T07:15:00Z 8
2016-08-28T07:30:00Z 8
2016-08-28T07:45:00Z 7
2016-08-28T08:00:00Z 8
2016-08-28T08:15:00Z 15
2016-08-28T08:30:00Z 15
2016-08-28T08:45:00Z 17
2016-08-28T09:00:00Z 20
示例1 只配置执行时间间隔
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
GROUP BY time(1h)At 8:00, cq_advanced_every 执行时间范围 time >= '7:00' AND time < '8:00'.
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
At 8:30, cq_advanced_every 执行时间范围 time >= '8:00' AND time < '9:00'.
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 12.6667
At 9:00, cq_advanced_every 执行时间范围 time >= '8:00' AND time < '9:00'.
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 13.75
(8+15+15）/ 3 = 12.6667(8+15+15+17) / 4=13.75最终结果：
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
示例2 只配置查询时间范围
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
GROUP BY time(30m)At 8:00 cq_advanced_for 查询时间范围：time >= '7:00' AND time < '8:00'.
写入两条记录。
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
At 8:30 cq_advanced_for 查询时间范围：time >= '7:30' AND time < '8:30'.
写入两条记录。
name: average_passengers
------------------------
time mean
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
At 9:00 cq_advanced_for 查询时间范围：time >= '8:00' AND time < '9:00'.
写入两条记录。
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
cq_advanced_for最终结果：
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
示例3 同时配置执行时间间隔和查询时间范围
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
这里配置了执行间隔为1小时，而查询范围90分钟，最后分组是30分钟，每次插入了三条记录。执行流程如下：
At 8:00 cq_advanced_every_for 查询时间范围 time >= '6:30' AND time < '8:00'.
插入三条记录
name: average_passengers
------------------------
time mean
2016-08-28T06:30:00Z 3
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
At 9:00 cq_advanced_every_for 查询时间范围 time >= '7:30' AND time < '9:00'.
插入三条记录
name: average_passengers
------------------------
time mean
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
最终结果：
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T06:30:00Z 3
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
示例4 配置查询时间范围和FILL填充
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
在前面值配置查询时间范围的基础上，加上FILL填充空的记录。执行流程如下：
At 6:00, cq_advanced_for_fill 查询时间范围：time >= '4:00' AND time < '6:00'，没有数据，不填充。
At 7:00, cq_advanced_for_fill 查询时间范围：time >= '5:00' AND time < '7:00'. 写入两条记录，没有数据的时间点填充1000。
------------------------
time mean
2016-08-28T05:00:00Z 1000 <------ fill(1000)
2016-08-28T06:00:00Z 3 <------ average of 2 and 4
[…] At 11:00, cq_advanced_for_fill 查询时间范围：time >= '9:00' AND time < '11:00'.写入两条记录，没有数据的点填充1000。
name: average_passengers
------------------------
2016-08-28T09:00:00Z 20 <------ average of 20
2016-08-28T10:00:00Z 1000 <------ fill(1000)
At 12:00, cq_advanced_for_fill 查询时间范围：time >= '10:00' AND time < '12:00'。没有数据，不填充。
最终结果:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T05:00:00Z 1000
2016-08-28T06:00:00Z 3
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
2016-08-28T09:00:00Z 20
2016-08-28T10:00:00Z 1000